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Analysis of motor performance variability in tasks with redundancy affords insight about synergies underlying central nervous system ( CNS ) control . Preferential distribution of variability in ways that minimally affect task performance suggests sophisticated neural control . Unfortunately , in the analysis of variability the choice of coordinates used to represent multi-dimensional data may profoundly affect analysis , introducing an arbitrariness which compromises its conclusions . This paper assesses the influence of coordinates . Methods based on analyzing a covariance matrix are fundamentally dependent on an investigator's choices . Two reasons are identified: using anisotropy of a covariance matrix as evidence of preferential distribution of variability; and using orthogonality to quantify relevance of variability to task performance . Both are exquisitely sensitive to coordinates . Unless coordinates are known a priori , these methods do not support unambiguous inferences about CNS control . An alternative method uses a two-level approach where variability in task execution ( expressed in one coordinate frame ) is mapped by a function to its result ( expressed in another coordinate frame ) . An analysis of variability in execution using this function to quantify performance at the level of results offers substantially less sensitivity to coordinates than analysis of a covariance matrix of execution variables . This is an initial step towards developing coordinate-invariant analysis methods for movement neuroscience .
A study of multivariable behavior naturally raises the question of which reference frames the central nervous system ( CNS ) may use to coordinate its actions . For example , Morasso [1] studied planar reaching movements and showed that translation and rotation of the start and target positions evoked systematic variation of joint kinematics ( angles of shoulder and elbow ) but much less variation of hand kinematics ( Cartesian coordinates of the hand ) . This indicated that hand motion in “visual space” is an important consideration in central coordination of these movements . That implied a need for the CNS to transform between representations in different coordinates , e . g . visual to motor , as one challenge of coordination and control . Evidence that at least one such transformation is implemented in the parietal cortex was presented by Andersen and Zipser [2] . Soechting and Flanders [3] provide a comprehensive review of other evidence from eye , head , and body movements elicited by vestibular and visual stimuli and arm movements with their neural correlates in motor cortex . Stochastic variation provides another source of evidence about how the CNS may control and coordinate behavior . Patterns in variability—especially when they are invariant across experimental conditions—can reveal underlying control strategies that are inaccessible to direct measurement . The structure of variability over repeated performances can be especially meaningful when a task is redundant , i . e . , the task presents a multiplicity of equivalent ways to achieve the same end goal . A paradigmatic example is multi-joint movement , where the limbs have more degrees of freedom than minimally required to perform an intended task . Structure in this variability can reveal the organization of the neuromechanical control system . In a study of the postural responses of cats to tilting of their support surface , Lacquaniti and Maioli [4] , [5] showed that while three joint angles of the limbs ( scapula , shoulder and elbow of the forepaw; hip , knee and ankle of the hindpaw ) exhibited a large variability ( on the order of 30° ) they co-varied to lie close to a plane within the three-dimensional configuration space . This is presented as evidence of a synergy that reduces the dimensionality of the control problem—one solution to the “degrees-of-freedom” problem [6] . An obvious but critical fact is that the structure of observed variability is defined in a space with coordinates selected by the researcher . There is no a priori reason to believe that these external coordinates are the same as any internal coordinates of a putative neural representation . For example , in the analysis of multi-joint limb movements , these coordinates may be the angles of the biomechanically defined joints . However , joint angles can be described following many different conventions as many standard textbooks in biomechanics and robotics document [7] , [8] . While these alternative angle conventions provide equivalent descriptions of physical reality , the choice becomes important when the focus is on inferring CNS control strategies for multi-joint movement generation . In fact , the question of which coordinates or control variables may be represented in the CNS is a deep and difficult problem that lies at the heart of the study of motor control . Until that question is answered , the coordinates for data analysis remain a choice of the experimenter . If this choice should affect the outcome of the analysis , an uncomfortable arbitrariness would result . In which coordinate space should patterns be sought ? Does the structure of variability change when the problem is described in alternative coordinates ? To what degree do the conclusions drawn from analyses in alternative coordinates agree ? In this paper we show that some widely-used methods of analysis intended to illuminate CNS control are exquisitely sensitive to assumed coordinates and cannot provide unambiguous inferences about control . We further show that an alternative method which includes two levels of variables—those that characterize task execution and those that quantify its result , with a function or mapping relating the two—promises to be less sensitive .
Using the example of a redundant reaching task we simulated performance variability in two different joint angle coordinates that have been used in the literature . In accordance with the most widely-used methods , analysis of variability was based on the data covariance matrix . Because these approaches proved to be exquisitely sensitive to coordinate choice , we first analyzed the influence of linear coordinate transformations on orthogonality , a core assumption of covariance-based analysis . We then used the special eigenstructure of a covariance matrix to analyze the influence of linear coordinate transformations on the anisotropy of a data distribution , another core assumption of covariance-based analysis . An alternative method based on two sets of variables ( one describing how a task is executed , the other describing the corresponding result ) was analyzed by examining the geometric structure of the function relating these two levels . In particular , we studied the manifold defined by the extremal values of this function and the curvature of the function in the neighborhood of that manifold . We analyzed the sensitivity of both of these geometric features to general transformations of the coordinates chosen for the execution variables . To ground the abstract analysis in realistic data , we computed the three different quantitative features of performance variability–Tolerance , Noise , and Covariation ( TNC ) –in exemplary data of one subject performing a throwing task . The task and the method of computing these measures are detailed in Cohen and Sternad ( 2009 ) . To test the sensitivity of this TNC method to the coordinates chosen for the execution variables , we considered two plausible choices that are related by a non-trivial nonlinear transformation . We conducted the analysis in these two coordinate frames and compared the resulting variation observed in 2 , 880 performance attempts over a period of 16 days . To understand the outcome of this comparison we analyzed the sensitivity of these three measures of performance variability to linear and nonlinear transformations of the coordinates chosen for the execution variables .
One reasonable approach to identifying structure in variability is to focus on the covariance matrix derived from a set of observations . This is at the heart of principal component analysis and also many other related methods . The difficulties are perhaps best illustrated by the so-called Un-Controlled Manifold ( UCM ) method which purports to identify features of CNS control based on analysis of variability [9] , [10] , [11] , [12] . Using multi-joint reaching as an exemplary task , the problem is how n execution variables ( e . g . , seven upper-extremity degrees of freedom , assuming the shoulder is at a fixed location in space and that the hand and fingers may be treated as a single rigid body ) are coordinated to achieve an m-dimensional result ( e . g . , location of the hand in external Euclidean space with three degrees of freedom ) . Given n>m , a multiplicity of solutions exist that equally satisfy the task requirement . Further , for every particular hand location , the set of solutions form a manifold in the space of execution variables , e . g . , joint angles . This manifold may be visualized as analogous to a curved surface in execution space; every point on the surface corresponds to a combination of joint angles that yield the same hand location . Changes of the joint angles that are coordinated to remain on that surface do not change the hand location . To seek evidence of CNS control strategies , the UCM method examines variability over repeated performances . To simplify analysis , a locally linear approximation to the manifold is defined . Specifically , the result variable ( hand position in space ) is mathematically defined as a function of the execution variables ( joint angles ) . The Jacobian matrix of that function—a matrix of partial derivatives of each result variable ( hand position coordinate ) with respect to each execution variable ( joint angle ) —is defined . In general , the Jacobian matrix varies with limb configuration but it can be evaluated at any point to yield a matrix of constants . In UCM analysis , the Jacobian matrix is typically evaluated at the mean of the observed distribution of execution variables . Using standard methods of linear algebra the Jacobian matrix is analyzed to identify its kernel or nullspace . The nullspace may be visualized as analogous to a plane that is tangent to the curved manifold at the evaluation point . It defines the “do-not-matter” directions: small changes of the execution variables about that point which are coordinated in such a way as to remain within that plane do not matter because they produce negligible changes in the result [13] , [14] . Conversely , all deviations in the orthogonal complement of this nullspace affect the result . The orthogonal complement may be visualized as analogous to directions perpendicular to the tangent plane described above . Scholz and Schöner [10] hypothesized that if execution variability is smaller in those directions for which the result is more sensitive to deviations than in those directions for which deviations do not matter , control is indicated . To quantify the degree of control , execution variability is projected onto the nullspace ( the do-not-matter directions ) and onto its orthogonal complement . If the variability per degree of freedom in the do-not-matter directions is larger than in the orthogonal directions , this is taken as evidence of skill , as control is not exerted where it does not matter . Hence , the manifold ( and its tangent , defined by the nullspace of the Jacobian matrix ) are termed the uncontrolled manifold [15] . In the early papers an explicit goal was to identify variables that may be controlled by the CNS . Using a shooting task as an example , Scholz , Schöner and Latash ( 2000 ) hypothesized that rather than controlling all elements of the arm directly , a candidate controlled variable was the orientation of the pistol barrel , as it ultimately determines the accuracy of pointing . An alternative variable was the center of mass of the arm configuration . Using these quantities to define the hypothesized task , and treating joint angles as execution variables , joint angle variability at selected points along the limb trajectory was assessed with respect to its effect on these two alternative result variables . Relatively more variability in the do-not-matter directions was taken as support for the orientation of the pistol barrel as the more likely candidate for a controlled variable than the center of mass of the arm . The idea that the CNS focuses its control effort on variables that matter while allowing inevitable variability to be distributed along do-not-matter directions has considerable conceptual appeal . The same feature can be generated by a stochastic optimal feedback control strategy [16] , [17] which has been proposed as a theory of CNS control . Unfortunately , although these studies pursue an important question in a hypothesis-driven way , this analysis of a covariance matrix has major weaknesses as we detail below . Given the general appeal of the idea , we also attempt to identify a means to overcome these weaknesses . To illustrate the general problem , consider a simplified hypothetical pointing task: reaching in the horizontal plane to point to a line . Assume the thorax is stationary and only the shoulder and elbow joints may move , so that the upper extremity may be modeled with only two segments . Assume the line is oriented diagonally with respect to a line through the shoulders ( Figure 1A ) . Successful pointing is achieved by moving the hand to any location along the line . Because placing the hand at every location on a line achieves the same zero error , the task may successfully be completed with infinitely many combinations of joint angles . Next , consider how the joint angles may be defined: two common conventions found in the literature are illustrated in the figure: “absolute” coordinates measured with respect to a stationary frame; and “relative” coordinates measured with respect to adjacent limb segments . Figure 1A illustrates absolute joint angles; the orientation of the upper arm , , and forearm , , are both measured with respect to the same stationary reference , a line through the shoulders . Alternatively , Figure 1B illustrates relative joint angles; the orientation of the upper arm , , is measured as before but the orientation of the forearm , is measured with respect to a movable reference , the orientation of the upper arm . These are only two of an uncountably infinite set of alternatives , any of which fully define the configuration of the upper extremity . However , these two alternatives are related by simple linear equations: and ( see Figure 1B ) . Absolute angles are advantageous because the forward kinematic equations expressing hand location in the horizontal plane as a function of limb configuration have a particularly compact form which simplifies computation of the Jacobian matrix ( Scholz & Schöner , 1999 ) . Note , however , that among the infinity of alternatives , there is no principled reason aside from computational convenience for giving primacy to either of these two conventions . Assume a hypothetical set of 500 trials that scatter the hand location on and around the target line . If this set of data is represented in the space of relative angle coordinates , they exhibit the anisotropic distribution visible in Figure 1C . The color code denotes deviations from the target line , with darker colors referring to larger distances . The curved white line denotes the UCM , the set of joint angle combinations for which the endpoint is exactly on the target line and the deviation is zero . The dashed straight line denotes the nullspace of the Jacobian matrix evaluated at the mean of the data distribution , providing a linear approximation that is tangent to the UCM at that point . According to the rationale of the UCM method , this data distribution has structure such that the projection onto the UCM or its linearization is larger than the projection onto its orthogonal complement . The putative interpretation would be that this performance shows an ability to identify and take advantage of the redundancy of the task; the variability is not randomly scattered but channeled preferentially along the do-not-matter direction . This interpretation would be premature . Consider Figure 1D , which shows exactly the same 500 data points but represented in the space of absolute joint angles: The data distribution which was previously anisotropic and well-aligned with the UCM becomes isotropic simply due to this change of coordinates . According to the logic of the UCM method , the putative interpretation would now be that the data shows no signs of this particular skill . Clearly , both interpretations cannot be supported simultaneously . In the absence of an objective argument for choosing one joint angle definition over another , any conclusion or interpretation drawn from this analysis would be quite arbitrary . In fact , when the data is represented in the space of hand coordinates shown in Figure 1E , any directional structure of its distribution disappears completely . ( For simplicity of exposition , a bivariate Gaussian distribution with equal variance in both directions and mean on the target line was assumed . ) Any claim that this data variability illuminates how the CNS organizes its control of behavior would be specious at best . To check this qualitative impression with quantitative analysis , we randomly selected 100 data points from the set of 500 and conducted UMC analysis . The random selection was repeated 10 times with replacement and the same analysis was performed . Following Scholz and Schöner ( 1999 ) , we report the results as the ratio of parallel over orthogonal variance . Table 1 summarizes the means and standard errors of the results . Evidently , the UCM ratio is very different for the two coordinate choices . For comparison , we also analyzed TNC components for both coordinate choices , which will be described below . Despite this sensitivity the core idea remains appealing: a preferential distribution of performance variability along do-not-matter directions suggests skilful CNS control . With a view to overcoming these weaknesses , we identify two reasons why this analysis is sensitive to coordinates: it relies on orthogonality; and it relies on anisotropic distribution of data . As reviewed above , central to the UCM approach is the projection of performance variability into the nullspace of the Jacobian matrix and its orthogonal complement . Unfortunately , orthogonality is exquisitely sensitive to the coordinates of the space within which it is defined . Figure 2 illustrates this fact: simply changing the scale of the abscissa ( multiplying by a constant ) changes an orthogonal intersection of straight lines to an intersection at an angle . Is this a realistic concern ? An argument might be made that joint angles should always have the same units . While a joint space with homogenous units is physically reasonable , the physical identity of angular displacements of different joints does not guarantee that they are represented as identical in the CNS . One hypothetical alternative is that joint angles may be represented in the CNS scaled by their range of motion . Different joints have different ranges of motion such that 30 degrees may constitute 100% of maximum range in one joint but only 50% in another . If this were the case , orthogonal directions in a physically-defined space would no longer be orthogonal when transformed into a space that is meaningful to the CNS . The fundamental problem is that orthogonality requires a metric ( a function defining the distance between two points in a space ) yet plausible coordinates of CNS representations may not admit a metric . For example , Todd and colleagues present convincing evidence that visual space does not have a metric structure [18] , [19] . Behavioral evidence of an equivalent finding for the motor system was provided by Fasse and colleagues who showed that at least some aspects of human perceptual-motor behavior do not admit a metric structure [20] . To underscore the behavioral evidence , if joint angles are perceived with respect to an external spatial reference , as reported by Soechting and Ross [21] then they cannot admit a metric because finite rotations with respect to an extrinsic spatial reference do not commute and hence violate one of the fundamental requirements to define a space with a metric . In sum , an assumption of orthogonality requires far more structure than may reliably be assumed of CNS representations and hence does not provide a sound basis from which to study CNS control . As summarized above , the UCM method tests an experimental data distribution for direction-dependent or anisotropic variance in order to assess support for its hypotheses . However , anisotropy of a covariance matrix can always be eliminated by a sequence of coordinate transformations ( see Text S1 ) . Figure 3 illustrates this basic fact . Panel A shows a hypothetical data distribution in x , y space . The ellipse denotes the covariance of this distribution . The solid line represents a hypothetical uncontrolled manifold that cuts through the distribution at an angle slightly different from the major axis of the ellipse . Applying simple vector addition , this distribution can be shifted so that its mean coincides with the origin of new coordinates denoted by x′ , y′ ( Figure 3B ) . With a simple coordinate rotation , the major axis of the distribution can be aligned with one of the coordinate axes , now defined as x″ , y″ ( Figure 3C ) . Finally , re-scaling these axes so that the major and minor axes of the ellipse are equal yields new coordinates , now denoted by x′″ , y′″ , in which the covariance is completely isotropic ( Figure 3D ) . In sum , for any data distribution , alternative coordinates can always be found in which the directional dependence of variance disappears . If analysis of covariance matrix anisotropy is applied to seek evidence for the coordinates of CNS control , then this line of argument is troublesome . A set of coordinates is assumed for the execution variables; a particular form of data anisotropy is presented as evidence that those coordinates are , in fact , used by the CNS—but the anisotropy of the data is completely determined by the coordinates initially assumed . There are always alternative coordinates in which the data anisotropy may be eliminated . There are even alternative coordinates in which data anisotropy may be constructed to argue for the opposite conclusion . Unless the coordinates of execution space are objectively known a priori , the presence of data anisotropy cannot serve as evidence of control . These concerns are by no means confined to the UCM method . Covariance-based analyses of variability are in widespread use . They include principal component analysis , factor analysis , ridge regression , proper orthogonal decomposition , linear discriminant analysis , Karhunen-Loève or Hotelling transform , the Isomap method , and non-negative matrix factorization [22] , [23] , [24] . Most of them depend similarly on assumptions about coordinates . In the study of motor control , covariance-based analysis has been used to infer synergies underlying multi-dimensional motor behavior [12] , [25] , [26] and many others ) . For example , Cusumano and Cesari [27] proposed an analysis of variability with respect to a Goal-Equivalent Manifold ( GEM , formally equivalent to the UCM ) . While some details of the GEM method differ from the UCM method ( e . g . , the use of a singular-value decomposition ) , most steps are similar—most importantly the analysis of a covariance matrix with respect to the nullspace of a Jacobian matrix . Although the authors do not interpret their findings as identifying the coordinates of CNS control , their results similarly rely on orthogonality and data anisotropy and hence are exquisitely sensitive to the coordinates assumed for the analysis . In the same vein , Todorov and colleagues have developed a stochastic optimal feedback control framework where , again , variability in the execution of redundant tasks is evaluated to adduce evidence of feedback control [16] , [17] . Deviations from a desired target behavior that are preferentially distributed along do-not-matter directions are taken as evidence of optimal control following a Minimum Intervention Principle . As before , although this is an intuitively appealing idea and uses sophisticated mathematical tools , experimental evidence derived from analysis of a covariance matrix is fundamentally sensitive to assumed coordinates . Unless the coordinates of control are objectively known a priori , anisotropy of a covariance matrix cannot provide reliable evidence . Can alternative methods be formulated which are less sensitive to coordinates ? Sternad and colleagues introduced the so-called TNC analysis ( Tolerance – Noise – Covariation ) with the goal of quantifying skilled performance and how it changes with practice [28] , [29] , [30] , [31] . In TNC analysis , variability in performance is parsed into three components: Tolerance ( or T cost ) quantifies to what degree variability is in regions of execution space that are tolerant of error; Noise ( or N cost ) quantifies to what degree random variation affects performance; Covariation ( or C cost ) quantifies to what degree covariation among execution variables takes advantage of the structure of the manifold of solutions . The principal goal of this method is to afford a more differentiated view of how the acquisition of skill not only decreases variability but also takes advantage of the structure of the task . Adjusting execution variability affords three conceptually at least different routes to improve performance , and T cost , N cost , and C cost are measures of these three distinct strategies . In addition , the TNC method differs from those discussed above in one key aspect: instead of evaluating the structure of a covariance matrix in the space of execution variables , the quantification of variability is performed in the space of the result variable ( s ) [29] , [32] . In a well-posed task , result variables typically have an unambiguous physical meaning and are expressed in a space with a natural , physically-meaningful metric . For that reason , a suitably formulated analysis of performance variability in the space of result variables may be insensitive to their coordinates . In the following we assess the sensitivity of the TNC method presented in Cohen and Sternad ( 2009 ) to the experimenter's choice of coordinates . TNC analysis begins with a model of the task , unambiguously described in physical variables that are measured . For example , analyzing a challenging throwing task where a subject throws a tethered ball around a central post to hit a target , execution is fully determined by two variables ( for a detailed description of the task see [28] . They may be the angular position and velocity of the hand at the moment of release of the ball , though other variables may also be chosen ( see below ) . Together they define a two-dimensional execution space , X . Given these execution variables , the subsequent ball trajectory—and hence the outcome of any throw—is fully determined from elementary mechanical physics . The result of any particular execution is an error , specifically , the closest approach of the ball to the target . It defines a one-dimensional result space , R . The task is redundant as multiple combinations of the two execution variables yield the same result; a function is readily identified which describes a “many-to-one” map from execution space into result space . Perfect execution of this task with zero error defines the solution manifold shown in white in Figures 1C , 1D and 4 . Non-zero errors are defined by the result function f and determine a landscape—an elongated “valley” with the solution manifold as its bottom—with error magnitudes expressed in colors with darker denoting larger errors ( for details see Cohen & Sternad , 2009 ) . The solution manifold ( and , indeed , the entire result function ) is highly nonlinear because the tether pulls the ball towards the central post , giving it a curved flight path . To be strictly correct , the nonlinear result function is itself a 2D manifold in the 3D space formed by the composition of the result and execution spaces , . However , to facilitate comparison with related methods , in this paper we reserve the term “manifold” for the zero error result ( the solution manifold ) though , technically , it defines a 1D sub-manifold of the 2D result function . The solution manifold , SM , is formally equivalent to the UCM and the GEM . Note that the existence of a solution manifold ( UCM or GEM or SM ) with a dimension of one or higher is a requirement for a task to be redundant . An important point is that the definition of the solution manifold is independent of any assumptions about the coordinates of execution space . It is always possible to establish a complete equivalence between the solution manifolds expressed in any two alternative choices for execution space coordinates . The reason is simple: if we visualize the result function as a 2D landscape in 3D space , the solution manifold is always at the “bottom of the valley” . While the curve corresponding to the bottom of the valley may appear different with different coordinates of execution space , it always corresponds to zero result . This is illustrated in Figure 4 , which depicts the result function of the skittles task for two plausible choices of the execution variables: the angular position and velocity of the hand at the moment of release of the ball , which may loosely be termed polar coordinates ( Figure 4A ) ; and two orthogonal components of the velocity of the hand at the moment of release , which may be termed Cartesian coordinates ( Figure 4B ) . The corresponding result functions are shown in Figures 4C and D , respectively . Because the relation between these two coordinate frames is nonlinear , each result function is a distorted copy of the other and the solution manifold traces a different curve in each space . However , in both cases , the solution manifold corresponds to identically zero result—it is at the bottom of the valley . Of course , because the UCM , GEM and SM are equivalent , all enjoy this property . However , the UCM and GEM methods confine their variability analysis to execution space and take no advantage of this fact . The TNC method analyzes observed performance in the context of the result function and distinguishes several related aspects of imperfect performance . First , it is commonly observed that subjects do not use the entire solution manifold , even though all combinations of execution variables that lie on it yield equally perfect performance . Instead , performance attempts tend to be clustered around a preferred location on the solution manifold ( Cohen & Sternad , 2009 ) , most likely because different locations have different tolerance of errors . Tolerance cost provides a measure of how observed performance exploits error tolerance by shifting the observed data distribution to different locations in execution space and evaluating the greatest ensuing improvement in average result . Second , subjects are not only inaccurate but also imprecise . Noise cost provides a measure of how this random scatter around the mean execution affects performance . Noise cost is calculated by shrinking the set of data incrementally and uniformly towards its mean in execution space . The greatest improvement in average result that ensues is taken as Noise cost . Third , even if variable errors are not reduced , they may be structured to advantage . Covariation cost provides a measure of how observed performance capitalizes on the structure of the solution manifold . It is calculated by recombining observed data in execution space and evaluating any improvement in average result . The important point for present purposes is that these three features of performance variability are estimated in result space , in units of the result variable . They are expressed as costs indicating how much observed performance could have been improved by an appropriate change of tolerance , noise and covariation . One obvious reason to prefer some locations on the solution manifold over others is the sensitivity of the result to variability ( or , equivalently , tolerance of error ) . This is determined in part by the mechanical physics of the task , expressed as the curvature of the result function . Figures 4C and 4D show the result function of the skittles task ( depicted as a plan view of a curved valley ) . Due to the nonlinear mechanics of the task , the immediate neighborhood of the solution manifold has different curvature at different positions , making some locations more tolerant of errors than others . Note that all locations on the solution manifold have identical height and there is no global minimum or “best” location based on error alone . It is the “width” of the valley that varies with location ( or , equivalently , how close its bottom is to being flat ) . Looking beyond the one-dimensional solution manifold to the many-dimensional result function opens up additional ways to quantify the consequences of variability , such as to assess the effect of curvature on error tolerance . Remarkably , important features of the result function's curvature are completely independent of any assumptions about the coordinates of execution space . As discussed above , the solution manifold itself is independent of coordinates . In addition , if the result function smoothly maps execution space into result space , the Hessian matrix ( a matrix of second partial derivatives ) of that map evaluated at any point determines its curvature at that point . Because the result function is real-valued and continuous , its Hessian matrix is real-valued and symmetric and has real eigenvalues . The eigenvalues determine the maximum and minimum curvatures ( known as principal curvatures ) of the result function . However , the result ( error ) is identically zero at all points on the solution manifold and is positive at all other points . Therefore we may deduce that: ( i ) the smallest principal curvature is always zero; ( ii ) the largest principal curvature is always non-negative . As a result , the Gaussian curvature ( the product of the principal curvatures ) of the result function is identically zero at all points along the solution manifold . For any coordinates that may be used for execution space , the Gaussian curvature on the solution manifold is zero . These geometric considerations justify a guarded optimism that methods based on analyzing subject performance in the context of a result function may enjoy less sensitivity to the coordinates assumed for execution space . Does empirical evidence support this conjecture ? While the result function for the skittles task is derived from simple mechanical physics , the same physical principles can be expressed in many alternative coordinate systems . One reasonable candidate is the “polar” coordinate frame used above ( and detailed in Figure 4A and 4C ) —angle and angular velocity at the moment of release . An equally reasonable alternative is the Cartesian components of linear velocity at the moment of release ( detailed in Figure 4B and 4D ) . Either pair of variables fully determines the subsequent ball trajectory and the ensuing error at the target . Note that the relation between these two coordinate systems is significantly more challenging than the simple linear transformation between absolute and relative joint angles in the hypothetical example of Figure 1 . To assess the sensitivity of TNC analysis to this coordinate transformation , we calculated Tolerance , Noise , and Covariation costs for a particular set of experimental data expressed in polar and Cartesian coordinates . The specific data were taken from a study by Cohen and Sternad ( 2009 ) and represent one expert subject practicing the skittles task for 16 days with 180 throws on each day . The study was approved by the Institutional Review Board of Pennsylvania State University ( IRB#: 16237 ) . As expected , a pronounced learning curve is observed in the distance error data ( Figure 5A ) . The daily average of the three different costs together with their standard deviations are displayed in Figures 5B , 5C and 5D , respectively . The cost calculations performed in polar and Cartesian coordinates are shown in dashed and solid lines respectively . The greatest influence of coordinates is on Tolerance cost on day 1 , when the error is also highest . By day 2 this influence has largely disappeared and the error has declined dramatically ( the largest day-to-day performance improvement observed ) . As is typical , this subject's initial execution attempts on day 1 were widely scattered , covering a large range of execution space . Over this range , the relation between the different coordinates is highly nonlinear . On subsequent days the execution attempts were more tightly clustered covering a smaller range of execution space ( as evidenced by the decline of Noise cost over the first few days ) . Over this narrower range , the relation between the different coordinates is closer to linear . As we show below , while Tolerance cost may be affected by a nonlinear transformation of coordinates , it is completely insensitive to any linear transformation of coordinates . More tightly clustered execution attempts yield smaller Noise cost; the transformation between coordinates becomes progressively closer to linear; and the choice of coordinates has progressively less influence on the analysis . Indeed , from about day 10 onwards , Tolerance costs are effectively indistinguishable in the different coordinates . Noise costs and Covariation costs are also remarkably similar . In sum , although the choice of coordinates produces some early quantitative differences , the qualitative trends for each of the three costs are remarkably similar and the quantitative differences vanish as skill improves . Although these two coordinate frames are substantially different and nonlinearly related , those features of the data analysis that convey the most potential meaning for studies of motor coordination and learning—the order-of-magnitude differences , the overall trend over successive days , the rank-ordering of the costs—are largely unaffected . Is this insensitivity to coordinates a general property of TNC analysis or a fortuitous outcome of analyzing a “favorable” data set ? In the following we consider each part of TNC analysis in turn .
In physics it is generally expected that descriptions of natural phenomena should not depend on an arbitrary choice of the coordinates in which the descriptions are cast . This principle has thus far received little consideration in movement neuroscience , which is surprising given that neuroscience similarly seeks fundamental descriptions of the function of the neuromuscular system . Because the tensor calculus is one of the classical methods to formulate analysis independent of coordinate frames , the “tensor theories” of sensory-motor transformations within the CNS ( proposed by Pellionisz and Llinas [39] ) might appear to address this matter . Unfortunately , as detailed in the review by Arbib and Amari [40] , their use of tensor calculus was at best metaphorical and could not achieve the required independence of coordinate frames . Sensorimotor transformations within the CNS might alternatively be approximated by a weighted combination of suitable basis functions , an approach that could plausibly be implemented by e . g . three-layer networks of neurons . Soechting and Flanders ( 1992 ) point out that “…activity in intervening ( hidden ) layers need not be in any frame of reference…” . Pouget and Sejnowski [41] propose that single neuron responses serve as basis functions which “have the advantage of not depending on any coordinate system or reference frame . ” The substance of this statement is that the different nonlinear functions required to represent the same sensory event or motor response in different reference frames may be approximated as different linear combinations of the same set of basis functions . As a result , the different representations are related by linear transformations . However , this does not achieve the required independence of coordinates that we seek . The “relative” and “absolute” joint angles considered in the example of Figure 1 are related by a linear transformation , yet the difference between them profoundly affects an analysis of the distribution of experimental observations . An important distinction should be made between the coordinates of a putative internal neural representation and the coordinates of external observations of behavior that may be used to infer neural processes . Because the complexity of the central nervous system and the limitations of available measurements create boundless opportunities for confusion , it seems prudent ( perhaps even mandatory ) to seek descriptions and analysis techniques that are minimally affected by an investigator's choice—however sensible—of measures and coordinates . If that should prove to be impractical , it is at least necessary to understand how a change of coordinates may affect the conclusions drawn; this was the primary motivation for the study reported here . Given the difficulties inherent in any method based on covariance , we considered an alternative analysis of data structure , the TNC method . One of its distinguishing features is that quantitative assessment of structure in execution variability is evaluated in the space of the result ( see Müller and Sternad , 2009 ) . The key point is that while different coordinates of execution space may be chosen , the result does not change . In the example cited above , the result space was one-dimensional ( the distance of closest approach to the target ) but that is not essential . Though the clarity of one-dimensional measures of task success affords substantial advantages , multi-dimensional result spaces could be envisioned . Alternative result measures are discussed in Müller and Sternad ( 2003 ) . However , in any unambiguously defined task , the result space should admit a natural metric so that any improvement ( or decline ) in performance could be identified unambiguously . For example , in the hand space depicted in Figure 1E , distance is naturally quantified by the usual Euclidean metric . Therefore , orthogonality is uniquely defined in hand space . In addition , physical distance is invariant under changes of hand coordinates . If the experimenter chose to use , say , polar coordinates to quantify hand position , a different well-defined metric ( obtained by suitably transforming the Euclidean metric ) applies to these coordinates . In some tasks it might be advantageous to define a result space whose elements were the complete time-histories of performance attempts . To be defined unambiguously , this result space should be an infinite-dimensional Hilbert space . Though we anticipate no fundamental barriers to dealing with these more challenging cases , their analysis is deferred . Provided the result space has a well-defined metric , any analysis of behavior confined to result space may be made completely independent of the choice of its coordinates . For example , though Lacquaniti and Maioli ( 1994b ) arrive at their main result ( planar co-variation of joint angles ) by principal component analysis in the space of joint angles ( which is sensitive to the choice of joint angles ) this may be interpreted in terms of CNS control of leg length and orientation ( Maioli & Poppele , 1991 ) . While the existence of any metric for any configuration space of joint angles is debatable , there is a natural choice for the location of the forepaw or hindpaw relative to the shoulder or hip: the Euclidean distance between the proximal joint and its distal support . Insofar as conclusions are drawn from observations of minimal variability of foot trajectory in space [42] , they are also insensitive to the choice of spatial coordinates . However , it would be difficult to extract convincing evidence of synergies or how the CNS may solve the problem of controlling redundant degrees of freedom from any analysis that is confined to result space alone . For that reason TNC analysis maps execution space onto result space . Sensitivity to coordinate transformations is only brought about by operations that are performed in execution space . In TNC analysis , those operations consist of translation , uniform shrinking , and re-combination of the observed data . Linear transformations of the execution coordinates do not affect the translation used to calculate Tolerance cost . For example the change from absolute to relative joint angles , which profoundly affected UCM analysis , makes no difference whatsoever . This is not to say that Tolerance cost is indifferent to all coordinate changes; it is clearly affected by nonlinear coordinate transformations . Its sensitivity is determined by how much a nonlinear coordinate transformation departs from linearity over the region of analysis . Sufficiently “gentle” transformations ( i . e . , those sufficiently close to linear ) will have little influence . We presented empirical evidence suggesting that a nonlinear transformation between polar and Cartesian coordinates has minimal effect . Nonetheless , it would be advantageous to develop a ( revised ) measure of tolerance that was insensitive to coordinates . That is a topic of ongoing investigation . Linear transformations of the execution coordinates are expected to have little effect on the shrinking operation used to calculate Noise cost . In this case , the sensitivity to coordinates will be determined by the curvature of the result function over the region occupied by the data . Again , empirical evidence suggests that a nonlinear transformation between polar and Cartesian coordinates has little effect . As described above , the re-combination operation used to calculate Covariation cost is fundamentally sensitive to rotation of the coordinate axes [43] , [44] . Even so , this measure has some singular merits: it is not affected by the core weaknesses of methods based on covariance matrix factorization because ( i ) it makes no use of orthogonality , and ( ii ) it does not require anisotropic distribution of the data . It is therefore worth considering how it might be improved . As outlined previously , the reason Covariation cost is sensitive to coordinates is clear . The difficulty is illustrated in Figure 7 . Panels A and E depict how any two observations x1 , y1 and x2 , y2 ( schematically shown as two filled dots ) may be re-combined to produce new data x1 , y2 and x2 , y1 ( shown as open circles ) . Exchanging their x-coordinates ( or y-coordinates ) does not change the marginal data distributions but might change the corresponding results . If the solution manifold and adjacent lines of constant result are ( approximately ) straight and aligned parallel to one of the coordinate axes as in Figure 7A–D , this re-combination will have no effect . As a result , Covariation cost will be ( approximately ) zero . Figure 7B–D illustrates this for schematic data with a distribution that is completely misaligned with the solution manifold ( Figure 7B ) , or exhibits no apparent alignment ( Figure 7C ) , or is well-aligned with the solution manifold ( Figure 7D ) . In contrast , if the solution manifold and adjacent lines of constant result are ( approximately ) straight but aligned diagonally with respect to the coordinates as in Figure 7E–H , then recombination of x- and y-elements can substantially improve the average result . The effect will be greatest if the data distribution is aligned along a direction different from the solution manifold ( illustrated in Figure 7F ) , intermediate , if it exhibits no apparent alignment ( Figure 7G ) , and close to zero if it is well-aligned with the solution manifold ( Figure 7H ) . This suggests an obvious way that Covariation cost may be revised to minimize its sensitivity to coordinates: For any choice of execution coordinates , a rectangular region may be identified that bounds some appropriately large proportion of the marginal data distributions ( less than 100% to minimize the influence of outliers ) . Within that region the best straight-line approximation to the solution manifold may be found . From that information , a rotation of the coordinate axes may be identified to define a new coordinate frame in which the solution manifold approximately intersects opposite corners of the rectangular region containing the ( new ) marginal data distributions . Provided the solution manifold is mildly curved throughout the region occupied by the data , the recombination procedure described previously will yield a ( revised ) Covariation cost that will approach zero only if the data is distributed along the solution manifold . Furthermore , this revised Covariation cost will be insensitive to the initial choice of execution coordinates , provided again that the solution manifold is approximately straight throughout the region occupied by the data . This revised measure of covariation adds a step to the analysis to circumvent problems due to an untoward relation between the coordinate axes ( chosen by the experimenter ) and the solution manifold ( defined by the physics of the task ) . Essentially this revision recognizes the original weakness and turns it to advantage . Nonetheless , it may not confer complete insensitivity to coordinates . A method to do so is a topic of ongoing investigation . The heart of TNC analysis is identification of a result function . In a well-posed task , the goal is explicit and meaningful and presents an unambiguous , objective benchmark for evaluating performance . Incorporating the result function avoids implicit limitations on an analysis of variability . For example , the result function determines the consequences of both inaccuracy ( constant error ) and imprecision ( variable error ) for task performance . In contrast , covariance matrix factorization methods are necessarily performed on deviations around a mean , and must remain silent on the consequences of constant error . Geometric details of the result function may be particularly informative . Though manifestly true , it may not be obvious that all perfect solutions are not equivalent but may differ in their forgiveness of error . Recognizing this fact is potentially a rich source of new insight into central nervous system control , suggesting new perspectives and hypotheses . For example , it seems reasonable to postulate that actors may “exploit” variability to assess error tolerance . This is consistent with the hypothesis that variability is necessary to explore execution space and find good solutions [45] , [46] , [47] , [48] . This hypothesis may be rendered explicit by observing that variability affords a way to assess the curvature of the result function , and may be testable by offering a way to quantify error tolerance via the result function curvature . This hypothesis is strongly reminiscent of the concept of “persistent excitation” that is essential for effective adaptive control [49] . An essential point is that , if this hypothesis is correct , the best strategy may not be to confine variability in the directions that affect task performance to its irreducible minimum and channel the remainder to the do-not-matter directions . As described above , the curvature of the result function along the do-not-matter directions ( the solution manifold ) is identically zero , so variability in this direction adds little new information . Instead , tolerance of error depends on directions independent of the do-not-matter directions . Variability in these “do-matter” directions may be essential to identify the best location along the solution manifold at which to cluster performance . Exploration of this possibility is a topic of ongoing investigation . | Over the past decade the identification of synergies has become a prominent theme in motor neuroscience . Like other aspects of neural organization ( e . g . , vision ) the control of coordinated movement is almost certainly hierarchical with synergies a key feature of this hypothesis . In pursuit of identifying synergies , whether flexible or hard-wired in biomechanical or physiological structures , many studies have analyzed variability with techniques of dimensionality reduction such as principal component analysis . Results have been interpreted as evidence for controlled variables in motor control . Our paper demonstrates that such analyses and conclusions based on these methods are exquisitely sensitive to the coordinates of the variables that are the basis for this analysis . As these coordinates are often chosen for convenience of measurement or analysis , any conclusions about neural control are therefore ambiguous at best . The development of coordinate-independent analyses was an important step in the development of modern physics . Here we highlight the problems induced by coordinate-dependency in studies of neural control and present initial steps towards coordinate-independent analyses relevant to computational biology . We critically examine an alternative method proposed to analyze variability for identification of structure and show that it is significantly less sensitive to assumed coordinates than conventional analyses . |
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Eumycetoma is a progressive and destructive chronic granulomatous subcutaneous inflammatory disease caused by certain fungi , the most common being Madurella mycetomatis . The host defence mechanisms against fungi usually range from an early non-specific immune response to activation and induction of specific adaptive immune responses by the production of Th-1 and Th-2 cytokines . The aim of this study is to determine the levels of Th-1 and Th-2 cytokines in patients infected with Madurella mycetomatis , and the association between their levels and disease prognosis . This is a descriptive cross-sectional study conducted at the Mycetoma Research Centre , University of Khartoum , Sudan , where 70 patients with confirmed M . mycetomatis eumycetoma were enrolled; 35 with , and 35 without surgical excision . 70 healthy individuals from mycetoma endemic areas were selected as controls . The levels of serum cytokines were determined by cytometric bead array technique . Significantly higher levels of the Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) were recorded in patients treated with surgical excision , compared to those treated without surgical excision . In contrast , the Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly lower in patients treated with surgical excision compared to those treated without surgical excision . In conclusion , the results of this study suggest that cell-mediated immunity can have a role to play in the pathogenesis of eumycetoma .
Mycetoma is a chronic subcutaneous infection caused by certain bacteria ( actinomycetoma ) or fungi ( eumycetoma ) [1] . It is characterised by a slow progressive infection and a granulomatous inflammatory response that can result in severe soft tissue and muscle damage along with destruction of the underlying bone [1 , 2] . Mycetoma is endemic in tropical and subtropical regions; however , it has been reported globally . Eumycetoma in Sudan , is predominately caused by the fungus Madurella mycetomatis [2] . The disease is characterised by extensive subcutaneous masses , usually with multiple draining sinuses and fungal grains [1] . Mycetoma disease has significant negative medical health and socio-economic impacts on patients and communities , affects individuals of all ages , but is more frequently seen in adults who work outdoors . The host defence mechanisms against fungi usually range from germline encoded immunity which present early in the evolution of microorganisms , to highly specialised and specific adaptive mechanisms that are induced by infection and disease . The innate response to fungi serves two main purposes; a direct antifungal effector activity and activation or induction of specific adaptive immune responses . In general , the direct antifungal effector activity mediates non-specific elimination of pathogens through either a phagocytic process with intracellular killing of internalised pathogens or through the secretion of microbiocidal compounds against undigested fungal molecules . The activation and induction of the specific adaptive immune responses is accomplished by the production of pro-inflammatory mediators , including chemokines and cytokines , providing co-stimulatory signals to naive T cells , as well as antigen uptake and presentation to CD4+ and CD8+ T cells [3 , 4] . Many individuals in mycetoma endemic areas are exposed to the causative aetiological agents , but only few develop the disease . This may suggest variable responses of the host immune system towards the invading agent . In this respect , the role of the innate immunity in host resistance to mycetoma infection has been studied in vitro and in animal models , but few studies have been performed in humans . T cell–mediated immune response to eumycetoma fungi in humans was studied by Mahgoub and associates who suggest that patients with eumycetoma have a weak cell-mediated response as determined by skin reaction to dinitrochlorobenzene [5] . Decreased lymphocyte proliferative response to phytohemagglutinin in those patients was also reported . However , no evidence was provided to confirm whether this is a primary immune deficiency or a secondary response to a severe infection . In addition , the same study showed high levels of IgA and IgM and low levels of IgG antibodies in mycetoma patients [5] . In actinomycetoma , Gonzalez-Ochoa and Baranda [6] found that patients with severe lesions and extensive tissue destruction displayed a weak skin reaction to some pathogenic bacteria polysaccharides such as , Nocardia brasiliensis [6] . However , it was not clear whether this represented a T-helper-1 ( Th-1 ) or T-helper-2 ( Th-2 ) response . To date there has been limited data on the immune response to mycetoma infection and how patients can modulate their response against M . mycetomatis . With this background , the present study aims to determine the Th-1 and Th-2 cytokines response of patients infected with M . mycetomatis and to find out the association between the measured Th-1 and Th-2 cytokine levels and the disease prognosis and outcome .
This descriptive cross-sectional hospital based study was conducted at the Mycetoma Research Centre , University of Khartoum , Khartoum , Sudan . In this study 140 individuals were enrolled; 49 ( 35% ) were females and 91 ( 65% ) were males ( Table 1 ) , with an overall median age of 25 years ( range 12–70 years ) . 70 patients with confirmed mycetoma infection due to Madurella mycetomatis were recruited . The study population was divided into three groups; group I: healthy controls ( n = 70; median age 25 years ( range 12 to 70 years ) ) , matched for sex , age and locality with the patients group . Group II: mycetoma patients without surgical excision ( n = 35 patients; median age 25 ( range 13 to 70 years ) ) , these patients were not treated with surgical excision and were under medical treatment ( 200 mg bd Itraconazole or 400 mg bd ketoconazole ) . Group III: mycetoma patients who underwent surgical excision ( n = 35 patients; median age 25 years ( range 12 to 70 years ) and medical treatment ( 200 mg bd Itraconazole ) . One hundred μl of blood were collected on filter paper ( Whatman qualitative filter paper , Grade 1 , circles , diam . 42 . 5 mm from SIGMA-ALORICH , KSA ) for cytokine’s determination . The use of filter paper dried whole blood spots ( DBS ) for specimen collection was preferred to facilitate collection , storage and transportation of specimens in addition to being recommended by the World Health Organization ( WHO ) and also used in several previous studies [7–9] . A hole puncher with a diameter of 6 mm was used for cutting out discs from the filter paper in the middle of the blood spot , where the blood was assumed to be evenly spread . The discs were put in 10 ml tubes and 500 μl of PBS containing 0 . 05% Tween and 0 . 5% BSA was added . The discs were then incubated for 2 hrs at room temperature on a shaker . Finally , after vortexing the samples for 30 seconds , the supernatants ( eluted serum ) were collected with a Pasteur pipette and aliquoted in new 1 . 5 ml cryo tubes and stored at −20°C until analysis . The extract corresponds to a serum dilution of ~1:100 . This method was modified from a previous report by Mercader and colleagues [8] . Measurements of cytokines were performed in sera by flow cytometry using Cytometric Bead Array ( CBA ) technology , as detailed by Cook and associates [10] . Human Inflammation CBA kit ( BD Biosciences , San Jose , CA ) was used to quantitatively measure IFN-γ , TNF-α , IL-1β , IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , and IL-13 levels . The sensitivity of Human Inflammation CBA was comparable to conventional ELISA [11] . Samples were analysed using a BD FACSCalibur flow cytometer ( BD Biosciences , San Jose , CA ) , according to the manufacturer’s instructions . The data was managed by SPSS statistics software version 23 for Windows ( IBM , SPSS statistics ) . The one-way analysis of variance ( ANOVA ) and Tukey’s test for post hoc analysis were used to compare mean levels of cytokines between various study groups . The difference in cytokine levels across groups was analysed using ANOVA test ( Table 2 ) . Linear regression models were used to predict each cytokine level ( Table 3 ) . Unstandardised coefficient ( B ) regression is the determination of the statistical relationship between two or more variables [12] . B analysis was adjusted for each cytokine according to gender ( Female = 0 and male = 1 ) , medical treatments ( Itraconazole = 0 and Ketoconazole = 1 ) , size of mass ( >10 cm = 1 ) , presence of grains ( No = 0 and Yes = 1 ) and age , as independent variables . This study was approved by the Ethics Committee of Soba University Hospital , Khartoum , Sudan . Written informed consent was obtained from the participants prior to their enrolment in the study . Informed consent was also obtained from children and their guardians before participation . The work described here was performed in accordance with the Declaration of Helsinki [13] .
A higher proportion of mycetoma patients were males ( 80% ) compared with females ( 20% ) . Combined ( both with and without surgical intervention ) males and females among mycetoma patients groups were 56/70 and 14/70 , respectively ( p <0 . 001; Table 1 ) . Patients with mycetoma received various antifungal drugs , which were used in combination with or without surgical excision . Of the 70 individuals who received oral medication in this study , 46 patients ( 66% ) received Itraconazole . Out of the patients who were treated with Itraconazole , Eleven patients ( 24% ) were treated without surgical excision and 35 patients ( 76% ) were surgically treated along with Itraconazole 200 mg bd . Twenty four patients ( 34% ) received Ketoconazole 400 mg bd [p value <0 . 001 and 95% confidence interval 95%CI; ( 0 . 55 to 0 . 80 ) ] ( Table 1 ) . Ketoconazole 400 mg bd was only used among patients without surgical excision and not following surgery , whereas Itraconazole 200 mg bd was the only choice postoperatively [p value <0 . 001 and 95% CI; ( 0 . 58 to 0 . 93 ) ] . The proportion of lesions that were more than 10 cm in diameter were significantly higher in the surgically treated group compared to the non-surgically treated patients [p value = 0 . 037 and 95% CI; ( -0 . 58 to 0 . 13 ) ] ( Table 1 ) . Patients with mycetoma infection had significantly higher cytokine levels including IFN-γ , TNF-α , IL-2 , IL-4 , IL-5 , IL-6 , IL-10 and IL-13 , compared to the control group ( overall p value for each cytokine <0 . 001 ) ( Table 2 ) . In contrast; no significant difference was observed in the levels of IL-1β between the study groups ( overall p value = 0 . 913 ) ( Table 2 ) . Linear regression analysis showed significantly higher levels of Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) among mycetoma patients treated with surgical excision than in those treated without surgical intervention . Unadjusted B ( 95% CI ) for: IFN-γ = [5 . 64; 95% CI ( 1 . 33 to 9 . 96 ) , p value = 0 . 011] . For TNF-α = [14 . 58; 95% CI ( 11 . 56 to 17 . 60 ) , p value <0 . 001] . For IL-1β = [-0 . 36; 95% CI ( -0 . 67 to -0 . 05 ) , p value = 0 . 022] . For IL-2 = [7 . 55; 95% CI ( 5 . 61 to 9 . 50 ) , p value <0 . 001] ( Table 3 ) . When B was adjusted for gender , medical treatment , size of lesions and the presence of grains; similar statistical analysis indicated significantly higher levels of Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) among mycetoma patients treated with surgical excision than in those treated without surgical excision . Adjusted B ( 95% CI ) for: IFN-γ = [6 . 62; 95% CI ( 1 . 42 to 11 . 81 ) , p value = 0 . 017] . For TNF-α = [12 . 69; 95% CI ( 9 . 94 to 16 . 32 ) , p value <0 . 001] . For IL-1β = [-0 . 75; 95% CI ( -1 . 13 to -0 . 37 ) , p value <0 . 001] . For IL-2 = [6 . 59; 95% CI ( 3 . 91 to 9 . 28 ) , p value <0 . 001] ( Table 3 ) . In contrast , a similar linear regression analysis model for Th-2 cytokines showed significantly lower levels of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) among mycetoma patients treated with surgical excision , compared to those treated without surgical excision . Unadjusted B ( 95% CI ) for: IL-4 = [-2 . 57; 95% CI ( -3 . 14 to -2 . 0 ) , p value <0 . 001] . For IL-5 = [-2 . 08; 95% CI ( -2 . 54 to -1 . 62 ) , p value <0 . 001] . For IL-6 = [-10 . 09; 95% CI ( -13 . 68 to -6 . 51 ) , p value <0 . 001] . For IL-10 = [-5 . 33; 95% CI ( -7 . 79 to -2 . 87 ) , p value <0 . 001] ( Table 3 ) . When B was adjusted for gender , medical treatment , size of lesions and presence of grains , a similar statistical analysis model showed significantly lower levels of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) among mycetoma patients treated with surgical excision compared to those treated without surgical excision ( Table 3 ) . Adjusted B ( 95% CI ) for: IL-4 = [-2 . 82; 95% CI ( -3 . 65to -1 . 99 ) , p value <0 . 001] . For IL-5 = [-2 . 38; 95% CI ( -3 . 04 to -1 . 72 ) , p value <0 . 001] . For IL-6 = [-7 . 66; 95% CI ( -12 . 88 to -2 . 44 ) , p value = 0 . 005] . For IL-10 = [-3 . 58; 95% CI ( -7 . 16 to -0 . 01 ) , p value = 0 . 05] ( Table 3 ) .
It is known that fungi release antigens ( Ag ) on the skin surface , and the antigens that penetrate the skin are subsequently captured by an antigen-presenting cell ( APC ) such as dendritic cells ( DCs ) [14] . Fungal antigens can also play an important role in the DCs maturation . Furthermore , production of inflammatory cytokines such as IFN-γ and TNF-α by other innate cells such as natural killer cells ( NK ) further enhance the activation of microbiocidal functions of phagocytic cells as well as maturation of DCs [15] . In the present study , Th-1 cytokines ( IFN-γ , TNF-α , and IL-2 ) were found to be significantly higher in mycetoma patients than in controls . Besides , the levels of Th-1 ( IFN-γ , TNF-α , IL-1β and IL-2 ) were significantly higher in mycetoma patients treated with surgical excision compared to those who were only medically treated . These findings go a long way to explain the earlier findings of van de Sande and associates [16] , that neutrophils are attracted to the site of infection by mycetoma antigen , secrete TNF-α and IFN-γ cytokines in the presence of IL-17 [17] . Interestingly , in a previous study , Cassatella and colleagues suggested that neutrophils are multipurpose cells which play many roles , not only in inflammatory progressions but also in immune and antitumor processes [18] . The same group had also added that , IFN-γ activated neutrophils release biologically active TNF-α related apoptosis-inducing ligand ( TRAIL/APO2 ligand ) , a molecule that exerts selective apoptotic activities towards tumours [18] . Additionally , Elagab and associates , showed that , the peripheral blood mononuclear cells ( PBMC ) of mycetoma patients react differently to M . mycetomatis antigens than healthy controls [19] . In general , when PBMCs produce IFN-γ upon stimulation with the antigen , no production of IL-10 was detected [19] . There is also no significant differences between the cytokines TNF-α and TGF-β levels in patients and controls [19] . The discrepancy between Elagab’s findings [19] and our findings may be explained by the differences in the study design . IL-1 is an essential host defence cytokine against a broad range of pathogens , ranging from bacteria to parasites and fungi [20] . IL-1β is primarily produced by innate immune cells such as monocytes , macrophages and dendritic cells upon activation , and is also an important cytokine for the control of fungal infection [21] . It is also an important proinflammatory mediator whose production is controlled by multiprotein complexes called inflammasomes [22 , 23] . Although IL-1β plays an active role in containing infection caused by different fungi , its role in controlling fungal infections remains unclear [24] . The results of the current study has shown that higher levels of IL-1β cytokine are strongly associated with mycetoma patients treated with surgical excision , compared to those treated without surgical intervention . It is of interest to note that , IL-1β can play a crucial role in the activation of complement protein-3 ( CR3 ) , dectin-1 as well as caspase-8 in coordinating cell death and inflammasome responses to β-glucans [25] . Our findings led us to suggest that the observed higher levels of IL-1β cytokine play an important role in reducing the risk of M . mycetomatis infection . However , more studies are needed to confirm farther this observation . As mentioned earlier cytokine IL-2 exerts critical functions during immune homeostasis via its effects on Treg cells , and by optimising the effector lymphocyte responses of both T-cells and B-cells . In addition , IL-2 receptors ( IL-2R ) were shown to be present on human neutrophils , and that IL-2-neutrophil interactions are believed to be important in both tumour rejection and increased susceptibility to bacterial infections [26 , 27] . It is relevant to add that a previous study on mycetoma patients from an endemic area [16] , demonstrated that neutrophils are attracted to the site of infection by the mycetoma antigen . In the current study IL-2 levels were significantly higher in mycetoma patients compared to controls . In addition , IL-2 cytokine levels were elevated significantly in mycetoma patients treated with surgical excision , compared to those treated without surgical intervention . We take this finding to indicate that , IL-2 cytokine plays a major role in the pathogenesis of mycetoma infection . This novel finding on an association of IL-2 and neutrophils should pave the way to new avenues of research on IL-2-neutrophil interactions to better understand the response of patients to mycetoma infection . The cytokines IL-4 , IL-5 , IL-13 and GM-CSF are produced by T-helper-2 cells at the site of inflammation but also they have important functions in haematopoiesis . These cytokines , individually or collectively along with chemokines such as CCL11 , play a major role in coordinating the maturation and mobilisation of leukocytes ( Monocytes/Macrophages and Neutrophils ) and mast cell progenitors , ensuring the continued supply of leukocytes to the site of the inflammation [28 , 29] . In present study , the in vivo effect of M . mycetomatis infection on the production of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) was clearly reflected by the , significantly higher levels of Th-2 cytokines in mycetoma patients compared to controls . Moreover , lower levels of Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly associated with mycetoma patients treated with surgical excision , compared to those treated without surgical intervention . This finding is in line with the earlier hypothesis that Th-2 cytokines play an important role in the activation of the humoral immune response [28 , 29] . It is well stablished that the type of cell-mediated immunity ( CMI ) is critical in determining resistance or susceptibility to fungal infection . In general , Th1-type CMI is required for the clearance of fungal infections , while Th2 immunity usually enhances the susceptibility to infection and allergic responses [30] . Additionally , Th-1 cells are concerned mainly with production of cytokines such as IFN-γ , and promote CMI and phagocyte activation , while in contrast , Th-2 cells predominantly produce cytokines such as IL-4 and IL-5 and tend to promote antibody production [30–32] . Besides , IL-4 and IL-5 cytokines can play an important role in the activation of B-cells to differentiate to plasma cells that secrete IgM antibody and also generate memory B cells [33] . A previous similar study found elevated levels of IgM antibody in mycetoma patients [5] . Besides , another study on immune responses against mycetoma Sudanese patients , demonstrated the presence of immunoglobulins G , M and complement on the surface of the grains and on the filaments inside the grains of mycetoma lesions [34] . Also , both neutrophils and macrophages were recruited into the lesion by complement and were involved in the fragmentation of the grains . The cytokines profile in the lesion and regional lymph nodes was of a dominant Th-2 pattern ( IL-10 and IL-4 ) [34] , and these elevated levels of Th-2 cytokines in mycetoma patients may trigger the increased production of IgG , IgM and complement . The significance of this phenomenon needs further investigations . We noted with great interest higher levels ofTh-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) in mycetoma patients treated with surgical excision than in those patients treated without surgical intervention . However , in contrast the Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly lower in patients treated with surgical excision compared to those treated without surgical intervention . These results suggest that , the defence against the fungus M . mycetomatis is based on the adaptive effector phase and the duration of the infection as well as the size of the mycetoma mass and presence of grains . The effects of CMI can also play a critical role in reducing the risk of localised infection in mycetoma patients treated with surgical excision compared to those treated without surgical intervention . The essential role of the CMI response is to destroy the fungi and produce an immuno-protective status against infection . At this moment the exact explanation of this finding is not clear and requires further investigation in mycetoma patients . | Madurella mycetomatis is the most common causative agent for eumycetoma , which is a progressive and destructive subcutaneous inflammatory disease . It is a neglected tropical disease affecting the population in poor and remote endemic tropical and subtropical areas . Currently , the susceptibility and resistance to mycetoma are not well defined , and many factors can be incriminated , including immunological , genetic , or environmental ones . The current descriptive cross-sectional study was conducted to determine the Th-1 and Th-2 cytokine levels among 70 patients with Madurella mycetomatis eumycetoma and 70 healthy controls . It aimed to find out the association between the disease prognosis and the level of these cytokines . Significantly higher levels of the Th-1 cytokines ( IFN-γ , TNF-α , IL-1β and IL-2 ) were found in patients treated with surgical excision compared to those treated without surgical intervention . However , the Th-2 cytokines ( IL-4 , IL-5 , IL-6 and IL-10 ) were significantly lower in patients treated with surgical excision compared to those treated without surgical excision . These findings suggested that , cell-mediated immunity has a prime role in the pathogenesis of eumycetoma . |
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The use of high quality disease surveillance data has become increasingly important for public health action against new threats . In response , countries have developed a wide range of disease surveillance systems enabled by technological advancements . The heterogeneity and complexity of country data systems have caused a growing need for international organizations such as the World Health Organization ( WHO ) to coordinate the standardization , integration , and dissemination of country disease data at the global level for research and policy . The availability and consistency of currently available disease surveillance data at the global level are unclear . We investigated this for dengue surveillance data provided online by the WHO . We extracted all dengue surveillance data provided online by WHO Headquarters and Regional Offices ( RO’s ) . We assessed the availability and consistency of these data by comparing indicators within and between sources . We also assessed the consistency of dengue data provided online by two example countries ( Brazil and Indonesia ) . Data were available from WHO for 100 countries since 1955 representing a total of 23 million dengue cases and 82 thousand deaths ever reported to WHO . The availability of data on DengueNet and some RO’s declined dramatically after 2005 . Consistency was lacking between sources ( 84% across all indicators representing a discrepancy of almost half a million cases ) . Within sources , data at high spatial resolution were often incomplete . The decline of publicly available , integrated dengue surveillance data at the global level will limit opportunities for research , policy , and advocacy . A new financial and operational framework will be necessary for innovation and for the continued availability of integrated country disease data at the global level .
Threats to public health around the world have become increasingly complex and the importance of high quality disease surveillance for preparedness and disease control will continue to grow [1] . Scientific progress and global cooperation against emerging threats will depend on the availability and sharing of disease surveillance data between countries . Global health and funding agencies emphasized this in an appeal for greater availability and use of data for global health [2 , 3] . Formally , the 2005 International Health Regulations require the use and sharing of data in response to new threats [4 , 5] . The central role of the World Health Organization ( WHO ) in global disease surveillance and data dissemination has been stated in World Health Assembly resolutions for specific diseases [6] . The WHO has developed various data systems to integrate and disseminate country surveillance data such as the Global Health Observatory [7] , the Global TB Database [8] , DengueNet [9] , RabNet [10] and FluNet [11] . In addition to these global databases , WHO Regional Offices ( RO’s ) also provide disease surveillance data through their websites to inform member countries on disease patterns and trends in their region . Increasingly , country Ministries of Health post their own disease surveillance data online for their constituency , mostly in the form of epidemiological bulletins but sometimes using sophisticated online data repositories such as those developed by Brazil and Indonesia [12 , 13] . The public availability of disease surveillance data from various heterogeneous sources provides new opportunities for research , training , and policy making but can also lead to confusion on data discrepancies between sources . Limited information on methodology used at various steps along the data trail from within countries to the global level has further complicated this data landscape . Although it is generally known that surveillance data reported by different agencies may not be identical due to reporting methods and definitions , few studies have quantified the availability and consistency of publicly available disease surveillance data across sources . This information can guide policy makers , scientists , students , and others to use available data more effectively . We used the example of dengue to assess the availability and consistency of surveillance data provided online by WHO . We also provided examples of online data provided by the Ministry of Health of Brazil and Indonesia .
We extracted all online dengue surveillance data from WHO ( WHO DengueNet [9] and from the websites of the Pan American Health Organization ( PAHO ) [14] , the WHO Southeast Asia Regional Office ( SEARO ) [15] and the WHO Western Pacific Regional Office ( WPRO ) [16 , 17] ) , and by the Ministries of Health ( MOH ) of Brazil and Indonesia [12 , 13] . Brazil and Indonesia were selected as examples because they provided open access to detailed dengue surveillance data online in computer readable format . All available data up to April 12th 2013 were extracted at the highest possible spatiotemporal resolution . To obtain standardized data across these sources , we extracted data for all ages and for both genders combined . We did not extract serotype specific data because these were minimally available . We standardized indicators reported by different sources across spatial and temporal scales and also harmonized country names using the United Nations ISO country name standard ( ISO 3166 ) [18] . We assessed the availability of dengue data from each source and also measured data consistency between and within sources . We defined consistency between sources as the percent agreement of data reported for overlapping countries and time periods . We defined consistency within a source by the percent agreement of indicators that were recomputed by us from data within the source and the corresponding indicators provided by the same source . All data used in this study are made publicly available through the University of Pittsburgh Project Tycho online data system ( www . tycho . pitt . edu ) .
We extracted a total of 71 , 460 counts for 100 countries from DengueNet and WHO RO websites ( Fig 1 ) . These data represented a total of ~23 million dengue cases and ~82 , 000 deaths that have been reported to WHO between 1955 and 2012 . Of these , ~13 million cases ( 56% ) and ~20 , 000 deaths ( 24% ) were reported between 2000 and 2012 . A total of ~4 . 6 million cases were reported by WPRO ( 20% ) , ~3 . 2 million ( 14% ) by SEARO , and ~15 million ( 66% ) by PAHO countries ( Table 1 ) . The majority of dengue deaths were reported by SEARO ( 49% ) and WPRO ( 44% ) . Each source provided counts for a range of different indicators ( Fig 2 ) . Data for “all” dengue cases ( dengue fever and dengue hemorrhagic fever combined ) and “all” dengue deaths were available from DengueNet and all RO’s . Data for DHF cases were predominantly from PAHO , few counts were from WPRO and none from SEARO . Across time , DengueNet provided counts for the longest time period ( 1955–2011 ) compared to SEARO ( 1985–2006 ) , WPRO ( 2000–2011 ) , and PAHO ( 1995–2012 ) ( Fig 3 and S1 Fig ) . Across sources , data for “all” cases were provided for the longest time periods , followed by mortality data . Data for DHF counts were available for the shortest time periods ( Fig 3 and S1B Fig ) . In general , many counts were missing across years and countries . We compared data from DengueNet and RO’s to assess consistency across sources ( Table 2 and S2 Fig ) . The overall percent agreement was 83 . 8% across all indicators . Data from SEARO were the most consistent with a percent agreement of 92 . 2% and data from WPRO were the least consistent at 72 . 3% . Data for DHF cases were more consistent compared to the other indicators at 92 . 4% compared to 76 . 1% for “all” cases and 89 . 4% for “all” deaths . DengueNet values for all indicators were generally lower compared to values from RO’s . In total , DengueNet reported 426 , 808 fewer “all cases” , 17 , 854 fewer DHF cases , and 245 fewer deaths compared to RO’s ( Table 2 ) . We recomputed the number of “all” cases for DengueNet from separately reported dengue fever ( DF ) and DHF cases . We also recomputed the case fatality rate ( CFR ) for DengueNet from reported cases and deaths . Our recomputed data for “all” cases corresponded with 98 . 9% of original values and for CFR with 99 . 5% ( Table 3 ) . We also recomputed the annual number of dengue cases at country level from monthly cases at the provincial level ( in DengueNet , data were either reported at the country level by year or at the provincial level by month ) . Our recomputed annual country level data for “all” cases was > 3 million cases lower compared to reported data at that level . The recomputed values for “all” deaths were about 2000 deaths lower compared to reported mortality at country level by year . This discrepancy was likely due to missing data at the lower administrative levels . We found that provincial level data were not available for all calendar months in years before 1997 and after 2004 ( S3 Fig ) . In addition , provincial level data for countries were only available for a median of 3 . 5% of provinces before 1996 and for 85 . 7% of provinces after 1996 ( using The Second Administrative Level Boundaries data set project ( SALB ) [19] for the expected number of provinces per country ) . We also assessed dengue surveillance data provided online by the Ministries of Health of Brazil and Indonesia ( Fig 4 ) . Both these countries are dengue endemic and have developed online databases that provide publicly available dengue surveillance data . The annual number of “all” cases reported by the Brazil and Indonesia MOH corresponded to WHO data for most years except 2008 ( Brazil ) and 2000/2004 ( Indonesia ) . No WHO data were available for Indonesia after 2005 . We found discrepancies within the data provided by the MOH of Indonesia for years after 2007 . Our recomputed number of cases per year at the country level from reported provincial data ( 1st administrative level ) was higher than country level values recomputed from district data ( 2nd administrative level ) . This suggested that data from lower administrative levels were incomplete .
We integrated publicly available online dengue surveillance data from various WHO and country sources to describe the availability and consistency of globally available dengue surveillance data . We found that consistency of overlapping data between DengueNet and WHO Regional Offices was lacking and that data at subnational levels were often incomplete . This incompleteness was difficult to recognize since the absence of data for provinces or districts was not indicated explicitly . DengueNet systematically reported lower values compared to the RO’s . This may be due to a difference in timing of data reports made by countries and a lack of updating DengueNet as countries updated their figures . DengueNet was created by the WHO Headquarters in 2002 as part of the Global Health Atlas [9 , 20] . Focal points were appointed and trained in every country to upload standardized reports into the DengueNet repository [21] . This has successfully led to public sharing of dengue data across countries through a central global repository . In addition to DengueNet , RO’s also routinely release dengue surveillance data from member countries through their websites . PAHO and SEARO provide links to surveillance data sheets in PDF format and WPRO has developed an online Health Information and Intelligence Platform ( HIIP ) . The WHO is the only source of integrated disease surveillance data across countries . Numerous studies have used WHO dengue surveillance data to describe trends and patterns of this disease at the global [22 , 23 , 24] and regional level [25 , 26 , 27] . Despite their role as a core resource for international dengue surveillance data , DengueNet and some RO data have not been regularly updated over the past decade , most likely due to capacity and funding constraints . With the decline of WHO as a central global resource for dengue surveillance data , the data landscape will become increasingly scattered and difficult to navigate . Other agencies or institutes can contribute additional capacity or alternative frameworks for global disease surveillance data may be needed , such as a distributed network instead of centralized databases . Increasingly , individual countries disseminate their own disease surveillance data online in various formats ranging from epidemiological bulletins to sophisticated databases . This has greatly advanced the availability of disease data at the global level . In 2010 the 63rd World Health Assembly stated that “the WHO urges member states to improve the collection of reliable health information and data and to maximize , where appropriate , their free and unrestricted availability in the public domain” [28] . Country data systems however use a large diversity of surveillance methodology and definitions that often lack detailed documentation . The potential biases and lack of comparability of data across countries are limiting the efficient use of these data . The reporting process of dengue surveillance data from countries to WHO also lacks detailed documentation and may vary across countries . Future research should formally compare country data systems and country vs . WHO data to gain more insight in potential biases of the various sources . A standardized and curated global data system can maximize opportunities for the efficient use of country disease data for science and policy . Data standardization and curation are essential for a global data system . For example we found that ~16% of country names in DengueNet were different from country names used by the RO’s ( S1 Table ) . Across all WHO sources , ~19% of country names were different from the UN ISO standard for country names [19] . In the absence of up-to-date global platforms for disease surveillance data , alternative data systems have emerged such as Google Dengue and Flu Trends and the HealthMap project that automatically integrate data from search queries or online news items respectively [29 , 30 , 31] . Innovative technological solutions and capacity used by these projects should be applied to integrate country disease surveillance data as well to establish a state-of-the-art 21st century global data system . This system can be coordinated by WHO but can be implemented by external institutes that have already created large scale public health data systems such as the Institute of Health Metrics and Evaluation , the Malaria Atlas Project , or Project Tycho . A new and sustainable framework will be required to ensure that integrated and curated disease surveillance data from countries around the world will continue to be available to stakeholders at all levels . Innovative technology should be used for data integration that minimizes the burden on countries but maximizes data availability and use . Academic and private sector partners should step up to support international agencies with this increasingly complex mandate . | The use of high quality data and information has become essential for public health agencies to monitor and protect population health . Technological advancement has enabled the development of sophisticated disease surveillance systems by many countries . Increasingly , countries are making surveillance data publicly available to their constituencies . A key role of international agencies such as the World Health Organization is the integration and curation of country data at the global level . Because it can be confusing to navigate the current online disease data landscape , we assessed the availability and consistency of online available surveillance data for dengue provided by the World Health Organization and two example countries ( Brazil and Indonesia ) . We found that data availability declined substantially after 2005 and that consistency between sources was limited to 84% , representing a discrepancy of half a million cases . These limitations reduce opportunities for the efficient use of country data to counter public health threats . A new financial and operational model is needed to advance the use of disease data at the global level . Industry and academic partners need to step up to support this mandate . |
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Telomerase is a telomere dedicated reverse transcriptase that replicates the very ends of eukaryotic chromosomes . Saccharomyces cerevisiae telomerase consists of TLC1 ( the RNA template ) , Est2 ( the catalytic subunit ) , and two accessory proteins , Est1 and Est3 , that are essential in vivo for telomerase activity but are dispensable for catalysis in vitro . Est1 functions in both recruitment and activation of telomerase . The association of Est3 with telomeres occurred largely in late S/G2 phase , the time when telomerase acts and Est1 telomere binding occurs . Est3 telomere binding was Est1-dependent . This dependence is likely due to a direct interaction between the two proteins , as purified recombinant Est1 and Est3 interacted in vitro . Est3 abundance was neither cell cycle–regulated nor Est1-dependent . Est3 was the most abundant of the three Est proteins ( 84 . 3±13 . 3 molecules per cell versus 71 . 1±19 . 2 for Est1 and 37 . 2±6 . 5 for Est2 ) , so its telomere association and/or activity is unlikely to be limited by its relative abundance . Est2 and Est1 telomere binding was unaffected by the absence of Est3 . Taken together , these data indicate that Est3 acts downstream of both Est2 and Est1 and that the putative activation function of Est1 can be explained by its role in recruiting Est3 to telomeres .
Telomeres , the DNA-protein structures at the ends of most eukaryotic chromosomes are essential for genome integrity: they protect chromosomes from degradation and end-to-end fusions , distinguish chromosome ends from DNA breaks , position chromosomes for pairing in meiosis and ensure the complete replication of chromosome ends ( reviewed in [1]–[3] ) . Telomeric sequences are comprised of highly repetitive DNA in which the strand running 5′ to 3′ towards the chromosome end is G-rich and extended to form a 3′ single stranded tail . For example , throughout most of the cell cycle , each Saccharomyces cerevisiae telomere has ∼300 bps of duplex C1-3A/TG1-3 DNA ending with a short ∼12–14 base TG1-3-tail [4] . In most eukaryotes , a specialized reverse transcriptase called telomerase provides the basis for an RNA templated replication mechanism that elongates the G-rich strand of telomeric DNA . The S . cerevisiae telomerase consists of the catalytic reverse transcriptase subunit , Est2 [5] , the templating RNA component , TLC1 [6] , and two regulatory proteins Est1 [7] and Est3 [8] , [9] . Eliminating any one of these four gene products results in the est ( ever shorter telomeres ) phenotype , characterized by gradual telomere shortening and death in most cells after ∼50–100 generations [6]–[8] . In addition , certain alleles of CDC13 , such as cdc13-2 , a gene that encodes an essential protein that binds the 3′ single stranded TG1-3 tails in vivo [10] , [11] , are telomerase defective [12] . Cdc13 is a multi-functional telomere binding protein that is essential to protect chromosome ends from degradation [13] and has a key function in telomerase recruitment [14] , [15] . Most of the yeast telomere is replicated by standard semi-conservative DNA replication , which occurs at the end of S phase [16] , [17] . This replication is followed by C-strand resection , which generates long ( ∼50–100 base ) transient single-stranded G-tails [17]–[19] . Telomerase action is also restricted to late S/G2 phase [20] , [21] , even though Est2 is telomere associated throughout most of the cell cycle with peak binding in both G1 and late S/G2 phase [22] . Est2 telomere association during G1 and early S phase requires a specific interaction between TLC1 and the heterodimeric Ku complex [23] . Est2 telomere association in late S/G2 phase is low in cdc13-2 cells [22] , requires a specific interaction between a stem-bulge region on TLC1 RNA and Est1 [24] , and is lost entirely in tlc1Δ cells [22] , [24] . Est1 telomere binding , which occurs only in late S/G2 phase , coincident with telomerase action [22] , is low when it cannot interact with TLC1 RNA or in cdc13-2 cells and is eliminated altogether in est2Δ cells [24] . Moreover , Est1 abundance is cell cycle regulated , low in G1 and early S phase , and peaking in late S/G2 phase [22] , [25] Although both Est1 and Est3 are essential for telomerase action in vivo [8] , the requirement for Est1 ( but not Est3 ) can be bypassed by expressing a DBDCdc13-Est2 fusion protein ( DBD , DNA binding domain ) [14] . This result is consistent with a model in which a Cdc13-Est1 interaction recruits the telomerase holoenzyme to the telomere , an interpretation supported by biochemical and genetic data that show that the two proteins interact in vivo [26] , [27] . However , Est1 has a role other than recruitment as it is needed for the hyper-elongation of telomeres that occurs in cells expressing a DBDCdc13-Est2 fusion [14] . This extra function can be seen in vitro as well: Est1 is required for long extension products in a PCR based in vitro assay [28] , and its addition to a primer extension assay increases the amount of product [29] . In Candida albicans , Est1 affects both initiation and processivity of telomerase in vitro in a primer-specific manner [30] . Thus , Est1 appears to function in both recruitment and activation of telomerase . The telomeric role of Est3 is separable from that of Est1 as an Est3-DBDCdc13 fusion cannot bypass the requirement for Est1 and an Est1-DBDCdc13 fusion cannot rescue the telomerase defect of an est3Δ strain [9] . Nonetheless , the two proteins are interconnected . In C . albicans , Est3 and Est1 mutually depend on each other for assembly into the telomerase holoenzyme [30] . However , the situation in S . cerevisiae is unclear as using co-immunoprecipitation , one group found that Est3 association with Est2/TLC1 is Est1 dependent [25] while one did not [9] , [31] . In vitro , extracts prepared from a C . albicans est3Δ strain show the same initiation and processivity defects in telomerase assays as extracts from est1Δ cells [30] , while all primers are extended less efficiently in extracts from an est3Δ S . castellii strain [31] . Est3 from both S . cerevisiae and C . albicans has structural similarity to TPP1 within an OB-fold domain [32] , [33] , a mammalian telomere structural protein that has roles in both telomere end protection and promoting telomerase activity [34]–[36] . Here we used chromatin immuno-precipitation ( ChIP ) in mutant and WT cells to determine the temporal pattern and genetic dependencies for S . cerevisiae Est3 telomere binding . We show that Est3 telomere binding occurred mainly in late S/G2 phase and was at background or close to background levels in tlc1Δ , est1Δ and est2Δ cells . In contrast , the late S/G2 phase association of both Est1 and Est2 was not reduced in est3Δ cells , making est3Δ the first telomerase deficient strain where the temporal and quantitative pattern of Est2 telomere binding is indistinguishable from that in WT cells . As purified Est1 and Est3 interact in vitro , the putative activation role of Est1 can be explained by its role in recruiting Est3 to telomeres . We also determined the absolute copy number for each of the three Est proteins , the first such determination for any protein subunit of telomerase in fungi .
As in previous work from our lab , we used chromatin immuno-precipitation ( ChIP ) to determine protein association with telomeres in vivo ( e . g . [22] ) . Previous studies from other labs used an HA3-tagged version of Est3 [9] , [25] to study its association with other telomerase subunits , but this protein was not detectable at telomeres by ChIP ( our unpublished results ) . Est3 directly tagged with nine Myc-epitopes was not functional ( data not shown ) . Therefore , we epitope tagged Est3 at its carboxyl-terminus with a glycine linker ( G8 ) , which improves the functionality of epitope tagged proteins [37] , followed by either 9 or 18 Myc epitopes . As with all of the epitope tagged proteins used in this paper , Est3 was expressed from its own promoter as the only copy of EST3 in the strain . Cells expressing these Est3 alleles did not senesce and maintained stable telomere length , although as in the HA3-tagged strain [9] , [25] , telomeres were shorter than in WT cells ( see methods and Figure S1A for more details ) . Both Myc-tagged proteins were detectable by an anti-Myc antibody in western blotting of whole cell extracts ( Figure 1C , Figure S1B ) , but only Est3-G8-Myc18 gave reliable results in a ChIP assay . We used real-time PCR quantitation to evaluate the association of Est3-G8-Myc18 to two native telomeres , the right arm of chromosome VI ( TEL-VI-R ) and the left arm of chromosome XV ( TEL-XV-L ) throughout a synchronized cell cycle ( Figure 1 , Figure 2 ) . For all synchrony experiments , cells were arrested in late G1 phase with alpha factor and then released into the cell cycle . The quality of each synchrony was evaluated by flow cytometry , which revealed no major reproducible differences in cell cycle progression among the various strains used in this study ( Figure 1A , Figure 2A ) . We used real-time PCR to determine the amount of telomeric DNA in the immuno-precipitate . Synchronies were done at least three times with the data presented as the average telomere association +/− one standard deviation . At each time point , we normalized the telomeric signal to the signal at the non-telomeric ARO1 locus in the same sample . At both telomeres , the profile of Est3 telomere association in WT cells was bi-phasic , with a small peak in G1 phase ( 0 and 15 min ) and 2-2 . 5-fold higher binding in late S/G2 phase ( 60 min ) ( Figure 1B ) . This late S/G2 binding was ∼10-fold above the no tag control . This biphasic binding pattern was reminiscent of Est2 telomere association except that for Est2 , the peaks in G1 and late S/G2 phases were of similar magnitude [22] . Compared to the untagged strain , the G1 telomere binding was significant at TEL-VI-R ( P = 0 . 020 ) but not at TEL-XV-L ( P = 0 . 078 ) , while the late S/G2 phase Est3-G8-Myc18 association was significant at both telomeres ( P = 0 . 0086 , TEL-VI-R; P = 0 . 0079 , TEL-XV-L ) . Differential binding throughout the cell cycle was not due to cell cycle variations in protein abundance as levels of tagged Est3 were constant throughout the cell cycle ( Figure 1C; Figure S1C . ) . We conclude that the telomere association of Est3 occurs mainly at late S/G2 phase , which coincides temporally with the peak of Est1 telomere association , the second peak of Est2 telomere binding , and the time of telomerase action ( see Introduction ) . Next , we determined if Est3-G8-Myc18 telomere binding requires the presence of other telomerase components by examining its telomere association in synchronized est2Δ , tlc1Δ ( Figure 1B ) , and est1Δ ( Figure 2B ) cells . In the absence of either Est2 or TLC1 RNA , Est3-G8-Myc18 telomere association was not detected at any point in the cell cycle at either TEL-VI-R or TEL-XV-L ( see legend of Figure 1 and Figure 2 for P values ) . The reduced Est3 telomere binding in est2Δ and tlc1Δ cells was not due to reduced Est3 abundance ( Figure 1D ) . Therefore , both Est2 and TLC1 RNA are absolutely required for Est3 telomere association . In est1Δ cells , the amount of telomere associated Est3-G8-Myc18 at late S/G2 phase was significantly reduced at TEL VI-R ( 60 min , P = 0 . 0007 ) and at TEL XV-L ( 60 min , P = 0 . 0028 ) compared to the level of binding in WT cells ( Figure 2B ) . When compared to the no tag control , the level of Est3-G8-Myc18 binding from 45 to 75 minutes at telomere VI-R was low but still significant while the level of binding to XV-L was not significant ( see Figure 2 legend for P values for each time point ) . Est3-G8-Myc18 association in G1 and early S phase ( Figure 2B , 0 to 30 min ) was also reduced but was significantly higher than in the no tag control at both telomeres ( see Figure 2 legend for P values ) . Although there was a small amount of Est3-G8-Myc18 telomere binding in the absence of Est1 , Est3 telomere association was largely Est1 dependent . Est1 could affect Est3 telomere binding directly or indirectly . To distinguish between the two possibilities , we purified C-terminally strep-tagged [38] Est1 from S . cerevisiae and N-terminally strep-tagged Est3 from E . coli , and removed the Est3 strep-tag post-purification . Both proteins were purified to near homogeneity ( Figure 3A ) , and their identities verified by MS/MS mass spectrometry . When expressed from its own promoter on a CEN plasmid as the only copy in the cell , strep-tagged Est1 supported WT length telomeres ( data not shown ) . We tested the ability of the two purified proteins to interact using a magnetic bead pull down experiment ( Figure 3B ) . Purified Est1 was mixed with streptavidin-coated magnetic beads that capture the C-terminal affinity tag of Est1 ( lane 4 ) . Est3 was not pulled down by the beads in the absence of Est1 ( lane 5 ) . However , in the presence of Est1 , Est3 was bead-associated ( lane 6 ) . BSA , which was used as a negative control , was not bead associated either in the presence or absence of Est1 . Further evidence for specificity is provided by similar assays where Est1 did not interact with the DBD region of Cdc13 , and Est3 did not interact with full length Cdc13 ( YW and VAZ , in preparation ) . As monitored by whole cell and immuno-precipitate western experiments [22] , [23] , [25] , [39] , Est1 abundance is cell cycle regulated , low in alpha factor arrested G1 phase cells and peaking at late S/G2 phase . Consistent with our previous studies and coincident with its peak in abundance , Est1 telomere binding occurred at late S/G2 phase ( 60 min ) at both telomeres in WT cells ( Figure 4B ) . In est3Δ cells , Est1 binding at TEL-VI-R was indistinguishable from WT ( 60 min , P = 0 . 68 ) . Est1 association at TEL-XV-L was marginally lower in est3Δ compare to WT cells ( 60 min ) , but this difference was not significant ( P = 0 . 26 ) . Est1 abundance was also not Est3 dependent ( Figure 4C ) . We conclude that the telomere association of Est1 is Est3 independent . Likewise , Est2 telomere binding ( Figure 4E ) and its abundance ( Figure 4F ) were very similar in WT and est3Δ cells . As shown previously [22] , in WT cells , Est2 bound to TEL-VI-R and TEL-XV-L throughout the cell cycle with peak binding in G1 ( 0 , 15 min ) and late S/G2 phases ( 60 min ) ( Figure 4E ) . In est3Δ cells , Est2 binding was indistinguishable from WT throughout the cell cycle at both telomeres except at 30 min ( p = 0 . 05 ) at TEL VI-R . We conclude that the telomere association of the catalytic subunit Est2 is also Est3 independent . Telomerase action is cell-cycle regulated [20] , [21] , and Cdc13 and each of the three Est proteins binds telomeres in a cell cycle dependent manner ( [22] and Figure 1 ) . Nonetheless , Est1 is the only one of these proteins whose abundance is cell cycle regulated [22] , [25] . The checkpoint kinase Tel1 binds telomeres in a cell cycle dependent manner , and this binding is necessary for preferential association of Est2 and Est1 with a short VII-L telomere [40] . Cdk1/Cdc28 activity is required for C-strand degradation [41] , [42] , and Cdc13 is phosphorylated by Cdk1 in a cell cycle dependent manner that affects the level of Est1 telomere association [43] , [44] . Since Est1 , Est2 and Est3 contain one or more candidate Tel1 and Cdk1 phosphorylation sites , one possibility is that their cell cycle regulated telomere binding is due to cell cycle regulated phosphorylation . However , there is no evidence for slower migrating species for any of the three Est proteins when analyzed by conventional polyacrylamide gels ( [22] and Figure 1 , Figure 4 ) . To address in more detail the possibility that Est proteins are phosphorylated , we prepared protein extracts from synchronized cells expressing either Est1-Myc9 , Myc9-Est2 , or Est3-G8-Myc9 and separated the extracts in gels containing Phos-tag ( Wako ) , a reagent that binds phosphate groups resulting in slower mobility of phosphorylated proteins [45] . In the extracts containing Est1-Myc9 or Myc9-Est2 , there was no detectable fraction of the protein with reduced mobility in Phos-tag gels ( Figure S2 ) . This pattern was seen for extracts resolved in 25 µM Phos-tag ( as shown in Figure S2 ) as well as in 100 µM phos-tag ( data not shown ) . Likewise , the mobility of Est3-G8-Myc9 was not affected by 25 or 50 µM Phos-tag ( data not shown ) . However , extracts from cells expressing Est3-G8-Myc9 and resolved in gels containing 100 µM Phos-tag had about equal amounts of a slower migrating form of Est3 ( Figure 5 , right panels ) that was not detected in the absence of Phos-tag ( Figure 5 , left panels ) . The two species were of similar levels in extracts from asynchronous ( Figure 5A ) , G1 arrested ( Figure 5B , 0 min ) or from throughout a synchronous cell cycle ( Figure 5B , 15–90 min ) . Thus , the apparent phosphorylation of Est3 revealed in the presence of Phos-tag was not cell cycle regulated . As part of our efforts to understand Est protein function , we generated Myc9-tagged versions of each of the three Est proteins ( here and [23] ) . We used these tagged alleles to generate a strain in which Est1 , Est2 , and Est3 were each marked with nine Myc epitopes . The triply tagged strain maintained stable telomeres that were ∼75–125 bps shorter than WT telomeres ( Figure 6A ) and showed no evidence of senescence even after >6 restreaks ( data not shown ) . We measured the absolute abundance of each of the Myc9-tagged proteins using quantitative western blot analyses . In order to provide a standard to convert western signals to absolute protein levels , a Myc9-tagged Cdc13 protein was fused to a C-terminal tandem 5X Strep-Tag II , over-expressed in S . cerevisiae , and purified to homogeneity ( Figure 6B ) . Untagged yeast extract containing serial dilutions of purified Myc9-tagged Cdc13 protein ranging from 0 . 5 to 10 femtomoles were run on a gel along with whole cell extracts from the triply tagged strain ( Figure 6C ) . Comparison of the signals of Myc9-tagged Est proteins to the known standards allowed us to determine that there are 1 . 18±0 . 32×10−22 , 0 . 62±0 . 11×10−22 , and 1 . 40±0 . 22×10−22 moles ( or 71 . 1±19 . 2 , 37 . 2±6 . 5 , and 84 . 3±13 . 3 molecules ) of Est1 , Est2 , and Est3 per cell , respectively ( Figure 6D ) . These results were statistically identical to those obtained from singly Myc9-tagged strains ( data not shown ) .
Although Est1 and Est3 were discovered over 15 years ago [7] , [8] , their exact roles in telomerase-mediated telomere maintenance have been difficult to establish . Evidence from diverse approaches indicates that Est1 has a key role in recruiting telomerase to DNA ends by virtue of its ability to interact with Cdc13 [14] , [15] , [26] , [27] . This step is likely direct as purified Cdc13 and Est1 interact in vitro , and this interaction facilitates Est1 association with telomeric DNA ( YW and VAZ , in preparation ) . However , as summarized in the introduction , current data suggest that Est1 also has a telomerase activation function that is poorly understood . Even less is known about the function ( s ) of Est3 except that it is clearly essential in vivo , and its role cannot be bypassed by a variety of fusion proteins ( see Introduction ) . Using ChIP , we find that while Est3 telomere binding was bi-phasic , occurring in both G1 and late S/G2 phase , late S/G2 binding was 2 to 3 times higher than G1 phase association . Thus , peak Est3 binding correlated with the time in the cell cycle when telomerase is active ( Figure 1B ) . In contrast , Est3 abundance was not cell cycle regulated ( Figure 1C , Figure S1C ) . Even though at least two kinases , Tel1 and Cdk1/Cdc28 are important for telomerase action [39] , [40] , [42]–[44] , [46]–[48] , cell cycle regulated telomere binding of Est3 ( Figure 1B ) , as well as Est1 and Est2 [22] , is probably not due to their being phosphorylated in a cell cycle dependent manner . By the criterion of phos-tag induced changes in protein mobility , we found no evidence for phosphorylation of Est1 or Est2 ( Figure S2 ) , and although Est3 appeared to be phosphorylated , this modification was not cell cycle regulated ( Figure 5 ) . In addition , mutation of the single Cdk1/Cdc28 ( S56A ) or the single Tel1 ( S96A ) consensus site in Est3 did not affect telomere length or senescence ( CTT and VAZ , data not shown ) . Therefore , we found no evidence that phosphorylation of Est1 , Est2 , or Est3 is important for their telomere functions . We also examined Est3 telomere binding in the absence of other telomerase components . Est3 telomere binding was at background levels in both tlc1Δ and est2Δ cells ( Figure 1B ) and very low in est1Δ cells ( Figure 2B ) , even though none of these mutations affected Est3 abundance ( Figure 1D , Figure 2C ) . Neither Est1 nor Est2 is telomere associated in tlc1Δ or est2Δ cells [24] . Therefore , the lack of Est3 telomere binding in these backgrounds could be due to the absence of either Est1 or Est2 . However , in est1Δ cells , Est2 telomere binding in G1 phase is at wild type levels and reduced but still high ( 40–50% of wild type binding ) in late S/G2 phase [24] . The simplest explanation for these data is that an Est1-Est3 interaction is critical for Est3 telomere binding , especially in late S/G2 phase , since Est3 telomere binding was very low in the absence of Est1 , even in situations where there are substantial levels of telomere associated Est2 . This interpretation is particularly appealing given our demonstration that purified Est1 and Est3 interact in vitro ( Figure 3B ) . Together , these results provide one of the most important mechanistic implications of our data because they suggest that the activation function of Est1 is due to its recruitment of Est3 to telomeres , as proposed previously [30] . Indeed structural considerations have led to the proposal that Est3 is a homologue of the mammalian TPP1 protein [32] , [33] . Human TPP1 cooperates with POT1 , the human G-strand telomere binding protein , to increase telomerase processivity [35] , [36] It has been proposed that the direct interaction between Est3/TPP1 and the G-strand binding protein was lost in S . cerevisiae , requiring a new link , Est1 , to allow Cdc13-Est3 cooperation [49] . Our data support this hypothesis . Recent in vitro data using S . castellii Est3 are also consistent with Est3 having a positive effect on telomerase processivity [31] . However , a role for Est3 in promoting processivity is not sufficient to explain all of the Est3 data as Est3 activity seems to be required for even a minimal level of telomere repeat addition in vivo . In the case of the mammalian system , in the absence of TPP1 , POT1 alone inhibits telomerase activity in vitro [35] , [50] . Thus , like TPP1 , Est3 may function in facilitating telomerase to overcome the inhibition by Cdc13 [51] , explaining the complete lack of telomerase activity in the est3Δ cell . Although our data argue that Est1 is the main factor recruiting Est3 to telomeres , our results also suggest that there is a secondary pathway for Est3 recruitment , which is likely Est2-mediated . A minor role for Est2 in Est3 recruitment can explain the low levels of Est3 at telomeres in G1 arrested cells ( Figure 1 , Figure 2 ) , when Est2 binding is high but Est1 telomere binding is not detected [22] . It can also explain why Est3 was found at low but detectable levels at telomeres in both G1 and late S/G2 phase est1Δ cells ( Figure 2B ) . In support of this interpretation , while the Est3 binding at the 60 min time point at both telomeres was reduced over 80% in est1Δ cells , telomere binding was reduced only 30% at both telomeres in G1 arrested cells ( 0 time point ) in this background ( Figure 2B ) . Genetic evidence provides strong support for interaction between the TEN domain of Est2 and Est3 , although a direct interaction has not yet been established [52] . Our data can also help resolve a discrepancy where one group finds that Est3 association with the holoenzyme is Est1 dependent [25] and one finds that it is Est2 , not Est1 , dependent [9] , [31] . Our data argue that Est1 has a key role and Est2 a more minor role in recruiting Est3 to telomeres ( Figure 1 , Figure 2 ) . Unlike our telomere binding studies which used synchronous cultures , these other studies were done with asynchronous cells . However , the Est1/Est3 interaction that brings Est3 to telomeres in late S/G2 phase occurred in a relatively narrow window of the cell cycle ( Figure 2B ) , and it is easy to imagine that this dependence could be missed in asynchronous cells . It is possible that the study that found that Est1 was not needed for Est3 to co-immunoprecipitate with TLC1 RNA had a larger fraction of G1 phase cells than in the other study , and therefore , the Est3-TLC1 interaction they detect is Est2 , not Est1 mediated . Another key mechanistic finding from our study is that Est3 acts downstream of both Est1 and Est2 . This interpretation is based on the finding that the temporal and quantitative patterns of both Est1 ( Figure 4B ) and Est2 ( Figure 4E ) telomere association were not altered in est3Δ cells . These findings indicate that Est3′s essential role in telomere maintenance is not to support telomerase binding to Cdc13 coated telomeric DNA , although it might be needed for correct positioning or engagement of the holoenzyme at the very end of the G-tail . Thus , the presence of normal levels of telomere bound Est2/TLC1 RNA , the catalytic core of telomerase , at the appropriate time in the cell cycle , is not sufficient in vivo for telomere maintenance even though it supports telomerase action in vitro . Finally our analysis of the abundance of the three Est proteins puts limits on models for how Est3 regulates telomerase . Previous studies that monitored the levels of all yeast proteins as fusions to a GFP or TAP tag [53] detected no signal for any of the Est proteins . Indeed the only core subunit of yeast telomerase whose abundance is known is TLC1 , the telomerase RNA , which is estimated to be present in 29 . 9±3 . 6 molecules per haploid cell [54] . By using a strain expressing Myc9 tagged versions of Est1 , Est2 , and Est3 , we confirmed that all three Est proteins were present in low amounts , with Est2 being the least ( ∼37 ) and Est3 the most ( ∼84 molecules per cell ) abundant ( Figure 6D ) . Telomeres in the triply tagged strain were short but stable ( Figure 6A ) , and the protein levels obtained from this strain were indistinguishable from those obtained in three strains where only one of the three Est proteins was Myc-tagged , and telomere length was less affected ( YW and VAZ , data not shown ) . Therefore , these low abundances are probably not an artifact of partially active subunits , although we cannot rule out this possibility . So far Est2 is the only subunit whose abundance is known to be lower in the absence of another subunit ( TLC1 ) [22] while Est1 is the only subunit whose abundance is cell cycle regulated [22] , [25] . Even if Est3 acts as a dimer , as suggested by an earlier study [55] , Est3 is probably not the limiting protein subunit . This interpretation is supported by the effects of subunit over-expression on telomere length . While Est3 over-expression does not cause telomere lengthening [52] , [56] , Est1 over-expression does [56] , [57] . These effects can be explained by Est1 dependent , cell cycle and concentration limited recruitment of Est3 to telomeres with a resulting increase in telomerase processivity . Thus , the protein abundance data presented here make it clear that Est3′s unique and essential role in telomerase mediated telomere lengthening is unlikely due to its being the limiting telomerase component .
All experiments , unless noted otherwise , were conducted in the YPH background [58] ( see Table S1 for strain list ) . For cell cycle synchrony and chromatin immuno-precipitation ( ChIP ) experiments , the BAR1 gene was deleted and replaced with KanMX6 . All epitope tagged genes were expressed from their own promoters at their endogenous loci . Epitope tagged EST1 and EST2 were previously described [22] , [23] , [37] . Est3 was similarly tagged at its carboxyl terminus with a flexible linker and 9 or 18 Myc epitopes . Telomere lengths were stable and cells did not senesce in either EST3-G8-MYC9 or Est3-G8-MYC18 cells but telomeres were shorter than WT in both strains ( Est3-G8-Myc9 , 50-75 bp shorter and 75 to 125 bps shorter in Est3-G8-Myc18; Figure S1A ) . Although both Est3- G8-Myc tagged proteins were detectable in whole cell westerns ( Figure S1B ) , only Est3-G8-Myc18 telomere binding was reliably detected by ChIP . Therefore , the EST3-G8-MYC18 allele was used for ChIP ( Figure 1B , Figure 2B ) and the EST3-G8-MYC9 allele was for westerns ( Figure 1C , Figure 5 , Figure 6 ) . Both chromosomal constructs were verified by sequencing , and thus , the larger apparent molecular weight of the fusion proteins in SDS-PAGE was not due to incorrectly fused proteins . In addition , the tagged loci segregated 2 2 with the TRP1 marker used to select its integration into the genome , indicating that an unknown protein was not accidently tagged . The est1Δ , est2Δ and tlc1Δ mutations were complete gene deletions and were generated as heterozygous diploids expressing EST3-G8-MYC18 tagged protein . Likewise , the est3Δ mutation was generated in a heterozygous diploid expressing EST1-MYC9 or EST2-G8-MYC18 tagged proteins . For all experiments in telomerase deficient strains , newly dissected estΔ or tlc1Δ spores were replica plated to verify the genotype and then cultured for immediate use so that analysis could be done before cells began to senesce . Cell cycle synchrony experiments were carried out as previously described [22] , [23] , [39] . Briefly , cells were cultured in rich media to an OD660 ∼0 . 15 and arrested with 0 . 01 µg/ml alpha factor for 3 . 5 hr at 24°C with shaking . Aliquots of cells were removed from the alpha factor arrested culture ( 0 min time point ) and at 15 min intervals after release from G1 arrest and processed for fluorescence-activated cell sorting ( FACS ) analysis and ChIP at each time point . ChIP was performed as described [22] , [23] , [39] and quantitated on an iCycler iQ Real-Time PCR detection system ( Bio-Rad Laboratories ) . The relative fold enrichment of a protein with telomeres was determined by ( TELIP/TELIN ) / ( ARO1IP/AROIN ) , where IP is the amount of DNA sequence that was amplified from the anti-Myc immuno-precipitate and IN is the amount of DNA sequence that was amplified in the input DNA prior to immuno-precipitation . Each synchrony was repeated at least three times; error bars represent one standard deviation . Where applicable , a two-tailed Student's t test was used to determine statistical significance ( P values ≤0 . 05 were considered significant ) . Whole cell extracts from epitope tagged strains were probed with an anti-Myc monoclonal antibody ( 9E10 , Clontech ) as previously described [22] , [23] , [39] . Briefly , cells were grown in rich medium to mid-log phase for asynchronous cell growth or to early log phase as described for cell cycle synchrony . Cells were pelleted , resuspended in CE lysis buffer ( 50 mM HEPES pH 7 . 5 , 140 mM NaCl , 1 mM EDTA pH 8 . 0 , 10% glycerol , 0 . 1% IGEPAL CA-630 , 1 mM DTT , 1 mM PMSF and 1 tablet protease inhibitor EDTA-free/10mL ) and frozen in liquid nitrogen . Cells were thawed quickly then lysed for 1 min by mechanical disruption with the addition of 425–600 µm glass beads using a beat beater at 4°C . Total cell extracts were pre-cleared by centrifugation at 10 , 000 g for 30 min at 4°C . Protein concentration was determined by Coomassie Plus protein reagent ( Pierce ) and equivalent protein levels were separated in an 8% SDS-PAGE gel . Proteins were transferred to Immobilon PVDF ( 0 . 45 µM ) membranes ( Millipore ) , probed with an α-Myc primary antibody and goat-anti-mouse-HRP secondary antibody , and exposed to film . Myc9-tagged Cdc13 and Est1 were purified from yeast BCY123 carrying an arc1-K86R mutation . Cdc13-Myc9 and Est1 were cloned into a pYES2 vector ( Invitrogen ) with a carboxyl terminal tag consisting of a G8 linker , 5x Strep-Tag II , and a HAT tag ( Clontech ) . Protein over-expression was induced with 2% galactose at 30°C for 12 hr . Cdc13-Myc9 was purified by 0 . 1% polyethyleneimine precipitation , streptactin agarose ( Novagen ) , and Talon Metal Affinity resin ( Clontech ) and was concentrated and buffer exchanged to TDEG/100 buffer ( 25 mM Tris-Cl , pH 7 . 5 , 0 . 1 mM DTT , 0 . 1 mM EDTA , 10% glycerol , 100 mM NaCl ) on an Amicon Ultra-4 ( MWCO 50 kDa ) concentrator . Concentration was determined using an extinction coefficient of 87 , 050 M−1cm−1 at 280 nm . Est1 was purified by 0 . 1% polyethyleneimine precipitation , 45% ammonium sulfate precipitation , and a streptactin column . Fractions from the streptactin column were pooled and buffer exchanged to Est1 storage buffer ( 25 mM Na-HEPES , pH 7 . 0 , 200 mM NaCl , 0 . 1 mM EDTA , 0 . 1 mM DTT , 0 . 05% Triton X-100 , 20% glycerol ) using a PD-10 column ( GE Healthcare ) . Protein concentration was determined by comparison to known quantities of tagged Cdc13 in western blot analysis against anti-streptag II antibody ( Novagen ) . Est3 was corrected for its natural +1 frameshifting [59] and cloned into a pET21d vector ( Novagen ) fused to an amino terminal tag consisting of a HAT tag , 4x streptag II , a G8 linker , and a HRV 3C site . Fresh E . coli Rosetta2 ( DE3 ) ( Novagen ) transformants were grown at 18°C , and protein over-expression was induced with 0 . 1 mM isopropyl β-D-1-thiogalactopyranoside for 24 hr . Cells were lysed by sonication and Est3 was purified by streptactin and Talon columns . The N-terminal Strep-tag was cleaved off by HRV 3C protease and the tag-removed Est3 was concentrated-buffer exchanged to TDEG/100 buffer using an Amicon Ultra-4 ( MWCO 10 kDa ) concentrator . Protein concentration was determined using an extinction coefficient of 37 , 410 M−1 cm−1 at 280 nm . A complete reaction contained 1 µM each of Est1 and Est3 in 20 mM Tris-HCl , pH 7 . 5 , 100 mM NaCl , 0 . 2% Triton X-100 , 20 µg/mL BSA , and protease inhibitors ( Roche ) . Reactions were pre-incubated for 15 min on ice before addition of Dynabeads streptavidin M280 ( Invitrogen ) . Reactions were incubated with the beads on a rotary shaker at 4°C for 30 min before separating the unbound from bound proteins on a magnet . The beads were washed 3 times with 200 µl of washing buffer ( 20 mM Tris-HCl , pH 7 . 5 , 50 mM NaCl , 0 . 2% Triton X-100 , 5% glycerol ) before being resuspended in 20 µl loading buffer ( 36 mM Tris HCl , pH 6 . 9 , 1% SDS , 1% β-mercaptoethanol , 6% glycerol , 0 . 05% bromophenol blue ) , boiled , and loaded onto a 10% SDS-PAGE gels along with 15% of input materials . The gel was visualized by Coomassie brilliant blue staining . Extracts were prepared from cells expressing either EST1-Myc9 , Myc9-EST2 , or EST3-G8-Myc9 that were grown asynchronously at 30°C until an OD660 of 0 . 5 or synchronized as described above . Extracts were prepared by TCA precipitation as described [60] . Briefly , ∼10 mL of culture was centrifuged and washed once with 20% TCA . Cells were resuspended in 20% TCA , glass beads ( 400-600 µm ) were added and cells were lysed in a bead beater for 2 minutes at 4°C ( FastPrep 96; MPBiomedical ) . Extracts were pre-cleared by centrifugation at 3 , 000 rpm for 10 minutes at 4°C . The pellets were resuspended in buffer and run on western gels . Extracts from cells expressing either Est1 or Est2 were separated on 7 . 5% SDS-PAGE with or without acrylamide-pendant Phos-tag ( Phos-tag AAL-107 , Wako ) [45] . Extracts from cells expressing Est3 were separated on 10% SDS-PAGE with or without Phos-tag . Two different positive controls were used: S . pombe cells expressing Chk1-3HA treated ( or not ) with 40 µM Camptothecin to induce Chk1 phosphorylation or S . cerevisiae cells expressing Rfa1-Myc13 UV irradiated ( or not ) using a Strategene Stratalinker 1800 at 60 J/m2 . HA monoclonal antibody ( Santa Cruz ) was used to detect Chk1 . The α-Tubulin loading control was detected with monoclonal antibody ( Abcam ) . Cells were grown in rich medium to early-log phase , briefly sonicated , and the cell density determined on a Beckman Coutler Z2 cell counter . To achieve complete protein extraction , whole cell extracts were prepared as described [61] . Indicated amount of purified standard protein ( 10 , 6 , 4 , 2 , 1 , and 0 . 5 femtomoles ) was mixed with extracts from 2×107 cells of untagged strain and heated at 95°C for 3 min before loaded alongside with extracts from 4 , 2 , 1×107 cells of the triply tagged strain on a 9% SDS-PAGE for anti-Myc Western blot analysis . The tagged proteins were detected using chemiluminescence by a FluroChem CCD camera and quantified by AlphaView software ( Alpha Innotech ) . | Owing to the biochemical properties of DNA polymerases , the free ends of linear chromosomes , called telomeres , cannot be replicated by the same mechanisms that suffice for the rest of the chromosome . Instead they are maintained by a telomere-dedicated reverse transcriptase called telomerase that uses its integral RNA component as the template to make more telomeric DNA . In baker's yeast , telomerase is composed of a catalytic subunit ( Est2 ) , the templating RNA ( TLC1 ) , and two accessory proteins , Est1 and Est3 . Here we show that Est3 associates with telomeres late in the cell cycle , at the same time when telomerase is active , and this binding was Est1-dependent , even though Est3 abundance was neither cell cycle–regulated nor Est1-dependent . Since purified Est3 and Est1interacted in vitro , Est1-dependent recruitment of Est3 is probably due to direct protein–protein interaction . Neither Est1 nor Est2 telomere binding was Est3-dependent . Thus , Est3 acts downstream of telomerase recruitment to promote telomerase activity , and the telomerase activation functions of Est1 can be explained by its recruiting Est3 to telomeres . |
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Taenia solium is a neglected zoonotic parasite endemic throughout many low-income countries worldwide , including Zambia , where it causes human and pig diseases with high health and socioeconomic burdens . Lack of knowledge is a recognized risk factor , and consequently targeted health educational programs can decrease parasite transmission and disease occurrence in endemic areas . Preliminary assessment of the computer-based education program ‘The Vicious Worm’ in rural areas of eastern Zambia indicated that it was effective at increasing knowledge of T . solium in primary school students . The aim of this study was to evaluate the impact of ‘The Vicious Worm’ on knowledge retention by re-assessing the same primary school students one year after the initial education workshops . Follow-up questionnaires were administered in the original three primary schools in eastern Zambia in 2017 , 12 months after the original workshops . In total , 86 pupils participated in the follow-up sessions , representing 87% of the initial workshop respondents . Knowledge of T . solium at ‘follow-up’ was significantly higher than at the initial ‘pre’ questionnaire administered during the Vicious Worm workshop that took place one year earlier . While some specifics of the parasite’s life cycle were not completely understood , the key messages for disease prevention , such as the importance of hand washing and properly cooking pork , remained well understood by the students , even one year later . Results of this study indicate that ‘The Vicious Worm’ may be an effective tool for both short- and long-term T . solium education of rural primary school students in Zambia . Inclusion of educational workshops using ‘The Vicious Worm’ could be recommended for integrated cysticercosis control/elimination programs in sub-Saharan Africa , particularly if the content is simplified to focus on the key messages for prevention of disease transmission .
Taenia solium is a zoonotic parasite known as the pork tapeworm , which infects over 50 million people worldwide [1] . Invasion of the human brain by the larval stage of the parasite is known as neurocysticercosis ( NCC ) , which can cause neurological deficits including severe progressive headache , stroke and hydrocephalus , and is the world’s leading cause of preventable epilepsy [2] . Other impacts of human infection include treatment costs , productivity losses and social stigmatization of epilepsy sufferers [3] . Porcine infections ( porcine cysticercosis , PCC ) cause substantial economic losses from carcass condemnation , and reductions to farmer income and food safety that exacerbate the poverty cycle in many developing countries in which the parasite is endemic [4 , 5] . Despite global ‘tool readiness’ for control of T . solium [6] , high levels of active parasite transmission persist in many endemic countries throughout Latin America , Asia and sub-Saharan Africa , including Zambia . Transmission is to a large extent socially determined , with inadequate sanitation , poor hygiene practices , minimal access to medical or veterinary services , and low levels of health education enabling parasite transmission in areas where pigs are raised . A lack of knowledge of the parasite has been identified as one of the barriers for control , and targeted health education interventions have been shown to be an effective addition to other T . solium control measures [7–11] . Education is recognized by the World Health Organization as an important part of the multisectoral approach needed for control of zoonotic pathogens such as T . solium [12] . Computer-based tools have the advantages of providing standardized educational messages , reduce training costs , are able to be widely disseminated and can be updated more easily , compared to traditional paper-based learning systems [13] . ‘The Vicious Worm’ ( https://theviciousworm . sites . ku . dk ) is a freely-downloadable computer-based educational program designed to provide comprehensive information about T . solium in a fun and interactive way . It is set in a sub-Saharan African context and has different levels of detail to allow tailoring of the educational content to suit the needs of the target audience [13] . Studies with medical and agricultural professionals in Tanzania demonstrated significant knowledge uptake and retention , and reported behavioral changes and knowledge dissemination directly attributable to exposure to ‘The Vicious Worm’ [14 , 15] . The program had not previously been evaluated for use in school-going children , who have been shown to be effective ‘health change agents’ capable of effectively disseminating educational messages to family and community members [16 , 17] . A preliminary study conducted by the authors of this manuscript in three primary schools in the highly T . solium–endemic Eastern Province of Zambia in 2016 demonstrated significant uptake of T . solium-associated knowledge in adolescent primary school pupils in the short-term [18] . The study at hand revisited the same primary school pupils one year later , to evaluate the longer-term impact of ‘The Vicious Worm’ on T . solium–associated knowledge retention .
The study took place in the Nyembe ( Katete district ) , Chimvira and Herode ( Sinda district ) communities in the Eastern Province of Zambia . As discussed in [18] , the region is highly endemic for T . solium; prevalence of active human and pig infections are among the highest in the world , and over 57% of human epilepsy cases are attributable to NCC [19 , 20] . ‘CYSTISTOP’ is a prospective , large-scale community-based T . solium intervention study , which commenced in three study arms in the Katete and Sinda districts in the Eastern Province of Zambia in 2015 . The study has two intervention arms designed to compare integrated human- and pig-based interventions ( elimination study arm ) versus pig-only ( control study arm ) interventions , as compared to a negative control study arm . Health education was also conducted at four- ( elimination study arm ) and twelve-monthly ( control and negative control study arms ) intervals ( Fig 1 ) . Health educational methods included village-based educational sessions during sensitization , conducted in Chewa ( the local language ) by a trained bilingual CYSTISTOP program member . These sessions included descriptions of the parasite’s life cycle and ways to prevent its transmission in the villages , and utilized visual aids including a large canvas life cycle poster , a five-meter long ribbon to represent the adult tapeworm , and life-sized plasticine models of human stool demonstrating expelled tapeworm proglottids . Participation in village-based sensitization sessions was higher in the elimination study arm than in the control study arm ( 89% compared to 46% , [35] ) , and sessions were primarily attended by women , very young children , and few men ( personal observation . ) Large color posters of the parasite’s life cycle were permanently displayed at the rural health centers in each of the three study areas . Simplified A4-sized paper copies of the life cycle poster were also distributed to each household in the two intervention study areas ( elimination and control study arms ) during the baseline visits in October 2015 . The final component of CYSTISTOP’s health education intervention was workshops in primary schools using the ‘The Vicious Worm’ computer program . The educational workshops were conducted in Nyembe ( elimination study arm ) in July 2016 , and in the Kondwelani ( control study arm ) and Gunda ( negative control study arm ) primary schools in November 2016 as described in [18] . The initial workshops comprised a ‘pre’ questionnaire to assess baseline knowledge , an educational session using ‘The Vicious Worm’ , followed immediately by a ‘post’ questionnaire to evaluate knowledge uptake ( see Fig 2 ) . Follow-up sessions were scheduled in the same primary schools in July ( elimination study arm ) and early December ( control and negative control study arms ) 2017 , one year after the initial workshops . There were two questionnaires ( QS ) used in the sessions as per Hobbs et al [18]: the original questionnaire ( QS1 ) , modified from the original questionnaire [14] to include Zambian terminology , was used in the elimination study arm and had 24 questions grouped into eight categories . As the QS was deemed too long and complicated for primary school pupils , a simplified version ( QS2 ) containing 15 questions in three categories was subsequently used in the control and negative control study arms . Both QS were designed to test knowledge of human tapeworm infections , known as taeniosis ( TS ) ; human ( neuro ) cysticercosis ( NCC/CC ) ; and PCC , including the linkages between the disease states and methods of transmission , diagnosis , and prevention . ( The QS used in the sessions are provided in the data repository . ) All of the pupils who had attended the initial educational workshops in 2016 were invited to return for a follow-up session , conducted in the same primary schools in July ( elimination study arm ) and December ( control and negative control study arms ) 2017 . Follow-up sessions were conducted as per the ‘post’ QS used in the initial workshops , as described in [18] , and were conducted by one of the same two trained bilingual CYSTISTOP project members as in the original workshops . Briefly , QS were projected onto a classroom wall , and questions and answer options were read aloud in Chewa and repeated at least once for clarity . Using Bluetooth-connected TurningPoint clicker devices , all pupils had to individually submit their answer to each question before the group could proceed to the next question . At the conclusion of each session , the group was taken through the QS again to discuss the correct answers and address any remaining misconceptions . The sessions were between 30 ( QS2 ) and 45 ( QS1 ) minutes in duration . The differences in the two questionnaires prevented direct comparison of response data , so QS1 data ( elimination study arm ) were analyzed separately from QS2 ( control and negative control study arms ) . Each question was scored as either correct ( 1 ) or incorrect ( 0 ) , resulting in a maximum score of 24 for QS1 and 15 for QS2 . Some questions in QS1 had more than one correct answer; selection of any one of these answers resulted in a ‘correct’ outcome . Group ( QS1 and QS2 ) and individual ( QS2 only; a technical problem prevented the collection of individual QS1 data during the initial elimination study arm workshop ) responses to each session were exported into an Excel ( Microsoft Corporation , 2010 ) spreadsheet for descriptive statistics . Responses were assessed individually and by category . Grouped result data for QS1 were analyzed using a generalized linear model , using the number of positive and negative answers as binomial response variable , and study time point as categorical covariate . The absence of individual data did not allow taking the within-respondent correlations across study time points into account . Pairwise comparisons of mean scores by study time point were performed using Tukey’s all-pair comparisons method . Individual result data for QS2 collected at both baseline and follow-up allowed further analyses . The analysis of the correlated ‘pre’ , ‘post’ and ‘follow-up’ scores was carried out using a generalized linear mixed model using individual respondent as random effect , the number of positive and negative answers as binomial response variables , and study time point as categorical covariate . Pairwise comparisons of mean scores by study time point were performed using Tukey’s all-pair comparisons method . Additional multivariable analyses were performed adding the respondents’ age , gender , and school . This model was applied to the total scores and to each of the three categories . The analyses were performed using the lme4 and multcomp packages for R 3 . 5 . 1 [21–23] . This study was conducted as part of the ongoing CYSTISTOP project ( https://clinicaltrials . gov/ct2/show/NCT02612896 ) . Ethical clearance was obtained from the University of Zambia Biomedical Research Ethics Committee ( 004-09-15 ) and the Ethical Committee of the University of Antwerp , Belgium ( B300201628043 , EC UZA16/8/73 ) . The study was introduced and explained to all project participants , both in village group settings and within individual households , prior to each field visit . Written informed consent to participate in the workshops , voluntarily provided by a parent or guardian , was obtained for each pupil , and attendance at the educational sessions was voluntary . The sessions took place outside of normal school hours . There was no incentive for participation , but light refreshments were provided after the sessions .
This follow-up study indicates that educational workshops using ‘The Vicious Worm’ may have lasting positive effects on T . solium knowledge uptake and retention in rural adolescent primary school pupils in eastern Zambia . Knowledge levels at ‘follow-up’ were significantly higher than at baseline one year earlier , with increases of 14% and 10% compared to ‘pre’ levels in QS1 and QS2 , respectively . Compared to ‘post’ knowledge levels immediately following the educational component one year earlier , however , knowledge at ‘follow up’ was similar ( QS1 ) or significantly lower ( QS2 ) . The questions relating to general knowledge of TS and NCC , diagnosis of PCC , and prevention of PCC/TS/NCC were answered very well in both QS at ‘follow-up’ , with 63% of categories in QS1 and 66% of categories in QS2 answered correctly by at least 75% of the groups . The knowledge regarding prevention of the parasite’s transmission was both the best answered category , and showed the lowest decrease in knowledge from the ‘post’ round one year earlier . This indicates that although some aspects of the parasite’s life cycle remained imperfectly understood at ‘follow-up’ , the pupils generally retained the main aspects of T . solium and the key messages for disease prevention one year after ‘The Vicious Worm’ educational workshops . The parasite’s life cycle is complex , and certain aspects remained imperfectly understood by the pupils at ‘follow-up’ . Transmission of PCC was not well understood , nor was transmission of NCC/CC in humans . Many respondents from both QS selected the incorrect answer responses stating that NCC/CC is obtained via ingestion of raw or undercooked pork that is infected with PCC , which given the complexity of the T . solium life cycle is not surprising . Indeed , many other field studies have demonstrated similar results with adults , farmers and even veterinary and medical professionals showing imperfect understanding of the life cycle despite educational interventions [7 , 9 , 10 , 14 , 17 , 24] . However , what is of concern from these data is that some respondents apparently believed that people with NCC/CC or specifically epilepsy can transmit the disease to others ( 24% , QS2 ) . Epilepsy is often stigmatized in many low-income countries including Zambia , and the social and psychological effects of stigmatization can substantially decrease quality of life for epilepsy sufferers and their families [25 , 26] . While the majority of other respondents correctly indicated that NCC is not transmissible to others , this message should be particularly emphasized in future educational interventions . Many pupils again selected destruction of the pig and/or carcass as the most suitable method for management of live or slaughtered pigs with PCC , as was also seen in the initial workshops and discussed in [18] . While the ‘correct’ answers for the purposes of the QS scoring were treating pigs with oxfendazole or properly cooking pork , destruction of and proper disposal of heavily infected pork is in fact the recommended approach mandated by World Organization for Animal Health’s ( OIE ) Terrestrial Animal Health Code [27] and the Zambian Public Health Act [28] , and this should be reflected in the marking of these questions in future workshops . However , the OIE Terrestrial Animal Health Code also states that the meat of carcasses infected with less than 20 cysticerci can be consumed after treatment ( that is , freeze- or heat-treatment , with the latter reaching a core temperature of 80°C ) . As ‘backyard’ animal slaughter is frequently conducted in rural and remote communities in many developing countries including Zambia , meat inspection is often rudimentary or absent . Given the limited availability of nutrition and particularly protein in many rural and remote developing communities , insisting on strict measures pertaining to meat inspection and condemnation is not always realistic , and may foster resistance and/or resentment in some situations . We therefore feel it is important to also highlight the alternative options to carcass destruction , especially considering the nutritional needs of these and many other low-resource communities that are endemic for T . solium . Consequently , we would recommend that future educational messages and workshops should recommend destruction of heavily infected meat and carcasses wherever possible , while also promoting proper cooking of lightly infected meat and/or anthelmintic treatment of pigs as more realistic alternatives for some resource-poor endemic communities . The reason for the decreased knowledge regarding PCC transmission routes seen in students from the control and negative control study arms at ‘follow-up’ ( more students indicating that infection arises after pigs being mated with an infected pig , or after eating moldy feedstuff ) is unclear , but may be related to the decreased frequency of educational delivery in these study arms compared to in the elimination study arm . Adolescent primary school pupils were selected to participate in these educational workshops because studies have shown that school students can be ‘health change agents’ capable of effectively disseminating educational messages to family and community members [16 , 17] . A cluster-based education trial in northern Tanzania utilized leaflets and videos containing T . solium-specific health education in primary and secondary schools , and demonstrated generally increased knowledge and attitudes in pupils from intervention schools compared to control schools [17] . Using computer-based programs allows standardization of educational messages , while allowing flexibility and adaptation of the content to specific audiences . The recent release of ‘The Vicious Worm’ as a multiplatform smartphone app and the completed translation of the online version into Kiswahili [29] , will allow expansion of the program across the African continent . Other language translations are currently underway ( personal communication , C . Trevisan ) , and with adaptation of the illustrations and contexts for Latin American , Asian or other specific settings , this tool could be implemented worldwide . Other electronic educational media including short animated videos , talking books , songs and DVDs are increasingly used in public health campaigns around the world , with encouraging results [30] . In a Chinese study , a short animated cartoon called ‘The Magic Glasses’ was shown to halve infection rates of parasitic worms in school-aged children ( 8 . 4–4 . 1% , P<0 . 0001 ) , and observed occurrence of handwashing increased from 54% to 98 . 9% ( P<0 . 0001 ) in the intervention group compared to the control group [31] . Tablet-based educational interventions have also been successful at raising awareness and changing behaviors for prevention of other , non-parasitic diseases , including cervical cancer and human papilloma-virus infections [32] . It should be emphasized that increased knowledge and awareness of a topic does not necessarily translate into behavioral change , and there may be underlying sociocultural and/or economic factors contributing to parasite transmission in endemic communities that can override even known adverse health outcomes associated with certain behaviors [33 , 34] . Student responses given during these assessment situations may indicate what the students believed to be technically correct answers , rather than reflecting their actual behaviors and beliefs . Feedback from focus group discussions conducted in the elimination and control study arms indicated that behavioral changes have been initiated in the villages since the start of the CYSTISTOP project [35] , and follow-up observational visits to the study areas are planned for 2019 to corroborate these reports . The effectiveness of information transfer from educated individuals to others is difficult to quantify , and evaluation of such knowledge transfer was not within the scope of this study . A primary school-based health education trial in Tanzania demonstrated significant knowledge uptake in pupils from intervention schools compared to control schools , whereas evaluation of knowledge transfer to the community showed mixed results [36]: some parents reportedly implemented behavioral changes such as building toilets and boiling drinking water based on knowledge passed on from their children; others reportedly wished to do more but lacked resources to do so; and some parents found it improper for children to instruct their parents . Mwidunda et al [17] reported that secondary school students are often more respected in their families and communities than primary school pupils , and suggested that focusing health educational messages on secondary schools may increase effects of knowledge transfer to communities . No secondary schools are present in the study areas , as is typically the case for many remote and rural regions of Zambia , but conducting Vicious Worm workshops in secondary schools would be encouraged where possible . This study has limitations . The project activities including health education were conducted more frequently in the elimination study arm ( four-monthly ) than in the control and negative control study arms ( annually ) , which could have been at least partially responsible for the seemingly better knowledge retention at ‘follow-up’ demonstrated by the elimination study arm students ( QS1 ) . The use of two different QS prevented direct comparison of knowledge uptake and retention from individuals across all three study arms , which would have allowed even more robust analyses . In addition , because the technical error in the initial elimination study arm workshop prevented collection of individual response data , we only had grouped result data for QS1 , and were consequently not able to take the within-respondent correlation across study time points into account . This led to an underestimation of variances , and consequently an increased probability of ( falsely ) detecting significant associations . The comparisons across study time points for QS1 should therefore be interpreted with caution . The loss of twelve of the original students to follow-up in this study is another limitation , however statistical significance was nevertheless achieved . Evaluating the effects of knowledge uptake on behavioral change or the extent of knowledge transfer from students to others was outside the scope of this study , but would be useful to attempt in future studies . In future educational workshops using ‘The Vicious Worm’ it may be beneficial , as per the authors’ previous recommendations [18] , to modify the educational component to focus on the main methods for prevention of disease transmission , rather than detailing the T . solium life cycle . Tailoring educational materials to the specific sociocultural context , including use of non-textual media to include individuals with low literacy skills , may further enhance education uptake in endemic communities . The use of locally-broadcast radio programs or simple , illustrative printed material such as posters , leaflets and comic books may also add value to educational programs [7 , 8 , 11 , 37] , especially in areas where access to smartphones or computers is limited . Some standardized educational posters are available for T . solium education [38] , including several recently published online by the European Network on Taeniosis/Cysticercosis ( CYSTINET , COST Action TD1302 , http://www . cystinet . org/ ) ( see S1 File ) . The results from this follow-up study demonstrate that educational workshops using ‘The Vicious Worm’ can contribute to significantly increased T . solium knowledge in rural Zambian primary school students in both the short- and long-term . Despite some confusion regarding the precise relationships between TS , NCC/CC and PCC , in general the data indicate that the key messages for prevention of disease transmission , including the importance of hand washing and of proper cooking of pork , remained well understood by the students one year after the educational sessions . The flexible nature of ‘The Vicious Worm’ program , combined with recent and ongoing translations into languages other than English and the development of the app for smartphones , provides standardized educational content that can be tailored to the specific educational and sociocultural context of the target audience . For village-level educational interventions in rural endemic communities it may be advised to simplify or omit the more scientific aspects of ‘The Vicious Worm’ in favor of promoting key behavioral messages , to enhance knowledge uptake and retention . Focusing education on school-going children as key change agents may also increase community awareness and engagement . Tailored ‘Vicious Worm’-based educational interventions should be considered for incorporation with integrated T . solium control or elimination programs in future . | The zoonotic parasite Taenia solium , commonly known as the pork tapeworm , causes substantial public health and economic losses worldwide . It is commonly found in low-income countries where pigs are raised in areas of poor sanitation , including Zambia . The links between the parasite and its different disease forms in humans and pigs are not very well known , and ignorance of the parasite is a known risk factor for infection . Health education can significantly increase knowledge and awareness of the parasite and can inspire behavioral change that reduces disease transmission . ‘The Vicious Worm’ is a computer-based program designed to provide T . solium education in a fun and interactive way . We conducted educational workshops in three primary schools in rural areas of eastern Zambia , and preliminary assessment indicated that the ‘Vicious Worm’ educational content significantly improved students’ knowledge of T . solium . We also conducted follow-up studies in the same students one year later , and discovered that the students’ knowledge was still significantly higher than at baseline . We conclude that ‘The Vicious Worm’ may be a useful educational component to enable targeting of school students , and would recommend its inclusion in integrated T . solium control programs in future . |
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Although retinoic acid ( RA ) has been implicated as an extrinsic signal regulating forebrain neurogenesis , the processes regulated by RA signaling remain unclear . Here , analysis of retinaldehyde dehydrogenase mutant mouse embryos lacking RA synthesis demonstrates that RA generated by Raldh3 in the subventricular zone of the basal ganglia is required for GABAergic differentiation , whereas RA generated by Raldh2 in the meninges is unnecessary for development of the adjacent cortex . Neurospheres generated from the lateral ganglionic eminence ( LGE ) , where Raldh3 is highly expressed , produce endogenous RA , which is required for differentiation to GABAergic neurons . In Raldh3−/− embryos , LGE progenitors fail to differentiate into either GABAergic striatal projection neurons or GABAergic interneurons migrating to the olfactory bulb and cortex . We describe conditions for RA treatment of human embryonic stem cells that result in efficient differentiation to a heterogeneous population of GABAergic interneurons without the appearance of GABAergic striatal projection neurons , thus providing an in vitro method for generation of GABAergic interneurons for further study . Our observation that endogenous RA is required for generation of LGE-derived GABAergic neurons in the basal ganglia establishes a key role for RA signaling in development of the forebrain .
The embryonic forebrain , deriving from the most anterior part of the neural tube , comprises a complex set of structures in the developing brain . This complexity arises mainly due to the heterogeneity of the neurons comprising it in terms of morphology , structure , function , and genetic specification . During forebrain development , the dorsal domain ( pallium ) gives rise to the cortex while the ventral region ( subpallium ) generates the basal ganglia , i . e . the pallidum and the striatum , which , respectively , originate from the medial and lateral ganglionic eminences ( MGE , LGE ) [1] . The progenitor zones of the subpallial ganglionic eminences are the origin of chemically diverse populations of gamma-aminobutyric acid ( GABA ) ergic interneurons and projection neurons . GABAergic interneurons are inhibitory local circuit neurons modulating neuronal activity and synaptic plasticity . GABAergic neurons comprise ∼20% of all neurons within the cortex and hippocampus and ∼95% of the neurons within the striatum [2]–[4] . Whereas GABAergic projection neurons generated in the germinal zones of the LGE migrate radially to the adjacent striatum , GABAergic interneurons arise from both the MGE and LGE and migrate using multiple tangential routes to the olfactory bulb , cortex , and hippocampus [5]–[8] . Disturbed GABAergic neuron function has been associated with several neurological disorders including Huntington's disease , autism , schizophrenia , bipolar depression , and epilepsy [9]–[12] . Thus , a source of GABAergic neurons for cell replacement therapy may be useful for treatment of these neurological diseases . GABAergic neuronal diversity emerges during embryogenesis and depends on both the timing and the creation of specific anteroposterior and dorsoventral progenitor domains by the coordinated action of several transcription factors expressed by distinct progenitor populations [13] , [14] . In contrast , little is known about the extrinsic signaling pathways coordinating GABAergic specification in the basal ganglia . Retinoic acid ( RA ) functions as an extrinsic signal that regulates patterning of rhombomeres in the hindbrain and neuronal differentiation in the spinal cord [15]–[17] , but the role of RA in forebrain development remains unclear . RA is derived from vitamin A through a two-step enzymatic process , employing retinol dehydrogenase ( Rdh10 ) for oxidation of retinol to retinaldehyde , and retinaldehyde dehydrogenases Raldh1 ( Aldh1a1 ) , Raldh2 ( Aldh1a2 ) , and Raldh3 ( Aldh1a3 ) for oxidation of retinaldehyde to RA , which then functions as a ligand for nuclear RA receptors [18] . A role for RA signaling during mouse striatal development is evident after E12 . 5 when Raldh3 , expressed in the subventricular zone of the LGE [19] , plays a required role in the up-regulation of dopamine receptor D2 expression [20] . Consistent with this finding , loss of RA receptor-beta ( RARβ ) in null mutant mice is associated with defects in striatal dopaminergic neurogenesis after E13 . 5 resulting in motor behavioral defects [21] . A recent study using Rdh10 mutant embryos with reduced RA synthesis in the meninges suggested that RA is required for normal radial expansion of the dorsal cortex [22] . However , other studies have suggested that RA may not act in the embryonic cortex , as RA activity was detected in the LGE but not the cortex [23] . Here , we employ null mutants for Raldh3 ( Aldh1a3 ) and Raldh2 ( Aldh1a2 ) to ascertain the anatomical sites , cellular targets , and consequences of RA signaling in the embryonic forebrain . Our results provide evidence that Raldh3 expression in the LGE is a major source of RA production in the embryonic forebrain and is required for GABAergic differentiation from LGE-derived progenitors in the basal ganglia . Furthermore , our findings suggest that RA generated in the meninges by Raldh2 is not required to stimulate radial expansion of the cortex as previously suggested . We also report that RA induces GABAergic differentiation in neurons generated from LGE-derived neurospheres and human embryonic stem cells , thus implicating a role for RA as a GABAergic differentiation factor both in vivo and in vitro .
Although Raldh3 expression in the LGE from E12 . 5 to early postnatal stages suggests the LGE is a major site of RA action in the embryonic forebrain [19] , [24] , Raldh2 and Rdh10 are expressed in the meninges beginning at E12 . 5–E13 . 5 , suggesting that RA synthesized there may regulate corticogenesis [22] , [25] . In order to better define the timing and location of RA signaling in the developing forebrain from E12 . 5–E14 . 5 , we employed a tissue explant RA reporter cell assay [26] . Cortex and LGE tissues were dissected from E12 . 5 to E14 . 5 embryos and grown as explants in co-culture with the RA-reporter cells . As positive controls , eye ( E12 . 5 to E13 . 5 ) , which expresses Raldh1 and Raldh3 , and meninges ( E14 . 5 ) , which expresses Raldh2 , were dissected from the same embryos . Reporter cells co-cultured with cortex or LGE from E12 . 5 wild-type embryos displayed no RA activity , whereas eye explants did ( Figure 1A–C ) ; lack of RA activity in E12 . 5 LGE explants may be due to low initial Raldh3 expression . In accordance with the increase in Raldh3 expression in the LGE after E12 . 5 [19] , LGE explants from both E13 . 5 and E14 . 5 induced strong RA activity in the surrounding reporter cells ( Figure 1F , I ) . In contrast , E13 . 5 and E14 . 5 cortical explants remained unable to induce RA activity , whereas meninges and eye explants at these stages exhibited RA activity ( Figure 1D , E , G , H ) . To verify that RA activity detected in the LGE is due to Raldh3 expression , we found that loss of RA synthesis by Raldh3 resulted in lack of RA activity in Raldh3−/− LGE explants but had no effect on RA activity in meninges ( Figure 1M–O ) . Using an Raldh2−/− mutant model we found that all RA activity detectable in wild-type meninges at E14 . 5 was eliminated in Raldh2−/− meninges ( Figure 1G , J ) . Raldh2−/− cortical explants contained no RA activity as observed in wild-type ( Figure 1H , K ) , while RA activity was still observed in Raldh2−/− LGE ( Figure 1I , L ) . Together , the above findings demonstrate that RA produced by Raldh3 in the LGE can activate transcription in the basal ganglia , whereas RA produced by Raldh2 in the meninges does not activate transcription in the adjacent cortex . Exposure of the reporter cells to a range of RA concentrations between 1 nM and 1 µM provided a dose-response for RA activity ( Figure S1 ) ; a concentration of 1 nM was sufficient to activate the reporter line as previously reported [26] . Thus , the fact that RA activity was not detected in cortical explants from E12 . 5 to E14 . 5 indicates that RA is present at very low levels in the cortex . However , recent studies proposed a role for RA in corticogenesis and additionally reported a value for the concentration of RA in the mouse E14 . 5 cortex ( 0 . 28 µmole/mg ) [22] , which is seven orders of magnitude higher than that previously reported for mouse E13 . 5 forebrain ( 12 pmol/g ) [27] . The former value ( presented as µmole/mg rather than pmol/g ) is most likely in error as other studies reported RA concentrations in adult mouse cortex as 16 pmol/g and adult striatum as 78 pmol/g [28] , but this leaves in doubt how much RA was actually detected in E14 . 5 cortex . Our observation that RA activity during forebrain development is due primarily to Raldh3 expression in the LGE prompted us to investigate if neural precursors isolated from E14 . 5 LGE maintain their Raldh3 expression and RA activity when expanded in vitro under mitogen stimulation . Hence , we employed immunocytochemistry with a Raldh3 antibody together with the tissue explant RA bioassay using neurospheres generated from E14 . 5 LGE and cortex of wild-type and Raldh3−/− embryos . RA activity and Raldh3 immunostaining were detected in neurospheres derived from wild-type E14 . 5 LGE ( Figure 2C–D ) . In contrast , both Raldh3 immunostaining and RA activity were eliminated in LGE-derived neurospheres from Raldh3−/− embryos ( Figure 2A–B ) . Neither Raldh3 immunostaining nor RA activity were found in neurospheres derived from the cortex of either wild-type or Raldh3−/− embryos ( Figure 2E–H ) . These results further confirm that Raldh3 expression in the LGE is responsible for RA synthesis and additionally showed that neurospheres expanded from LGE cells maintain their RA activity . We investigated whether RA has an effect on the differentiation potential of regionally derived neurosphere cultures . Neurospheres from LGE and cortex of E14 . 5 wild-type and Raldh3−/− embryos were differentiated for 7 d and subsequently analyzed immunocytochemically with antibodies against the pan-neuronal marker β-tubulin-III ( Tuj1 ) , the GABA-synthesizing enzyme glutamic acid decarboxylase ( Gad67 ) , the neural progenitor marker nestin , and the astrocytic marker glial fibrillary acidic protein ( GFAP ) . Many wild-type LGE neurospheres untreated with RA were found to co-express Tuj1 and Gad67 , indicating they have differentiated and matured into a GABAergic phenotype ( 44 . 3%±13 . 0% ) , however very few Tuj1/Gad67-positive cells were detected in differentiated cultures of Raldh3−/− LGE neurospheres ( 13 . 2%±4 . 1% ) ( Figure 3A–B , K ) . Tuj1-expressing neurons that differentiated from cortical Raldh3−/− neurospheres appeared to have a similar morphology to those generated from cortical wild-type neurospheres , and Gad67 was never detected ( Figure 3C–D ) . Nestin-positive progenitors and cells with an astrocytic morphology expressing GFAP appeared similar in LGE and cortical differentiated cultures derived from wild-type and Raldh3−/− neurospheres ( Figure 3E–H ) . LGE neurospheres from both wild-type and Raldh3−/− embryos were differentiated in the presence of RA in order to further test the effect of RA on GABAergic neuronal differentiation . After 1 wk of differentiation in the presence of 100 nM RA , the majority of the generated neurons in Raldh3−/− and wild-type cultures were GABAergic , as observed by double staining for Tuj1 and Gad67 ( Figure 3I–J ) . However , when cortical neurospheres were differentiated in the presence of RA , Gad67 was never detected in either wild-type or Raldh3−/− differentiating cultures ( Figure 3K–L ) . Quantification of GABAergic neuron differentiation from wild-type and Raldh3−/− LGE neurospheres with or without added RA showed a significant increase of Gad67-positive neurons in the presence of RA . The proportion of GABAergic cells derived from Raldh3−/− LGE neurospheres increased significantly from 13 . 2%±4 . 1% under control conditions to 83 . 37%±11 . 75% when the neurospheres had been differentiated in 100 nM RA ( Figure 3M ) . The proportion of GABAergic neurons in cultures of wild-type LGE neurospheres increased from 44 . 3%±13 . 0% under control conditions to 76 . 45%±11 . 75% after treatment with 100 nM RA ( Figure 3M ) . Thus , E14 . 5 LGE derived cells from Raldh3−/− embryos can be expanded as neurospheres and are able to differentiate into neurons and glia , but they are unable to differentiate into GABAergic neurons unless RA is added . The observation that RA generated by Raldh3 induces GABAergic differentiation of neural precursors in vitro prompted us to investigate if RA signaling is required for GABAergic differentiation in the developing forebrain . We examined a panel of markers for both neural progenitors and differentiated neurons in forebrains from E14 . 5 wild-type and Raldh3−/− embryos . Raldh3 protein was observed at high levels in the SVZ of the LGE; Raldh3 was not detected in the Raldh3−/− forebrain ( Figure 4A–B ) . Nestin and RC2 immunoreactivity was not significantly changed in Raldh3−/− versus wild-type basal ganglia at E14 . 5 , suggesting that generation of neural progenitors is not affected when RA signaling is disrupted ( Figure 4C–F ) . In order to determine the proliferative capacity of these neural progenitors , double immunohistochemistry was performed with the proliferation marker Ki67 and the radial glia/progenitor marker nestin ( Figure S2A–B ) . We observed no reduction in the number of LGE proliferating progenitors in Raldh3−/− embryos compared to control embryos , showing that both generation and proliferation of neural progenitors is not affected when RA signaling is disrupted ( Figure S2C ) . MAP2 immunostaining marking postmitotic neurons [29] was unaffected in Raldh3−/− basal ganglia ( Figure 4G–H ) . The numbers of MAP2-expressing neurons were quantified in the striatum , cortex , and septum from wild-type and Raldh3−/− embryos . MAP2-expressing cells did not change in number in these regions of the forebrain in Raldh3−/− embryos , confirming that neurogenesis was not affected ( Figure S2D ) . A defect in GABAergic differentiation was observed when RA signaling is lost in the basal ganglia . Gad67-positive cells are normally present along the SVZ from the LGE to the septum ( Figure 4K ) , and the pattern of GABA immunoreactivity is normally similar to Gad67 although GABA-positive cells extend into the ventricular zone ( Figure 4I ) ; this is probably due to the fact that while Gad67 immunoreactivity marks only cells with GABA production ( i . e . GABAergic cells ) , GABA immunoreactivity could mark cells synthesizing GABA plus cells that uptake GABA released by Gad67-positive cells . In Raldh3−/− embryos , detection of both Gad67 and GABA was nearly eliminated in the LGE and septum ( Figure 4J , L ) . At E12 . 5 , when Raldh3 expression in the LGE has just initiated , we observed that GABA was detected in the MGE and LGE in a pattern that was not significantly different between wild-type and Raldh3−/− forebrain; GABA detection in the LGE at E12 . 5 was at a lower level than that seen at E14 . 5 ( Figure S3A–B ) . As RA activity is not yet detected in the LGE at E12 . 5 but is seen by E13 . 5 ( Figure 1C , F ) , these findings demonstrate that RA signaling initiating after E12 . 5 is required to stimulate the high level of GABAergic differentiation normally observed in the LGE by E14 . 5 . The cellular source of RA in the LGE has previously been associated with newly born neurons in the subventricular zone expressing Raldh3 [24] . We analyzed Raldh3 immunoreactivity in two distinct types of cells , radial glia and postmitotic neurons of the LGE at E14 . 5 . Double-labeling studies demonstrated that none of the RC2-positive radial glia exhibit Raldh3 detection , although the radial processes of these cells were observed next to Raldh3-positive cells localized in the SVZ that did not possess radial processes ( Figure S4A–C ) . In contrast , most Raldh3-expressing cells were also labeled with neuronal marker MAP2 ( Figure S4D–F ) . The above results provide further evidence that Raldh3-expressing cells in the LGE are newly born neurons defining a discrete region of the SVZ . In E18 . 5 wild-type embryos , Raldh3 detection remains strongest along the SVZ of the LGE ( particularly high in the dorsal LGE ) with weaker detection further ventrally along the septum; Raldh3 immunoreactivity was eliminated in Raldh3−/− forebrain ( Figure 5A–B ) . As observed at E14 . 5 , MAP2 immunostaining was unaffected in Raldh3−/− versus wild-type forebrain at 18 . 5 ( Figure 5G–H ) . At E18 . 5 , GABAergic differentiation in the striatum was nearly eliminated in Raldh3−/− forebrain as monitored by Gad67 immunoreactivity ( Figure 5C–D ) ; GABA detection was reduced in striatum but less so than Gad67 possibly due to diffusion of GABA still generated ventral of the striatum ( Figure 5E–F ) . Thus , not all regions of the basal ganglia were affected by the disruption of RA signaling , as both Gad67 and GABA immunoreactivity appear relatively normal in the pallidum and septum at E18 . 5 ( Figure 5C–F ) . The subcortical telencephalon is known to be the source of GABAergic projection neurons that migrate radially from the ventricular progenitor zone to reach their final destination . The LGE gives rise to GABAergic striatal projection neurons [1] , [30]–[35] , while the MGE gives rise to GABAergic projection neurons of the pallidum , septum , and nucleus basalis [1] , [30] . In order to determine if differentiation of striatal projection neurons is affected in E18 . 5 Raldh3−/− embryos , we examined Foxp1 , a marker for these neurons [36] , and found that Raldh3−/− embryos display normal Foxp1 immunoreactivity in the striatum ( Figure 5I–J ) . This observation supports our previous findings demonstrating that Raldh3 is not required to generate DARPP32-positive neurons , another marker of striatal medium-sized spiny projection neurons [20] . Instead , our findings demonstrate that RA is required for striatal projection neurons to acquire a GABAergic fate . Our results indicate that RA is required to stimulate GABA synthesis in LGE-derived progenitors . To characterize other aspects of the GABAergic phenotype in Raldh3−/− mutants , we analyzed expression of the vesicular GABA transporter ( VGAT , Viaat ) , a transporter that mediates accumulation of GABA into the synaptic vesicles before exocytotic release to the synaptic cleft [37] . Expression of VGAT appeared normal in Raldh3−/− forebrain ( Figure S5A–B ) , indicating that RA is not required for this aspect of GABAergic differentiation . Next , we wanted to investigate whether disruption of RA signaling could induce defects in the specification of other neuronal populations . Previous studies have shown that loss of Raldh3 or RARβ in the striatum results in down-regulation of dopamine receptor D2 in the nucleus accumbens [20] , [21] . However , in Raldh3−/− forebrain no difference was found in the expression of tyrosine hydroxylase ( TH ) , a marker of dopaminergic neurons ( Figure S5C–D ) . Also , we observed no difference in vesicular glutamate transporter ( VGLUT ) , a marker of glutamatergic neurons ( Figure S5E–F ) . In addition to neuronal markers , we examined whether loss of RA affects glia . We showed above that radial glia differentiation is not affected by loss of Raldh3 ( Figure 4E–F ) . We also analyzed astrocyte differentiation by analyzing expression of the astrocytic marker GFAP . We did not detect any difference in GFAP immunoreactivity in E18 . 5 Raldh3−/− forebrain compared to wild-type controls ( Figure 5K–L ) . Our findings thus suggest that RA is not required for gliogenesis or generation of radial projection neurons . Taken together , our observations at E12 . 5–E18 . 5 demonstrate that RA is required to stimulate a high level of GABAergic differentiation first in the LGE and then later in the striatum but that a Raldh3-independent mechanism for GABAergic differentiation occurs in the MGE/pallidum and septum . In addition to GABAergic projection neurons , progenitor cells in the LGE produce GABAergic interneurons that migrate tangentially mostly within the cortical intermediate zone , whereas GABAergic interneurons migrating from the MGE disperse into the cortical plate [5] , [38]–[40] . Also , cells derived from the dorsal SVZ of the LGE generate many olfactory bulb interneurons via a rostral migratory pathway [40]–[42] . At E18 . 5 , Gad67 immunoreactivity normally extends from the striatum into the intermediate zone of the cortex marking a population of LGE-derived interneurons , but this zone of Gad67 detection was markedly reduced in Raldh3−/− cortex ( Figure 5C–D ) . Dlx2 is an early marker of GABAergic progenitors in the basal ganglia that is required for GABAergic interneuron migration to the cortex [14] . Dlx2 immunoreactivity was not changed in Raldh3−/− forebrain from E12 . 5–E18 . 5 , demonstrating that interneurons are generated in the basal ganglia and migrate to the cortex in the absence of RA signaling ( Figure S3C–H ) . Additionally , detection of Gad67 in the Raldh3−/− olfactory bulb was also clearly reduced compared to wild-type ( Figure 5M–N ) . Apart from the dorsal LGE , recent studies have shown that additional telencephalic areas may also contribute to olfactory bulb interneurons [43]–[45] , which may explain our observed partial elimination of Gad67 immunoreactivity in the Raldh3−/− olfactory bulb . The above findings provide evidence that RA synthesis controlled by Raldh3 is required for GABAergic differentiation of interneurons that originate from progenitors in the LGE then migrate tangentially to the cortex and olfactory bulb . We investigated whether our findings may be useful to generate GABAergic neurons from human embryonic stem ( ES ) cells for potential cell replacement therapies . Following RA treatment of embryoid bodies and propagation of neural rosettes , cultures were processed immunocytochemically with antibodies against Pax6 ( a marker for neural progenitors ) , Doublecortin ( DCX; a marker for immature migrating neurons ) , the pan-neuronal marker Tuj1 , plus GABA and Gad67 . With no RA added in the differentiation medium a large proportion of the cells were Pax6-positive ( 79 . 9%±5 . 01% ) , indicating they were neural progenitors ( Figure 6J , M ) , and many cells exhibiting neuronal processes were Tuj1-positive ( 32 . 8%±10 . 3% ) and colocalized with DCX ( 31 . 3%±8 . 6% ) , suggesting they were immature neurons ( Figure 6G , M ) . However , very few Gad67 ( 2 . 7%±0 . 6% ) positive cells were detected in cultures with no RA added , suggesting that GABAergic differentiation is not favored under these conditions ( Figure 6A , D ) . Treatment of embryoid bodies with 1 µM RA resulted in a significant increase in both the number of Tuji1/DCX+ neurons that were forming extensive neuronal networks and GABAergic neurons detected with Gad67 ( 16 . 2%±2 . 1% ) ( Figure 6B , E , M ) . Moreover , the proportion of Pax6-positive cells was reduced to almost half with 1 µM RA ( 49 . 9%±11 . 6% ) , suggesting that more progenitor cells had differentiated to immature migrating neurons co-expressing Tuj1 and DCX ( Figure 6H , K , M ) . Addition of 10 µM RA further increased the percentage of GABAergic neurons positive for Gad67 ( 41 . 9%±10 . 7% ) ( Figure 6C , F , M ) and further decreased the number of Pax6-positive progenitors ( 17 . 9%±4 . 9% ) ( Figure 6L , M ) . Under these differentiation conditions , RA was able to drive GABAergic differentiation in almost half of the cells generated ( Figure 6M ) , suggesting that RA treatment is quite useful for induction of GABAergic differentiation in vitro . To gain further insight on the subtype identity of the RA-induced GABAergic neurons generated in our cultures , we examined expression of region-specific transcription factors previously associated with the specification of both GABAergic interneurons and projection neurons . Many Tuj1-positive cells were also immunopositive for Dlx2 , which is expressed in GABAergic precursors in the basal ganglia of the telencephalon , differentiating into both interneurons and striatal projection neurons ( Figure S6A ) . However , no Tuj1-positive cells were found to be positive for Foxp1 , a striatal projection neuron marker ( Figure S6B ) . Lim1/2 is a LIM homeodomain protein marking interneurons of the diencephalon and spinal cord [17] , [46]–[48] . Many GABA-positive neurons also expressed Lim1/2 , suggesting they acquire a GABAergic interneuron phenotype ( Figure S6C ) . Islet1 ( Isl1 ) , another LIM homeodomain protein , is expressed in the ventral forebrain where it marks telencephalic GABAergic projection neurons; Isl1 also marks diencephalic interneurons when co-expressed with Lim1/2 and GABA [46] , [48] . No cells in our culture co-expressed GABA and Isl1 , further suggesting that GABA-positive neurons in our cultures do not acquire striatal projection neuron or diencephalic interneuron identities ( Figure S6D ) . We found that 43 . 7%±11 . 94% of GABA-positive cells expressed Lim1/2 and 37 . 5%±11 . 5% expressed Dlx2 ( Figure S6E ) . Together , these data show that our human ES cell differentiation protocol induces a heterogeneous population of GABA-positive interneurons acquiring either telencephalic or spinal cord identities , but that it does not favor generation of GABA-positive striatal projection neurons . Previous studies using an Rdh10 ethylnitrosourea ( ENU ) mutant , which reduces retinaldehyde needed for Raldh2 to catalyze RA synthesis in the meninges , suggested that this source of RA is required for radial expansion of the cortex; a reduction in radial expansion of the cortical postmitotic neuronal layer was proposed to result in a concomitant lateral increase in the proliferative progenitor population in the ventricular zone [22] . Raldh2−/− embryos , which completely lack meninges RA activity ( Figure 1G , J ) , present an excellent model to examine whether RA is required for corticogenesis since Raldh2 catalyzes the final step of RA synthesis in the meninges . At E14 . 5 , the head region of Raldh2−/− embryos appeared to have developed relatively normally while they invariably displayed stunted forelimbs ( Figure 7A–B ) , which we have previously shown is due to a lack of RA synthesis by Raldh2 in trunk mesoderm [49] , [50] . Thus , Raldh2−/− mutants do not exhibit massive head deformities like those reported for Rdh10 mutants [51] . We analyzed expression of Tuj1 and the proliferative marker Ki67 in coronal brain sections of both wild-type and Raldh2−/− embryos at E14 . 5 . The medial-lateral length of the ventricular zone in the dorsal forebrain of the Raldh2−/− mutant appeared similar to that of the wild-type embryo ( Figure 7C–D ) . Moreover , double immunostaining for Tuj1 and Ki67 revealed no changes in radial expansion of the postmitotic Tuj1-expressing cortical layer nor the Ki67 proliferative zone in the Raldh2−/− cortex when compared to wild-type ( Figure 7E–J ) . Examination of MAP2 , another marker for postmitotic neurons , also demonstrated no difference in medial-lateral length for the mutant dorsal ventricular zone ( Figure S7A–B ) and no difference in radial width for the mutant cortex ( Figure S7C–D ) . Finally , examination of RC2 , a marker of radial glia whose somata reside in the ventricular zone of the cortex and whose radial processes span the entire distance to the pial surface , showed no difference between the Raldh2−/− and wild-type cortex ( Figure S7E–F ) . The fact that Raldh2−/− embryos retain a normal ratio of cortical progenitors to postmitotic neurons with no apparent morphological defects , in conjunction with a complete lack of RA activity in mutant meninges and cortical explants , suggests that RA is not required for embryonic corticogenesis . The contradiction between our results with Raldh2−/− embryos and the results of others with Rdh10 mutants [22] may be explained by the observation that Rdh10 mutants , unlike Raldh2−/− embryos , exhibit severe craniofacial defects that distort the cranium and forebrain possibly resulting in a thinner cortex [51] . Previous studies have shown that RARα and RARβ are expressed during mouse forebrain development , whereas RARγ is undetectable [52] , [53]; RARα was reported to be widespread in the embryonic forebrain , while RARβ was detected primarily in the striatum and is induced by RA . We examined expression of RARα and RARβ in E18 . 5 wild-type forebrains by in situ hybridization . RARα mRNA was widespread in the E18 . 5 forebrain including both the striatum and cortex , but expression was low or undetectable in the ventricular zone ( Figure S8A ) . RARβ mRNA was detected in the striatum but not in the cortex or ventricular zone ( Figure S8B ) . Taken together with our observation that the LGE/striatum is a major localized site of RA synthesis during forebrain development due to Raldh3 expression , overlapping expression of both RARα and RARβ in the striatum further suggests that this is a major site of local RA-mediated induction and signaling .
In this study we demonstrate a novel requirement of RA generated by Raldh3 for GABAergic differentiation in the basal ganglia . In contrast , RA activity is not detected in the cortex at any stages examined despite detection of RA activity in the adjacent meninges , which requires Raldh2 . Even if the cortex does receive a small amount of RA from the meninges that we cannot detect , our findings with Raldh2−/− embryos lacking RA activity in the meninges demonstrate that this source of RA is unnecessary for cortical expansion as suggested by a recent study [22] . Thus , unlike the cortex , the LGE represents an unambiguous site of RA action during forebrain development , and loss of RA in the LGE results in a loss of GABAergic differentiation . Our studies revealed that at E12 . 5 , when Raldh3 expression is barely detectable in the LGE and RA activity is not yet detected , Raldh3−/− embryos maintain expression of the regulatory gene Dlx2 and early aspects of GABAergic differentiation in the progenitor domains of the basal ganglia . However , by E14 . 5 , when Raldh3 expression has intensified in the LGE and RA activity is easily detectable , RA generated by Raldh3 is required to stimulate GABAergic differentiation in the LGE by inducing Gad67 needed for GABA synthesis . At E18 . 5 , Raldh3 is required to maintain GABAergic differentiation in the LGE , whereas a Raldh3-independent mechanism controls GABA synthesis in the MGE and septum . This observation suggests that the LGE is the main site of RA action along the SVZ . We observed that Raldh3 expression in newly generated neurons at the border of the proliferative and postmitotic zones in the LGE coincides with a region that generates both GABAergic striatal projection neurons and GABAergic interneurons [5] , [31]–[34] , [40] . Our studies in Raldh3−/− embryos revealed that RA signaling stimulates a GABAergic phenotype in LGE-derived interneurons migrating to the olfactory bulb and cortex and that RA is required for Foxp1-positive striatal projection neurons to further differentiate to a GABAergic fate . As Dlx2 and VGAT were still expressed normally in the absence of RA , the role of RA in GABAergic differentiation may be limited to stimulation of Gad67 activity in the LGE to promote GABA synthesis . As the appearance of GABAergic interneurons in the olfactory bulb and cortex is reduced rather than eliminated , our findings suggest that interneurons can still migrate to these locations in the absence of RA but that less interneurons have matured to a GABAergic phenotype . Other studies have shown that Gsx2 ( Gsh2 ) , a homeobox gene specifying ventral character in the forebrain , is required for Raldh3 expression in the LGE [54] and that Gsx2 is required for specification of GABAergic interneurons that migrate from the LGE to the olfactory bulb [13] . In addition , differentiation of DARPP-32 striatal projection neurons is greatly reduced in Gsx2 null embryos but not in conditional Gsx2 mutants when Gsx2 is progressively inactivated from E10 . 5–E18 . 5 [13] and also not in Raldh3−/− mutants [20] . Thus , early expression of Gsx2 is required for correct DARPP-32 striatal projection neuron development , a time when there is no Raldh3 expression in the forebrain . Taking into consideration the above , one can conclude that RA signaling exerts a specific role in specifying the GABAergic phenotype both for production of GABAergic interneurons and for further differentiation of striatal projection neurons to a GABAergic fate . Examination of the Gad67 promoter proximal region revealed no evidence of a canonical RA response element ( unpublished data ) , suggesting that Gad67 may be an indirect target or may be controlled post-transcriptionally by RA signaling in the basal ganglia during GABAergic differentiation . As it is clear that RARα and RARβ are both expressed in the basal ganglia , null mutants or antagonists for these RA receptors may be useful to further examine the mechanism through which RA functions during stimulation of GABAergic differentiation . Further , as we show that endogenous RA signaling is preserved in primary LGE neurosphere cultures and is required to generate GABAergic neurons in vitro , such cells may prove useful in studying the mechanism of RA action during GABAergic differentiation . A previous study suggested that Foxc1 mutants fail to form a complete forebrain meninges and exhibit increased lateral expansion of the cortical ventricular zone and reduced neurogenic radial expansion due to the loss of RA produced by Rdh10 and Raldh2 in the meninges [22] . The major conclusions of that study were drawn by comparison of the cortical phenotype of the Foxc1 mutants with that of an Rdh10 ENU mutant [22] , [55] . However , our studies on Raldh2−/− embryos lacking RA activity in the meninges demonstrate that RA is not required for radial expansion of the embryonic cortex . Additionally , RA receptors were not detected in the ventricular zone of the developing cortex , where RA was proposed to be required to induce neurogenic division of cortical progenitors . Together , these findings suggest that the dorsal forebrain phenotype in Foxc1 mutants is RA-independent . The Rdh10 ENU mutant employed for those forebrain studies [22] exhibits a very similar phenotype to another published Rdh10 ENU mutant , which has severe neural crest-derived craniofacial defects that are responsible for distortion of the cranium as well as forebrain [51] . Thus , reduced radial expansion of the cortex and increased lateral expansion of the ventricular progenitor zone reported for Rdh10 mutants [22] may not be due to a specific effect of RA on corticogenesis but rather a defect in cranial neural crest migration and differentiation that leads to the altered cortical morphology . Indeed , Rdh10 mutants lack all RA activity in the head during the time when cranial neural crest is migrating due to loss of all retinaldehyde synthesis [51] , whereas Raldh2−/− embryos still retain most cranial RA synthesis during this time due to expression of Raldh1 and Raldh3 in ocular and olfactory tissues [56] . Thus , head development in Raldh2−/− embryos is not grossly altered , allowing us to conclude that a lack of cranial RA activity specifically in the meninges does not lead to a defect in radial expansion of the cortex . Furthermore , Raldh2 is not expressed in the dorsal meninges until E12 . 5 [22] or E13 . 5 [25] , while the lengthening of the dorsal forebrain in Foxc1 mutants is already evident at E12 . 5 [22] . Based on the above , it seems unlikely that RA produced and secreted in the dorsal meninges could be the neurogenic factor inducing the switch from symmetric to asymmetric division in the ventricular zone to affect embryonic cortical expansion . Alternatively , RA generated in the meninges by Rdh10 and Raldh2 might have another function . RA could diffuse in the opposite direction and control development of the skull , which is populated by cranial neural crest cells . Interestingly , a recent study showed that ablation of all three RA receptors ( RAR alpha , beta , and gamma ) in cranial neural crest cells results in agenesis or malformations of most of the craniofacial skeletal elements including the frontal and parietal bones , which are adjacent to the dorsal meninges [57] . Additionally , Foxc1 hypomorphic mutants also exhibit malformation of the frontal bone [58] , providing further evidence that RA generated in the meninges downstream of Foxc1 may function in cranial neural crest differentiation . RA treatment is known to facilitate terminal differentiation of neural progenitors derived from ES cells [48] , [59]–[63] . Here we demonstrated that exposure of human ES-derived embryoid bodies to high concentrations of RA promotes differentiation of neuronal precursors to a high percentage of immature GABAergic neurons . Interestingly , although a low endogenous concentration of RA is sufficient to stimulate GABAergic differentiation of cells in the LGE at E14 . 5 , a high concentration of RA is needed for GABAergic differentiation in embryoid bodies derived from ES cells . This may be due to the much more primitive nature of cells in an embryoid body ( similar to cells in an early gastrula ) compared to neuroepithelial cells of the late embryonic forebrain . As RA binds directly to DNA-bound nuclear receptors that interact with co-repressors and co-activators , we suggest that high concentrations of RA may exert tremendous epigenetic effects on embryoid body cells , driving them to both a neuronal and GABAergic fate . In addition , our RA treatment protocol generated GABAergic neurons exhibiting expression of interneuron transcription factors of either anterior ( forebrain ) or posterior ( spinal cord ) identity , but not striatal projection neuron identity . A previous study proposed that mouse ES cells differentiating in medium without RA acquired a GABAergic identity of ventral forebrain co-expressing Gad67 and Isl1 ( most likely striatal projection neurons ) , while exposure to RA resulted in acquisition of a spinal cord interneuron identity [48]; those studies differed from ours in that RA treatment occurred at a later window during embryoid body formation and a lower concentration of RA was used . Thus , differences in the effects of RA on GABAergic interneuron identity in various culture systems may be dependent upon the timing and concentration of RA used . Production of the inhibitory neurotransmitter GABA in the central nervous system depends on local neurons , and disturbed GABAergic neuron function has been associated with numerous neurological disorders including Huntington's disease , autism , schizophrenia , bipolar depression , and epilepsy [9]–[12] . GABAergic interneurons are a particularly attractive cell population for cell-based therapies of these disorders due to their ability to migrate , differentiate , and function following transplantation [64]–[66] . GABAergic interneuron precursors derived from mouse ES cells were shown to migrate , survive for several months , and exhibit neurochemical and electrophysiological characteristics of mature interneurons when transplanted into postnatal cortex [67] . Additionally , transplantation of GABAergic interneuron precursors reduced the number of seizures in epileptic mice [68] . Thus , generation of GABAergic interneurons from RA-treated human ES cells as we report here coupled with isolation of cells with forebrain character may provide useful candidate cells in cell replacement therapies for one or more of these neurological conditions .
Raldh3−/− embryos exhibiting postnatal lethality just after birth were previously described [20] . Raldh2−/− embryos exhibiting midgestation lethality have been described previously [69] . To prevent Raldh2−/− early lethality , the maternal diet was supplemented for a short time with a very low dose of RA as described previously [50] . Briefly , 0 . 1 mg of all-trans-RA ( Sigma Chemical Co . ) was added per gram of standard mouse chow and provided fresh to pregnant females at E6 . 75–E8 . 75 . At E9 . 25 mice were returned to standard chow until embryos were collected at E14 . 5; Raldh2−/− mutants obtained with this method invariably exhibit stunted forelimbs and interdigital defects , demonstrating a loss of RA function in regions where Raldh2 is responsible for RA synthesis [50] . Dietary supplementation with this dose of RA is indeed low as HPLC measurements have shown that it provides less RA to embryos than Raldh2 normally generates [70] . Administered RA is cleared within 12–24 h after treatment ends [69] , thus allowing one to examine embryos at E10 . 5–E14 . 5 that now lack RA activity normally generated by Raldh2 . Embryos were genotyped by PCR analysis of yolk sac DNA and were staged by designating noon on the day of the vaginal plug as E0 . 5 . All mouse studies conformed to the regulatory standards adopted by the Animal Research Committee at the Sanford-Burnham Medical Research Institute . Lateral ganglionic eminence ( LGE ) and cortex were dissected from E14 . 5 wild-type and Raldh3−/− mutant embryos and incubated in DMEM containing 0 . 1% trypsin and 0 . 05% DNase for 15 min at 37°C followed by mechanical dissociation . The cells were spun down and resuspended at a concentration of 100 , 000 cells/ml in basic medium DMEM-F12 supplemented with B27 , 10 ng/ml basic fibroblast growth ( bFGF ) factor , and 20 ng/ml epidermal growth factor ( EGF ) . No apparent differences in growth rate or appearance were observed for wild-type compared to Raldh3−/− neurosphere cultures . Neurospheres were differentiated by culturing on plates pre-coated with poly-L-ornithine . EGF and bFGF were removed from the expansion medium and 1% serum was added ( differentiation medium ) . The spheres were maintained under differentiation conditions for 7 d in the presence or absence of 100 nM RA before fixation . Tissue explants or neurospheres from wild-type and mutant embryos were cultured overnight on Sil-15 F9-RARE-lacZ RA reporter cells followed by detection of β-galactosidase activity as previously described [26] . E12 . 5–E14 . 5 heads and E18 . 5 brains were fixed overnight at 4°C in 4% paraformaldehyde and paraffin sections ( 7 µm ) were processed immunohistochemically as described [71] . The primary antibodies included rabbit anti-Raldh3 1∶50 [72] , mouse anti-nestin 1∶100 ( Millipore ) , mouse anti-RC2 1∶100 ( Developmental Studies Hybridoma Bank at University of Iowa; DSHB ) , mouse anti-MAP2 1∶200 ( Sigma; M4403 ) , rabbit anti-GABA 1∶500 ( Millipore ) , mouse anti-Gad67 1∶50 ( Millipore; MAB5406 ) , rabbit anti-GFAP 1∶1000 ( Dako ) , rabbit anti-Foxp1 1∶100 ( Abcam ) , rabbit anti-Ki67 1∶200 ( Abcam ) , rabbit anti-Dlx2 1∶100 ( Abcam ) , mouse anti-VGAT 1∶200 ( Synaptic Systems ) , rabbit anti-VGLUT ( Synaptic Systems ) , and mouse anti-TH ( Sigma ) . In situ hybridization of E18 . 5 brain sections was performed as described [73] using RARα and RARβ riboprobes . Human ES cells ( line H9 ) were cultured and passaged weekly on a feeder of irradiated embryonic mouse fibroblasts as described previously [74] . The protocol for RA-induced GABAergic differentiation was based on previous methods [75] . Briefly , human ES cell-derived embryoid bodies ( EBs ) were cultured in EB growth medium in non-adherent plates for 3 d , followed by RA treatment for 3 d ( RAd3 ) . RA was removed and the RAd3 EBs were plated on culture dishes pre-coated with poly-L-ornithine and fibronectin and cultured for an addition 4 d ( RAd7 ) in serum-free neuronal induction medium , comprised of neurobasal medium supplemented with B27 , bFGF , and EGF . At RAd7 neuroepithelial rosettes were isolated mechanically from the differentiation cultures with a 2 ml serological pipette . After isolation , rosettes were replated in the same neuronal induction medium for an additional 14 d ( RAd21 ) before fixation . Immunocytochemistry on neurospheres and ES cells was carried out as described [75] . Primary antibodies used included rabbit anti-GABA 1∶1 , 000 ( Millipore ) , mouse anti-Gad67 1∶200 ( Millipore; MAB5406 ) , rabbit anti-GFAP 1∶1 , 000 ( Dako ) , mouse anti-Tuj1 1∶1 , 000 ( Covance ) , and guinea pig anti-DCX 1∶500 ( Millipore ) , rabbit anti-Dlx2 1∶200 ( Abcam , AB18188 ) , rabbit anti-Foxp1 1∶100 ( Abcam ) , mouse anti-Islet-1 1∶100 ( 40 . 2D6 ) ( DSHB ) , and mouse anti-Lim1/2 ( 4F2 ) 1∶200 ( DSHB ) . Immunopositive cells and total DAPI-stained nuclei were counted to calculate the percentage of immunopositive cells . Five randomly picked areas from three independent experiments were counted for each marker . Data were presented as mean ± SEM; for pair-wise analysis of treatment conditions and/or genotypes , an ANOVA test was used . | The vitamin A metabolite retinoic acid is an important signaling molecule needed for development of the central nervous system . Previous studies have shown a role for retinoic acid in regulating genes involved in the generation of motor neurons both in the hindbrain and spinal cord , but the role of retinoic acid in the forebrain has remained elusive . Here , we investigated mice that lack the ability to metabolize vitamin A into retinoic acid in the forebrain . Although no defects were observed in the generation of forebrain cortical neurons , we did observe a serious deficiency in GABAergic neurons , which provide inhibitory input to cortical neurons . Specifically , our results reveal that retinoic acid is required for forebrain neurons to activate an enzyme that converts glutamate to the inhibitory neurotransmitter GABA . We also find that retinoic acid treatment of human embryonic stem cells could stimulate production of GABAergic neurons . Deficiencies in GABAergic neurons have been associated with several neurological disorders , including Huntington's disease , autism , schizophrenia , and epilepsy . Knowledge of how GABAergic neurons are generated may aid efforts to treat these diseases . |
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Low oxygen conditions ( hypoxia ) can impair essential physiological processes and cause cellular damage and death . We have shown that specific hypoxic conditions disrupt protein homeostasis in C . elegans , leading to protein aggregation and proteotoxicity . Here , we show that nutritional cues regulate this effect of hypoxia on proteostasis . Animals fasted prior to hypoxic exposure develop dramatically fewer polyglutamine protein aggregates compared to their fed counterparts , indicating that the effect of hypoxia is abrogated . Fasting also reduced the hypoxia-induced exaggeration of proteostasis defects in animals that express Aβ1–42 and in animals with a temperature-sensitive mutation in dyn-1 , suggesting that this effect was not specific to polyglutamine proteins . Our data also demonstrate that the nutritional environment experienced at the onset of hypoxia dictates at least some aspects of the physiological response to hypoxia . We further demonstrate that the insulin/IGF-like signaling pathway plays a role in mediating the protective effects of fasting in hypoxia . Animals with mutations in daf-2 , the C . elegans insulin-like receptor , display wild-type levels of hypoxia-induced protein aggregation upon exposure to hypoxia when fed , but are not protected by fasting . DAF-2 acts independently of the FOXO transcription factor , DAF-16 , to mediate the protective effects of fasting . These results suggest a non-canonical role for the insulin/IGF-like signaling pathway in coordinating the effects of hypoxia and nutritional state on proteostasis .
In order to survive in changing conditions , organisms need to successfully integrate a number of environmental signals and respond appropriately in order to maintain homeostasis . Aerobic heterotrophs must meet their requirements for food and oxygen by taking in these resources from the environment . An inadequate response to low levels of oxygen ( hypoxia ) can lead to cellular damage or death , an unsurprising outcome given oxygen’s central role in cellular metabolism . Like hypoxia , food deprivation presents an obstacle to homeostasis by impinging on cellular metabolism and disturbing anabolic pathways . However , in many cases food restriction can have beneficial effects , such as extending lifespan and delaying the onset of neurodegenerative diseases and their associated pathologies [1] . In a mouse model of Alzheimer’s disease , 12 weeks of caloric restriction reduces Aβ plaque burden [2] , and mice expressing human mutant huntingtin maintained on an alternate-day-feeding diet have reduced brain atrophy and decreased huntingtin aggregate formation [3] . Similarly , depriving C . elegans of their bacterial food source reduces damage associated with expressing polyglutamine proteins [4] . The protective effect of fasting is not limited to symptoms of neurodegeneration–there are many studies that show fasting can protect against damage associated with hypoxia in mammals . For example , mice on an alternate-day feeding regimen have higher survival rates after myocardial ischemia induced via coronary occlusion [5] . Similar results have been obtained with ischemic damage to the liver . Mice on a calorically restricted diet have reduced infarct damage compared to ad-libitum fed controls [6] , and mice that have been fasted for 3 days display reduced hepatocellular apoptosis and damage [7] . Reduced food intake also improves outcomes after cerebral ischemic injury by protecting cortical and striatal neurons [8] and reducing neurological deficits and infarct volume [9] . These observations suggest that understanding the mechanistic basis underlying the protective effects of fasting in hypoxia could provide novel insight into therapeutic strategies to treat pathological conditions associated with ischemia and reperfusion injury . We have previously shown that in C . elegans the cellular response to specific hypoxic conditions involves a disruption of proteostasis–the coordination of protein synthesis , folding , degradation , and quality control required to maintain a functional proteome [10] . Here we show that fasting prevents the hypoxia-induced disruption of proteostasis . Our data indicate that the nutritional context of an animal at the onset of hypoxia has the power to alter hypoxia’s effect on proteostasis and that the insulin-like signaling ( IIS ) pathway plays a role in fasting’s ability to protect against proteostasis decline independently of the canonical downstream transcription factor DAF-16/FOXO .
In order to investigate the effect of nutritional status on proteostasis in hypoxia , we first used transgenic C . elegans that express yellow fluorescent protein ( YFP ) fused to a polyglutamine tract in the body wall muscles [11] . We refer to these animals as QX::YFP , where X refers to the number of glutamine residues fused to YFP , such that Q35::YFP animals express YFP with 35 glutamine residues . In these animals , the number of YFP foci , which correspond to large protein aggregates , can be used as an in vivo measure of cellular proteostasis [12] . Exposing animals to 1000 ppm O2 ( 0 . 1% , with balance N2 ) for 24 hours while fed resulted in an increase in the number of YFP foci ( Fig 1B–1D ) , consistent with previous reports that hypoxia inhibits proteostasis [10] . However , we found that the number of YFP foci that formed in hypoxia was dramatically reduced if the animals were removed from food for six hours before the hypoxic exposure and remained off of food for the duration of hypoxia ( Fig 1A ) . Hypoxia-induced protein aggregation ( HIPA ) was prevented by fasting in fourth-stage larvae ( L4 ) Q35::YFP animals ( Fig 1C ) as well as in first-stage larvae ( L1 ) Q40::YFP ( Fig 1D ) . We verified that the bright fluorescent puncta in animals exposed to hypoxia were aggregated protein using fluorescence recovery after photobleaching ( S1 Fig ) , as had been previously observed for age-associated aggregates [11] . We also determined that the abundance of Q35::YFP was the same in animals exposed to hypoxia when fed and fasted ( S2 Fig ) . This is consistent with previous observations that the expression of Q35::YFP did not change even after nine days without food [4] . In control experiments we found no change in the number of protein aggregates between animals in room air that were fed and fasted ( S3 Fig ) . This is likely because we initiated these experiments before much age-associated protein aggregation had occurred , in order to avoid confounding factors from the effects of fasting and hypoxia on aging . From these data , we conclude that fasting prevents HIPA . We originally chose to fast animals for 6h before exposure to hypoxia to allow animals time to alter gene expression [13] . However , there is no a priori evidence that the protective effects of fasting in hypoxia requires changes in gene expression . Therefore , we measured how long of a fasting period was required to mitigate the effects of hypoxia on aggregation of polyglutamine proteins . To determine the pre-hypoxia fasting duration required to protect against HIPA , we removed Q35::YFP animals from food for varying lengths of time before being exposed to hypoxia ( schematic in Fig 2A ) . We found that animals removed from food immediately before exposure to hypoxia developed significantly fewer YFP foci in hypoxia as compared to controls that remained on food in hypoxia ( Fig 2A , 6h fed compared to fed ) . We conclude that extended fasting before exposure to hypoxia is not required to prevent HIPA . Instead , our data show that the protective effects of fasting occur very rapidly . In fact , the full protection against HIPA is realized with only 2h fasting before exposure to hypoxia ( Fig 2A ) . These results suggest that at least some of the protective effects of fasting do not require a period of adaptation to fasting prior to the hypoxic exposure . Instead , we conclude the environment at the onset of the exposure to hypoxia dictates at least some aspects of the response to hypoxia . Work in other systems has shown that fasting can have a protective effect that persists even after animals are returned to food [14] . To further explore the requirements for fasting to protect against HIPA we next asked whether the protective effects of fasting against HIPA could be reversed . In these experiments ( Fig 2B ) , we began fasting animals 6h before exposure to hypoxia but then returned the animals to food prior to initiation of hypoxia . We observed that animals fasted for a full 6h and then returned to food immediately before exposure to hypoxia ( Fig 2B , 6h fasted ) developed significantly more YFP foci than animals that were fasted for 6h and then exposed to hypoxia in the absence of food ( Fig 2B , fasted ) , suggesting that the nutritional context of an animal as it experiences hypoxia is able to mediate the effect of hypoxia on proteostasis . Furthermore , we found no protection from HIPA if animals were fasted for 4h , but then fed for 2 h before exposure to hypoxia ( Fig 2B , 4h fasted ) , even though 4h of fasting was sufficient for complete protection against HIPA in the absence of food ( Fig 2A , 2h fed ) . This result indicates that the protective effects of fasting are fully reversed within 2h of return to food . We conclude that the protective effects of fasting in hypoxia are rapidly reversed . Shorter exposures to hypoxia , which do not immediately increase the number of polyglutamine protein aggregates , still disrupt long-term proteostasis as evidenced by the increased rate of age-associated protein aggregation after return to room air [10] . We therefore asked whether fasting could protect against these long-term proteostasis deficits in addition to HIPA . We exposed Q35::YFP L4 animals to hypoxia for only 10h either in the fed state or after fasting for 6h ( F = 6 hours , H = 10 hours as per Fig 1A ) . Control animals remained on food in room air . Immediately after this short hypoxic exposure , there was no observed increase in the number of YFP foci in animals exposed to hypoxia regardless of whether food was present ( Fig 3 , 0 hours post-hypoxia ) . As expected , the animals exposed to hypoxia in the fed state accumulate aggregates faster than control animals . In contrast , animals exposed to hypoxia while fasted accumulate YFP foci at the same rate as control animals . Animals that were fasted in room air also accumulated YFP foci at the same rate as room air , fed controls ( S3 Fig ) . These data indicate that fasting both prevents HIPA and protects against the long-term effects on proteostasis induced by a short exposure to hypoxia . The cellular role of protein aggregates is controversial , with some reports finding a protective role and others suggesting a cytotoxic effect [15] . We have previously found that aggregates induced by hypoxia are likely cytotoxic , as they accelerate polyQ-associated paralysis even after animals are returned to room air [10] . We therefore next asked if fasting would protect against increased proteotoxicity in addition to HIPA . To address this , we exposed cohorts of L1 Q40::YFP animals to hypoxia for 24 hours while fed or fasted , then returned the animals to room air and measured the onset of paralysis in each cohort . We found that fasting slowed the rate at which paralysis developed relative to animals exposed to hypoxia while fed ( Fig 4A ) . There was no difference in the rate of paralysis onset if animals were fasted in room air ( S3 Fig ) . This result indicates that fasting protects against hypoxic effects of increased protein aggregation and proteotoxicity . We next sought to determine whether fasting’s protective effects on proteostasis extend to other models of proteotoxicity . Human amyloid β ( Aβ ) 1-42 peptide expressed in the body wall muscles of C . elegans results in cytoplasmic plaque formation , with a subsequent phenotype of progressive paralysis [16] . C . elegans expressing Aβ1–42 in their body wall muscles become paralyzed more quickly when they are exposed to hypoxia [10] . We found that this effect of hypoxia was reversed by fasting , as the rate that paralysis develops is slowed if animals expressing Aβ1-42 are exposed to hypoxia while fasting ( Fig 4B ) . Because Aβ1–42 and Q40::YFP are both expressed in body wall muscles , we also evaluated if fasting protected animals expressing a metastable version of the neuronal dynamin protein DYN-1 from the effects of hypoxia . The dyn-1 ( ky51 ) mutant contains a temperature-sensitive mutation , such that the DYN-1 protein is functional and dyn-1 ( ky51 ) mutant animals exhibit wild-type motility at the permissive temperature ( 20°C ) , but become uncoordinated at the restrictive temperature ( 28°C ) due to improper folding of the DYN-1 protein [17] . Genetic and environmental factors that disrupt proteostasis , including hypoxia , prevent the proper folding of the DYN-1 protein at the permissive temperature , thereby rendering the dyn-1 ( ky51 ) animals uncoordinated [10 , 18] . Similar to our experiments with Q40::YFP and Aβ1–42 , we found that fasting dyn-1 ( ky51 ) mutant animals before exposure to hypoxia results in a partial rescue of hypoxia-induced uncoordination at the permissive temperature ( Fig 4C ) . Together , our results suggest that fasting has a general protective effect against proteostasis defects induced by hypoxia , and that this protective effect is not specific to a particular tissue , developmental stage , or misfolded/aggregation prone model . Dysregulation of insulin-like signaling ( IIS ) has been tied to protein aggregation and neurodegeneration in a number of model organisms [19] . As the IIS pathway links food availability to growth , development , stress resistance , and aging , we hypothesized that changes in IIS could explain how fasting modulates the effect of hypoxia on proteostasis . The IIS pathway is widely conserved in metazoans [20] . We therefore explored the hypothesis that IIS would mediate the effects of fasting to prevent HIPA . We first looked at the localization of DAF-16::GFP in animals exposed to hypoxia to determine if IIS is active in hypoxia . DAF-16 is the C . elegans orthologue of the FOXO transcription factor . When active , the insulin/IGF-like receptor DAF-2 initiates a phosphorylation cascade that results in the phosphorylation and nuclear exclusion of DAF-16 protein [21 , 22] . Conversely , when nutrients are scarce , DAF-16 remains unphosphorylated by upstream kinases and is able to enter the nucleus and bind to its target genes [22 , 23] . We found that DAF-16::GFP remained diffuse and cytoplasmic in control worms maintained in room air on food ( Fig 5B and 5C ) , but accumulated in the nucleus of animals that were removed from food in room air ( Fig 5B and 5C ) or were exposed to hypoxia on food ( Fig 5B and 5C ) . These results suggest that IIS activity is reduced by fasting and hypoxia , consistent with previous reports [24 , 25] . Surprisingly , DAF-16::GFP did not accumulate in the nuclei of animals exposed to hypoxia after fasting ( Fig 5B and 5C ) , despite hypoxia and fasting both individually resulting in nuclear accumulation . These DAF-16::GFP localization patterns led us to interrogate requirements for DAF-16 and the upstream IIS receptor DAF-2 in mediating fasted and fed responses to hypoxia . To this end , we crossed the Q35::YFP transgene into daf-2 ( e1370 ) and daf-16 ( mu86 ) backgrounds . The fact that DAF-16::GFP is localized to the nucleus in fed animals exposed to hypoxia suggests the possibility that DAF-16 facilitates HIPA . However , we found that Q35::YFP; daf-16 ( mu86 ) mutant animals exhibit robust HIPA on food ( Fig 5D ) , indicating that DAF-16 is not required for HIPA despite its nuclear accumulation in fed hypoxic animals . We also asked if there was a genetic requirement for the IIS receptor DAF-2 . Our data indicate that IIS does not mediate the effects of hypoxia on proteostasis in fed animals , as Q35::YFP; daf-2 ( e1370 ) mutant animals exhibit robust HIPA when fed ( Fig 5D ) . Thus , neither DAF-16 nor DAF-2 activities are required for HIPA in fed animals . Given the IIS-independent nature of HIPA in fed animals , we next investigated whether fasting protection requires IIS . We discovered that DAF-2 , but not DAF-16 is required for fasting protection against HIPA . Fasting protects the Q35::YFP; daf-16 ( mu86 ) similar to wild-type ( Fig 5D ) ; however , we observe significant HIPA when Q35; daf-2 ( e1370 ) and Q35; daf-2 ( e1368 ) mutant animals are exposed to hypoxia when fasted ( Figs 5D and S4 ) . These results show that protective effects of fasting in hypoxia require DAF-2 , but not DAF-16 . This is consistent with our observation that DAF-16::GFP is not localized to the nucleus in fasted animals exposed to hypoxia ( Fig 5B and 5C ) . We found that the insulin/IGF-like receptor DAF-2 mediates the protective effects of fasting on HIPA , while the FOXO transcription factor DAF-16 is not required for protection . Given this finding , we also checked the DAF-16::GFP localization pattern in daf-2 ( e1370 ) animals . These mutants have constitutively nuclear DAF-16 in the fed state due to decreased signaling through the IIS pathway [22] . Since DAF-16::GFP is not localized to the nucleus in fasting-protected wild-type animals exposed to hypoxia , we sought to investigate whether the nuclear localization of DAF-16 in daf-2 ( e1370 ) mutants , which are not protected by fasting , would be altered by hypoxia . We found that DAF-16::GFP is fully nuclear in all conditions , including fasted hypoxia , in these animals ( S5 Fig ) . In C . elegans , DAF-16 mediates the effects of decreased signaling through DAF-2 . Mutations in daf-16 suppress most daf-2 mutant phenotypes including increased lifespan , enhanced dauer formation , increased fat storage , reproductive delays , and increased resistance to heat and oxidative stress [26 , 27] . This coupled with the nuclear localization of DAF-16::GFP in daf-2 mutants led us to hypothesize that daf-16 would be required for the HIPA in fasted Q35; daf-2 ( e1370 ) mutant animals . While Q35; daf-16 ( mu86 ) mutant animals were protected from HIPA by fasting similar to wild-type controls , Q35; daf-2 ( e1370 ) ; daf-16 ( mu86 ) animals still exhibit significant HIPA when fasted ( Fig 5D ) . These results indicate that DAF-2 mediates the effects of fasting to prevent HIPA at least partly independently of DAF-16 . We took a candidate approach to attempt to identify factors that act downstream of daf-2 to protect proteostasis in hypoxia . We focused first on hif-1 , the hypoxia inducible transcription factor [28] . We previously demonstrated that HIF-1 activity helps to blunt the effects of hypoxia on proteostasis [10] . Moreover , increased lifespan of C . elegans exposed to hypoxia depends on both hif-1 and daf-16 [25] . To determine if hif-1 is acting downstream of daf-2 to protect proteostasis in hypoxia we compared daf-16; daf-2; Q35::YFP mutant animals with hif-1; daf-16; daf-2; Q35::YFP animals . We find that deletion of hif-1 does not suppress increased protein aggregation in fasted animals exposed to hypoxia ( S6 Fig ) . We similarly investigated the role of skn-1 , which is required for increased lifespan of daf-2 mutants [29] . Our data show that fasted skn-1; daf-16; daf-2; Q35::YFP exhibit HIPA that is indistinguishable from the daf-16; daf-2; Q35::YFP mutant animals ( S6 Fig ) . Finally , we evaluated whether the heat-shock factor hsf-1 was involved . HSF-1 is required downstream of DAF-2 for increased lifespan [30] . However , we find that expression of HSF-1 targets hsp-16 . 2 , hsp-70 , and hsp-4 is induced to the same degree in fed and fasted animals exposed to hypoxia ( S7 Fig ) , suggesting that differential activity of HSF-1 does not underlie the protective effects of fasting . In fact , the expression of a variety of genes induces by proteotoxic stress , including the unfolded protein response and autophagy , are similarly induced in fed and fasted animals ( S7 Fig ) . Together , these results suggest that the mechanism ( s ) by which fasting can protect proteostasis in hypoxia is independent of daf-16 , hif-1 , hsf-1 , and skn-1 . The components of this daf-2 dependent pathway that can modulate proteostasis are , as yet , a mystery .
This study illustrates the power of fasting to ameliorate the deleterious effects of hypoxia on proteostasis . These findings are consistent with phenomena that have been observed in mammals–fasting mice for a single day increases survival after kidney ischemia and also reduces ischemic damage to the liver [31] . Our results suggest that the nutritional milieu present at the onset of hypoxia can dictate the effect of hypoxia on proteostasis , as fasting protection against hypoxia can be induced quite quickly . Animals that are removed from food immediately before hypoxia are protected against HIPA to a significant degree , even after being maintained on food for the entire pre-hypoxic period . This implies that worms are integrating information about their environment , including nutrient availability , concurrently with the perception of hypoxia . The importance of the nutritional environment of the animal as it experiences hypoxia is further supported by the fact that we also see a rapid reversal of fasting protection . Worms fasted for six hours but that are moved onto food immediately preceding hypoxia are not as protected against HIPA compared to worms that were fasted and remained off of food for the duration of hypoxia . The speed with which fasting protection can be induced and reversed indicates that protection cannot be explained solely by changes in gene expression resulting in a hypoxia-resistant pre-adapted state . Furthermore , the rapidity with which fasting protection can be reversed suggests that altered gene expression or metabolism resulting from the fasting period is alone insufficient to protect against HIPA . Although C . elegans enter a reproductive and developmental diapause in 1000 ppm O2 [32] , the protection conferred by fasting does not represent a simple delay in the onset of proteostasis decline due to the time spent in hypoxia . Rather , fasting provides long-term protection against the accrual of protein aggregates and toxicity even after the return to room air . We found that the IIS receptor DAF-2 is required for fasting to prevent HIPA . This is somewhat counterintuitive , as decreased function of daf-2 mutants could be thought of as “phenocopying” the fasted situation . Consistent with this , both fasting and mutation of daf-2 lead to increased nuclear localization of DAF-16 . However , our results show that daf-2 mutant animals do not phenocopy wild-type , fasted animal ( which show little HIPA ) . In contrast , daf-2 mutant animals exhibit robust HIPA regardless of whether they are fed or fasted . Moreover , we found that while hypoxia and fasting individually promote the nuclear localization of DAF-16::GFP , there is no nuclear accumulation in in fasted animals exposed to hypoxia . These results suggest that activation of DAF-2 in fasted animals is required to prevent hypoxia-induced perturbations of proteostasis . Although required for the protective effects of fasting in hypoxia , our data show that IIS is not required for the normal response to hypoxia in fed animals–both daf-16/FOXO and daf-2/IR mutants have relatively normal HIPA when fed . This contrasts with previous studies that show C . elegans daf-2 mutant animals are resistant to anoxia , displaying reduced muscle and neuronal cell death following anoxia [33 , 34] . Similarly , flies with defective insulin signaling due to mutations in the insulin receptor InR , or Chico , the insulin receptor substrate , are protected against anoxia/reoxygenation injury [35] . The discordance between our results and these previous studies may be due to the fact that the phenotypic and genetic responses to hypoxia depend strongly on the precise concentration of O2 available ( reviewed in [36] ) ; in our studies we focused on hypoxic conditions with 1000 ppm O2 , whereas the anoxic conditions used in these previous studies had far less O2 available . Our results suggest that , in fed animals , IIS is not required for nor can it protect against hypoxia-induced disruption of proteostasis . Mammalian systems offer precedents of insulin receptor mutations causing sensitivity to hypoxic stress . Knockdown of neuronal insulin-like growth factor 1 receptor ( IGF-1R ) exacerbates hypoxic injury and increases mortality in mice [37] , and IGF-1R is required in order for IGF-1 to protect myocardial cell exposed to ischemia [38] . However , data on the role of mammalian IIS in response to hypoxia are mixed , and are complicated by the fact that different types of insulin receptors mediate distinct cellular functions [39] . As such , the simplified C . elegans IIS system may be useful for understanding contextual inputs that alter IIS outputs . DAF-16 is believed to be the main nexus of IIS [22 , 30 , 40 , 41] , which makes the DAF-2-dependent , but DAF-16-independent nature of the protective effect of fasting we have described unusual in C . elegans . Decreased DAF-2 activity results in phenotypes such as increased lifespan , reproductive delays , and increased resistance to heat and oxidative stress , all of which require DAF-16 [27] . However , a few other examples exist in the literature of DAF-2 dependent , DAF-16 independent phenomena: dauer formation at 27° , meiotic progression of oocytes , salt chemotaxis learning , and regulation of the dao-3 and hsp-90 genes [40 , 42–45] . Our studies suggest that fasting-mediated protection against HIPA is mediated by factors that act downstream of DAF-2 , but separate from DAF-16 . Understanding the nature of these factors could reveal new aspects of how IIS modulates stress responses and proteostasis in animals .
Animals were maintained on nematode growth media ( NGM ) with OP50 E . coli at 20°C [46] . See S10 Table for worm strains . Strains were obtained from the Caenhorabditis Genetics Center at the University of Minnesota . Double and triple mutants were generated using standard genetic techniques , and genotypes were verified using PCR . Hypoxic conditions were maintained using continuous flow chambers , as described in [49] . Compressed gas tanks ( 1000 ppm O2 balanced with N2 ) were Certified Standard ( within 2% of target concentration ) from Airgas ( Seattle , WA ) . Oxygen flow was regulating using Aalborg rotameters ( Aalborg Intruments and Controls , Inc . , Orangeburg , NY , USA ) . Hypoxic chambers ( and room air controls ) were maintained in a 20°C incubator for the duration of the experiments . Synchronous cohorts of L1 YFP::polyQ40 animals were generated by either bleaching first-day adult animals in a 20% alkaline hypochlorite solution or allowing first-day adult animals to lay eggs for 1–2 hrs on seeded NGM plates . The adults were then removed , and the plates were incubated at 20°C . The next morning , cohorts of hatched L1 larvae were suspended in M9 and mouth-pipetted to new NGM plates for hypoxic exposure . Synchronous cohorts of L4 YFP::polyQ35 animals were generated by picking L4 animals from well-fed , logarithmically growing populations . Cohorts of 25–35 YFP::polyQ animals were exposed to hypoxia for approximately 24 h at 20°C on unseeded 3 cm NGM plates with 40mg/mL carbenicillin or NGM plates seeded with live OP50 food . Plates were ringed with palmitic acid ( 10mg/mL in ethanol ) , creating a physical barrier around the edge of each plate to discourage animals from leaving the surface of the agar . To quantify the number of YFP foci , worms were mounted a 2% agar pad in a drop of 50mM sodium azide as anesthetic . Control experiments showed that azide did not affect the aggregation of YFP::polyQ35 or YFP::polyQ40 [47] . YFP foci were identified and quantified as described in [11] and [48] . A Nikon 90i fluorescence microscope with the YFP filter and 10x objective ( Nikon Instruments Inc . , Melville , NY , USA ) was used to visualize and quantify aggregates . In all experiments , the number of aggregates was counted blind to treatment and genotype . Statistical significance was evaluated by calculating P-values between conditions using a Kruskal-Wallis test and Dunn’s multiple comparisons post hoc analysis in GraphPad Prism version 7 . 0c for Mac OSX ( GraphPad Softare , San Diego , California , USA ) In all cases , P < 0 . 05 was considered statistically significant . Animals expressing Aβ1–42 or YFP::polyQ40 were exposed to 1000 ppm O2 for 24 at 20°C as L4 or L1 , respectively . For both , animals were grown on seeded NGM plates until 6 hrs before hypoxic exposure , at which point fasted animals were transferred to unseeded NGM plates , where they remained until the end of the hypoxic exposure . Fed animals were transferred to new seeded NGM plates . After hypoxic exposure , all animals were returned to food and normoxia , and incubated at 20°C . Paralysis was scored daily . Worms were considered paralyzed if they failed to respond , other than with movement of the nose or pharyngeal pumping , when tapped with a platinum wire pick 3 consecutive times . Dead or bagged worms were censored from the experiment on the day of death/bagging . Paralyzed worms were removed from the plate on the day of paralysis . Live worms that were not paralyzed were moved to a new plate each day until all worms were scored as either paralyzed or dead . Statistical significance was calculated using Kaplan-Meier log-rank ( Mantel-Cox ) tests and a Bonferroni correction for multiple comparisons using GraphPad Prism version 7 . 0c for Mac OSX ( GraphPad Softare , San Diego , California , USA ) . Synchronous cohorts of L2 animals expressing DAF-16::GFP were exposed to hypoxia for 24 h at 20°C on unseeded 3 cm NGM plates with 40mg/mL carbenicillin or NGM plates seeded with live OP50 food . Plates were ringed with palmitic acid ( 10mg/mL in ethanol ) , creating a physical barrier around the edge of each plate to discourage animals from leaving the surface of the agar . To visualize the localization of DAF-16::GFP , worms were mounted a 2% agar pad in a drop of 10mM levamisole as anesthetic . A Nikon 90i fluorescence microscope with the GFP filter and 10x objective ( Nikon Instruments Inc . , Melville , NY , USA ) was used to visualize DAF-16::GFP . For quantification , percent of animals with nuclear GFP was scored immediately after removal from hypoxia . In all experiments , the GFP localization was scored blind to treatment and genotype . Statistical significance was evaluated by calculating P-values between conditions using a Kruskal-Wallis test and Dunn’s multiple comparisons post hoc analysis in GraphPad Prism version 7 . 0c for Mac OSX ( GraphPad Softare , San Diego , California , USA ) . P < 0 . 05 was considered statistically significant . Animals were grown on NGM plates seeded with OP50 E . coli at 20°C . When animals reached gravid adult , synchronized embryos were obtained by a 5-minute bleach in 1:1:5 water:KOH:hypochloric acid solution . For each strain/condition , ~9 , 000 embryos were plated onto a 150 mM NGM plate seeded with live OP50 E . coli . Animals were not allowed to starve out the plate at any time during the experiment . When animals reached L4 , they were exposed to hypoxia with or without a 6 hour fasting period , or were left in room air as controls . Animals were harvested into 1 mL Trizol solution and immediately frozen in liquid nitrogen , as described previously [49] . RNA was isolated from the Trizol preparation as described previously [50] . cDNA was made using Invitrogen SuperScript III First Strand Synthesis System . qPCR was performed using Kappa SYBR FAST qPCR Kit . PCR cycle was as follows: 95C for 3 min , 95C for 15 sec , 55C for 15 sec x40 . 4°C to hold . qRT-PCR values were analyzed as described in [51] . In summary , ΔCt for each gene product was calculated by subtracting Ct values from the geometric mean of the control targets that are not altered in response to fasting or hypoxia ( hil-1 , irs-2 , and tba-1 ) . ΔCt were averaged across experiments . Student’s t-test was used to evaluate differences between ΔCt values of treated samples and untreated controls . For differences between genotypes , p-values were calculated with a one-way ANOVA from summary statistics ( mean , standard error , n ) . Reported fold-changes were calculated as 2^-ΔΔCt where ΔΔCt = ΔCt ( experimental condition ) —ΔCt ( control condition ) . Error bars on graphs represent standard error of the mean . | When blood flow to various parts of the body becomes restricted , those tissues suffer from a lack of oxygen , a condition called hypoxia . Hypoxia can cause cellular damage and death , as in stroke and cardiovascular disease . We have found that in the model organism C . elegans ( a roundworm ) specific concentrations of hypoxia cause aggregation of polyglutamine proteins–the same kind of proteins that are found in an aggregated state in the neurodegenerative disorder Huntington’s disease . Here , we show that that worms can be protected from hypoxia-induced protein aggregation if they are fasted ( removed from their food source ) prior to experiencing hypoxia . Furthermore , we show that the insulin receptor is required for this protection . The insulin receptor is responsible for detecting insulin , a hormone that is released after feeding . Worms with a nonfunctional version of the insulin receptor displayed hypoxia-induced protein aggregation despite being fasted before the hypoxic exposure . Our results highlight a new role for the insulin signaling pathway in coordinating the effects of both hypoxia and nutritional state on protein aggregation . |
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The Gram-positive bacterium Staphylococcus aureus , similar to other pathogens , binds human complement regulators Factor H and Factor H related protein 1 ( FHR-1 ) from human serum . Here we identify the secreted protein Sbi ( Staphylococcus aureus binder of IgG ) as a ligand that interacts with Factor H by a—to our knowledge—new type of interaction . Factor H binds to Sbi in combination with C3b or C3d , and forms tripartite Sbi∶C3∶Factor H complexes . Apparently , the type of C3 influences the stability of the complex; surface plasmon resonance studies revealed a higher stability of C3d complexed to Sbi , as compared to C3b or C3 . As part of this tripartite complex , Factor H is functionally active and displays complement regulatory activity . Sbi , by recruiting Factor H and C3b , acts as a potent complement inhibitor , and inhibits alternative pathway-mediated lyses of rabbit erythrocytes by human serum and sera of other species . Thus , Sbi is a multifunctional bacterial protein , which binds host complement components Factor H and C3 as well as IgG and β2-glycoprotein I and interferes with innate immune recognition .
In order to establish an infection pathogens have developed multiple mechanisms to avoid immune recognition and to escape host immune attack [1] , [2] . Complement , which mediates a powerful immediate innate immune defense of vertebrate hosts , is activated , within seconds upon entry of a foreign invader [1] , [2] . Activation of the complement system occurs through three pathways , the alternative , the classical , or the lectin binding pathway . The activated system cleaves the central complement protein C3 into the fragments C3a and C3b , and deposits C3b onto the surface of a microbe , which normally results in opsonization and elimination of the microbe by phagocytosis . This surface deposited C3b initiates further activation of the complement cascade and results ultimately in the formation of the membrane attack complex ( MAC ) , which forms a pore in the membrane and destroys the microbe by complement-mediated lyses . However for Gram positive bacteria MAC mediated lyses seems of minor significance . The cleavage products C3a and C5a serve as potent anaphylatoxins , which attract immune effector cells to the site of infection . Non-pathogenic microbes are effectively killed and eliminated by the complement system [3] . In order to restrict complement activation to the surface of an invading microbe host cells are protected from complement attack by membrane bound and soluble regulators . Factor H is the major fluid-phase complement regulator that controls alternative pathway activation at the level of C3 . The 150-kDa Factor H protein is exclusively composed of 20 structural repetitive protein domains , termed short consensus repeats ( SCR ) [4] . Factor H is a member of a protein family , that includes the Factor H like protein 1 ( FHL-1 ) , encoded by an alternatively spliced transcript of the Factor H gene , and five Factor H related proteins ( FHRs ) that are encoded by separate genes [5] . Factor H controls complement activation by acting as a cofactor for the serine protease Factor I , which cleaves surface-bound C3b into iC3b . In addition , by competing with Factor B for C3b binding Factor H accelerates the decay of the alternative pathway C3 convertase . Thus , Factor H blocks C3b deposition and amplification of the complement cascade on the cell surface [5] , [6] . In order to survive and to establish an infection , pathogens need to inhibit the host complement attack and apparently utilize diverse escape mechanisms . Several pathogens acquire host fluid-phase complement regulators , like Factor H , FHL-1 , FHR-1 and C4b-binding protein ( C4BP ) from host plasma and body fluids . Bound to the surface of a pathogen , these host regulators retain complement regulatory functions , and inhibit complement activation . Therefore , acquisition of host regulators masks the pathogenic surface , which results in survival of the pathogen [7] , [8] . This common strategy of complement evasion has been identified for multiple pathogens , including Gram-positive and Gram-negative bacteria , human pathogenic fungi , parasites and viruses and several of the corresponding surface proteins were identified [1] . The vast majority of these pathogenic surface proteins bind additional host plasma proteins and display multiple functions . The M protein of Streptococcus pyogenes binds the complement regulators Factor H , FHL-1 and C4BP as well as other plasma proteins , i . e . plasminogen , fibronectin , thrombin , fibrinogen , IgA , IgG and kininogen [1] , [9]–[14] . The Candida albicans surface protein Glyceratphosphat-Mutase 1 ( Gpm1 ) binds Factor H , FHL-1 and plasminogen [15] . In addition , Complement Regulator Acquiring Surface Protein 1 ( CRASP-1 ) of Borrelia hermsii and Tuf of P . aeruginosa , bind Factor H , FHR-1 and plasminogen [16] , [17] . The additional Factor H binding pathogenic surface proteins e . g . CRASP1 of Borrelia burgdorferei , PspC of S . pneumoniae and porin protein 1A of Neisseria gonorrhoeae are candidates for combined Factor H and plasminogen binding [18]–[20] . These pathogenic surface proteins display multiple functions and interfere with the complement regulation and coagulation . Thus , multiple or potentially all pathogens acquire soluble host factors and utilize these proteins for immune evasion [1] . S . aureus is a major human pathogen responsible for hospital- and community-acquired infection . The Gram-positive bacterium permanently colonizes the human skin and mucous membranes of approximately 20% of the population [21] . Once the pathogen has crossed host immune barriers S . aureus can cause superficial skin infection , toxin-mediated diseases or serious invasive infections depending on the interaction of the pathogen's virulence factors and the defense mechanisms of the host [22] . The pathogen utilizes complex strategies to survive and disseminate within the host and expresses several virulence factors to block both innate and adaptive immune response [23] . S . aureus utilizes several proteins to control and evade the host complement attack . The cell wall-anchored protein A ( SpA ) binds the Fc region of IgG [24] . S . aureus expresses the zymogen staphylokinase , that cleaves human plasminogen into active plasmin , which in turn cleaves IgG . In both cases recognition of the pathogen by C1q , the initial component of the classical complement activation pathway , is inhibited [25] , [26] . Sbi is an additional staphylococcal IgG-binding protein that similar to SpA interacts with the Fc part of IgG [27] . Furthermore , Sbi binds β2-glycoprotein I , which is also termed apolipoprotein H [28] . Recently , additional effector molecules of S . aureus are identified , that directly interfere with complement activation at the level of C3 . The extracellular fibrinogen-binding protein ( Efb ) , the Efb homologous protein ( Ehp ) , and the extracellular complement-binding protein ( Ecb ) , bind C3 and C3d , prevent further activation of C3b and consequently block the activity of C3b-containing convertases [29] , [30] , [31] , [32] , [33] . The staphylococcal complement inhibitor ( SCIN ) acts on surface-bound C3 convertases , C3bBb and C4b2a , by stabilizing these complexes , thereby reducing the enzymatic activity [34] , [35] . Here we show binding of Factor H and FHR-1 to the surface of intact S . aureus and in addition identify the secreted staphylococcal Sbi protein as a Factor H binding protein . Native Factor H from human serum binds to Sbi , and this binding is mediated by a second serum factor , which was identified as C3 . Factor H binding is increased in the presence of C3b or C3d suggesting formation of a tripartite complex . This complex blocks activation of the alternative complement pathway . The Factor H binding site of Sbi which was located to domains III and IV is distinct from the IgG binding sites which are contained in the N-terminal domains I and II [28] . Here , we demonstrate a novel mechanism for Factor H binding by Sbi . Sbi forms a tripartite complex with Factor H and C3b or C3d and this complex interferes with complement activation .
In order to analyze binding of host complement regulators to S . aureus , strain H591 was incubated in human serum . After extensive washing bound proteins were eluted , separated by SDS-PAGE , transferred to a membrane and analyzed by Western blotting . This approach identified three bands of 150 , 43 and 37 kDa , which represent Factor H , FHR-1β and FHR-1α , respectively ( Figure 1A , lane 2 ) . These proteins were absent in the final wash fraction , thus suggesting specific binding ( Figure 1A , lane 1 and lane 2 ) . The same proteins were also identified in human serum ( Figure 1A , lane 3 ) . When bacteria were incubated with purified Factor H binding of the purified protein was also detected in the eluted fraction ( Figure 1B , lane 2 ) . In order to characterize the bacterial ligand mediating this interaction we hypothesized that the staphylococcal Sbi protein might represent the binding protein . The N-terminal region of Sbi ( i . e . Sbi-E ) is composed of four domains and includes the IgG binding domains I and II , whereas domains III and IV lack antibody binding properties ( Figure 2A and C ) ( [28] , Burman et al . JBC in press ) . IgG binding of Sbi-E and Sbi-I was confirmed for one polyclonal antiserum and two monoclonal antibodies ( mABs ) , which are directed to Factor H ( Figure 2C , columns 1 and 2 ) . Antibody binding was rather strong and exceeded the reactivity for the specific ligand Factor H ( Figure 2 , compare columns 5 and 1 ) . Sbi is an IgG binding protein , therefore Sbi-E and Sbi-I interaction with additional ligands cannot be studied by standard ELISA . Consequently we used the previously described combined ELISA and Western blot approach ( CEWA ) to study binding of human serum proteins to Sbi [36] . CEWA , which allows the identification of Sbi bound serum proteins by size and by reactivity with specific antisera , revealed that Factor H as well as both FHR-1α and FHR-1β bind to Sbi-E , comprising domains I–IV ( Figure 3A , lane 1 ) . Both Factor H and FHR-1α/FHR-1β bound to the deletion constructs Sbi-III/IV and with lower intensity to Sbi-IV ( Figure 3A , lanes 3 and 4 ) . The IgG binding domain Sbi-I did not bind the host complement regulators ( Figure 3A , lane 2 ) . As described previously Factor H bound to borrelial CRASP-1 and CRASP-5 and FHR-1α/FHR-1β to CRASP-5 ( Figure 3A , lanes 5 and 6 ) [36] . Having demonstrated binding of Factor H , FHR-1α and FHR-1β from human serum to Sbi via domains III and IV , we wanted to confirm this interaction with purified proteins . However purified Factor H did not bind to Sbi , but did bind to CRASP-1 and CRASP-5 ( Figure 3B ) . These results suggest that binding of Factor H to Sbi is mediated by an additional serum factor . In order to identify the additional serum factor that mediates binding of the host complement regulatory , we hypothesized that the central complement component C3 , which binds to the staphylococcal inhibitors Efb , Ehp and Ecb [29] , [30] , [32] might be such a mediator . Consequently binding of purified Factor H in the presence of the complement proteins C3b and C3d was analyzed by CEWA . When coincubated with either C3b or C3d Factor H bound to Sbi-E , Sbi-III/IV and Sbi-IV , but not to Sbi-I ( Figure 4A ) . This binding suggests that Sbi forms a tripartite complex with Factor H and C3 . Factor H binds to domains III and IV of Sbi , but not to the IgG binding domain I . The interaction to the non-IgG binding domains was confirmed by standard ELISA . Purified Factor H together with C3b or C3d bound to Sbi-III/IV ( Figure 4B , columns 1 ) . Binding of Factor H together with C3b or C3d to Sbi-IV was rather low . In this assay the binding of Factor H together with C3b/C3d to Sbi-III/IV was more pronounced as compared to borrelial CRASP-1 ( Figure 4B , compare columns 1 and 3 ) . In addition the C3 fragment responsible for complex formation with Factor H was assayed by CEWA and ELISA ( Figure 4D and 4E ) . The C3d-containing fragments C3 , C3b and C3d mediate complex formation of Factor H with Sbi , but not C3a , C3c nor to iC3b . This result reveals a novel mechanism of capturing host immune regulators , as Sbi binds Factor H in combination with a second host ligand , namley C3 . Having identified staphylococcal Sbi as a protein that binds the host complement components Factor H together with C3b or C3d , we analyzed C3 binding and tripartite complex formation in more detail . First binding of the various forms of C3 was analyzed to immobilized Sbi-E in real time using surface plasmon resonance . C3 showed a strong association and a relative fast dissociation ( Figure 5A: C3 ) . C3b , used at the same molar ratio showed slower association , but the Sbi∶C3b complex was rather stable ( Figure 5A: C3b ) . In addition C3d , the degradation product of C3 , showed a more pronounced association and also a slow rate of dissociation ( Figure 5A: C3d ) . This slow dissociation profile of both C3d and C3b suggests a high stability of the Sbi∶C3b and Sbi∶C3d complexes . Based on the apparent stronger association of C3d to Sbi-E , this interaction was analyzed in more detail . Sbi-E showed a dose-dependent binding to immobilized C3d when used at a range of 200 , 400 and 800 nM ( Figure 5B ) . The same dose-dependent binding was observed in a reverse setting with immobilized Sbi-E ( data not shown ) . These results demonstrate that C3 , C3b and C3d bind directly to the staphylococcal Sbi . In order to further analyze the interaction and complex formation Sbi-E representing domains I-IV were immobilized and complex formation was followed in real time . In this setting purified Factor H bound rather weakly to immobilized Sbi-E , while C3b binding was stronger ( Figure 5C ) . An increase was observed in the presence of both Factor H and C3b confirming formation of a tripartite complex ( Figure 5C ) . Formation of the tripartite complex was also analyzed with Factor H and C3d ( Figure 5D ) . In this setting binding of C3d was similar to that of C3b ( compare Figure 5D and Figure 5C ) and based on the RLUs the tripartite Sbi∶C3d∶Factor H complex showed more pronounced interaction . Binding and tripartite complex formation was analyzed to immobilized Sbi-constructs , i . e . Sbi-E , Sbi-I and Sbi-III/IV , to localize the C3 binding domains in Sbi . C3d did not bind to the IgG binding domain Sbi-I , but to Sbi-E and also to the construct Sbi-III/IV ( Figure 5E ) . C3d interaction to Sbi-E and Sbi-III/IV was comparable , thus confirming the role of domains III and IV for the contact . Based on the strong interaction of the Sbi∶C3d∶Factor H complex to Sbi-E and to Sbi-III/IV ( Figure 5E ) it is concluded that the C3/Factor H interaction region of Sbi is located exclusively in Sbi domains III and IV . To characterize the formation of Sbi∶C3d∶Factor H complex Sbi-E was coupled to an NTA-chip and complex formation was followed upon sequential addition of C3d and Factor H . Immobilization of Sbi-E was observed ( Figure 5F , phase I ) and upon addition of C3d formation of the Sbi∶C3d complex was followed in real time ( Figure 5F , phase II ) . Upon addition of Factor H , a further association was detected by the increase in the surface plasmon resonance signals . These results demonstrate that Factor H binds directly to the Sbi∶C3d complex and that Factor H does not compete with C3b for Sbi-E binding ( Figure 5F , phase III ) . The observed mass increase at the surface of the sensor chip during association of Factor H to the Sbi∶C3d complex was higher than that of Factor H to immobilized C3d ( data not shown ) . To further characterize this novel type of Factor H acquisition with C3 , we decided to identify the Factor H domains that are involved in this interaction . Factor H deletion constructs were immobilized and used in an ELISA experiment . In the presence of C3b , Sbi-E and Sbi-III/IV , but not to Sbi-I bound to immobilized Factor H SCRs 19–20 and SCRs 15–20 ( Figure 6 , columns 5 and 6 ) . In addition Sbi-I did not bind to any Factor H deletion construct . Thus the Sbi binding site was localized within the C-terminal surface binding region of Factor H , within SCRs 19–20 and is restricted to Sbi domains three and four . Factor H bound to pathogenic ligands maintains complement regulatory activity which relates to complement evasion [1] . It was therefore of importance to assay if Factor H fixed in this tripartite complex is functionally active and has complement regulatory activity . Factor H and C3b were incubated simultaneously with immobilized Sbi-E or the deletion fragments Sbi-I , Sbi-III/IV and Sbi-IV . Subsequently , Factor I was added and the mixture was incubated further . Following this treatment the proteins were eluted , separated by SDS-PAGE and after transfer to a membrane the C3b degradation products were identified by Western blotting . Factor H bound to Sbi-E in the presence of C3b displayed cofactor activity as indicated by the disappearance of the α' band and the appearance of the α'68- and α'43 bands ( Figure 7 , lane 1 ) . The same degradation profile of C3b was observed when Factor H was bound to Sbi-III/IV ( Figure 7 , lane 3 ) or to borrelial CRASP-1 ( Figure 7 , lane 5 ) . In the absence of Factor H no degradation of C3b was observed ( Figure 7 , lanes 7 and 8 ) . These results show that Factor H attached to Sbi in a tripartite complex maintains complement regulatory activity . Tripartite Sbi∶C3d∶Factor H complexes represent –to our knowledge- a novel mechanism for Factor H attachment . Factor H has a C3b/C3d binding region within the C-terminal recognition region , which also forms the major contact with Sbi . Therefore we asked whether the tripartite complex is based on a sandwich type interaction , by which Sbi binds first intact C3 , C3b or C3d and then Factor H . Alternatively a tripartite complex may be formed , in which Factor H directly contacts Sbi and C3 . Inhibition experiments were performed to test this hypothesis and to characterize this interaction in more detail . First Factor H and C3b were incubated in the presence of Factor H antiserum and Factor H binding to immobilized Sbi was studied . Preincubation of Factor H with the specific antiserum decreased binding to Sbi-E and blocked binding to the fragments Sbi-III/IV and Sbi-IV ( Figure 8A , lower panel ) . The weak binding of antiserum treated Factor H to intact Sbi-E and to Sbi-I is explained by binding of the Factor H∶IgG complex via the IgG binding site of Sbi located within domain I . First binding of Factor H to immobilized Sbi-III/IV in the presence of increasing amounts of C3d was studied . Already 1 ng of C3d , resulting in a molar Factor H∶C3d ratio of 25∶1 enhanced Factor H∶Sbi interaction ( Figure 8B ) . Secondly , Sbi-III/IV was immobilized , C3d was added and Sbi-III/IV bound C3d was blocked with increasing amounts of specific C3d antiserum . Subsequently , the binding of Factor H was analyzed . Factor H binding was not impaired with antisera titers up to 1∶1000 , and was reduced but not completely blocked at the highest titers ( 1∶100 and 1∶10 ) ( Figure 8C ) . This result shows direct binding of Factor H to Sbi and indicates that the presence of C3d , Sbi enhances formation of the tripartite complex . Similarly , Sbi-III/IV was immobilized and a saturating amount of C3d was bound . In order to block C3d binding sites on the Factor H protein , Factor H was preincubated with increasing concentrations of C3d prior to binding . The preincubated Factor H∶C3d complexes were added to the immobilized Sbi∶C3d complexes and after incubation Factor H binding was analyzed . Again tripartite Sbi∶C3d∶Factor H complexes were detected and complex formation was independent of the amount of C3d used for preincubation ( Figure 8D ) . This result is in agreement with a direct Factor H∶Sbi contact . In summary the inhibition and blocking experiments reveal that Factor H binds directly to Sbi and that binding is assisted by C3d . Staphylococcal Sbi forms a tripartite complex with host complement proteins Factor H and C3 . Consequently the complement inhibitory activity of Sbi was assayed in a standard hemolysis assay , using human serum and rabbit erythrocytes . In this assay Sbi-E and also Sbi-III/IV inhibited complement-mediated lyses of rabbit erythrocytes in a dose-dependent manner . Complete inhibition was observed at a concentration of 600 ng of either Sbi-E or Sbi-III/IV ( Figure 9A ) . In contrast , Sbi-I had no effect ( data nor shown ) indicating that C3b and Factor H binding is relevant for complement inhibitory activity . These results demonstrate that Sbi acts as a potent complement inhibitor . Hemolysis of rabbit erythrocytes in human serum was dose-dependent over a range from 5 to 15% and Sbi blocked hemolysis efficiently at all serum concentrations ( Figure 9B ) . To analyze the species range of Sbi-E the inhibitory effect of Sbi-E was tested using sera of different species . Complement mediated inhibition was observed in human , mouse and guinea pig sera , and no effect was detected in dog , goat and sheep sera ( Figure 9C ) . Thus Sbi acts in human serum but also displays a broader species range . Sbi is a potent complement inhibitor . Therefore we investigated the inhibitory effect of Sbi-E in all three complement pathways . Sbi-E clearly inhibited alternative pathway activity ( Figure 9D , column 2 and 3 ) . When all pathways were activated hemolysis was reduced in a dose-dependent manner , indicating that the alternative pathway , which was blocked by Sbi-E , is involved and that the classical and lectin pathway are unaffected ( Figure 9D , columns 7 and 8 ) . This effect was confirmed upon analyzing the impact on the classical and the lectin pathway . Sbi-E did not inhibit hemolysis of rabbit erythrocytes when complement was activated via the classical and the lectin pathway ( Figure 9D , columns 12 and 13 ) . As Sbi inhibits complement we asked whether Sbi protects S . aureus from phagocytosis mediated killing . S . aureus was incubated with complement active human serum in presence or absence of Sbi-E . Subsequently bacteria were harvested and incubated together with activated phagocytic THP-1 macrophages . At the indicated times points bacteria were recovered and the survival rate was quantitated . The presence of Sbi increased bacterial survival ( Figure 9E ) , thus indicating that the inhibitor Sbi protects bacteria from opsonisation and phagocytosis . These results demonstrate that Sbi-E efficiently inhibits the alternative , complement pathway and aids in bacterial resistance against complement mediated phagocytosis .
The Gram-positive bacterium S . aureus , similar to other human pathogens binds the complement regulators Factor H and FHR-1 from human serum . We identify the staphylococcal Sbi protein as a ligand for the two host complement regulators . Apparently Sbi binds Factor H by a new mechanism , as this human regulator binds to Sbi together with C3 , which likely results in formation of a tripartite Sbi∶C3∶Factor H complex . Arranged in this tripartite complex Factor H is functionally active and displays complement regulatory activity . Thus Sbi is a potent complement inhibitor , and inhibits the hemolytic activity of human and rodent serum on rabbit erythrocytes via the alternative pathway . Thus the multifunctional bacterial Sbi protein interferes with innate immune recognition , by acquisition of multiple host proteins in form of the complement components Factor H , C3 as well as IgG and β2-glycoprotein I . Purified Factor H bound to intact bacteria , but dependent on the assay showed weak or even no binding to Sbi ( compare Figure 1 , Figure 3B and Figure 5C , D ) . This difference in binding suggests that intact S . aureus bacteria express an additional Factor H binding surface protein . The identification of this protein is subject to further studies . The staphylococcal Sbi protein was identified as a ligand for Factor H . However Factor H binding is enhanced in the presence of an additional complement protein C3 . A tripartite Sbi∶C3b∶Factor H complex is formed ( Figure 4 and Figure 5 ) . The Factor H contact region for Sbi is located within SCRs 19–20 ( Figure 6 ) . Very similar contact domains were identified for other microbial Factor H binding proteins , e . g . CRASP-1 and CRASP-2 of B . burgdorferi , Tuf of P . aeruginosa and Gpm1 of C . albicans [12] , [14] , [16] , [37] . Inhibition experiments showed that polyclonal Factor H antiserum blocks Factor H binding to Sbi ( Figure 8A ) . In the proposed tripartite complex the regulatory region of Factor H ( i . e . SCRs 1–4 ) is freely accessible as demonstrated by the Factor I mediated cleavage of C3b ( Figure 7 ) . The staphylococcal Sbi protein is composed of four globular N-terminal domains connected to a tyrosine-rich C-terminal domain via a prolin-rich region ( Figure 2A ) [38] . A recombinant fragment with domains I–IV ( Sbi-E ) , as well as constructs Sbi-III/IV and Sbi-IV , but not Sbi-I bound Factor H in combination with C3b or C3d ( Figure 4 and Figure 5 ) , thus localizing the Factor H binding region to Sbi domains III and IV . As the Factor H/C3b binding sites in domain III and IV and the IgG binding sites in domain I and II are separated , the Sbi protein may simultaneously bind several host proteins . The binding properties of Sbi are unique , as –to our knowledge– Sbi is the first bacterial protein identified that forms such a tripartite complex with Factor H and C3 , C3b or C3d . It will be of interest to demonstrate whether other proteins of pathogen origin or virulence factors form similar tripartite complexes . The Sbi∶C3 interaction appears rather complex , as intact C3 and the two processed forms C3b and C3d display different binding profiles resulting in different stabilities ( Figure 5A ) . C3d complexed to Sbi showed the highest binding intensity of binding , and C3b or C3 a lower interaction . In addition the rate constants of C3d and Sbi-III/IV when assayed by surface plasmon resonance did not fit a 1∶1 langmuir model of interaction , but rather fit a bivalent analyte model ( Figure S2A and S2B , Figure S3 ) . The proposed bivalent analyte interaction together with the different binding profiles for the three C3 forms suggest that C3 undergoes a conformational change upon binding to Sbi and exposes additional binding epitopes , which affect Sbi interaction , or that these C3b/C3d binding region ( s ) is/are differently accessible to the bacterial Sbi protein . During complement activation C3 is cleaved , the C3 cleavage products bind to Sbi , increase Factor H binding and enhance the stability of the tripartite complex . Such a feed back regulation may increase the amount of inhibitory host regulators like Factor H at the site of infection and result in protection of the pathogen from complement attack and thus improves bacterial survival ( Figure 9E ) . This inhibition of the alternative pathway by Sbi indicates that Factor H bound to Sbi affects the C3 convertase . Within the tripartite complex Factor H displays complement regulatory activity ( Figure 7 ) and seems responsible for hemolytic activity ( Figure 9A and data not shown ) . This explains why Sbi domains III and IV display an inhibitory effect . Compared to the other staphylococcal complement regulators Efb and Ecb , Sbi does not interfere with the activity of the classical pathway and did not affect hemolysis mediated by the classical pathway ( Figure 9D ) [29] . Thus Sbi forms a tripartite complex with the two human complement proteins Factor H and C3 , revealing- to our knowledge- a novel mechanism for complement inhibition . The inhibitory activity of Sbi is not restricted to human complement as the protein also blocks complement of other species i . e . mouse and guinea pig . Demonstrating that Sbi is a staphylococcal virulence factor with a broader species range as compared to the human specific inhibitor SCIN , which acts specifically in the human system [29] , [34] . Sbi is a potent complement inhibitor , which interferes with the hemolytic activity of human serum . In hemolytic assays with rabbit erythrocytes Sbi used at 2 µg/ml ( = 0 . 3 µg ) exclusively blocked the alternative pathway whereas the classical and the lectin pathways were unaffected ( Figure 9D ) . However when used at higher concentrations of 1000 µg/ml in an ELISA approach the Sbi-III/IV fragment blocks all three activation pathways of human complement but the Sbi-IV fragment is a specific inhibitor for the alternative pathway ( Burman et al . JBC in press ) . This activity differs from that of SCIN and its homologues SCIN-B and SCIN-C , which affects all three complement pathways [29] , [34] . The staphylococcal Sbi protein is a multifunctional protein which binds the complement effectors Factor H , FHR-1 and C3 and also the processed forms C3b and C3d , as well as IgG and β2-glycoprotein I . Thus Sbi mediates innate and adaptive immune escape ( i ) by acquiring host complement inhibitors , which correlates with the activation state of complement , ( ii ) by inhibiting complement activation at the level of alternative pathway C3 convertase , ( iii ) by binding and inactivation of IgG to avoid recognition by phagocytes , and ( iv ) most likely by blocking C3dg binding to complement receptor 2 ( CR2 ) ( Burman et al . JBC in press ) .
S . aureus strain H591 ( MSSA clinical isolate , UK ) was grown at 37°C in tryptic soy broth ( TSB , Sigma ) . The strain was characterized for the presence of Sbi on DNA and protein level ( Figure S1A , S1B and S1C ) . Overnight cultures of S . aureus were diluted to OD600 = 0 . 2 in TSB and incubated for about 1 . 5 h at 37°C to OD600 = 1 . 0 ( approximately 1 . 2×109 cfu ) . Cells ( 2×109 cfu ) were harvested by centrifugation ( 6000 g , 8 min at room temperature ) , resuspended in veronal buffered saline ( GVB2+ , Sigma ) supplemented with 10 mM EDTA and incubated with either normal human serum ( NHS , diluted 1∶10 ) or Factor H ( 100 µg/ml , Aventis Behring ) for 1 h at 37°C with agitation . Subsequently , the cells were washed four times with EDTA-GVB2+ and bound proteins were eluted with SDS buffer ( 60 mM Tris-HCl , pH 6 . 8 , 2% SDS , 25% glycerine ) for 5 min at 98°C . Wash and elute fractions were separated by SDS-PAGE , transferred to a membrane and analyzed by Western blotting using a polyclonal goat Factor H antiserum ( Merck ) and horseradish peroxidase ( hrp ) coupled rabbit anti goat antiserum ( DAKO ) for detection . Recombinant fragments of the N-terminal region of Sbi ( adjacent to the poly-proline region ) were engineered , expressed and purified as described previously by ( Burman et al . JBC in press ) . The following Sbi constructs were used in this study: Sbi-E ( amino acids 28–266 ) containing IgG-binding domains I and II and C3 interacting domains III and IV; Sbi-I ( amino acids 42–94 ) ; Sbi-III-IV ( amino acids 150–266 ) and Sbi-IV ( amino acids 197–266 ) . The Factor H deletion mutants SCRs 1–7 , SCRs 8–11 , SCRs 11–15 , SCRs 15–18 and SCRs 19–20 were expressed in insect cells infected with recombinant baculovirus as described earlier [39] . Briefly , Spodoptera frugiperda cells ( Sf9 ) were grown at 28°C in monolayer cultures in protein-free expression medium for insect cells ( BioWhittaker ) . Adherent Sf9 cells were infected with recombinant virus using a multiplicity of infection of five . The culture supernatant was harvested after 9 days and recombinant Factor H constructs were purified by affinity chromatography using Ni-NTA-Agarose ( Qiagen ) . The complete extra cellular region , Sbi-E , and the extra cellular deletion mutants Sbi-I , Sbi-III/IV , Sbi-IV , BSA ( 2 µg/ml each ) and Factor H ( 1 µg/ml ) were immobilized onto a microtiter plate for 2 h at room temperature . Unspecific binding sites were blocked with 0 . 2% gelatine in DPBS ( Lonza ) over night at 4°C . After extensive washing with PBSI ( 3 . 3 mM NaH2PO4×H2O , 6 . 7 mM Na2HPO4 , 145 mM NaCl , pH 7 . 2 ) supplemented with 0 . 05% Tween 20 a polyclonal rabbit SCR1–4 antiserum and the two mABs B22 and C18 ( all specific for Factor H ) were added for 2 h at room temperature . Protein-antibody complexes were detected using secondary horseradish peroxidase ( HRP ) -coupled antiserum ( e . g . rabbit anti goat-hrp ( DAKO ) rabbit anti mouse-hrp ( DAKO ) ) Respectively . All antibodies and antisera were used at 1∶1000 dilutions . The reaction was developed with 1 , 2-phenylenediamine dihydrochloride ( OPD , Dako ) and the absorbency was measured at 490 nm . A combined ELISA and Western blot approach ( CEWA ) was used to assay Factor H binding to Sbi-E and the deletion constructs Sbi-I , Sbi-III/IV and Sbi-IV [36] . The proteins ( 10 µg/ml ) were immobilized onto a microtiter plate over night at 4°C . After blocking with 0 . 2% gelatine in DPBS ( Lonza ) for 6 h at 4°C , NHS ( diluted 1∶10 ) , Factor H ( 5 µg/ml ) , a combination of Factor H ( 5 µg/ml ) and C3b ( 10 µg/ml , Merck ) , or Factor H ( 5 µg/ml ) and C3d ( 2 , 6 µg/ml , Merck ) were added . For the C3-CEWA a mixture of Factor H ( 5 µg/ml ) and C3b or C3 , iC3b , C3d ( each 10 µg/ml , Merck ) , C3c , C3a ( each 10 µg/ml , Comptech ) were added . Samples were incubated over night at 4°C . After extensive washing protein complexes were removed with SDS buffer , separated by SDS-PAGE and analyzed by Western blotting using a polyclonal anti C3 antibody ( Calbiochem ) and anti-goat – hrp ( DAKO ) was used for the detection of C3 and its degradation products . For Factor H detection the mAB C18 , which is specific for SCR 20 of Factor H and rabbit anti mouse-hrp ( DAKO ) as secondary antibody was used . As positive controls the borrelial Factor H binding protein CRASP-1 , and also the Factor H/FHR1 binding protein CRASP-5 ( kindly provided by Dr . Peter Kraiczy ( University of Frankfurt a . M . ) and by Prof . Dr . Reinhard Wallich ( University of Heidelberg ) ) and as negative control BSA were used . The Factor H deletion constructs SCRs 1–7 , SCRs 8–11 , SCRs 11–15 , SCRs 15–18 , SCRs 19–20 and SCRs 15–20 were immobilized equimolar onto a microtiter plate over night at 4°C . After blocking with Blocking Buffer I ( AppliChem ) for 2 h at 37°C , a combination of C3b ( 5 µg/ml ) and the Sbi deletion mutants Sbi-E , Sbi-I and Sbi-III/IV used at equimolar amounts were added and incubated for 1 h at room temperature . After excessive washing bound Sbi deletion mutants were detected with polyclonal Sbi antiserum ( 1∶1000 ) and a secondary horseradish peroxidase-coupled anti rabbit antiserum ( 1∶1000 , DAKO ) . To analyze the complex formation , Sbi-III/IV ( 10 µg/ml ) was coated and a combination of Factor H ( 15 µg/ml ) and C3 , C3b , C3d , C3c ( Calbiochem ) or C3a ( 15 µg/ml; Comptech ) was added . The complex was detected by polyclonal goat anti Factor H ( 1∶1000 ) and rabbit anti goat-hrp ( 1∶1000 , Dako ) . The reaction was developed with 1 , 2-phenylenediamine dihydrochloride ( OPD , Dako ) and the absorbency was measured at 490 nm . Sbi-E and the extra cellular deletion mutants Sbi-I , Sbi-III/IV , Sbi-IV or CRASP-1 , and BSA ( 10 µg/ml ) were immobilized and unspecific binding sites were blocked as described . Factor H ( 5 µg/ml ) , polyclonal Factor H antiserum ( diluted 1∶100 ) and C3b ( 10 µg/ml ) were preincubated for 2 h at 4°C . Subsequently the mixture was added to the immobilized proteins and incubated over night at 4°C . After extensive washing protein complexes were removed from the well with SDS buffer , separated by SDS-PAGE and analyzed by Western blotting with the polyclonal rabbit SCR1–4 antiserum and swine anti rabbit-hrp ( DAKO ) as secondary antibody . For determining the regulatory activity of Sbi-bound Factor H , the regulator ( 3 µg/ml ) together with C3b ( 6 µg/ml ) or C3b ( 6 µg/ml ) alone were added to immobilized Sbi-E , Sbi-I , Sbi-III/IV , Sbi-IV , CRASP-1 , or BSA ( 10 µg/ml ) incubated over night at 4°C . After extensive washing Factor I ( 0 . 8 µg ) was added and the mixture was incubated for 30 min at 37°C . C3b conversion to inactive C3b ( iC3b ) was detected after separating the protein solution by SDS-PAGE with Western blot analysis using a polyclonal goat C3 antiserum ( 1∶1000 , Merck ) and rabbit anti goat-hrp ( DAKO ) as secondary antibody . Samples were separated by SDS-PAGE using 10% and 12% gels . After the transfer of the proteins onto nitrocellulose membranes by semi-dry blotting [40] , the membranes were blocked with 5% ( w/v ) dried milk in PBSI for 30 min at room temperature and incubated with the indicated primary antibodies over night at 4°C . Antibodies were diluted in 2 . 5% ( w/v ) dried milk in PBSI . The proteins were detected by ECL using appropriate secondary antisera that was coupled with horseradish peroxidase . Protein-protein interactions were analyzed by the surface plasmon resonance technique using a Biacore 3000 instrument ( Biacore AB ) as described [41] . Briefly , the staphylococcal proteins Sbi-E , Sbi-I , Sbi-III/IV or human C3d were coupled to the surface of the flow cells of the sensor chip via a standard amine-coupling procedure ( carboxylated dextran chip CM5 , Biacore AB ) until about 2000 resonance units were reached . A control cell was prepared under identical conditions that lacked a protein . Sbi-E , Factor H , C3 , C3b or C3d were diluted in DPBS ( Lonza ) , adjusted to equal molarities and injected with a flow rate of 5 µl/min at 25°C . Alternatively , Ni2+ and Sbi-E was loaded to a NTA-chip , and C3d followed by Factor H were injected at equimolar amounts . Each interaction was analyzed at least three times . In order to analyze the complement regulatory effect of Sbi , hemolytic assays were performed using rabbit erythrocytes ( rE , Rockland ) . Rabbit erythrocytes represent activator surfaces for human serum and lyse due to MAC formation . Thus the complement activity correlates directly with the erythrocyte lysis as monitored by the increase in absorbance . Following preincubation of NHS with Sbi-E or Sbi-III/IV for 30 min at 37°C , 5×106 rE were added ( 150 µl total volume ) and further incubated for 30 min at 37°C . After centrifugation ( 2 min , 5000 rpm ) the absorbency of the supernatant was measured at 414 nm . NHS , Sbi-E and Sbi-III/IV were used at the indicated concentrations . Samples were diluted in HEPES buffer ( 20 mM HEPES , 144 mM NaCl , 7 mM MgCl2 , 10 mM EGTA , 1% BSA , pH 7 . 4 ) . The effect of Sbi on different animal sera ( Innovative Research ) was assayed using 30% animal serum and 2 µg ( 13 µg/ml ) Sbi-E . In order to analyze and distinguish between the alternative and the classical/lectin pathway complement activation was pursued in different buffers . Alternative pathway activity was measured in EGTA-HEPES buffer . Activation of all three pathways was assayed in Ca2+-HEPES buffer ( 20 mM HEPES , 144 mM NaCl , 5 mM CaCl2 , 2 , 5 mM MgCl2 , pH 7 . 4 ) . The effect of the classical and the lectin pathway was assayed in Factor B deficient serum ( Complement Technology Inc . ) and the Ca2+-HEPES buffer . All three approaches ( AP , AP+CP/LP and CP/LP ) were analyzed in the presence of none , 0 . 3 µg ( 2 µg/ml ) and 1 . 0 µg ( 6 , 7 µg/ml ) Sbi-E . Bacteria S . aureus strain H591 ( 6×104 ) were incubated in 40% NHS supplemented with HEPES EGTA in presence or absence of Sbi-E for 15 min at 37°C . Samples were added to 8×105 PMA primed THP-1 macrophages in antibiotic free RPMI-1640 resulting in a final Sbi-E concentration of 2 µg/ml . THP-1 cells incubated without S . aureus were used as negative control . After shaking 20 µl sample were plated hourly . Plates were incubated overnight and colonies were counted . National Centre for Biotechnology Information ( www . ncbi . nlm . nih . gov ) : Homo sapiens complement factor H ( CFH ) , gi|62739185|ref|NM_000186 . 2|[62739185]; Homo sapiens complement factor H-related 1 ( CFHR1 ) , NM_002113 . 2 GI:118442838; Homo sapiens complement component 3 ( C3 ) , NM_000064 . 2 GI:115298677; immunoglobulin G-binding protein Sbi [Staphylococcus aureus subsp . aureus str . Newman] , YP_001333351 . 1 GI:151222529 . | Staphylococcus aureus is a Gram-positive bacterium that can live as a commensal but can also cause severe life threatening infections in humans . Upon infection the bacterium is attacked by the host immune system , and in particular by the complement system which forms the immediate , first defence line of innate immunity . In order to survive , S . aureus has developed multiple evasion strategies and uses several virulence factors to evade and inactivate the host complement attack . Here we show that this pathogen binds the host complement regulators Factor H from human serum with the secreted and surface exposed Sbi protein , by a—to our knowledge—new type of interaction . Factor H binds to Sbi in combination with another host complement protein C3 , C3b or C3d , and forms tripartite Sbi∶C3∶Factor H complexes . As part of this tripartite complex , Factor H is functionally active and inhibits further complement activation . Sbi , by recruiting Factor H and C3b , acts as a potent complement inhibitor , and inhibits alternative pathway-mediated lyses of rabbit erythrocytes by human serum and sera of different species . Thus , Sbi is a multifunctional bacterial protein , which binds host complement components Factor H and C3 as well as IgG and β2-glycoprotein I and interferes with innate immune recognition . |
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The burden of malaria in Vietnam has drastically reduced , prompting the National Malaria Control Program to officially engage in elimination efforts . Plasmodium vivax is becoming increasingly prevalent , remaining a major problem in the country's central and southern provinces . A better understanding of P . vivax genetic diversity and structure of local parasite populations will provide baseline data for the evaluation and improvement of current efforts for control and elimination . The aim of this study was to examine the population genetics and structure of P . vivax isolates from four communities in Tra Leng commune , Nam Tra My district in Quang Nam , Central Vietnam . P . vivax mono infections collected from 234 individuals between April 2009 and December 2010 were successfully analyzed using a panel of 14 microsatellite markers . Isolates displayed moderate genetic diversity ( He = 0 . 68 ) , with no significant differences between study communities . Polyclonal infections were frequent ( 71 . 4% ) with a mean multiplicity of infection of 1 . 91 isolates/person . Low but significant genetic differentiation ( FST value from -0 . 05 to 0 . 18 ) was observed between the community across the river and the other communities . Strong linkage disequilibrium ( IAS = 0 . 113 , p < 0 . 001 ) was detected across all communities , suggesting gene flow within and among them . Using multiple approaches , 101 haplotypes were grouped into two genetic clusters , while 60 . 4% of haplotypes were admixed . In this area of Central Vietnam , where malaria transmission has decreased significantly over the past decade , there was moderate genetic diversity and high occurrence of polyclonal infections . Local human populations have frequent social and economic interactions that facilitate gene flow and inbreeding among parasite populations , while decreasing population structure . Findings provide important information on parasites populations circulating in the study area and are relevant to current malaria elimination efforts .
Vietnam has been extremely successful in decreasing the country’s malaria burden , thanks to the large scale implementation of control interventions such as insecticide-treated bed nets , indoor residual spraying , and prompt , free-of-charge diagnosis and treatment; the number of cases fell from 130 , 000 in 2004 to 27 , 868 in 2014 [1] . Malaria has been virtually eliminated from Northern and Southern Vietnam [1 , 2] . In 2014 , 80% of malaria cases occurred in nine “hot provinces” where annual incidence peaked at 3 . 1 cases per 1000 , indicating a highly heterogeneous transmission , with hot spots of transmission ( mostly in mountainous and forested areas ) surrounded by areas of low transmission [1–5] . Vietnam aims at eliminating malaria by 2030 [6] . Such ambitious goal is threatened by P . vivax , whose characteristics ( dormant liver forms that relapse weeks or months after clearance of the primary infection and gametocytes production before the occurrence of symptoms ) together with the relative high occurrence of sub-patent and asymptomatic infections that remain undetected and thus untreated [3 , 7–10] , make its transmission much more difficult to interrupt than that of P . falciparum . In addition , as already reported , elimination efforts are threatened by the emergence of drug resistance , for P . falciparum to artemisinin derivatives and partner drugs and for P . vivax to chloroquine ( CQ ) [2 , 11 , 12] . Current efforts to eliminate malaria are targeted to districts and communes reporting an increased number of malaria cases over time [2 , 6] . In this context , understanding parasite genetic diversity and its population structure is relevant for ( i ) monitoring temporal changes in transmission following control efforts , ( ii ) elucidating the spatial distribution of parasite populations and predicting outbreaks , population resilience , and the spread of drug-resistant parasites , and ( iii ) identifying ecological and behavioral risk factors that can inform malaria control and elimination efforts [13–15] . A previous study conducted in Binh Thuan province in central Vietnam reported high levels of genetic diversity ( average expected heterozygosity ( He ) = 0 . 86 ) and all infections being multi-clonal despite low transmission [13] ( similar to what has been previously reported in South-East Asia ) [16] . The aim of this study was to provide baseline data on the P . vivax parasite populations in four rural communities in the Vietnamese Quang Nam province .
Samples were collected from April 2009 to December 2010 in four communities ( Fig 1 ) in the South Tra My district of Quang Nam , Central Vietnam during a prospective cohort study aiming to assess the short- and long-term efficacy of CQ and high-dose piperaquine ( PQ ) for the treatment of P . vivax mono-infections [12] . Detailed sociodemographic characteristics of the local population have been already reported elsewhere [3] . In 2009 , the prevalence of malaria by light microscopy was 7 . 8% , while by polymerase chain reaction ( PCR ) prevalence was estimated at 22 . 6% ( ranging from 16 . 4 to 42 . 5% ) , with a high proportion of P . vivax mono infections ( 43% ) . Sub-patent infections accounted for 58 . 7% of all infections , evidencing the existence of a substantial hidden human reservoir of malaria [3] . Malaria transmission is seasonal , and peaks during the rainy season ( May to November ) . Based on data from the Provincial Malaria Station , between 2009 and 2013 the mean prevalence of P . falciparum , P . vivax , and mixed malaria cases for all age groups in the study area was 64 . 1% , 31 . 5% , and 4 . 4% , respectively [17–18] . The main malaria vectors in the area are Anopheles dirus sensu stricto and An . minimus , though An . vagus , An . aconitus , and An . philippinensis are also present [17–18] . A finger prick blood sample was collected at day 0 ( before treatment ) for diagnosis by light microscopy and two blood spots were collected on grade 3 filter paper ( Whatman Ltd . , Springfield Mill , Maidstone , United Kingdom ) for molecular diagnosis and microsatellite ( MS ) genotyping . The study was approved by the National Institute of Malariology , Parasitology and Entomology in Hanoi , the Ministry of Health of Vietnam , and the review boards of the Institute of Tropical Medicine and Antwerp University Hospital ( UZA ) in Antwerp , Belgium . Adult participants ( in case of minors one of the parents/guardians ) provided written informed consent . Thick and thin film blood slides were stained with a 3% Giemsa solution for 45 minutes , and the number of asexual parasites was calculated following World Health Organization ( WHO ) guidelines [19] . Parasite density was estimated by dividing the number of asexual parasites for 200 white blood cells ( WBCs ) counted and expressed as the number of asexual parasites per microliter of blood , assuming 8000 WBC/μL . All blood slides were double-read by two technicians and in case of disagreement , slides were read by a third senior technician . The final results were expressed as the mean of the two closest results . A slide was declared negative if no parasites were found after counting 1 , 000 WBCs . DNA was extracted from filter paper blood spots cut into 5-mm-diameter disks with the QIAamp DNA Micro Kit following the manufacturer’s recommendations ( Qiagen , Hilden Germany ) . P . vivax mono infections were confirmed by species-specific multiplexed semi-nested PCR , as described by Rubio et al . [20] . All samples were genotyped with 14 MS ( MS1-MS10 , MS12 , MS15 , MS20 , and PvSal1814 ) following previously described PCR protocols [13–14] . The PCR products of four MS were pooled and analyzed by capillary electrophoresis in a 3730 XL ABI sequencer ( Applied Biosystems ) and 1200 Liz was used as the internal size standard . Negative samples were repeated once . Allele calling was performed using GeneMarker version 2 . 4 . 0 . After pooling capillary electrophoresis fsa files from all samples , a standard cut-off value of 500 relative fluorescence units was defined and peaks below this limit were considered background noise . In addition , all samples were double-checked manually to confirm true alleles . At each locus the predominant allele ( the one giving the highest peak ) and minor alleles within at least two-thirds of the height of the predominant allele were scored [13 , 15 , 21] . Only predominant alleles were used to define haplotypes to ensure an unbiased estimate of minor allele frequency in polyclonal infections [15 , 22–24] . Samples were defined as polyclonal if at least one locus presented more than one allele [13–14] . Polyclonal/locus ( % ) describes the percentage of samples identified as polyclonal by a given MS out of the total number of samples [13] . Multiplicity of infection ( MOI ) , defined as the minimum number of different clones observed in a sample , was estimated by taking the maximum number of alleles at the two most polymorphic markers [13 , 25] . Average MOI was defined as the sum of MOIs detected across all samples divided by the total number of samples . Average MOI and the proportion of monoclonal and polyclonal infections were compared with the Kruskal-Wallis and Pearson χ2 test , respectively . A value of p < 0 . 05 was considered significant . The predominant alleles in each sample were used to calculate the number of haplotypes by GenAlEx 6 . 5 [26] . Haplotypes that appear only once in the population were defined as unique haplotypes . Genetic diversity , defined as the probability of observing different genotypes at a given locus in two unrelated parasites , was assessed by calculating the expected heterozygosity ( He ) for each community using the formula: He = [n/ ( n-1 ) * ( 1-∑pi2] , where n is the total number of alleles and p is the allele frequency . He ranges between 0 and 1 , with values close to 1 indicating high genetic diversity [27] . Allelic richness , defined as the number of alleles per locus independently of sample size , was calculated using FSTAT v2 . 9 . 3 [28] . He and allelic richness were compared between communities using the Kruskal-Wallis test . The presence of bias due to false assignment of predominant haplotypes was investigated by comparing He in the database containing all the alleles and the database containing the predominant alleles [29] . A standardized index of association ( IAs ) , calculated with LIAN v3 . 5 , was used to assess the presence of multilocus linkage disequilibrium ( LD ) in the parasite population [30] . The significance of the IAs estimate was assessed with Monte Carlo simulation using 10 , 000 random permutations of the data . To differentiate between clonal propagation and epidemic expansion , we compared LD in the predominant allele dataset and the unique haplotype dataset [31] . Pairwise LD was used to evaluate the physical linkage between loci located within the same contig using the G statistic in FSTAT v2 . 3 . 9 [28 , 32] . Genetic differentiation between pairs of communities was estimated using a pairwise unbiased estimator of F-statistics FSTAT v2 . 3 . 9 with no assumption of Hardy-Weinberg equilibrium within samples [28 , 32] . A matrix of p values corresponding to each pairwise FST was calculated after Bonferroni correction , with a value of p < 0 . 05 considered significant . Crude FST values were adjusted for sample size using Recode Data v . 0 . 1 [33] and standardized FST estimates were obtained by dividing crude FST values by adjusted FST values . FST estimates ranged from 0 ( no genetic differentiation between communities ) to 1 ( full differentiation ) . As a complementary approach , population structure was investigated using the software programs STRUCTURE v2 . 3 . 2 [34] , CLUMPP [35] , DISTRUCT [36] , and GENODIVE [37] . STRUCTURE was used to identify clusters of genetically related samples . The number of clusters ( K ) was set from 1 to 10 with 10 replications per K , and 150 , 000 Markov Chain Monte Carlo steps after a burn-in period of 50 , 000 iterations using the admixture model . The loc-prior model was used for accurate inference of population and individual ancestry . Next , we used STRUCTURE HARVESTER v0 . 6 . 94 [38] to calculate the most likely number of K clusters . Additional data parsing and formatting of the STRUCTURE output was performed using CLUMPP and DISTRUCT [38–39] . CLUMPP permutes the clusters’ output by performing multiple replicate runs for the selected K . Samples with an average pairwise similarity ( H value ) of over 85% in one of the K clusters were considered to belong to that particular population; all other samples were considered admixed samples . DISTRUCT performs geographical displays of the aligned cluster assignment . We confirmed the optimal K , i . e . the K with the highest pseudo-F statistic , using AMOVA-based K-means clustering analysis in GENODIVE V2 . 0b23 ( OS X 10 . 6 operating system ) . This method divides a number of individuals into an a priori assigned number of clusters ( K ) in such a way that minimizes within-group diversity and maximizes between-group diversity . The pseudo-F statistic was calculated by setting up simulated annealing runs with 150 , 000 steps and 50 algorithm repetitions to determine optimal clustering ( highest pseudo- F-statistic ) . eBURST v3 was used to identify clusters of closely related haplotypes , or haplogroups ( HGs ) , which were defined as haplotypes sharing at least 9 loci from the 14 MS analyzed [40] . Haplotypes unrelated to any haplogroup ( HG ) were classified as singletons . The relationship between haplotypes following the defined K clusters was further analyzed by PHYLOViz [41] . Finally , to investigate the relationship between geographic and genetic distances in the study population , we performed principal coordinate analysis with the Mantel test for matrix correspondence in GenAlEx 6 . 5 [26] . Allele frequency was calculated in GenAlEx 6 . 5 using the predominant allele data set with 14 loci . The existence of a recent population bottleneck was investigated by evaluating the allele frequency distribution in the population ( alleles at low frequencies are less abundant in populations with a recent bottleneck ) [42–43] .
In total , 234 individuals with P . vivax mono infection from the 260 recruited in the original study [12] were successfully genotyped and included in the analysis . Baseline characteristics of study participants are described in Table 1 . Successful genotyping , with at least 12 of the 14 MS , was achieved in 194 patients ( 82 . 9% ) . Allele data were successfully recovered in more than 83% of the samples for all MS except MS20 and Pvsal1814 , for which successful amplification was achieved in 66% and 75% of samples respectively . The MS characteristics are described in Table 2 . Overall , genetic diversity was moderate , with an average He = 0 . 68 ( 95%CI 0 . 58–0 . 77 ) for all MS . MS3 and MS9 were the least polymorphic markers ( He = 0 . 45 and 0 . 31 respectively ) , while MS10 and Pvsal1814 were the most polymorphic markers ( He = 0 . 99 and 0 . 92 respectively ) , which were therefore used to calculate MOI . The number of alleles per MS ranged from 3 to 14 . All MS had non-significant differences in He values in the database containing all alleles per locus and the predominant allele datasets ruling out bias in the construction of haplotypes from polyclonal infections ( p = 0 . 68 ) . The average number of alleles per locus was 5 . 5 ( 95%CI 3 . 82–7 . 17 ) , the average number of alleles detected in a sample by any locus was 1 . 14 ( 95%CI 1 . 0–1 . 27 ) and the average allelic richness was 5 . 05 ( 95%CI 4 . 11–5 . 98 ) . The proportion of polyclonal infections , similar in all communities ( p >0 . 05 ) , was 71 . 4% ( 167/234 ) when all 14 MS were used , but 64 . 1% ( 141/220 ) ( N = 220 as 14 samples had missing data for MS10 and Pvsal1814 ) when only Pvsal1814 and MS10 , the most polymorphic markers were used . The same two MS were used to calculate MOI ( based on 220 samples with completed data ) , whose mean in the four communities was 1 . 91 ( 95% CI 1 . 81–2 . 02 ) , with no significant differences between communities ( p = 0 . 52 ) , age groups ( MOI≤15years = 2 . 0 vs MOI>15years = 1 . 9 , p = 0 . 50 ) , gametocyte carriage ( MOIgametocytes present = 1 . 93 vs MOIgametocytes absent = 1 . 80 , p = 0 . 42 ) , sex ( MOImale = 1 . 89 vs MOIfemale = 1 . 95 , p = 0 . 55 ) , symptomatic vs asymptomatic ( defined as fever at enrolment vs no fever at enrolment , MOI = 1 . 98 vs MOI = 1 . 80 , respectively ) ( p = 0 . 10 ) , season ( MOIrainy season = 1 . 90 vs MOIdry seaoson = 1 . 97 , p = 0 . 64 ) , and ethnic minority ( MOICadong = 1 . 89 vs MOIM’nong = 2 . 0 , p = 0 . 46 ) . No significant differences were found between the four communities for either level of He ( p = 0 . 08 ) or allelic richness ( p = 0 . 31 ) . We identified 101 haplotypes from 144 samples of which 84 haplotypes were defined as unique haplotype with complete genotyping data for 13 MS . MS20 was excluded because it had the lowest successful genotyping rate ( 66% ) . Of these haplotypes , 25 . 7% ( 26/101 ) were found in monoclonal infections and 16 . 8% ( 17/101 ) were found in both monoclonal and polyclonal infections; 6 . 93% of haplotypes ( 7/101 ) had a frequency of over 2 in 40 samples and the two most frequent haplotypes were detected in 7 . 6% ( 11/144 ) and 6 . 2% ( 9/144 ) of samples . One haplotype was shared between the four communities , 4 haplotype found in community 1 were also present in community 2 and one haplotype shared between community 3 and community 4 . To evaluate the existence of a recent population bottleneck we analyzed the allele frequency distribution in the population . Fig 2 shows an L-shaped distribution of allele frequencies , as would be expected from neutral evolution . MS20 was also excluded from the LD analysis to maximize sample size and avoid bias due to an imbalanced number of samples between communities . Hence at least 25% of samples per community were included in the analysis . Significant LD was observed in each community ( IAs ranged from 0 . 10 to 0 . 17 ) and in the overall study population ( IAs = 0 . 113 , p < 0 . 001 ) . LD remained significant ( IAs = 0 . 059 , p < 0 . 001 ) when only the unique haplotypes were used . We then examined patterns of LD between pairs of MS ( Fig 3 ) . Pairwise LD was observed between loci located within the same contigs ( MS4-MS5 , MS7-MS8 , and MS12-MS15 ) and also within different contigs . Even though lower pairwise LD was observed in communities 3 and 4 , the fact that the overall LD was significant ( p = 0 . 008 ) suggests the existence of a clonal parasite population . We first compared the datasets containing only monoclonal infections ( n = 67 ) with the predominant allele ( n = 234 ) and found low genetic differentiation ( FST = 0 . 05 ) , indicating absence of bias . Then , we calculated FST values for pairwise genetic differentiation between the four communities ( Table 3 ) . We observed moderate genetic differentiation between community 4 and the other communities ( FST = 0 . 15–0 . 18 ) and low differentiation for the other combinations ( FST < 0 . 1 ) ( n = 144 ) , indicating that the parasite population in community 4 is moderately , although significantly , differentiated from parasite populations in communities 1 , 2 , and 3 ( p = 0 . 008 ) . Similar FST values were obtained when only unique haplotypes ( n = 84 ) were used . Structure analysis identified the most likely clusters in the population to be ( i ) K = 7 ( ΔK = 9 . 4 ) , ( ii ) K = 2 ( ΔK = 5 . 2 ) , and ( iii ) K = 3 ( ΔK = 3 . 2 ) ( n = 144 ) . The AMOVA-based K-means clustering analysis identified K = 2 as the optimal number of clusters ( pseudo-F = 30 . 5 ) . We further analyzed the parasite population divided by K = 2 ( cluster 1 and cluster 2 ) with CLUMPP and DISTRUCT ( Fig 4 ) , and found that 33 . 3% ( 48/144 ) of the samples ( with complete haplotypes ) observed in the study population belonged to cluster 1 and 20 . 1% ( 29/144 ) belonged to cluster 2 and 46 . 6% ( 67/144 ) were admixed samples . Community 4 had the highest proportion of admixed samples ( 62 . 1% ) , followed by community 3 ( 53 . 9% ) , while community 1 and 2 had similar rates ( 40 . 9% and 41 . 3% respectively ) . The proportion of admixed samples remained high when the number of clusters was set to K = 3 and K = 7 ( 48 . 6% and 38 . 9% , respectively ) . Of note , cluster 1 samples were absent from community 4 , which supports a moderate degree of population structure between communities 1–3 and community 4 . However , principal coordinate analysis failed to detect geographical clustering ( per community ) in the population . Then , we investigated genetic relatedness , defined as haplotypes sharing at least 10/13 loci ( n = 101 ) by eBURST . Eight different HGs and 13 singletons were identified . HG1 contained 51 . 5% ( 52/101 ) of all haplotypes in the four communities , while the other 7 HGs contained between 2 . 0% ( 2/101 ) to 11 . 9% ( 12/101 ) haplotypes . However , when relatedness was defined as haplotypes sharing at least 9/13 loci , only 2 HGs and 2 singletons were identified . HG1 included 95 . 0% ( 96/101 ) of all haplotypes detected . PHYLOVIZ analysis supported the existence of related haplotypes among all study communities ( with slightly clustering of community 4 samples ) ( Fig 5A ) and confirmed the absence of cluster 1 haplotypes in community 4 ( Fig 5B ) .
We analyzed the genetic diversity and population structure of 234 P . vivax pre-treatment clinical isolates collected in a forested area of Central Vietnam between April 2009 and December 2010 [12] . We observed moderate levels of heterozygosity in all four study communities , with a high proportion of polyclonal infections and significant LD , suggestive of inbreeding across parasite populations circulating in the study communities . Genetic differentiation and population structure between study communities was low but present between villages at each side of the river defining a moderate geographical barrier to gene flow . In this study we used eight MS ( MS1 , MS4 , MS6 , MS9 , MS10 , MS12 , MS15 , and MS20 ) with balanced diversity , three ( MS2 , MS5 and Pvsal1814 ) with unbalanced diversity , one ( MS8 ) with significant excess diversity , and two ( MS3 and MS7 ) with significant reduced diversity [25] . Mean He in the study population ( He = 0 . 68 ) using those 14 MS was non-significantly different to He when only MS with balanced diversity ( recommended for measuring population diversity ) were used . Therefore , all MS were kept in the analysis to assess both diversity parameters and polyclonal infections , but only the two most polymorphic markers ( MS10 and Pvsal1814 ) were kept to investigate MOI . The mean He in our study population ( He = 0 . 68 ) was similar to figures seen in areas of north-west Brazil with similar transmission intensities ( He = 0 . 74 and He = 0 . 68 ) [43–44]; higher than those observed in South Korea ( He = 0 . 43 ) [45] and the Loreto district , Peru ( He = 0 . 37 ) [16] , and lower than those seen in Sri Lanka ( He = 0 . 89 ) [46] , Pursat , Cambodia ( He = 0 . 84 ) [16] , and Binh Thuan , Central Vietnam ( He = 0 . 88 ) [13] . In our study MS9 , MS3 , and MS7 displayed the lowest number of alleles per locus ( n = 3 ) and MS9 and MS3 had the lowest He values ( HeMS9 = 0 . 31 and HeMS3 = 0 . 45 ) . MS3 , MS7 , and MS9 would therefore appear to be poorly informative markers in the study area and their use in future studies is not recommended . Polyclonal infections were frequent ( 71 . 4% ) when the results from 14 MS were combined and moderately lower ( 64 . 1% ) when just Pvsal1814 and MS10 were used [47] . Mean MOI was 1 . 91 , with similar MOI observed in symptomatic and asymptomatic study participants , possibly because of the high parasite density ( mean 3 , 919/μL; 95%CI 2 , 852–4 , 986 ) detected in asymptomatic participants at day 0 . In the literature , the proportion of polyclonal infections vary considerably depending on the MS markers used [16 , 48] , highlighting the need for a standardized methodology that allows comparison between studies and geographical regions . High proportions of polyclonal infections have also been reported in hypo-endemic areas in Sri Lanka ( 60% ) [49] , Colombia ( 60–80% ) [48] , the Amazon Basin in Brazil ( 50% ) [43] , and more recently , in a pre-elimination context in Sri Lanka ( 69% ) [46] . It is noteworthy in a study carried out ( 1999–2000 ) in Binh Thuan province , central-south Vietnam , where the entomological inoculation rate was estimated at 1 infective bite/person/year , 100% of vivax infections were polyclonal with a mean MOI of 3 . 7 [13] . The high levels of genetic diversity and polyclonal infections in low transmission areas [13 , 23 , 48] can be , at least partially , explained by the unique biology of P . vivax which result in ( i ) a high prevalence of asymptomatic and low parasite density infections ( which last longer because are difficult to detect , increasing the likelihood of repeated infections with divergent clones , resulting in increased polyclonality ) and ( ii ) relapse from dormant liver stages ( the reactivation of heterologous clones increases the likelihood of peripheral superinfections ) . Since a high proportion of study participants were asymptomatic at recruitment ( 59 . 0% ) and poor adherence to PQ radical cure is known in the study area [3] , the high proportion of polyclonal infections found in this study may reflect peripheral superinfection fed by heterologous clones from both relapses and reinfections . Despite those high rates of polyclonal infections , we observed a significant LD ( IAs = 0 . 113 , p < 0 . 001 ) in the overall study population . Asexual clones present in one infection produce gametocytes that , taken by the vector , recombine during meiosis and generate new haplotypes in a process known as outcrossing . Consequently , the breakdown of pre-existing associations between unlinked loci would reduce LD to low levels [50] as opposed to recombination between gametes from the same parasite [51] . As transmission decreases , fewer parasite types will be present in the population and recombination will often occur between related parasites , increasing the level of inbreeding in the population . This is supported by the fact that 53 . 8% of all polyclonal infections were identified by multiple alleles at just one locus . Indeed , LD remained significant in the analysis using only unique haplotypes , indicating that it is a result of inbreeding rather than expansion of few haplotypes due to outbreaks or epidemics [31] . Closely related parasites in hypoendemic areas have been previously reported [52 , 53] . In addition , inbreeding was further supported by overall significant pairwise LD [21 , 31] . LD combined with high levels of polyclonality has been reported in rural Amazonia [54] and more recently in Sri Lanka [46] . The authors of these studies offered two alternative interpretations for this phenomenon . First , the MS may not be strictly neutral ( 10/14 MS mapping to loci encoding either hypothetical or annotated proteins may be subject to natural selection ) [22–23] . And second , replication-slippage events during mitotic ( asexual ) replication could result in the generation of new alleles due to the addition or deletion of repeats [49 , 55] . If the replication-slippage rate is higher than that of effective recombination ( the probability of producing a recombinant genome ) , the clones generated would increase polyclonality , without altering LD . It has been previously reported that replication-slippage events ( and therefore number of alleles per locus and He ) correlate positively with increasing repeat length and non-perfect repeats motifs , i . e . interrupted or compound motifs [25 , 56] . Pvsal1814 MS used in this study , which had an ( AGA ) 44 motif structure with an interrupted/compound motif , He = 0 . 91 and 14 different alleles with frequencies ranging from 1 . 3% to 16% , identified 53 . 8% of all polyclonal samples in the study population . Indeed , inherent mutability in this MS has been described to produce excess diversity , which in turn is recommended to identify MOI [25] . We identified 101 haplotypes , of which 84 appeared only once in the population . Ninety percent of them were grouped in a single haplogroup ( HG1 ) , defined by identical alleles in at least 9/13 loci , indicating a high degree of relatedness among parasites across the communities . These results support the view that despite a high level of polyclonality , inbreeding among highly related haplotypes maintains LD . The adjusted genetic differentiation was low between communities 1 , 2 , and 3 ( FST < 0 . 05 ) and moderate when community 4 was included ( FST = 0 . 15–0 . 18 ) , indicating limited geographical boundaries between neighboring communities 1–3 but higher differentiation with the community across the river . In concordance with the FST values , the STRUCTURE analysis detected two main parasite populations . Two clusters of haplotypes , with a high proportion of mixture haplotypes ( 60 . 4% ) were observed in all four communities . The fact that a majority of haplotypes found in community 4 belonged to cluster 2 , which was the minor cluster in the other 3 communities , supports a certain degree of differentiation between communities 1–3 and 4 . Moderate population differentiation between these communities can be explained by geographical proximity and socioeconomic relationships between the communities’ inhabitants as previously described [3] . Inhabitants of community 1–3 ( located at one side of the river ) belong to the Cadong ethnic group and therefore share some degree of kinship , facilitating social exchange . Conversely , community 4 , whose inhabitants belong to the M’nong ethnicity , is located at the other side of the river with limited access during the rainy season . Malaria incidence in the Quang Nam province has dropped by 78 . 0% over the last decade thanks to the implementation of efficient control strategies [1 , 17 , 57] . At the time of the study , malaria prevalence in the study area was 7 . 8% as assessed by light microscopy and 23 . 6% as estimated by PCR [3] . Therefore , the moderate-to-high levels of genetic diversity detected , together with the high polyclonality and low population structure are consistent with an epidemiological context of transition from moderate to low endemicity [58–59] . Future studies aiming at identifying changes in genetic diversity and population structure to support the development or improvement of control and elimination interventions should include isolates collected at several time points from all areas where malaria is prevalent ( or has been recently eliminated ) . Ideally , a molecular surveillance system should be implemented within the existing network of sentinel sites for drug resistance across the country to support evaluation of interventions and improve response strategies at the provincial level . Parasite populations with strong LD and the presence of gene flow could fuel the spread of resistant parasites in the event of the emergence of drug resistance , threatening current treatment efforts and achievements towards malaria elimination in Central Vietnam . Temporal analysis to investigate haplotype persistence and the risk of clonal expansion is urgently needed in order to inform decision makers . | In Vietnam , Plasmodium vivax ( P . vivax ) is the second most frequent human malaria parasite and a major obstacle to countrywide malaria elimination . Knowing the local parasite structure is useful for elimination efforts . Therefore , we analyzed , with a panel of 14 microsatellite markers , 234 P . vivax mono infections in blood samples collected from 4 communities in central Vietnam . Genetic diversity in the population was moderate; a high occurrence of polyclonal infections and significant linkage disequilibrium were detected , suggesting inbreeding or recombination between highly related haplotypes . In addition , both genetic differentiation and population structure was low and only detected between communities at each side of the river . Those results suggest gene flow between study communities with the river defining a moderate geographical barrier . Future studies should determine how this genetic variation is maintained in an area of extremely low transmission . |
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While circulating levels of soluble Intercellular Adhesion Molecule 1 ( sICAM-1 ) have been associated with diverse conditions including myocardial infarction , stroke , malaria , and diabetes , comprehensive analysis of the common genetic determinants of sICAM-1 is not available . In a genome-wide association study conducted among 6 , 578 participants in the Women's Genome Health Study , we find that three SNPs at the ICAM1 ( 19p13 . 2 ) locus ( rs1799969 , rs5498 and rs281437 ) are non-redundantly associated with plasma sICAM-1 concentrations at a genome-wide significance level ( P<5×10−8 ) , thus extending prior results from linkage and candidate gene studies . We also find that a single SNP ( rs507666 , P = 5 . 1×10−29 ) at the ABO ( 9q34 . 2 ) locus is highly correlated with sICAM-1 concentrations . The novel association at the ABO locus provides evidence for a previously unknown regulatory role of histo-blood group antigens in inflammatory adhesion processes .
ICAM-1 is a member of the immunoglobulin superfamily of adhesion receptors and consists of 5 immunoglobulin-like extracellular domains , a transmembrane domain and a short cytoplasmic domain . ICAM-1 , present on endothelial cells , serves as a receptor for the leukocyte integrins LFA-1 ( lymphocyte function-associated antigen-1 ) and Mac-1 ( CD11b/CD18 ) , facilitating leukocyte adhesion and migration across the endothelium [1] . A soluble form of ICAM-1 ( sICAM-1 ) is found in plasma and consists of the extra-cellular domains of ICAM-1 . Although the process leading to the formation of sICAM-1 is not entirely clear , sICAM-1 is thought to be shed from the cell membrane via proteolytic cleavage of ICAM-1 . Because sICAM-1 binds to LFA-1 , it is capable of inhibiting lymphocyte attachment to endothelial cells [2] . Furthermore , sICAM-1 has been shown to bind human rhinoviruses , the etiologic agent of 40–50% of common colds , and to inhibit rhinovirus infection in vitro [3] . Likewise , a circulating fragment of sICAM-1 binds to erythrocytes infected with Plasmodium falciparum , the etiologic agent of malaria [4] ( MIM 611162 ) . Finally , plasma concentration of sICAM-1 has been shown to provide unique predictive value for the risk of myocardial infarction ( MIM 608446 ) , ischemic stroke ( MIM 601367 ) , peripheral arterial disease ( MIM 606787 ) and noninsulin-dependent diabetes mellitus ( MIM 125853 ) in epidemiological studies [5]–[7] . Despite relatively high heritability estimates ( from 0 . 34 to 0 . 59 ) [8] , [9] for sICAM-1 , few genetic variants are known to influence its concentrations . Two recent linkage studies have shown evidence for genetic association at the ICAM1 ( GeneID 3383 ) locus ( 19p13 . 3-p13 . 2 ) [8] , [9] and two candidate SNPs within the extracellular domains of ICAM-1 itself , G241R ( rs1799969 ) and K469E ( rs5498 ) , have been correlated with circulating sICAM-1 levels [10] , [11] . By contrast , a recent genome wide association study ( GWAS ) from the Framingham investigators involving 1006 participants and 70 , 987 SNPs revealed no association reaching a genome-wide level of significance , including the ICAM1 locus itself , although this study had no genetic marker within 60 kb of the gene [12] . To more comprehensively explore this issue , we performed a larger GWAS , evaluating 336 , 108 SNPs in 6 , 578 apparently healthy women .
All participants in this study were part of the Women's Genome Health Study ( WGHS ) [13] . Briefly , participants in the WGHS include American women from the Women's Health Study ( WHS ) with no prior history of cardiovascular disease , diabetes , cancer , or other major chronic illness who also provided a baseline blood sample at the time of study enrollment . The WHS is a recently completed 2×2 randomized clinical trial of low-dose aspirin and vitamin E in the primary prevention of cardiovascular disease and cancer . For all WGHS participants , EDTA anticoagulated plasma samples were collected at baseline and stored in vapor phase liquid nitrogen ( −170°C ) . Circulating plasma sICAM-1 concentrations were determined using a commercial ELISA assay ( R&D Systems , Minneapolis , Minn . ) ; the assay used is known not to recognize the K56M ( rs5491 ) variant of ICAM-1 [14] and the 22 carriers of this mutation were therefore excluded from further analysis . This study has been approved by the institutional review board of the Brigham and Women's Hospital . Additional clinical characteristics of these subsets are provided in Table S1 . Genotyping was performed in two stages , a first sample being used to discover new associated loci and the second sample being used to validate them by replication . These two samples were genotyped independently of one another in two batches . The first ( WGHS-1 ) and second ( WGHS-2 ) batches included 4 , 925 and 2 , 056 self-reported Caucasian WGHS participants , respectively . No related individuals were detected when tested with an identity by state analysis [15] . Samples were genotyped with the Infinium II technology from Illumina . Either the HumanHap300 Duo-Plus chip or the combination of the HumanHap300 Duo and I-Select chips was used . In either case , the custom content was identical and consisted of candidate SNPs chosen without regard to allele frequency to increase coverage of genetic variation with impact on biological function including metabolism , inflammation or cardiovascular diseases . Genotyping at 318 , 237 HumanHap300 Duo SNPs and 45 , 571 custom content SNPs was attempted , for a total of 363 , 808 SNPs . Genetic context for all annotations are derived from human genome build 36 . 1 and dbSNP build 126 . SNPs with call rates <90% were excluded from further analysis . Likewise , all samples with percentage of missing genotypes higher than 2% were removed . Among retained samples , SNPs were further evaluated for deviation from Hardy-Weinberg equilibrium using an exact method [16] and were excluded when the P-value was lower than 10−6 . Samples were further validated by comparison of genotypes at 44 SNPs that had been previously ascertained using alternative technologies . SNPs with minor allele frequency >1% in Caucasians were used for analysis . After quality control , 307 , 748 HumanHap300 Duo SNPs and 28 , 360 custom content SNPs were left , for a total of 336 , 108 SNPs . From the initial 4925 WGHS-1 and 2056 WGHS-2 individuals genotyped , 4582 WGHS-1 individuals and 2014 WGHS-2 individuals were kept for further analysis . Because population stratification can result in inflated type I error , a principal component analysis using 1443 ancestry informative SNPs was performed using PLINK [17] in order to confirm self-reported ancestry . Briefly , these SNPs were chosen based on Fst >0 . 4 in HapMap populations ( YRB , CEU , CHB+JPT ) and inter-SNP distance at least 500 kb in order to minimize linkage disequilibrium . Different ethnic groups were clearly distinguished with the two first components . Out of 4582 WGHS-1 and 2014 WGHS-2 self-identified Caucasians , 12 and 6 were removed from analysis because they did not cluster with other Caucasians , leaving 4570 ( WGHS-1 ) and 2008 ( WGHS-2 ) participants for analysis , respectively . Two more analyses were undertaken to rule out the possibility that residual stratification within Caucasians was responsible for the associations observed . First , association analysis was done with correction by genomic control . This method estimates the average effect of population substructure in the sample ( based on median T values ) and accordingly corrects the test statistics [18] . Second , a principal component analysis [19] was performed in Caucasians ( only ) using 124 , 931 SNPs chosen to have pair-wise linkage disequilibrium lower than r2 = 0 . 4 . The first three components were then used as covariates in the association analysis . As adjustment by these covariates did not change the conclusions , we present analysis among the WGHS-1 and WGHS-2 Caucasian participants without further correction for sub-Caucasian ancestry unless stated otherwise . To identify common genetic variants influencing sICAM-1 levels , we first attempted to discover which loci significantly contributed to sICAM-1 concentrations in WGHS-1 . Plasma concentrations of sICAM-1 were adjusted for age , smoking , menopause and body mass index using a linear regression model in R to reduce the impact of clinical covariates on sICAM-1 variance . The adjusted sICAM-1 values were then tested for association with SNP genotypes by linear regression in PLINK [17] , assuming an additive contribution of each minor allele . A conservative P-value cut-off of 5×10−8 was used to correct for the roughly 1 , 000 , 000 independent statistical tests thought to correspond to all the common genetic variation of the human genome [20] . Replication of genome-wide significant associations was performed on adjusted sICAM-1 values from the replication sample ( WGHS-2 ) , using a Bonferroni correction to account for multiple hypothesis testing . To further define the extent of genetic associations , a forward selection linear multiple regression model was used at the previously identified loci . Briefly , all genotyped SNPs within 100 kb of the most significantly associated SNP at each replicated locus and passing quality control requirements were tested for possible incorporation into a multiple regression model . In stepwise fashion , a SNP was added to the model if its multiple regression P-value was less than 10−4 ( to account for all the SNPs being considered ) and if it had the smallest P-value among all the SNPs not yet included in the model . This analysis was done on WGHS-1 individuals using adjusted sICAM-1 values . We then proceeded to validate our multiple regression model in WGHS-2 samples . Using only the SNPs previously selected in WGHS-1 , we added them in a multiple regression model in the same order as they were chosen in WGHS-1 . We considered the model validated if each time a SNP was included in the model , its regression P-value was lower than 0 . 01 ( to account for multiple testing ) and the direction of effect consistent . Plasma from A blood group individuals was mixed 1∶1 or 1∶2 with a monoclonal anti-A antibody ( Ortho-Clinical Diagnostics , Rochester NY ) , and allowed to incubate 10 minutes or 60 minutes at room temperature , or 60 minutes or 12 hours at 4°C before assaying sICAM-1 levels by the standard technique . To exclude the possibility that the antibody itself interfered with the assay , the same procedure was repeated with plasma from O blood group individuals . Finally , plasma from O group individuals , which is expected to contain both anti-A and anti-B polyclonal antibodies , was mixed with plasma from A group individuals in 1∶1 ratio , again with incubation as above and measurement of sICAM-1 levels .
As shown in Table 1 , 19 SNPs passed our stringent genome-wide significance threshold when tested in WGHS-1 individuals , clustering within two loci in the vicinity of the ICAM1 ( 19p13 . 2 ) and ABO ( GeneID 28 ) ( 9q34 . 2 ) genes ( Figure 1 ) . The replication threshold in WGHS-2 was conservatively set at a 2-sided P-value of 0 . 002 , applying a Bonferroni correction to account for 19 tests . Using this cutoff , we were able to replicate 17 of the 19 associated SNPs , including SNPs at both the ICAM1 and ABO loci . Only rs2116941 ( 19p13 . 2 ) and rs7256672 ( 19p13 . 2 ) did not replicate using this standard . Nevertheless , each of these SNPs had a P-value lower than 10−9 when tested on the combined sample ( i . e . WGHS-1 and WGHS-2 pooled together ) . Among the replicated SNPs , only rs7258015 ( 19p13 . 2 ) deviated from Hardy-Weinberg equilibrium ( p = 0 . 00007 ) , but visual inspection of the raw genotyping signal for this SNP did not reveal any obvious artifact . Major and minor alleles are shown in Table S2 . We then applied our model selection algorithm in WGHS-1 individuals ( see Methods ) using 54 SNPs at 19p13 . 2 ( ICAM1 locus ) and 68 SNPs at 9q34 . 2 ( ABO locus ) . As can be seen in Table 2 , 3 out of 54 SNPs at 19p13 . 2 were selected by our algorithm and 1 out 68 SNPs at 9q34 . 2 was selected . All four SNPs selected in WGHS-1 were validated in WGHS-2 . Pairwise linkage disequilibrium between these SNPs was low . For instance , r2 was lower than 0 . 35 between ICAM1 SNPs while it was lower than 0 . 002 between the ABO SNP rs507666 and the ICAM1 SNPs . Among these SNPs , there was no strong evidence for non-additive effects of the minor allele as judged by lack of significance for a likelihood ratio test comparing the additive regression model to an alternative genotype model with an additional degree of freedom . Interestingly , one of the four selected SNPs ( rs281437 ) was non-significant in univariate analysis , illustrating that its inclusion in the model and significant association are conditional on the genotypes at rs5498 and rs281437 . No gene-gene interaction was observed between ICAM1 and ABO SNPs . The 3 SNPs at 19q13 . 2 ( ICAM1 ) collectively explained 6 . 9% of the total variance in sICAM-1 concentrations ( pooling WGHS-1 and WGHS-2 together ) , whereas the ABO SNP rs507666 explained 1 . 5% . In comparison , clinical covariates accounted for 18 . 8% of the variance ( Table 3 ) , and together the candidate loci and the clinical variables accounted for 27 . 3% of total variance . It should be noted that the estimated effect sizes of the ICAM1 and ABO loci are minimums since the genotyped variants might not be the actual functional variants . The 3 SNPs at the 19p13 . 2 ( ICAM1 ) locus selected by our algorithm were also used in haplotype analysis using WHAP [21] , as implemented in PLINK [17] ( Table 4 ) . The estimate of the proportion of variance attributable to haplotypes , as well as their regression coefficients , is consistent with the linear model of these same SNPs , reinforcing the adequacy of a strictly additive model to explain the association . The ABO histo-blood group antigen is the most important blood group system in transfusion medicine . Using data from Seattle SNPs ( http://pga . mbt . washington . edu ) as well as from the Blood Group Antigen Mutation Database ( www . ncbi . nlm . nih . gov ) , it can be demonstrated that rs507666 is a perfect surrogate for type A1 histo-blood group antigen . Moreover , using rs687289 as a marker for the O allele , rs8176746 for the B allele and rs8176704 for the A2 allele , complete blood group antigen phenotype can be re-constructed by haplotype analysis ( no serotype data is available in WGHS ) . Imputed haplotypes perfectly fitted the pattern expected from the literature and their association with sICAM-1 is shown in Tables 5 and 6 . The A1 allele is associated with the lowest sICAM-1 concentrations while the A2 allele is associated with low concentrations , intermediate between the A1 and O allele . In comparison , the B allele is associated with slightly higher concentrations than the O allele . Because ABO histo-blood group antigens are known to vary in frequency among Caucasian sub-populations , we sought to investigate the potential effect of population stratification on the observed association even though adjustment of sICAM-1 values for the top ten components of our principal component analysis did not change our conclusions ( see Methods ) . Visual inspection of the clustering pattern from the top two components confirmed a match with previously published work of sub-Caucasian stratification [22] ( data not shown ) . Since these two components were reproducibly shown to correspond to a Northwest-Southeast European gradient [22] and the A1 allele follows such a gradient [23] , we hypothesized that they would be tightly linked to A1 allele frequencies . Indeed , the second component showed evidence of association with A1 allelic frequencies ( p = 2 . 5×10−6 ) , while the first component was only weakly associated ( p = 0 . 08 ) . Nevertheless , neither the first nor second component was very tightly linked to sICAM-1 values ( p = 0 . 69 and 0 . 0006 respectively with corresponding R2 of 3 . 8×10−5 and 0 . 0019 ) , implying that stratification has no major effect on the sICAM-1 association . Furthermore , the weak association with the second component could be partially explained by the correlation with A1 alleles , with corrected P-value of 0 . 004 and R2 of 0 . 0013 . Adjustment of sICAM-1 values for the first and second components did not substantially change the association between the A1 allele and sICAM-1 ( unadjusted p = 5 . 1×10−29 and adjusted p = 5 . 5×10−28 ) , demonstrating that stratification on a Northwest-Southeast European axis is not responsible for the association . We conclude that the data does not support the hypothesis that Northwest-Southeast sub-Caucasian stratification is responsible for the association of ABO variants with sICAM-1 concentrations since the A1 allele varies in frequency according to a Northwest-Southeast European axis while the slight variation in sICAM-1 among this same axis is at least partially dependent on the A1 allele . Indeed , there is no evidence in the literature that mean sICAM-1 concentrations vary at all among Caucasian sub-populations , and this lack of evidence is supported by an overall R2 of 0 . 005 ( P-value of 0 . 0007 ) for the association between sICAM-1 concentrations and the top 10 principal components . The Secretor phenotype ( as defined by rs601338 on chromosome 19q13 . 33 ) and the Lewis antigen phenotype ( as defined by rs812936 on chromosome 19p13 . 3 ) are additional important members of the histo-blood group antigen system . These were therefore tested for association with sICAM-1 levels as well as for interaction with rs507666 . No significant effect was observed . Although the sICAM-1 molecule itself is not known to bear the ABO histo-blood group antigen , this possibility could not be ruled out , especially given its extensive glycosylation [24] , [25] . We therefore sought to exclude the remote chance that the association between A histo-blood group antigen and lower sICAM-1 values was the consequence of a lower affinity of the antibodies used in the sICAM-1 assay for sICAM-1 carrying the A antigen . In other words , if sICAM-1 does carry ABO histo-blood group antigen , then the allelic composition at the ABO locus could dictate the glycosylation status of the sICAM-1 molecule and possibly interfere with the immunoassay used . While there is no evidence that the two plasma proteins known to contain ABO histo-blood group antigen ( von Willebrand factor and alpha 2-macroglobulin ) [26] suffer from such analytical interference , immunoassays are potentially susceptible to differential glycosylation of their target protein [27] . We thus hypothesized that blocking the A antigen sites with either polyclonal or monoclonal antibodies would result in spuriously low sICAM-1 values if sICAM-1 does indeed carry ABO histo-blood group antigen and if the A antigen is located in the vicinity of one of the two antibody binding sites used by the immunoassay . No differential effects of the mixing procedures ( see Methods ) were observed suggesting that the A blood group antigen was not interfering with measurement of sICAM-1 levels . We therefore conclude that the genetic association of the ABO variant is not due to analytic interference . However , we can not exclude that sICAM-1 bears the ABO histo-blood group antigen . Finally , we sought to assess the presence of other associations that did not pass our stringent genome-wide P-value cut-off . We therefore repeated the whole-genome association analysis on the combined sample ( i . e . WGHS-1 and WGHS-2 pooled together ) . While no new locus was associated at a genome-wide level , rs9889486 had the lowest p-value ( outside of 9q34 . 2 and19p13 . 2; p = 3 . 2×10−6 ) with a false discovery rate [28] of 0 . 03 . This SNP is intronic to CCDC46 ( GeneID 201134 ) ( 17q24 . 1 ) , a gene whose function is not well characterized . Among other low p-value SNPs , we note rs1049728 ( p = 1 . 3×10−5 ) with a false discovery rate of 0 . 08 and the 51st most strongly associated SNP overall . This SNP is located in the 3′ untranslated region of RELA ( GeneID 5970 ) ( 11q13 . 1 ) , which is part of the NFKB signaling complex , arguably the most important known regulator of ICAM1 expression [29] . The non-synonymous coding ICAM1 SNPs rs1799969 ( G241R ) and rs5498 ( K469E ) were previously described as being associated with sICAM-1 levels[10] , [11] whereas the association involving rs281437 is unreported . The later SNP is in the 3′ untranslated region of ICAM-1 . Of interest , the minor allele of rs1799969 ( arginine ) is correlated with lower sICAM-1 and has been associated with lower risk of type I diabetes[30] , while the minor allele of rs5498 ( glutamic acid ) is correlated with higher sICAM-1 levels and has been associated with lower risk of asthma [11] ( MIM 600807 ) , inflammatory bowel disease [31] ( MIM 266600 ) and type I diabetes [32] ( MIM 222100 ) . Furthermore , it has been demonstrated in vitro that this SNP affects ICAM-1 mRNA splicing pattern and apoptosis in human peripheral blood mononuclear cells [33] . It is also noteworthy that sICAM-1 has been shown to inhibit insulitis and onset of autoimmune diabetes in a mouse model of type I diabetes [34] whereas ICAM1 itself was proven to be crucial to the priming of T cells against beta cells [35] . The most striking result of this report is the association between sICAM-1 levels and rs507666 , a SNP intronic to the ABO gene . The ABO gene encodes glycosyltransferase enzymes which transfer specific sugar residues to a precursor substance , the H antigen . There are three major alleles at the ABO locus: A , B and O . Variation at the ABO locus is remarkable in that these alleles encode enzymes with different specificities as well as activities . The A allele encodes the enzyme alpha1→3 N-acetylgalactosamyl-transferase which forms the A antigen from the H antigen . The A allele ( as well as the B and O alleles ) is itself heterogeneous and comprises several subgroups , of which A1 and A2 are the most important . As compared to A1 , the A2 allele has 30–50 fold less A transferase activity [36] . The B allele encodes the enzyme alpha1→3 galactosyltransferase which forms the B antigen from the H antigen . The O allele does not produce an active enzyme [37] . Consistent with the A antigen being associated with lower sICAM-1 concentrations and with the A1 allele having 30–50 fold more A transferase activity than the A2 allele , the A1 allele is associated with the lowest sICAM-1 concentrations while the A2 allele is associated with low concentrations as well , but still higher than the A1 allele ( Table 5 ) . Although we excluded the possibility of an analytical interference to explain the association , the exact mechanism linking histo-blood group antigen to sICAM-1 concentrations remains elusive . Among the different hypotheses , it remains possible that sICAM-1 bears the A antigen , a modification that might increase its clearance by increasing its affinity for its receptor ( s ) and/or decrease its secretion , perhaps by decreasing its affinity for the protease ( s ) producing sICAM-1 from membrane-bound ICAM1 . Alternatively , lower sICAM-1 concentrations might be the result of the presence of the A antigen on its receptor ( s ) and/or protease ( s ) . ABO histo-blood group phenotype has been linked to a plethora of diseases , including infectious diseases , cancers and vascular diseases [38] . Particularly interesting is the association of non-O histo-blood groups — and group A in particular [39] , [40] — with a higher risk of myocardial infarction , peripheral vascular disease , strokes and venous thromboembolism [41] ( MIM 188050 ) . While this phenomenon is partially explained by higher concentrations of the coagulation factors vonWillebrand and VIII ( presumably because of decreased clearance ) [42] , the exact mechanism is not entirely understood . Underlining the complex nature of the biological processes involved , the A1 group ( rs507666 ) is associated with lower levels of sICAM-1 , a ( positive ) predictor of vascular diseases in epidemiological studies [5] , [6] , [43]–[46] . Among potential explanations as to this apparent disparity , it is possible that decreased sICAM-1 leads to increased adhesion of leukocytes on endothelial surface and therefore increased vascular inflammation , an important component of atherosclerosis [47] . Moreover , because group A individuals have been shown to have higher blood cholesterol [48] and coagulability [42] , the decrease in sICAM-1 seen in these individuals could be offset by the increased susceptibility to vascular diseases conferred by these risk factors , even if sICAM-1 mechanistically causes these diseases . Alternatively , sICAM-1 might merely be a marker of increased inflammation and coagulation [49] , both risk factors for vascular diseases . Also of special interest , group A antigen carriers have been recognized as having a higher risk of suffering from severe malaria when infected by Plasmodium falciparum [50] . Plamodium infected erythrocytes express a receptor ( PfEMP-1 ) that binds specifically to cell-surface group A and B antigen as well as ICAM-1 [51] , a major step in the sequestration of infected erythrocytes leading to the clinical complications of severe and cerebral malaria . The lower concentrations of sICAM-1 found in A1 group carriers could therefore be hypothesized to contribute to this higher risk either directly , if sICAM-1 can inhibit the sequestration process , or indirectly , if sICAM-1 levels reflect differences in the processing of the ICAM1 receptor itself . Several limitations warrant discussion . First , this study was conducted in Caucasian women . It is therefore difficult to generalize our results to other ethnicities or to men . Second , effect estimates derived from this study might be higher than in other populations as these are initial findings and because of the winner's curse [52] . Third , although we were able to rule out a technical artifact as the cause of our results , no mechanistic link is identified to explain the association between ABO histo-blood groups and sICAM-1 . In particular , one pending question is whether or not ICAM-1 bears any ABO antigen at all . In this report , we demonstrate that sICAM-1 concentrations are associated with genetic variation at the ABO and ICAM1 loci in women . To our knowledge , this represents the first published genetic evidence that ABO may have a regulatory role on an inflammatory mediator , a finding with potential implication on a diverse array of immune-mediated disorders . Especially interesting is the fact that both ABO and ICAM1 have been previously related to vascular disease and malaria , two major causes of mortality and morbidity worldwide . The current study indicates a genetic link between histo-blood group antigen and inflammatory adhesion processes , providing the basis for physiological studies of this interaction . | Soluble Intercellular Adhesion Molecule 1 ( sICAM-1 ) is an inflammatory marker that has been associated with several common diseases such as diabetes , heart disease , stroke , and malaria . While it is known that blood concentrations of sICAM-1 are at least partially genetically determined , our current knowledge of which genes mediate this effect is limited . Taking advantage of new technologies allowing us to interrogate genetic variation on a whole genome basis , we found that a variation in the ABO gene is an important determinant of sICAM-1 blood concentrations . Since the ABO gene is responsible for the ABO blood groups , this discovery sheds light on a new role for blood groups and offers novel mechanisms to explain the association between sICAM-1 blood concentrations and various common diseases . |
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Root-knot nematodes secrete effectors that manipulate their host plant cells so that the nematode can successfully establish feeding sites and complete its lifecycle . The root-knot nematode feeding structures , their “giant cells , ” undergo extensive cytoskeletal remodeling . Previous cytological studies have shown the cytoplasmic actin within the feeding sites looks diffuse . In an effort to study root-knot nematode effectors that are involved in giant cell organogenesis , we have identified a nematode effector called MiPFN3 ( Meloidogyne incognita Profilin 3 ) . MiPFN3 is transcriptionally up-regulated in the juvenile stage of the nematode . In situ hybridization experiments showed that MiPFN3 transcribed in the nematode subventral glands , where it can be secreted by the nematode stylet into the plant . Moreover , Arabidopsis plants that heterologously expressed MiPFN3 were more susceptible to root-knot nematodes , indicating that MiPFN3 promotes nematode parasitism . Since profilin proteins can bind and sequester actin monomers , we investigated the function of MiPFN3 in relation to actin . Our results show that MiPFN3 suppressed the aberrant plant growth phenotype caused by the misexpression of reproductive actin ( AtACT1 ) in transgenic plants . In addition , it disrupted actin polymerization in an in vitro assay , and it reduced the filamentous actin network when expressed in Arabidopsis protoplasts . Over a decade ago , cytological studies showed that the cytoplasmic actin within nematode giant cells looked fragmented . Here we provide the first evidence that the nematode is secreting an effector that has significant , direct effects on the plant’s actin cytoskeleton .
Root-knot nematodes ( Meloidogyne spp ) are small endoparasites with a host range that includes most flowering plants [1] . During the compatible interaction , the motile second stage juveniles ( J2 ) enter the host roots and migrate intercellularly to the plant vasculature [2] . The primary nematode secretory organs , the esophageal glands , produce secretions . The gland secretions are exuded through the nematode stylet into the plant . Secretions that promote nematode parasitism are called effectors [3 , 4] . Effectors help nematodes with successful parasitism by altering host defenses and/or modifying the plant cells to form the nematode feeding sites [3–5] . The feeding sites are comprised of 2–10 host cells ( typically 6 ) that are reprogrammed to form large , multinucleate “giant cells” [6] . Root-knot nematodes are completely reliant on their giant cells as their sole source for food , and thus , the giant cells must be maintained throughout the nematode’s life for its survival . As part of nematode effector research , several groups have worked to identify secreted root-knot nematode proteins and the plant processes that they affect [7–20] . Work from Bellafiore et al ( 2008 ) directly identified secreted proteins from Meloidogyne incognita by exposing the nematodes to root exudates before treating them with resorcinol to induce esophageal gland secretion [8] . Using sensitive mass spectrometry methods , 486 proteins were identified in the M . incognita “secretome” [8] . Although the proteins were categorized based on bioinformatic analyses , the roles of these potentially secreted proteins in plant-nematode interactions have been largely unexplored . Recently , Lin et al . ( 2016 ) used the secretome to identify a transthyretin-like protein 5 ( TTL5 ) . They showed that M . javanica TTL5 homolog plays an important role in promoting parasitism by suppressing the host’s basal defense responses [21] . This work highlights the utility of studying the nematode secretome for identifying important nematode effectors . Previous reports have used chemical and genetic experiments to show that actin cytoskeletal rearrangements are necessary for giant cell development [22 , 23] . Therefore , we investigated it the nematodes were secreting effector ( s ) into the plant cells that were directly targeting actin filaments . In an effort to identify these effectors , we searched the published secretome for proteins that could interact with actin . Of the 486 peptides identified in the M . incognita secretome , 33 fell into the category “cell shape , ” and predicted to interact with actin or microtubules [8] . Of these 33 proteins , two were annotated as profilins ( PFNs ) : Proteins #131 and #240 [8] . Profilins are small actin binding proteins found in all eukaryotes whose main function is to bind globular ( G ) actin [24 , 25] . Profilin can also bind to barbed ends of actin filaments , albeit with lower affinity than to G actin [26 , 27] . In addition to binding to actin , profilin can also bind to polyphosphoinositide molecules , Arp2/3 complex , annexin , and proline-rich ligands [28–32] . Plant profilins do not share high amino acid similarity with profilins from other organisms , but they can complement profilin mutants in yeast and Dictyostelium , suggesting profilins have conserved functional roles across kingdoms [33 , 34] . By focusing on nematode profilins found in the previously published secretome , we discovered that a nematode profilin , called MiPFN3 , is an effector that facilitates parasitism . Our work shows that MiPFN3 expression in plant cells causes a disruption to plant actin filaments .
We were interested in specifically studying the two peptides found in the M . incognita secretome that were homologous C . elegans profilins ( pfam00235 ) : M . incognita #131 ( Mi131 ) and #240 ( Mi240 ) [8] . Using the peptide sequence for Mi131 , a tBLASTn search of the Expressed Sequence Tag ( est ) database identified six ESTs with 100% identity to the Mi131 sequence ( Genbank sequence IDs: JK291082 . 1 , JK291081 . 1 , JK267125 . 1 , JK298994 . 1 , JK303909 . 1 , JK306024 . 1 ) . These sequences contain an open reading frame of 381 bp that encodes a protein of 126 aa ( S1 Fig ) . The protein sequence is 100% identical to the Mi131 peptide sequence , and it contains a profilin domain ( pfam00235 ) ( S1 Fig ) . When this protein sequence was used in a BLASTp search of C . elegans Sequencing Consortium genome project , the best hit was to C . elegans Profilin 3 ( CePFN3 ) , with 63 . 5% amino acid identity ( Figs 1A and S2 ) . In NCBI BLASTp search of Genbank’s non-redundant ( nr ) database with Mi131 , the top four hits were to profilin 3 in parasitic and free-living nematodes: Trichinella spiralis PFN3 , 65 . 6% identity; C . elegans PFN3 , 63 . 5% aa identity; Caenorhabditis remanei , -PFN-3 , 63 . 5% identity , and Caenorhabditis brenneri -PFN-3 , 63 . 5% identity . Based on this sequence similarity to profilin 3 in C . elegans and other nematodes ( Fig 1A ) , we refer to the gene as MiPFN3 . According to Bellafiore et al . ( 2008 ) , Mi240 had a BLASTp top hit to C . elegans Profilin 1 ( CePFN1 ) . Using this information , we found significant tBLASTn hit in the M . incognita genome to one predicted full-length M . incognita gene , Minc11290 , which encodes a 133 aa protein . Using primers based on this sequence , we amplified a 402 bp sequence . When this 402 bp sequence was used in a nucleotide blast search of the NCBI EST database , it was 100% identical to four M . incognita ESTs ( Genbank Sequence IDs: JK295768 . 1 , CF802658 . 1 , CF802625 . 1 , CK984306 . 1 ) . This sequence encodes a 133 aa protein that is 95 . 5% identical to the protein encoded by Minc11290 ( Fig 1B ) . In addition , a BLASTp search using the non-redundant ( nr ) database showed that the protein encoded by our amplified sequence had highest sequence homology to the hookworm profilin [Necator americanus , 70% aa identity] . There were also hits to Toxocara canis profilin [TC-PFN1 70% identity] , Caenorhabditis remanei profilin , [CRE-PFN-1 protein 64% aa identity] , and Caenorhabditis brenneri profilin [CBN-PFN-1 63% aa identity] ( Figs 1B and S2 ) . Based on this sequence homology to PFN1 in parasitic and free-living nematodes , we refer to the Mi240 protein as MiPFN1 . Because genes involved in pathogenicity may be upregulated during the parasitic life stages of the nematode , we asked if either gene was up-regulated during nematode developmental life-stages associated with parasitism . Using primers specific for MiPFN1 and MiPFN3 , we measured the genes’ transcript levels in eggs , second stage juveniles ( J2 ) , and in nematodes-infected tomato roots at 7 , 14 , and 21 days post-inoculation , which represent the parasitic life-stages . The expression at the egg stage was set to 1 and used to calculate the fold change of the expression at the other time points . The expression of MiPFN1 was not differentially expressed in the egg , J2 and in the parasitic nematodes life stages ( Fig 2A ) . In contrast , MiPFN3 expression was up-regulated in the J2 compared to the egg stage . In a later developmental life-stage ( 7 dpi ) , the MiPFN3 expression level was equivalent to its expression in eggs ( Fig 2B ) . Expression then decreased in nematodes in the 14 and 21 dpi samples . To study earlier time points of nematode infection , we collected infected roots from Arabidopsis grown on MS-media at 4 dpi . We also collected infected roots at 7 , 14 , and 42 dpi . Acid fuchsin staining of the infected roots showed that at 4 dpi , the J2s penetrated the roots and were migrating as parasitic J2 ( S3 Fig ) . At 7 dpi , visible galls had formed , but the parasitic nematodes still look like slim , non-feeding J2 . By 14 dpi , we noticed that some nematodes had begun to looked fatter , an indication that the nematodes had initiated feeding . By 42 dpi , the nematode females had laid eggs in a gelatinous matrix on the surface of the root ( S3 Fig ) . The expression of MiPFN1 and MiPFN3 was measured in the eggs , J2 , and in the parasitic nematodes ( 4 , 7 , 14 , 42 dpi ) . The expression at the egg stage was set to 1 and used to calculate the fold change of the expression at the other time points . Overall , MiPFN1 was not differentially expressed at any time point ( Fig 2C ) . However , MiPFN3 expression was significantly up-regulated compared to the egg stage in the J2 and early parasitic stages ( 4 and 7 dpi ) ( Fig 2D ) . Because MiPFN3 was strongly expressed in the J2 , the infective stage of the nematode , and during early parasitic stages , it may be playing a role in the initial stages of plant parasitism , and therefore , we focused on MiPFN3 for further characterization . To determine where the MiPFN3 transcript is expressed in the J2 , we performed in situ hybridization using a DIG-labelled antisense MiPFN3 cDNA probe on fixed juveniles . The probe hybridized to the esophageal glands ( Fig 3A and 3B ) . The sense probe for MiPFN3 did not hybridize to the nematodes ( Fig 3C and 3D ) . Thus , we found MiPFN3 transcripts present in a nematode secretory organ , which indicates that it encodes a protein secreted by M . incognita . We next wanted to investigate if MiPFN3 has a role in nematode parasitism . Arabidopsis Col-0 was transformed with full-length MiPFN3 driven by the Cauliflower Mosaic Virus 35S promoter . We obtained two T2 lines for plants with a dwarf phenotype , in which the rosettes were significantly smaller than Col-0 ( lines G and M ) ( Fig 4 ) . However , we were also able to generate two homozygous transgenic lines with single transgene insertion ( MiPFN3 B . 2 and I . 3 ) and which did not exhibit size defects ( Fig 4 ) . The size of the plants corresponded to the amount of MiPFN3 transcript measured in the plants , with lines G and M having significantly higher levels of MiPFN3 than plants from lines B . 2 and I . 3 , which had wild-type root growth ( S4 Fig ) and rosette sizes ( Fig 4 ) . Because the dwarf plants had smaller roots that can affect the number of nematode infection sites , we only tested lines that had wild-type root length phenotype in the root-knot nematode infection assays . Therefore , the T3 generation of MiPFN3 lines B . 2 and I . 3 were infected with M . incognita . Both independent transgenic lines showed increased levels of galling ( Figs 5A and S4 ) . The size of the galls in B . 2 and I . 3 were not significantly different to the control ( Figs 5B and S5 ) . There also was no obvious qualitative difference in the giant cells formed in the wild-type and transgenic lines ( S6 Fig ) . Overall , the expression of MiPFN3 in the plant leads to increased number of galls , suggesting that MiPFN3 promotes nematode infections . A BLASTp search against the five profilin proteins in Arabidopsis showed that MiPFN3 had highest homology to Arabidopsis Profilin 4 AtPRF4 ( 38% amino acid identity ) ( S7 Fig ) . AtPRF4 can bind to actin monomers , and is specifically expressed in reproductive tissues ( pollen and flowers ) [35] . Based on its organ-specific expression pattern , AtPRF4 forms a complex with actin monomers that are also expressed in the plant’s reproductive organs ( ACT1 , ACT3 , ACT4 , ACT11and ACT12 ) [36] . A previous report showed that mis-expression of the reproductive actin ACT1 leads to an aberrant actin architecture in the plant , causing severe dwarfing of the plants . However , co-expression of AtPRF4 in these plants could suppress the ACT1-mediated dwarf phenotype [37] . Because MiPFN3 has similarity to AtPRFN4 , we investigated whether MiPFN3 could also suppress the ACT1-induced dwarf phenotype . Arabidopsis Col-0 and two transgenic MiPFN3 lines ( B . 2 and I . 3 ) were transformed with 35S::AtACT1 . When the T1 seedlings started to produce inflorescences , the rosette size and the leaf morphology were graded into three categories: 1 ) small rosette ( dwarf ) , 2 ) intermediate rosette size and 3 ) wild-type-like rosette size . When AtACT1 was ectopically expressed in Col-0 , approximately 30% of the T1 population exhibited a dwarf phenotype ( Fig 6 ) . When the transgenic MiPFN3 lines were transformed with 35S::AtACT1 , none of the T1 plants exhibited a small rosette ( Fig 6 ) , indicating that MiPFN3 could suppress the AtACT1-induced dwarf phenotype . To clarify the effects of MiPFN3 on actin in more detail , in vitro actin sedimentation assays were performed using non-muscle actin and recombinant His-tagged MiPFN3 . The His- MiPFN3 was added to soluble G actin prior to actin polymerization . After actin polymerization and sedimentation by centrifugation , we measured the ratio of soluble G actin to filamentous ( F ) -actin . When buffer or BSA ( bovine serum albumin ) were added to the G actin before polymerization , there was significantly more actin in the pellet fraction compared to the supernatant , indicating that most of the G actin had polymerized into F actin . On the other hand , when G actin was incubated prior to actin polymerization with purified recombinant MiPFN3 , there was an increase in the G actin observed in the supernatant fraction after ultracentrifugation ( Fig 7 ) . In other words , relatively less F-actin polymerized if the actin monomers were incubated with MiPFN3 prior to polymerization . To study the effects MiPFN3 expression on the actin cytoskeleton in plant cells , we expressed MiPFN3 in Arabidopsis leaf protoplasts constitutively expressing 35S::ABD2-GFP . The 35S::ABD2-GFP construct encodes a fibrin protein fused to GFP , and it can bind to and fluorescently label actin filaments [38] . We found that protoplasts expressing 35S::RFP-MiPFN3 showed disrupted actin filaments and reduced the visible levels of ABD2-GFP compared to untransformed ABD2-GFP protoplasts , which showed dense , GFP-labeled F-actin ( Fig 8 ) . To determine whether the effects on the actin cytoskeleton was specific for MiPFN3 expression , the M . incognita Peptidyl-prolyl cis-trans isomerase ( Minc06346 ) [39] was expressed in the ABD2-GFP protoplasts . The peptide corresponding to Minc06346 coding sequence was present in the M . incognita secretome [8] , and the protein does not contain any predicted actin binding domains ( Interpro Scan ) . When we transiently expressed full length cDNA Minc06346 driven by the 35S promoter fused at the N-terminus to RFP in the ABD2-GFP leaf protoplasts , the RFP fusion protein could be detected , and these cells had fluorescently labeled , dense actin filaments , similar to the un-transformed control ( Fig 8 ) . Therefore , MiPFN3 expression in the protoplasts can specifically affect the organization and structure of the actin filaments .
We have identified a nematode profilin gene called MiPFN3 as a novel nematode effector that is up-regulated in expression during parasitic life stages and when expressed in plants , it enhanced plant susceptibility to nematodes . MiPFN3 has homology to C . elegans profilin 3 ( CePFN3 ) ( Fig 1A ) . There are three profilin genes in the C . elegans genome . All three profilins are expressed in the worm and all behave as classical actin binding proteins [40] . In the CePFN1 knockdown , cytokinesis of embryonic cells was affected [41] , but the gene knockouts of CePFN2 and CePFN3 did not have phenotypes , indicating that these genes are non-essential [40] . The profilins in other free living and parasitic nematodes have not been characterized . Interestingly , the parasite Toxoplasma gondii possesses a profilin-like protein that is released by the parasite to facilitate its invasion of host cells [42 , 43] . The profilin-like protein can bind actin , but it has also evolved a role at the host-parasite interface [42] . MiPFN3 was originally found in the root-knot nematode secretome [8] . Surprisingly , MiPFN3 lacks a canonical secretion signal sequence . A root-knot nematode effector protein devoid of canonical secretion signal is not without precedent , and there are several examples of root-knot nematodes effectors , such as MI-14-3-3 and Mi-GSTS-1 , which do not have canonical signal peptides but play key roles in plant-nematode interactions [20 , 44–46] . The secretion of these proteins may be though a non-canonical secretory pathway that functions independently of the endoplasmic reticulum -Golgi network [20 , 45 , 46] . MiPFN3 may play a role in the secretome , and is expressed in the nematode esophageal glands , indicating that MiPFN3 is secreted from the nematode . Since these glands are connected by the esophagus to the stylet , it is possible for MiPFN3 to be delivered through the stylet directly into the plant [5] . In an effort to functionally characterize this profilin from root-knot nematodes , we performed in vitro actin polymerization assays . In these assays , actin-binding proteins bind and sequester actin monomers , preventing them from polymerizing into actin filaments . We found that pre-incubation of actin monomers with purified MiPFN3 prevented the formation of new actin filaments . Our results suggest that MiPFN3 sequesters G actin and inhibits nucleation of actin polymers in vitro . Profilins are found in all eukaryotes , including plants [47] . Arabidopsis has five profilin genes that can be divided into two groups based on the tissues in which they are expressed: vegetative and reproductive [33 , 37] . MiPFN3 has highest identity ( 38% ) to AtPFN4 ( At4g29340 ) , a profilin expressed in plant reproductive organs . A previous report showed that the mis-expression of the reproductive actin AtACT1 in plants caused dwarf plants , but overexpression of AtPRF4 in these plants suppressed this phenotype [37] . By binding and sequestering the AtACT1 monomers , AtPRF4 prevented the deleterious effects on cytoskeletal architecture caused by reproductive actin misexpression [37 , 48] . Because a root-knot nematode profilin that is secreted into the plant and may functionally mimic plant profilins , we tested MiPFN3 for its ability to suppress the ACT1-induced dwarf phenotype . Expression of MiPFN3 in the 35S::ACT1 plants resulted in plants with wild-type rosette size . Because MiPFN3 could suppress the dwarf morphological phenotype caused by ACT1 mis-expression , we conclude that MiPFN3 could bind to ACT1 in planta and sequester the excess reproductive actin monomers to a level that allowed for normal growth and development . When MiPFN3 was expressed at high levels in Col-0 ( wild type ) , the resulting transgenic lines ( G and M ) showed growth defects ( Fig 4 ) . This indicates that MiPFN3 can also bind to the vegetative class of actin , which is found in vegetative organs such as leaves and roots . Overall , this data indicate MiPFN3 binds to both reproductive and vegetative classes of actin; there is no actin-class specific interaction between MiPFN3 and actin monomers . We also wanted to investigate the effects of a nematode profilin on the actin filament dynamics of the plant . When MiPFN3 was expressed in protoplasts , the GFP-labeled actin filaments appeared fragmented . In plant cells that were not transfected or transfected with another nematode gene , the actin cytoskeleton appeared intact . Thus , 35S::MiPFN3 expression in Arabidopsis protoplasts affected the plant actin filaments . The fragmented actin phenotype was similar to the actin phenotype of Tradescantia stamen hair cells injected with birch profilin . The excess of birch profilin led to actin filament depolymerization and a reduction of actin microfilamants in the stamen hair cells [49 , 50] . It was proposed that injected birch profilin sequestrated monomeric actin , leading to an inhibition actin polymerization and a depletion of F actin . Paradoxically , later studies showed that profilin facilitates actin polymerization by interacting with cytoskeletal proteins like formin and promoting the turnover of actin monomers . Profilin was also shown to promote actin filament assembly at the barbed-ends , competing with barbed end regulators and filament branching machinery [25 , 27 , 51 , 52] . Thus , profilins regulate actin homeostasis through its roles in actin polymerization and depolymerization . The effect on actin depends on the concentration of profilin and other actin binding proteins [53] . The strong expression of MiPFN3 in the protoplasts may be disrupting the balance of profilin in the cell , and this leads to aberrant actin filaments . The transgenic lines with the highest level of MiPFN3 expression showed aberrant , small rosettes , suggesting that the quantity of MiPFN3 correlates with developmental defects [54 , 55] . The two transgenic lines that had the lowest levels of MiPFN3 transcript ( B . 2 and I . 3 ) had roots that looked similar to wild type plants . Interestingly , although the lines B . 2 and I . 3 showed increased galling compared to the control , the overall gall phenotypes morphologies were not obviously different . Thus , MiPFN3 has no role in gall expansion . Based on the expression of MiPFN3 in the nematode during early parasitism , we postulate that MiPFN3 facilitates early infection and feeding processes that lead to a higher percentage of nematodes that are successful in forming galls . One possibility is that MiPFN3 plays a role in facilitating multinucleate giant cell formation and possibly maintenance . Previous cytological work showed that phragmoplasts , which act as scaffolding to support the newly formed cell wall between divided nuclei , are disordered and do not fully develop in giant cells [56–58] . The malformed phragmoplast results in aborted cell division and this leads to multinucleate giant cells [22 , 23 , 56 , 59] . Work looking at the actin filaments associated with the phragmoplasts in giant cells showed that these actin filaments are disorganized [22] . Because the nematode effector MiPFN3 is linked to actin reorganization , MiPFN3 may be injected into the plant cell to play a role in the phragmoplast failure causing a blockage of cytokinesis in giant cells . Interestingly , cross sections of galls showed that giant cells in the wild-type and transgenic ( B . 2 and I . 3 ) lines did not exhibit any obvious phenotypic differences ( S6 Fig ) . The transgenic lines had relatively low levels of MiPFN3 expression , and the lack of any obvious giant cell irregularities may reflect the delicate balance between the level of MiPFN3 and actin monomers in the infected-transgenic plants . Our data showed that high levels of MiPFN3 could tip the balance , leading to abnormal plant phenotypes . For example , high levels of MiPFN3 in two transgenic lines ( G and M ) resulted in stunted growth , and strongly expressing MiPFN3 in protoplasts affected the actin filaments . The MiPFN3 in the transgenic lines B . 2 and I . 3 did not have a negative effect on galls size or giant cell phenotype , and MiPFN3 may help nematodes to establish giant cells so that a higher percentage of infective juveniles are successful in infections and making galls . In giant cells , the appearance of fragmented cytoplasmic actin filaments has been shown to be accompanied by transcriptional activation of actin and actin-related genes [22 , 60–62] . Two representative of the actin gene family , ACT2 and ACT7 , are transcriptionally upregulated during giant cell development [22] . The up-regulation of these genes may be in response to wounding by nematode feeding or it may be suggestive that a pool of G-actin is necessary in feeding cells [22 , 59] . In Arabidopsis , there is also an upregulation of formins ( AtFH1 , AtFH6 and AtFH10 ) , which are involved in actin remodeling [56 , 60 , 61 , 63] . The actin dynamics in the giant cells have also been linked with an increase in Arabidopsis actin-depolymerizing factor ( ADF ) gene expression . ADF/cofilins sever actin filaments and increase the rate at which actin monomers fall off the pointed end of the actin filaments [64] . Recently , specific ADF genes were also shown to be up-regulated in M . incognita infected cucumber ( Cucumis sativus L ) roots . The up-regulation of specific cucumber ADF genes correlated with the changes in plant actin structure that occurred during root-knot nematode infection [65] . Since ADFs can be involved in severing and depolymerizing actin filaments from their pointed ends , the increase ADF family gene expression in giant cells may be related to the fragmentation of the actin filaments that is observed in the feeding sites . In Arabidopsis , AtADF2 RNAi knockdowns exhibited an accumulation of actin bundles , and in these plants , feeding cell expansion was inhibited [60] , indicating the important role of ADFs for nematode feeding site development . Considering the roles of ADFs and profilins , it may be possible that the nematode is enhancing the expression of endogenous ADFs to increase the pool of ADP–G-actin that can bind to MiPFN3 . This data suggest that diminishing the actin network density is important for facilitating nematode feeding . Up to now , the data has shown that the expression of plant genes , such those encoding formins and actin depolymerizing factors , can affect the organization of the actin filaments in giant cells . Because MiPFN3 is found in the secretome [8] and the transcript localizes to the esophageal glands , the protein is likely secreted into the plant . Here we have shown that a presumably secreted MiPFN3 can bind actin monomers to manipulate plant actin in conjunction with changes in plant gene expression .
Meloidogyne incognita ( Morelos ) was used in all experiments . To collect nematode eggs , roots from infected tomato ( Solanum lycopersicum Green Zebra ) were mixed vigorously in 10% commercial bleach for 5 min . The eggs were collected on a 25 μm sieve and were further surface sterilized by vigorously shaking them in 10% bleach for 5 min . followed by three washes with sterile H2O . The bleach and wash steps were performed twice . After the last wash , the eggs were pelleted by a final centrifugation ( 4 , 000 rpm for 5 min ) and re-suspended in 5 ml water with 0 . 1% SDS and 0 . 2% Plant Preservative Mixture ( Plant Cell Technology ) . Freshly hatched J2 were collected on a modified Baermann Funnel as described [66] . Sequences for MiPFN1 and MiPFN3 were obtained by BLAST searches of databases available online , such as WormBase , WormBase ParaSite [67] and NCBI , www . ncbi . nlm . nih . gov . M . incognita J2 cDNA was the template for amplifying the coding sequences of MiPFN3 and the coding sequence for Minc06346 [39] by PCR . ( See S1 Table for primer sequences ) . The amplified products were cloned into the Gateway pENTR Directional vector ( Invitrogen ) and then into the Gateway vectors pB2GW7 , to generate constructs for Arabidopsis plant transformation , into the Gateway vector pB7WGR2 , to generate the 35S::RFP-N terminal fusions for protoplast transformation [68] , or into pDEST17 for expression in Escherichia coli for protein purification . For stable plant transformation with 35S::MiPFN3 , the construct was introduced to the A . tumefaciens strain GV3101 by heat shock transformation [69] , and this was used to transform Arabidopsis thaliana Col-0 ( N1093 ) , using the floral dip method [70] . The seeds of the primary transformants were selected for BASTA resistance ( Bayer CropScience , Wolfenbüttel , Germany ) . In the T2 generation , we selected lines segregating 3:1 ( BASTA-resistant/BASTA-susceptible ) . At least seven BASTA resistant plants for each segregating T2 line were transferred to new pots . We found two lines that showed dwarf rosettes and two lines that had plants with normal growth and developmental phenotypes . The two wild-type looking lines were grown for seed , and homozygous lines were confirmed by 100% survival on BASTA-containing media in the T3 generation . For the cloning of AtACT1 , Arabidopsis Col-0 cDNA was used as the template for PCR , and the product was cloned into the entry vector pDONR207 and then destination vector pK2WG7 . This construct was introduced to the A . tumefaciens strain GV3101 by heat shock transformation [69] , which was then used to transform Arabidopsis thaliana Col-0 [70] . Seedlings ( T1 ) from each background were first grown on plant media containing kanamycin to select for transformants containing the 35S::AtACT1 construct . At 10 days post germination on selective media , healthy plants were transferred to soil . When seedlings started to produce inflorescences ( approx . 4 weeks at 14h light/10h dark , 22°C ) , the rosette size and the leaf morphology were graded into three categories: 1 ) severe abnormal leaf curling/small rosette , 2 ) intermediate rosette size and 3 ) wild-type-like rosette size . Arabidopsis seeds were surface sterilized in 70% ethanol for 10 minutes , washed in 95% ethanol and allowed to air-dry . Seeds were placed on Murashige and Skoog media [71] with 2% sucrose and incubated in a growth chamber at 22°C/ 18°C , 80–100 μmol Photons/m2/s , 14h light/10h dark . The 14 day old seedlings were inoculated with 100–200 J2 of M . incognita . The inoculated plants were kept in the dark at 22°C as this facilitates infection for root-knot nematode bioassays [72] . Galls per root were counted at 4 weeks post-inoculation . For sectioning , galls at 23 dpi were dissected from plants , fixed overnight at 4°C in 2% PFA , 2% GA 0 . 1M Cacodylate buffer . After the overnight incubation , fresh fixative was added to the samples and the samples were microwaved at 200 w until they reached 30°C . The samples were then incubated for 5 minutes at room temperature , rinsed then post-fixed with 1% OsO4 for overnight 4°C . The samples were dehydrated in an ethanol series ( 30% - 100% ) , then propylene oxide ( PO ) . Galls were infiltrated with Spurrs resin prior to embedding and polymerization at 70°C overnight . Thick sections between 500 and 1000 nm were cut on a Leica EM UC7 , stained for 45 seconds with 1% toluidine blue in 1% borax aq . , then mounted for light microscopy . Samples were observed using a Zeiss Axio Observer A1 microscope . Total RNA was extracted from eggs , freshly hatched J2 , and gall enriched tissue . The gall enriched tissue was collected from infected tomato ( Rutgers ) roots grown in sand at greenshouse conditions at 7 , 14 , and 21 days post-inoculation ( dpi ) . To monitor earlier time points , 2 week old Col-0 seedlings grown on MS were inoculated with freshly hatched nematodes . Root tissue was collected at 4 , 7 , 14 , and 42 dpi . To monitor the nematode life stage in the Arabidopsis plants , the roots were stained with acid fuschin [73] . Nematodes and infected plant tissue from each time point was pooled for RNA extraction . cDNA synthesis and qRT-PCR was performed as previously described [74] . MiPFN1 and MiPFN3 expression was normalized to reference gene MiGAPDH [75] . Calculations were done according to the 2–ΔCT method [76] . For qRT-PCR analysis of transgene expression in the stable transgenic lines , RNA extraction from Arabidopsis seedlings or whole plants ( dwarves ) . qRT-PCR analysis for transgenic plants were performed as described [74 , 77] . Calculations were done according to the 2–ΔCT method . AtUBQ5 served as a reference gene [78] . Primers for the qRT-PCR are listed in S1 Table . Using MiPFN3 purified PCR product as a template , an asymmetric PCR was performed in the presence of of DIG-labelled deoxynucleotide triphosphates ( dNTPs ) ( Roche ) to generate sense and anti-sense cDNA probes . In brief , the PCR contained 1x Advantage 2 buffer , 1x DIG-labelled nucleotides , 0 . 4 μM forward or reverse primer ( The sense probe primer 5’-AACTGGCCATGTCTCAAAGG-3’; the anti sense probe primer 5’-TTAATAATTGATGCTTCGAAAGTAA-3’ ) , approximately 200–800 ng PCR product , 1x Advantage 2 Polymerase Mix . The reaction performed for 1 cycle 95°C , 1 min and then 35 cycles at 95°C for 30 seconds , 59°C for 30 seconds , and 68°C for 30 seconds . The PCR probes were precipitated by mixing in 1 volume of 3M sodium acetate and 3 volumes 100% ethanol and kept at -20°C for at least one hour . The probe was centrifuged and the pellet resuspended in 300 μl hybridization buffer 50% deionized formamide , 4X SSC buffer , 1X Blocking Reagent , 2% SDS , 1X Denhardt's , 1 mm EDTA , pH 8 , 200 μg/ml Fish sperm DNA , 3 . 125 yeast tRNA ) . Freshly hatched J2 were fixed and probed following the protocol of de Boer et al . , 1988 [79] . The DIG-labeled probes were detected by incubation with the Alkaline phosphatase-conjugated anti-digoxigenin antibody ( Roche Molecular Biochemicals ) and the alkaline phosphatase substrate . Representative images were collected with a digital camera on a Leica microscope . The E . coli strain BL21 was transformed with either pDEST17-Mi131 ( 6xHis-Mi131 ) . BL21 was cultured in 3 ml of LB + 100 μg/μl of ampicillin ( Amp ) overnight . The overnight culture was transferred into 30 ml of LB-Amp and grew until OD600 = 0 . 5 . The protein expression was induced by adding 1 mM Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) and incubating cells transformed with pDEST17-Mi131 construct at 37°C with 200 rpm shaking for 2 hours . Cells were harvested by centrifugation and resuspended in the lysis buffer and lysed by sonication at 60% power input for 5 minutes on ice . For the His-Mi131 purification , columns were prepared by adding 200 μl of Profinity IMAC resin into a Micro Bio-spin column . The spin columns were centrifuged at 1000g for 15 sec and washed with 250 μl deionized water . Columns were equilibrated by twice adding 250 μl of His purification wash buffer and centrifuging at 1000 x g for 15 sec . 200 μl of the bacterial lysate was added onto equilibrated columns and gently mixed by pipette . Lysates were incubated with resin for at least 5 minutes before centrifugation . The excess unbound proteins were removed by washing the column 3 more times with 250 μl of wash buffer . The bound protein was eluted with 100 μl of His purification elution buffer . Prior to the actin assays , the lysates , BSA , α-actin and purified His-Mi131 were prepared by ultracentrifugation at 150 , 000 x g for 60 min at 4°C and the supernatants were transferred into new Eppendorf tubes . The G actin sequestration and F actin binding assays were performed following the manufacturer’s protocol ( Cytoskeleton #BK013 ) . In brief , a G actin solution was prepared by diluting 1 mg/ml of non-muscle actin with 225 μl of general actin buffer . The G actin solution was mixed by pipetting up and down several times and incubated on ice for 60 min prior to the assay . After the incubation , 40 μl of G actin solution was added into each tube with either 10 μl of test proteins or 10 μl of actin buffer . The mixture was mixed several times by pipetting up and down and incubated at RT for 30 mins . After the incubation , 2 . 5 μl of 10x polymerization buffer was added into each tube , mixed and incubated at room temperature for 30 min . To separate F actin from G actin , the mixtures were centrifuged at 150 , 000 x g for 90 min at 24°C . The supernatant was carefully removed and 5x reducing Laemmli buffer was added to each sample . The samples were centrifuged and the pellets were resuspended in 30 μl of Milli-Q water and incubated on ice for 10 min . Then 30 μl of 2 x Laemmli buffer was added to each sample . Samples were run on 4–20% SDS-gels and visualized by Coomassie staining . Arabidopsis Col-0 was transformed with pCAMBIA2300-ABD2 ( Department of Cell Biology , Goettingen ) using the floral dip method [70] . Approximately 10–15 leaves from 4–6 weeks old 35S::ABD2-GFP plant ( T2 ) , grown at 22°C/ 18°C , 80–100 μmol Photons/m2/s , 12h light/12h dark , 60% humidity , were collected . The leaf tissue was lysed using a 'Tape-Arabidopsis Sandwich' technique [80] , in which pealed leaves were placed into 10 ml of enzyme solution ( 1 . 25% ( w/v ) Cellulase R-10 , 0 . 3% Macerozyme R-10 , 0 . 4 M mannitol , 20 mM KCl , 10 mM CaCl2 , 20 mM MES ( pH 5 . 7 ) . The leaves were incubated at room temperature for 2 hours with constant slow rotation until the protoplasts were released into the enzyme solution . Then the protoplasts were carefully collected by centrifugation at 750 rpm for 5 minutes . The pellet was washed twice with 10 ml W5 buffer ( 2 mM MES ( pH 5 . 7 ) , 154 mM NaCl , 125 mM CaCl2 , 5 mM KCl ) . The cells were chilled on ice for 30 minutes prior . Prior to PEG transformation , the W5 buffer ( 2 mM MES ( pH 5 . 7 ) , 154 mM NaCl , 125 mM CaCl2 , 5 mM KCl ) was removed by centrifugation , and the pellet was gently resuspended in 5 ml MMG buffer ( 4 mM MES ( pH 5 . 7 ) , 0 . 4 M mannitol , 15 mM MgCl2 ) . For PEG transfection of the protoplasts , up to 15 . 0 μg of the plasmid DNA was placed in a 2 ml Eppendorf tube containing 300 μl of 40% PEG 4000 solution and gently mixed with Protoplasts resuspended in 300 μl MMG buffer . The solution was gently mixed and incubated at 22°C for 30 minutes . At the end of the incubation , 800 μl of W5 buffer was added and gently mixed . The supernatant was removed after centrifugation at 750 rpm for 2 minutes and protoplasts were washed with 800 μl of WI buffer ( 4 mM MES ( pH 5 . 7 ) , 0 . 5 M mannitol , 20 mM KCl ) . The supernatant was removed and the pellet was suspended in 300-μl WI buffer , mixed gently and incubated at 22°C , overnight . On the next day , the incubated protoplasts were transferred onto a glass slide for the observation under the confocal laser scanning microscope ( x40 ) . | Root-knot nematodes are microscopic plant pests that infect plant roots and significantly reduce yields of many crop plants . The nematodes enter the plant roots and modify plant cells into complex , multinuclear feeding sites called giant cells . The formation and maintenance of giant cells is critical to nematode survival . During giant cell organogenesis , the progenitor plant cells undergo many morphological changes , including a re-organization of the cytoplasmic actin cytoskeleton . As a result , the giant cell cytoplasmic actin appears fragmented and disorganized . Plant cells can regulate their actin filament assembly , in part , through the expression of actin binding proteins such as profilins . Here we show that infectious nematode juveniles express a profilin called MiPFN3 . Expression of MiPFN3 in Arabidopsis plants made the plants more susceptible to root-knot nematodes , indicating that MiPFN3 acts as an effector that aids parasitism . We show evidence that the expression MiPFN3 in plant cells causes the fragmentation of plant actin filaments . The work here demonstrates that nematode effector MiPFN3 can directly affect plant actin filaments , whose reorganization is necessary for giant cell formation . |
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Formalin-inactivated Japanese encephalitis virus ( JEV ) vaccines are widely available , but the effects of formalin inactivation on the antigenic structure of JEV and the profile of antibodies elicited after vaccination are not well understood . We used a panel of monoclonal antibodies ( MAbs ) to map the antigenic structure of live JEV virus , untreated control virus ( UCV ) , formalin-inactivated commercial vaccine ( FICV ) , and formalin-inactivated virus ( FIV ) . The binding activity of T16 MAb against Nakayama-derived FICV and several strains of FIV was significantly lower compared to live virus and UCV . T16 MAb , a weakly neutralizing JEV serocomplex antibody , was found to inhibit JEV infection at the post-attachment step . The T16 epitope was mapped to amino acids 329 , 331 , and 389 within domain III ( EDIII ) of the envelope ( E ) glycoprotein . When we explored the effect of formalin inactivation on the immunogenicity of JEV , we found that Nakayama-derived FICV , FIV , and UCV all exhibited similar immunogenicity in a mouse model , inducing anti-JEV and anti-EDII 101/106/107 epitope-specific antibodies . However , the EDIII 329/331/389 epitope-specific IgG antibody and neutralizing antibody titers were significantly lower for FICV-immunized and FIV-immunized mouse serum than for UCV-immunized . Formalin inactivation seems to alter the antigenic structure of the E protein , which may reduce the potency of commercially available JEV vaccines . Virus inactivation by H2O2 , but not by UV or by short-duration and higher temperature formalin treatment , is able to maintain the antigenic structure of the JEV E protein . Thus , an alternative inactivation method , such as H2O2 , which is able to maintain the integrity of the E protein may be essential to improving the potency of inactivated JEV vaccines .
Japanese encephalitis virus ( JEV ) , the most important etiological agent of viral encephalitis in Asian countries , causes regular outbreaks in eastern and southeastern Asia , India , and more recently in Australia [1 , 2] . Annually , 30 , 000 to 50 , 000 Japanese encephalitis ( JE ) -confirmed cases are reported in the JEV endemic areas , and 20% to 60% of symptomatic CNS infections are fatal [3–6]; 25% to 50% of symptomatic survivors have long-term neurological sequelae [7] . Asymptomatic JEV infection is about a thousand-fold higher than confirmed cases [8–10] . JEV is transmitted by virus-infected Culex mosquitos from inapparently infected viremic-amplifying hosts such as pigs or aquatic birds to symptomatic accidental hosts , such as horses and humans . Migratory birds have been implicated as the source of virus been introduced into new geographic regions , and associated with JE epidemics and replacement of genotype III ( GIII ) - with genotype I ( GI ) - JEV from southeast Asia to east Asia [11 , 12] . The genome of JEV consists of a ~11-kb , positive-sense , single-stranded RNA , which is translated and processed by viral and host proteases to three structural proteins—capsid , precursor membrane/membrane protein ( prM/M ) and envelope glycoprotein ( E ) —and seven nonstructural proteins ( NS ) —NS1 , 2A , 2B , 3 , 4A , 4B and 5 . The mature virion consists of 180 E proteins forming 90 homodimers and 180 processed M proteins . The immature virion is formed by 60 E and prM hetero-trimers [13 , 14] . E protein is the most critical protein eliciting protective immunity in hosts after viral infection , offering critical protection in mice [15] and inducing protective antibodies in recovering humans [16] . The ectodomain of E protein can be separated into three structural domains: E domain I ( EDI ) to III ( EDIII ) . The fusion peptide in EDII elicits group cross-reactive non- or low-neutralizing antibodies; EDIII , the receptor-binding domain , elicits potent type-specific neutralizing antibodies; and EDI , the center domain connecting EDII and EDIII , elicits complex cross-reactive high- or non-neutralizing antibodies after viral infection [16–18] . Vaccination remains the most effective strategy to control JE epidemics [19] . Live-attenuated and formalin-inactivated JEV vaccines are available for human use , but only live-attenuated vaccines are available for domestic animals , such as swine and horses . The first generation inactivated JEV vaccine , developed by BIKEN in Japan , was the mouse brain-derived , formalin-inactivated GIII Nakayama strain; manufacture of this vaccine has ceased since 2005 because of undesirable adverse effects [20] . Second generation tissue culture-derived , formalin-inactivated SA-14-14-2 vaccines are formulated with aluminum-hydroxide–adjuvant ( IC51 or IXIARO ) . IC51 vaccine has been licensed for use in adult and children older than 2 months [21] . In addition , a live-attenuated JEV SA14-14-2 vaccine , developed in China , is used in some Asian countries such as China , India , and Nepal [22–24] . The vaccine effectiveness has been estimated to be 85% to 90% after two doses of inactivated Nakayama vaccine , and 91% after one dose of the live-attenuated SA14-14-2 vaccine [25–27] . Unlike the live-attenuated vaccine , the formalin-inactivated JEV vaccines require boost immunization to retain the protective neutralizing antibodies [22 , 28] . Significant numbers of JEV endemic countries still depend on the locally produced , mouse brain-derived formalin-inactivated GIII JEV vaccine to control JE epidemics [19] . Formalin is the chemical most commonly used for inactivation to manufacture viral vaccines such as hepatitis A virus , polio , influenza virus , rabies virus , and simian immunodeficiency virus [29–34] . Formalin reacts with amino acids of target proteins to form reversible Schiff-base adducts and non-reversible methylene bridges . It has also been used as isotopic agent to label protein by introducing isotope to specific amino acid and as a cell and tissue fixation agent . Formalin functions chemically when it is used to inactivate virus , and the chemical reaction may modify the antigenic structure of the virion [35 , 36] . It has been shown formalin inactivation alters antigenic properties and reduces the immunogenicity of vaccines , such as hepatitis A and B virus , polio virus , bovine herpes virus 1 and influenza virus in mouse models [37–41] . Formalin-inactivated JEV vaccine remains the most widely distributed vaccine used to control JE epidemics . However , the potential effects of formalin on the antigenic structure of JEV and the antibody profile elicited by this vaccine remain unclear . The use of a low concentration of formalin and short inactivation time can yield antigens capable of inducing high neutralizing titers in mice , but the association between these inactivation procedures and the alteration of antigenic structure of E and the antibody profile elicited by this vaccine remain undetermined [42] . In this study , we used a panel of E-specific , murine monoclonal antibodies ( MAbs ) to analyze the effect of epitope modification of JEV E protein in a formalin-inactivated commercial vaccine ( FICV ) and laboratory grown , formalin-inactivated GIII and GI viruses ( FIV ) . We showed that formalin-inactivation , indeed altered the binding pattern of a JEV-derived , serocomplex cross-reactive neutralizing antibody , T16 . Interestingly , antibodies recognizing formalin-modified epitope were significantly lower in titer and had weaker neutralizing activity in serum from mice vaccinated with FICV and FIV-Nakayama than with untreated control Nakayama virus ( UCV-Nakayama ) . H2O2 inactivated JEV and was a superior approach that retained the antigenic reactivity of the virus with all tested MAbs including T16 as compared to conventional inactivation methods such as formalin and UV .
Animal experiments were approved by the Institutional Animal Care and Use Committee ( IACUC ) of National Chung Hsing University , Taiwan ( Approval No: 101–88 ) , and performed according to a protocol , which adhered to principles in the Guide for the Care and Use of Laboratory Animals ( NRC 2011 ) and meet the requirement in an Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . The serum samples used in this study were collected from anonymous children who had received JEV vaccination and were without JEV infection during 2010; they were part of an already-existing collection housed at Tungs’ Taichung Metroharbor Hospital in Taichung . The clinical protocol was reviewed and approved by the institutional review board of the hospital ( 99006 ) for serum sample collection . Serum was recovered from blood after clotting and then centrifuged , and stored at -70°C until use . Vero , COS-1 , and C6/36 cells ( kindly provided from Dr . Chang GJ of US CDC , Fort Collins , CO ) were grown in Dulbeco’s modified Eagle’s minimal essential medium ( DMEM , Gibco ) containing 5% , 10% , and 10% heat-inactivated fetal bovine serum ( FBS , Gibco ) , respectively . BHK cells ( kindly provided from Dr . Chen WJ of Chang Gung University , Taiwan ) were grown in Minimum Essential Medium ( MEM , Gibco ) with 10% heat-inactivated FBS . The JEV vaccine strains used were the GIII strains Nakayama and SA14-14-2 , naturally attenuated GIII T1P1 isolate [43] and GI circulating strain YL2009-4 [44] . The FICV used in this study was the mouse brain-derived , formalin-inactivated Nakayama virus vaccine manufactured by ADImmune Corp . in Taiwan . Monoclonal antibodies ( MAbs ) used for antigenic characterization were flavivirus group cross-reactive MAbs ( 4G2 , 6B3B-3 , 6B6C-1 and 23–2 ) , JEV serocomplex cross-reactive MAbs ( T16 , 2B5B-3 , 6B4A-10 , 1B5D-1 and 7A6C-5 ) and JEV-specific MAbs ( 2H4 and 2F2 ) [45–47] . Vero cells , infected with strains of JEV , namely Nakayama , SA14-14-2 , T1P1 , and YL2009-4 , at a multiplicity of infection of 1 ( MOI = 1 ) , were grown in serum-free medium ( SFM4MegaVir; HyClone , Logan , UT ) for 4 days . Supernatant was clarified by centrifugation at 10 , 000 rpm for 30 min; virion particles in the supernatant were pelleted by a second centrifugation at 19 , 000 rpm for 16 hr . Viral pellets from the second centrifugation were resuspended in 1X phosphate buffered saline ( PBS ) . These concentrated viruses were used to derive FIV . An amount of 37% formaldehyde ( Sigma-Aldrich , St . Louis , MO ) was diluted with 1X PBS to 0 . 5% and adjusted to pH 7 . 2 with 10 N NaOH ( Sigma-Aldrich , St . Louis , MO ) . The mixture was added to concentrated JEV viruses give a final formalin concentration of 0 . 05% . The formalin-treated virus ( FIV ) or untreated virus ( untreated control virus; UCV ) was incubated at 4°C for 49 days ( the manufacture procedure for FICV provided by Adimmune Corporation in Taiwan ) , or at 22°C for 10 days [42] . FIV and UCV samples incubated at 4°C were collected every week and stored at -70°C for analysis . Nakayama virus specimens were inactivated by short-wavelength UV light at a distance of 3 cm on ice for 30 min or with a final concentration of 3% H2O2 ( Fisher Scientific ) , pH 7 . 2 , at 22°C from 2 to 8 hr , then stored at -70°C . The residual infectious viral titers of FIV , UCV or UV- or H2O2-treated viruses were assessed by micro-plaque assay . Antigen-capture ELISA ( Ag-ELISA ) , described previously [48] , was used to estimate E proteins concentrations in samples with anti-JEV mouse hyper-immune acitic fluid ( MHIAF ) ( immunized with purified and live JEV ) and determine the binding activity of MAbs . Briefly , a 96-well plate ( Sigma-Aldrich , St . Louis , MO ) was coated with rabbit anti-JEV polyclone ( generated from rabbit immunized with pVAX-JEi VLP-expressing plasmid [49 , 50] , and obtained from Dr . Chang GJ of US CDC , Fort Collins , CO ) at 37°C for 1 hr , blocked with StartBlock blocking buffer ( Pierce , Rockford , Ill . ) , then antigen was added at 40 ng per well for incubation at 4°C overnight . Antigen was incubated with MAbs and MHIAF , diluted with 5% skim milk , at 37°C for 1 hr , then peroxidase-conjugated goat anti-mouse IgG ( H+L ) ( Jackson ImmunoResearch , West Grove , PA ) at 37°C for 1 hr . Finally , 3 , 3’ , 5 , 5’-tetramethylbenzidine substrate ( TMB; Neogen Corp . , Lexington , KY ) was added at 100 μl per well for the color reaction and reactions were stopped with 2N H2SO4 added at 50 μl per well; the OD450 values were recorded . The antigen concentration of UCV and FIV was estimated by the OD450 of the MHIAF . The MAb binding activities for UCV or FIV were determined by percentage reactivity estimated by the OD450 of UCV or FIV at the time relative to that at 0-day , respectively . All binding activities were adjusted by fold difference of antigen concentration , estimated by the OD450 of MHIAF against UCV or FIV at the time relative to that at 0-day , respectively and shown as mean±SD of two duplicates of two independent assays . JEVs and JEV virus-like particles ( VLPs ) were mixed with 5X SDS non-reducing sample buffer ( 315 mM Tris , pH 6 . 8 , 50% glycerol , 5% SDS , 0 . 025% bromophenol blue ) , then loaded onto a 10% SDS gel . After separation , proteins were transferred to a nitrocellulose membrane , which was blocked with 5% skim milk . The proteins on the membrane were detected by use of mouse anti-JEV polyclonal antibody , MAbs or MHIAF and visualized by incubation with peroxidase-conjugated goat anti-mouse IgG ( H+L ) ( Jackson ImmunoResearch , West Grove , PA ) ; bands were developed by use of the LumiGOLD ECL Western Blotting Detection Kit ( SignaGen Laboratories , Gaithersburg , MD ) . The intensities of bands were calculated by use of ImageJ version 1 . 44 ( NIH , Bethesda , MD ) . The binding activity of anti-EDII 101/106/107 and anti-EDIII 329/331/389 antibodies against VLP was eliminated by introducing 101/106/107 and 329/331/389 mutations on VLP , respectively . JEV VLPs were produced with the pVAX-JEi plasmid derived from the pCBJE plasmid [50] , which encodes the prM and E protein regions of the SA14 strain genome . This plasmid was also used as the template for introducing mutations into the E protein by use of a site-directed mutagenesis kit ( Stratagene , La Jolla , CA ) as described [50 , 51] . The pVAX-JEi 101/106/107 , 306 , 329 , 331 , 332 and 389 amino acid mutants were introduced by mutagenesis primers ( S1 Table ) , according to the manufacturer’s protocols , and mutation was confirmed by sequencing . The JEV VLP-expressing plasmids were electroporated into COS-1 cells by use of a 0 . 4-cm–electrode-gap cuvette and a Bio-Rad Gene Pulser II ( Bio-Rad Laboratories , Hercules , CA ) at 250 V and 975 μF; electroporated cells were recovered overnight at 37°C and incubated at 28°C to enhance VLP secretion . The secreted VLPs were analyzed by Ag-ELISA and used to evaluate the presence of epitope-specific antibodies . To measure the neutralizing activity of the MAbs or in serum samples , briefly , 2 . 48×104 Vero cells were added into 96-well plates for 24 hr at 37°C with 5% CO2 . MAbs in pre-attachment assay or serum samples were inactivated at 56°C for 30 min , diluted in a two-fold series , mixed with 100 pfu JEV Nakayama strain for 1 hr , then shaken every 20 min . Monolayers of Vero cells were infected with the virus-antibody mixture for 1 hr at 37°C with 5% CO2; in contrast , in post-attachment assay , virus was bound on monolayers of Vero cells at 4°C for 1 hour , then incubated with a two-fold series diluted and inactivated MAbs at 4°C for 1 hour , and then was shift into 37°C incubator with 5% CO2 for 1 hour . After incubation , 1% methyl cellulose in DMEM containing 2% FBS was added to the 96-well plates for incubation for 36 hr at 37°C with 5% CO2 , then plates were washed with PBS , fixed with 75% acetone , and air-dried in a hood . The fixed cells were stained with anti-JEV MHIAF for 40 min at 37°C . After a washing , peroxidase-conjugated goat anti-mouse IgG ( Jackson ImmunoResearch , West Grove , PA ) was added for 40 min , and virus-infected foci were identified by use of a Vector-VIP peroxidase substrate kit SK-4600 ( Vector Laboratories , Burlingame , CA ) . The foci were counted manually under microscopy and used to calculate a sigmoidal dose-response for the focus reduction micro-neutralization test ( FRμNT50 ) titers with use of GraphPad Prism v5 . 01 . Groups of 6-week old BALB/c mice ( n = 5 mice per group ) were vaccinated with three doses of Freund’s incomplete adjuvanted UCV-Nakayama , FIV-Nakayama or FICV . The first booster vaccination was given 2- week after the primary immunization and followed by a final booster at 4 weeks after the first booster vaccination . Serum samples were collected 2 weeks after the final booster vaccination . An IgG antibody-capture ELISA ( GAC-ELISA ) described previously [52] was used to determine the titer of the epitope-specific antibodies in immunized mouse serum . Briefly , goat anti-mouse IgG ( H+L ) ( KPL , Gaithersburg , MD ) was coated on 96-well plates at 37°C for 1 hr , then plates were blocked with StartBlock blocking buffer ( Pierce , Rockford , Ill . ) . Serum samples were serially diluted with wash buffer and added to plates at 37°C for 90 min . After a washing , 40 ng of the JEV antigens , wild-type ( WT ) or mutant JEV VLPs was added and mixtures were incubated at 4°C overnight . The IgG-capture antigens were detected by use of rabbit anti-JEV polyclonal antibody and peroxidase-conjugated goat anti-rabbit IgG ( H+L ) ( Jackson ImmunoResearch , West Grove , PA ) to detect antigen-bound rabbit anti-JEV polyclonal antibodies . The above steps were described for the Ag-ELISA . Total anti-immunogen IgG antibody was determined as endpoint titer . The epitope-specific antibody response was determined by the decreased reactivity titer against mutant VLP compared to WT VLP and was calculated as endpoint titer of ( WT—mutant VLP ) . The epitope-specific neutralizing antibody activity was determined as follows . The pVAX-JEi WT and EDIII 329/331/389 mutant plasmids were electroporated into COS-1 cells , cultured for 24 hr , and cells were resuspended in PBS . Diluted mouse serum samples were mixed with 107 transformed COS-1 cells at 37°C on a shaker for 2 hr . Then the transformed COS-1 cells were removed by centrifugation at 3 , 000 rpm for 10 min and the supernatant containing unbound antibodies was serially diluted and mixed with 100 plaque-forming units ( pfu ) of JEV Nakayama strain at 37°C for 1 hr . The neutralizing activity was determined by FRμNT assay as described previously and neutralizing activity ( % ) calculated by [1- ( plaque numbers of serum mixed with virus/plaque numbers of virus-only control ) ]* 100 . The curves showing the neutralizing antibody activity at different dilutions were fitted by non-linear regression in GraphPad . The percentage of neutralizing antibodies that recognized the JEV EDIII 329/331/389 epitope was calculated by plaque numbers of serum post-adsorbed with [ ( WT VLP—EDIII 329/331/389 mutated VLP ) / ( WT VLP—COS-1 cells ) ]* 100 . The pVAX-JEi WT and EDIII 329/331/389 mutant plasmid-transformed COS-1 cells were seeded into wells of a chamber slide ( Millipore , Billerica , MA ) , and cultured at 37°C overnight , then wells were fixed with 4% paraformaldehyde ( Sigma , St . Louis , MO , USA ) in PBS at room temperature for 20 min and washed with PBS . The fixed cells were made permeable by treatment with 0 . 1% Triton X-100 at 4°C for 5 min and washed with PBS , then wells were blocked with 3% bovine serum albumin ( Sigma , St . Louis , MO , USA ) in PBS at 37°C for 1 hr . Wells were stained with anti-JEV MHIAF and reacted with FITC-conjugated goat anti-mouse IgG ( KPL , Gaithersburg , MD ) in 1% Evans blue . Images were viewed under an OLYMPUS CKX41 microscope . Data are presented as mean±SD from two repeated experiments . Two-tailed Student’s t test was used for all analyses , and statistical significance was set at p <0 . 05 .
Most of the antibodies elicited by JEV infection or immunization are conformation-dependent and , for the most part , recognize the viral E protein and are able to help prevent virus infection [15 , 53] . Formalin inactivation of several human vaccines has been shown to result in antigenic alteration to the viral particles , which can be measured by the binding activity of specific MAbs [38 , 40] , but the effect of formalin inactivation on commercially available JEV vaccines has not been evaluated . Previously , an established Ag-ELISA protocol was successfully used to determine the antigenic structure of JEV using a panel of anti-E protein MAbs [49 , 53] . First , we evaluated the antigenic differences between the live Nakayama virus and FICV by Ag-ELISA using a panel of eleven anti-flavivirus E-protein MAbs [45–47] . The same antigen concentration ( estimated by Ag-ELISA using JEV-specific MHIAF ) of live virus and FICV was used throughout the experiments . Live Nakayama virus and FICV showed similar binding pattern for ten of the eleven tested MAbs , with the exception being T16 MAb ( Fig 1 ) . The binding activity with T16 MAb was significantly lower for FICV than for the live Nakayama virus ( p<0 . 05 ) with end-point titers of 105 . 39 and 103 . 48 for the live Nakayama virus and FICV respectively . The MAb binding pattern suggests that the antigenic structure of FICV differs from that of the live Nakayama virus . The decrease in binding activity of T16 MAb against FICV might be due to procedure variation during vaccine manufacture , which include differences in the formalin inactivation , differences in the virus purification process , changes in the sub-strains used and differences in the passage history of the Nakayama virus used between the vaccine production virus strain and the live Nakayama virus used in this experiment . To rule out the potential influence of sub-strain differences on the E structure and focus on the effect of formalin treatment on antigenic modification of Nakayama virus , we subjected laboratory-grown concentrated Nakayama virus to either formalin inactivation ( FIV-Nakayama ) or without formalin at 4°C for 49 days ( untreated control virus-Nakayama virus , UCV-Nakayama ) . The antigenic reactivity of FIV-Nakayama and UCV-Nakayama , as determined by Ag-capture ELISA with anti-JEV MHIAF , remained constant ( Fig 2A ) . The infectivity of FIV-Nakayama decreased drastically to below the detection limitation after 7 days treatment under these conditions; however , the infectivity of UCV-Nakayama decreased gradually over time and only became undetectable after 49 days ( Fig 2B ) . Ten of the eleven MAbs , with the exception of T16 , showed similar binding activity with FIV-Nakayama and UCV-Nakayama preparations collected at most time points by Ag-ELISA ( Fig 2C ) . The binding activity of the JEV serocomplex cross-reactive T16 MAb against FIV-Nakayama was significantly decreased at the 14-day collection point ( 14-DC ) with this sample having only 83% of the binding activity of the 0-DC sample . This decrease in binding of T16 against FIV-Nakayama was time-dependent; with only 55% binding activity remaining at 49-DC ( Fig 2C , panel e ) . Unlike T16 , 2B5B-3 and 2F2 binding against FIV-Nakayama declined at an early time point compared to UCV-Nakayama , but this was not observed at later time points . This result is consistent with the observation that only T16 exhibiting a decreased binding activity against FICV by Ag-ELISA ( Fig 1 ) . Therefore , we believed that the decreased binding activity of T16 MAb against FICV is the result of formalin inactivation and is not related to potential antigenic differences related to the sub-strain of virus or associated with the passage history of virus . To rule out formalin-induced antigenic modification occurring at strain-specific amino acids [36 , 54] , we prepared three different strains of JEV , the SA14-14-2 GIII vaccine-strain virus , the T1P1 naturally attenuated GIII virus and the YL2009-4 GI virus [44] , and then applied the same formalin inactivation procedures to all three viruses; this was followed by measurement of their MAb binding by Ag-ELISA using a subset of eight MAbs . The pattern of MAb binding activity obtained with these viruses was similar to that obtained with the Nakayama virus with or without formalin treatment ( Fig 3 and S1 Fig ) . Again , at 49-DC , the binding activity of T16 MAb was significantly decreased to 75% , 75% , and 72% for FIV-SA14-14-2 , FIV-T1P1 , and FIV-YL2009-4 , respectively ( Fig 3A ) . To further confirm that the decrease in binding activity of T16 MAb against the E protein was due to formalin inactivation , these viral preparations with or without formalin treatment were analyzed by non-reducing SDS PAGE followed by Western blotting using the T16 . 4G2 and 7A6C-5 MAbs , which have similar Ag-ELISA binding activity against the FIV-JEV and the UCV-JEV antigens , were included for comparison ( Figs 2C and 3 and S1 Fig ) . The intensity of the E protein band , when detected by 4G2 and 7A6C-5 of the various FIV-JEV and UCV-JEVs , including Nakayama , SA14-14-2 , T1P1 , and YL2009-4 , were similar; however , the intensity detected by T16 was lower against the FIV-JEVs than against the UCV-JEVs ( Fig 3B ) . By way of comparison , at 49-DC , the formalin-treated Nakayama , SA14-14-2 , T1P1 , and YL2009-4 viruses were found to have reduced T16 binding intensities of only 36% , 38% , 57% , and 40% of the UCV-JEVs , respectively ( Fig 3C ) . Therefore , formalin inactivation , when it affects the antigenic structure of JEV E protein , would seem not to be viral strain-specific and is likely to occur at the virion level , perhaps affecting the E monomer containing disulfide bonds . To localize the formalin-modified epitope on E protein , we mapped the epitope recognized by T16 MAb . The antigenic structure of non-infectious JEV VLP is similar to that of the virion particle [50 , 51] . T16 MAb is a JEV serocomplex cross-reactive antibody and we previously found that amino acid residues 101 , 104 , and 106 , which are present in EDII , and amino acid residues 315 , 331 and 389 , which are present in EDIII , are important for the binding of JEV serocomplex cross-reactive MAbs [49 , 53] . Thus we used a VLP-expressed plasmid to locate the formalin-modified epitope recognized by T16 MAb . JEV VLPs with EDIII amino acid substitutions S329A , S331K , and D389G , but not JEV VLPs with amino acid substitutions W101G/G106K/L107D , E138K , E306G , A315G and D332R , showed decreased binding to T16 MAb ( Fig 4A ) . Amino acids 329 and 331 are located within the BC loop of the EDIII of JEV and amino acid 389 is located within the FG loop of the EDIII of JEV; these three amino acids are likely candidates to undergo modification during formalin inactivation ( Fig 4B ) . The BC loop of EDIII contains critical residues recognized by neutralizing MAbs against JEV and West Nile virus , while the FG loop of EDIII is involved in host tropism [55–57] . Therefore , we analyzed the neutralizing ability of T16 by FRμNT assay ( Fig 5A ) . It was found that the FRμNT50 potency of T16 MAb was 21 . 8 μg/ml . 4G2 MAb neutralizes and inhibits flaviviral infection at the post-attachment step [58] . Thus , we used both 4G2 and T16 MAbs to determine the mechanism of viral neutralization . MAb was used to bind to JEV before infecting Vero cells in order to carry out a pre-attachment assay . Alternatively , MAb was added to JEV-bound cells in order to carry out a post-attachment assay . The neutralizing patterns of 4G2 and T16 MAbs were similar regardless of whether either MAb was added before or after viral attachment ( Fig 5B ) , which indicates that T16 MAb seems to inhibit JEV at a post-attachment step . To evaluate the influence of the T16 epitope ( EDIII 329/331/389 ) on the immunogenicity of formalin-treated JEV antigens , we further investigated IgG antibody responses and the properties of antibodies against EDIII 329/331/389 in vaccinated mice . Female BALB/c mice were vaccinated with UCV-Nakayama , FIV-Nakayama , or FICV and then post-vaccination serum samples were analyzed by the IgG-capture ELISA using wild-type , EDIII 329/331/389-mutated and EDII 101/106/107-mutated VLPs . The EDII 101/106/107-mutated VLPs eliminate the immunodominant B-cell epitope , conserved in all flaviviruses as well as inducing cross-reactive , non-neutralizing and/or low-neutralizing antibodies [49 , 59] . The total JEV-specific IgG elicited by all three immunogens were similar ( p>0 . 05 ) with the average titer end-points being 8 . 5×103 , 1 . 5×104 , and 1 . 1×104 for UCV-Nakayama , FIV-Nakayama , and FICV-immunized mice , respectively ( Fig 6A , panel a ) . We determined the antibody responses that recognized the EDII 101/106/107 epitope and the EDIII 329/331/389 epitope by calculation the decreased reactivity titer against EDII 101/106/107-mutated and EDIII 329/331/389-mutaed VLP compared to the wild-type VLP . The titer of antibodies recognizing the EDII 101/106/107 epitope was similar ( p>0 . 05 ) for all serum from mice vaccinated with UCV-Nakayama ( 103 . 9 , range 103 . 2–104 . 3 ) , FIV-Nakayama ( 104 . 1 , range 103 . 7–104 . 5 ) , and FICV ( 103 . 9 , range 103 . 1–104 . 5 ) ( Fig 6A , panel b ) . In contrast , the titer of EDIII 329/331/389 epitope-specific antibodies was significantly lower ( p<0 . 05 ) for serum from mice vaccinated with FIV-Nakayama ( 102 . 7 , range 102 . 4–103 ) and FICV ( 103 , range 102 . 5–103 . 4 ) compared to UCV-Nakayama ( 103 . 6 , range 103−104 . 1 ) ( Fig 6A , panel c ) . Based on the above , we suspected that FICV-immunized children might produce a similarly lower proportion of EDIII 329/331/389 epitope-specific antibody . Twelve FICV-immunized children serum samples were found to show a lower level for the EDIII 329/331/389 epitope-specific IgG antibodies , namely 23% ( 10–46% ) ( S2 Table ) ; this result closely resembles the antibody reactions elicited in the FIV-Nakayama–immunized and FICV-immunized mice . To confirm the results obtained by epitope-specific IgG ELISA , the FIV-Nakayama and FICV immunized mouse serum samples were examined by Western blot analysis using the same concentration of WT , EDII 101/106/107-mutated VLP and EDIII 329/331/389-mutated VLP ( Fig 6B ) and the results quantified against standardized protein concentrations ( Fig 6C ) . The prM protein of all of the JEV VLPs , including the WT antigen , the EDII 101/106/107-mutated antigen and the EDIII 329/331/389-mutated antigen , were equally recognized by the anti-JEV MHIAF . Furthermore , the EDII 101/106/107-mutated VLP and EDIII 329/331/389-mutated VLP could not be recognized or showed significantly decreased recognition with the 4G2 and T16 MAbs , namely <1% and 36% reactivity , respectively . The serum collected from mice vaccinated with UCV-Nakayama was less able to bind to the EDIII 329/331/389-mutated VLP ( 35% ) than FIV-Nakayama and FICV ( 65% and 64% , respectively ) , but this was not true for the EDII 101/106/107-mutated VLP ( 12% , 11% , and 17% , respectively ) ( Fig 6B and 6C ) . The results of the epitope-specific IgG ELISA and Western blot analysis are consistent and indicate a stronger immunogenicity of the EDIII 329/331/389 epitope on UCV-Nakayama than that on FIV-Nakayama and FICV . Therefore , formalin-inactivated Nakayama virus or vaccine in immunized mice was only able to affect the induction of antibodies recognizing the EDIII 329/331/389 , but was not able to affect the induction of antibodies recognizing the EDII 101/106/107 epitope . The protective efficacy of vaccines against JEV infection is positively associated with the presence of neutralizing antibodies in mice [60] . Based on this we evaluated the correlation between the production of neutralizing antibodies binding to EDIII 329/331/389 across various vaccines . The contribution of EDIII 329/331/389-specific antibodies to the viral neutralizing activity was determined by FRμNT using mouse serum specimens elicited by UCV-Nakayama , FIV-Nakayama or FICV . Serum samples were pre-adsorbed with the same number of normal COS-1 cells ( adsorption control ) , or COS-1 cells expressing WT or EDIII 329/331/389-mutated JEV VLPs . The level of VLP-expressing COS-1 cells was estimated by staining with anti-JEV MHIAF at 24 hr after transformation with the JEV WT or EDIII 329/331/389 mutant plasmid . The IFA positive rates were similar at about 85% for COS-1 cells transformed with either of the plasmids ( S2 Fig ) . The pre-adsorption neutralizing antibody titers of mouse serum immunized with UCV-Nakayama , FIV-Nakayama or FICV were similar , with FRμNT50 titers of 52 ( 20–80 ) , 46 ( 20–160 ) , and 52 ( 20–80 ) , respectively ( Fig 7A ) . We then measured the post-adsorption FRμNT50 titers ( Fig 7B ) to determine the contribution of the EDIII 329/331/389-specific antibodies to the viral neutralizing activity . The neutralizing antibody titers were lower for serum post-adsorbed with the WT JEV VLP-expressing COS-1 cells than for serum post-adsorbed with normal COS-1 cells using serum samples elicited by all three vaccines ( Fig 7B ) . However , the post-adsorption serum specimens using JEV EDIII 329/331/389-mutant VLP-expressing COS-1 cells showed a significant reduction in their neutralizing antibody titers activity when serum from either FIV-Nakayama-immunized mice or FICV-immunized mice was used , but not when the serum from UCV-Nakayama–immunized mice was used . The differences in neutralizing activity of the serum samples after adsorption with COS-1 cells expressing the WT VLP or EDIII 329/331/389-mutant VLP may have been due to the contribution made by EDIII 329/331/389-specific antibodies . When the results were fitted using non-linear regression analysis ( Fig 7C ) , this showed that the contribution of EDIII 329/331/389-specific antibodies to neutralizing antibody activity was proportionally higher ( 69% , range 62–78% ) using the serum from UCV-Nakayama-immunized mice than when the serum from FIV-Nakayama-immunized mice ( 38% , range 31–48% ) or FICV-immunized mouse serum ( 44% , range 35–58% ) was used . Thus , formalin modification of the EDIII 329/331/389 epitope would seem to affect the production of neutralizing antibodies . A previous report has suggested that JEV inactivation by formalin at 22°C for 10 days might be more immunogenic than inactivation at 4°C for 49 days [42] . Therefore , we asked if inactivation temperature ( 4°C vs . 22°C ) and inactivation duration ( 49 days vs . 10 days , respectively ) is able to influence T16 modification . We measured the T16 MAb binding activity of the JE Nakayama , SA14-14-2 , T1P1 , and YL2009-4 viruses treated with formalin at 4°C or 22°C for 10 days ( Fig 8A ) . At 10-DC , the T16 MAb binding activity against FIV-Nakayama , FIV-SA14-14-2 , FIV-T1P1 , and FIV-YL2009-4 were all lower at 75% , 77% , 63% , and 43% at 22°C than at 4°C , where the results were 94% , 98% , 120% , and 94% , respectively . Thus T16 epitope modification is present on FIV-JEVs treated either at 22°C for 10 days ( remaining 43–77% of T16 MAb binding activity ) or at 4°C for 49 days ( remaining 55–75% of T16 MAb binding activity ) ( Figs 2C and 3A ) . UV has also been used to inactivate viruses in the past and such UV-inactivated viruses are able to induce protective humoral immunity [61 , 62] . Surprisingly , UV-inactivated Nakayama virus only weakly bound anti-JEV MHIAF and T16 MAb when assessed by Western blot analysis , which indicates that the antigenic structure of Nakayama virus might be severely altered by UV irradiation ( Fig 8B ) . Hydrogen peroxide ( H2O2 ) can be used as a biocide and is known to interact with amino or sulfhydryl groups on antigens . Amanna et al . recently reported that H2O2-inactivated viruses are still able to induce protective cellular and humoral immunity [63 , 64] . Therefore , we followed their protocol and inactivated the Nakayama virus with 3% H2O2 at 22°C from 2 to 8 hr . Two-hours of H2O2 treatment reduced viral infectivity by at least 42000-fold to under the detection limitation ( Fig 8C ) . Ag-capture ELISA revealed that the binding activities of the T16 and other cross-reactive MAbs against the UCV-Nakayama and the H2O2-treated Nakayama virus were the same after 2-hr of treatment at 22°C . This suggests the antigenic structure of the Nakayama virus remained intact after H2O2 inactivation ( Fig 8D ) .
Several countries , including Japan , South Korea , and Taiwan , have successfully reduced the number of JE clinical cases by using inactivated JEV vaccines , but more effective and safe alternative vaccines are still needed [65–67] . The factors that affect the effectiveness and safety of JEV vaccines are the virus strain , method for viral cultivation , vaccine purity and vaccine formulation [68]; however , the effect of formalin inactivation on the quality of vaccine has never been studied . This is important because formalin-induced hypersensitivity has been found associated with risk of enhanced disease during subsequent infection with respiratory syncytial virus ( RSV ) , and formalin inactivation altered the antigenicity of poliovirus [38 , 41 , 69] . Antigenic characterization of formalin-inactivated poliovirus vaccine by using a panel of MAbs revealed that modification of antigenicity is time-dependent [38] . Using a previously collected panel of anti-flavivirus MAbs and the established Ag-ELISA [49 , 53] , we found that only the T16 MAb binding domain was time- and temperature-dependently altered by formalin inactivation . This observation suggested it might be valuable to evaluate the effect of residual formalin in formulated bulk on vaccine shelf life in the future . Importantly , regardless of the JEV strain used , formalin treatment altered the T16 epitope of all tested JE viruses . In contrast , epitopes recognized by 2B5B-3 and 2F2 MAbs on FIV-Nakayama were temporarily modified for specimen collected at early time point . Modification of these two epitopes was Nakayama strain-specific and was reversible since this phenomenon was only observed in the early time point specimen of formalin-treated Nakayama alone . Among anti-flavivirus antibodies , most of the virus-specific , non–cross-reactive , and EDIII-recognizing antibodies have strongly neutralizing activity , and most of the cross-reactive and EDII- or EDI-recognizing antibodies have weak or no neutralizing activity [16 , 70] . T16 MAb is a JEV-derived , JEV-serocomplex cross-reactive antibody . It shows weakly neutralizing activity at the post-attachment step in vitro . However , antibodies that comprise a large portion of the antibody response after WNV infection have only weak neutralizing activity in vitro but still provide therapeutic protection in vivo via the immune complement system [71] . The amino acid residues in both EDII and EDIII of the E protein are important to the binding of JEV serocomplex cross-reactive MAbs [49 , 53] . We determined the binding of T16 MAb to JEV VLPs by the amino acid positions EDII-104 , -329 , -331 , and -389 but used only EDIII 329/331/389-mutated VLPs to analyze epitope-specific antibody responses because EDII-104–mutated VLPs showed reduced secretion . Interestingly , the T16 epitope overlaps with the JEV-specific highly neutralizing E3 . 3 MAb epitope [56] . This result provides additional support that most E-protein epitopes within flaviviruses are overlapping [53] . Formalin is known to mainly react with the amino and thiol groups of amino acids to form methylol groups , which is followed by the formation of Schiff-base adducts; this reaction is reversible . These Schiff-bases adducts can cross-link to functional groups of various amino acids , such as arginine , tyrosine , tryptophan , histidine , glutamine , lysine , and cysteine , forming non-reversible methylene bridges [35] . Thus , the epitope of T16 MAb , namely glycine 104 , serine 329 , serine 331 and aspartic acid 389 , are likely not directly modified by formalin but are possibly influenced by nearby amino acids , including those at 105 , 335 , 336 , 387 , 390 , and 391 , and such cross-linking might directly or indirectly affect the conformational structure of the T16 epitope . Formalin treatment did not alter T16-overlapped epitopes recognized by 4G2 and 6B6C-1 . T16 MAb might be more sensitive to this formalin-generating modification on the non-overlapped residue ( s ) essential for T16 recognition . Currently we still do not know residues specifically reacting with formalin . Structural differences and amino acid variation in flavivirus immunogens , such as whether the virions are mature or immature , VLPs , or EDIII alone , may also affect the immunogenicity , antibody profile , and neutralizing potency elicited [72–75] . For example , EDIII-reacting antibodies show high neutralizing potency , but the recombinant EDIII immunogen induces low avidity and low titers of neutralizing antibodies against the virus [72] . In this study , we found that formalin inactivation altered the structure of the JEV E protein and thus affected the profile of induced antibodies . In this study , T16 epitope was the only epitope affected by the formalin inactivation; however , whether the T16 epitope is the only E structure alteration affecting the profile of antibodies elicited by formalin-inactivated vaccines/viruses is unknown because the T16 epitope , EDIII 329/331/389 , was not directly reactive with formalin . The formalin-modified EDIII 329/331/389 region was found less immunogenic and had less of a contribution to the neutralizing activity , despite non-significant differences in neutralizing antibody titers among UCV-Nakayama–immunized mice and FIV-Nakayama–immunized mice . Weak-neutralizing and non-neutralizing epitopes were located in the fusion peptide , and the introduction of mutations into the fusion peptides of the VLP disrupted the binding activity of anti-fusion loop MAbs . The fusion peptide mutant reduced the immunogenicity of the fusion peptide but retained its ability to evoke neutralizing antibodies [76 , 77] . Thus , the formalin-modified region affects the profile of vaccine-induced antibodies and alters the distribution of neutralizing antibodies . We did not determine the effect of formalin inactivation on the T-cell response , which needs to be addressed because a negative effect of formalin-inactivation on the influenza-virus T-cell response has been documented and T-cell immunity plays a role in how vaccines protect against JEV infection [37 , 78 , 79] . The use of epitope scaffolds or deglycosylation has successfully exposed immunorepressive and cryptic epitopes and enhanced immunogenicity in HIV or redirected the antibody response in simian immunodeficiency virus [80 , 81] . We found the titers of EDIII 329/331/389-reactive antibodies higher among UCV-Nakayama–than FIV-Nakayama–or FICV-immunized mice and use of EDII 101/106/107-reactive antibodies gave similar results . Previously , we found that EDII 101/106/107 and EDIII 329/331/389 form an overlapped epitope for flavivirus group cross-reactive MAbs , such as 4G2 and 6B6C-1 [53] . Thus , the EDII 101/106/107 region may be less likely to cooperate with the EDIII 329/331/389 region in inducing an antibody response when the immunogen been modified by formalin . The formalin-inactivated , lactate dehydrogenase-elevating , virus-elicited antibodies differ from antibodies after natural infection . Formalin-inactivated influenza virus could not induce a T-cell response and was less protective in mice against homologous and heterologous influenza virus challenge as compared with γ-ray–inactivated virus [37 , 82] . However , another study indicated that the use of low formalin concentrations , short inactivation period , and high incubation temperature improved the immunogenicity of formalin-inactivated JEV vaccine and elicited high titers of neutralizing antibodies in mice [42] . Here , we showed that the binding activity of T16 MAb was reduced more by virus inactivation at 22°C than 4°C for the same treatment duration . Surprisingly , UV-inactivated Nakayama virus failed to be recognized by MHIAF and T16 . Adjusting the condition for UV irradiation may maintain the antigenic structure of JEV . UV light inactivates virus by cross-linked viral nucleic acid and viral proteins . Cross-linked by oxidation between the amino acid residues may increase the susceptibility of protease cleavage [83 , 84] , and degradation of aromatic side chain of amino acid and disulfide bond forming cysteine in protein has been indicated after UV treatment [85 , 86] . The loss of viral antigenicity was also observed in UV-inactivated virus including poliovirus ( showing both antigenic and morphologic change ) , and influenza A virus ( exhibiting low hemagglutination activity ) [37 , 87] . Murray Valley encephalitis virus , belonging to JEV serocomplex , inactivated with UV showed lower immunogenicity compared to non-infectious VLP but the UV-induced antigenic change wasn’t described [88] . In conclusion , formalin and UV inactivation alter the antigenic structure of E protein in JEV and reduce the immunogenicity of associated vaccines . H2O2 inactivation seems to be a better alternative for JEV vaccine production . It maintained the antigenic structure of E protein , measured by a panel of MAbs . Further study should focus on identifying an optimal inactivation procedure and testing the immunogenicity of H2O2-inactivated JEV vaccine . Finally , to prevent unexpected modification of the various epitopes on the JEV vaccine during inactivation , a non-infectious JEV VLP or DNA vaccine should be developed . Formalin inactivation introduces an antigenic modification that affects the EDIII of JEV and thus distorts the profile of vaccine-induced neutralizing antibodies . Antigenic-stable inactivation methods are needed to develop better-inactivated JEV vaccines . | We demonstrated that formalin inactivation of Japanese encephalitis virus ( JEV ) alters the antigenic structure of the JEV envelope glycoprotein ( E ) , in particular an epitope in domain III , and that this reduces the ability of the inactivated vaccine to elicit protective neutralizing antibodies . Ours and others’ previous studies have highlighted the importance of improving the immunogenicity of genotype III ( GIII ) -derived JEV vaccine in order to provide cross-protection against genotype I ( GI ) viruses , which are emerging and replacing GIII viruses in many JEV-endemic regions . Encouraging the wide use of live-attenuated or chimeric vaccines , such as SA14-14-2 or yellow-fever 17D/JEV vaccines , respectively , developing GI virus-derived inactivated or premembrane/E–containing , noninfectious virus-like particle ( VLP ) vaccines are two other possible ways to address this potential problem . In this exploratory study , we highlight an alternative inactivation method , such as H2O2 treatment , which may improve the antigenic stability and immunogenicity of JEV . |
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The whole-genome duplication ( WGD ) that occurred during yeast evolution changed the basal number of chromosomes from 8 to 16 . However , the number of chromosomes in post-WGD species now ranges between 10 and 16 , and the number in non-WGD species ( Zygosaccharomyces , Kluyveromyces , Lachancea , and Ashbya ) ranges between 6 and 8 . To study the mechanism by which chromosome number changes , we traced the ancestry of centromeres and telomeres in each species . We observe only two mechanisms by which the number of chromosomes has decreased , as indicated by the loss of a centromere . The most frequent mechanism , seen 8 times , is telomere-to-telomere fusion between two chromosomes with the concomitant death of one centromere . The other mechanism , seen once , involves the breakage of a chromosome at its centromere , followed by the fusion of the two arms to the telomeres of two other chromosomes . The only mechanism by which chromosome number has increased in these species is WGD . Translocations and inversions have cycled telomere locations , internalizing some previously telomeric genes and creating novel telomeric locations . Comparison of centromere structures shows that the length of the CDEII region is variable between species but uniform within species . We trace the complete rearrangement history of the Lachancea kluyveri genome since its common ancestor with Saccharomyces and propose that its exceptionally low level of rearrangement is a consequence of the loss of the non-homologous end joining ( NHEJ ) DNA repair pathway in this species .
Centromeres and telomeres are essential genetic and structural elements of eukaryotic chromosomes . To maintain the accurate transmission of the genome to the next generation , each chromosome must have exactly one centromere and two telomeres . Evolutionary changes in an organism's number of chromosomes are caused by , or result in , structural rearrangements at centromeres and telomeres . Some particular chromosome number changes have been studied in detail in other eukaryotes , such as the fusion of two chromosomes in human since the divergence from chimpanzee [1]–[2] and the insertions of whole chromosomes into other centromeres that occurred during grass evolution [3]–[4] . Here we present the first study of this kind in yeast species . Centromeres in all eukaryotes are the site at which the kinetochore forms and is attached to spindle microtubules , which segregate sister chromosomes to opposite poles of a dividing cell during anaphase I of meiosis , and sister chromatids during mitosis and anaphase II of meiosis . They also play a role in the pairing of homologous chromosomes during meiosis [5] . Centromere malfunction can lead to aneuploidy , resulting in inviable cells or severe genetic conditions . With few exceptions , centromeres are limited to one location per chromosome , because having more than one can lead to differential attachment to opposite spindle pole bodies during cell division , causing chromosome breakage by mechanical shearing during chromosome segregation . There are several different types of centromeres in eukaryotes [6] . Most species have ‘regional’ centromeres that are defined epigenetically and can range in size from a few kilobases , to hundreds of kilobases . These regions are often heterochromatic and contain repetitive arrays of DNA satellites . Several diverse eukaryotic species have holocentric chromosomes which are thought to have evolved independently , where the centromeric function is spread along the entire chromosome [7] . Yeasts related to Saccharomyces cerevisiae have a unique type of centromere , known as point centromeres [8]–[9] . These are generally less than 200 bases long and are defined by specific sequences , the CDEI , CDEII and CDEIII regions which are bound by CEN DNA-binding proteins [10]–[11] . Point centromeres are probably an evolutionary state derived from epigenetic centromeres , as more divergent fungal lineages have epigenetic centromeres that cannot be identified by sequence [12]–[13] . It has been proposed that point centromeres evolved from the partitioning elements found on selfish plasmids , which supplanted the epigenetic centromeres in the Saccharomycetaceae lineage [6] . The point centromeres in yeast are some of the fastest diverging regions in the genome [11] . Telomeres are also ubiquitous and essential in all eukaryotes . They are heterochromatic regions that serve a protective function for the chromosomes [14]–[17] . Telomeres prevent the degradation of chromosomes from their ends and stop them from being recognized as double strand breaks ( DSBs ) . Wild type telomeres are ‘capped’ with a combination of binding proteins , chromatin structure and DNA secondary structure folding into t-loops or other higher order chromatin structures [18]–[21] . Uncapped telomeres act and are recognized as DSBs , which initiate cell cycle arrest and DSB repair pathways [19] , [22] . Telomeres of S . cerevisiae chromosomes consist of a heterogeneous repeating sequence ( basic unit TGGGTG ( TG ) 0–3 ) that is maintained by the enzyme telomerase in an array 325±75 bp long [23]–[24] . Other species such as Naumovozyma castellii and Candida glabrata have a similar organization though the sequence and length can vary [25] . Proximal to the telomere itself is a ‘subtelomeric’ region , which in S . cerevisiae consists of larger repeat sequences such as the Y′ element . Further proximal again are the first genes on the chromosome , which tend to be members of subtelomere-specific repeat families such as the DAN/TIR and FLO gene families in S . cerevisiae . Many species from the Saccharomycetaceae family [26] have had their genomes sequenced ( Figure 1 ) [27]–[33] . Central in this phylogeny is a whole genome duplication ( WGD ) event that occurred roughly 100 million years ago and gave rise to several extant paleopolyploids with reduced duplicate gene content [34] . Multiple genome sequences are available representing lineages that arose both before and after the WGD ( Figure 1 ) , referred to as non-WGD and post-WGD species , respectively [28] , [33] , [35] . We previously inferred the gene order and core genome structure of the ancestral species that existed immediately before the WGD [36] . This ancestral genome contained a minimum complement of roughly 4 , 700 genes arranged on 8 chromosomes . The WGD doubled this basal chromosome number from 8 to 16 . However , many of the post-WGD species do not have exactly 16 chromosomes; C . glabrata for instance has only 13 . Karyotype data from pulse field gel electrophoresis ( PFGE ) also indicates a chromosome complement that ranges between 8 and 16 chromosomes for a range of post-WGD species [37]–[38] . Similarly , some of the non-WGD species have fewer than 8 chromosomes , such as Kluyveromyces lactis with 6 . The ancestral reconstruction has allowed us to trace the genomic rearrangements that gave rise to the genome structures of extant species . Here , we mapped the locations of the ancestral centromeres and telomeres to sites in extant species , and identified the rearrangements that caused the chromosome number to change during the evolution of these species .
We previously inferred the structure of the yeast genome as it existed immediately before the WGD occurred [36] . We refer to this genome as the ‘Ancestral genome’ , and to the organism that contained it as the ‘Ancestor’ . It corresponds to the point marked ‘WGD’ on the phylogenetic tree in Figure 1 . The approximate locations of telomeres in this genome are already known [36] . We inferred centromere locations in the Ancestral genome by using the same parsimony approach as in [36] combined with available centromere annotations from sequenced species . The inferred Ancestral centromere locations have been included in YGOB [39] . In summary , if a centromere is present in an orthologous intergenic region in at least one non-WGD and one post-WGD species , or in paralogous ‘sister’ regions of a post-WGD species , then that centromere was inferred to have been present in the Ancestral genome ( WGD node in Figure 1 ) . We extended the inferences of centromeres and telomere locations further back along the phylogeny to the common ancestor of the non-WGD and post-WGD species ( Node ‘B’ in Figure 1 ) to allow for inferences about the evolution of centromeres and telomeres in the genera Kluyveromyces , Lachancea and Ashbya . While inferring node B we found that the genome of the non-WGD species L . kluyveri differs from the Ancestor by only 15 rearrangements ( not including inversions within synteny blocks ) as shown in Figure 2 ( details are given in Table S1 ) . We then assigned these rearrangements to different branches of the tree based on their presence or absence in other non-WGD species and the outgroup Candida and Pichia clades ( Figure 1 ) . The centromere and telomere locations are nearly identical between L . kluyveri and the Ancestor , allowing us to infer the centromere and telomere locations in the common ancestor of the non-WGD and post-WGD species ( Node ‘B’ in Figure 1 ) . Interestingly , by examining which Ancestral genes were not present in L . kluyveri , we noticed that four genes involved in non-homologous end joining ( NHEJ ) ( DNL4 , POL4 , NEJ1 and LIF1 ) are missing from the genome of L . kluyveri with only a degraded DNL4 pseudogene and weak traces of an NEJ1 pseudogene remaining in the ancestral locations . These four proteins are part of the end-processing complex which plays a role in NHEJ [40]–[42] , and DNL4 , NEJ1 and LIF1 are also part of the end-bridging complex [40]–[41] . NHEJ is generally limited to haploid yeast cells because the expression of NEJ1 , a major regulator of NHEJ , is down-regulated in MATa/MATα diploid cells [43]–[44] . DNL4 is required for NHEJ , and NEJ1 regulates NHEJ , so it appears that the NHEJ pathway is missing in L . kluyveri . POL4 , NEJ1 and DNL4 have also been shown to play roles in the alternative microhomology-mediated end joining ( MMEJ ) pathway , and deletions of these genes reduce the efficiency of this process several-fold [45]–[47] . We hypothesize that the loss of the NHEJ and MMEJ pathways ( or a large reduction in their efficiency ) in L . kluyveri may be linked to the low number of genomic rearrangements and lack of telomere-to-telomere fusions in this lineage . It may also be linked to the predominantly diploid lifecycle of this yeast [48] , which also suggests that most DSB repair in L . kluyveri is through homologous recombination . Although the NHEJ machinery is not essential , to our knowledge L . kluyveri is the only eukaryote so far identified that lacks it . Genes for all members of the MRX and Ku complexes are still present in L . kluyveri , and the related species L . thermotolerans has a complete set of NHEJ genes . The locations of centromeres were already inferred bioinformatically by the original sequencing groups for all species except Saccharomyces bayanus , Vanderwaltozyma polyspora ( previously called Kluyveromyces polysporus ) and Naumovozyma castellii ( previously called Saccharomyces castellii or Naumovia castellii ) . We identified and annotated centromeres in S . bayanus and V . polyspora by extracting the intergenic regions in these species orthologous to the inferred Ancestral centromeres , and used MEME [49] to generate consensus CDEI and CDEIII profiles ( full sequences of all centromeric loci are in Table S2 ) . For N . castellii , Cliften et al . [50] were unable to identify any consensus centromere sequence . We too were unable to identify consensus centromere sequences at the Ancestral centromeric locations in N . castellii ( Dataset S1 ) . We also searched the whole N . castellii genome using the consensus motif for Saccharomycetaceae point centromeres derived from all identified centromeres in all species , but still could not find any candidates . Inspection of the intergenic regions corresponding to Ancestral centromeres in preliminary genome sequence data from the related species N . dairenensis also failed to locate any candidate point centromeres ( data not shown ) . We hypothesize that these species may represent a novel transition of centromere structure in Naumovozyma which could be analogous to the earlier replacement of epigenetic centromeres by point centromeres in yeasts [6] . The system that has potentially superseded point centromeres in Naumovozyma will require functional characterization in the laboratory . The correspondence between Ancestral centromere locations and current centromeres for all other extant species in the YGOB species set are shown in Table 1 . All but one current centromere mapped in a straightforward manner to a corresponding Ancestral centromere with full or partially conserved syntenic gene content bordering the centromeres relative to the Ancestor . The exceptional case was CEN9 of C . glabrata , which maps to Ancestral CEN6 and has undergone a series of rearrangements with breakpoints on both sides of the centromere which have eliminated all traces of synteny at this locus ( Figure S1 ) . We traced the evolution of telomere locations in all the species for which completely finished genome sequences are available , but not for those whose genomes consist of numerous scaffolds , due to the uncertainty in identifying real telomeric regions in scaffold data ( Table 2 ) . In most of the genomes , mapping the current telomeres to Ancestral locations is relatively trivial as there is a direct correspondence without genome rearrangements at those locations ( Table 2 ) . However in C . glabrata , A . gossypii and K . lactis several telomeres mapped to Ancestral locations through a complex set of rearrangements including breakpoint reuse . The genomes of these species are also the most rearranged of those examined . By contrast , members of the Lachancea clade have had relatively few genomic rearrangements on the evolutionary path between them and the Ancestor . The mapping of telomeres to Ancestral telomeres is more tentative than for the centromeric mapping , due to the inherently unstable nature of telomeres , and the possibility of movement of the telomeric boundaries . For example , if we had genome sequences from more species , it might become possible to extend the Ancestral genome inference further towards the telomeres and so reveal rearrangements that are presently inaccessible that may alter the mapping . The current telomere assignments represent the most parsimonious mappings given the data that is currently available . We identified nine losses of a centromere , corresponding to nine decreases of chromosome number . Three of these occurred in C . glabrata , two each in V . polyspora and K . lactis , and one each in Z . rouxii and A . gossypii ( Figure 1 ) . The major mechanism of centromere loss was associated with the telomere-to-telomere fusion of two chromosomes with the loss of one of the centromeres . This mechanism is illustrated by the chromosome fusion and single centromere loss that occurred in Z . rouxii , whose details are shown in Figure 3 . In this example , the process also resulted in the internalization of many genes that were previously located near telomeres . All but perhaps one of the nine centromere losses occurred in this fashion , resulting in the loss of at least 14 of the 112 telomere locations examined . The removal of centromeres appears to have been quite specific , generally leaving adjacent genes intact . In some cases a centromere and some adjacent genes are missing , but all these cases occur in post-WGD species where gene deletion is relatively common due to the redundancy created by the WGD . None of the centromere losses in non-WGD species is accompanied by loss of centromere-adjacent genes . The majority of centromere losses in yeast appear to have involved the fusion of whole chromosomes . In these cases , two possible scenarios exist that differ only in the order of events . The first scenario is the initial fusion of the chromosomes at telomeric locations , with subsequent loss of one of the two centromeres . In this case selection would likely act to suppress one of the two centromeres to avoid problems during cell division . The second scenario is that the centromere of a chromosome is first lost or disabled , with the chromosome subsequently being rescued from cellular loss by fusion to another chromosome with a functional centromere . Under the latter scenario , selection acts to maintain the genes contained on the chromosome without a centromere , because cells missing a whole chromosome will certainly be inviable . Chromosome fusions have been generated experimentally in S . cerevisiae by the inactivation of a centromere [51] . Interestingly , if the centromere is reactivated , it often leads to fission of the resulting chromosome at or near the fusion site to reconstitute the parental karyotype [51] , indicating that the fusion point may be a fragile site . This fragility might explain the reuse of fission/fusion breakpoints like those shared between Translocations 1 , 2 and 3 in Figure 3 . The unique case observed in A . gossypii appears to have occurred by the breakage of a chromosome in the intergenic region that contained Ancestral centromere Anc_CEN5 ( Figure 4 ) . The resulting two chromosome arms then fused to two other chromosomes , joining the previously centromere-proximal sequences to the telomeres of the other chromosomes . The exact nature of this fission and fusion is not known , and we cannot tell the difference between chromosome breakage and religation to new locations , or translocation events . It is also not possible to infer whether the centromere was destroyed in the fission event , or whether it was still intact at the end of one of the arms that subsequently fused to another telomere and was lost later due to the constraint of having one centromere per chromosome . We observed no cases of de novo centromere gain . Apparently , the only mechanism by which chromosome number has increased during the evolution of Saccharomycetaceae is WGD ( Figure 1 ) . This discovery is quite surprising , because the spontaneous formation of aneuploids with duplications of single centromeres or chromosomes has frequently been reported , both in S . cerevisiae [52]–[53] and C . glabrata [54] . Interestingly , from the sequenced genomes only species in the genus Saccharomyces have retained all 16 centromeres from the WGD , while the other sequenced post-WGD species ( V . polyspora , N . castellii and C . glabrata ) all have a reduced chromosome complement that arose independently in their respective lineages ( Figure 1 ) . Previous PFGE karyotype analyses indicated that some strains of Kazachstania exigua may also have a chromosome complement of 16 [37]–[38] , the most likely explanation of which is that this species has also retained all of its centromeres since the WGD . We compiled and compared the CDE consensus sequences for all sequenced yeasts with point centromeres ( Figure S3 ) . All the centromeres of S . cerevisiae have been characterized functionally [8]–[9] , and a few have been cloned from other yeasts: S . bayanus [55]–[56] , C . glabrata [57] , Z . rouxii [58] and K . lactis [59] . The genome sequencing groups made bioinformatic predictions about centromere locations for most of the other chromosomes and species , based on matches to the CDEI–III consensus sequences [27]–[28] , [30] , [33] . We used these in our analysis , though we revised the coordinates of two L . waltii centromeres ( Table S4 ) . We identified CDE regions for centromeres in S . bayanus ( Table S5 ) and V . polyspora ( Table S6 ) , finding 16 and 14 centromeres respectively . Although the genome sequence of V . polyspora is incomplete [32] , there is complete intergenic sequence spanning both of the lost centromeres meaning we are confident of their absence . Our count of 14 centromeres is one more than the previous estimate of chromosome number in this species [60] . With over a hundred yeast centromeres in our dataset we searched for features common to all point centromeres ( Figure 5 ) . For consistency with S . cerevisiae , in this analysis we delineated the boundaries of CDEI , CDEII and CDEIII regions in the same way across all genomes disregarding small differences in the boundary choices made by different sequencing groups . The CDEI regions have an 8 bp consensus motif with four invariant sites ( NNCAVBTG ) . The CDEIII regions have an invariant 5 bp motif ( CCGAA ) and the whole CDEIII consensus is 26 bp . Within a given species there are often further invariant sites in their CDEI or CDEIII regions , for example G at positions 2 and 8 in S . cerevisiae CDEIII . The intervening CDEII regions are always highly AT-rich ( 76–98% ) . The length of CDEII varies twofold among species , but there is remarkably little CDEII length variation within each species , and a clear correlation of CDEII lengths among related species ( Figure 5C ) . Hegemann and Fleig [61] compiled and summarized mutagenesis studies on S . cerevisiae CEN6 [62]–[64] , measuring the frequency of chromosome fragment loss resulting from point mutations at many sites in CEN6 . There is a strikingly strong correlation between their results and the evolutionary conservation of individual sites in CDEI and CDEIII ( Figure 5A , 5B ) . None of the 13 nucleotide changes with the most severe phenotypes ( chromosome fragment loss rates >10−2 per mitotic cell division ) at CEN6 occurs as a natural variant in the 102 centromeres we compiled . Thus the evolutionary conservation of these regions over hundreds of millions of years correlates well with the highest impact point mutations from the mutational data . Due to these constraints , we suggest that the de novo formation of a point centromere in these yeast species is much less likely than the de novo creation of regional centromeres in other species such as Candida albicans [65] because heritable epigenetic changes can occur on a much smaller timescale than sequence-based evolution . Reciprocal translocation and inversion breakpoints were observed adjacent to centromeres in C . glabrata , V . polyspora , A . gossypii and K . lactis , as were orientation changes of the centromeres ( Table 1 ) . V . polyspora and A . gossypii each show only one such event , and in both cases the rearrangement breakpoints coincide with the site of a centromere loss in these species . K . lactis has three rearrangement breakpoints adjacent to centromeres , and C . glabrata has six , none of which coincide with centromere loses in either species . Interestingly , the breakpoints adjacent to the three centromeres in K . lactis are all part of one rearrangement cycle ( Figure S2 ) , indicating that there have been reciprocal translocations between intergenic locations containing centromeres . Translocations causing a terminal segment of one chromosome to be transferred and joined to another chromosome were observed in Z . rouxii ( Figure 3 ) , S . cerevisiae , C . glabrata , K . lactis and A . gossypii . As well as physically moving an existing telomere to a new chromosome , this type of rearrangement results in some previously subtelomeric DNA becoming internal to chromosomes where the fusion occurred ( Figure 3 ) . These events can be inferred at the level of synteny blocks , but they probably occurred millions of years ago and there is currently no telomere-like DNA sequence at the rearrangement points . Conversely , previously internal regions on the chromosomes located at the breakpoints of telomeric translocations become novel telomere sites ( e . g . , gene ZYRO0G15554 after Translocation 1 in Figure 3 , before it became the join-site of another telomeric translocation ) . Analogous birth and death of telomere locations can occur by inversions and are found in S . cerevisiae , A . gossypii , Z . rouxii and K . thermotolerans ( Table 2 ) . Telomeric translocations and inversions have resulted in the turnover of more than a quarter ( 33/112 ) of telomere locations relative to the ancestor . As well as inversions and translocations , the death of telomere locations can be caused by telomere-to-telomere fusions . The gain of novel telomere sites is presumably by telomere capture , a process that has been observed in cells that survive the absence of telomerase or defective telomere capping . Novel telomeres can also be generated at the site of a DSB by telomerase , a process that is enhanced by G-rich telomeric seed sequences lying close to the DSB [66]–[69] . Internal chromosomal positions differ from subtelomeric locations in terms of their chromatin configurations , which in turn affect the expression of nearby genes [70]–[72] . In general , subtelomeric regions tend to have higher nucleosome occupancy and silencing protein association , both of which generally reduce gene expression [70]–[72] . Subtelomeric genes are likely to be under less evolutionary constraint than genes in internal locations , are less essential and have higher variance in their expression profiles [73] . The rate of sequence evolution is negatively correlated with expression and essentiality , but positively correlated with the variance of gene expression [74]–[77] . Thus relocating a gene from telomeric to internal regions is likely to increase the evolutionary constraints on its sequence . Conversely , evolution may proceed at a faster pace at telomeres due to more relaxed selective constraints . If this higher evolutionary rate leads to an advantageous allele at a telomere , we hypothesize that it may be beneficial to relocate the gene to somewhere else in the genome where selection will maintain the advantageous allele under higher constraint . This could potentially constitute an ongoing cycle over evolutionary time , where the telomeres act as the cooking pots of evolution [78] , with successful innovations moving to more stable regions . Rearrangements that internalize genes appear to be more common in genomes that have high rates of genome rearrangement . In S . cerevisiae , which is the least rearranged post-WGD species [36] , only two genes ( GAL2 and SRL2 , which are in the same breakpoint location ) were internalized by rearrangement from a telomere ( Table S3 ) . In C . glabrata , arguably the most rearranged post-WGD species [36] , there are at least 17 internalized genes in 8 locations ( Table S3 ) even though the telomeres of C . glabrata contain many fewer annotated genes than those of S . cerevisiae . Non-WGD genomes that have high levels of rearrangement such as K . lactis and A . gossypii [36] contain high numbers of these genes ( at least 48 genes in 19 locations and 15 genes at 8 locations respectively ) ( Table S3 ) . In Z . rouxii , which is intermediate in terms of rearrangement , there are at least 27 genes at 7 locations , while in the rearrangement poor L . thermotolerans , there are 6 genes at a single location . There are no internalized genes in the L . kluyveri , the least rearranged non-WGD species . These numbers also somewhat reflect the overall numbers of subtelomeric genes annotated in these species . Large scale genomic rearrangements like the fusions of telomeres to other telomeres or internal chromosomal sections inferred in this work are generally considered to be detrimental to cells although they are not necessarily so . Many cancers involve similar types of rearrangements , and there are several pathways and mechanisms in place in cells to prevent and repair them , including proteins involved in telomere structure and maintenance , cell cycle arrest signalling , homologous recombination ( HR ) and NHEJ repair pathways [19] , [22] , [69] , [79]–[81] . Interestingly , many of the components of the HR and NHEJ machinery such as the MRX complex , Yku70/80 proteins and Rad17/Mec3/Ddc1 complex also play roles in telomere structure and stability and are associated to telomeres [19] , [22] , [79]–[81] . Experimental deletions of genes involved in these pathways as well as those involved in telomeric structure have helped to tease apart their functions at telomeres , and many of the deletions result in chromosomal rearrangements such as telomere-to-telomere fusions and non-reciprocal translocations , similar to those inferred in our work [19] , [22] , [80]–[82] . The gross chromosomal rearrangements observed in these mutants generally manifest through a NHEJ-like mechanism requiring Dnl4 ( Lig4 ) , an NHEJ ligase [79]–[81] . Spontaneous rearrangements involving telomere fusions to other telomeres or DSBs occur in wild type S . cerevisiae cells at a rate of 1–6×10−7 events per genome per cell division [80] , but have only been fixed a few times throughout Saccharomycetaceae evolution . Together with evidence that S . cerevisiae is capable of rescuing cells from DSBs by telomere capture at the edge of the DSB from the centromere-containing part of the chromosome [66] , [68] , [83] , it appears that telomeric rearrangements such as telomere-to-telomere fusions and non-reciprocal translocations likely represent rare errors in the systems that protect and cap telomeres or repair DSBs that have been fixed over evolutionary time . It is only possible to speculate about the exact causes of the rearrangements , how they became fixed in populations , and whether they were selectively advantageous , neutral or disadvantageous . The observed rearrangements are in the order of millions of years old , and are thus unlikely to contain any sequence information that could provide empirical evidence about their mechanism of formation . We suggest that the rearrangements probably occurred in haploid cells , as in a diploid it would be expected that DSBs would be repaired via homologous recombination using the homologous chromosome as templates . In the Saccharomycetaceae where mating-type switching occurs [28] , [84] , rearrangements in haploids would also avoid mating incompatibilities that could arise in a diploid due to meiotic segregation difficulties [85] . A haploid cell could divide , change mating type and then mate with the daughter cell , thus avoiding potential chromosome pairing problems and aneuploidy . Among the species studied here ( the family Saccharomycetaceae ) [26] , we find that chromosome number has evolved by two very different mechanisms . The only mechanism of increase was polyploidization . We suggest that the lack of any other new centromere formation is a consequence of the sequence-defined nature of point centromeres , but it is unclear why the formation of a new centromere by small-scale DNA duplication of an existing centromere , as seen in C . glabrata drug resistance isolates [54] , is not seen during evolution . The mechanism of decrease in chromosome number was by rearrangements involving telomeres , primarily telomere-to-telomere fusions with the loss of a centromere belonging to one of the fused chromosomes . The temporal sequence of the chromosome fusion and centromere loss is ambiguous . Telomeric rearrangements have also frequently moved genes from subtelomeric locations to internal genomic locations . These movements have the potential to change the selective constraints on the genes and could be evolutionarily adaptive .
The Ancestral centromere locations were generally trivial to find because numerous comparisons among extant non-WGD and post-WGD species can be made , most centromere locations are in syntenic regions among species , and most rearrangements that might obscure these relationships are species specific . Ancestral centromere loci were added to YGOB following the same parsimony rules as in [36] , by using species for which centromere annotations have already been made . These Ancestral centromere locations were then used to guide the search for unannotated centromeres in orthologous intergenic regions by searching for CDEI and CDEIII sequence motifs using MEME [49] . To map the rearrangements that had occurred at a centromere in any particular species , we examined the breakpoints between synteny blocks in that species relative to the Ancestor and tried to locate the reciprocal breakpoint elsewhere in the genome . In some cases , a reciprocal breakpoint did not exist; these cases represent breakpoint reuse [36] . They can be solved by following one edge of the breakpoint ( A|B ) locating the reciprocal edge at another location ( B′|C ) , then finding the breakpoint partner's reciprocal edge ( C′|D ) and iterating this process until reaching the original breakpoint's other edge ( D′|A′ ) . This process identifies a cycle of breakpoint edges that eventually leads back to the adjacent edge of the centromeric breakpoint . Telomeric locations were mapped between the Ancestor and extant species in a similar way , except the extant telomere positions were defined as the regions at the ends of chromosomes where it is no longer possible to define Ancestral genes based on synteny across species , i . e . the regions in extant species that lie beyond the edges of the Ancestral chromosome reconstruction . As telomeres have a very high rate of rearrangement , we regard telomeres as locations rather than as any particular genes . Thus the telomere locations of a chromosome were defined as the locations beside the leftmost and rightmost genes on that chromosome that have orthologs in the Ancestral genome . We only analyzed the evolution of telomere locations in species whose genomes are completely sequenced , because for incompletely sequenced species we cannot be sure that there is a telomere at the end of each scaffold . To trace the evolution of centromere and telomere positional evolution in the non-WGD species , which are not direct descendants of the Ancestor ( Figure 1 ) , we mapped the translocational rearrangements between the Ancestor and the non-WGD species L . kluyveri onto the phylogeny by comparing their presence and absence in other extant species in the Saccharomycetaceae and outgroups ( Pichia pastoris [86] and the Candida clade of species [87] ) . The four genes involved in NHEJ that are missing from L . kluyveri were identified by compiling a list of genes in the YGOB database that are present in the Ancestral genome but not in the L . kluyveri genome . We noticed that four genes in the list had a role in NHEJ . We then examined the L . kluyveri intergenic locations where these genes would be expected to reside , to make sure that they were not present but unannotated . No potentially coding ORFs were found in these regions , but pseudogene relics of DNL4 and NEJ1 were identified . Finally , protein sequences from the four genes from the closely related L . thermotolerans were used as TBLASTN queries against the L . kluyveri chromosome sequences to make sure they were not present elsewhere in the genome . | The number of chromosomes in organisms often changes over evolutionary time . To study how the number changes , we compare several related species of yeast that share a common ancestor roughly 150 million years ago and have varying numbers of chromosomes . By inferring ancestral genome structures , we examine the changes in location of centromeres and telomeres , key elements that biologically define chromosomes . Their locations change over time by rearrangements of chromosome segments . By following these rearrangements , we trace an evolutionary path between existing centromeres and telomeres to those in the ancestral genomes , allowing us to identify the specific evolutionary events that caused changes in chromosome number . We show that , in these yeasts , chromosome number has generally decreased over time except for one notable exception: an event in an ancestor of several species where the whole genome was duplicated . Chromosome number reduction occurs by the simultaneous removal of a centromere from a chromosome and fusion of the rest of the chromosome to another that contains a working centromere . This process also results in telomere removal and the movement of genes from the ends of chromosomes to new locations in the middle of chromosomes . |
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Visceral leishmaniasis is a major neglected tropical disease , with an estimated 500 , 000 new cases and more than 50 , 000 deaths attributable to this disease every year . Drug therapy is available but costly and resistance against several drug classes has evolved . Despite all efforts , no commercial , let alone affordable , vaccine is available to date . Thus , the development of cost effective , needle-independent vaccines is a high priority . Here , we have continued efforts to develop live vaccine carriers based on recombinant Salmonella . We used an in silico approach to select novel Leishmania parasite antigens from proteomic data sets , with selection criteria based on protein abundance , conservation across Leishmania species and low homology to host species . Five chosen antigens were differentially expressed on the surface or in the cytosol of Salmonella typhimurium SL3261 . A two-step procedure was developed to select optimal Salmonella vaccine strains for each antigen , based on bacterial fitness and antigen expression levels . We show that vaccine strains of Salmonella expressing the novel Leishmania antigens LinJ08 . 1190 and LinJ23 . 0410 significantly reduced visceralisation of L . major and enhanced systemic resistance against L . donovani in susceptible BALB/c mice . The results show that Salmonella are valid vaccine carriers for inducing resistance against visceral leishmaniasis but that their use may not be suitable for all antigens .
The leishmaniases are regarded as neglected tropical diseases . The causative protozoan parasites are transmitted through the bite of sandfly vectors . Currently an estimated 12 million people are infected , while 350 million people in 88 countries worldwide are at risk to develop one of the diseases associated with Leishmania parasites ( http://www . who . int/leishmaniasis/burden/en/; [1] ) . The most severe form is visceral leishmaniasis ( VL; also known as kala azar in India ) a disease that is fatal if untreated . An estimated 500 000 new cases and 50 000 deaths are reported every year , with 90% occurring in Bangladesh , Nepal , India , Sudan , Ethiopia and Brazil ( [2] ) . VL caused by L . infantum/chagasi is zoonotic with dogs being the main reservoir; however , in areas endemic for L . donovani ( e . g . India and Sudan ) the disease is anthroponotic . In many cases infection remains asymptomatic , most likely indicating immune control . However , patients with symptomatic VL experience fever , fatigue , weight loss and weakness often accompanied by hepato-splenomegaly and anaemia and , if untreated , may die from bacterial co-infections , internal bleeding and anaemia ( reviewed by ( 2] ) . Chemotherapy is available , but due to high toxicity , adverse side effects and emerging parasite resistance , treatment options are limited [3]–[6] . Long treatment regimens and associated costs are additional critical factors preventing patient access and compliance . For example paromomycin , a newly registered drug , is given by intra-muscular injections over a period of 21 days . Though the cheapest drug available , treatment still costs between 5 and 10 US$ per course , making this drug too expensive in relation to household income [5] . This economic burden of treatment is likely to remain for the foreseeable future . Thus , developing a vaccine for VL ( and indeed for other forms of leishmaniasis ) is high on the agenda of the World Health Assembly ( resolution EB118 . R3 , Geneva 05/07 ) . Vaccination is considered possible because of the efficacy of the century-old practice of leishmanization against old world cutaneous leishmaniasis ( CL ) , a treatment that affords life long protection as proven during its large scale use to protect military personnel in Israel , Iran and the former Soviet Union [7]–[9] . However , in some individuals , development of non-healing lesions , exacerbation of chronic disease and immunosuppression as a result of this procedure has been observed [10] . The unsatisfactory safety profile , its questionable efficacy against infection with heterologous species and logistic hurdles render leishmanization problematic . Vaccines that relied on autoclaved or merthiolate-killed whole promastigotes formulated with or without Bacillus Calmette-Guerin as adjuvants were developed to remedy some of the shortcomings of leishmanization but a recent meta-analysis of clinical studies evaluating these vaccines did not support their efficacy [11] . Clinical testing of vaccines based on recombinant Leishmania antigens or fractionated parasite material is much less advanced , although numerous antigenic proteins have been shown to have vaccine potential in pre-clinical models ( see reviews by [12]–[14] ) . These antigens were usually discovered by classical approaches , i . e . by screening with immune or hyperimmune sera from patients or infected animals . Antibody reactivity may not be an ideal criterion since protection is cell mediated and is thought to depend on both CD4+ and CD8+ T lymphocytes [15]–[18] . More recently , however , parasite genome information has become available and vaccine-antigen discovery exploiting this information has been promoted [19] . Recombinant DNA technology enables the formulation of subunit vaccines consisting of one or few specified antigens as DNA- and vectored vaccines , the latter exploiting viruses or bacteria as vaccine vehicles ( summarised by [12] , [20] , [21] ) . Indeed , expressed sequence-tag based vaccine antigen discovery has been explored [22] . However , of 100 ORFs tested only 14 showed detectable protective effects when tested in a high dose infection model of murine CL . This was probably not surprising given that gene expression is regulated mainly post transcriptionally in Leishmania and suggests a need to improve sequence selection criteria . Here , we adapted a reverse vaccinology [23] approach to define novel candidate vaccines , starting from proteomic data sets that were generated recently [24] and ignoring whether or not proteins would be recognized by sera from infected hosts . Moreover , we optimized recombinant attenuated Salmonella as a vaccine carrier platform since they had been explored before as vectors for anti-Leishmania vaccines [25]–[27] and have already been developed for vaccination purposes in humans [28]–[30] .
Female BALB/c mice were purchased from Harlan UK , Charles River UK or bred and maintained under specific pathogen-free conditions in individually ventilated cages in the animal facilities of the School of Biological Sciences at the University of Edinburgh and the University of York . Animals were used at 6–9 weeks of age and were age matched within each experiment . All animal experiments adhered to the UK Animals ( Scientific Procedures ) Act 1986 and were conducted under Project Licenses granted by the UK Home Office and with local ethical approval ( License # PPL 60/03581 to TA and PPL 60/03708 to PK ) . To inducibly express antigens on the surface of Salmonella , the E . coli adhesin involved in diffuse adherence ( AIDA ) autotransporter system was adapted and a variant of plasmid pKRI143 [31] was constructed , pAIDA0 , as previously described [32] . Briefly , the sequence encoding cholera toxin B subunit signal peptide was followed by SpeI/BglII sites for in frame directional cloning of ORF of interest fused with downstream sequences coding for a hemagglutinin epitope ( HA ) -tag and the transporter domain of AIDA , all under the control of the in vivo inducible Mg2+ responsive PpagC promoter [33] . Vaccine antigen ORFs encoding L . donovani KMP-11 ( LinJ35_V3 . 2260 ) , ORF LinJ08 . 1190 ( LinJ08_V3 . 1190 ) , ORF LinJ09 . 1180 ( LinJ09_V3 . 1180 ) , ORF LinJ23 . 0410 ( LinJ23_V3 . 0420 ) , ORF LinJ25 . 1680 ( LinJ25_V3 . 1670 ) and ORF LinJ35 . 0240 ( LinJ35_V3 . 0140 ) were amplified from L . donovani ( MHOM/INI/03BHU-55 ) genomic DNA using primers shown in table 1 . ORF nomenclature and accession numbers are indicated in Table 2 . Amplifications were carried out with the Platinum® Pfx DNA Polymerase kit ( Invitrogen ) . PCR products were digested with SpeI and BglII and cloned into the equally digested pAIDA0 for transformation into SL3261 and E . coli JK321 ( UT5600 zih::Tn10 dsbA::kan ) [34] , respectively . To differentially regulate protein expression levels , point mutations were introduced into the Shine-Dalgarno ribosomal binding sequence ( RBS; underlined ) using site directed mutagenesis . Forward primer for RBS3 ( 5′-GATCAATCTAGATTTAAGAAGCAGATATACATATGATTAAATTAAAATTTGGTG-3′ ) , RBS4 ( 5′-GATCAATCTAGATTTAAGAAGGGAATATACATATGATTAAATTAAAATTTGGTG-3′ ) and RBS5 ( 5′-GATCAATCTAGATTTAAGAAAGAAATATACATATGATTAAATTAAAATTTGGTG-3′ ) were designed to amplify the cholera toxin signal peptide , HA-tag and antigen while simultaneously introducing the mutated Shine-Dalgarno sequence upstream of the signal peptide . The resulting PCR product was SpeI/BglII digested and re-ligated into pAIDA-Antigen . All resulting surface expression plasmids were subsequently named psVAC[# of RBS mutation]-antigen . For expression of antigens in the salmonella cytosol L . donovani ORFs were amplified using primers described in Table 1 . Resulting PCR products flanked by 5′ NdeI and 3′ BamHI sites were digested and first cloned downstream of a PpagC promoter into a pBR322-derived plasmid series already containing mutated Shine-Dalgarno sequences ( RBS1 – AGGAA , RBS2 – GGGAA and RBS3 – AGCAG ) described in [35] for transformation into SL3261 . The resulting plasmids were subsequently named pcVAC[# of RBS mutation]-antigen . Preparation of live vaccine stocks , immunizations and determination of bacterial fitness by in vivo colonisation have been performed exactly as described before [32] . For generating recombinant proteins , Leishmania antigen ORFs were cloned into pET28a ( + ) ( Novagen ) . All antigens were amplified using the NdeI and BamHI site containing primers described above . Recombinant proteins were purified as described previously [32] . KMP-11 , the only soluble protein was directly purified on a Nickel column ( 1 ml , HisTrap FF , GE Healthcare ) . All other antigens formed inclusion bodies which needed to be isolated and dissolved prior purification under denaturing conditions with an on-column refolding step [32] . Recombinant protein containing fractions eluted from columns ( see Fig . S1 ) were pooled and protein concentrations determined using amidoblack [36] ) . Proteins LinJ08 . 1190 , LinJ09 . 1180 , LinJ23 . 0410 and LinJ25 . 1680 became insoluble when imidazole was removed; hence 50 µl/well of a 50 µg/ml protein eluate was used to coat 96-well plates ( MaxiSorb , Nunc ) for ELISA . Plates were sealed and stored at 4°C until needed . For T cell re-stimulation assays imidazole was removed by dialysis against TBS/150 mM NaCl and subsequently concentrated by ultrafiltration using Centricons® ( Millipore ) of appropriate pore size . ELISA for antigen-specific antibodies of different isotypes ( IgG1 and IgG2a ) from mouse serum has been performed as previously described [32] . In brief , serial dilutions of individual sera were analysed . To estimate relative antibody concentrations , titers were determined corresponding to the value of the serum dilution giving a half maximal ELISA signal . L . major promastigotes were grown in semi-defined medium until late stationary phase was reached . Two million parasites were injected into the left hind footpad and lesion size was measured as the difference in thickness between infected and uninfected footpad using a calliper . For determination of parasite numbers in organs mice were sacrificed by cerebral dislocation and organs ( spleen , draining lymph node , footpad ) were removed and homogenized . The single cell suspensions were adjusted to equal volumes and subjected to serial dilutions in 96-well tissue culture plates filled with SDM medium [24] supplemented with 20 µg/ml hygromycin and 50 µg/ml kanamycin , which was carried out in quadruplets . After 14 days at 27°C , parasite growth was scored microscopically and parasite load in the infected organs was calculated using the dilution where at least 2 of 4 wells ( >37 . 5% ) were positive [37] . This dilution was multiplied by the total volume ( in multiples of 0 . 1 ml ) to derive the total number of parasites per organ . Mice were killed by cervical dislocation and livers and spleens were removed and weighed . The body-mass index ( BMI ) was calculated as the organ weight in percentage of body weight . To determine parasitic burden in spleen and liver , impression smears were prepared on microscopic glass slides , fixed in methanol and stained with Giemsa . The number of parasites per 1000 host cell nuclei was counted using a light-field microscope and an immersion oil lens . Leishman-Donovan units ( LDU ) were calculated by multiplication of the number of parasites/1000 nuclei with the organ weight [38] . Liver sections were processed for immunohistochemistry as described in detail elsewhere [39] . Briefly , confocal microscopy was performed on acetone fixed 8 µm frozen sections stained with Alexa 488-conjugated F4/80 ( eBioscience , United Kingdom and purified rabbit anti-mouse inducible nitric oxide synthase ( iNOS ) ( Abcam , United Kingdom ) detected with donkey anti-rabbit Alexa 647 . Sections were counterstained with 4′ , 6′-diamidino-2-phenylindole ( DAPI ) , and mounted in Pro-Long Gold antifade ( Invitrogen ) for examination on a LSM META 510 confocal microscope ( Zeiss ) . Quantification of NOS2 staining was performed on randomly selected fields for each mouse , using Adobe Photoshop CS3 to determine the area of iNOS reactivity ( as number of positively stained pixels ) relative to total granuloma area ( as pixels stained with F4/80 ) . Granuloma maturation was assessed from hematoxylin-eosin ( H&E ) -stained tissue sections as described elsewhere [39] . Statistical analysis was performed using GraphPad Prism Program ( Version 4 . 0 , GraphPad Software , San Diego , California ) . Depending on data passing normality tests , ANOVA was performed with appropriate post-tests for pairwise comparisons or Mann-Whitney tests were computed . P values less than 0 . 05 were considered significant .
For the selection of novel antigen candidates , we conducted a bioinformatic analysis of a proteomic dataset that compared the proteomes of pro- and amastigote stages of L . mexicana [24] . This data set was chosen because to date this is the only dataset containing information on truly intracellular parasites and because a comparison with data from a proteomic analysis of L . donovani axenic amastigotes suggested a very high degree of overlap with respect to abundant proteins [24] . From a total of 509 proteins that reflect the set of highly abundant proteins , we selected five novel antigen candidates based on abundance , conservation throughout the genus and lack of homologies to host proteins ( Figure 1 ) . These criteria and , in addition , predicted subcellular localization were found before to be valuable to identify antigens for induction of protective T cell responses from complex organisms , operationally defined here as expressing ≫103 different protein antigens , e . g . to select antigens for vaccines against Helicobacter pylori [40] . A further , Leishmania-relevant criterion was the expression of the potential antigen in appropriate life cycle stages . Preference was given to proteins expressed in the disease-causing intracellular amastigote stage , but , since early stages of infection after transmission of promastigotes were also considered relevant , antigen expression in both life cycle stages was not an exclusion criterion . Four selected antigens were present in the proteomic datasets of both stages while the LinJ23 . 0410 corresponding protein was present only in the amastigote dataset . Homologues of the encoding genes were found in all cases in L . major , L . infantum , L . donovani and L . braziliensis genomes with a very high degree of conservation ( ranging from 78 . 9% to 95 . 8% identity of amino acid sequence , increasing to 87 . 6% to 99% when including conserved substitutions ) . Sequence homologies to proteins of mouse and human ( human as final target and mouse as a model host ) were excluded by BLAST searches . This approach was biased and preferentially excluded similar sequence-dependent epitopes . It was used here because it was assumed to enhance the likelihood of antigens to be recognized by T cells as “foreign” and to reduce the risk of potential autoimmune sequelae . Novelty and expressability in our salmonella expression systems were additional final selection criterion but we also included the well characterized antigen KMP-11 as a reference vaccine antigen . This antigen has been shown to be protective against L . donovani , when administered as a DNA vaccine [41] , [42] . Subcellular localization and protein amount are not only useful criteria to select T cell vaccine antigens , they are also crucial parameters to consider in the construction of recombinant live vaccine carriers - such as bacteria - to induce antigen-specific cell mediated immunity [43] , [44] . Thus , two expression systems were adapted that directed antigens either to the cytosol or the surface of Salmonella and allow induced expression via the in vivo inducible promoter PpagC . We choose to control antigen production at the translational level and introduced a set of point mutations into a canonical ribosomal binding site ( RBS ) creating a set of four plasmid cassettes each for cytosolic and surface antigen expression . These mutations resulted in staggered protein expression levels when Salmonella strains carrying the respective plasmids were grown under conditions that activate the PpagC promoter ( Figure S2 ) . Heterologous protein expression can greatly reduce fitness of the carrier bacteria in vivo , thereby critically affecting the amount of total antigen delivered to the immune system and thus vaccine immunogenicity . This relationship is schematically shown in Figure 2A ( left panel ) and , as an example , is shown for vaccine strains engineered for cytosolic expression of LinJ23 . 0410 ( Figure 2A , right panel ) . Colonisation of the Peyer's patches seven days after oral administration of 109 CFU was determined as a measure of bacterial fitness . Expression of LinJ23 . 0410 was clearly negatively correlated with the number of CFU found in Peyer's patches , i . e . vaccine strain fitness . Use of a canonical , non-mutated RBS ( RBS0 ) resulted in high amounts of protein but greatly reduced bacterial fitness . Introduction of point mutations ( RBS1 , 2 , 3 ) lowered expression levels from intermediate ( RBS1 ) to very low ( RBS2 and 3 ) which brought fitness back to the level of the empty carrier strain ( Figure 2A right panel ) . A reduction of bacterial fitness far below 104 CFU in this assay , based on past experience ( JS and TA unpublished ) , rendered vaccine strains non-immunogenic with respect to the recombinantly expressed antigen . Thus , out of 48 bacterial strains constructed and evaluated as shown for the example above , 10 strains were selected for further testing . Their respective fitness and antigen expression characteristics were as shown in Figure 2B . Interestingly , antigens LinJ08 . 1190 , LinJ09 . 1180 , LinJ25 . 1680 and LinJ35 . 0240 could not be expressed in the cytosol ( data not shown ) but vaccine strains could be obtained , with the exception of LinJ35 . 0240 , when the antigens were targeted to the bacterial surface . In consequence , only two vaccine strains expressing the antigens KMP-11 and LinJ23 . 0410 cytosolically could be included in the panel ( Figure 2B , right panel ) . In addition , eight surface expression strains were selected ( Figure . 2B , left panel ) . Surface expression of antigen LinJ35 . 0240 could not be detected via western blot despite a clear influence on bacterial fitness ( Figure 2B left panel ) . Based on the latter , it was therefore decided to include psVAC5-35 . 0240 as an example for the respective antigen . All selected strains were next tested in vivo for their ability to protect BALB/c mice against visceralising L . major infection . These mice are highly susceptible to L . major infection , and have been suggested to provide a good mouse model for VL . Mice were vaccinated with a single dose of Salmonella vaccine strains , the carrier control SL3261 or treated with PBS . Mice were subsequently challenged with 2×106 late-stationary phase L . major promastigotes into the left hind footpad . Lesion size was monitored over a course of several weeks after which mice were randomized and selected for analysis of parasitic burden in footpad , lymph node and spleen . A pilot study involving all 10 selected vaccine strains showed that vaccination with Salmonella carrying antigens LinJ08 . 1190 and LinJ23 . 0410 reduced lesion size and parasitic burden compared to the controls ( see Figure S3 ) . Interestingly , vaccination with antigen LinJ25 . 1680 expressing Salmonella exacerbated disease while the other vaccines including the KMP-11-expressing strains had no effect on disease progression compared to controls ( Figure S3 ) . Thus , the presumably protective vaccine strains psVAC5-08 . 1190 , pcVAC1-23 . 0410 and psVAC0-23 . 0410 as well as a mixture of these ( from hereon named ‘vaccine allstars’ ) , were further evaluated ( Figure 3 ) . Vaccination , especially with psVAC5-08 . 1190 and vaccine allstars , significantly delayed the onset and progression of footpad swelling in mice challenged nine weeks later ( Figure 3A ) . Five weeks after infection , five animals per groups were selected randomly and parasitic burden in spleen , popliteal lymph node and footpad was determined . Parasite numbers in footpads and lymph nodes were not significantly different in the vaccine groups ( Figure 3B , C ) although a trend towards lower burdens was notable in mice vaccinated with psVAC0-23 . 0410 , psVAC5-08 . 1190 and vaccine allstars ( Figure 3B , C ) . The discrepancy between lesion size and parasite burden was surprising but is not without precedence . The inverse situation has been described in murine L . major infection when analysing TNR-p55 receptor deficient mice [45] or when mapping susceptibility loci [46] , [47] . However , mechanisms are currently not fully understood . The parasitic burden in the spleen was assessed as a surrogate marker of protection against visceral leishmaniasis . Immunisation with the psVAC5-08 . 1190 and allstars vaccines significantly reduced parasite numbers in the spleen compared to challenged only mice and a similar trend was noted for the surface expressing psVAC0-23 . 0140 vaccine ( Figure 3D ) . Of note , five animals amongst those vaccinated with psVAC5-08 . 1190 and vaccine allstars had no detectable parasites in the spleen ( Figure 3D ) . Hence , a single oral dose of Salmonella vectored vaccines that delivered both LinJ08 . 1190 with LinJ23 . 0410 significantly reduced visceral L . major parasite burdens in these highly susceptible BALB/c mice . Since conservation of the antigens among Leishmania species was a key selection criterion , we hypothesised that antigens which were protective against L . major would also protect against the causative agent of human VL , L . donovani . To test this hypothesis , we immunised BALB/c mice with strains psVAC5-08 . 1190 and vaccine allstars . Leishmania surface antigen KMP-11 had been shown to be protective against L . donovani in mice [42] . Therefore and despite its poor performance in previous experiments , Salmonella strain pcVAC1-KMP , expressing KMP-11 in the cytosol , was included together with the carrier strain SL3261 and sham-immunisation in this study . Mice vaccinated with a single oral dose were challenged intravenously with 3×107 L . donovani amastigotes six weeks later . A characteristic for L . donovani infection in BALB/c mice is hepato-splenomegaly and the organ-specific control of the infection . Half of the mice were sacrificed on day 28 p . i . , when liver parasite burden has usually reached its peak before the onset of self cure and when splenic parasite burden has begun to increase . The remaining animals were analysed at day 68 p . i . to assess long term control , particularly in the spleen . An increased ratio of liver/spleen weight to body weight is an indirect measure of L . donovani infection induced inflammation and disease severity . Thus , body and organ weights were determined at necropsy ( Table S1 ) . The ratio for both liver ( Figure 4A ) and spleen ( Figure 4B ) increased between day 28 and day 68 in non-vaccinated animals and mice treated with either the carrier salmonella alone or the pcVAC1-KMP vaccine . In contrast , in animals vaccinated with psVAC5-08 . 1190 or the allstars vaccine , this ratio either increased less dramatically or not at all ( Figure 4A , B ) . Mice immunized with psVAC5-08 . 1190 or the allstars vaccine had a mean liver parasite burden of 84 . 20±39 . 30 and 69 . 75±20 . 74 LDU , respectively at day 68 p . i . significantly reduced in comparison to the non-immunized group ( 361 . 0±66 . 79 LDU ) , the SL3261 carrier ( 189 . 0±63 . 79 LDU ) or the pcVAC1-KMP treated animals ( 232 . 2±30 . 02 LDU; Figure 4C ) . Of note , the decrease noted after SL3261 treatment in comparison with the naïve controls was also significant ( Figure 4C ) . The effects of the vaccines on splenic parasite burdens followed the same pattern ( Figure 4D ) . Mice immunized with psVAC5-08 . 1190 or the allstars vaccine controlled parasite replication while numbers increased significantly between day 28 and 68 in all other study groups ( Figure 4D ) . Immunisation with pcVAC1-KMP also did not protect mice from L . donovani infection and parasite burden increased over time ( 69 . 80±18 . 67 to 182 . 8±61 . 53 ) , which was similar for SL3261 treated mice ( Figure 4D ) . In summary , a single oral dose of salmonella vectored vaccines delivering LinJ08 . 1190 and/or LinJ23 . 0410 significantly reduced hepato-splenomegaly and visceral infection in mice infected with L . donovani , the causative agent of human VL . To assess immune responses during vaccination and infection , we measured antigen-specific antibody isotype titres as a surrogate of the underlying CD4+ T cell response , given the known correlation between IL-4 and IgG1 responses and between IFNγ and IgG2a [48] . Serum was assessed in vaccinated mice four weeks after immunisation and on day 28 and 68 post infection with L . donovani to test for antigen-specific antibodies . Four weeks after vaccination but before infection , vaccine antigen-specific antibody titers were below the limit of detection ( Figure 5A–F ) . In agreement with the fact that KMP-11-specific antibodies are produced during human VL [49] , infected non-vaccinated mice or SL3261 carrier immunized mice generated anti-KMP-11 antibodies ( Figure 5A , B ) . This anti-KMP-11 response was very similar in the pcVAC1-KMP vaccinated group ( Figure 5C ) . In contrast , vaccines expressing LinJ08 . 1190 and/or LinJ23 . 0410 primed animals for the production of specific antibodies that became detectable after the boosting infection on day 28 and 68 post infection ( Figure 5D–F ) but no antibodies against the respective recombinant proteins were detectable by ELISA ( detection limit of assay was at titers ≤20 ) during infection in naïve , SL3261 or pcVAC1-KMP treated animals ( not shown ) . This indicated that LinJ08 . 1190 and/or LinJ23 . 0410 were not naturally immunogenic during infection of BALB/c mice . Next , the ratios of vaccine antigen-specific IgG1 and IgG2a were calculated for each mouse and time point ( Figure 6 ) to seek evidence for a bias in type 1 vs . type 2 immune response . Over the course of infection significant and different skewing was noted between the treatment groups . Anti-KMP-11 IgG1 to IgG2a ratios were above 1 in pcVAC1-KMP vaccinated mice which was therefore not different from the response to KMP in infected only or SL3261 vaccinated mice . In comparison , anti-vaccine antigen specific IgG1 to IgG2a ratios , however , were significantly different in sera from psVAC5-08 . 1190 or allstars vaccinated mice with values around 1 or below ( Figure 6; p<0 . 05 ) . Finally , to assess the underlying cellular response in a more direct manner , we examined the level of granulomatous inflammation in infected mice that were either unvaccinated or had been vaccinated with control SL3261 Salmonella or with allstars ( Figure 7 ) . At day 28 p . i , there was a small but significant increase in the number of granulomas observed in the liver of allstars vaccinated mice ( Figure 7A ) . We next measured the maturation stage of each granuloma , using established scoring criteria [39] . Granuloma maturation was similar between all groups of mice at day 28 p . i . ( with a small but not significant trend towards enhanced maturation in allstars vaccinated mice ) . By day 68 p . i . , however , mice vaccinated with either SL3261 or allstars showed enhanced granuloma maturation compared to non vaccinated mice . Although the results of this analysis are in keeping with the enhanced ability of these vaccinated mice to reduce parasite burden , it was not a sufficiently sensitive technique to discriminate between the resistance induced by SL3261 and allstars ( c . f . Figure 4D ) . Finally , we measured the area within each granuloma that stained positive for iNOS , as one measure of functional capacity at these inflammatory foci . There were no significant differences in the iNOS response between vaccinated and non-vaccinated mice at either time point by this criterion ( Figure 7D ) . Hence , the main tissue correlate of protection induced by allstars vaccination was an increase in the rapidity of granuloma formation , suggesting that vaccination may have heightened the frequency of CD4+ and/or CD8+ T cells able to facilitate this focal inflammatory response .
We had previously reported on the proteome of the intracellular amastigote stage of L . mexicana [24] which showed extensive overlap with proteins identified in L . donovani axenic amastigotes [50] . Because of this overlap , the former proteomic dataset was exploited here to adapt a reverse vaccinology approach to develop a vaccine against VL . We applied the criteria of protein abundance , within parasite genus conservation , and absence of homologous proteins in host organisms to select novel candidate vaccine antigens aimed to induce cellular immunity . These criteria may not be optimal though to select targets for inducing antibody-dependent immunity . Four of five selected candidates could be expressed in recombinant form and when delivered by recombinant Salmonella two reduced and one exacerbated disease progression in a murine L . major infection model . These results suggest that the frequency of identifying immunologically relevant proteins by this method is high and may well be superior to previous strategies that relied on mRNA expression and genome data for antigen selection [51] , [52] with a hit frequency of ∼15% . Leishmania like other kinetoplastids regulate gene expression mostly post-transcriptionally and mRNA abundance data alone may not be informative to predict protein abundance . However , as in shown in other systems [40] and Leishmania [53] actual protein abundance in amastigotes is highly relevant if the protein is to become a target of the immune response [54] . Analysis of the proteome data sets suggested that bias in codon usage indicates translational bias and therefore is highly correlated with protein abundance [24] . Hence , codon usage may be used to rank ORFs and serve as a substitute parameter for protein abundance in the absence of real protein expression data to refine pure in silico selection of candidate antigens . This becomes particularly relevant for selecting membrane proteins that are severely underrepresented in current proteomic data sets . The two protective antigens , LinJ08 . 1190 and LinJ23 . 0410 , expressed by Salmonella carriers were immunogenic in these vaccines yet , based on antibody responses , were not a target of the immune response to L . donovani infection , at least not in mice . This is noteworthy since many Leishmania vaccine antigens including KMP-11 currently favoured by other groups have been identified using sera from patients [55]–[58] . Our findings with the salmonella vectored KMP-11 vaccine suggest that these immunoselection approaches may introduce an extra hurdle for vaccine development since the natural antigen-specific response may be skewed and , possibly , even be disease exacerbating [59] , [60] . The requirement for an additional type 1 immune response inducing adjuvants , IL-12 , to achieve protective effects with a KMP-11 DNA vaccine in the murine L . major model [61] is in good agreement with this idea . Furthermore , a fusion protein called LEISH-F1 - also known as Leish-110F , Leish-111f or MML – was derived from the sequence of three immunoselected parasite antigens . LEISH-F1 is the most advanced protein-based subunit Leishmania vaccine in trial to date and has shown promising effects when tested in a therapeutic setting against human American CL [62] . However , this is not the case when used to prevent visceral canine disease after high dose experimental infection [63] or to treat naturally acquired VL in dogs [64] . In contrast , Leishmune® , a vaccine based on a glycoproteic fraction of L . donovani that was not immunoselected , is licensed for the prevention of canine VL in Brazil and has shown efficacy in the field [65] . Interestingly , the Leishmune® vaccine antigens are poorly recognized by sera from dogs suffering from VL and vaccination therefore is not interfering with sero-surveillance programs [65] . Thus , reverse vaccinology based approaches as presented here are likely to significantly broaden the choice of protective antigens . A number of subunit vaccine delivery platforms , including purified proteins or mixtures of glycans and glycoproteins , recombinant DNA , viral and bacterial vectors have been evaluated experimentally in murine models of leishmaniases ( review by [12] . However , very few have entered or passed clinical testing and amongst them no vectored vaccine . We have chosen Salmonella as a carrier since these bacteria had already been positively evaluated by several groups in experimental models of leishmaniases [25]–[27] . Moreover , they are being developed as recombinant carriers against a number of pathogens including Helicobacter pylori , Hepatitis B virus and Plasmodium falciparum [66] , [67] . In the context of a major neglected disease such as VL , their main advantages are their excellent safety profile , simple and low-cost production at industrial scale , possibility to store as lyophilized product at room temperature , and oral application route , thus reducing the requirements for extensive infrastructure . In addition , Salmonella are potent inducers of long-lived cell-mediated immunity including CD8+ T cells [28] , [68] . Induction of CD8+ T cells is particularly efficient by vaccines delivered by viral or bacterial carriers and may be a crucial characteristic of anti-Leishmania vaccines , since both CD4+ and CD8+ T cells are required for optimal anti-leishmanial immunity and granuloma formation [15]–[18] . While we do not yet have formal proof that our vaccines induced antigen-specific CD8+ T cells , bioinformatics analysis using CD8 T cell epitope/HLA-binding peptide prediction algorithms suggested epitopes presentable by major HLA alleles e . g . of human populations in VL endemic areas in India [69] . In the context of VL , Salmonella have the additional property to generate viscerotropic immune responses which may explain that the main protective effect was observed at the level of visceralizing infection in the L . major model . Moreover , depending on serovar , S . enterica exhibits broad or narrow host ranges . Serovar Typhimurium that was used here has the potential to deliver vaccine antigens in humans [70] as well as in dogs [71]–[73] while attenuated S . enterica Typhi can be engineered to deliver human vaccines [30] , [66] . In summary , we report the identification of two novel candidate vaccine antigens against VL by reverse vaccinology and the optimized construction of live Salmonella carriers . These VL vaccines could potentially be used to combat VL in the zoonotic , as well as the anthroponotic cycle of the disease . | The leishmaniases are tropical diseases that affect the poorest of the poor . They are caused by Leishmania species , protozoan parasites transmitted by blood sucking insects and the visceral form of the disease is fatal . Vaccines that would tremendously boost disease control strategies need to be designed cost-efficiently and for the existing infrastructure . Salmonella-based live vaccines could fulfil these requirements as they can be cheaply produced on an industrial scale and the lyophilized product can be stored at room temperature and upon rehydration is ready for oral , needle-free application . Salmonella , like Leishmania , are intracellular pathogens that primarily target host macrophages . The bacteria induce a viscerotropic immune response . Herein lies a potentially significant advantage of using attenuated Salmonella as delivery vehicles for parasite antigens for vaccination against visceral leishmaniasis . We used in vivo inducible promoters and optimized expression systems to construct attenuated Salmonella carriers that deliver novel vaccine antigens and show a host protective effect in small rodent models of visceral leishmaniasis . These proof-of-concept studies should serve to further promote exploration of live Salmonella as a cost effective and widely applicable carrier for vaccination against leishmaniases . |
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Protein-protein interaction networks ( PINs ) are rich sources of information that enable the network properties of biological systems to be understood . A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance ( HOT ) networks that are similar to the router-level topology of the Internet . This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes . Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks . Degree distributions of essential genes , synthetic lethal genes , synthetic sick genes , and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes . Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects .
There is a growing awareness that networks of protein interactions and gene regulations are the keys to understanding diseases and finding accurate drug targets [1] . With the increasing availability of genome-wide data including those on protein interactions and gene expressions , numbers of studies have been done on the structure and statistics of protein interactions and how diseased genes and drug targets are distributed over the network [2] , [3] . Understanding the topological and statistical properties of interaction networks and their relationships with lethal genes as well as currently identified drug targets should provide us with insights into robust and fragile properties of networks and possible drug targets for the future . We studied budding-yeast and human protein-protein interaction networks ( PINs ) to identify the architectural properties of network structures . PINs have often been argued to be “scale-free” [4] , [5] , which mostly means they have power-law frequency-degree distributions . However , this definition diverges from the original meaning of being scale-free in terms of the self-similarity of geometric properties of subject systems and there have been reports that claim such distributions are “more normal than normal”; thus , they are not considered to be particularly exotic by themselves [6] . In addition , there are different network topologies with different robustness and performance properties that maintain power-law distributions [7] . Therefore , it is very important to identify the architectural features of the network bearing the specific utilization of analysis results in mind . Our goal in this study was to identify the network topology of PINs and their relationship with lethal genes and possible drug targets so that the statistical likelihood of novel drug targets could be inferred . A particularly interesting issue in the field of systems engineering , physics , and systems biology is the trade-off between the properties of robustness , fragility , and efficiency . Highly optimized tolerance ( HOT ) theory is a conceptual framework that can be used to explain this issue . Although a system conforming to HOT theory is optimized for specific perturbations and has highly efficient properties , such a system is extremely fragile against unexpected perturbations [8] , [9] . Doyle et al . [8] demonstrated that the Abline Internet2 router-level topology network conformed to HOT theory . Nodes in the Abline network with extremely high-degree nodes connect to a large number of low-degree nodes , while links between these high-degree nodes are suppressed and thus they do not form a core backbone for the whole network . A network having similar structures to the Abline network is defined as a HOTnet [8] . It would be very interesting to clarify whether PINs are HOTnets or not . The two questions addressed in this paper are: ( 1 ) what is the global architecture of PINs ? Do they follow the possible architectural features of scale-free networks created by preferential attachments or conform to HOT theory , and ( 2 ) are there specific statistical features for proteins that are likely to be drug targets ? To answer these questions , budding yeast and human PINs were used to analyze their structural properties using a series of analysis methods .
Scale-free Network vs . Highly Optimized Tolerance Network: A series of analyses was carried out using budding yeast and human PIN data to identify the topological features of PINs . In this study , we defined low-degree nodes as nodes with degrees of less than 5 because Han et al . [10] and Partil and Nakamura [11] defined hubs as nodes with degrees of more than 6 . We then developed a method called moving stratification by degrees ( MSD ) to extract sub-networks consisting of hubs with specific degree distributions where indices such as average cluster coefficients would be computed ( see Materials and Methods for details ) . The analyses revealed that the average cluster coefficient was very high for sub-networks consisting of hubs with degrees from 6 to 38 , while it was very low for hubs with degrees of more than 39 in the yeast PIN ( see Figure S1 and Table S1 ) . Notably , for hubs with degrees of less than 38 , the difference in cluster coefficients was generally significant between the yeast PIN and random network , while there were no significant differences in cluster coefficients for hubs with degrees of more than 39 ( see Figure S1 ) . Therefore , we defined middle-degree nodes as those with degrees from 6 to 38 and those with degrees of more than 39 as high . In the same manner , we defined middle- ( from 6 to 30 ) and high-degree ( more than 31 ) nodes in the human PIN ( see Figure S2 and Table S2 ) . Note that , when we used more stringent thresholds for middle- ( from 10 to 50 ) and high-degree ( more than 51 ) nodes , the results did not change essentially , i . e . , the average cluster coefficient for middle-degree nodes was much higher than that for high-degree nodes ( see Tables S3 and S4 ) . The analyses revealed three findings: ( 1 ) the network structure for middle-degree nodes ( from 6 to 38 for yeast and from 6 to 30 for human PINs ) , and high-degree nodes ( more than 39 for yeast and more than 31 for human PINs ) has different structures , ( 2 ) middle-degree nodes are tightly connected and form a structure often called a “stratus” , and ( 3 ) high-degree nodes do not connect , but connect with low-degree nodes , and form an “altocumulus” structure ( Figures 1 and 2 ) . Notably , we used more stringent thresholds for middle- ( degrees from 10 to 50 ) and high-degree nodes ( degrees more than 51 ) , and found that changing the thresholds did not essentially affect the results ( see Figure S3 and S4 ) . These results suggests that PINs have an architecture where highly interconnected middle-degree nodes form a core backbone for the whole network and large numbers of low-degree nodes connect to high-degree nodes ( see Figure 2 ) . This architecture is a type of network that is suggested as a HOTnet , i . e . , a network with HOT properties , also seen in the Internet router-level topology [8] . To further confirm this observation , we calculated a graph-theoretic quantity , s ( g ) , that defines the likelihood high-degree nodes will be connected to one another ( see Materials and Methods for details ) . S ( g ) , a value normalized against smax , indicates that networks with tightly interconnected high-degree nodes tend to be closer to 1 . 0 , whereas networks with only sparsely interconnected high-degree nodes tend to be closer to 0 . 0 ( see Materials and Methods for details ) . Doyle et al . reported randomly generated preferential-attachment-type scale-free networks had relatively high values such as 0 . 61 , whereas a HOTnet exemplified by a network abstracted from an actual Abilene Internet2 router topology network had a value as low as 0 . 34 [8] . We found that the value of S ( g ) for the yeast PIN was 0 . 25 and that of the human PIN was 0 . 38 . Thus , we could conclude that PINs are HOTnets . PINs are networks with a modular structure [12]–[14] . Here , modularity is defined as characteristics where there are fewer links between nodes with similar degrees . This only means there are limited links between high-degree nodes ( hubs ) , whereas there are links between hubs and low-degree nodes . This is a feature that was also confirmed in this study ( see Figure 2 ) . Modularity in PINs implies that networks have two features [13]: First , functional units may be composed of many low-degree nodes that are directly connected to a hub node . Second , confusion between modules is avoided by avoiding direct connection between hubs . While there are arguments against this claim that hubs are tightly connected because they need to influence one another to achieve an integrated function for the whole system [15] , analysis results indicate that such integration is most likely to take place via middle-degree nodes instead of high-degree nodes ( see Figure 2 ) . The distribution of essential genes , synthetic genes , and other genes are shown in Figure 3 . It is interesting to note that both essential genes and synthetic lethal genes have similar distributions . The average degree of essential proteins is 4 . 95 and that of synthetic lethal proteins is 4 . 40 . However , the Wilcoxon rank sum test demonstrated that there is no statistical significance between them ( P = 0 . 334 ) . In either case , essential and synthetic lethal proteins are concentrated on middle-degree nodes and high-degree nodes . However , the average degree among synthetic sick genes is 4 . 07 and this is significantly lower than that among synthetic lethal genes ( P = 0 . 0015 ) . This means genes that have less severe impact are distributed toward regions with a lower-degree distribution . Scale-richness: The power law distribution often characterized for scale-free networks only means that local frequency-degree distributions are independent of location along the degree axis , rather than self-similarity of network structures . However , Tanaka demonstrated that bacterial metabolic networks are scale rich in the sense there are different categories of metabolites and enzymes depending on the degree of nodes [16] . A group of nodes with high degree tends to be composed of currency molecules such as ATP and a group of nodes with low degree mostly consists of enzymes involved in specific cellular functions . In this study , we investigated if the frequency-degree distribution of proteins for each functional category exhibited the scale-rich characteristics reported by Tanaka . Figures 4 and S5 correspond to frequency-degree plots for proteins in different functional categories in the yeast PIN and the human PIN . The functional categories were assigned based on the GO slim ontology . As shown in the figures , the degree distribution patterns differ among functional categories . Moreover , proteins with different GO slim annotations have different average degrees ( See Tables S5 and S6 ) . Note that many functional categories have significantly higher ( or lower ) average degrees than the whole PINs ( See Tables S5 and S6 ) . These results suggest that the yeast and human PINs are scale-rich . Drug Targets: Drug-target molecules are distributed over low- to middle-level degree nodes with higher probability on middle-degree nodes . Consistent with reports already published , the average degree among drug-target nodes ( 4 . 74 ) is higher than the average degree among all nodes ( 4 . 06 ) . The distribution of known drug targets is shown in Figure 5 and this is predominantly distributed to middle-degree nodes and mostly on backbone of the network . There are almost no drug targets for high-degree nodes . The distribution of drug targets for cancer and non-cancerous diseases are in sharp contrast . While the average degree of target nodes for cancer drugs was 7 . 82 , the targets for non-cancerous diseases scored only 4 . 24 ( P = 0 . 01 ) . Moreover , we found that the proportion of drug targets among low-degree proteins were similar to random expectation . Figure 6 shows distribution of drug targets marked on degree-rank plot . The drug target molecule that has highest degree is Src with 41 which is the target for drugs such as Dasatinib . Target molecules for anti-cancer drugs are shifted toward high degree nodes compare against average and non-anti-cancer drugs .
A series of analyses revealed that both the budding yeast and human PINs are scale-rich and have HOT networks . There are extensive interconnections among middle-degree nodes that form the backbone of the network ( see Figure 2 ) . Most drug-target genes concentrate on middle-degree nodes and parts of low-degree nodes , but not on high-degree nodes . Interestingly , Feldman et al . ( 2008 ) [17] reported that genes harboring inherited disease mutations also concentrated on middle-degree nodes . Because of the potential lethality observed in budding yeast ( Figure 3A ) and reported high lethality in mouse knockout [2] , high-degree nodes are unlikely to be preferred drug targets or genes with disease mutations . Since oncogenes tend to be high-degree nodes , they are less likely to be drug targets , or one has to accept major potential side effects . The fact that the degree distribution of cancer-drug targets is higher than that of non-cancer-drug targets is consistent with the report by Yao and Rzhetsky [18] . Since high-degree nodes are predominantly connected with low-degree nodes ( Figures 1 , 2 , S3 , and S4 ) , the elimination of high-degree nodes is likely to affect large numbers of low-degree nodes . This may result in unacceptable side effects since a group of genes that bear certain functions may be made collectively dysfunctional . Detailed case studies are warranted to test and verify this possible interpretation . However , the average degree distribution of synthetic sick genes ( 4 . 07 ) is less than that of essential genes ( 4 . 95 ) and synthetic lethal genes ( 4 . 40 ) . This implies that a drug design strategy to generate synergetic effects by targeting less important targets can be a reasonable option because each compound in such drugs can select targets that have less impact on the overall system alone . We found that middle-level degree nodes are the optimal targets for therapeutic drugs . A similar observation was reported by Yao and Rzhetsky [18] , although they measured the mean degree among drug targets . In this study , we investigated the degree distribution of drug targets in greater detail , because we measured a fraction of drug targets to all nodes with degree k as well as mapping drug targets on the network structure . It was clearly identified most of drug targets for drugs that are currently on the market are concentrated on middle degree nodes that are back bone of the network and low-degree nodes that tends to have specific function specific effects . One of novel findings here is that the distribution of drug targets for low-degree nodes is similar to random expectation , indicating that there are a certain number of low-degree drug targets . From these results , we can expect that the most advantageous targets for combinatorial drugs could be among low-degree nodes because these could have less severe impact on the overall system of the human body . This is consistent with the idea of “long-tail drugs”[19] . Are there any relationships between structures in molecular networks ( i . e . , scale-richness in PINs ) and the properties of their underlying genome ? Rzhetsky and Gomez [20] proposed a stochastic model describing the evolutionary growth of molecular networks . Their model predicts that , in a molecular network , the shape of the degree distribution will be similar to the shape of the distribution of domains in the genome . Actually , they showed that , in the case of the entire yeast PIN , both the degree distribution and the distribution of the domain followed a power law . Therefore , it might be interesting to see whether , for each functional category , the shape of the degree distribution was similar to that of the domain distribution , when the entire architecture of domains in genomes becomes available . In this study , we assumed that the PINs represented all functions of genes . However , the PINs are just composed of binary protein-protein binding and proteins have other types of functions , such as catalyzing reactions with non-protein substrates . Therefore , PINs reflect a subset of the entire cellular function . This indicates that , if the complete picture for cellular protein functions could be considered , our conclusions from the PINs may diverge from what we presented here . Moreover , at present , the yeast and human PINs represent incomplete pictures of the actual entire PINs of these organisms . When data on all the actual entire PINs become available , we intend to examine all the actual entire PINs to see whether similar observations to those in this study can be made or not . It is interesting to note that both PINs and the Internet topology are HOTnets . Many of the observed properties in Internet router topology may be applied to PINs as well . Such properties include robustness against node failure and optimized performance [21] . It has been reported that analysis using several possible router topologies found that a HOTnet configuration was most efficient , providing more maximum overall bandwidth to users than that with other network-configuration approaches such as random and preferential attachment [21] . The implication is that biological PINs have evolved to become efficient and error tolerant . The series of analyses presented in this report indicate that there are changes whereby we can rationally design drugs by taking into account network properties , and additional insights from engineering and physics may further extend our opportunities for exploring network-based biology . | Genome-wide data on interactions between proteins are now available , and networks of protein interactions are the keys to understanding diseases and finding accurate drug targets . This study revealed that the architectural properties of the backbones of protein interaction networks ( PINs ) were similar to those of the Internet router-level topology by using statistical analyses of genome-wide budding yeast and human PINs . This type of network is known as a highly optimized tolerance ( HOT ) network that is robust against failures in its components and that ensures high levels of communication . Moreover , we also found that a large number of the most successful drug-target proteins are on the backbone of the human PIN . We made a list of proteins on the backbone of the human PIN , which may help drug companies to search more efficiently for new drug targets . |
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The occurrence of outbreaks of human rabies transmitted by Desmodus rotundus in Brazil in 2004 and 2005 reinforced the need for further research into this zoonosis . Studies of knowledge and practices related to the disease will help to define strategies for the avoidance of new cases , through the identification of gaps that may affect the preventive practices . A semi-structured questionnaire was applied to 681 residents of twelve communities of northeastern Pará state involved in the 2004 and 2005 outbreaks mentioned above . The objective was to evaluate the local knowledge and practices related to the disease . We found a highly significant difference ( p<0 . 0001 ) in the knowledge of rabies among education levels , indicating that education is a primary determinant of knowledge on this disease . More than half of the respondents ( 63% ) recognized the seriousness of the zoonosis , and 50% were aware of the importance of bats for its transmission , although few individuals ( 11% ) were familiar with the symptoms , and only 40% knew methods of prevention . Even so , 70% of pet owners maintained their animals vaccinated , and 52% of the respondents bitten by bats had received post-exposure vaccination . Most of the respondents ( 57% ) reported being familiarized with rabies through informal discussions , and only a few ( 23% ) mentioned public health agents as the source of their information . We identified many gaps in the knowledge and practices of the respondents regarding rabies . This may be the result of the reduced participation of public health agents in the transfer of details about the disease . The lack of knowledge may be a direct determinant in the occurrence of new outbreaks . Given these findings , there is a clear need for specific educational initiatives involving the local population and the public health entities , with the primary aim of contributing to the prevention of rabies .
Rabies is an acute form of viral encephalomyelitis , which is almost invariably fatal , and affects mammals on all continents except Antarctica [1] . Transmission occurs through the inoculation of the virus , typically through bites , scratches or contact between skin lesions and the saliva of an infected animal [2–4] . The etiological agent is a member of the order Mononegavirales , family Rhabdoviridae , and the genus Lyssavirus [5] . This genus has a number of different variants that may be hosted by one or more species , acting as regional reservoirs . The classic rabies virus ( RABV ) is considered to be the most important form of the genus , and it is responsible for more than 55 thousand cases of human rabies worldwide every year , mostly in Asia and Africa [6 , 7] . In Latin America , dogs have always been considered the principal reservoirs of RABV , although vaccination campaigns for domestic animals have resulted in a 90% reduction in the number of cases of rabies transmission by these animals since the 1980s [8] . In 2004 , however , the participation of the hematophagous bat , Desmodus rotundus ( E . Geoffroy 1810 ) in the transmission of rabies on this continent began to attract increasing attention [9] . This shift in the epidemiological profile of the disease was especially relevant in the Brazilian Amazon basin , due to outbreaks of human rabies caused by this bat species in 2004 and 2005 [10–12] , which represented a major public health crisis in the rural zone . In fact , during this period , 38 cases were recorded in the northern Brazilian state of Pará , and 24 in the neighboring state of Maranhão [12] . Together , these two states cover 1 , 579 , 891 . 27 km2 of the Brazilian Legal Amazon region [13] , an area smaller only than that of Argentina ( 2 , 780 , 092 km2 ) in comparison with the other 12 countries that make up South America . The establishment of a wild rabies cycle is probably due to the gradual disequilibrium of the natural dynamic of the relationship between the pathogenic agent and its wild host [14] , which is likely to have been a response to the increasingly negative environmental impacts affecting this region of Brazil . The outbreaks occurred primarily in northeastern Pará [12] , although after 2005 , there were no new cases in humans , and the number of cases in animals declined considerably . Even so , the recent serological study of Costa et al . [15] reported the presence of rabies-neutralizing antibodies in 24 of the 28 bat species currently known to occur on the coast of Pará , indicating that the virus may still be circulating in the region . These authors also found that the most abundant species , Uroderma bilobatum Peters , 1866; Dermanura cinerea ( Gervais , 1856 ) ; Carollia perspicillata ( Linnaeus , 1758 ) and Artibeus planirostris Spix , 1823 , had a seroprevalence of over 40% . While D . rotundus was relatively rare , 43% ( 3/7 ) of the specimens collected were seropositive . The two other hematophagous bat species , Diaemus youngi ( Jentik , 1893 ) and Diphylla ecaudata Spix , 1823 , have not yet been recorded in the region [15] , although they are not directly involved in the transmission of rabies , given their preference for the blood of birds [16] . Currently , D . rotundus has been reported attacking domestic stock in this region , which means that it can still be considered to be a risk zone . In this context , we evaluated the knowledge and practices related to rabies among the residents of this risk zone , comparing the levels of knowledge in communities where cases of human rabies transmitted by D . rotundus had been recorded on the coast of Pará with those of communities where no cases of human rabies had been recorded . In addition to the community , this investigation considered the age , sex and education level of the residents interviewed , recording their perception with regard to the principal means of transmission of the disease and the practices that help prevent it .
The Institutional Review Board ( IRB ) at federal Chico Mendes Institute for the Conservation of Biodiversity ( ICMBio ) authorized this study through license number 39818–1 , obtained on June 20 2013 . Before administering questionnaires , all the respondents were informed verbally of its aims and objectives , and that their responses would be treated in absolute anonymity . We interviewed only participants who verbally agreed . Oral consent was obtained to ensure anonymity and accommodate illiterate participants , and was documented by the interviewer via voice recording . The ICMBio IRB includes the use of oral consent for the collection of interview data without collecting biological samples from humans in the case of responses that will be kept anonymous , so written consent was not necessary . The present study focused on protected areas in three municipalities in northeastern Pará , Brazil– ( i ) the Araí-Peroba Marine Extractivist Reserve in Augusto Corrêa ( 46°38’06” W , 01°01’18” S ) , ( ii ) the Caeté-Taperuçu Marine Extractivist Reserve in Bragança ( 46°45’56” W , 01°03’13” S ) , and ( iii ) the Gurupi-Piriá Marine Extractivist Reserve in Viseu ( 46°08’15” W , 01°12’15” S ) . These three municipalities together cover an area of approximately 8098 . 544 km2 , and have a total population of around 210 , 345 inhabitants [13] . The region is relatively flat , with altitudes of no more than 29 m , and is characterized by a mixture of habitats , with a predominance of Amazon forest , mangroves , and marshlands . The local economy is based on cattle ranching , farming , crabbing , and fisheries . We applied questionnaires in twelve communities of these municipalities ( Fig 1 ) . In six of these communities—Araí , Piçarrera , Cachoeira and Porto do Campo in Augusto Corrêa , and Firmiana and Curupaiti in Viseu—cases of human rabies transmitted by D . rotundus had been recorded during the outbreaks . In the other six communities ( Vila Soares and Bacanga in Augusto Corrêa , Benjamin Constant and Treme in Bragança , and Açaiteua and Serra do Piriá in Viseu ) , no cases of human rabies had been recorded . In all these communities , the settlements are essentially rural , with a variety of living conditions , including houses made of wattle and daub , timber , and brick , located among forest patches ( Fig 2 ) . In some cases , the corral in which the livestock is held is located within a short distance of the landowner’s house , which , together with the proximity of forested areas , contributes to an increased risk of contact with the hematophagous bat , D . rotundus ( Fig 2E ) . During 2013 , we applied the questionnaires ( S1 Appendix ) in households selected randomly , with residents being selected according to their availability at the moment of the visit . Data were obtained using a paper-based survey . We interviewed a total of 681 residents ( approximately 10% of the 12 communities surveyed ) , of which , 445 were from RR communities , that is , communities in which cases of human rabies transmitted by D . rotundus have been recorded , while the other 236 respondents were from NR communities ( no human rabies cases recorded ) . To begin with , data were collected on the sex , age , and education level of each respondent . The semi-structured questionnaire was then applied in order to document the perception of the respondents with regard to rabies . Questions were asked on animal ownership , the vaccination of these animals against rabies , possible attacks on these animals and humans by D . rotundus and , when positive , if post-exposure prophylactic vaccination was sought , as well as details on the severity of the disease , its symptoms , transmission , and methods of prevention , as well as the source of this information ( S1 Appendix ) . The knowledge of the respondents was evaluated using an approach adapted from Kaliyaperumal [17] , and classified as Insufficient , Basic , Intermediate or Advanced . The response to each question related to the knowledge of the respondents on rabies was scored 0–3 , depending on its completeness and accuracy ( S1 Appendix ) . At the end of the questionnaire , the points were summed . The maximum score is 14 , with scores of between 10 and 14 being classified as Advanced knowledge , those between 6 and 9 as Intermediate , 3–5 as Basic , and 0–2 as Insufficient . Following the application of the questionnaires , the respondents received basic information on different aspects of the rabies zoonosis , such as the means of transmission and prevention . The Chi-square test ( χ2 ) was used to evaluate possible differences in the knowledge of the respondents on rabies according to their ( i ) sex ( ii ) age class , and ( iii ) education level . The same test was used to evaluate differences in the knowledge of the residents of communities with ( RR ) and without ( NR ) recorded cases of human rabies . The level of knowledge on rabies of the respondents was also evaluated in relation to ( i ) the severity or lethal nature of the disease , and ( ii ) its prevention , through the vaccination of animals , and the application of the vaccine following attacks by D . rotundus . A logistic regression ( Logit ) was used to evaluate the probability that the residents of the RR and NR communities ( i ) are familiar with different methods of prevention , ( ii ) vaccinate their animals regularly ( in the case of owners of pets or domestic stock ) , and ( iii ) seek post-exposure vaccination following attacks by D . rotundus . The odds ratios were calculated with a confidence interval ( CI ) of 95% . A p = 0 . 05 significance level was considered for all the statistical analyses , which were run in BioEstat 5 . 0 [18] .
The majority of the respondents were female ( n = 436 , 64% ) , adult ( n = 555 , 81% ) , and had no more than a primary school education ( n = 435 , 63% ) . No significant difference ( p = 0 . 21 ) was found between communities ( RR vs . NR ) in relation to the knowledge of the residents with regard to rabies ( Table 1 ) . Perceptions were also investigated in more detail relation to the sex , age class , and education level of these respondents . No significant difference ( p = 0 . 32 ) was found between male and female respondents , given that approximately 80% of both sexes in both types of community had either basic or insufficient knowledge ( Table 2 ) . Similarly , no significant difference ( p = 0 . 06 ) was found among age classes , once again , with more than 80% of respondents having basic or insufficient knowledge , although a higher percentage of elderly residents had insufficient knowledge ( Table 3 ) . By contrast , highly significant differences ( p<0 . 0001 ) were found among the four levels of education ( Table 4 ) . Most ( around 80% ) of the illiterate respondents and those with a primary school education had only basic or insufficient knowledge on rabies , while approximately 70% of those with a high school education had basic or intermediate knowledge . The majority of the 17 respondents with a college education had basic-level knowledge , although a relatively high percentage had advanced knowledge ( Table 4 ) . Most of the respondents were aware that rabies is a grave and potentially lethal zoonosis , in both the RR ( n = 276 , 62 . 02% ) and NR ( n = 156 , 66 . 10% ) communities , with no significant difference between localities ( p = 0 . 72 ) . However , when questioned on the symptoms of the disease , the residents of the NR communities had no specific knowledge . The residents of the RR communities were barely more knowledgeable , although 18% were aware of some symptoms . Aggressiveness and intense salivation were the symptoms most frequently cited , for both humans and non-humans ( Table 5 ) . The residents of the two types of community were familiar with the principal rabies transmission routes and thus , the potential vectors . The bites of a number of different animals were mentioned specifically , and bats were mentioned most often in the two areas , being cited by 51% of the residents in the RR communities and 47% in the NR communities ( Table 6 ) . While most residents were unfamiliar with measures for the prevention of this zoonosis ( 52% in the RR communities and 61% in the NR communities ) , vaccination was the most cited in both types of community ( 24% in RR and 23% in NR ) , while washing the bite with soap and water was mentioned by only one resident from an RR community ( Table 6 ) . A majority of the respondents ( n = 475 , 70% ) have pets or domestic stock , with slightly higher ownership being recorded in the NR communities ( n = 171 , 72% ) in comparison with the RR communities ( n = 304 , 64% ) . Of the 475 animal owners , 79% ( n = 239 ) in the RR communities , and 74% ( n = 127 ) in the NR communities have their animals vaccinated regularly against rabies , with no significant difference in vaccination rates between the two types of community ( p = 0 . 74 ) . This represents an important preventive measure , considering that 8% of RR residents and 5% of NR residents confirmed that their animals are being attacked by D . rotundus . Despite these attacks , none of the respondents confirmed being bitten recently ( at the time of the survey ) by these hematophagous bats . However , 35% ( n = 154 ) of RR residents and 41% ( n = 96 ) of NR residents reported having being bitten at some time in their lives . When attacked by D . rotundus , approximately 60% ( n = 87 ) of the interviewees from RR communities , and 40% ( n = 41 ) from NR communities confirmed having sought post-exposition prophylactic vaccination ( PEP ) , with no significant difference in this response between the two types of community ( p = 0 . 19 ) . The logistic regression ( Logit ) provided an estimate of the probability that residents of the two types of community ( RR and NR ) were familiar with prevention measures for rabies transmitted by D . rotundus ( Table 7 ) . This showed that the chance that an RR resident was familiar with a given prevention measure was only slightly higher ( odds ratio = 1 . 09 ) than that of an NR resident . In fact , the probability that an RR resident was familiar with prevention measures was only 20% , in comparison with 18% in NR residents , with no significant difference being found between the types of community ( p = 0 . 71 ) . A similar tendency was found with regard to animal vaccination rates , with 79% of RR residents vaccinating their animals , in comparison with 74% of NR residents , with no significant difference between types of community ( p = 0 . 27 ) . However , RR residents were significantly more likely ( p = 0 . 02 ) to seek prophylactic care following attacks by D . rotundus , with 57% against 42% of NR residents ( odds ratio = 1 . 77 ) . The residents of both types of community identified informal conversations as the principal source of their knowledge on rabies and preventive measure ( RR = 60%; NR = 53% ) . In both cases , less than a quarter of the respondents confirmed receiving information on rabies from public health agents . Importantly , 38% of RR residents and 34% of NR residents had no specific knowledge on this zoonosis ( Table 8 ) .
The present study is the first of its kind to be conducted in the Brazilian Amazon basin , with the aim of evaluating the perceptions and practices of the residents of areas of risk for the transmission of human rabies transmitted by the hematophagous bat Desmodus rotundus . The basic knowledge gaps identified among the residents of the study communities were significant and have far-reaching implications for the prevention of this zoonosis , and may contribute to an increase in the risk of new cases or outbreaks . No significant difference was found in the perceptions of the residents of the two types of community ( RR and NR ) , that is , in which cases of human rabies transmitted by D . rotundus had or had not been recorded , respectively . This similarity between communities is almost certainly a result of the fact that they are separated by relatively short distances , of no more than 50 km , and in some cases , only 5 km ( Fig 1 ) . In addition , residents of NR communities obtained information on rabies through informal conversations with neighbors from RR communities , given the existence of family and economic ties in many cases . In this case , it is important to note that the proximity of the study communities may represent a methodological limitation of the present study , and it is possible that the perceptions of the residents of NR communities located further away from RR communities may be far less similar . The knowledge of the respondents was analyzed according to sex , age class , and education , and our study showed that males and females had similar levels of knowledge , in contrast with the results of studies in Bhutan [19] and Ethiopia [20–22] , where the male residents were more knowledgeable than females . In the region of the present study , there are major cultural differences , with females playing a more active role in daily economic activities , in comparison with the regions studied in Asia ( Bhutan ) and Africa ( Ethiopia ) , where these activities are dominated by males , with a clear influence on the distribution of knowledge [20 , 21] . The lack of any clear difference among age classes found in the present study was similar to the situation found in these previous studies [20 , 21] , indicating that the age of the individual does not have a significant influence on their knowledge of rabies . Despite this , the present study did identify a tendency for the elderly informants to have more insufficient knowledge . This may be related to the reduced educational opportunities available to this generation , given the limited educational infrastructure of the study region . In fact , education appeared to be the principal factor determining levels of knowledge on rabies , as shown in the previous studies in Asia and Africa [19–22] . These previous studies also found that knowledge of rabies was directly related to education levels . One possible explanation for this is that individuals with a better education have more access to information , resulting in a better understanding of the features of this zoonosis [21] . It is important to note that one of the limitations of the present study is the differences in the numbers of respondents in the different categories , i . e . , sex , age , and education levels ( see Table 1 ) . This is related to the fact that the participants of the study were selected according to their availability at the moment of the visit . Given this , it was not always possible to obtain an optimal number of respondents from each category . However , it seems likely that the data set was consistent with reality of the study area . With regard to the perception of the residents of the two types of community with regard to the seriousness and potential lethality of rabies , more than 60% of the respondents were aware of this aspect of the disease , as in the study of Moran et al . [23] . Despite this , in our study , few of the respondents were able to describe specific symptoms . In fact , only RR resident were familiar with specific symptoms , primarily because they would have had the opportunity to observe the symptoms in relatives o neighbors . Symptoms such as aggressiveness and intense salivation were reported most frequently , in both humans and animals ( pets or domestic stock ) , and in fact , these symptoms are typical of the neurological phase of the disease , while the other symptoms mentioned are observed during either this phase or the prodromal phase [4] . With regard to the transmission of this zoonosis , most respondents referred to bat bites . This is almost certainly due to the fact that these individuals live close to locations at which cases of human rabies transmitted by D . rotundus had been recorded . It is important to note that this species of bat is considered to be the principal reservoir of RABV in Latin America [24–26] . Given this , understanding the potential risks of direct contact with these animals can be considered to be an essential preventive measure for this zoonosis , especially in high risk areas or where this bat is known to attack humans and animals . While most of the respondents identified bat bites as an important source of the transmission of rabies , around 36% are unaware of the causes of the disease , and perhaps more importantly , more than half were not familiar with any specific prevention measures . Only one of the respondents referred to washing the bite with soap and water as a preventive measure . This measure was also mentioned by few of the residents ( 8% of the respondents ) of high-risk areas in a study in Guatemala , Central America [23] . In fact , this is the primary treatment recommended following an attack by a potential rabies vector , which may reduce by one fifth the risk of developing the disease [27] , which reinforces the importance of immediate treatment of the site of the bite , as a preventive measure . Other post-exposure prophylactic ( PEP ) measures include ( i ) disinfection of the wound with alcohol or iodine , in order to inactivate the viral envelope; ( ii ) application of rabies vaccine on days 0 , 3 , 7 , 14 and 28; and ( iii ) infiltration of anti-rabies serum with the aim of blocking the proliferation and progression of the virus at the site where it was inoculated [4] . Individuals exposed to a potential risk of rabies should obtain pre-exposure ( PrPEP ) prevention , which involves the application of three doses of the vaccine at days 0 , 7 and 8 [4] . Since the 1980s , the World Health Organization ( WHO ) has recommended that countries substitute the vaccines produced in animal nerve tissue by those produced in cell cultures . In practice , there are currently only two options of rabies vaccine produced from cell culture—the PCECV ( Purified Chick-Embryo Cell Vaccine ) and the PVRV or PVCV ( Purified Vero Cell Rabies Vaccine ) , a lineage established from the kidney cells of the green monkey , Cercopithecus aethiops ( Linnaeus , 1758 ) [4] . There have been no recent reports of attacks by hematophagous bats on humans in the study communities . However , approximately 37% of the respondents reported having been attacked at some time during their lives . Some individuals reported that the number of attacks decreased after their community was connected to the national grid of electric power , and that leaving the lights on in the house is an effective measure to keep the bats away , as is keeping the house shut during the night , which stops the animals entering the household . Moran et al . [23] also refer to the sealing of doors and windows as a way of reducing the risk of exposure to the bat , as well as the use of mosquito nets . In fact , these measures may be effective in reducing the number of attacks and as a consequence , the number of cases of human rabies , given that the outbreaks of rabies transmitted by D . rotundus were recorded in areas with no electricity supply , which were dominated by substandard housing at the time . Schneider et al . [12] and Gilbert et al . [28] concluded that housing conditions are among the principal risk factors for the infection of humans by RABV . They also argued that poor quality housing is typical of many rural areas in Latin America , which have suffered outbreaks of human rabies transmitted by D . rotundus . Finally , the authors concluded that substandard housing may facilitate the access of hematophagous bats to human prey . Just over half the individuals attacked by bats reports receiving post-exposure vaccination , although an additional limitation of the present study was the lack of proof of vaccination ( e . g . , vaccination cards ) to confirm adequate preventive treatment . Even so , the results of the present study indicate that contact with cases of rabies in humans and animals was important to increase consciousness of the need for post-exposure vaccination , as well as the vaccination of animals . This has also been supported by the intensive animal vaccination campaigns sponsored by the Pará state government , which included the free vaccination of dogs and cats , in some cases , conducted door-to-door by community public health agents . These campaigns have resulted in the elimination of rabies cases in domestic animals in the study area . These vaccination campaigns have contributed to a major reduction—approximately 90%–in the number of cases of rabies in domestic animals and humans in Latin America since the 1980s [8] . Vaccination campaigns have also been established for farm animals , and while not distributed freely , they represent an important government incentive aimed at guaranteeing the vaccination of domestic stocks . Fernandes et al . [29] showed that an increase in the production of beef resulted in an increase in the number of rabies cases in the Brazilian Amazon basin . This emphasizes the importance of maintaining cattle stocks vaccinated , given that beef production tends to be directly proportional to the number of rabies cases . Over the past few decades , the growing number of cases of bovine rabies in many Latin American countries has caused major impacts on both public health and local farming practices [30 , 31 , 14 , 11] . The results of the present study indicate that the majority of the residents of the study area ( both types of community ) have either Basic or Insufficient knowledge , as in the study Moran et al . [23] . In our study , the respondents were poorly informed with regard to measures that can prevent rabies , and that informal conversations were the primary source of their knowledge . While these conversations may have been based on formal sources of information , the informal transfer of this information among residents may have been subject to distortions and alterations . While community health agents have a primary role in the transfer of information , only 26% of respondents reported receiving information from this source , and even them , only during outbreaks . This emphasizes the need for complementary training with regard to the importance of the transfer of reliable information to local populations . Overall , then , the implementation of these and other measures designed to guarantee and refine the knowledge of local residents with regard to the potential risks of contracting rabies and means of prevention , may be fundamental to the avoidance of new outbreaks in humans and animals . These objectives may be achieved through the development of educational initiatives , primarily through the relevant public health authorities , and should be directed at both men and women of all ages and education levels . These recommendations are directly relevant to the reality of the Brazilian Amazon basin , although they may provide a practical model for other regions of the world where there is a high risk of lethal outbreaks of human rabies . | In 2004 and 2005 , the occurrence of outbreaks of human rabies caused by the hematophagous bat , Desmodus rotundus in the Brazilian Amazon , highlighted the role of this bat in the transmission of the disease and the importance of further research on this zoonosis in this region . In the present study , we investigated the local knowledge and practices related to rabies , in some areas affected by the outbreaks , with the aim of identifying gaps , which may affect the preventive practices . Our results show that education influences the level of knowledge , and many residents are aware of the seriousness of the disease and the role of bats in its transmission , although less than half of the respondents knew how to prevent transmission . We also discovered that public health agents were not effective in the transfer of information on rabies , which may be an important determinant of the low levels of knowledge about it . These findings indicated a clear need to increase public consciousness with regard to the potential risk of rabies and the means of avoiding the disease , through educational initiatives directed at the local population , which should involve the public health authorities responsible for the control and prevention of the disease . |
You are an expert at summarizing long articles. Proceed to summarize the following text:
When mitochondrial respiration or ubiquinone production is inhibited in Caenorhabditis elegans , behavioral rates are slowed and lifespan is extended . Here , we show that these perturbations increase the expression of cell-protective and metabolic genes and the abundance of mitochondrial DNA . This response is similar to the response triggered by inhibiting respiration in yeast and mammalian cells , termed the “retrograde response” . As in yeast , genes switched on in C . elegans mitochondrial mutants extend lifespan , suggesting an underlying evolutionary conservation of mechanism . Inhibition of fstr-1 , a potential signaling gene that is up-regulated in clk-1 ( ubiquinone-defective ) mutants , and its close homolog fstr-2 prevents the expression of many retrograde-response genes and accelerates clk-1 behavioral and aging rates . Thus , clk-1 mutants live in “slow motion” because of a fstr-1/2–dependent pathway that responds to ubiquinone . Loss of fstr-1/2 does not suppress the phenotypes of all long-lived mitochondrial mutants . Thus , although different mitochondrial perturbations activate similar transcriptional and physiological responses , they do so in different ways .
Mitochondria generate most of the cell's energy as well as its reactive oxygen species ( ROS ) , and mitochondrial dysfunction can cause disease and accelerate aging . Paradoxically , mitochondrial dysfunction can also increase longevity . Yeast petite mutants , which lack mitochondrial DNA and do not carry out respiration , have an increased replicative lifespan [1] . In C . elegans , two types of mutations that affect mitochondrial function also increase lifespan . The first type reduces respiration substantially . One such mutant , isp-1 ( qm150 ) , was identified in an EMS screen for mutants with delayed development and defecation rates . These animals harbor a mutation in an iron-sulfur protein in complex III of the electron transport chain and have reduced rates of oxygen consumption [2] . In addition , two independent RNA interference ( RNAi ) longevity screens revealed that knock-down of genes encoding components of the respiratory chain or ATP synthase decreased ATP production and rates of respiration , reduced behavioral rates and increased lifespan [3] , [4] . Respiratory-chain RNAi-treated animals are smaller than isp-1 mutants [3] , [5] , implying either a more severe reduction in respiration or , conceivably , a qualitatively different response . Interestingly , a mutation that reduces the level of the respiratory-chain component cytochrome c oxidase extends the lifespan of mice [6] , suggesting that the underlying mechanism may be conserved in higher animals . The second type of mitochondrial mutant is exemplified by clk-1 mutants , which are also long lived and have reduced behavioral rates [7] . clk-1 mutants lack a mitochondrial hydroxylase necessary for synthesis of ubiquinone , a prenylated benzoquinone required for shuttling electrons from complexes I and II to complex III during respiration [8] . Oxidative phosphorylation measurements in isolated mitochondria have shown that clk-1 mutations reduce electron transport between complex I and III , but not between complex II and III [9] . In yeast , the clk-1 homologue COQ7 is necessary for respiration , and coq7 mutants are unable to grow on non-fermentable carbon sources . In contrast , C . elegans clk-1 mutants are not only viable , but they have nearly normal levels of respiration and ATP [10] , [11] . clk-1 mutants compensate for the lack of endogenous ubiquinone , Q9 ( the subscript refers to the number of isoprene units ) with bacterial Q8 , provided in their diet [12] , [13] . In the absence of clk-1 , the animals accumulate the Q9 precursor demethoxyubiquinone ( DMQ9 ) . There is some debate over what role DMQ9 plays in the clk-1 phenotypes , but the data suggest that they are caused by the absence of Q9 [13] , [14] , [15] . Mice with reduced levels of Mclk-1 are also long lived [16]; though curiously , these mice do not have reduced Q9 levels [17] . How do these mitochondrial mutations extend lifespan ? Because respiration is the major source of ROS , which could potentially accelerate aging , a simple explanation for the increased longevity of animals with reduced respiration is that they generate less ROS as the animal ages . However , timed RNAi experiments indicate that respiratory-chain activity must be reduced during development for lifespan extension . Adult-only RNAi treatments reduce ATP levels and slow behavioral rates but do not extend lifespan [3] , [18] . If reducing mitochondrial respiration extended lifespan by reducing the level of ROS produced during the aging process itself , then one might expect reducing respiration at any time would extend lifespan . In addition , lifespan extension does not correlate with resistance to the oxidative stressor paraquat [4] or levels of protein carbonylation [18] . Likewise , little is known about the mechanism by which clk-1 mutations , which have relatively small effects on respiration , extend lifespan . Overexpression of clk-1 shortens lifespan and increases movement rates in C . elegans [11] suggesting that whatever the mechanism , its influence on longevity and rates of living is rate limiting in the animal . In previous studies , mutations that suppress the slowed defecation phenotype ( but not other defects ) of clk-1 mutants have been found [19] . One of these suppressor genes , dsc-1 , has been cloned and found to a encode homeodomain protein [20]; however , in general their mode of action is not well understood . Several interesting studies have associated a clk-1 germ-line phenotype with altered ROS signaling , and showed that sod-1 mutations can partially suppress clk-1 mutant's developmental delays [21] , [22] . This is suggestive that clk-1 mutations may alter ROS signaling in cells . How might mutations affecting respiration and ubiquinone biosynthesis slow behaviors and extend the lifespan of C . elegans ? One possibility is that these perturbations trigger a transcriptional response that alters the animal's physiology and lifespan . In yeast , loss of mitochondrial DNA is known to induce a robust transcriptional response . This change in gene expression has been called the “retrograde response” , because it implies a reversal in the normal direction of information flow between the mitochondria and nucleus [1] , [23] , [24] . The genes expressed during the yeast retrograde response lead to a metabolic remodeling of the cell , heat-shock resistance , and increased mitochondrial biogenesis [25] , [26] , [27] , [28] . The retrograde response has been shown to be required for the increased longevity of these so-called yeast “petites” . Thus , lifespan extension in these yeast cells is actively regulated , and not simply a passive consequence of decreased respiration . A gene expression profile similar to the yeast retrograde response has been observed in cultured mammalian cells when mitochondrial DNA is depleted using ethidium bromide , suggesting that this transcriptional response has been conserved evolutionarily [28] . The retrograde response may be a compensatory reaction to the normal decline in mitochondrial function seen with age , since it is observed in older cells [1] . Whether it could potentially play a role in longevity determination in multicellular organisms is not known . Consistent with this possibility , C . elegans isp-1 mutants have increased levels of expression of at least one protective gene , the superoxide dismutase sod-3 [2] . In this study , we carried out microarray analysis of C . elegans mitochondrial mutants to test the hypothesis that a transcriptional response to mitochondrial perturbation slows the animal's rates of behavior and aging .
clk-1 mutants are enigmatic because they exhibit a respiration-defective behavioral and longevity phenotype without having major changes in respiration [16] . For gene expression profiling , we grew synchronized populations of clk-1 ( qm30 ) mutants and wild-type ( N2 ) animals and collected them as pre-fertile adults . One thousand seven genes ( listed in Supplementary Table 5 ) were found to be differentially expressed and were ranked using the SAM ( Significance Analysis of Microarrays ) tool [29] using a False Discovery Rate ( FDR ) of ∼0 . 1 as cut-off . Interestingly , the majority of the genes in this group ( 99% ) were up-regulated relative to wild type , as was also the case in yeast petites [25] , [26] . The genes were assigned to Gene-Ontology ( GO ) categories using the software BiNGO [30] , and several GO categories were found to be overrepresented ( Figure 1 ) . The nature of these categories suggested that clk-1 mutants undergo significant metabolic reorganization and , in addition , activate a stress response similar to that elicited by xenobiotics . For example , GO categories 6006 , 6007 , and 6096 include genes involved in glycolysis; GO categories 16835 and 44275 include genes potentially involved in glycolysis , gluconeogenesis or anaplerotic pathways; GO category 32787 contains genes involved in anaplerotic reactions ( which generate Krebs cycle intermediates ) ; GO categories 9072 , 9074 and 30170 encompass genes involved in amino acid metabolism; GO categories 6825 and 5375 include genes involved in Cu transport; GO category 6629 includes genes involved in lipid metabolism; GO category 46040 includes genes involved in nucleotide metabolism; and GO categories 4499 and 16758 include genes involved in xenobiotic response and maintenance of cellular redox state . When we looked at individual genes , we observed up-regulation of genes encoding enzymes required for glycolysis , such as GPD-2 , GPD-3 , ( gliceraldehyde 3-phosphate dehydrogenase ) , T05D4 . 1 ( aldolase A homologue ) , and LDH-1 ( lactate dehydrogenase ) . GEI-7 , which is an enzyme necessary for the glyoxylate cycle in worms ( see Discussion ) , was also up-regulated . We also observed increased expression of an isocitrate dehydrogenase , C30G12 . 2 , likely involved in the Krebs cycle , and other alcohol dehydrogenases ( dhs-29 , dhs-3 ) that could potentially act in anaplerotic pathways . We found increased expression of proteins involved in oxidative phosphorylation , such as asg-2 ( subunit of ATP synthase complex ) and F17A9 . 4 ( NADH oxidoreductase ) . Also , several genes coding for enzymes involved in amino acid and nucleotide metabolism were up-regulated in clk-1 mutants . There was also a significant increase in enzymes involved in cellular detoxification , including UDP-glycosyl transferases ( UGT-53 , UGT-13 , UGT-43 , UGT-6 , UGT-39 ) , gluthathione S-transferases ( GST-4 , GST-13 , GST-36 ) , superoxide dismutase ( SOD-3 ) , flavin-containing monooxygenases ( FMO-1 , FMO-3 ) and other gene classes potentially involved in xenobiotic metabolism ( cytochrome P450s , alcohol dehydrogenases and ABC transporters ) . To compare the transcriptional profile of clk-1 mutants to the yeast retrograde-response genes , we referred to two previous publications studying the effects of inhibiting mitochondrial respiration in yeast petites [25] , [26] . We applied BiNGO software to the most differentially expressed genes reported in those studies and compared the yeast-petite GO categories to those of clk-1 mutants . Among the top ranking GO categories ( p<0 . 1 ) , we observed a remarkable degree of similarity ( p<0 . 001 ) between the clk-1 and yeast petite BiNGO categories ( Figure 1 ) . One of the hallmarks of the yeast response to respiration inhibition is an increase in mitochondrial biogenesis [31] . To test whether there might be an increase in mitochondrial biogenesis in clk-1 mutants , we used Real-Time qPCR to quantify mitochondrial DNA . Total mtDNA was measured relative to genomic DNA , which provided the average number of mitochondrial genomes per cell in a population of worms ( see Methods ) . We observed a significant increase in mitochondrial DNA levels ( Figure 2 ) . Together , these data suggest that clk-1 mutants exhibit a response to mitochondrial dysfunction that is similar to the yeast retrograde response . To identify genes necessary for the increased longevity of clk-1 ( − ) animals , we compiled a list of differentially expressed genes from populations of L4 ( late larval stage ) as well as prefertile adults ( see Methods ) . We included L4 animals in this set because previous work has shown that the L4 period is the critical period for lifespan determination in at least some mitochondrial mutants [18] ( and data not shown ) . We picked the 75 top differentially expressed genes ranked using the SAM software package , inhibited their functions using RNAi and measured lifespan . Out of the initial list of 75 , we collected lifespan data for 63 genes over two independent trials ( Table S1 ) . We established a significance cut-off of p<0 . 05 and selected RNAi clones that decreased clk-1 longevity significantly in both trials or were statistically significant in one experiment and showed a decrease of at least 5% in the other ( a 5% decrease in overall lifespan corresponds to an ∼25% decrease in the lifespan extension produced by clk-1 mutation ) . We retested the positive clones in clk-1 ( − ) and wild-type animals for effects on longevity ( Table S2 ) . Out of 63 RNAi clones tested , only two decreased clk-1 mutant longevity in all three trials ( Figure 3A and 3B ) . Neither of these clones significantly shortened wild-type lifespan , suggesting that they may play a role specifically in clk-1 mutant lifespan ( Figure S1 ) . One of these clones corresponded to aqp-1 , which encodes a glycerol channel [32] . aqp-1 RNAi decreased the lifespan extension that would normally be produced by clk-1 mutations from 26% to 7% ( p<0 . 05 ) and from 17% to 0% ( p<0 . 0001 ) , and did not affect wild-type longevity in two separate experiments ( Figure S1 ) . Interestingly , aqp-1 ( also called dod-4 ) has already been shown to contribute to the long lifespan of daf-2 insulin/IGF-1-receptor mutants [33] . The other RNAi clone , corresponding to a gene we call fstr-1 ( for “faster” , also known as gfi-1 ) decreased the lifespan extension produced by clk-1 mutations from 26% to 3% ( p<0 . 01 ) and from 17% to 0% ( p<0 . 0001 ) while not affecting wild-type lifespan in two separate experiments . The effects of aqp-1 RNAi and fstr-1 RNAi on the longevity of clk-1 mutants were tested three times , with consistent results , although the extent of suppression varied between experiments ( Tables S1 and S2 , Figure S1 ) . We examined the genome for the possibility that fstr-1 RNAi might cross-inhibit another gene , and found that the RNAi clone was likely to knock down a close homolog ( with 96% protein sequence identity ) located next to fstr-1 that did not exhibit clk-1-dependent regulation in our microarray analysis . We call this gene fstr-2 , and henceforth we refer to their combined functions , as inferred from RNAi , as fstr-1/2 function . In addition to increased longevity , the most striking phenotypes of C . elegans mitochondrial mutants are their decreased behavioral rates . In principle , these rates could decrease as a direct consequence of impaired mitochondrial function . Alternatively , it is possible that their slowed behavioral rates reflect a regulated response to mitochondrial perturbation; simply speaking , they slow down to conserve energy . To look for genes that slow the behaviors of clk-1 mutants , we inhibited the top 100 up-regulated genes from microarrays of L4 and pre-fertile adults using RNAi and measured time it took for L1 larvae to develop to adulthood . We found that knockdown of fstr-1/2 in clk-1 mutants consistently increased the rate of growth to adulthood ( Figure 4A ) . This phenotype was most striking when the animals were examined 75–80 hours after hatching . At this time , no control clk-1 ( − ) mutants had reached adulthood , whereas 95–100% of the fstr-1/2 RNAi treated animals were adults . fstr-1/2 RNAi treatment also increased the behavioral rates of clk-1 ( − ) animals , as measured by thrashing and pumping ( Fig 4B and 4C ) . These effects were not observed in wild type; in fact , in wild type , knock-down of these genes had the opposite effect , slowing development and decreasing rates of thrashing and pumping . To test whether fstr-1/2 RNAi somehow restored wild-type clk-1 function , we examined ubiquinone profiles . Using HPLC , we observed the expected decrease in UQ9 and increase in DMQ9 in clk-1 mutants , and we observed the same mutant pattern of ubiquinone species in clk-1 mutants subjected to fstr-1/2 RNAi ( Figure 4D ) . Thus , fstr-1/2 RNAi suppresses the phenotypes of animals that still have an altered , Clk-1 ( − ) , pattern of ubiquinone species . This suggests that the wild-type fstr-1/2 gene slows behavior and extends lifespan in response to the changes in ubiquinone produced by clk-1 mutations . Next we asked whether FSTR-1/2 modulates the clk-1 mutant phenotype by influencing the retrograde response . Using real-time qPCR , we looked at the effects of fstr-1/2 RNAi on the expression levels of five of the genes whose expression was most significantly up-regulated in the microarrays: gpd-2 , a glyceraldehyde 3-phosphate dehydrogenase involved in glycolysis; T22B7 . 7 , an Acyl-CoA thioesterase , involved in anaplerotic reactions; dhs-26 , an alcohol dehydrogenase; ugt-43 , an UDP-glucoronosyl transferase; and the aquaporin gene aqp-1 . The qPCR data confirmed the microarray studies , in that all of these genes were up-regulated in clk-1 mutants . We found that fstr-1/2 RNAi significantly and consistently decreased expression of these genes in a clk-1 ( − ) background ( Figure 5 ) but not in wild type ( Figure S2 ) . Thus , the gene expression changes observed in clk-1 mutants are at least partially dependent on fstr-1/2 . Together these findings suggest that in clk-1 mutants , fstr-1/2 ( + ) decreases rates of behavior and extends lifespan by triggering downstream changes in gene expression . Because fstr-1 is up-regulated in clk-1 mutants , we were particularly interested to learn where in the animal it was expressed . To investigate this , we generated transgenic animals expressing the fluorescent protein mCherry under the control of the fstr-1 promoter . We observed strong expression in three neurons located in the head and throughout the intestine , particularly in the anterior intestinal cells ( Figure 6 ) . We identified the three neurons as RIH and I1L/R . RIH is a nerve-ring interneuron of unknown function and I1L/R are pharyngeal interneurons that regulate pharyngeal pumping rates in response to touch and removal of bacteria . We saw the same pattern of expression in the clk-1 ( − ) mutant and wild-type , but the intensity of expression was increased in the mutant , consistent with our qRT-PCR and microarray data . Together these findings suggest that fstr-1 acts in the intestine and/or in specific neurons to slow the rates of aging and behavior in clk-1 mutants . To compare the pattern of gene expression in clk-1 mutants to that of respiration mutants that have more strongly reduced levels of oxygen consumption and ATP , we performed microarray analysis of isp-1 ( qm150 ) and cyc-1 ( RNAi ) animals . ( cyc-1 encodes cytochrome c reductase , which is a component of complex III of the electron transport chain . ) We grew synchronized populations of isp-1 ( qm150 ) mutants and cyc-1 RNAi-treated animals in parallel with wild-type control animals , collected them as young , pre-fertile adults and analyzed the resulting microarray data as described above for clk-1 mutants . The SAM algorithm with a false discovery rate of ∼0 . 1 yielded 814 significant genes for isp-1 ( − ) mutants and 7662 significant genes for cyc-1 ( RNAi ) animals . ( For the complete list , please see Tables S6 and S7 . ) Thus , it seems that cyc-1 RNAi induces a broader transcriptional response than do clk-1 and isp-1 mutations , which correlates with the increased severity of the Cyc-1 ( RNAi ) phenotype . In addition , cyc-1 RNAi-treated animals , when compared to isp-1 and clk-1 mutants , showed increased expression of additional cell-protective genes , including chaperones ( hsp-6 , hsp-70 ) , superoxide dismutases ( sod-4 , sod-3 ) and xenobiotic detoxification enzymes ( ugt-2 , ugt-47 , ugt-36 , gst-8 , gst-22 , gst-24 , dhs-5 , dhs-28 ) . Interestingly , other genes encoding detoxification enzymes were down regulated , possibly implying the deployment of a specific detoxification program . Using BiNGO analysis , we identified several GO categories that were overrepresented in each mutant ( Figure 1 ) . A highly significant fraction of the top GO categories ( p<0 . 1 ) was shared between either two , or all three , of the mutant strains ( p<0 . 001; Figure 1 ) . By chance , one would expect 2 of the 10 GO categories shared by all three mitochondrial mutants and annotated in yeast to also be significant in the yeast petite cells . In contrast , we observed 8 categories in common ( p<0 . 001 ) . In addition to identifying GO categories , we looked for individual genes that were expressed in a similar way in the three C . elegans mitochondrial mutants . We found a highly significant ( p = 7 . 19E-15 ) overlap set of 73 differentially-expressed genes ( Table S3 ) . In addition , we observed an increase in mitochondrial DNA levels in isp-1 mutants and a smaller increase in cyc-1 ( RNAi ) animals that did not reach statistical significance ( p = 0 . 354 ) ( Figure 2 ) . Taken together , these data suggest that the gene expression profiles of different C . elegans mitochondrial mutants are similar to one another and to the yeast retrograde response . Since isp-1 ( − ) , clk-1 ( − ) and cyc-1 ( RNAi ) animals are all long-lived , gene expression patterns that are shared between all three might be particularly likely to contribute to lifespan extension . Double RNAi experiments in C . elegans can be difficult to interpret , so we did not attempt RNAi knockdowns in cyc-1 ( RNAi ) animals . However , we did attempt to knock down the top thirty statistically-significant shared genes , individually , in an isp-1 background . ( Of these , 21 were not present in the clk-1 set we described above , which contained only the top 75 differentially expressed genes ) . We obtained data for all of these genes ( Table S4 ) . Of these , only one RNAi clone , cdr-2 , consistently made our significance cut-off [p<0 . 05 and a 10% decrease in isp-1 mutant longevity , which corresponds to a 50% decrease in the lifespan extension produced by the isp-1 mutation . ] cdr-2 RNAi reduced the lifespan extension produced by the isp-1 mutation from 43% ( control RNAi ) to 26% ( p<0 . 001 ) , while not affecting wild-type longevity ( Figure 3C ) . cdr-2 encodes a member of the glutathione S-transferase family . These enzymes catalyze the conjugation of reduced glutathione to electrophilic centers on different substrates . This activity contributes to detoxification of both endogenous toxins and xenobiotics , suggesting that the increased longevity of isp-1 ( − ) mutants is at least partially dependent on a cellular detoxification response . We note that in one of our three trials , cdr-2 RNAi significantly shortened the lifespan extension produced by clk-1 mutation ( from 21% to 14% ) . This finding suggests that cdr-2 may be involved more generally for lifespan extension in mitochondrial mutants . Given the remarkable reversal of the clk-1 mutant phenotype by fstr-1/2 RNAi , we were interested in examining its function in the respiratory-chain mutants . We found that fstr-1 was significantly up-regulated in isp-1 mutants but , unexpectedly , not in cyc-1 ( RNAi ) animals . Using RNAi , we asked whether fstr-1/2 might influence the behavioral phenotypes of isp-1 ( qm150 ) mutants . We restricted our analysis to lifespan and time to adulthood because isp-1 mutants did not move often enough to provide consistent behavioral rates . We found that the developmental rates of isp-1 mutants were severely decreased in the presence of fstr-1/2 RNAi , leading to developmental arrest of many animals . We found that fstr-1/2 RNAi had no effect on the lifespan of isp-1 mutants that reached adulthood ( Figure 3D ) . Thus the effect of fstr-1/2 RNAi on isp-1 mutants was more similar to the effect of fstr-1/2 RNAi on wild type than to its effect on clk-1 mutants . We wanted to know whether fstr-1/2 was necessary for gene expression changes in an isp-1 ( − ) background , but because isp-1 mutants subjected to fstr-1/2 RNAi grew very slowly and asynchronously , we could not use qRT-PCR . Instead , we assayed gene expression in vivo by introducing the isp-1 mutation into a strain expressing GFP under the control of the gpd-2 promoter , which drives expression of a glycolysis gene that is up-regulated by these mitochondrial mutations ( Figure 7 ) . We found that fstr-1/2 RNAi prevented the up-regulation of this reporter in a clk-1 background but not in an isp-1 background . Together , these data suggest that the Isp-1 ( − ) and Clk-1 ( − ) behavioral and longevity phenotypes are established by distinct mechanisms .
Gene expression profiling of these mutants was quite revealing , because each of their expression profiles exhibited striking similarity to the yeast retrograde response . The yeast retrograde response , which also lengthens lifespan , appears to remodel the cell's metabolism . Without respiration , the Krebs cycle cannot be completed , as succinate cannot be oxidized to fumarate ( Figure 8 ) . This prevents the formation of oxaloacetate ( OAA ) , which in turn decreases the availability alpha-ketoglutarate , which is the precursor of glutamate , an essential metabolite in amino acid metabolism . In order to generate precursors of glutamate , respiration-deficient cells must activate alternative ( anaplerotic ) pathways that supply the mitochondria with OAA and acetyl-CoA [27] . Activation of anaplerotic pathways was observed in respiration-defective yeast [25] , [26] and human cells [28] . Our microarrays reveal transcriptional activation of genes encoding several metabolic enzymes that have roles in anaplerotic reactions , such as the glyoxylate cycle and fatty acid oxidation . The glyoxylate cycle occurs mainly in the peroxisomes and bypasses the succinate-to-fumarate step of the Krebs cycle through the formation of glyoxylate , eventually leading to the formation of succinate , which can be fed back into the Krebs cycle ( Figure 8 ) . We observed increased expression of the gene encoding the major C . elegans glyoxylate-cycle enzyme , GEI-7 , in the three mitochondrial mutants we examined . We did not observe an RNAi phenotype for this clone in the clk-1 mutant; however , because a gei-7 mutant was available , we examined cyc-1 ( RNAi ) ; gei-7 animals and found a large suppression of the cyc-1 ( RNAi ) longevity phenotype , decreasing lifespan extension from 80% to 15% with little effect on wild type ( Figure S4 ) . Malate dehydrogrenases catalyze synthesis of OAA from malate , which is also an important step in recycling Krebs cycle intermediates . F46E10 . 10 encodes a malate dehydrogenase and is significantly up-regulated in all three long-lived mitochondrial mutants we examined . Fatty-acid oxidation provides acetyl-CoA , which feeds into the Krebs cycle by reacting with OAA to form citrate . This pathway is activated in long-lived yeast lacking mitochondrial DNA [27] . We also detected increased expression of several genes that are involved in fatty acid oxidation . clk-1 ( − ) animals exhibited increased expression of acs-2 ( acetyl-CoA synthetase ) and fat-6 ( fatty acid desaturase ) ; and isp-1 mutants exhibited increased expression of T02G5 . 4 ( acetyl-CoA thiolase ) and T05G5 . 6 ( enoyl-CoA hydratase ) . The expression profiles of cyc-1 ( RNAi ) animals , however , contained fewer significant genes involved in fatty acid oxidation , suggesting there may be some differences in metabolic adjustments between different mitochondrial mutants . Interestingly , recent work has shown that long-lived mice with decreased levels of Mclk-1 have an increase in α-ketoglutarate dehydrogenase activity , consistent with an up-regulation of the Krebs cycle [17] . Furthermore , the animals have a decrease in overall NAD levels , which in itself could hamper normal Krebs cycle activity and further decrease glutamate synthesis [26] . In all three long-lived mitochondrial mutants , we observed a significant increase in expression of genes involved in glycolysis . This was expected , since glycolysis becomes a more important source of ATP when oxidative phosphorylation is inhibited . In addition to these metabolic shifts , we also observed increased expression of a significant number of stress response genes in all three mitochondrial mutants , ranging from genes increasing xenobiotic drug resistance to protein chaperones . This is consistent with previous in vitro observations that impairment of electron flow during oxidative phosphorylation is actually likely to generate more ROS [34] , and suggests these animals may be responding to this additional cellular insult . Finally , in all three strains , genes involved in the oxidative phosphorylation process itself were up-regulated , and we observed increased levels of mitochondrial DNA in clk-1 and isp-1 mutants . Thus , apparently C . elegans mitochondrial mutants , like yeast petites [26] , attempt to compensate for reduced levels of respiration . Together these findings indicate that the transcriptional response triggered by conditions that inhibit respiration in C . elegans is similar to that triggered in yeast , and suggest the presence of a conserved underlying mechanism for lifespan extension . While this manuscript was in preparation , Falk et al . reported the gene expression pattern of a mixture of long-lived and short-lived respiration-defective mutants compared to wild type [35] . In the future , it will be interesting to learn whether the expression patterns of short-lived mitochondrial mutants differ from those of the long-lived mutants . The lifespan of one such short-lived mutant , mev-1 ( kn1 ) was increased when respiration was lowered further using respiratory-chain RNAi ( Figure S3 ) , arguing that their short lifespans are not due simply to insufficient respiratory-chain activity . It was interesting to find that clk-1 mutations trigger a conserved transcriptional response even though they only have a mild effect on respiration . There are at least two possible interpretations for this finding . The first is that the transcriptional response to clk-1 mutation need not be triggered by reduced respiration itself , but instead can be triggered by signals that are generally associated with reduced respiration , such as fluctuations in ubiquinone levels . Such fluctuations could have acquired the ability to induce the retrograde response during evolution because they allowed the animal to conserve energy in the face of a perceived energy shortage . Alternatively , perhaps the clk-1 mutation does inhibit respiration more severely initially , but the physiological changes elicited by the retrograde response restore the steady-state level of respiration closer to normal . In yeast , the retrograde response is induced via helix-loop-helix transcription factors that do not appear to be present in C . elegans . When the genes encoding these transcription factors are deleted in yeast , inhibiting respiration does not induce the retrograde response , and lifespan is not extended [1] . Thus , in yeast , the retrograde response likely increases lifespan . Our data suggest that this is the case in this multicellular animal as well . First , inhibiting the activity of at least some of the genes up-regulated in mitochondrial mutants was sufficient to shorten their lifespan without obviously affecting the lifespan of wild type . In particular , the glutathione S-transferase gene cdr-2 was up-regulated in all of the long lived mutants and it contributed to lifespan extension consistently in isp-1 respiration-defective mutants and , at least in some trials , in clk-1 mutants as well . This finding suggests that the prominent cell-protective gene expression response that we observe contributes to longevity . In addition , the metabolic shifts we observed are also likely to influence lifespan , as the longevity of cyc-1 ( RNAi ) animals required the glyoxylate-cycle gene gei-7 , and the longevity of clk-1 mutants was promoted by the glycerol channel aqp-1 . Our failure to observe effects on lifespan with most of the RNAi clones we tested does not necessarily mean that they do not influence lifespan ( though this may be the case ) . It seems possible that many of these genes could act cumulatively to influence lifespan . The lifespan extension of clk-1 and isp-1 mutants was only ∼20% in most experiments , so only perturbations that are fairly strong would be visible in our assays . In general , although we have no reason to discount the importance of genes whose knockdowns produced statistically significant effects on lifespan , because the magnitude of the effects were small , we remain cautious in our interpretation . The second argument for the importance of the C . elegans transcriptional response in the longevity of mitochondrial mutants comes from our studies of fstr-1/2 . fstr-1 was up-regulated in clk-1 mutants , and this gene , and/or its constitutively-expressed homolog fstr-2 , is required , in turn , for a robust transcriptional retrograde response . Knocking down fstr-1/2 activity with RNAi did not suppress the primary ubiquinone defect . However , none of the five up-regulated genes we tested was up-regulated in the presence of fstr-1/2 RNAi . These genes included metabolic as well as cell-protective genes , arguing that fstr-1/2 may be a major regulator of the retrograde response in clk-1 mutants . This restoration of a normal transcriptional profile correlated with a suppression of the behavioral , growth and longevity phenotypes of clk-1 mutants . Together all of these findings support the hypothesis that a conserved mitochondrial retrograde response extends lifespan in metazoans as well as in yeast . We note , however , that fstr-1/2 RNAi had stronger effects on the induction of the retrograde-response genes we tested than it had on the clk-1 behavioral phenotypes . This suggests either that part of the retrograde response is expressed independently of fstr-1/2 , or that mechanisms that do not involve transcription also influence the clk-1 phenotype . How does FSTR-1/2 regulate gene expression ? Little is known about the molecular function of FSTR-1/2 . The predicted FSTR-1 and FSTR-2 proteins contain 21 ET modules , which are domains of unknown function containing 8–10 conserved cysteines predicted to form 4–5 disulphide bridges and a C-terminal putative transmembrane domain . Sequence alignment studies using the BLAST algorithm showed weak similarities to a secreted yeast protein ( AGA1 ) and a predicted mouse membrane protein ( Zonadhesin ) . We also looked for structural homologues of fstr-1/2 using the software package PHYRE [36] and found highly significant predicted structural similarities to portions of ErbB1/2/3/4 . ErbB proteins belong to a highly conserved family of receptor tyrosine kinases that play many roles in cell biology and disease . Members of the ErbB family usually contain an extracellular region ( ∼620 amino acids ) that recognizes and binds ligands , a single membrane spanning region and an intracellular tyrosine kinase domain . However , we did not find a predicted tyrosine kinase domain or a secretion signal in fstr-1/2's sequence , which makes a potential connection to the ErbB family unclear . Finally , FSTR-1 ( originally called GFI-1 , for GEX-interacting protein ) was identified in a two-hybrid screen as a potential binding partner of UNC-68 , a muscle-specific C . elegans ryanodine receptor . Our attempts to find phenotypic similarities or functional interactions between these two genes were unsuccessful ( data not shown ) , so their potential relationship remains unclear . Our finding that FSTR-1 is expressed in the intestine ( which is C . elegans entire endoderm , including its site of fat storage ) , as well as a small number of neurons , raises the possibility that these two tissues are particularly important for the response to mitochondrial perturbation . Interestingly , the glycerol channel aqp-1 is expressed exclusively in the intestine and pharynx [32] , further implicating the intestine in the clk-1 longevity pathway . In the future , it will be very interesting to learn how FSTR-1 and FSTR-2 function at the molecular level to initiate a response to altered ubiquinone levels . Long-lived C . elegans mitochondrial mutants share many phenotypes; however , there are also some significant differences between them . As mentioned above , they differ in their respiration rates , ATP levels and body size . In addition , respiratory-chain RNAi and clk-1 mutations both extend the lifespans of daf-2 insulin/IGF-1 receptor mutants [3] , [7] , whereas isp-1 mutations do not [2] ( DC and CK , unpublished ) . These differences have prompted the question of how similar the C . elegans mitochondrial mutants really are to each other [5] . Our microarray observations suggest that the overall nature of the response is similar , though the specific genes affected and the extent to which they are activated varies between mitochondrial mutants . Perhaps these differences are phenotypically significant; for example , clk-1 mutants may have near normal respiration rates because they can compensate more fully than the other mutants to a primary respiratory-chain defect . On the other hand , there is a clear difference in the regulation of the clk-1 and isp-1 mutant phenotypes , since fstr-1/2 is necessary for the clk-1 mutant phenotypes but not for the isp-1 mutant phenotypes ( Figure 9 ) . It is possible that there exist yet-unidentified regulators that control the transcriptional response to mitochondrial perturbation in all of these mutants , thus unifying their phenotypes . In any case , it will be interesting to learn how the isp-1 and cyc-1 retrograde responses are regulated and at what point these pathways converge to control the same downstream genes .
The strains used in this study were: N2-Bristol ( WT ) , fer-15 ( b26 ) ; fem-1 ( hc17 ) , clk-1 ( qm30 ) [37] , isp-1 ( qm150 ) [2] , gei-7 ( ok531 ) , mev-1 ( kn1 ) [38] , muEx491[pfstr-1::mCherry+podr-1::cfp] , clk-1 ( qm30 ) ; muEx491 , sEx11128[pgpd-2::gfp] , isp-1 ( qm150 ) ; sEx11128[pgpd-2::gfp] . All strains used except for mev-1 ( kn1 ) were outcrossed to our laboratory's wild-type N2 strain 4 times . We constructed microarrays using single-strand DNA oligos representing 20 , 374 unique C . elegans genes . These were purchased from Illumina [39] . Populations were starvation-synchronized as L1s overnight and collected at two different times: as L4s staged based on vulval morphology and as pre-fertile adults soon after the L4-to-adult molt , to guarantee maximum synchronicity between animals that grew to adulthood at different rates . Hybridizations were performed using standard techniques described in [33] . Total RNA was purified using TriZol reagent , mRNA was purified using Oligotex ( Qiagen ) and cDNA was labeled using Cy-dyes prior to hybridization . The chips described are direct comparisons between N2 and either clk-1 ( qm30 ) or isp-1 ( qm150 ) animals; or between fer-15 ( b26 ) ; fem-1 ( hc17 ) animals subjected to control ( vector only ) RNAi versus cyc-1 RNAi . The animals were harvested as L4 larvae or young ( pre-fertile ) adults . We performed four independent biological repeats for each condition with the exception of clk-1 ( − ) and isp-1 ( − ) L4-staged populations , where we only collected data for two biological repeats . Dye-swaps and technical repeats were averaged and analyzed as one biological repeat . Scanning was done using a GenePix 4000B scanner , and initial spot quality check was done using Genepix 6 . 0 software . During the analysis we used two different sets of chips for each mutant: the “combined set” includes a combination of all L4 and adult chips , and the “adult-only set” only includes chips from populations collected as adults . The microarray data were analyzed twice over the several-year period spanned by these studies . The initial analysis , used to generate candidate genes that may be functional in the extended longevity of mitochondrial mutants , was performed on the combined set ( L4 and adults ) of microarrays . In this analysis , standard ratio-based normalization and default program settings for flagging missing or “bad” spots were used in the Acuity 4 . 0 software package . Gene significance was calculated using the SAM software package . All of the lifespan analysis described herein was based on genes at the top of the list when the data were analyzed in this way . The second analysis , used to generate genes for use in the comparative GO analysis , was performed on the adult-only set of arrays . These data were renormalized using lowess as well as ratio-based normalizations . On the assumption that genes within the same operon should in general have similar expression patterns , flagging parameters were adjusted to those that maximized the expression correlation of genes within the same operon on a “training” subset of the arrays . Genes not present in at least three of the arrays were not considered . These data were analyzed using the SAM software package and genes were considered significant below a FDR ( false discovery rate ) of 0 . 1 . An algorithm , which we named the “p-q algorithm” , was designed and implemented in the Python programming language to determine the set of genes that are differentially regulated in all three combined ( L4 and adult ) microarray data sets . The algorithm takes as input a set of microarray hybridization data and estimates the q-value for each gene using the method described in [40] and p-values estimated using Student's t-test . It then iterates through each q-value and calculates the probability of seeing the observed number of genes that would be significant in all three data sets , should that q-value be used as the threshold for significance . The algorithm reports the set of genes that overlap between the three data sets at the q-value cut-off that achieved maximum overlap significance , as well as the probability of seeing such a degree of overlap by random chance . Probabilities are calculated using the hypergeometric distribution when possible , or the Poisson approximation when necessary . GO categories were found using the BINGO software starting from a list of differentially expressed genes obtained from running SAM on the set of adult-only microarrays , with a significance cut-off of FDR< = 0 . 1 for each C . elegans mutant . Yeast GO categories were obtained by analyzing a dataset that we constructed pooling the differentially-expressed genes from two different publications [25] , [26] . Bacterial feeding RNAi experiments were performed as described previously [33] . Clones were picked from Julie Ahringer RNAi library and were all verified by DNA sequencing . Lifespan analysis was conducted as previously described [3] . All assays were done at 25°C unless otherwise stated . The lifespan measurements depicted in Supplementary Table 1 were done in the presence of 20 mM FUDR ( fluorodeoxyuridine ) to inhibit progeny growth . The Stata 8 . 0 software package ( Stata Corporation ) was used for statistical analysis and to calculate means and percentiles . In all cases p-values were calculated using the logrank ( Mantel-Cox ) method . Mitochondrial DNA was quantified using Real Time-qPCR . We used two primer sets for mitochondria DNA graciously provided by Dana Miller from the Roth lab at the Fred Hutchinson Cancer Research Center: Mito1 Forward: GTTTATGCTGCTGTAGCGTG , Reverse: CTGTTAAAGCAAGTGGACGAG; Mito2- Forward: CTAGGTTATATTGCCACGGTG , Reverse: CAATAAACATCTCT-GCATCACC . The results were normalized to genomic DNA using a primer pairs specific for ama-1 and nhr-23: ama-1- Forward: TGGAACTCTGGAGTCACACC , Reverse: CATCCTCCTT-CATTGAACGG; nhr-23 – Forward: CAGAAACACTGAAGAACGCG , Reverse: CGATCTGCAGTGAATAGCTC . Animals were grown and collected as described above for microarray studies and lysed in a standard buffer containing proteinase K for 1 hour at 65°C . qPCR was performed using SYBR GREEN PCR Master Mix ( Applied Biosystems ) . Each comparison pools 5 biological repeats . Results were normalized to wild type using 7300 System SDS Software . Time to adulthood was measured as time ( ±2 hours ) at which 95% of animals reached adulthood . Measurements shown represent pooled data from five independent experiments , error bars represent SEM . Pumping rate was measured as the average number of pharyngeal pumps per minute ( n = 10 ) over three independent trials . Thrashing rate was measured as the average number of body thrashes in M9 buffer in one minute ( n = 10 ) over three independent trials . All measurements were conducted on day three of adulthood . Real-time RT-PCR was carried out using the 7300 Real Time PCR System ( Applied Biosystems , Foster City , CA , USA ) . Primers and probes were designed specifically for each gene using Primer3 software . To generate pfstr-1::mCherry-expressing animals , a pfstr-1::mCherry construct was made using the Invitrogen Gateway Cloning technology . The promoter was amplified from genomic DNA using a primer set obtained from Mark Vidal's online promoterome database ( Forward: ggggacaactttgtatagaaaagttgaggccagctttagataat; Reverse: ggggactgcttttttgtacaaacttgtcatctgaaatttgaatgtgttagt ) . The construct obtained was sequenced and injected as described ( Mello and Fire , 1995 ) at 10 ng/µl into N2 animals to generate a transgenic line ( indicated by muEx491 designation ) . The coinjection marker Podr-1::gfp was injected at 50 ng/µl . | Mitochondrial respiration generates energy in the form of adenosine triphospate ( ATP ) , a molecule that powers many cellular processes . When respiration is inhibited in C . elegans , rates of behavior and growth are slowed and , interestingly , lifespan is extended . In this study , we investigated the mechanism of this response . We find that inhibiting respiration increases the expression of genes predicted to protect and metabolically remodel the animal . This pattern of gene expression is reminiscent of the expression profile of long-lived respiration-defective yeast , suggesting ancient evolutionary conservation . Mutations in clk-1 , which inhibit the synthesis of the respiratory-chain factor ubiquinone , produce gene expression , longevity , and behavioral phenotypes similar to those produced by inhibiting components of the respiratory chain . We find that knocking down the activities of two similar genes—fsrt-1 and fstr-2—accelerates the behaviors and aging rates of clk-1 mutants and inhibits the clk-1 ( − ) transcriptional response . Thus , fstr-1/2 , which encode potential signaling proteins , appear to be part of a mechanism that actively slows rates of growth , behavior , and aging in response to altered ubiquinone synthesis . Unexpectedly , fsrt-1/2 are not required for the longevity and behavioral phenotypes produced by inhibiting the gene isp-1 , which encodes a different component of the respiratory chain . Our findings suggest that different types of mitochondrial perturbations activate distinct pathways that converge on similar downstream processes to slow behavioral rates and extend lifespan . |
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During Dec-2013 , a chikungunya virus ( CHIKV ) outbreak was first detected in the French-West Indies . Subsequently , the virus dispersed to other Caribbean islands , continental America and many islands in the Pacific Ocean . Previous estimates of the attack rate were based on declaration of clinically suspected cases . Individual testing for CHIKV RNA of all ( n = 16 , 386 ) blood donations between Feb-24th 2014 and Jan-31st 2015 identified 0·36% and 0·42% of positives in Guadeloupe and Martinique , respectively . The incidence curves faithfully correlated with those of suspected clinical cases in the general population of Guadeloupe ( abrupt epidemic peak ) , but not in Martinique ( flatter epidemic growth ) . No significant relationship was identified between CHIKV RNA detection and age-classes or blood groups . Prospective ( Feb-2014 to Jan-2015; n = 9 , 506 ) and retrospective ( Aug-2013 to Feb-2014; n = 6 , 559 ) seroepidemiological surveys in blood donors identified a final seroprevalence of 48·1% in Guadeloupe and 41·9% in Martinique . Retrospective survey also suggested the absence or limited "silent" CHIKV circulation before the outbreak . Parameters associated with increased seroprevalence were: Gender ( M>F ) , KEL-1 , [RH+1/KEL-1] , [A/RH+1] and [A/RH+1/KEL-1] blood groups in Martiniquan donors . A simulation model based on observed incidence and actual seroprevalence values predicted 2·5 and 2·3 days of asymptomatic viraemia in Martiniquan and Guadeloupian blood donors respectively . This study , implemented promptly with relatively limited logistical requirements during CHIKV emergence in the Caribbean , provided unique information regarding retrospective and prospective epidemiology , infection risk factors and natural history of the disease . In the stressful context of emerging infectious disease outbreaks , blood donor-based studies can serve as robust and cost-effective first-line tools for public health surveys .
Chikungunya virus ( CHIKV ) , an Aedes-borne alphavirus first identified in Tanzania in the early 1950's , infects humans through a "zoonotic cycle" ( i . e . , starting from a non-human primate reservoir and sylvatic mosquitoes ) or a "dengue-like cycle" ( i . e . , via direct human-mosquito-human transmission by peridomestic Aedes aegypti and Ae . albopictus mosquitoes ) . During the past decade the epidemic transmission cycle of CHIKV has caused large outbreaks throughout Asia , Africa and the islands in the Indian Ocean . The disease is usually mild and characterised by acute febrile arthralgia . Severe forms of infection have been reported , notably encephalitic syndromes in newborns following late infection of the mother during pregnancy . In addition , debilitating persistent arthralgic sequelae are observed in a proportion of patients [1] . In December 2013 , the first autochthonous cases of chikungunya fever in the Americas were recorded in the French-Dutch Caribbean Saint-Martin Island [2] . Subsequently , the virus spread to other islands of the French West Indies ( Saint-Barthelemy , Martinique and Guadeloupe ) , to the majority of Caribbean islands and to continental America . By now , this episode has probably involved more than one million people [3] . In the most populated French Caribbean islands ( Guadeloupe and Martinique ) , the only potential vector of CHIKV is Ae . aegypti . This species is abundant and also responsible for dengue virus epidemics [4] . It was therefore anticipated that Ae . aegypti would transmit CHIKV locally . Indeed , in 2014 , at least 81 , 200 presumed clinical cases of chikungunya fever were recorded in Guadeloupe , and 72 , 500 in Martinique [5] . Consequently , special attention was paid to minimizing the risk of virus transmission via blood transfusion . However , a temporary ban on local blood donation would have presented a major challenge for supplies of fresh blood products from France , due to local phenotypic distribution of blood groups . Accordingly , CHIKV-specific molecular screening was implemented by the French blood bank ( Etablissement Français du Sang , EFS ) [6] and collections of human sera were provided by EFS for serological analyses . Here , we report an epidemiological follow-up of the chikungunya outbreak in Guadeloupe and Martinique islands , based on the large-scale prospective molecular detection of incident cases in blood donors and on seroprevalence analyses performed in donors at different time intervals during the epidemic .
Only volunteer blood donors were included . All of them were specifically informed that samples would be tested for blood-borne pathogens in order to prevent transfusion-transmitted infections and also might be used for epidemiological studies . They provided signed written informed consent . The study was approved by the scientific direction of the EFS . No specific sampling dedicated to the study was performed . All data used for epidemiological studies were de-identified . In 2014 , 403 , 750 inhabitants were living in Guadeloupe ( sex ratio = 0·86 ) and 381 , 326 in Martinique ( sex ratio = 0·85 ) [7] . The distribution according to age-groups and gender is available in S1 Fig ( supporting information section ) . Association of the presence of CHIKV-IgG with other epidemiological and biological factors was analysed using records from 8 , 653 donors tested during the epidemic period ( Jan-1st 2014 to Jan-31st 2015 ) , in both Guadeloupe ( 2 , 984 , sex ratio = 0·94 ) and Martinique ( 5 , 669 , sex ratio = 0·81 ) ( pop#6; Fig 1 , see S1 Table for details ) , and tested using the Chi-square test . To avoid possible bias , when a donor was associated with several blood donations during the period considered , he/she was counted only once ( the day of the first donation if serology remained negative , otherwise the day of the first positive serology ) . Analyses were performed separately for Guadeloupe and Martinique . The main parameters considered were gender , age and blood grouping phenotypes . Statistical analysis relied on Chi2 analysis were performed online using "Chi-Square Test Calculator" ( http://www . socscistatistics . com/tests/chisquare2/Default2 . aspx ) . Multivariate analysis was performed using binary logistic regression with the IBM-SPSS Statistics v 23 . 0 . 0 . 0 software . Results were considered statistically significant when p-value was lower than 0·05 . The population of blood donors tested by NAT screening for CHIKV ( blood donations = pop#1 ) in Martinique included 6 , 911 donors . The complete duration of the study in pop#1 was 338 days , including 224 days open for blood donation . Accordingly , during these 224 days , our model randomly attributed 1 or 2 days of possible donation to 4 , 147 donors who gave blood once and 2 , 764 who gave blood twice , respectively . It also randomly attributed 1 , 2 , 3 or 4 days of viraemia to each donor in a 338 day period and the number of days where viraemia and blood donation coincided were counted . The mean value obtained in 1 , 000 simulation replicates provided the expected number of detected incident cases in Martiniquan pop#1 donors , assuming a 1–4 day duration of detectable asymptomatic viraemia and a final seroprevalence of 100% . Results were then adjusted in proportion with the actual seroprevalence observed at the end of the study period . The same analysis was performed in pop#1 Guadeloupian blood donors ( 4 , 613 donors , including 2 , 768 who gave once and 1 , 845 who gave twice ) . This model was used to determine which estimated duration of detectable asymptomatic viraemia provided the best fit to positive viral RNA detection following NAT screening . This estimate was therefore dependent upon the specific LOD of the detection method used .
In the population of donors used for sero-epidemiological analyses ( #pop6 ) , the distribution of ABO blood groups was: O: 54·7%; A: 27·4%; B: 14·9% , AB: 2·90% . The prevalence values of Rhesus positive ( RH+1 ) and Kell positive ( KEL+1 ) phenotypes were 88·2% and 4·1% , respectively . The manual of the kit used in the current study indicated that the limit of detection ( LOD ) of the assay ( Probit analysis based on serial dilutions of quantified synthetic control RNAs ) was 1 . 268 copies/μL of eluate [95% confidence interval ( CI ) : 0 . 610 copies/μl—4 . 053 copies/μl] . In our experimental conditions , this would correspond to a LOD of ca 450 genome copies per mL of plasma . Using the same control RNAs , our results were in a similar range ( 300–400 synthetic RNA copies per mL of plasma ) . Further evaluation using titrated culture supernatants allowed for both the Asian and ECSA lineages of CHIKV the reproducible detection of viral RNA in dilutions corresponding to a titre ≥ 1 TCID/mL . Amongst 16 , 386 donations tested by RT-PCR ( pop#1 ) , 37/10 , 197 ( 0·36% ) and 26/6 , 189 ( 0·42% ) were positive in Martinique and Guadeloupe respectively . Monthly detection of CHIKV RNA is presented in Fig 2 , together with suspected clinical cases in the general population . None of the donors with a positive CHIKV RNA NAT screening result did repeat blood donation over the study period . The results clearly showed different epidemic kinetics in Guadeloupe and Martinique . The outbreak started earlier in Martinique ( threshold of 1 , 000 weekly suspected clinical cases from early Mar-2014 ) than in Guadeloupe ( same threshold from early Apr-2014 ) with a clear and very intense epidemic peak in Guadeloupe during May-Aug-2014 ( up to 6 , 500 weekly cases , ca . 1 , 500/100 , 000 inhabitants ) and a flatter curve in Martinique ( peak at ca . 3 , 000 weekly cases , ca . 800/100 , 000 inhabitants ) . The curve of incidence produced from CHIKV RNA detection in blood donations faithfully correlated with that of suspected clinical cases in the general population in Guadeloupe , but less accurately in Martinique . No significant statistical relationship between CHIKV RNA detection and age-classes or blood groups was identified . Monthly prevalence values of CHIKV-IgG in populations #2–5 are presented in Fig 3 for Guadeloupe and Martinique . This covers the period from Aug-1st 2013 to Jan-31st 2015 and includes both results from a prospective study ( starting at the end of Feb-2014 ) and a retrospective study ( Aug-2013 to Feb-2014 ) limited to Martinique . Statistical analyses were performed using results collected from population #6 , i . e . donors sampled during the outbreak ( see Fig 1 , Tables 1 and 2 ) . Equivalent associations with AB , O , and B groups were insignificant . The goodness of fit test of Hosmer and Lemeshow was 0·502 . Significant association were identified at week level as follows ( global analysis of both islands ) : For a final seroprevalence of 41·9% , in the pop#1 of Martiniquan blood donors our model predicted , 15 , 31 , 46 , and 61 RNA positive detections ( with a viral load above 450 genome copies/mL of plasma ) associated with asymptomatic viraemia durations of 1 , 2 , 3 , and 4 days respectively . Based on this model , the observed number of RNA positive detections ( 37 ) corresponded to a predicted asymptomatic viraemia of ca . 2·5 days . For a final seroprevalence of 48·1% in the pop#1 of Guadeloupian donors , 12 , 24 , 35 , and 47 RNA positive detections ( with a viral load above 450 genome copies/mL of plasma ) were associated with asymptomatic viraemia durations of 1 , 2 , 3 , and 4 days respectively . Thus , the observed number of RNA positive detections ( 26 ) corresponded to a predicted asymptomatic viraemia of ca . 2·3 days .
It has been previously speculated that historical reports would suggest previous circulation episodes of Chikungunya virus in the Americas [3] . The only documented introduction of the virus ( Asian genotype ) in the region occurred at the end of 2013 [2] and has been responsible for large outbreaks in the Caribbean islands , and numerous countries of Central and South-America . Moreover , 12 Floridian autochthonous cases [9] were reported in 2014 . After the discovery of CHIKV clusters in Saint-Martin and Saint-Barthelemy islands ( Dec-2013 ) , the virus , transmitted by Aedes aegypti mosquitoes , dispersed rapidly to Martinique and later to Guadeloupe . In La Martinique , according to French health authorities the disease reached epidemic proportions on Jan-3rd 2014 , and the alert was cancelled on Jan-8th 2015 . On Guadeloupe the epidemic alert started on Apr-10th 2014 and ended on Nov-27th 2014 . The epidemic kinetics were different on the two islands , with a shorter and more intense outbreak in Guadeloupe . The peak of clinically suspected cases occurred during week-23 in both Guadeloupe ( ca . 6 , 500 cases ) and Martinique ( ca . 3 , 250 cases ) [5] . The precise origin of the observed differences is unknown . They may be related to ecological and environmental factors ( e . g . , climatic and geographical conditions , density and distribution of mosquito and human populations , land use… ) , but also to anthropogenic factors such as pressure of vector control . The current study was based on volunteer blood donors . Limitations to the interpretation of epidemiological data are therefore those of classical blood donor studies ( individuals studied were 18–70 years old , and had no history of virus-like illness in the 28 days before donation ) . Important assets of the current study design were , the opportunity to compare results from similar populations in different locations , the high number of individuals enrolled , access to pre-epidemic samples , the possibility of performing individual nucleic acid tests and having access to asymptomatic or pre-symptomatic viraemiac individuals . The first important finding relates to the occurrence of cases in Martinique before the detection of cases by the public health services ( Dec-18th 2013 ) . Because the clinical symptoms of dengue and chikungunya fever are similar ( flu-like disease ) , the ongoing dengue outbreak may have hidden the emergence of CHIKV and delayed the detection of the first cases . Our retrospective seroprevalence analysis identified only two donors with antibodies to CHIKV between August and December 2013 ( both being detected in Oct-2013 ) : one associated with previous infection during the Indian Ocean outbreak and the other that could not be investigated . Overall , this suggests the absence of significant circulation of the virus during several decades before the outbreak , and also the rapid detection of the first outbreak cases . Another interesting observation was the impact of different epidemic kinetics on our ability to identify risk factors associated with antibodies to CHIKV . In Guadeloupe , the epidemic eruption was intense but the very high transmission rate was not associated with identifiable risk factors . By contrast , the flatter epidemic curve in Martinique correlated with an overrepresentation of males and individuals with [RH+1 & KEL-1] , [A & RH+1] and [A & RH+1 & KEL-1] phenotypes and CHIKV-IgG ( using univariate analysis ) . Being a female or having a blood group different from these risk factors was apparently "protective" . However , this protection was relatively minor taking into account the overall situation in Guadeloupe . In multivariate analysis , the only significant associations identified were gender ( risk increased in males vs females ) and age ( global increase of risk with age ) , both with low odd ratio values . The observed higher risk of CHIKV infection in males has been previously reported [10–13] . However , in other studies higher prevalence in females has been reported [14–17] . The reasons underlying these divergent observations remain elusive and are presumably linked with different habitats , economic factors and lifestyles . Regarding blood types and association with CHIKV infection , there is very limited information available . Kumar et al . claimed that Rhesus-positive individuals had increased susceptibility to acquiring CHIKV infection [18] , but their results did not support this . In a genetic predisposition study in 100 Indian families , Lokireddy et al . identified infection in all Rhesus-positive blood groups [19] but none amongst Rhesus-negative individuals . However , the proportion of Rhesus-negative individuals was too low to draw robust conclusions . The association between erythrocyte phenotypes and susceptibility to viral infection remains difficult to interpret as long as the mechanisms and cellular receptors involved in CHIKV infection are not fully identified . The risk associated with specific blood groups may be explained by several non-mutually exclusive factors: individuals with these blood groups may be more prone to mosquito bite ( for biological , epidemiological or sociological reasons ) ; alternatively , they may have different susceptibilities to infection or capacities to eliminate the virus via specific innate immune responses . It is interesting to observe that on both islands the outbreak declined when a 40–50% herd immunity level was reached . This threshold is strikingly similar to that observed at the end of the outbreak on Reunion island ( 38·2% ) and Mayotte ( 37·2% ) [12 , 20] . This is despite the fact that different virus genotypes were implicated in the Caribbean and Indian Ocean epidemics . In contrast the threshold for decline was much higher in Kenya ( 72% ) , Kerala ( 68% ) and Thailand ( 62·1% ) [10 , 13 , 21] . This possibly reflects differing levels of social and economic development together with local efficacy of mosquito control measures . Based on the situation observed in the Indian Ocean countries , the level of immunity detected in the Martiniquan and Guadeloupian population should reduce the likelihood of a major recurrence of chikungunya fever in the French West Indies during the next few years . Finally , concerning the implications for blood transfusion , our study clearly shows that the risk of collecting blood from asymptomatic viraemiac patients is significant during a chikungunya fever outbreak . Modelling performed in Martiniquan and Guadeloupian blood donors suggested that the duration of asymptomatic viraemia was close to 2 . 5 days . This estimated period may be extended by the use of molecular assays with an improved limit of detection . However our results are consistent with the post-donation survey of 48 viraemiac blood donors , in which clinical symptoms were reported 1–5 days after donation ( 39·6% at day 1 , 39·6% at day 2 , 14·5% at day 3 , 4·2% at day 4 and 2·1% at day 5; mean value = 1·9 days ) . The actual mean duration of asymptomatic viraemia corresponds to this observed mean delay before the symptoms , plus the duration of viraemia prior to donation , i . e . it should be very close to the value provided by our model . This new information suggests that the optimal quarantine period for blood products during a chikungunya fever outbreak should be at least 5 days . The length of the asymptomatic viraemic period identified in the current study is higher than previous proposals ( 1·5 days according to Brouard et al . ) [22] . This divergence could reflect either different durations of asymptomatic viraemia in the case of infection by CHIKV ECSA when compared with the Asian genotype , or underestimation of the actual duration due to the limited number of cases previously analysed . In conclusion , this study demonstrates the ability of blood donor-based investigations to be implemented promptly even during an intensely rapid onset of virus emergence . It also provides both retrospective and prospective data relating to epidemiological characteristics and infection risk factors without the requirement for a de novo cohort , hospitalisation of patients and specific blood sampling . Moreover , by combining the biological and post-donation follow-up data we have gained new knowledge relating to the natural history of the disease . In the stressful context of emerging infectious disease outbreaks , appropriate blood donor-based studies have now been shown to be excellent first-line tools for public health surveys . This is particularly applicable to situations in which the proportion of asymptomatic individuals is high and seroprevalence information is required to estimate the attack rate as , indeed , exemplified by the currently emerging Zika virus . | Chikungunya virus ( CHIKV ) is an emerging mosquito-borne arbovirus responsible of a large outbreak since December 2013 in the Americas from French islands in the Caribbean . Documentation of the epidemic was based on the survey of clinically suspected cases , providing limited information on the incidence of the disease overtime and the herd immunity of the general population at the end of the outbreak . Our study improved blood donors specimen collection and data obtained from the Nucleic Acid Testing ( NAT ) screening implemented during the outbreak in order to prevent CHIKV transmission by blood products . After an 11 month follow up , we determine for Martinique and Guadeloupe islands the CHIKV-RNA positive rate: 0 . 42% and 0 . 36% respectively and the final IgG seroprevalence: 41 . 2% and 48 . 1% . Using a simulation model , we estimate the CHIKV duration of asymptomatic viremia to be between 2 . 3 and 2 . 5 days . Our findings will help in the comprehension of the natural history of infection and provide helpful data for prevention of Transfusion transmitted infections . Our study provides evidence that monitoring of Chikungunya infection based on NAT screening of voluntary blood donors can be implemented rapidly and provides real-time epidemiological information . This should be of specific relevance to the case of epidemics caused by viral infections with high numbers of asymptomatic forms such as observed with the currently emerging Zika virus . |
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Cancer is a disease of cellular regulation , often initiated by genetic mutation within cells , and leading to a heterogeneous cell population within tissues . In the competition for nutrients and growth space within the tumors the phenotype of each cell determines its success . Selection in this process is imposed by both the microenvironment ( neighboring cells , extracellular matrix , and diffusing substances ) , and the whole of the organism through for example the blood supply . In this view , the development of tumor cells is in close interaction with their increasingly changing environment: the more cells can change , the more their environment will change . Furthermore , instabilities are also introduced on the organism level: blood supply can be blocked by increased tissue pressure or the tortuosity of the tumor-neovascular vessels . This coupling between cell , microenvironment , and organism results in behavior that is hard to predict . Here we introduce a cell-based computational model to study the effect of blood flow obstruction on the micro-evolution of cells within a cancerous tissue . We demonstrate that stages of tumor development emerge naturally , without the need for sequential mutation of specific genes . Secondly , we show that instabilities in blood supply can impact the overall development of tumors and lead to the extinction of the dominant aggressive phenotype , showing a clear distinction between the fitness at the cell level and survival of the population . This provides new insights into potential side effects of recent tumor vasculature normalization approaches .
Cancer is a disease of multicellular regulation , in which malfunctioning cells can break free of homeostatic regulations imposed by the host environment [1] . One of the main characteristics of cancer is the increased proliferation and mutation of cancerous cells due to malfunctioning control of growth and proliferation [1] . As these behavioral changes typically originate from mutations in the cells’ genetic material , excessively proliferating cells accumulate further alterations , leading to a possible amplification of malignancies . Traditional studies of altered cell traits primarily focus on genetic mutations , but neglect the multicellular nature and genetic variety of tumors . Tumor heterogeneity has been demonstrated experimentally and is an active field of research [2–4] . Neutral mutations may accumulate and contribute to intratumor heterogeneity [5] . An intermediate level of heterogeneity is correlated with low survival probability [6] . Heterogeneity may even promote the collapse of tumor development by inducing a clone population that supports and enhances the growth of other clones in mice [7] . Marusyk and colleagues [7] claim that these supporter clones may be outcompeted by the more aggressive subpopulation , leading to the disappearance of the supporters and to the consequent collapse of the tumor . Heterogeneity questions the validity of previous whole–tumor analyses , as “the most abundant cell type might not necessarily predict the properties of mixed populations” [8] , and emphasizes the need for more detailed approach . Initial phases of tumor development are increasingly thought to give rise to a Darwinian process [1 , 9] , where individual cells compete for growth space and nutrients . Selection is imposed by the microenvironment , a highly complex entity spanning several ranges in size from the endocrine regulation of the whole body down to the extracellular matrix ( ECM ) and neighboring cells . During cancer development this environment is changed due to changes in the cellular component , and due to the tissue and organism level reactions to the tumor . Intrinsic coupling between neighboring cells forms the basis of the plasticity-reciprocity model [10]: as cells alter their behavior ( plasticity ) , their contribution to the local environment changes through for example ECM remodeling , nutrient uptake , or adhesion molecule expression . In turn , the changed environment imposes an altered selection on the cells ( reciprocity ) , creating a feedback between cells and their local microenvironment . This cascading change in behavior is reminiscent of the behavioral changes associated with stages of cancer development [11] that was later described by the accumulation of mutations through which cells become increasingly malignant [12] . Although a strict sequence of mutations was not found , a general pattern was observed in the majority of cases [12] , linking the macroscopic stages to the cell-level changes . Computational modeling is an excellent tool for exploring , studying , and understanding such complex systems , because it provides complete control over assumptions and reveal the consequent behavior emerging from them . This allows the dissection of complex interactions and exploration of experimentally challenging cases . A variety of models have been applied to study cancer . Using a population level description , Basanta and co-workers studied how the combination of tumor treatments , p53 cancer vaccine and chemotherapy , can be optimized to yield the best results [13] . In a similar study , Sreemati Datta and coworkers [14] model tumor development with the inclusion of evolving mutation rates and show that the balance between inducing driver mutations and mutation rates plays a key role in tumor growth: at high mutation rates , genetic instability may counter tumor progression . Combined with close experimental verification , Marusyk and colleagues [7] used a similar model to suggest that interactions among clones may lead to an overall collapse of tumor development . This approach is able to incorporate evolutionary games to help cope with the development of treatment resistance . Such space-free models assume that all cells within the tumor are able to interact with all other cells directly , and are unable to deal with intra-tumor spatial heterogeneities . Studies incorporating this spatial aspect have mostly worked with cellular automata ( CA ) models . For example , Gerlee and colleagues were able to explain the ‘go or grow’ hypothesis through the emergence of haptotaxis in their CA model [15] . In a recent study , Waclaw and colleagues [16] used a CA model to show that cell motility together with cell turnover may prevent intratumor heterogeneity . Of particular interest is the study of Anderson and colleagues [17] , where the evolution of a growing cell population and the effect of a heterogeneous environment is explored . They represent the tumor environment as a distribution of ECM molecules that together with oxygen serve as nutrient after degradation . Cell evolution is modeled in the phenotype by selecting a set of cellular parameters ( matrix degradation rate , proliferation rate , etc . ) from a number of predefined phenotypes upon cell division . The new phenotype was either selected randomly or according to a predefined sequence progressing towards more aggressive behavior . The authors found that cells evolved into a similar , aggressive phenotype when applying the random mutation scheme . Heterogeneity in the population was found to give rise to irregular tumor surface , whereas environmental heterogeneity ( heterogeneity in the ECM distribution ) reduced population heterogeneity and favored the most aggressive cell types . Low concentration of oxygen was also found to reduce population heterogeneity and promote invasive finger formation; applying two bursts of oxygen in these simulations led to the segregation of the mixed population . Similar models have been used to show emergent progression of phenotypes related to hypoxia , glycolysis , and acid-resistance , by including neuronal networks in cells [18] , or angiogenesis by introducing blood vessels in hypoxic regions [19] . Enderling and co-workers [20] introduced the cancer stem cell ( CSC ) hypothesis in a similar model by incorporating cells with unlimited proliferation potential ( CSCs ) and cells with limited proliferation potential . They found that cell death induced by tumor therapy could lead to a more aggressive , proliferative tumor , as the CSCs were no longer competing for space after the treatment . Using a combination model of phenotype evolution and the CSC hypothesis , Sottoriva and colleagues showed that the presence of CSCs in the model tumors led to more invasive tumor morphology [21] . The CA model further allowed exploring efficient drug delivery through the neovasculature [22 , 23] , the role of spatial arrangement of the vasculature in radiation therapy [24] and even the effect of the different 2D representations of the 3D vasculature [25] . Using a more detailed model allows to explore the evolution of many other aspects of cell behavior , such as cell flexibility , cell adhesion , or cell shape . One such model used in tumor modeling is the cellular Potts model [26 , 27] . It has been used to describe , for example , the effect of nutrient limitation on tumor growth morphology [28] , or to compare the emergence of distinct developmental stages in terms of morphology and growth in vascular versus avascular tumors [29] . Using a heterogeneous evolutionary model of CSCs , a recent study reported the emergence of spatial stratification of tumors based on the evolution of adhesion molecule expression [30] . A further line of models explore the effect of oxygenation of tumors [31] in the light of chemotherapy [32] . An alternative way of introducing more detail is a spatial continuum representation of the cells . One example of this is the phase field method which has been used in combination with discrete modeling elements to investigate tumor growth morphology and its interaction with the vasculature [33 , 34] . Most of the above models applies a sharp distinction between the tumor and the healthy tissue . While the tumor is described in a cell-based detail , the environment is usually presented as a continuum . These models implicitly assume that stromal cells surrounding the tumor do not participate in the development of the tumor , and that these cells are fundamentally different from the cancerous tissue . The transition between the stromal and cancerous cell states is neglected . A recent exception from this is the work of Powathil and colleagues [35] where the effect of irradiation on a small set of “healthy” bystander cells is investigated . As a result of irradiation and induced signals , stromal cells may apoptose or may be converted to the tumor cell type in the model . However , in this and almost all of the above models the phenotypes available for evolving cells are restricted to a small , discrete subset of all possibilities . A continuous range of cell states could reflect a more biological picture including more subtle , for example epigenomic , changes . Furthermore , the nutrients are typically modeled as a single component , however , one signature of cancerous cells is the reduced oxygen consumption and increased glucose uptake . For this a more detailed nutrient description is required . Another less explored point of interest is the change in the temporal behavior of the larger environment , the nutrient supply . Initial tumors experience a stable blood supply in healthy tissues , that presents them with a relatively constant environment . In contrast , angiogenic tumors at a later stage develop neovasculature that is tortuous and leaky , presenting a fluctuating , unstable environment for the cells [36 , 37] . How does this temporal variation affect the population behavior ? Would the same aggressive phenotype dominate the population as in the stable environment , or would it fit less than the less aggressive and stable phenotype ? Here we present a cellular Potts model of a closely packed , mutating cell population representing an epithelial tissue within an organism ( Fig 1 ) . Mutation in the model allows cellular behaviors to vary continuously in a wide range of phenotype space , therefore evolution is governed by selection emerging naturally from the limitations of space , glucose , and oxygen . Cellular metabolism is modeled as a mixture of aerobic glycolysis and respiration . Small scale changes in the microenvironment are represented by the changes of the immediate cellular neighborhood , both by mutations and cell rearrangements . We model large scale environmental fluctuations through the fluctuating activity of spatially fixed nutrient sources , which represent the cross-section of blood vessels . Constructed in this way our model includes both the plasticity-reciprocity model of Friedl and Alexander [10] , as well as the large scale fluctuations imposed by the host . In the following sections we analyze the behavior of the model with and without mutating cells . We show that our model reproduces the Warburg shift , and exhibits stages of development similar to those observed in previous studies ( such as [17–19] ) . In our model cells undergo clonal expansion , hypoxia , followed by starvation , with the development of segregated populations around blood vessels . The spatial differentiation of cell populations is somewhat similar to the spatial diversity in real tumors as described by Alfarouk et al . [38] . Whereas Alfarouk and colleagues describe two main habitat zones concentrically surrounding the blood vessel , we observe only one of the zones with high proliferation rates and a robust cellular outflow from near the nutrient source . Finally , our results indicate that the dominant aggressive phenotype is more sensitive to fluctuations in the environment than the ones maintaining a stable phenotype without mutation .
To investigate the above questions , we model a monolayer of cells using a modified cellular Potts model ( CPM ) based on the CompuCell3D implementation [39] which can be obtained from http://www . compucell3D . org . Customized code for the simulations and example parameter and initial condition files can be found in S1 File . In the following we give an overview of the model; for more detail see the Methods section . Cells in the CPM are represented as confluent domains on a lattice on which an integer σ ( x → ) at every position x → indicates which cell is occupying the location x →; cell-free areas are designated by σ ( x → ) = 0 . Cell movement results from a series of elementary steps in which an attempt is made to copy σ ( x → ) at a randomly selected location x → to one of its randomly selected neighboring location x ′ → . This attempt is accepted with a probability based on a Hamiltonian goal function H that defines cell dynamics ( Eqs 1 and 2 ) . H is usually defined such that cells maintain a controlled size , perform amoeboid-like cell movement , and may exhibit adhesion or contact-repulsion . A time step in the model is defined as the Monte Carlo Step ( MCS ) consisting of N elementary steps where N is the total number of lattice sites in the model . In our model we apply the usual calibration by relating 1 MCS to 1 minute real time , and 1 lattice site to 2 μm . Diffusion of soluble substances are simulated on a lattice identical to the cellular-lattice . This calibration relates the simulated tissue area to 400μm × 400μm , and diffusion coefficients of simulated nutrients to realistic values ( glucose and lactate: Dg = 10−9 m2/s; oxygen: D O 2 = D l = 10 - 11 m 2 / s ) [40] . We implemented a metabolism whereby cells consume glucose and oxygen from their environment and use a mixture of lactic acid fermentation and cellular respiration . Cells metabolize oxygen and glucose to generate an abstract cellular energy that is used for their maintenance and growth . The amount of energy required for cells is controlled by the expression levels of glucose transporters in the cell membrane which is determined by an intracellular growth signal parameter ( N0 ( i , t ) for cell i at time t ) . The mode of metabolism is determined by an internal hypoxia inducible factor ( h ( i , t ) for cell i at time t ) , that is controlled through the oxygen levels inside and around the cell and the amount of intracellular reactive oxygen species ( ROS ) ( Eq 15 ) . This factor determines the ratio of respiratory and fermentative modes of cellular metabolism . In our model the speed of energy production , and hence cell growth , is independent of the mode of metabolism in order to avoid a selection bias towards the faster metabolic mode . If a cell reaches a pre-defined doubling size it divides , hence cell cycle time is determined by cell metabolism . Cell death may occur either due to starvation or age . If a cell generates less energy from metabolism than is required for maintenance , it converts the necessary amount of its cell mass into energy ( catabolism ) . Once the cell mass is exhausted , the cell is considered dead and is taken out of the simulation . Cells are also killed in the simulation with a 0 . 1% probability after each MCS to maintain cell turnover . Glucose and oxygen are supplied by a separate set of designated immobilized cells that play the role of blood capillaries ( Fig 1a ) . To allow the temporal control of nutrient supply , capillaries can be in an active or a blocked state . Nutrient levels are kept at a fixed concentration in the blood stream , that is inside the capillaries , when the capillaries are active . When a capillary is blocked , nutrient levels are kept at zero within the capillaries . The activity of the vessels are changed with a probability ( nutrient switching probability ) after each MCS . A high switching probability leads to a stable supply while a low switching probability results in an inconsistent supply , mimicking blocked or tortuous vessels without affecting the average activity time of the vessels . Lactate produced by cells as a waste from lactic acid fermentation is cleared out of the system by active capillary cells . For more detail on the model see the Methods section . We start by first verifying that the model is capable of simulating a sustainable homeostatic tissue . Therefore we studied the behavior of a healthy tissue in the absence of mutation and stable nutrient supplies . Fig 2a shows the model setup . We consider a two-dimensional square lattice , corresponding with a slice of tissue of 400 μm × 400 μm ( 40 × 40 cells , 200 × 200 lattice sites ) , containing four blood vessels arranged in a square formation ( Fig 1a ) . To achieve constant nutrient supply , the probability of a vessel to be blocked or unblocked in every MCS is 0 . 5 . In this case each vessel switches between active ( depicted in white ) and blocked ( depicted in gray ) states rapidly . As the nutrients diffuse away from the source , this rapid switching results in a continuous supply of nutrients ( Fig 2c ) . The color of tissue cells in Fig 2a indicates the intracellular pressure , defined as the difference between target volume ( biomass ) and actual volume of the cell ( π ( i , t ) = VT ( i , t ) − V ( i , t ) ) . This measure differs from the extracellular pressure as it includes any contribution of the contractile actin cortex surrounding biological cells . Note that the pressure within the population is distributed without any specific pattern . Fig 2b shows the dynamics of the tissue for 105 MCS corresponding to approximately 70 days . The number of cells fluctuates around a constant value throughout the simulation ( Fig 2b ) , and is sufficient to cover the whole system: the ratio of the cell-covered region over the total simulation area is close to one ( Fig 2b ) . As the average intracellular pressure does not increase over the course of the simulation ( Fig 2b ) , cell growth and proliferation are kept in balance with the basal metabolism and the constant cell turnover . Excessive growth is prevented by the lack of growth space through a negative feedback between the intracellular pressure and cell growth ( Eq 18 ) . Nutrient levels remain constant during the simulations and cells remain respiratory as indicated by the absence of lactate ( Fig 2d ) . Next we investigated how a cancerous tissue would behave in our model . Cancerous tissues are characterized by large number of mutations , chromosomal rearrangements and changes in gene expression level , all of which may result in phenotypic changes of the cells [1 , 41–43] . To mimic such changes , we allowed the set of 10 assigned phenotypic properties of cancerous cells to change upon division ( Fig 1b ) , including the division volume or adhesion parameters ( see Table 1 and Methods ) . After cell division , the daughter cells inherit the phenotypes of their parents with some small mutations . Every parameter is allowed to change with a fixed probability ( mutation rate μp for parameter p ) and independently of one another . The change in parameter p is drawn from a normally distributed random variable with a standard deviation of σp , that is: p ′ = p + N ( 0 , σ p ) . The parameters are allowed to change freely within a pre-defined range ( Table 1 ) with reflective boundary conditions . This allows an unbiased parameter to uniformly explore the available range ( Fig 1b inset ) . To determine how a single mutating cell would perturb the homeostasis of the above tissue , we inserted a cell with a mutation potential either near ( Fig 3a , red cell ) or far from ( Fig 3b ) the nutrient source . The single mutating cell persisted in 28 and 26 simulations out of 100 for the two different initiation positions . When the cell persisted , it expanded within the first 5000 MCSs and eventually colonized the population completely ( Fig 3a and 3b ) . In order to study the internal dynamics of the tumor , we will only focus on the case where the mutating cell persists and has colonized the population; therefore we will initiate our simulations with populations where all cells are allowed to mutate . Fig 3c–3f shows the behavior of the model with mutating cell populations , with 10% mutation rate . In comparison with the non-mutating populations , the intracellular pressure is higher ( Fig 3c ) . This shows that cells overcome the initial growth control mechanism . The number of cells initially increases , reaches a peak , and then declines to approximately half of the peak value , well below the initial numbers , where the population size stabilizes ( Fig 3d ) . These changes in cell numbers are followed by the intracellular pressure as well: in the expansion phase the pressure increases , but before the peak in cell number it sharply declines . After the population size settles to a lower value , the pressure settles to an approximately constant value . The full coverage of the tissue drops to approximately half coverage , showing that the cancerous cells cannot maintain a complete monolayer . Nutrients are depleted further from the blood vessels , resulting in a shortage of oxygen in the distant regions ( Fig 3e ) , shortly followed by the depletion of glucose ( Fig 3f ) . The depletion of oxygen triggers the cells to switch to fermentation , resulting in an increase in lactate . This switch accelerates the depletion of glucose , causing the decline in population size . After the population size is reduced , oxygen levels return to the same level as in the non-mutating populations , while cells still rely on fermentation as can be seen from the maintained lactate levels ( Fig 3f ) . Previous studies have reported distinct stages of development including hypoxia , glycolysis , or acid-resistance [17–19] . However , in these studies the evolution occurred in isolation from the stromal tissues and vasculature either using a limited set of phenotypic behaviors potentially constraining the degree of freedom of the evolutionary trajectories or with immobile cells . Therefore , we first asked if stages of tumor progression also occurred in our less constrained model . Fig 4 shows the behavior of our model with the nutrient concentrations and cell numbers averaged from 10 independent simulations with stable vasculature ( switching probability = 0 . 5 ) and 10% mutation rate . Based on this , we identified distinct stages in our model: expansion ( 1 ) , hypoxia ( 2 ) , starvation ( 3 ) . Insets show cell configurations characteristic of the three stages , color scale on the insets indicates the oxygen concentrations . These stages emerge as a result of an interplay between the cells and their environment , as in the proposed plasticity-reciprocity hypothesis of Friedl and Alexander [10] . Stages in our model relate to: 1 . Conditioning the environment; 2 . A reaction to the environmental change in the behavior of cells ( new phenotypes emerging , old phenotypes disappearing ) ; 3 . New environment created by the new population . Remarkably , this shows that despite the much larger number and freedom of mutating parameters in our model , we still find the same phenomena of emergent stages . In the first stage of our model the population expands by cell growth and division . A high intracellular growth signal N0 is selected for in the population , favoring fast growing cells ( Fig 4c ) . This parameter evolves much faster than any other of the 10 mutating parameters ( see also Table 1 ) while most of the other parameters do not exhibit such a strong and clear drift by the end of the simulations ( Fig 4d ) . Indeed , the overall behavior of the model did not change qualitatively in simulations where only N0 or N0 and the chemotactic parameters are allowed to evolve ( S1 Fig ) . Since nutrients in the environment are not limiting due to the assumed prior homeostasis , these cells simply outgrow the slower ones , creating patches of high growth ( Fig 4e ) . This leads to the expansion of fast growers , and as a result , cells from newer generations appear in clumps of growth hot-spots ( Fig 4f ) . At this stage expansion can occur at any position in the population since nutrients are available at any location . In the second stage of our model the population turns hypoxic . As the number of cells grows , the intracellular pressure increases rapidly in the tightly packed tissue until about t = 5000 MCS ( see Fig 3d ) . At this time oxygen is depleted at areas further away from the source ( see Fig 4a middle inset at t = 6000 MCS ) . Fast growing cells ( high N0 , Fig 4g ) are unable to fuel their increased metabolic need through oxidative respiration and turn hypoxic ( Fig 4h ) . These cells further increase their glucose uptake ( Fig 4i ) due to the HIF1-α→ GLUT signaling pathway in our model ( Eq 15 ) , and start the production of lactate . Finally , glucose is gradually depleted at regions far from the sources as a result of the elevated glucose consumption rate of fast growing cells . In the depleted areas cells die out , and with them the cell population is gradually decreased ( Fig 3b and 3d ) . The only cells remaining are around the vessels , that eventually hijack the source ( Fig 4j and 4k ) . These cells continue to compete as in stage 1 , since the change in the environment near the vessels is minimal , and keep increasing their internal growth signal from generation to generation ( Fig 4j ) . Increasing intracellular pressure near vessels ( Fig 4k ) exerted by neighboring cells and counteracts growth . Changes in nutrient levels indicate that our model selects for cells exhibiting the well-known Warburg effect , whereby cells metabolize glucose through glycolysis even in the presence of oxygen ( aerobic glycolysis ) [44] . Cells in our model initially shift to aerobic glycolysis to support their metabolic need escalated through competition , shown by the increasing levels on intracellular hypoxia and ROS ( Fig 4l ) . This results in an increase in extracellular oxygen ( Fig 4a ) . Despite the availability of oxygen , cells are unable to revert to a more efficient full respiratory metabolism due to production of ROS which stabilizes HIF1−α and limits the amount of metabolic flux through respiration ( Eq 15 ) , thus keeping it in a state of hypoxia in our model ( Fig 4l ) . Nevertheless , cells do consume oxygen but it is significantly lower than glucose uptake ( Fig 4m ) . Taken together , these results show that our model exhibits different stages of development similar to previously published studies . Remarkably , this progression emerges in spite of an almost completely unrestricted evolution of a large number of phenotypic parameters . Tumors in this model are initialized at random positions , but due to the explicit representation of localized nutrient sources , we show that they occupy the vicinity of blood vessels at later stages . This is enhanced by the more realistic representation of cells in the CPM where cell shape and compressibility allow cell rearrangements within the packed tissue as opposed to the more rigid CA models exploring progression [17–19] . Secondly , we show that our model selects for cells exhibiting the Warburg effect despite the lack of growth advantage of fermenting cells . To test if the stages of tumor progression depend on the phenotypic mutation rates , we simulated the model for a series of mutation rates . Whereas the non-mutating population keeps a constant size , all mutating populations exhibit an initial increase in cell numbers ( Fig 5a ) . In highly mutating populations ( 5% and 10% ) this increase is followed by a drop in cell numbers . This drop is observed later in populations with 5% mutation rate , and population decrease is just starting at the end of the simulations in populations with 1% mutation rate . Note that the repetitions reproduce the behavior fairly well , suggesting the robustness of the system . Therefore we suggest that similar stages occur at lower mutation rates , and the time needed for reaching each stage depends on the mutation rate . This is supported by the changes in nutrient levels in the simulations , which react faster to change than the total number of cells ( Fig 5b–5f ) . In healthy , non-mutating populations , nutrient levels and population size is stabilized ( Fig 2b ) . In mutating populations , cells evolve a higher metabolic demand in parallel with increased proliferation . This results in an increase in population size and decrease in oxygen levels , followed by a decrease in glucose levels . As oxygen becomes sparse , cells turn hypoxic and switch from oxidative phosphorylation to aerobic glycolysis , resulting in an increase in lactate levels . We have found the same behavior in populations with different mutational probabilities ranging from 0 . 1% up to 10% ( Fig 5b–5f ) , or higher ( S2a–S2h Fig ) . Again , a lower mutational probability only delayed the changes in the nutrient levels . We did not find a qualitative difference in the progression in our simulations at different mutation rates , showing that the emergent order of stages is robust in this system . To explore the structure of the population at different mutation rates in the system , we analyzed the distribution of cells in the space of normalized mutating parameters . After subtraction of the initial parameter values and normalizing with mutational step-size , the ten-dimensional parameter space of the population was reduced to the three most prominently changing axes within each population using principal component analysis ( see Methods ) . Fig 5g shows one example population at the final time point of the simulations for mutation rates 1% , 10% , and 20% with each dot representing a cell . At low mutation rate ( 1% ) the population splits up into well-defined clones which are more spread and less well-defined at higher mutation rates . The first three principal axes contain most of the information about the shape of the population , as can be seen by the normalized weights ( eigenvalues ) of these components ( Fig 5h ) . The composition of principal axes at the final time point of the simulations shown on Fig 5g differ ( Fig 5i ) with the growth signal N0 , cell rigidity λV , and glucose chemotaxis χg playing an important role at μ = 1% . At 10% mutation rate the lactate chemotaxis parameter χl plays a distinct role in segregating the population phenotypes , while at μ = 20% the segregation is less obvious ( Fig 5g ) and is driven mainly by parameters λV , doubling volume VD , and adhesions ( ρCAM , ρMAM ) . While the composition of the main axes varies across different simulation repeats with the same mutation rate , the populations are nevertheless well characterized by the first three components in all cases ( S2i and S2j Fig ) . To better understand population structure in the simulations , we categorized the cells at each time point in the 10-D phenotype space using hierarchical clustering ( Methods ) . We measured the displacement of each cluster as the Euclidean distance between the point of origin and its center of mass and its spread as the mean distance of points of the cluster from the cluster’s center of mass . As expected , we observed that populations with higher mutation rates reach further from the origin by the end of simulations; these clusters are more spread and less dense than clusters in populations with lower mutation rates ( Fig 5j and 5k ) . Considering the whole time course of the simulation , the cluster analysis reveals that as the population explores the phenotype space , clusters from the highly mutating populations first tend to spread out and dilute more than the clusters in populations from lower mutation rates ( Fig 5l and 5m ) . These results show that the population starts to dilute much faster in phenotype space at high mutation rates , but without affecting the progression of stages apparent from the nutrient levels . Next , we tested how feedback from a larger spatial organizational level , through the nutrient supply in our case , would affect populations of different mutation rates . In a healthy tissue , nutrient supply is relatively constant . The main source of fluctuations are the slow daily change according to the circadian rhythm , and the relatively fast blood pulse . In cancerous tissues the vasculature is remodeled through tumor vasculogenesis , resulting in tortuous and leaky vessels [36 , 37] . As these vessels are less reliable , here we assume that they dysfunction from time to time , for example by becoming temporarily blocked . To model vessel tortuosity , we introduced a blocking probability for the vessels . An open blood vessel will be blocked with a probability P at every time step , and a blocked vessel will be opened with the same probability . A high blocking probability ( P = 0 . 5 ) corresponds to a healthy situation , where the resulting fast switching of the vessel is smoothened out by nutrient diffusion . A lower blocking probability introduces longer periods of nutrient deprivation but also longer periods of nutrient supply . On average these systems receive the same amount of nutrients , but in different dosage . Healthy , non-mutating populations in our simulations survived over a wide range of nutrient fluctuations . The average number of cells from 10 simulation repeats showed that these populations keep roughly the same size at different blocking probabilities , as shown on Fig 6a . At the extreme blocking probability P = 0 . 001 , only 2 out of 10 populations died out . In comparison , mutating cell populations are unable to tolerate blocking probabilities lower than P = 0 . 01 , irrespective of their mutation rate ( Fig 6a ) . Note that at high P , populations with low mutation rates have an increased population size at the end of the simulations ( t = 105 MCS ) , compared to the healthy population . This increase results from the initial stage of progression , as these populations only reach the first stage ( expansion ) by the end of the simulations . However , this advantage disappears as P decreases . The observed reduction in population size due to decreasing P could work in two ways: either by killing cells through starvation , or by speeding up the progression of stages . In the previous section we showed that a higher mutation rate speeds up the progression of the population and thus results in a reduced population size ( Fig 5 ) . This might eventually lead to extinction . If the populations under fluctuating nutrient supply go through the same stages of progression as the ones in the stable environment , the environmental indicators used in Fig 5 ( levels of glucose , oxygen , lactate ) are unsuitable , as these might change in simulations with different P . Instead , we focus on the intracellular evolution of traits , that are not altered directly in these experiments . The first trait to be selected for is the intracellular growth signal of the cells ( N0 ( i , t ) ) that exhibits a run-away dynamics ( Fig 4c ) . If the blocking probability accelerates the progression through the stages , it should increase the selection pressure on the intracellular growth signal as well . Contrary to this expectation , we found that the trend in the average value of the intracellular growth signal remains approximately the same in simulations across different P values , and even slightly decreases at lower P ( Fig 6b ) . Similarly , other cellular measures ( such as hypoxia , ROS , or pressure ) , or cellular parameters ( such as the chemotaxis parameters ) show the same behaviors irrespective of P ( S3 Fig ) . Therefore , longer nutrient fluctuations do not accelerate the evolution of the population , and the reduction in population size is not a result of the acceleration of the same evolutionary dynamics . Instead we conclude that as cells deplete the nutrients in the environment due to their increased consumption , the chance for survival in systems with longer fluctuations is reduced . Next we altered P during the time of simulation runs . We tested how the population reacts if the blood vessels become increasingly tortuous , starting from a healthy state ( fast switching ) progressing to a tortuous vasculature ( slow switching ) . Blocking probability in these simulations is decreased gradually in the simulations , following a geometric progression P ( t + 1 ) = r P ( t ) with an initial value of P ( t = 0 ) = 0 . 5 and ratio of r = 0 . 999876 . Once the progression reaches P ( t f ) = 0 . 001 at tf ≈ 50 , 100 MCS the blocking probability is not decreased further ( P ( t > t f ) = P ( t f ) ) . Similar to the stable system , the mutating populations ( mutation rate μ = 0 . 1 ) are initially driven into the high consumer state by cell-cell competition ( Fig 6c solid red line showing growth signal parameter N0 ) . When the fluctuation probability reaches the order of P ( t ) = 0 . 01 ( t ≈ 31 , 500 MCS ) , the populations start to die out ( Fig 6c red dashed line showing number of surviving populations ) , similar to the case of static low blocking probabilities . Note however , that non-mutating populations ( Fig 6c blue ) are able to survive increasing fluctuations in the nutrient supply . This shows that a changing nutrient supply does not necessarily influence the direct competition among cells . Inconsistency of nutrients may emerge from the dysfunctional tissues of the emergent tumor occluding vasculature . To represent this feedback , we examined how the population behaves when the consistency of nutrient supply is related to the amount of cellular coverage in the tissue . We created a feedback between the density of the tissue ( measured as tissue surface coverage ρ , with 0 ≤ ρ ≤ 1 ) and the fluctuating source . For a fully populated tissue we kept the nutrient switching probability high , P = 0 . 5 , providing a smooth nutrient supply , and decreased it linearly with the cell density to model the variability in nutrient supply . Thus: P ( ρ ) = 0 . 499 ρ + 0 . 001 . In these simulations the mutating population persists at approximately the same level as in the healthy case ( Fig 6d ) . The emergent tissue coverage yields an approximate fluctuation probability of P ( ρ ) ≈ 0 . 27 , sufficiently high to support mutating populations . While the relationship between nutrient supply stability and living cell density is experimentally unclear , our results suggest that a linear relationship between stability and density is insufficient to drive the aggressive cells to extinction , similar to what is expected of an expanding pathological tumor . The effect of low nutrient levels with two consecutive surges of high oxygen levels has been shown to affect selection in a model of evolutionary tumor growth [17] . Here we used localized nutrient sources and stochastic supply , rather than two deterministic and uniform pulses . We show that in our system , inconsistent nutrient supply does not increase the speed of cellular evolution , however , it does reduce the viability of the unstable cell populations . We found similar results when inconsistency is considered progressively increasing or proportional to the tissue coverage . Our results suggest that , while consistent nutrient supply promotes cell-level selection of fast growing cells , inconsistent nutrient supplies that put a larger demand on the tissue exert selection at the tissue scale and provide higher chances of survival for populations with cellular quiescence . In a simulated healthy tissue devoid of mutants , cell growth is independent of spatial localization , growth is limited intrinsically by a constant growth signal balancing spatial confinement . After this intrinsic limitation is lifted through uncontrolled mutations and competition , growth becomes clustered in growth hot-spots ( Fig 4e ) . These spots are populated by overly proliferative cells , resulting in clonal expansion: Fig 7a shows configurations at two time points in the same simulation color-coded for descendants . At a later stage , when cells deplete nutrients , proximity to the sources dictates the growth rate ( Fig 4j ) . In the resulting environment cells closer to the source grow faster and divide , while cells further away starve and die . This differential growth gives rise to a directed cell movement from the vicinity of the sources to the depleted areas , apparent from the short cell trajectories shown in Fig 7b . To demonstrate that the segregation patterns were indeed linked to the nutrient sources and are not the result of finite system size , we performed simulations with randomly scattered blood vessels instead of the regular distribution , similar to previous studies [24 , 31] . The example trajectory plot on Fig 7c shows that the outflow of cells is correlated with the nutrient positions . The population becomes segregated , as the probability of a cell invading a neighboring region is diminished . As a result of this differential growth cells near the source take over the vicinity of the vessel and spread their phenotype in this region ( Fig 7d ) . While cells within each segregated part of the population have highly similar phenotype , the different parts evolve independently of one another . This is shown by the distribution of phenotypic traits over time in each population ( Fig 7e ) . This gives rise to independently evolving quasi-species-like populations within the tissue , and might be analogous to the observed heterogeneity in tumors . In simulations with low but constant vessel blocking probabilities cells around a blocked source are able to leave their region and migrate to another active source , as exemplified by the trajectories in Fig 7e . The ability to detect and to move to a neighboring active source is crucial for the cells for survival , and therefore is expected to be selected for . In our model , cells can achieve this by evolving chemotaxis towards glucose or oxygen , or chemotaxis away from lactate . Indeed , chemotaxis parameters are the second most affected parameters throughout the evolution of the population ( Fig 4c and 4d ) . Interestingly , chemorepulsion by lactate is strongly selected for although lactate in our model does not have any direct metabolic effect on the cells . This acquired property helps to orient the cells towards the nutrient sources better than oxygen , which becomes ubiquitous with more shallow gradients , or glucose , which does not diffuse far from the nutrient sources due to elevated uptake . The sum effect of these motility parameters can be expressed with a combined chemotaxis parameter defined as: χ ′ ( i , t ) = χ g ( i , t ) + χ O 2 ( i , t ) - χ l ( i , t ) . Fig 7g and 7h show the evolution of this combined chemotaxis parameter in populations from different vessel blocking probabilities and mutation rate of 10% . Indeed in all of the conditions the combined chemotaxis is selected for . In simulations with lower blocking probabilities the combined chemotaxis has increasingly higher fluctuations due to the random selection introduced by the blocking and opening of the vessels , often leading to extinction as well .
We show that a directional evolution emerges from random movement in phenotype space as the result of cell competition , driving cells from a healthy ( homeostatic ) state to a more aggressively expansive phenotype . This is consistent with previous findings of Anderson and colleagues [17] , who showed that a similar drift towards aggressive phenotypes emerges if cells are allowed to mutate randomly into one of a 100 predefined discreet phenotypes . In contrast to these abrupt changes in behavior , our model only allows small changes in mutation . This choice lends more persistence to the clones in the population , since more mutation events are required to diverge from an existing clone . Faster growing cells are selected in our model , which then go on to colonize the population by means of clonal expansion . Progression of phenotypes has been observed previously in other models of tumor evolution , where the authors also considered the toxic effect of acidification due to glycolysis [18 , 19] . In [18] , cells first develop the ability to survive in hypoxic environments by lowering their apoptotic response threshold to low oxygen levels . This is followed by a metabolic switch to glycolysis and finally the emergence of the acid-resistant phenotype . In contrast , acid-resistance was proposed to emerge first followed by evolution of glycolysis in another model where nutrient sources were represented as point sources [19] . The distinct stages exhibited in our model ( clonal expansion , oxygen depletion , and starvation ) are accompanied by a sudden increase in glycolytic activity which is then moderated onto a stable medium level ( Fig 4l ) . In our model the population turns hypoxic ( Fig 4l ) before developing a distinct chemo-repulsive response ( Fig 4c ) . We found the progression robust against changing the mutation rate , however , at higher mutation rates the population was able to explore the phenotype space faster and in more dispersed clusters ( Fig 5 ) . Although lactate in our model does not affect cells , cells gradually develop a chemorepulsion away from lactate ( a negative χl on Fig 4c and 4d ) . Lactate accumulates at regions where high consumers deplete glucose , therefore cells use lactate chemorepulsion as a compass to navigate away from the high-consumer niche towards a more supportive environment . This novel feature in our model highlights how phenotypes that show no apparent advantage at the cell level could be selected for based on the altered micro-environmental conditions . Local expansion in tumor growth models has recently been described by Waclaw and colleagues [16] . Their study focused on heterogeneity in passenger mutations while the probabilistically occurring driver mutations set the proliferation advantage . Cell motility there acts to blur intratumor heterogeneity by allowing proliferating cells to invade ‘cell-free’ areas where further proliferation is not inhibited by other cancer cells . While this model is able to reproduce the clonal expansion features of tumors , it neglects the potential inhibitory effect of spatial constraint produced by the surrounding stromal tissue . Proliferation diversity in tumors was shown to be essential to explain experimentally observed tumor morphology by an earlier study using the cancer stem cell hypothesis [20] which interprets the tumor as a conglomerate of self-metastases . The emergent clonal expansion patterns in our model ( Fig 7a ) are reminiscent of these self-metastases and therefore suggest that slight differences of proliferation rates within the population are sufficient to generate these patterns , as opposed to the sharp distinction between cancer stem cells and non-stem cells . Previous models of tumor evolution typically neglect the confining effect of the surrounding healthy tissue although spatial confinement can play an important role when studying the effects of treatment recovery in a tumor with a cancer stem cell population [21] . When a large portion of cells is killed by therapy , the internal cells have access to growth space and are able to regrow the tumor . In other words: space limitation keeps the growth inhibited . To mimic this situation here we restrict the growing tumor to a confined space , making them a model of an in vivo system . We note that simulations in our system do not develop a necrotic core as in other models , as dead cells simply shrink and disappear from the simulations without inducing any signaling response from neighboring cells or without increasing spatial confinement . Therefore our system focuses on live tumor cell evolution where necrotic cells may be considered as extruded from the simulated monolayer region into the underlying necrotic core , removed by the immune response and/or drained by the lymphatics . The source of nutrients in previous models is typically considered uniform , or is provided in the environment in a dispersed fashion . The source of nutrients are the blood vessels in our model , similar to the more recent studies of Shirinifard and colleagues [29] , Powathil and coworkers [31 , 32 , 35] , or Patel and colleagues using a hybrid cellular automaton model [45] . Two recent studies showed that arrangement of vessels and their 2D representation affects tissue oxygenation and may alter the outcome of a simulated radiotherapy [24 , 25] . Nutrient diffusion and utilization create a spatial gradient in the supplies which is translated into a differential growth pattern in our system . Differential growth creates a collective flow of cells outwards from the sources ( Fig 7b , 7c and 7f ) . This outward flow is counter-acted by the emergent chemotactic tendency of cells to move closer to the source ( Fig 4c and 4d ) . Chemotaxis in a changing environment provides additional advantage in locating active nutrient sources ( Fig 7f ) . Blood vessels in our model are immobilized entities , vascular remodeling is not included . Inconsistency in nutrient supply is implemented as stochastic switching of blood vessel activity , but without feedback from the pressure or hypoxia in the tissue as in the phase field model of Yan and colleagues [34] . Simulations where the blocking probability P is a function of time ( Fig 6c ) or tissue coverage ( Fig 6d ) showed that the progression of stages is not altered in our system . A higher number of connected loops have been shown to emerge in 3D models of tumor angiogenesis than in 2D , predicting that complete blockage of circulation is very rare [22 , 23] . Here we studied local vessel blockage which would still have a local effect on our system , even if at a reduced frequency . Previously Anderson and colleagues showed that surges of oxygen induce diversification into a population of simulated cells evolving under low oxygen levels [17] . A repeated , second surge was shown to induce phenotypic segregation in these simulations [17] . Here we studied how inconsistency of the nutrient supply affects the population using stochastic ( rather than deterministic ) switching . In contrast to Anderson’s study , cells do not receive a constant low oxygen supply in our model and are therefore prone to extinction . We found that populations with increasing mutation rate are increasingly sensitive to nutrient fluctuations ( Fig 6a ) , although the evolutionary trend at the cell level remained largely unaffected ( Fig 6b , S3 Fig ) . In our model the aggressive phenotype analogous to cancer is a natural consequence of selection on the cell-level and random mutation . When selection pressure is applied on the tissue level ( in the form of fluctuating nutrient supplies ) the overall fitness ( or survival ) of the cancerous population proves to be lower than that of the healthy population , potentially due to depletion of ambient nutrient resources as a form of competition . Cancerous cells create an insecure environment that is more sensitive to stress coming from outside the cell population . This theoretical exploratory study opens questions in how to best approach normalization of the tumor vasculature . In the model , healthy cells are able to withstand the irregularities in nutrient supply better than cancerous cells . Based on this observation , it is tempting to speculate that irregularities of the tortuous tumor vasculature might serve a similar role in real tumor development . It is important to bear in mind that these results are based on a rudimentary model of tumor development . In addition to the simplifications discussed above , cell-cycle regulation is overly simplified in our model as opposed to the study of Powathil and colleagues [31] where it is in the focus of the study; cells in our model lack an explicit way to store surplus energy as opposed to the cumulative health-factor of Swat and co-workers [30] representing the cells’ tolerance against starvation . Due to its 2D nature , our model is unable to account for the out of plane transport of nutrients or cells . However , using our quasi-2D model allows the study of several processes taking place in epithelial tissues , where most of the tumors arise . Importantly , the implemented cellular metabolism is overly simplified to enable the exploration of the system . After the foundation of this model framework is set out , it could be expanded with more detailed intracellular metabolic networks , for example , using spatial dynamic flux-balance analysis [46] . These multiscale models will lead to a tighter integration of computational and experimental work similar to the “symbiotic” approach described in a recent angiogenic sprouting study of Cruys and colleagues [47] . Further future work includes the more thorough exploration of different mutation rates for different traits . One of the ultimate goals of computational systems biology is constructing a virtual tissue in order to predict efficient treatment of various diseases . This can be achieved by adding homeostatic mechanisms to the tissues such as contact inhibition of growth , or introducing an internal energy storage as in the study of Swat and colleagues [30] . Inclusion of a dynamic angiogenesis model ( e . g . [22 , 29 , 48–50] ) might involve exploring the roles of different blood vessel placements , or implementing angiogenesis models already available in the same platform . Finally , the system enables the measurement of fitness at different spatial levels and in different ( micro- and macro- ) environments which could serve as a basis for exploring evolutionary trade-offs in using computational simulations . In this study we introduce an unbiased evolutionary approach to studying the evolution of interacting tissue cells . The model includes localized source of nutrients ( oxygen , glucose ) and sink for intermediate metabolites ( represented by lactate in our model ) , and a simplified cellular metabolism including glycolysis and respiration . Cells in the model are spatially confined and no explicit distinction is made between stromal or tumor cells . The model exhibits distinct stages of development with an emergent evolutionary drift towards rapid growth , high glucose consumption , and hypoxia . This is accompanied by a Warburg-effect , whereby cells become unable to return to respiratory metabolism even at high oxygen levels . The simulated tumor exhibits clonal expansion and eventually gives rise to similar phenotypes around each nutrient source which then evolve independently later on . Finally , we found that the emergent rapid growing population is highly sensitive to intermittent nutrient supply , such as caused by leaky tortuous blood vessels .
Cells are initialized in a monolayer with an initial volume and target volume of 25 lattice sites on a lattice of 200 × 200 and four endothelial cells ( Fig 1a ) . In the initial regime of the simulations , nutrients are allowed to diffuse into the system to obtain a natural distribution resulting in high glucose and oxygen and low lactate levels ( Fig 1b ) . During this equilibrating time cells are allowed to metabolize , but cannot grow , shrink , move , or mutate . This state represents a stable , homeostatic tissue with sufficient nutrient availability . When the temporal changes in diffusing nutrients is less than 5% in a time interval of 100 MCS , the initial regime is closed , cells are released and the simulation is started . Nutrient fields at the beginning of the initial regime ( Fig 1b ) are initialized using pre-generated concentration distributions to expedite equilibration . These initial concentration fields are generated by simulating a cell population in the initial regime starting with zero concentrations: ∀ x → ∈ Λ : g ( x → , t = 0 ) = 0 , O 2 ( x → , t = 0 ) = 0 , l ( x → , t = 0 ) = 0 . The concentration fields are saved when the total concentration levels remain within 5% over a 100 MCS iteration period: ∑ x → ( s ( x → , t = t save ) - s ( x → , t = t save - 100 MCS ) ) < 0 . 05 ∑ x → s ( x → , t = t save - 100 MCS ) for s ∈ {g , O2} . Note that no lactate is present as all cells are respiratory in the initial regime . All mutating parameters in all cells are initialized with the same value , after which all cells undergo a mutation attempt to provide an initial heterogeneity to the population . Adhesion parameters are set to J ( c , c ) = J ( c , EC ) = Jmax , and J ( c , m ) = Jmax/2 for the three region ( “cell” ) types: stromal cells ( c ) , endothelial cells ( EC ) , and cell-free areas ( m ) . To allow the full range of interactions for cells , we fix the adhesion molecule density values of the extracellular region and the endothelial cells as: k CAM , CAM × ρ CAM ( i ∣ τ ( i ) : EC ) = J ( c , EC ) = J max k MAM , MAS × ρ MAS ( i ∣ τ ( i ) : m ) = J ( c , m ) = J max / 2 ( 20 ) With this choice the effective Jeff values are allowed to change between 0 and Jmax for Jeff ( c , c ) , and Jeff ( c , EC ) . For interaction with the medium , Jeff ( c , m ) is allowed to change between 0 and Jmax/2 . Diffusion parameters for glucose , oxygen , and lactate were set to Dg = 10−9 m2/s and D O 2 = D l = 10 - 11 m 2 / s following the approximation of Jiang and coworkers [40] , and decay is neglected for all of these chemical species ( ωs = 0 , s ∈ {g , O2 , l} ) . Decay rate of ROS is set to a constant ωζ = 0 . 1 per MCS . The amount of ATP produced from 1 glucose molecule through respiration was chosen as αr = 38 . This approximation is a theoretical upper limit for the process , in reality this number is expected to be lower . However , due to the compensation with the number of glucose transporters ( see Eq 13 ) the exact value of this parameter is not expected to change the behavior of the system . To analyze the population behavior over the simulations we analyzed the evolving parameters in the following way . For every parameter p ( i , t ) of cell i at time t we calculated a normalized parameter as pn ( i , t ) = [p ( i , t ) − p ( i , t = 0 ) ]/σp where σp is the characteristic step size of the parameter . This way the normalized parameters reflect their distance from their origin in terms of mutational step-size . The distribution of cell phenotypes in this space was analyzed at time t by finding the principal components of the 10-dimensional set of pn ( i , t ) points for all i and p using singular value decomposition from scientific python ( SciPy ) . The axes corresponding to the first three largest eigenvalues were selected as the main components of the cloud . Clustering in the normalized phenotype space was performed using hierarchical clustering of SciPy with the Ward method and Euclidean distances . To distinguish clusters we established a cutoff cophenetic distance of 100 manually by evaluating a set of selected dendograms and distribution of clusters plotted on the first three main axes . Displacement of the clusters is calculated as the ( 10D ) Euclidean distance of the center of mass of the cluster from its initial point of origin . Spread of clusters was calculated as the mean distance of points in the cluster from its center of mass . Density of clusters was calculated by dividing the spread by the number of points in the cluster . | Multicellular organisms control their cells to facilitate higher level function of the whole organism . In tumors , this control is lost and cells are allowed to enhance their fitness by , for example , increased proliferation . Tumor cells continue to change their behavior through accumulating mutations , leading to a complex and highly heterogeneous structure . Several computational studies have investigated the emergent structures of such mutating group of cells and led to the recognition that cellular heterogeneity within tumors is essential to explain the observed morphologies . Most of these studies have considered a limited number of possible cell phenotypes , an isolated tumor cell population , unlimited growth space around the tumor , often with an inexhaustible source of nutrients . Here we introduce a modeling approach that takes into account the limited growth space around the tumor , localized nutrient sources , cellular metabolism , and mutation in a continuous phenotype space . The model reproduces the Warburg effect due to the limited nutrient supply leading to an irreversible switch in cellular metabolism , and , consistently with previous models , exhibits stages of development together with a natural selection for a rapidly growing phenotype . This phenotype locally emerges in stable environments , but when nutrient supply becomes erratic , these show less resilience and are outcompeted by slow growers . |
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Pathogenic bacteria such as Listeria and Yersinia gain initial entry by binding to host target cells and stimulating their internalization . Bacterial uptake entails successive , increasingly strong associations between receptors on the surface of bacteria and hosts . Even with genetically identical cells grown in the same environment , there are vast differences in the number of bacteria entering any given cell . To gain insight into this variability , we examined uptake dynamics of Escherichia coli engineered to express the invasin surface receptor from Yersinia , which enables uptake via mammalian host β1-integrins . Surprisingly , we found that the uptake probability of a single bacterium follows a simple power-law dependence on the concentration of integrins . Furthermore , the value of a power-law parameter depends on the particular host-bacterium pair but not on bacterial concentration . This power-law captures the complex , variable processes underlying bacterial invasion while also enabling differentiation of cell lines .
Pathogenic bacteria such as Yersinia pseudotuberculosis and Listeria monocytogenes exploit a latent phagocytic activity in gut epithelia to pass into deeper tissues that are optimal for survival and proliferation [1] . Particularly well studied is the 'zipper' phagocytic process used by Yersinia: Envelopment of a bacterium by a host membrane is aided by successive binding events between bacterial surface receptors called invasins and cognate β1-integrins exposed on the surface of host intestinal multifold cells ( M-cells ) in the gut lumen [2 , 3] . Zipper-mediated phagocytosis can be broken down into three successive steps: The first step is contact and adherence; second is phagocytic cup formation; and the final stage is phagocytic cup closure . Contact does not require active alterations to host actin cytoskeleton but rather hinges on strong associations between bacterial receptors and host ligands . In contrast , the creation and resolution of the phagocytic cup is a dynamic process involving numerous signal transduction and structural genes coordinated in receptor clustering , membrane phospholipid redistribution , and cytoskeletal re-organization [4] ( S1 Table ) . A particularly critical aspect is the high affinity of invasin for β1-integrins , which promotes phagocytosis by increasing cell-surface adhesion [5] and outcompetes extracellular matrix proteins for enough host integrins [6] to sustain bacterial engulfment . A striking observation emerging from these studies is the cell-cell variability in bacterial uptake: Isogenic host cells cultivated under the same conditions show differences in the number of bacteria that will invade . Past studies have reported that this variability manifests as bimodal uptake dynamics , where a fraction of mammalian cells take up bacteria while the other fraction is devoid of bacteria [7–10] . This property is not limited to cultured experiments since Yersinia introduced into the intestines of mice are often found in islands of M-cells surrounded by bacteria-free regions and that the number of bacteria in infected cells spans a wide range [11] . Such differences may arise from the stochastic nature of cellular interactions [12 , 13] . Also , the uptake may have resulted from pre-existing , long-lived differences in host cell properties ( e . g . cell surface , signal transduction network , cytoskeleton ) that have stochastic [14] and deterministic [15] origins . The complexity in bacterial uptake presents a challenge for explaining variability . Here , to characterize the fundamental property of bacterial uptake , we employ kinetic modeling and experiments that distill a simple power-law , relating uptake probability—the amount of bacteria per host cell scaled by the bacteria concentration—with host receptor levels . Our study demonstrates that a simplified model of successive binding events that occur in the zipper mechanism is sufficient to generate an ultrasensitive , threshold-dependent response to host receptors . Thus , minute cell-cell differences in host receptors are amplified into large differences in uptake . We describe how different hosts and bacterial strains translate into different power-law parameters which serves as the basis of a novel , operational definition of cell type .
We modified a previous cell culture protocol to measure bacterial uptake by HeLa human cervical cancer-derived cells [16] . In particular , we engineered non-pathogenic E . coli to express invasin from Yersinia pseudotuberculosis [17] ( Materials and Methods ) . Invasin-mediated bacterial entry via binding with b1-integrins is one of the best-studied zipper-mechanism systems and has been used in a number of biological and potential clinical applications [7 , 9 , 18 , 19] . Remarkably , binding of invasins to mammalian host receptors is sufficient to facilitate entry into non-phagocytic mammalian cells . In fact , non-pathogenic bacteria expressing invasins [20] or even beads coated with invasins [21] can enter into mammalian host cells . Thus , the entry dynamics of this engineered bacterial system can be attributed to the interactions between invasin and host receptors . To track and quantify uptake in individual hosts , we engineered E . coli to constitutively express a green fluorescent protein ( GFP ) . In each experiment , the engineered bacteria were co-cultured with HeLa cells for 90 minutes in well-mixed conditions to mitigate the effects of heterogeneity in the bacterial population . In addition , the mixture was co-incubated in the presence of sub-lethal gentamicin before washing and measurement ( S1A–S1C Fig ) . This gentamicin treatment served two purposes by inhibiting bacterial growth: First , it reduced differences between bacterial numbers and our calculation of MOI that could arise during co-culture . Second , it minimized alterations in GFP signals due to bacterial growth . Consistent with observations reported in the literature [18 , 22] , fluorescence microscopy confirmed drastic cell-cell variability in bacterial uptake by HeLa cells ( Fig 1A ) . Some cells were devoid of bacteria , whereas the others each contained a wide range . This property was consistent with flow cytometry measurements ( Fig 1B ) : At intermediate bacterial concentrations , a bimodal distribution of GFP fluorescence arises where within a single population there exist both uninfected cells ( i . e . low mode ) and infected cells ( i . e . high mode ) . Even within the infected subpopulation , there was drastic variability in fluorescence , indicating a wide range of bacterial numbers in individual cells . We note that the characteristics of bacterial uptake depended on the bacterial multiplicity of infection ( MOI ) as well as the host cell type ( Fig 1C ) . Uptake in all the cell lines we tested , most of which are cancer models , was positively correlated with MOI but the amount of uptake was quite variable . Bacterial uptake variability within an infected population of cells has been speculated to arise from cell-cell variability in host surface membrane properties [11] . A trivial explanation is that variability in uptake reflects a wide , bimodal distribution of host β1-integrins or GFP signals in bacteria . However , immunolabeling experiments revealed a relatively narrow , unimodal distribution of β1-integrins across HeLa cells ( S1D Fig ) . Similarly , GFP signals in bacteria showed a tight and unimodal distribution ( S2 Fig ) . Hence , we hypothesized that the dynamics arising from zipper-like interactions may be responsible for generating the observed bimodal uptake from a unimodal β1-integrin distribution . To examine the mechanistic basis of the variable bacterial uptake , we developed a kinetic model consisting of a set of ordinary differential equations that capture two important aspects of the zipper mechanism ( Fig 2A , supporting text ( S1 Text ) and tables ( S1–S4 ) Tables ) . First , invasin-integrin interactions are inherently cooperative [23] as sequential binding events are increasingly likely due to host-pathogen proximity and the stabilizing role of invasin-integrin clustering [21] . Second , bacteria must maintain a minimum number of invasin-integrin interactions to remain stably attached to host cells . We make the simplifying assumption that bacteria and β1-integrins interact as if they were in a homogeneous , well-mixed system . Furthermore , rather than explicitly describing every single receptor binding event , we divide the uptake process into three reversible stages ( i . e . the 3-stage model ) . Initially , bacteria bind weakly to a small number of β1-integrins ( state B1 ) and these initial interactions increase the avidity of subsequent binding events . In the second stage , weakly-bound bacteria can adhere in a more stable fashion by interacting with an intermediate number of β1-integrins ( state Bm ) , which we assume is sufficient for successful adherence even after repeated washing . In the final stage , a maximal number of β1-integrin interactions can be formed ( state Bn ) , representing the saturation of invasin receptors and a state that corresponds to bacterial engulfment and internalization [2 , 3] . Thus , we define bacterial uptake in this study is as the sum of bacteria achieving at least the intermediate binding state ( i . e . sum of Bm and Bn ) to represent both stably-adhered and internalized bacteria . In fact , immunostaining of a single infected host cell with anti-LPS shows both stably-adhered and fully internalized bacteria ( S3 Fig ) . We note that the number of β1-interactions depend on the amount of invasins expressed in bacteria cells , with successful adherence and internalization likely requiring a large number of interactions . Thus , rather than modeling each binding event explicitly to match experimental data , our goals with the 3-stage and the full model are to explore the conceptual basis for the experimentally observed variability in bacterial uptake . Indeed , the qualitative behaviors of our full model , in which each binding step is explicitly modeled , are not especially dependent upon the nominal values of intermediate ( m = 10 ) or maximal number ( n = 100 ) of interactions . Consistent with experimental measurements , our model predicts that both the mean uptake per cell and the fraction of the population infected increase with the bacterial MOI ( Figs 2B and S4A ) . Simulation results show that these behaviors arise from a threshold relationship between uptake and host receptor number . In particular , host cells with fewer than threshold numbers of receptors do not take up bacteria; above a threshold , uptake is positively correlated with the number of host receptors . Since we assume a bi-molecular binding reaction between bacteria and mammalian receptors for the initial binding step , a higher bacterial concentration would require a lower mammalian receptor concentration to enable bacterial uptake . Thus , bacterial concentration modulates overall uptake while reducing the minimal threshold of host receptors . For example , uptake only occurs in cells with more than 6∙105 β1-integrins at 25 MOI ( Fig 2B; dark green ) whereas only 1∙105 β1-integrins are needed at 1000 MOI ( Fig 2B; brown ) . The threshold response produced by the zipper model indicates a sensitive dependence of bacterial uptake on bacterial concentration . For a given host receptor concentration , our model predicts that the bacterial uptake increases linearly ( in log-log scale ) with the bacterial concentration ( Fig 2B , inset ) . This property could be expected as we have assumed independent binding of bacteria to receptors . In other words , from the perspective of a single bacterium , its uptake would solely depend on the host receptor concentration . Consistent with this notion , when scaled with respect to the corresponding MOI values , the different dose responses in Fig 2B collapse into a single curve , which is approximately linear ( Fig 2C ) . This curve summarizes how uptake depends on host receptor number . In essence , increasing bacterial MOI extends the uptake curve into a lower range of host receptor concentrations , as evidenced by a downward shift in the mean . Importantly , the sensitive threshold relationship and unified uptake trajectory were recapitulated by a zipper model explicitly describing all 100 binding/unbinding steps , indicating these behaviors are not specific to the simpler 3-stage model ( S4B Fig ) . The approximately linear relationship between the logarithm of uptake and host receptors indicates the power-law dependence . Indeed , the inset in Fig 2D show a fit by the equation logP = β·logR—logKDeff , where β and KDeff are fitted parameters . Sequential perturbation of zipper model parameters shows that these changes can be mapped to changes in both power-law parameters ( S4C Fig ) . In particular , changes to the dissociation constants for binding of B and B1 to receptors had the greatest effect . Altogether this analysis shows that a simple power-law provides a concise summary of zipper-mediated uptake . How does such a simple dependence arise ? Highly cooperative binding processes can often be approximated by the Hill equation [24] , in which the fraction of particle binding as a function of the log of the input follows a characteristic sigmoidal trajectory . Zipper model simulations reveal that uptake probability as a function of receptor behaves in this characteristic fashion according to P = Rβ / ( KDeff + Rβ ) ( Fig 2D ) . A power-law arises if the receptor concentration is much lower than the effective dissociation constant ( i . e . KDeff >> Rβ ) . A large KDeff can arise from the reversible , multi-step nature of uptake that makes achieving the uptake state less likely than if it were a single binding event . To test the predicted power-law , we needed to measure , in single host cells , the concentration of β1-integrins in addition to the bacterial uptake ( as measured by GFP ) . To this end , we used a non-activating antibody to fluorescently label β1-integrins [25 , 26] . In principle , labeling with antibody may inhibit bacterial uptake by serving as competitive inhibitor . To examine impact of antibody on uptake , we also extended the model to account for sequestration of β1-integrins ( S5A Fig ) . Modeling ( S5B Fig ) and experiments ( S5C and S5D Fig ) reveal that indeed , antibody addition could result in reduced uptake ( over 40% reduction at > = 1 . 5 μg/mL ) . We chose a relatively low concentration of antibody ( 0 . 15 μg/mL ) that reports the relative amount of receptors on host cells while not having a severe effect on bacterial uptake ( S5E Fig ) . While antibody-bound receptors may not be directly involved in bacterial uptake , we reasoned that , at a sufficiently low concentration , the antibody-bound receptors may be used as a proxy reporter for the relative amount of β1-integrins . After co-culture of increasing amounts of GFP-expressing E . coli harboring a plasmid permitting arabinose-inducible control of invasin ( pBACr-AraInv ) with HeLa cells , flow cytometry was conducted to obtain data shown in Fig 3A . In the absence of induction , bacteria carrying pBACr-AraInv cannot infect host cells , which was reported previously [27] and observed by us by co-incubating HeLa cells with uninduced bacteria ( S6A–S6C Fig ) . We further showed that bacterial uptake increased with increasing concentrations of invasin induction ( S6D–S6G Fig ) . Further validating the construct , our qPCR experiments showed significantly increased invasin expression in arabinose-induced bacteria compared to uninduced bacteria ( S7 Fig ) . Consistent with previous modeling ( Fig 2B ) , collected data suggested an increase in the fraction and overall amount of uptake with bacterial MOI . To simplify the data for comparison , a centroid and mean level of β1-integrins at a given fraction of infected host cells were computationally estimated from each flow cytometry sample at different MOIs ( Fig 3B ) . In each population , single-cell measurements revealed a positive correlation between the infected host cells and the corresponding level of β1-integrins ( Fig 3C ) . With increasing bacterial MOI , the major axes of GFP+ subpopulations were shifted towards higher GFP levels , consistent with an overall increase in uptake , and towards lower β1-integrin levels , indicative of a reduced threshold of receptors required for uptake . When GFP values were scaled with their respective MOI for the fraction of infected host cells , the curves collapsed into a single linear line that has high correlation ( Fig 3D ) , similar to our previous simulation results ( Fig 2C ) . This striking observation indicates that the correlation between the uptake and host receptor levels follows a power law as predicted by model . These behaviors were reproduced using the GFP-expressing E . coli strain with invasin expressed from a different promoter ( pSCT7Inv; S5F–S5H Fig ) . A final prediction of our model is that the location of probability curves could be modulated by varying the invasin KDeff ( Fig 3E ) . Indeed , when the bacterial MOI was fixed at 200 , increasing arabinose shifted the scaled GFP values to higher GFP and lower host receptor levels ( Fig 3F ) . Our analysis demonstrates that , for a wide range of system parameters , zipper-mediated uptake is captured by a power law . Changes in many kinetic parameters associated with the zipper mechanism can be mapped to changes in the power-law parameters ( Fig 4A; simulated data in S4C Fig ) . In a typical cell , changes in zipper-mechanism parameters reflect the changes in either the bacteria or the host cells . For instance , the initial binding rate ( kfB1 ) between bacteria and host cells is affected by factors including expression levels of invasin and β1-integrins along with the prevalence of β chain integrins in host cells . For the same bacterial strain , different host cell types would likely lead to different kinetic parameters for the zipper mechanisms , and thus different corresponding power-law parameters . If this notion is correct , the power-law parameters can be used as a quantitative phenotype for a host cell type in a specific growth environment . To test this hypothesis , we measured the uptake dynamics of our engineered bacteria ( E . coli expressing arabinose-inducible invasin ) in several mammalian cell lines , grown under the same condition . In theory , we expect that different uptake probability dose responses would be measured at different bacterial MOI to share a given power-law trajectory . However , measurements done at low MOI values would likely be prone to fluctuations due to the stochastic nature of the interaction between small numbers of bacteria and mammalian cells . This would then lead to greater uncertainty in the estimated power-law parameters . In fact , our experimental measurements show this property . In particular , we treated several cell lines with increasing MOI . And these measurements showed that the power-law parameter values stabilized with increasing MOI , which likely resulted from more accurate measurement and analysis when more cells were in the GFP+ mode ( S8A–S8C Fig ) . As such , for more extensive analysis of different cell lines , we chose a high MOI for bacterial infection . Results in Fig 4B reveal that power-law parameters β and 1/KDeff for different cell lines infected with bacteria at 1000 MOI appeared to fall along a single trajectory . This is reminiscent of modeling analysis showing that perturbation to zipper model parameters modulates power-law parameter values along the same , restricted path ( Fig 4A ) . In addition , for each host cell line , β and KDeff were lower for E . coli harboring pBAC-AraInv relative to pSCT7Inv plasmid , possibly resulting from the lower invasin expression from pBAC-AraInv under these conditions . The approximately linear correlation between β and 1/KDeff can be understood by examining the equation governing the emergent power-law ( Fig 2D ) . Every set of perturbations results in quasi-parallel lines for a range of specific receptor concentrations , thus enabling the data collapse over several orders of magnitude for biologically relevant values ( Fig 4A ) . However , as receptor concentration approaches zero , the linear fit breaks down and all lines approach a quasi-constant locus ( S9A Fig ) . Therefore , when examining the correlation between β and 1/KDeff , we can construct an inverse linear regression fit ( S9B Fig ) such that the slope and intercept of this line correspond to the locus of convergence for receptors and for probability uptake . We are therefore left with a nontrivial dependent relationship between the power-law fitted parameters , which fall along the linear correlation between β and 1/KDeff , : β = logP*logR*-log1KDefflogR* .
Zipper-mediated phagocytosis entails coordination of a diverse set of host cellular processes . Bacterial uptake through this mechanism has been found to be highly variable , even within isogenic cells grown under tightly controlled experimental conditions [18 , 22 , 28] . In general , heterogeneous behaviors have been believed to arise from the stochastic nature of biochemical reactions [29] and differences in the local microenvironment for each cell , for example , the placement of neighboring cells [30] . Phagocytosis is particularly complex as it is affected by quantitative changes in host signal transduction molecules [31] , cytoskeletal dynamics [32] , particle receptor affinity [6] , host receptor density , particle size , and shape [33] . It has been suggested that the hundreds of signaling molecules are potentially involved [33] . The sheer complexity presents a seemingly daunting challenge for attempts to incorporate detailed molecular knowledge into a theoretical model with predictive power [34] . We show that , despite the complexity , bacterial uptake by a 'zipper' mechanism can be modeled as sequential binding between bacterial invasin ligands to host receptors . Our detailed , quantitative , single-cell measurements revealed that , in the same population of host cells , the fraction of host cells infected with bacteria increases in a monotonic fashion with the levels of host β1-integrins—irrespective of MOI . Likewise , our modeling results demonstrate a positive correlation between invasin-mediated uptake of E . coli and the relative abundance of available β1-integrins expressed by hosts . A counterintuitive , striking notion from our analysis is that uptake may be distilled into a simple empirical relation defined by two lumped parameters . Indeed , phenomenological approaches have been routinely used when detailed description is impractical , for example , oxygen binding [24] , gene regulation [35] , and bacterial growth [36] . Similarly , power-law relations have a rich history in the description of scale-invariant processes in nature [37] . Here we show that an ultrasensitive host-pathogen relationship explains a large proportion of the variation in pathogen invasion . Our results demonstrate generation of broad cell-cell variability in bacterial uptake by a Hill-like mechanism involving surface receptor interactions , though other mechanisms can contribute to this variability . For example , there could be a positive feedback in host cell cytoskeletal signaling that determine phagocytosis of bound bacteria . The emergence of a power-law correlation suggests that microscopic understanding does not necessarily enhance our ability to describe some lumped system behavior [38] . Though simple , the power-law description of uptake is robust in describing invasin-mediated bacterial uptake by mammalian cells , and the extracted power-law parameters can be useful in distinguishing pairs of E . coli strains ( expressing varying levels of invasion ) and mammalian cell lines . This represents a complementary , simple and efficient means to characterize cell physiology . Each perturbation to the system produces a unique set of power-law parameters; a library of these parameters can be collected for easily accessible sample analysis . Also , this notion can be extended to other pathogens and particles that utilize a multi-step , reversible binding process such as viruses . Similar to bacterial uptake by host cells , viruses also employ receptor-mediated endocytosis to use biological machinery in the host cells for them to replicate [39] . This behavior plays an important role in viral infections including Influenza and Rubella [40] . By providing simple predictions for multi-step receptor-mediated uptake , the power-law description can enable us to capture cellular dynamics without identifying interconnected processes involved in each step .
GFP expression plasmid pTetGFP was created by fusing a PCR-derived fragment of the gfp gene into pPROTet . E using the KpnI and BamHI enzyme digestion sites . To construct the plasmid pSCT7Inv , the invasin gene was PCR amplified from pRI203 [17] with BamHI and EcoRI enzyme digestion sites at the end and inserted into similar sites of a plasmid that contained a Kanamycin resistant maker gene and sc101 replication origin . The arabinose-inducible plasmid , pBACr-AraInv , is provided by Dr . J Christopher Anderson , UC Berkeley [27] . Top10F’ bacterial strain was transformed with either pTetGFP only or with either pSCT7Inv or pBACr-AraInv . Cells were grown overnight at 37°C in Luria-Bertani ( LB ) and diluted in Dulbecco’s modified eagle medium ( D-MEM; GIBCO® Cat . No . 31053–036 ) supplemented with 2% L-glutaminine ( Cat . No . 25030–164 ) , 1% sodium pyruvate ( Cat . No . 11360–070 ) , and 0 . 02% bovine growth serum ( BGS ) until their absorbance reading ( Abs600 ) on a plate reader was approximately 1 . 0 . At this absorbance , the number of bacteria in 1μL of the culture was approximately 2 . 0 x 106 . All mammalian cells ( provided by Dr . Joseph Nevins , Duke University ) were grown in D-MEM supplemented with 2% L-glutaminine , 1% sodium pyruvate , and 10% BGS . Prior to co-culture with bacteria , cells were seeded in 6-well plates at approximately 2 . 0 x 105/well , and were allowed to grow overnight in D-MEM with 10% BGS . Typically , bacterial overnight cultures were co-incubated with mammalian host cells in the presence of gentamicin ( 50μg/ml ) for 90 minutes to allow infection . Then the co-cultured cells were rigorously washed twice with phosphate buffered saline ( PBS ) to remove bacteria in suspension , trypsinized to detach mammalian host cells for flow cytometry , fixed with PBS supplemented with 1% formaldehyde , 0 . 33% BGS and 0 . 001% of sodium pyruvate and assayed for their GFP signals by flow cytometry ( FACSCanto II , Becton Dickinson ) . For fluorescent microscopy ( DMI6000 microscope , Leica ) , cells were imaged immediately after rigorous washing with PBS twice . For quantification of β1-integrin levels , mammalian cells were pre-incubated with a monoclonal antibody ( Cat No . MAB2253 , Millipore ) followed by detection with anti-mouse antibody conjugated to Alexa647 ( Cat No . A21237 , Invitrogen ) . A single colony of E . coli Top10F’ strain containing pBACr-AraINV and pTetGFP was cultured overnight at 37°C in LB media for 16 hours . Sub-culturing were performed with 100-fold dilution in LB with appropriate antibiotics ( Kan and Cm ) and induced with 0 . 1% arabinose when appropriate . Induced and uninduced samples were cultured for additional 5 hours at 37°C , then RNA samples were isolated from the cell cultures using RNeasy Mini Kit ( QIAGEN , cat . no . 74104 ) in combination with RNAprotect Bacteria Reagent ( QIAGEN , cat . no . 76506 ) . Using Power SYBR Green RNA-to-CT 1-Step Kit ( Life Technologies , cat . no . 4389986 ) , qPCR was conducted with primers targeting invasin ( forward primer: CTCACTCAATGGTGGGCGAT; reverse primer: CATACCAAGGAGCCAGCCAA ) and FFH was used as a reference gene for normalization ( forward primer: TGTGACGAATAGAGAGCGCC; reverse primer: GGCCAATACGGCAAAAGCAT ) . The standard curve method for relative quantification was used . Approximately 2×104 HeLa cells/well were seeded onto glass coverslips placed in a 24 well plate . On the following day , HeLa cells were then infected with invasin-expressing E . coli at an MOI of 200 by plate rotation at an rpm of 50 , in a 37°C , 5% CO2 humidified incubator for 90min . After infection , HeLa cell were washed twice with phosphoate buffered saline ( PBS ) and fixed with 3% formaldehyde/0 . 025% glutaraldehyde at room temperature for 10 min . HeLa cells were then blocked with 5% BSA in PBS for 30 min at room temperature and stained with anti-LPS E . coli ( Abcam ) . Secondary antibody conjugated to Alexa Fluor 555 ( Life technologies ) , and Hoechst 33342 ( Life Technologies ) where then incubated for 30min . Our kinetic models ( 3-stage or 100-stage ) each consist of a set of coupled ordinary differential equations ( ODEs ) . The parameters are obtained either from the literature or fitted from experimental data . Numerical simulations and data analysis were performed using Matlab ( Natick , MA ) . The detailed models are described in the supporting text ( S1 Text ) and tables ( S1–S4 Tables ) . | Uptake of bacteria by mammalian cells is highly variable within a population of host cells and between host cell types . A detailed but unwieldy mechanistic model describing individual host-pathogen receptor binding events is captured by a simple power-law dependence on the concentration of the host receptors . The power-law parameters capture characteristics of the host-bacterium pair interaction and can differentiate host cell lines . This study has important implications for understanding the accuracy and precision of therapeutics employing receptor-mediated transport of materials to mammalian hosts . |
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Virulent Agrobacterium tumefaciens strains integrate their T-DNA into the plant genome where the encoded agrobacterial oncogenes are expressed and cause crown gall disease . Essential for crown gall development are IaaH ( indole-3-acetamide hydrolase ) , IaaM ( tryptophan monooxygenase ) and Ipt ( isopentenyl transferase ) , which encode enzymes for the biosynthesis of auxin ( IaaH , IaaM ) and cytokinin ( Ipt ) . Although these oncogenes are well studied as the tumor-inducing principle , nothing is known about the regulation of oncogene expression in plant cells . Our studies show that the intergenic regions ( IGRs ) between the coding sequences ( CDS ) of the three oncogenes function as promoters in plant cells . These promoters possess a eukaryotic sequence organization and cis-regulatory elements for the binding of plant transcription factors . WRKY18 , WRKY40 , WRKY60 and ARF5 were identified as activators of the Ipt promoter whereas IaaH and IaaM is constitutively expressed and no transcription factor further activates their promoters . Consistent with these results , the wrky triple mutant plants in particular , develops smaller crown galls than wild-type and exhibits a reduced Ipt transcription , despite the presence of an intact ARF5 gene . WRKY40 and WRKY60 gene expression is induced by A . tumefaciens within a few hours whereas the ARF5 gene is transcribed later during crown gall development . The WRKY proteins interact with ARF5 in the plant nucleus , but only WRKY40 together with ARF5 synergistically boosts the activation of the Ipt promoter in an auxin-dependent manner . From our data , we propose that A . tumefaciens initially induces WRKY40 gene expression as a pathogen defense response of the host cell . The WRKY protein is recruited to induce Ipt expression , which initiates cytokinin-dependent host cell division . With increasing auxin levels triggered by ubiquitous expression of IaaH and IaaM , ARF5 is activated and interacts with WRKY40 to potentiate Ipt expression and balance cytokinin and auxin levels for further cell proliferation .
Agrobacterium tumefaciens is a pathogenic bacterium that infects several plant species . A region in the tumor inducing ( Ti ) plasmid , the transfer DNA ( T-DNA ) , is integrated into the plant genome causing crown gall disease [1] . There are essentially two groups of genes encoded on the T-DNA of virulent A . tumefaciens strains [2] . The first is responsible for producing opines , so providing a carbon and nitrogen source for A . tumefaciens , with the second group expressing the oncogenes required for crown gall development . These oncogenes include IaaH , IaaM , Ipt , gene 6b and gene 5 . It is assumed that although gene 6b and gene 5 are expendable , IaaH , IaaM and Ipt are crucial for crown gall development [3–5] . IaaH and IaaM code for enzymes that catalyze biosynthesis of auxin and Ipt mediates cytokinin biosynthesis [5 , 6] . IaaM encodes a tryptophan monooxygenase that converts tryptophan ( Trp ) into indole-3-acetamide ( IAM ) , and IaaH an indole-3-acetamide hydrolase , converts IAM into indole-3-acetic acid ( IAA ) [7–9] . Ipt ( isopentenyl transferase ) catalyzes the rate-limiting step in cytokinin biosynthesis [2 , 5 , 10] . Cytokinins can also be synthesized in A . tumefaciens cells by the chromosomal encoded miaA enzyme [11 , 12] and the trans-zeatin synthesizing ( tzs ) enzyme encoded on the nopaline-type pTi-plasmid [13–15] . A . tumefaciens secretes auxin and cytokinin from the cells to initiate crown gall development [16] and pretreatment of plant tissues with auxin and cytokinin promotes A . tumefaciens-mediated transformation efficiency [14 , 17 , 18] . Very recently it was shown that cytokinins secreted by A . tumefaciens repress a Myb transcription factor in host plant cells , resulting in an enhanced transformation efficiency [18] . The increased production of auxin and cytokinin in T-DNA transformed plant cells expressing the IaaH , IaaM and Ipt oncogenes induces cell proliferation and differentiation [19 , 20] . Therefore , a T-DNA harboring plant cell needs to initiate transcription of the three oncogenes in order to express their function . In eukaryotic cells , the RNA polymerase II complex mediates transcription of mRNAs from protein-coding genes . This complex recognizes the TATA box and the transcription start site ( TSS ) [21] within upstream promoter regions that drive the expression of the downstream coding sequence ( CDS ) . These two sequence features build the core promoter and this is sufficient to transcribe a gene [21] . TATA boxes were predicted to be present 5’ upstream of the CDS of the IaaH , IaaM and Ipt oncogenes [22–24] . In addition to initiation of transcription by the RNA polymerase II complex , expression of eukaryotic genes is usually regulated by transcription factors . These bind to regulatory sequence elements localized in the promoter regions of many eukaryotic genes and are oriented in a sense or anti-sense direction distant from the TSS [21] . For the Ipt gene of the octopine Ti plasmid pTiAch5 , a 184 bp fragment upstream of the CDS is sufficient for transcription in plant cells [25] . In particular , the region between −185 and −139 bp from the translational start codon are essential [26] . Within that region , the 30 bp sequence cyt-1 binds an as yet unknown protein from tobacco nuclear protein extracts , designated CBF ( cyt-1 binding factor ) [27] . This suggests that expression of the agrobacterial oncogenes can be regulated by host transcription factors that await discovery . A well-known response of plants to microbial pathogens is the microbe associated molecular pattern ( MAMP ) -induced innate immunity response , which includes expression of several WRKY transcription factors [28] . The expression profiles of 72 WRKY genes in Arabidopsis revealed that 49 genes are responsive to salicylic acid ( SA ) and pathogen treatment [29] . The WRKY transcription factor binding elements , the W-boxes ( TGAC ) , are present in many defense related gene promoters [28] . In addition to the induction of pathogen defense responses , crown gall development requires cell proliferation and differentiation , such as vascularization [30] . These developmental programs are synergistically controlled by auxin and cytokinin signaling pathways that lead to changes in the regulation of gene expression . The expression of some auxin responsive factor ( ARF ) genes is induced by auxin , particularly in developing embryos and vascular tissues [31] . ARFs are known to induce the transcription of genes in an auxin-dependent manner by binding to auxin response elements ( AuxREs ) in auxin responsive promoters [31 , 32] . The regulation of ARF function involves auxin/indole acetic acid ( Aux/IAA ) proteins and TIR1 ( transport inhibitor response 1 ) [33 , 34] . Aux/IAA proteins interact and repress the transcriptional activity of ARFs [35 , 36] . The F-box auxin receptor TIR1 is part of the SCFTIR ubiquitin ligase complex [37 , 38] . At increasing auxin concentrations , Aux/IAA proteins are recognized and ubiquitinylated by the SCFTIR complex and subsequently degraded by the 26S proteasome [39 , 40] . The de-repressed ARF proteins can activate target promoters . This study focuses on the transcriptional regulation of the A . tumefaciens genes IaaH , IaaM and Ipt in the host plant . The intergenic regions between the CDSs of IaaH , IaaM and Ipt of the virulent T-DNA of A . tumefaciens strain C58 ( pTiC58 , AE007871 ) showed promoter activity in Arabidopsis cells . The IaaH and IaaM genes involved in auxin biosynthesis in T-DNA transformed cells , were ubiquitously expressed at low levels . In contrast , the Ipt promoter was activated by the transcription factor WRKY40 ( AT1G80840 ) , a transcription factor that responded rapidly to A . tumefaciens infection . WRKY40 together with ARF5 ( AT1G19850 ) , which is part of an auxin-dependent signaling pathway , boosted Ipt promoter activity in an auxin dependent manner . This enhanced activity correlated with cis-regulatory elements such as W-boxes and AuxREs in the Ipt promoter and the protein interaction of WRKY40 with ARF5 . Our findings suggest that A . tumefaciens recruits the WRKY-dependent pathogen defense pathway to activate Ipt gene expression . This can be substantially increased when the auxin-dependent developmental process mediated by ARF5 is switched on .
To discover how the expression of the agrobacterial oncogenes IaaH , IaaM and Ipt is regulated in plant cells , we analyzed the structure of the T-DNA region of the nopaline-type Ti plasmid pTiC58 . The CDS of the three oncogenes are sequentially arranged and interrupted by two non-coding intergenic regions ( IGR1 and IGR2; Fig . 1A ) . The IaaM and Ipt genes are transcribed from the sense strand and the IaaH gene is encoded on the opposite strand . If IGR1 functions as a promoter for both the IaaH and IaaM oncogenes , it must be a bidirectional promoter: one direction being 5’ upstream of the TSS of the IaaH CDS ( IGR1a ) and the other , 5’ upstream of IaaM ( IGR1b ) . To prove whether the IGRs function as promoters in plant cells , the complete IGR sequences were fused with the CDS of the green fluorescent protein ( GFP ) in a binary vector . The IGR1a and IGR1b sequences included the 5’ untranslated regions ( 5’ UTR ) of both the IaaH and IaaM genes , whereas IRG2 contained the 3’ UTR of IaaM and 5’ UTR of the Ipt gene . We generated stable transformed Arabidopsis crown gall tumor cell lines by infecting Arabidopsis root segments with the virulent A . tumefaciens strain C58 , which , in addition to their pTiC58 , harbor a binary vector with the IGR::GFP constructs . Detection of GFP fluorescence in the IGR1a::GFP , IGR1b::GFP and IGR2::GFP crown gall cell lines demonstrated that the IGRs drive GFP expression , so function as promoters in plant cells ( Fig . 1B ) . Furthermore , as the IGR1 sequence is a bidirectional promoter , it can drive transcription of both the IaaH and IaaM genes . Since IGR1a , IGR1b and IGR2 all function as promoters in eukaryotic cells , their sequences should contain the core promoter elements , such as the initiator ( Inr ) sequence and TATA box . To localize these in the promoters , we determined the TSSs of the IaaH , IaaM and Ipt genes using the 5’ rapid amplification of cDNA ends ( 5’ RACE ) assay , finding that the translational start codon of the IaaH , IaaM and Ipt CDSs are at positions +12 bp , +26 bp and +44 bp in respect to the TSS ( Table 1 ) . Upstream of the TSSs , the typical eukaryotic Inr box ( YYANWYY , TSS is underlined , Y = C/T , W = A/T , N = A/G/C/T ) was present in the three promoter sequences . This is in agreement with the plant specific “YR Rule” ( YR , TSS is underlined , Y = C/T , R = A/G [41 , 42] ) . The TATA boxes , the binding sites for the general transcription factor complex , are found in the promoter regions −25 bp to −35 bp and another feature of many eukaryotic promoters , the CAAT boxes , are localized approximately −70 bp upstream of the TSSs within the oncogene promoter regions ( Table 1 ) . To ascertain whether the regulatory promoter elements of pTiC58 are conserved , we performed a sequence alignment with the promoter and 5’ untranslated regions ( 5’ UTRs ) of the three oncogenes from different Ti plasmids . We compared the upstream sequences of the three oncogene CDSs of the Ti plasmids from two nopaline-types ( pTiC58 , pTiSAKURA ) , three octopine-types ( pTiA6NC , pTiAch5 , pTi15955 ) and one agropine-type ( pTiBo542 ) . The alignment shows that the TSSs ( arrows ) , TATA boxes and CAAT boxes of the promoters for IaaH ( S1 Fig . ) , IaaM ( S2 Fig . ) and Ipt ( S3 Fig . ) are conserved between the pTi plasmids of the different A . tumefaciens strains . In contrast , two TATA boxes are present 5’ upstream of the CDS in the Ipt genes from the octopine Ti plasmids ( S3 Fig . ) . In the Ipt promoter of pTiC58 , two CAAT boxes were predicted ( S3 Fig . ) , one of which ( GGTAAAGCC , from −72 to −64 bp ) is conserved and also found in other nopaline type and in the octopine type pTi plasmids , but not in the agropine type Ipt promoter where no CAAT box was predicted . The second CAAT box ( AAGGAATCT , −49 to −41 bp ) is specific for the Ipt promoters of the nopaline type Ti-plasmids ( S3 Fig . ) . Cis-regulatory binding elements for transcription factors were also determined in the IaaH , IaaM and Ipt promoters on the Watson and Crick strand using PLACE ( http://www . dna . affrc . go . jp/PLACE/index . html ) [43–45] . Several binding elements for different transcription factor families including MYB , DOF , WRKY , bHLH , ARR1 and ARF , were localized within the Ipt promoter ( Table 1 ) . In the IaaH and IaaM promoters , the binding element for the ARR1 ( AT3G16857 ) transcription factor was dominant and there were eight ARR1 elements altogether . To identify potential transcription factors that may be involved in enhancing the expression of the oncogenes , we analyzed existing microarray data of Arabidopsis crown galls [20 , 46] , based on the Arabidopsis transcription factors listed in the Plant Transcription Factor Database v3 . 0 [47] ( http://planttfdb . cbi . pku . edu . cn/index . php ? sp=Ath ) . A total of 151 transcription factor genes were found to be differentially transcribed in inflorescence stems inoculated with the virulent A . tumefaciens strain C58 compared to non-inoculated stems ( S1 Table; fold change ≥ 2 or ≤ 0 . 5 , p value < 0 . 01 ) . As early as three hours post inoculation ( hpi ) , three of these genes were up-regulated: WRKY53 ( AT4G23810 , 2 . 47 fold ) , WRKY40 ( 2 . 22 fold ) , and NAC102 ( AT5G63790 , 2 . 18 fold ) . WRKY53 was also up-regulated by the disarmed A . tumefaciens strain GV3101 ( 2 . 37 fold ) 3 hpi . Six days post inoculation ( dpi ) , the expression of six transcription factor genes was up- or down-regulated ( S1 Table ) . In Arabidopsis crown gall material of A . tumefaciens strain C58 , 141 transcription factor genes were transcriptionally changed compared to reference tissue 35 days post wounding ( dpw ) . Amongst these , 74 genes were up-regulated , with 67 down-regulated ( S1 Table ) and all belong to various families including WRKYs , MYBs , DOFs , and NACs . The DNA binding elements and the microarray data both suggest that the MYB , DOF , WRKY , bHLH , ARR1 and ARF transcription factors are potential candidates for involvement in the regulation of Ipt expression , while ARR1 could regulate transcription of the IaaH or IaaM genes . The core promoter sequence elements could contribute to the basal expression of the three oncogenes in plant cells , whereas the binding sites for transcription factors might function in enhancing their transcription . To begin to study the regulation of onocgene expression , we first used quantitative real-time PCR ( qRT-PCR ) . We assessed the relative transcript numbers of IaaH , IaaM and Ipt genes in 25-day-old Arabidopsis thaliana crown galls induced by the virulent A . tumefaciens strain C58 , finding that the transcript levels of IaaH and IaaM were much lower compared to those of the Ipt gene in the crown galls ( Fig . 2A ) . The high-throughput protoplast transactivation ( PTA ) system was then used [48] to identify transcription factors that could activate the three oncogene promoters in plant cells . To do so , the complete promoters of IaaH ( IGR1a , 337 bp ) , IaaM ( IGR1b , 337 bp ) and Ipt ( IGR2 , 697 bp ) of the pTiC58-encoded oncogenes ( Fig . 1A ) were fused with the CDS of the firefly luciferase ( LUC ) reporter gene . The plasmids containing the oncogene promoter-LUC constructs were transfected into Arabidopsis mesophyll protoplasts , either alone , or together with a second plasmid containing the CDS of a transcription factor fused to the constitutive cauliflower mosaic virus ( CaMV35S ) promoter . The relative luminescence , a measure for the oncogene promoter activity since it drives luciferase gene expression , was then determined . Mesophyll protoplasts transfected only with the oncogene promoter-LUC constructs showed the same pattern of promoter activity as that determined for the relative transcript numbers in crown galls ( Fig . 2B ) . The Ipt promoter induced a higher relative luminescence than the IaaH and IaaM promoters . Next , a library containing the CDS of more than 400 transcription factors was screened . Among the included family members , WRKY , AP2/ERF , bHLH , bZIP , DOF , MYB and NAC , only WRKY18 ( AT4G31800 ) , WRKY40 , WRKY60 ( AT2G25000 ) and ARF5 were found to specifically activate the Ipt promoter in protoplasts ( Fig . 3A ) . Protoplasts co-transfected with the WRKY or ARF effector and the Ipt-promoter-LUC reporter constructs exhibited a significantly higher promoter activity ( reflected by luciferase activity ) compared to the control samples that only harbored the reporter . Despite several attempts , no transcription factor was found to activate the IaaH and IaaM promoters . Comparison of the three WRKYs alone and in combination both showed that WRKY40 exerts the strongest impact on Ipt promoter-driven luciferase expression ( S4 Fig . ) . Even all three WRKYs together did not increase the relative luminescence more than WRKY40 alone . This observation points towards a dominant role for WRKY40 in Ipt promoter regulation . The transcript numbers of WRKY18 , WRKY40 , WRKY60 and ARF5 genes in crown gall tissues of A . tumefaciens strain C58 were determined using qRT-PCR . In agreement with the published microarray data [20 , 46] , the transcript levels were clearly elevated in crown gall tumors compared to inflorescence stems inoculated with the disarmed A . tumefaciens strain GV3101 ( Fig . 3B ) . It is already known that WRKY18 , WRKY40 and WRKY60 are induced early after bacterial and fungal pathogen infection [49 , 50] . To analyze the impact of A . tumefaciens on gene induction , we analyzed the time-dependent expression of the three WRKY genes in Arabidopsis thaliana ( Col-0 ) leaf tissues infiltrated with either the virulent A . tumefaciens strain C58 , the disarmed strain GV3101 or buffer as a control . The qRT-PCR results demonstrated that the three WRKY genes responded to a certain degree to the infiltrated buffer solution at all analyzed time points ( 2 hpi to 72 hpi ) , indicating that they respond to wounding ( Fig . 3C ) . The transcript levels of WRKY18 began to increase significantly at 8 hpi after infiltration by strain GV3101 . The WRKY40 and WRKY60 genes were significantly induced by both A . tumefaciens strains as early as 2 and 4 hpi , respectively ( Fig . 3C ) . In contrast , transcription of the ARF5 gene was still very low after 72 hpi , suggesting that this gene is not responsive to A . tumefaciens or wounding at the time points analyzed ( Fig . 3C ) . The gene expression patterns imply that at the very beginning of A . tumefaciens infection ( 2 to 4 hpi ) , WRKY40 and WRKY60 genes are already expressed . WRKY and ARF transcription factors bind respectively to specific DNA sequences , W-box ( TGAC ) and AuxRE ( TGTCNC or TGTCTN ) . Sequence analysis of the two IGRs of pTiC58 revealed that seven W-boxes ( one W-box is localized in the 5’ UTR of the Ipt gene ) and five AuxREs are located in IGR2 ( Table 1 , 2 ) , which are equally distributed along the promoter sequence ( S5 Fig . ) . IGR1 drives expression of IaaH and IaaM and contains only one W-box and AuxRE sequence motif , and this is more closely localized upstream of the IaaM than that of the IaaH TATA box . Sequence comparisons of IGR1 and IGR2 regions illustrate that W-boxes and AuxREs are also conserved in the T-DNA regions of several A . tumefaciens strains ( Table 2 ) . Similar to the pTiC58 , the majority of these elements are enriched in the Ipt promoters whereas only one or two of them are located in the IaaH and IaaM promoter sequences . From this in silico result , it can be concluded that the Ipt oncogenes , rather than IaaH and IaaM of the different A . tumefaciens strains are regulated by WRKY and ARF transcription factors in planta . To unravel the role of WRKY18 , WRKY40 and WRKY60 in A . tumefaciens-mediated crown gall development , we performed a crown gall growth assay with mutant plants of the three WRKY genes inoculated with the tumorigenic A . tumefaciens strain C58 , determining the crown gall weights 25 days later . All mutant genotypes developed smaller crown galls than the wild-type Col-0 ( Fig . 4A , B ) , with the double mutant wrky18/wrky40 and the triple mutant wrky18/40/60 developing the smallest crown galls . The triple mutant was most resistant to crown gall development; about 30% of the mutant plants did not development any crown gall material at all after 25 days . Unfortunately , the role of the ARF5-mediated auxin signaling pathway on crown gall development could not be analyzed due to the strong developmental phenotypes of arf5 mutant plants [51 , 52] . If WRKY18 , WRKY40 and WRKY60 activate the Ipt promoter , it would be expected that Ipt oncogene expression would be altered in the WRKY mutant plants . To investigate this , we used quantitative RT-PCR to measure the relative transcript numbers of the Ipt oncogene in Arabidopsis crown gall material of the wrky mutants inoculated with A . tumefaciens strain C58 . Compared to crown galls from the wild-type ( Col-0 ) plants , the Ipt transcript levels were similar in crown galls from the wrky18 , wrky40 and wrky60 mutants ( S6A Fig . ) . Due to this similarity , i . e . , no obvious impact of WRKY on long term Ipt gene expression in crown galls , earlier time points of C58 Arabidopsis stem inoculations were analyzed . At 2 dpi , the Ipt transcript levels were far too low to reliably quantify differences ( S6B Fig . ) . Only at 6 dpi did Ipt transcription reach a measureable level ( S6B Fig . ) and showed in the triple mutant ( wrky18/40/60 ) a moderate reduction compared to the wild-type ( Fig . 4C ) . The moderate reduction of Ipt transcription may be due to the function of ARF5 , which is still expressed in the wrky triple mutant . This assumption is supported by the observation that in crown galls of the wrky single mutants gene expression of ARF5 was elevated and that of IAA12 , an inhibitor of ARF5 function , was reduced ( S6C Fig . ) . The PTA data revealed that the Ipt promoter can be activated by WRKY18 , WRKY40 , WRKY60 and ARF5 . To test whether these transcription factors cooperatively regulate the Ipt promoter , we co-expressed the WRKY40 protein with ARF5 in the presence of the Ipt promoter-LUC construct in Arabidopsis mesophyll protoplasts . The Ipt promoter-driven luciferase activity was clearly higher , particularly in the presence of ARF5 and WRKY40 compared to ARF5 or WRKY40 alone ( Fig . 5A ) . In contrast , expression of ARF5 together with WRKY18 or WRKY60 did not further enhance the Ipt promoter activity . This also indicates that WRKY40 is more important than WRKY18 and WRKY60 for activating the Ipt promoter . These results imply that the WRKY40 and ARF5 proteins interact to synergistically activate Ipt gene expression . This was tested using the Bimolecular Fluorescence Complementation ( BiFC ) assay to study protein interactions between the WRKYs and ARF5 . The C-terminal half of the yellow fluorescent protein ( cYFP ) was fused to the C-terminus of the ARF5 and WRKY40 proteins to express ARF5- and WRKY40-cYFP fusion proteins , respectively . The N-terminal half of YFP ( nYFP ) was fused to the C-terminus of the three WRKY proteins as well as to ARF5 to generate WRKY18- , WRKY40- , WRKY60-nYFP and ARF5-nYFP . Observation of YFP-mediated fluorescence demonstrates that both WRKY40 and ARF5 interacted with themselves and with all the other expressed genes , when transiently co-expressed in Arabidopsis mesophyll protoplasts ( Fig . 5B , C ) . The fluorescence signal was always restricted to the nucleus . The free cYFP construct was used as negative control , and showed no YFP fluorescence when co-expressed with the WRKY-nYFPs and ARF5-nYFP in protoplasts ( Fig . 5D ) . It has been reported that the domain III and IV at the C-terminus of the ARF5 protein is important for dimerization and protein-protein-interaction [53–55] . To prove whether these domains are required for the interaction with the WRKY proteins , we fused a C-terminal deletion of ARF5 ( 1–722 aa ) to cYFP ( ARF5Δ722-cYFP ) and co-expressed them with either ARF5-nYFP or the three WRKY-nYFPs . Although stable [53] , the truncated ARF5Δ722 protein was unable to interact with the intact ARF5 protein or with WRKY18 , WRKY40 and WRKY60 ( Fig . 5E ) . This indicates that the domains III and IV are not only required for self-interaction , but also for interaction with the three WRKYs . The specificity of the interactions between ARF5 and the three WRKYs was confirmed by co-expressing ARF3 ( AT2G33860 ) -cYFP , which naturally lacks domain III and IV , and WRKY53-cYFP , expressed early after infection with A . tumefaciens strain C58 ( 3 hpi; S2 Table ) [20 , 53] . Neither ARF3 nor WRKY53 interacted with ARF5 , WRKY18 , WRKY40 , and WRKY60 , thus verifying that the interactions between the WRKYs and ARF5 are specific ( Fig . 5F , G ) . The PTA assays indicate that WRKY40 has a stronger potential to activate the Ipt promoter than WRKY18 and WRKY60 ( Fig . 3A and 5A ) . This implies that WRKY40 regulates the Ipt promoter directly . We therefore analyzed binding of WRKY40 to the Ipt promoter using the electrophoretic mobility shift assay ( EMSA ) . The recombinant WRKY40 protein fused to six histidine amino acids at the N-terminus ( 6×His-WRKY40 ) was expressed and purified from E . coli and a 50 bp fragment ( −184 bp to −135 bp ) of the Ipt promoter , which contains three of the six W-boxes located in the promoter region , was radioactively labeled and served as a probe for EMSA ( Fig . 6A ) . Only a weak band of the shifted Ipt promoter fragment ( Fig . 6B , WRKY40-Ipt complex ) was observed in the presence of 150 ng purified recombinant 6×His-WRKY40 protein , but a doubled amount of the His-tagged WRKY40 protein ( 300 ng ) exhibited a much stronger band . Addition of unlabeled Ipt promoter fragments as competitor to the reaction mixture significantly reduced the binding of WRKY40 to the labeled Ipt promoter probe . Thus , the WRKY40 protein binds to the Ipt probe in vitro , suggesting that the Ipt promoter is a direct target of the WRKY40 transcription factor in plant cells . That ARF5 enhances the WRKY40-mediated activation of the Ipt promoter suggests that the auxin signaling pathway is involved in regulating Ipt expression . Previous studies have shown that the levels of unconjugated IAA in infected Arabidopsis stems are more than two-fold higher six days after inoculation with A . tumefaciens strain C58 compared to non-inoculated plant stems [20] . We found that crown galls accumulate four times more unconjugated IAA than control tissues and the total level of cytokinins in Arabidopsis crown gall tissues infected with A . tumefaciens strain C58 are 10 times higher than in crown gall-free stem tissues ( 8414 vs . 849 ng/g dry weight ) . The dominant cytokinin forms in Arabidopsis crown gall tissues were zeatin conjugates , including zeatin nucleotide ( 3657 vs . 308 ng/g dry weight ) and zeatin riboside ( 2294 vs . 76 ng/g dry weight ) . The content of free zeatin was also higher in crown gall tissues than in mock-inoculated stems ( 544 vs . 34 ng/g dry weight ) . Based on these results , we used the PTA system to analyze the impact of auxin and cytokinin on IaaH , IaaM and Ipt promoter activity . The Ipt promoter was highly activated by the bioactive auxin type 1-naphthaleneacetic acid ( 1-NAA ) and the cytokinin type trans-zeatin ( Fig . 7A ) , with the latter much less effective . Increasing concentrations of auxin and cytokinin had no strong enhancing effect on the activity of the three oncogene promoters ( Fig . 7A ) . The Ipt promoter sequence contains five auxin response elements ( AuxREs , TGTCNC or TGTCTN ) for binding of ARF transcription factors , whereas only one AuxRE is present in the bidirectional IaaH and IaaM promoter sequence ( Table 1 , S5 Fig . ) and ARF transcription factors usually regulate their target genes in an auxin-dependent manner [33 , 34] . Thus , we analyzed the regulatory effect of ARF5 on the Ipt promoter in the presence of auxin in the PTA system . ARF5 activated the Ipt promoter , an activation that was even stronger when the mesophyll protoplasts were treated with auxin ( 1-NAA , Fig . 7B ) . Mutations in the AuxREs ( sense TGTCNC or TGTCTN , anti-sense GNGACA or NAGACA ) in the Ipt promoter abolished the auxin induction and the enhancing effect of ARF5 ( Fig . 7C ) . It is known that auxin/indole-3-acetic acid ( Aux/IAA ) proteins can inhibit ARF mediated promoter activation and the repressor of ARF5 is IAA12 ( also known as BODENLOS , BDL , AT1G04550 ) [54] . When we co-transfected Arabidopsis mesophyll protoplasts with the ARF5 and IAA12 plasmid constructs , a significant reduction in the Ipt promoter-driven luciferase activity was found compared to protoplasts transfected with only ARF5 ( Fig . 7B ) . Nonetheless , the level of the Ipt promoter activity was not as low as it was in the absence of any transcription factor , indicating that not all ARF5 proteins are inhibited by IAA12 . In addition to the W-boxes and AuxREs , the IaaH , IaaM and Ipt promoters also contain ARR1 binding elements ( GATT; Table 1 ) , suggesting that the three oncogenes are regulated by type-B ARR transcription factors to mediate cytokinin signaling . The ARR1 gene is expressed at low levels in crown gall tissue of the virulent A . tumefaciens strain C58 and in stems infected with the disarmed strain GV3101 according to real time PCR measurements ( S7A Fig . ) . ARR4 ( AT1G10470 ) , a type A transcription factor gene , was strongly expressed in crown gall tumors ( S7A Fig . ) . The ability of both the ARR1 and ARR4 transcription factors to activate the IaaH , IaaM and Ipt promoters was tested in the PTA system . Neither ARR1 nor ARR4 significantly increased luciferase activity driven by the three oncogene promoters , even in the presence of trans-zeatin ( S7B Fig . ) . Hence , the ARF5-mediated auxin signaling pathway , but not that of cytokinin , regulates Ipt expression , whereas the expression of IaaH and IaaM is not affected by either of the two signaling pathways .
Expression of a gene in a eukaryotic cell requires general sequence features ( e . g . TATA , CAAT ) and potentially cis-regulatory elements for the binding of transcription factors . For the Ipt promoter of the octopine Ti plasmid pTiAch5 , previous studies have shown that it binds CBF , a protein of unknown function from tobacco nuclear protein extracts [25–27] . This implies that at the least , expression of the Ipt oncogene is regulated by plant derived transcription factors . Nonetheless , using the PTA screening system we found that no transcription factor activated the IaaH and IaaM promoters of pTiC58 . This may be because the transcription factor collection used for screening did not cover all the encoded Arabidopsis transcription factors; candidates for binding to the IaaH and IaaM promoters may have been missed . However , the very few cis-regulatory elements in the relatively short promoter sequence and the low level of transcription in crown galls , in addition to the low promoter activity in protoplasts , suggest that the IaaH and IaaM genes are not strongly activated by transcription factors , but instead are constitutively expressed at low basal levels . In contrast , the Ipt oncogene promoter of pTiC58 contains several W-boxes and is activated by the WRKY18 , WRKY40 and WRKY60 proteins . The impaired crown gall growth on the wrky18 , wrky40 and wrky60 mutant plants indicates that these WRKY transcription factors have a positive effect on crown gall development . The three WRKYs are paralogous transcription factors that cooperatively regulate biotic and abiotic stress responses in Arabidopsis [49 , 58–63] and the respective wrky mutants are known to be more resistant to biotrophic pathogens such as Pseudomonas syringae and powdery mildew Golovinomyces orontii [50] . Hence , the smaller crown galls on these wrky mutants may result from both fewer transformation events due to the stronger resistance response towards biotrophic pathogens and/or from reduced Ipt expression due to the loss of wrky function . Unfortunately , these two processes are not easy to separate in infection-based assays . It is known that transcription of WRKY40 and WRKY60 is induced by fungal and bacterial pathogens [49 , 50] . Likewise , A . tumefaciens inoculation induced their transcription within two hours , indicating that they are expressed quite early in response to this pathogen . Thus , it is conceivable that the WRKYs are needed to trigger Ipt oncogene expression from the very beginning in a T-DNA transformed cell , so these pathogen responsive genes are already expressed when the T-DNA enters the host cell . Consequently , a reduction in Ipt promoter activity can be observed early on in the wrky triple mutant , vanishing at later infection stages . The relatively moderate difference in Ipt gene expression between the wrky triple mutant and wild-type most likely results from the increased expression of ARF5 and reduced expression of its inhibitor IAA12 in the mutant background . Thus , A . tumefaciens hijacks a host transcription factor , which is part of the plant pathogen defense machinery , to initiate expression of its own oncogene in the host cell . A . tumefaciens and T-DNA transformed plant cells produce auxin and cytokinin [13 , 20] . Cytokinin affects cell division , essential for cell proliferation and initiation of crown gall development . Only the activity of the Ipt promoter , not that of the IaaH and IaaM genes , increased upon application of trans-zeatin , the dominant cytokinin in Arabidopsis crown galls . Eight binding elements for the ARR1 transcription factor are located in the bidirectional promoter of IaaH/IaaM and seven in the Ipt promoter . ARR1 is a type-B ARR transcription factor that activates transcription of cytokinin responsive genes [64 , 65] . Nonetheless , the activity of all three oncogene promoters was not influenced either by ARR1 or ARR4 , even in the presence of trans-zeatin . This indicates that cytokinin signaling does not have a dominant role in oncogene expression . The auxin type 1-NAA was much more effective than trans-zeatin in activating the Ipt promoter , but again , not for the promoters of IaaH or IaaM . Elevated levels of free IAA are detectable in infected tissues six days after inoculation with A . tumefaciens strain C58 [20] and at the same infection stage , expression of the ARF5 gene begins to increase , as shown in the microarray data ( 1 . 49 fold , P value = 0 . 006 ) [20 , 46] . The Ipt promoter contains five AuxREs and is activated by 1-NAA and by the auxin response factor ARF5 upon release from inhibition by IAA12 in an auxin-dependent manner . Expression of the ARF5 gene is induced by auxin [66] and the elevated auxin levels in plant tissues infected and T-DNA transformed by A . tumefaciens most likely induce ARF5 gene expression and de-repress the ARF5 protein by proteolysis of IAA12 . The release of ARF5 inhibition in the presence of auxin leads finally to activation of the Ipt promoter in the T-DNA transformed plant cell and may contribute to the moderate differences of Ipt transcript numbers in the wrky mutants and wild-type . Taken together , the results indicate that auxin is an important factor in regulating Ipt oncogene expression , which exerts its function through the auxin-sensitive transcription factor ARF5 . Our study shows that WRKY40 binds directly to the Ipt promoter in vitro and has the strongest effect on Ipt promoter activation in plant cells , an activation that increases even further in the presence of the ARF5 transcription factor . It has been shown that WRKY transcription factors specifically interact with different kinds of proteins [67] and WRKY18 , WRKY40 and WRKY60 interact with each other and themselves [49] , a result confirmed in this study . Moreover , WRKY18 , WRKY40 , and WRKY60 interact with ARF5 . Most ARFs contain four important domains , except for ARF3 , ARF13 and ARF17 , which lack domain III and IV and ARF23 , which has only domain I [31] . Domain III and IV are localized at the C-terminus of ARF proteins and are important for dimerization and interaction with Aux/IAA proteins [53] . According to our study , the domain III and IV of ARF5 seem to be required for the interaction with the three WRKY transcription factors . The interaction of ARF5 with WRKY40 , but not that with WRKY18 and WRKY60 , greatly enhances the activation of the Ipt promoter , so emphasizing the role of WRKY40 as the most important transcriptional activator of Ipt gene expression . Moreover , the WRKY40-ARF5 interaction links two signaling pathways for the regulation of Ipt gene expression: the ARF5-dependent auxin and WRKY-mediated pathogen defense pathway . Both pathways are activated in the host plant upon infection with A . tumefaciens and synergistically boost expression of the Ipt gene in T-DNA transformed cells . This study suggests a bifactorial regulation of oncogene expression in T-DNA transformed plant host cells ( Fig . 8 ) . Just after A . tumefaciens infection , auxin and cytokinin levels are as low as in an untransformed plant cell . The WRKY40 gene is soon expressed in response to infection , and the protein binds to W-boxes in the Ipt promoter to induce gene expression ( Fig . 8A ) . Under low auxin conditions , ARF5 interacts with IAA12 , so is inactivated . Over time , the auxin concentration increases in the T-DNA transformed cell , the result of the ubiquitous expression of IaaH and IaaM , driven by binding the RNA polymerase II complex to the promoter and additional auxin that can be secreted from the A . tumefaciens cells into the apoplast . Under high auxin concentrations , the ARF5 inhibitor IAA12 is poly-ubiquitinylated and degraded , thus releasing the transcription factor ARF5 . The de-repressed ARF interacts via domain III and IV with WRKY40 , resulting in strong expression of the Ipt oncogene . Taken together , this transcription factor interaction integrates two signaling pathways: the WRKY-based pathogen defense pathway for initial induction of Ipt gene expression and later , the auxin signaling pathway to boost Ipt expression . Moreover , the alterations in Ipt expression levels may be a mechanism to fine-tune the cytokinin to auxin ratios in a transformed plant cell . The appropriate auxin/cytokinin balance is an important mechanism to control whether a crown gall will proliferate or grow and differentiate .
Arabidopsis thaliana ecotype Columbia ( Col-0 ) was used as the genetic background of the wrky18 ( GABI-Kat 328G03 ) , wrky40 ( SLAT_N40001 ) , and wrky60 ( SALK_120706 ) mutants [58] . Plants were grown on soil and cultivated in growth chambers ( Percival AR-66L2 , Perry , USA ) with 12 h light ( ca . 120 μmol·m−2·s−1 fluorescent white light , TL70 , Philips , Eindhoven , Netherlands ) at 22°C and 12 h dark at 16°C . The crown gall callus cell culture was generated by inoculating A . thaliana root segments of ecotype Wassilewskija ( WS-2 ) with the virulent Agrobacterium tumefaciens strain C58 and cultivated on MS agar plates [1× MS basal salts including vitamins and MES buffer ( Murashige and Skoog medium , Duchefa Biochemie , Haarlem , Netherlands ) , 10 g/L sucrose , 100 mg/L myo-Inositol ( Duchefa Biochemie , Haarlem , Netherlands ) , 7 . 5 g/L plant agar ( Duchefa Biochemie , Haarlem , Netherlands ) , pH 5 . 7] without the addition of phytohormones , but with 100 mg/L ticarcillin disodium/clavulanate potassium ( Duchefa Biochemie , Haarlem , Netherlands ) . The GFP expressing crown gall cell cultures were generated in the same manner except the A . tumefaciens strain C58 was used . This harbored , in addition to its pTiC58 plasmid , the binary vector pMDC206 [68] with the IaaH , IaaM and Ipt promoter-green fluorescent protein ( GFP ) constructs inserted in the T-DNA region . The antibiotic hygromycin ( 30 mg/L ) was added to the agar medium for selection of transformed cells . All callus cultures were transferred to fresh media every three weeks . The crown gall cell suspension cell cultures were grown in the dark at 22°C with gentle shaking at 160 rpm , and transferred to fresh medium [1× MS basal salts including vitamins and MES buffer ( Murashige and Skoog medium , Duchefa Biochemie , Haarlem , Netherlands ) , 20 g/L sucrose , 100 mg/L myo-Inositol ( Duchefa Biochemie , Haarlem , Netherlands ) , pH 5 . 7] at a 1:2 dilution ( v/v ) twice a week . The virulent A . tumefaciens strain C58 nocc ( nopaline catabolism , number 584; Max Planck Institute for Plant Breeding , Cologne , Germany ) and the disarmed derivative of C58 , strain GV3101 ( pMP90 ) were used for plant inoculations . The strains were cultivated on YEB-agar plates ( 5 g/L yeast extract , 5 g/L tryptone , 5 g/L sucrose , 50mM MgSO4 , and 15 g/L agar ) at 28°C for 2 days . GV3101 was cultivated in the presence of rifampicin ( 10 mg/L ) and gentamicin ( 25 mg/L ) . Before plant inoculation , the A . tumefaciens strains were transferred into King’s liquid medium ( 20 g/L protease peptone , 1 . 5 g/L K2HPO4 , 10 mL/L glycerol , 600 μM MgSO4 ) and grown overnight at 28°C and 140 rpm . King’s medium was removed by pelleting the bacteria three times at 8000 rpm for 1 min and resuspension in Agromix buffer ( 0 . 01 M MgCl2 , 0 . 01 M MES pH 5 . 6 ) . For recovery , the resuspended cells were cultured at 28°C and 140 rpm for 2 to 3 hours . The optical density ( OD600 ) was measured at 600 nm ( NanoDrop 2000c UV-Vis Spectrophotometer , Thermo , Waltham , USA ) and adjusted to OD600 1 . 0 for leaf infiltrations and OD600 0 . 5 for inflorescence stem inoculations . A . tumefaciens suspensions were infiltrated into the abaxial side of 5-week-old Arabidopsis ( Col-0 ) leaves by tightly pressing the orifice of a 1 mL syringe onto the leaf surface . For induction of crown gall growth , young inflorescence stems ( 3 to 10 cm ) of A . thaliana plants were inoculated by injecting A . tumefaciens suspensions four times with a 5 mL syringe and a needle attached to it . Crown galls were separated from the inflorescence stems 25 days after inoculation with a scalpel using a dissecting microscope ( Leica MZ6 ) and their weight was immediately determined . Leaves infiltrated or stems inoculated with A . tumefaciens strain GV3101 served as reference . For construction of the promoter-GFP fusions ( IGR1a::GFP , IGR1b::GFP and IGR2::GFP ) , the vector pMDC206 was used , which contains the coding sequence ( CDS ) of GFP including an intron [68] . The promoter sequences of the intergenic regions ( Fig . 2A ) between the IaaH and IaaM CDS ( IGR1 , 337 bp ) and between the IaaM and Ipt CDS ( IGR2 , 697 bp ) of the pTiC58 plasmid were inserted upstream of the GFP CDS using Gateway cloning technology [68] . IGR1 was cloned in both directions ( IGR1a and IGR1b , Fig . 2B ) . The ubiquitous cauliflower mosaic virus ( 2× CaMV35S ) promoter was used as a positive control . To construct the plasmids for the Bimolecular Fluorescence Complementation ( BiFC ) assay and the luciferase reporter constructs , the pSAT vector was altered to be used in the USER cloning strategy as described in [69 , 70] . For the BiFC assay , the ubiquitin 10 ( UBQ10 ) promoter and CDS of the C-terminal half ( Venus , 156–239 ) and N-terminal half ( Venus , 1–173aa , I152L ) of the yellow fluorescent protein ( cYFP , nYFP ) were inserted into the pSAT vector . The full CDSs , excluding the stop codon of WRKY18 , WRKY40 , WRKY53 , WRKY60 , ARF3 and ARF5 , and of the C-terminal deletion of ARF5 ( 1–722 aa ) , were inserted before the C-terminus of the cYFP or nYFP to generate the fusion proteins WRKY-cYFP , WRKY-nYFP , ARF-cYFP , ARF-nYFP and ARF5Δ722-cYFP . To generate the IaaH , IaaM , Ipt promoter-firefly luciferase reporter constructs ( IaaH promoter-LUC , IaaM promoter-LUC and Ipt promoter-LUC ) , DNA fragments of the luciferase reporter CDS and the CaMV-terminator were introduced into the pSAT vector first , then the sequences of IGR1a , IGR1b and IGR2 ( Fig . 2B ) were added upstream of the luciferase reporter CDS . To express a histidine-tagged WRKY40 protein in E . coli cells , full length CDS including the stop codon was cloned into the vector pET28b ( Novagen Merck Millipore , Darmstadt , Germany ) at the NdeI and XhoI restriction enzymes sites . This resulted in expression of a WRKY40 protein fused at its N-terminus with 6× histidine amino acids ( 6×His-WRKY40 ) . For site-specific mutagenesis of the AuxREs in the Ipt promoter ( Ipt promoter AuxREm ) , the QuickChange Site-Directed Mutagenesis Kit ( Agilent Technologies , Santa Clara , USA ) was used . All primer sequences used are listed in S2 Table . For analysis of the transcription start sites of the IaaH , IaaM and Ipt oncogenes of A . tumefaciens strain C58 in plant cells , the mRNA extracted from crown gall callus cells was used . The mRNA was extracted from approximately 50 mg crown gall callus material by using Dynabeads Oligo ( dT ) 25 ( Invitrogen , Carlsbad , USA ) following the manufacturer’s protocol . First-strand cDNA was generated by using SMARTScribe Reverse Transcriptase , the SMARTer II A Oligonucleotide primer and the 5’ RACE CDS primer A ( Clontech , Otsu , Japan ) . The fragments of the 5’ ends of the oncogene cDNAs were amplified using DreamTaq DNA Polymerase ( Fermentas , Thermo , Waltham , USA ) and the Universal Primer A Mix ( UPM ) and the gene specific primers ( IaaH reverse , IaaM reverse and Ipt reverse , S2 Table ) . The resulting PCR products were cloned using the pGEM-T Easy Vector ( Promega , Fitchburg , USA ) and transformed into the E . coli strain MRF ( Agilent Technologies , Santa Clara , USA ) . At least three independent clones were sequenced to determine the transcription start site of each gene . Total RNA from approximate 50 mg plant tissue was extracted by using the RNeasy Plant Mini Kit ( Qiagen , Hilden , Germany ) following the manufacturer’s protocol . Before reverse transcription , about 500 to 1000 ng of total RNA extracted from Arabidopsis tissue was digested by DNase I ( Fermentas , Thermo , Waltham , USA ) for 30 min at 37°C . DNase digestion was terminated by the addition of 25 mM EDTA and subsequent incubation at 70°C for 10 min . First strand cDNA synthesis was performed using oligo ( dT ) 18 primers ( Fermentas , Thermo , Waltham , USA ) and the Thermo Scientific RevertAid First Strand cDNA Synthesis Kit ( Thermo , Waltham , USA ) . Quantitative RT-PCR with the plant cDNA samples was performed as described in [20] . The primer sequences used are listed in S2 Table . The Arabidopsis mesophyll protoplast isolation and transfection procedures were performed as described in [48 , 71] . For transfection , 30 μL protoplast suspension ( approximately 1×104 cells ) , 1 μg plasmid DNA of oncogene promoter-LUC constructs ( IaaH promoter-LUC , IaaM promoter-LUC and Ipt promoter-LUC ) and 1 μg of the expression plasmids containing the CaMV35S::transcription factor constructs of the transcription factor library [48] were combined in each well of a microtiter plate ( Nunc U96; MicroWell Polypropylene Plates , Thermo , Waltham , USA ) . As an internal standard , 1 μg plasmid expressing the Renilla luciferase driven by the CaMV35S promoter ( CaMV35S::Renilla LUC ) was co-transfected . The protoplast suspension mixture was incubated overnight in the dark and at room temperature . The following day , a dual luciferase measurement was performed using the Renilla-Juice BIG Kit and Beetle-Juice BIG Kit ( PJK GmbH , Kleinblittersdorf , Germany ) . The protoplasts settled at the bottom of the wells by gravity , then the supernatant was removed from the protoplast suspensions and 20 μL Lysis-Juice 2 ( Renilla-Juice BIG KIT ) was added to each well and mixed by pipetting . After 15 min on ice , the microtiter plate was centrifuged ( 4000 rpm for 10 min ) . An aliquot of 10 μL of the supernatant was transferred into the wells of two new microtiter plates . As substrate for the two types of luciferase enzymes , 50 μL Renilla-Juice for renilla luciferase ( CaMV35S::Renilla LUC ) and 50 μL Beetle-Juice for firefly luciferase ( IaaH promoter-LUC , IaaM promoter-LUC and Ipt promoter-LUC ) were added via the liquid handling robotic device and the luminescence was measured by the Robion Solaris plate reader luminometer ( STRATEC Biomedical Systems AG , Birkenfeld , Germany ) . The relative luminescence intensity was calculated from the values of Firefly-LUC versus Renilla-LUC . The relative luminescence intensity calculated from the oncogene promoter-LUC constructs ( IaaH promoter-LUC , IaaM promoter-LUC and Ipt promoter-LUC ) in the absence of any expression plasmids containing the CaMV35S::transcription factor constructs or phytohormone treatments was set to 1 . The fold induction in luminescence represents the relative activity induced by certain transcription factors or treatments . Protein synthesis was induced in the bacterial suspension of the transgenic E . coli SoluBL21 strain ( Genlantis , San Diego , USA ) expressing the 6×His-WRKY40 fusion protein by adding 0 . 5 mM Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) at OD600 0 . 6 overnight at 16°C . Purification of the histidine-tagged WRKY40 protein was performed according to the protocol from Novagen ( Merck Millipore , Darmstadt , Germany ) . To generate the 50 bp of the Ipt promoter probe used in EMSA , two complementary oligonucleotides were synthesized by Sigma ( Sigma Aldrich , St . Louis , USA ) . The two oligonucleotides were mixed at a 1:1 molar ratio in annealing buffer ( 10 mM Tris , pH8 . 0 , 1 mM EDTA , 50 mM NaCl ) . The mixture was incubated at 95°C for 5 min and slowly cooled to room temperature and incubated overnight . The double-stranded oligonucleotides were purified from an 3% ( w/v ) agarose gel after electrophoresis , then radioactively labeled by using T4 polynucleotide kinase ( Fermentas , Thermo , Waltham , USA ) and [gamma-32P] adenosine 5’-triphosphate ( ATP; Hartmann Analytic GmbH , Braunschweig , Germany ) . About 5 ng labeled probe and 150−300 ng 6×His-WRKY40 protein were mixed in DNA-protein binding buffer [10 mM Tris-HCl pH8 . 0 , 0 . 5 mM ZnSO4 , 0 . 25 mM DTT , 0 . 1 μg/μL poly [dI-dC] , 5% ( v/v ) glycerol] . The binding reaction mixture was incubated on ice for 30 min and separated in a 6% ( w/v ) native polyacrylamide gel [45 mM Tris-borate , 1 mM EDTA , pH 8 . 6 , 15% ( v/v ) Rotiphorese Gel 40 ( 29:1; Roth , Karlsruhe , Germany ) , 0 . 1% ( w/v ) ammonium persulfate ( APS ) , 0 . 5% ( v/v ) TEMED] at 4°C for 3 h at 200 V in 0 . 5 × TBE buffer ( 45 mM Tris-borate and 1 mM EDTA; pH 8 . 6 ) . The gel was fixed in 5% acetic acid for 10 min and dried for approximately 1 h ( gel drying systems , Bio-Rad , Hercules , USA ) , exposed at −70°C to an x-ray film ( Eastman Kodak , Rochester , USA ) overnight and then developed . Crown gall materials used for cytokinin analysis were obtained from Wassilewskija ( WS-2 ) stems inoculated with A . tumefaciens strain C58 . The analysis was performed as described in [19] . For the BiFC assay , 20 μg of each cYFP and nYFP protein fusion constructs ( WRKY-cYFP , WRKY-nYFP , ARF-cYFP , ARF-nYFP and ARF5Δ722-cYFP ) were transfected into mesophyll protoplasts using the PEG-calcium transfection method [71] . After incubation for 16–18 h in the dark at room temperature , protoplasts were inspected and images were taken using a confocal laser scanning microscope ( Leica TCS SP5II , Leica Wetzlar , Germany ) . Fluorescing plant cells and tissues were inspected and documented using an epifluorescence microscope ( BZ 8000K , Biozero , Keyence , Osaka , Japan ) and the software program ( BZ observation application ) . For the inspection of intact plants , a dissecting microscope ( Leica MZ6 , Leica , Wetzlar , Germany ) was used and pictures of crown galls were taken using a Leica DFC500 camera ( Leica , Wetzlar , Germany ) . The Arabidopsis gene indexes ( AGI ) of genes mentioned in the text are AT2G33860 ( ARF3 ) , AT1G19850 ( ARF5 ) , AT3G16857 ( ARR1 ) , AT1G10470 ( ARR4 ) , AT1G04550 ( IAA12 ) , AT5G63790 ( NAC102 ) , AT4G31800 ( WRKY18 ) , AT1G80840 ( WRKY40 ) , AT4G23810 ( WRKY53 ) and AT2G25000 ( WRKY60 ) . AGI codes are from The Arabidopsis Information Resource database ( TAIR , http://www . arabidopsis . org ) . | Crown gall development requires the expression of agrobacterial genes in the plant host . These genes are transferred by the T-DNA of the plant pathogen Agrobacterium tumefaciens and include the oncogenes IaaH , IaaM and Ipt , which , according to the tumor-inducing principle , are essential for crown gall development . The oncogenes are involved in auxin and cytokinin production . This results , when at appropriate hormone ratios , in enhanced cell proliferation . The T-DNA transformation process and the encoded oncogene enzymes have been intensively studied , but knowledge of oncogene expression in plant cells and the regulatory host factors is missing . We set out to fill this gap , providing evidence that expression of the Ipt gene is host-cell controlled , whereas the IaaH and IaaM genes are ubiquitously expressed at low levels in T-DNA transformed tissue . This is achieved by A . tumefaciens , which first hijacks transcription factors of the plant pathogen response pathway to activate Ipt oncogene expression and initiates cell proliferation . With increasing auxin levels during the infection process , a transcription factor of the auxin-signaling pathway is recruited , potentiating Ipt gene expression . Thus , for crown gall development , two host-signaling pathways are combined through the interaction of transcription factors that adjust the ratio of cytokinin to auxin . |
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ClC-1 protein channels facilitate rapid passage of chloride ions across cellular membranes , thereby orchestrating skeletal muscle excitability . Malfunction of ClC-1 is associated with myotonia congenita , a disease impairing muscle relaxation . Here , we present the cryo-electron microscopy ( cryo-EM ) structure of human ClC-1 , uncovering an architecture reminiscent of that of bovine ClC-K and CLC transporters . The chloride conducting pathway exhibits distinct features , including a central glutamate residue ( “fast gate” ) known to confer voltage-dependence ( a mechanistic feature not present in ClC-K ) , linked to a somewhat rearranged central tyrosine and a narrower aperture of the pore toward the extracellular vestibule . These characteristics agree with the lower chloride flux of ClC-1 compared with ClC-K and enable us to propose a model for chloride passage in voltage-dependent CLC channels . Comparison of structures derived from protein studied in different experimental conditions supports the notion that pH and adenine nucleotides regulate ClC-1 through interactions between the so-called cystathionine-β-synthase ( CBS ) domains and the intracellular vestibule ( “slow gating” ) . The structure also provides a framework for analysis of mutations causing myotonia congenita and reveals a striking correlation between mutated residues and the phenotypic effect on voltage gating , opening avenues for rational design of therapies against ClC-1–related diseases .
CLC proteins comprise a large family of chloride ( Cl− ) -transporting integral membrane proteins with diverse physiological functions [1–3] . The first identified human member , ClC-1 , is essential for maintaining the permeability of Cl− across the plasma membrane of skeletal muscle fibers , gCl , accounting for approximately 80% of the resting membrane conductance and assuring precise neuronal control of muscle contraction [3] . Mutations of the ClC-1 gene cause myotonia congenita , a disease that allows a single nerve action potential to trigger a series of muscle action potentials ( myotonic runs ) , leading to prolonged muscle contraction [4–7] . Despite distinct roles as passively conducting Cl− channels and stoichiometrically coupled secondary active Cl−/H+ antiporters [2 , 3] , members of the CLC family share a common homodimeric core architecture , with each subunit harboring an independent ion translocation pathway [8 , 9] . The molecular mechanisms of ion transport in CLC antiporters have been extensively studied functionally and structurally [8 , 10–15] . Yet it is poorly understood how the antiporters and channels establish their separate functions . In addition , the complex gating processes that regulate CLC channel activity remain elusive , with only a single available structure of a channel member , namely , that of bovine ClC-K [9] . Each CLC monomer has a gate that operates independently from the other ( also known as “protopore” or “fast gate” ) , structurally attributed to a specific glutamate , “GluGATE” [10] . A slower gate controls both conducting pathways simultaneously ( “common” or “slow gate” ) [16] , but the principles and determinants of this regulation are enigmatic . Furthermore , activity of ClC-1 is modulated by cellular cues such as phosphorylation [17] , pH , and nucleotides [18 , 19] in an unknown manner . Such regulation is , however , physiologically essential because intense muscle exercise leads to acidosis , resulting in an increased nucleotide sensitivity of ClC-1 and consequent reduction of gCl , thereby assisting in preventing muscle fatigue [20 , 21] . The recent ClC-K structure provided the first insights into the differences between CLC channels and transporters; in particular , it revealed a pore widening on the intracellular side . Yet there are surprisingly few known structural differences between the CLC channels and transporters . However , ClC-K channels exhibit only limited gating as GluGATE is missing [2 , 3] , and their activity has not been reported to depend on nucleotide binding [22] . Therefore key questions concerning CLC channel function and regulation remain unanswered . Furthermore , a deeper understanding on structure–phenotype relationships of myotonia-causing mutations in ClC-1 is required to shed further light on how the muscle disease is manifested at a molecular level .
Here , we have determined structures of full-length human ClC-1 using single-particle cryo-electron microscopy ( cryo-EM ) , exploiting a purified protein sample that displays Cl−-dependent single-channel–derived ion conductance ( S1 Fig and S1 Data ) . For structural characterization , sample in the presence of 100 mM Cl− at pH 7 . 5 and in the absence of nucleotides or antibodies was initially employed ( Fig 1 ) . Three-dimensional ( 3D ) classification of particles resulted in several different groups , of which one yielded a 3 . 6 Å overall resolution density map for the transmembrane domain , allowing confident model building ( S2–S4 Figs ) . The final model represents the membrane-spanning portion ( note that the N terminus and intracellular αA helix are lacking ) as well as parts of two C terminal’s so-called cystathionine-β-synthase ( CBS ) domains present per monomer ( for which some cryo-EM density is left unmodeled ) and includes several features that were not observed in the ClC-K structure ( S5 Fig ) . The homodimeric architecture of ClC-1 is reminiscent of that of bovine ClC-K and available structures of CLC proteins from lower organisms ( Fig 2A ) . The monomers consist of membrane-spanning helices and half-helices ( αB to αR ) with connecting loops ( e . g . , αB–C , between αB and αC ) as well as the CBS domains ( Fig 1 ) . Each protomer holds a separate chloride conducting pathway across the membrane , established by a vestibule on either side of the membrane , and an interconnecting narrow and short pore . In CLC transporters , the Cl− conducting pore ( Fig 2B ) is marked by distinct Cl− binding sites ( denoted sext , scen , and sint , respectively , but no Cl− ions are resolved in the current structure ) , and the constricting Glu232 ( of αF , also known as GluGATE; ClC-1 numbering throughout ) and Tyr578 ( of αR , TyrC ) [9] . Furthermore , Ser189 ( of αC-D , SerC ) is located in the vicinity of the pore ( Fig 2B , 2C and 2E and S5A Fig ) . In ClC-1 , voltage-dependent gating is established by GluGATE , which is perhaps being displaced by competing Cl− ions and/or protonation . In contrast , in voltage-independent ClC-K channels , GluGATE is replaced by a valine , and , indeed , substitutions of GluGATE with uncharged residues render ClC-1 similarly voltage independent [24] . Unfortunately , the GluGATE side chain is not visible in our cryo-EM density maps ( S4 Fig ) , but carboxylate groups of interacting acidic residues are known to be frequently undetectable using cryo-EM due to radiation damage . A similar orientation of the side chain as observed in ClC-K would be in agreement with Cl− passage through a maintained scen , as a concomitant adaptation of αR significantly shifts the position of TyrC and thus maintains the GluGATE-TyrC distance ( Fig 2B–2F and S5G Fig ) . However , we cannot exclude that the side-chain of GluGATE is buried deep into the hydrophobic pocket established by Phe279 , Phe288 , and Phe484 ( S5H Fig ) . The pore aperture of the extracellular vestibule is constricted by a hydrophobic barrier with Met485 ( Met427 in ClC-K ) , but in contrast to ClC-K , the gate opening is also controlled by Lys231 ( of αE–F ) and Arg421 ( of αL ) ( Fig 2B–2F ) that may orchestrate Cl− permeation to or from the extracellular environment [25–27] . This difference can be attributed to αE–F , with its GluGATE and Lys231 adopting a more CLC-transporter–like configuration because this loop is considerably shorter than in ClC-K , alongside a side-chain reorientation of Arg421 ( Fig 2C–2F ) . We also observe a structural adjustment on the intracellular side of the pore , with αC–D being displaced as compared to the corresponding loop in ClC-K . This rearrangement opens the vestibule even deeper toward GluGATE ( Fig 2C–2F ) , providing intracellular access beyond the sint site present in CLC antiporters and suggesting that no tight Cl− binding occurs on the intracellular side , in agreement with electrophysiological data [28] . The wider intracellular vestibule of the CLC channels , as compared to the transporters , has been proposed to allow for the higher Cl− conductance in channels , lowering the kinetic barrier between scen and the cytosol [9] . We note that the vestibule width of ClC-1 is similar to that of ClC-K at SerC , with the side chain of this residue being positioned away from the Cl− permeation pathway in both channels , establishing the SerC location as another of the distinguishing features between CLC channels and transporters . It remains obscure whether the channel has been captured in the open configuration , a priori induced by the experimental conditions ( 0 mV , 100 mM Cl− ) . Molecular dynamics simulations of the ClC-1 structure suggest that Cl− from the intracellular side spontaneously interacts with GluGATE upon protonation of its side chain but that free energy is required to complete the passage across the membrane ( S6 Fig ) . We anticipate that GluGATE and the Lys231–Arg421 constricting interactions attenuate chloride flux , in agreement with the smaller conductance of ClC-1 versus ClC-K [2 , 3] , and we cannot exclude that Cl− shuttling occurs directly between protonated GluGATE and Lys231 across the Met485 barrier ( GluGATE overlays sext in some CLC transporters [8 , 14 , 29] ) ; chloride interaction with the latter may be unfavorable , however . The molecular mechanisms that govern slow gating in CLC proteins remain elusive . It is known that CBS nucleotide binding and low pH inhibit ClC-1 activity by favoring closure of the common gate [19 , 29] . Assessment of the 3 major cryo-EM maps obtained in our structural classification ( see also S2 Fig and Methods ) reveals different arrangements of the CBS domains , suggesting intrinsic domain flexibility at pH 7 . 5 ( Fig 3A and 3B and S7 and S8 Figs ) . To test this , we determined the structure of ClC-1 also at lower pH ( 6 . 2 ) in the presence of 0 . 3 mM of the nucleotide nicotinamide adenine dinucleotide ( NAD ) to unravel the regulation mechanism ( S2 , S3 and S8 Figs ) . In these conditions , the CBS domains appear significantly more rigid ( in comparison to pH 7 . 5; Fig 3A and 3B and S7 and S8 Figs ) . This observation is also supported by ClC-1 size-exclusion chromatography profiles ( S9 Fig ) , with samples at low pH being shifted toward lower molecular weight ( more compact ) . Therefore , the CBS arrangements seem to correlate with slow gating , being rigid at low pH in the presence of nucleotides and more flexible at higher pH in the absence of nucleotides , bringing to mind a mechanism that has been proposed based on electrophysiological data [29] . The complete effects of such putative rearrangements are , however , not demonstrated experimentally by our structures , because they remain closed also at the higher pH ( determined from particles in detergent environment ) . How then can the Cl− conductance of 2 separate pores be affected by structural shifts of the CBS domains ? Examination of the interface between the CBS and the transmembrane domain suggests that CBS2 interacts with αD–E , a loop previously shown to affect slow gating ( Fig 3C and 3D ) [25 , 31] . Nucleotides may also interact directly with the transmembrane domain when bound in the cleft between CBS1 and CBS2 ( the latter observed in structures of isolated CBS domains [13]; Fig 3E ) . It is conceivable that these structural arrangements and the direct physical connection between CBS and αR—all structural elements leading to the GluGATE constrictions site—allow structural adjustment of the transport pathway and thus chloride conductance regulation ( Fig 3C ) . Such structural effects will be propagated between the monomers via the CBS domains , in agreement with concurrent modulation of the 2 conducting pathways in the dimer [16] . We note that the CBS portions that interact with the transmembrane and the CBS domain of the adjacent monomer are structurally ( and at interaction sites also sequencewise; S10 Fig ) conserved ( Fig 3E ) , and therefore this may represent a unifying mechanism of slow gating for CLC proteins . ClC-1 defects cause recessive ( Becker type ) or dominant ( Thomsen type ) myotonia congenita , typically associated with complete disruption of channel function or with a dominant negative effect in heterodimeric wild-type ( WT ) -mutant complexes [7] , respectively . Our structure now allows mapping of such ( or other experimental ) ClC-1 substitutions for evaluation of structure–function–disease and -phenotype relationships ( Fig 4 ) . Several dominant and recessive mutations induce an alteration of the overall gating from depolarization to hyperpolarization activated , yielding a similar intracellular Cl−-sensitive gating as described for ClC-2 [32] . Therefore , the different gating profiles of ClC-1 and ClC-2 likely do not necessitate major structural differences . These residues are generally surface exposed and localized to the extracellular half , including the vestibule and the pore-constricting residues Lys231 and Arg421 ( Fig 4B ) [26 , 27 , 32–35] . In contrast , many dominant mutations exert a “shift” of the common gate to open probability to positive voltages , leading to significant reduction of gCl at the physiological membrane potential [36] . Such mutations cluster primarily at the dimer interface and in the intracellular vestibule and pore region ( Fig 4C , and 4D and S5D Fig ) . One is located in CBS2 , close to the membrane domain , in agreement with the above-mentioned mechanism of slow-gating regulation exerted via CBS2 . Residues that affect binding of one of the most commonly used ClC-1 inhibitors , the lipophilic 9-anthracene-carboxylic acid ( 9-AC ) , are all buried into a CAVER [37]-computed membrane-embedded cavity on the intracellular side that stretches to GluGATE , in agreement with the intracellular mechanism of action proposed for this compound ( Fig 4E and 4F and S11 Fig ) [24] . Because this pocket is lined by multiple hydrophobic and a few negatively charged residues , it is unlikely to allow chloride conductance ( proton access is possible ) but rather 9-AC–induced interference of flux across GluGATE and may thus represent a suitable site for future drug-discovery efforts .
In summary , we report the molecular structure of Cl−-conducting human ClC-1 , sharing an overall fold similar to other CLC proteins , with a narrow connecting pore and positively charged vestibules attracting Cl- ions similar to CFTR [38] . The structure exhibits several unique features , including shifts in the central GluGATE-TyrC pair , a more closed extracellular vestibule , and a wider penetration profile from the intracellular side , the latter representing a distinct feature of CLC channels separating them from transporters . We propose a model for adenine nucleotide and pH regulation of the common gate via CBS2 and the intracellular loops congruent with previous functional data . Overall , these findings significantly increase our understanding of Cl− conductance in physiology and open new opportunities for biomedicine . For example , the positively charged constriction of the extracellular vestibule and the putative 9-AC pocket may serve as favorable target sites for stimulators or inhibitors from outside or inside the cell , respectively . During the course of the preparation of this manuscript , the structure of human ClC-1 was reported by another group [39] . The ClC-1 structures display only limited differences despite that different overproduction hosts were exploited . The authors detected a similar putative 9-AC binding pocket ( the alternative pathway ) and conformational flexibility in the CBS region ( determined at pH 7 . 4 ) , in agreement with our findings . We anticipate that the pH-dependent conformational changes reported here—in conjunction with mutational efforts using , e . g . , single-channel recordings , as for the first time demonstrated in this work , will allow for more refined studies to further resolve the mechanism of slow-gating in CLC proteins .
Yeast codon-optimized cDNA encoding human ClC-1 ( UniProt accession P35523 ) was purchased from Genscript ( Genscript , USA ) . cDNA was inserted into pEMBLyex4 [40] along with yeast-enhanced GFP by homologous recombination to encode ClC-1 , followed by a Tobacco Etch Virus ( TEV ) cleavage site , GFP , and a His10 tag . The correct nucleotide sequence of the expression construct was verified by DNA sequencing ( Eurofins MWG Operon , Germany ) . Human ClC-1 was produced in the PAP1500 strain [41] grown in computer controlled 15-L bioreactors as previously reported but without addition of any chloride salts ( such as NaCl ) [42] . Yeast cells were harvested approximately 90 hours after induction of ClC-1 expression . For crude membrane preparations , approximately 25 g of yeast cells were resuspended in 25 mL lysis buffer ( 25 mM imidazole [pH 7 . 5] , 1 mM EGTA , 1 mM EDTA , 10% glycerol , 5 mM β-mercaptoethanol ) supplemented with protease inhibitors ( 1 μg/mL leupeptin , pepstatin , and chymostatin , and 1 mM PMFS ) . Cells were disrupted by addition of glass beads ( 0 . 4–0 . 8 mm ) and vortexed in 50-mL Falcon tubes 8 times for 1 minute . The supernatant was collected , and glass beads were washed several times in ice-cold lysis buffer . The cell lysate was centrifuged at 1 , 000g for 10 minutes to remove cell debris . Crude membranes were pelleted from the supernatant by ultracentrifugation at 160 , 000g for 90 minutes; resuspended in a buffer containing 50 mM Tris ( pH 7 . 5 ) , 300 mM NaCl , 10% glycerol , 1 mM PMSF , and EDTA-free protease inhibitors ( Sigma ) ; and homogenized in a Potter-Elvehjem homogenizer . Subsequently , membranes were solubilized by adding dodecyl-β-maltoside ( DDM ) and cholesteryl semi succinate ( CHS; from Anatrace ) at final concentrations of 1% and 0 . 33% , respectively , and incubated at 4°C for 3 hours under gentle stirring . Nonsolubilized material was removed by ultracentrifugation at 30 , 000 rpm for 30 minutes in a Beckman Ti 60 rotor . Ni-beads from 5 mL of slurry ( Thermofisher ) were incubated with the supernatant for 2 hours under gentle stirring . To prevent unspecific binding , 30 mM imidazole was added . Resin was transferred to a 5-mL Econo column ( Bio-Rad ) and washed with 10 column volumes of high-salt buffer ( 50 mM Tris [pH 7 . 5] , 800 mM NaCl , 5% glycerol , 0 . 4 mg/mL DDM , and 0 . 04 mg/mL CHS ) followed by 10 column volumes of low-salt buffer ( 50 mM Tris [pH 7 . 5] , 300 mM NaCl , 5% glycerol , 0 . 4 mg/mL DDM , and 0 . 04 mg/mL CHS ) . ClC-1 protein was liberated from the beads by overnight incubating in 10 mL low-salt buffer containing 0 . 2 mg of TEV protease . Ni-beads were washed twice with 5 mL of low-salt buffer , and all collections were pooled and concentrated to approximately 1 mL using a 100 , 000 kDa cutoff concentrator device ( Sartorius ) . Amphipol PMAL-C8 ( Anatrace ) was added to the purified protein at a mass ratio of 1:5 and incubated overnight . To remove DDM , protein was dialyzed overnight against final buffer ( 20 mM Tris [pH 7 . 5] , 100 mM NaCl , 0 . 2 mM TCEP ) supplemented with 100 mg of SM-2 Bio-Beads ( Bio-Rad ) . The protein-amphipol complex was applied to a Superdex-200 column equilibrated with final buffer . Peak fractions were collected and concentrated to approximately 0 . 5 mg/mL . For the low pH samples , the purification procedure was identical except for using 20 mM BisTris ( pH 6 . 2 ) ( instead of Tris [pH 7 . 5] ) in the final buffer ( final protein concentration only reached approximately 0 . 3 mg/mL due to precipitation ) . Single-channel ion current was recorded using 2 separate methods , as follows: Cryo-EM grids were prepared with the Vitrobot Mark IV ( FEI ) operated at 100% humidity at 4°C . Immediately prior to sample vitrification , Quantifoil 1 . 2/1 . 3-μm holy carbon grids were glow-discharged with Easyglow ( TedPella ) , and fluorinated fos-choline-8 ( Anatrace ) was added to the protein sample to a final concentration of 3 mM , which was an essential step for producing good quality thin ice . For each grid , an aliquot of 3 . 5 μL was applied and incubated for 20 seconds inside the Vitrobot . Blotting time was set to 2 . 5 seconds with 2 seconds of drain time . The low pH sample was treated identically , except for incubation with 0 . 3 mM NAD before freezing ( and that no fluorinated fos-choline-8 was added to obtain one of the pH 6 . 2 data sets ) . Cryo-EM data sets were collected on a Titan Krios electron microscope ( FEI ) operating at 300 keV with a Gatan K2 Summit direct electron detector attached to a Gatan imaging filter ( GIF ) . Movies were recorded under super-resolution counting mode at a pixel size of 0 . 535 Å and a dose rate of 0 . 876 e/pixel/frame for a total of 60 frames . The total electron dose was 45 electrons per Å2 per movie for 9 seconds . Cryo-EM movies were first gain-corrected and 2× binned to a final pixel size of 1 . 07 Å . Dose-weighted and nondose-weighted summed micrographs were generated with MotionCorr2 [43] using all frames except the first one . Defocus values were calculated with the nondose-weighted micrographs using Gctf [44] . Next , image processing was conducted using dose-weighted micrographs with the predetermined defocus . Template-free particle picking was done using Kai Zhang’s Gautomatch software ( https://www . mrc-lmb . cam . ac . uk/kzhang/Gautomatch ) . All following processing steps were done in Relion 2 . 0 [45] using a box size of 288 pixels . For the pH 7 . 5 data set , a total of 594 , 609 auto-picked particles from 4 , 475 micrographs with a defocus range of −1 . 0 to −3 . 0 μm were subjected to several rounds of reference-free 2D classification to remove defective particles . The selected 477 , 729 particles were sorted using 3D classification . Selected classes were refined using masks , either with the complete protein excluding the amphipol belt or with the membrane domain only . Multiple cryo-EM density maps were calculated demonstrating structural heterogeneity of the protein . 3D classification of particles into 5 classes provided the best class consisting of 176 , 871 particles ( representing more than 37% of all particles ) . A soft mask covering the entire protein without amphipol belt yielded a map with an overall resolution of 4 . 00 Å , and a tighter mask only containing the membrane domain resulted in map with resolution of 3 . 63 Å . To further investigate the structure heterogeneity in the cytoplasmic domain , the 2D selected particles were first refined , and then the refined per-particle parameters were applied for 3D classification , only performing local angular searches within ±10 degrees . This local 3D classification resulted in 9 classes , and the 2 major classes differed primarily in the cytoplasmic domain . Refinement of these 2 classes , each representing approximately 15% of all selected particles , yielded overall map resolutions of 4 . 34 Å and 4 . 28 Å , respectively . For the pH 6 . 2 data set collected with fluorinated fos-choline-8 , 552 , 914 particles were autoselected from 4 , 119 motion-corrected micrographs , and 300 , 572 particles were selected after 2D classification for further processing; 3D classification into 5 classes generated the best class , which eventually was refined to a final resolution of 4 . 47 Å . Combination of the data collected at pH 6 . 2 with and without fluorinated fos-choline-8 , and a similar local angular search strategy as for the pH 7 . 5 data set , generated a final map of 4 . 2 Å of the best class ( based on approximately 30% of the total particles ) . C2 symmetry was applied for all classification procedures , and all maps were sharpened with a B-factor of −100 Å2 . Local resolution was calculated using the postprocessed map , and the map was filtered according to the local resolution and used for model building . The initial model was generated using the SWISS-MODEL online server and the ClC-K structure [9] ( PDB-ID 5TQQ ) as a template . The model was first fitted into the cryo-EM density map and later manually built in COOT [46] . The 3 . 6 Å membrane domain density map was sufficient for building the entire membrane domain ( residues 115 to 589 ) with only 1 loop missing ( residues 254–261 ) . The built model was refined using phenix . real_space_refine of the Phenix software package [47] . C2 symmetry was imposed during the refinement by using strong non-crystallographic symmetry ( NCS ) restraints . Secondary structure restraints and Ramachandran restraints were also imposed during refinement . The resolution and connectivity of the cytoplasmic domain was insufficient for de novo model building . Instead , a homology model based on the available structure of the CBS domains of ClC-0 ( PDB-ID 2D4Z [30] ) was generated and docked into different maps . The refinement of the cytoplasmic domain was conducted by local grid minimization , model morphing , and simulated annealing implemented in the phenix . real_space_refine software [47] . To prevent overfitting , the map resolution was restricted to 5 Å , the local resolution of the cytoplasmic domains as determined by Relion postprocessing . After model building , the models were trimmed to only include the minimal CBS architecture , consisting of 2 helices and a β-sheet . The quality of the models were validated assessed using Molprobity [48] ( see S1 Table for statistics ) . All figures except for Fig 3A and 3B were generated using the model based on the 4 . 0 Å ( Map 1 ) . The ClC-1 dimer with Glu232 either protonated or deprotonated was inserted into a palmitoyloleoylphosphocholine ( POPC ) membrane , and CHARMM36 force field parameters [49 , 50] were generated using CHARMM-GUI [51] . The simulations were performed using the GROMACS 2016 . 4 simulation software [52] . Each system was energy minimized and equilibrated in a stepwise manner using 25-ps NVT simulations with decreasing restraints on the protein and lipid heavy atoms . In these simulations , a 1-fs time step was used and the temperature was maintained at 310 K with a Berendsen temperature-coupling scheme [53] . The following set of NPT simulations further released heavy-atom restraints for 0 . 1 ns , 10 ns , and 10 ns , respectively . Here , a 2-fs time step was used and the pressure was kept constant at 1 bar using a Berendsen pressure barostat [53] . In a 100 ns production simulation , all atoms were unrestrained , and the temperature and pressure coupling schemes were Nose-Hoover [54 , 55] and Parrinello-Rahman [56 , 57] , respectively . The GROMACS pull code with a force constant of 1 , 000 kJ mol−1 nm−2 was applied for 300 ps to the Cl− ion in closest vicinity of Glu232 in 1 monomer . The pull rate was 0 . 1 Å per ps , and the pull force was directed along the vertical axis of the membrane . The potential of mean force ( PMF ) was calculated using umbrella sampling from 1 Å windows along the ion path . The figures were generated using VMD software[58] . | Chloride transporting CLC proteins are expressed in a wide range of organisms , and the family encompasses several members with numerous roles in human health and disease by allowing movement of chloride ions across the membranes that encapsulate cells and cellular organelles . Structurally , CLCs form dimers possessing a separate ion translocation pathway in each monomer , and they can operate as either channels or transporters that exchange chloride for protons . The CLC channel ClC-1 is critical to skeletal muscle excitability and has been proposed as a target to alleviate neuromuscular disorders . Here , we have analyzed the structure of human ClC-1 and revealed the high similarity of its ion conducting pathway to those observed in other CLC members , including prokaryotic and algal transporters . Our data suggest how ClC-1 is regulated by environmental cues to allow opening and closure , thereby permitting attenuation of muscle function . Our results help with understanding the principal determinants that govern CLC proteins and may guide downstream translational applications to combat muscle pathologies . |
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Lymphatic filariasis ( LF ) , a morbid disease caused by the tissue-invasive nematodes Wuchereria bancrofti , Brugia malayi , and Brugia timori , affects millions of people worldwide . Global eradication efforts have significantly reduced worldwide prevalence , but complete elimination has been hampered by limitations of current anti-filarial drugs and the lack of a vaccine . The goal of this study was to evaluate B . malayi intestinal UDP-glucuronosyltransferase ( Bm-UGT ) as a potential therapeutic target . To evaluate whether Bm-UGT is essential for adult filarial worms , we inhibited its expression using siRNA . This resulted in a 75% knockdown of Bm-ugt mRNA for 6 days and almost complete suppression of detectable Bm-UGT by immunoblot . Reduction in Bm-UGT expression resulted in decreased worm motility for 6 days , 70% reduction in microfilaria release from adult worms , and significant reduction in adult worm metabolism as detected by MTT assays . Because prior allergic-sensitization to a filarial antigen would be a contraindication for its use as a vaccine candidate , we tested plasma from infected and endemic normal populations for Bm-UGT-specific IgE using a luciferase immunoprecipitation assay . All samples ( n = 35 ) tested negative . We then tested two commercially available medicines known to be broad inhibitors of UGTs , sulfinpyrazone and probenecid , for in vitro activity against B . malayi . There were marked macrofilaricidal effects at concentrations achievable in humans and very little effect on microfilariae . In addition , we observed that probenecid and sulfinpyrazone exhibit a synergistic macrofilaricidal effect when used in combination with albendazole . The results of this study demonstrate that Bm-UGT is an essential protein for adult worm survival . Lack of prior IgE sensitization in infected and endemic populations suggest it may be a feasible vaccine candidate . The finding that sulfinpyrazone and probenecid have in vitro effects against adult B . malayi worms suggests that these medications have promise as potential macrofilaricides in humans .
Lymphatic filariasis ( LF ) is a debilitating disease caused by the tissue-invasive nematodes Wuchereria bancrofti , Brugia malayi , and Brugia timori . Currently , there are ~ 70 million people infected worldwide and over a billion people at risk for infection [1] . Since 2000 , the Global Programme to Eliminate Lymphatic Filariasis has substantially reduced the number of people infected or at risk for infection [1] . However , it has become apparent that new strategies must be implemented in order to attain global eradication of LF [2 , 3] . Development of new therapeutics that target adult filarial worms would greatly enhance our ability to eliminate lymphatic filariasis . When given individually , the antifilarial drugs diethylcarbamazine ( DEC ) , ivermectin ( IVM ) , and albendazole are effective against the microfilaria ( Mf ) stage but exhibit little activity against adult filarial worms [4] . Use of all three medications together appears to have a macrofilaricidal effect [5] , but due to the adverse effects caused by their potent microfilaricidal activity DEC and ivermectin cannot be used for mass drug administration ( MDA ) in areas endemic for Loa loa or Onchocerca volvulus . Therefore , development of a short-course macrofilaricidal agent or a vaccine would be very valuable for eradication efforts . Unlike cestodes and trematodes , nematodes have a complete intestinal tract . Over the past 15 years , intestinal proteins of Necator americanus ( hookworm ) and Haemonchus contortus ( barber pole worms ) have been shown to be effective vaccine candidates in animal models [6–11] . Considering this work , our group performed a proteomic analysis of the intestine , body wall , and reproductive tract of adult B . malayi worms to potentially identify novel drug and vaccine targets for lymphatic filariasis [12] . We identified 396 proteins that were specific to the intestinal tract of the adult worms . Of these intestinal proteins , we selected a subset for evaluation as drug and vaccine candidates based on high homology with other filarial species , extracellular domains with accessibility to drugs and antibody , and predicted function . In this study , an adult B . malayi intestinal protein , UDP-glucuronosyltransferase ( Bm-UGT ) , was identified as a potential therapeutic target . The protein was predicted to have an enzymatic function that could be inhibited . Furthermore , structural analysis of Bm-UGT by InterPro revealed a large extracellular domain that could be targeted by therapeutics . We determined that this protein was essential for worm survival using small interfering RNA ( siRNA ) to knockdown expression . Importantly , we identified two FDA-approved commercially available UGT inhibitors that exhibit macrofilaricidal activity and display synergy with albendazole in vitro . Finally , we analyzed the antibody response against Bm-UGT in filarial patients and found that neither infected individuals nor endemic normals develop detectable levels of IgE against Bm-UGT , suggesting it would not induce allergic reactions if used in a vaccine .
Previously , we reported that Bm-UGT ( Bm17378 ) was a specific intestinal protein of B . malayi adult worms [12] . Sequence analyses indicated the presence of homologues in human filarial worms ( Brugia sp . , W . bancrofti , L . loa ) with significant homology ( >75% identity ) , and to a lesser extent ( ~35–40% identity ) in other nematodes such as Dirofilaria immitis , Haemonchus contortus , Ancylostoma sp . , Strongyloides sp . , Oesophagostomum dentatum and Toxocara canis . The most similar human proteins were UDP-glucuronosyltransferases as expected , but with low sequence identity <27% . Given the high predicted homology of Bm-UGT between B . malayi and D . immitis , we also evaluated orthologs in cat and dogs . Results of the sequence analyses revealed little homology to Bm-UGT . We then generated a phylogenetic tree by first aligning the Bm-UGT cDNA-derived peptide sequences using MUltiple Sequence Comparison by Log-Expectation ( MUSCLE ) and then creating a tree based on efficient maximum-likelihood estimation method by the LG model . As seen in Fig 1 , there is a high level of relatedness between Bm-UGT and several filarial orthologs , including other Brugia species , W . bancrofti , L . loa , and D . immitis . Interestingly , we could find no UGT ortholog in O . volvulus , and relatedness to the ortholog in Litomosoides sigmodontis , a common murine model of filarial infection , is low . Importantly , there is significant evolutionary distance between the Bm-UGT and orthologs in humans , cats , and dogs . Evaluation of data available from prior transcriptomic and proteomic studies of B . malayi demonstrates that Bm-UGT is not expressed in all the lifecycle stages ( Table 1 ) . A study by Li et al . shows that Bm-UGT transcript is only expressed in third stage larvae ( L3s ) and adult female and male worms [13] . In addition , an RNAseq study by Choi et al . on various lifecycle stages of B . malayi found that Bm-UGT was preferentially expressed during later larval stages . Consistent with these findings , Bm-UGT protein expression was found to be specific to these stages as well [14] . Predictive analysis using the InterPro database revealed that the protein contains a large luminally-expressed domain likely accessible to small molecules or ingested antibodies . Sequence analysis of the Bm-UGT ( Fig 2A ) indicates that residues 1–20 encode a signal peptide [15] followed by a two-domain UGT ( residues 21–278: N-terminal domain; 278–445: co-factor binding domain ) , a linker region , a transmembrane domain , and a short intra-cellular domain . The structure of the Bm-UGT was modeled using SWISS-MODEL with 43 template structures utilized . The final model was based on Protein Data Bank ( PDB ) : 5NLM ( the structure of the Polygonum tinctorium UGT ) with a sequence identity of 19 . 7% [PMID: 29309053] ( Fig 2B ) . UGTs add a glucuronic acid moiety to a substrate by transfer of the glucuronosyl group from uridine 5’-diphospho-glucuronic acid ( UDPGA ) . Further structure-based searches using the PDB identified the 2B7 UGT as a model for the co-factor binding domain , and based on the co-factor interacting residues , the Bm-UGT has a potential UDPGA-binding site ( Fig 2C and 2D ) . The enzyme nucleotide-sugar binding sites utilize a common structural scaffold; while specific interactions with the donor ligands vary between enzymes , analysis of the proposed binding site demonstrates it contains significant sequence homology to the active site of other UGTs . To evaluate whether siRNA is taken up by adult B . malayi worms , Cy3-conjugated Bm-UGT siRNA was added to the culture media of adult B . malayi worms for 24 hrs . Visualization of the adult worms shows clear uptake of siRNA throughout the intestinal tract ( Fig 3A and 3B ) . In contrast , imaging at the same exposure time reveals no apparent signal in the intestine of adult B . malayi cultured in media alone ( Fig 3C and 3D ) . In addition , we also confirmed that antibodies could access the lumen of the intestine via ingestion by the adult filaria ( S1 ) . Adult filaria worms were incubated with Cy3-labeled mouse IgG for 24 hours and then viewed under a fluorescent microscope . The labeled antibodies emitted a positive signal in the gut of the adult female worms while no signal was detected in worms incubated in media alone . We next evaluated reduction in target transcript and protein levels . We selected timepoints of 1 , 3 , and 6 days post-siRNA treatment based on a protein half-life of approximately 10 hrs for UDP-glucuronosyltransferases ( UGT ) [16] . After a 24-hr incubation with target-specific siRNA or scrambled siRNA , we compared Bm-ugt mRNA expression between the treated worms relative to the media control by RT-qPCR ( Fig 4A ) . Transcript expression was normalized employing the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase ( Bma-gapdh , Bm5699 ) . Bm-UGT siRNA treatment resulted in a 77 . 1% reduction in target mRNA compared to the controls 1-day post-siRNA treatment ( p = 0 . 0056 ) . Transcript knockdown was sustained throughout the experiment with a 76 . 2% decrease in Bm-ugt transcription 6 days post-siRNA incubation ( p = 0 . 0003 compared to controls ) . As expected , there was no significant difference in target transcription between the media control and scrambled siRNA groups . In addition , we did observe several worms ( n = 3 at day 2 post-siRNA incubation ) in the UGT siRNA group have no movement early into the experiment without any recovery throughout the course of the experiment . Bm-UGT knockdown was further substantiated by immunoblotting ( Fig 4B ) . Target protein expression was evaluated 24 hrs post-siRNA incubation using anti-Bm-UGT peptide antibodies . There was a robust reduction in UGT expression with the Bm-UGT siRNA treated worms compared to controls normalized to β-actin ( 68 . 1% reduction in Bm-UGT/β-actin , Fig 4C ) . After successfully demonstrating that siRNA reduces Bm-UGT expression , we evaluated for any resultant changes in worm motility , Mf release , and metabolism . Adult worm motility was scored on a scale from 0 to 4 , with 0 indicating no movement and 4 indicating active movement . At day 1 post-siRNA incubation , we observed a 77 . 1% reduction in motility with the Bm-UGT siRNA-treated group compared to the scrambled control ( p = 0 . 0006 , Fig 5A ) . This dramatic reduction in motility was maintained through day 6 ( 78 . 94% reduction , p = 0 . 0004 ) . We also observed a dramatic decrease in Mf release per adult worm per 24-hr period after Bm-UGT knockdown . At day 1 , there was a 62 . 5% reduction from the specific siRNA-treated worms compared to the scrambled siRNA-treated group ( p = . 0048 , Fig 5B ) . The greatest reduction in Mf release occurred at day 3 and was marked by a 95 . 3% difference between the Bm-UGT siRNA group and the scrambled control ( mean number of Mf release in 24h = 14 . 0 vs 294 . 8 , p = 0 . 0096 ) . Finally , we evaluated metabolism using a ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) reduction assay . Decreased MTT reduction by the Bm-UGT siRNA treated B . malayi was observed at all three timepoints ( Fig 5C ) . The values observed were similar over the course of the experiment with day 6 post-siRNA incubation showing the greatest difference between the specific and scrambled siRNA groups ( 64 . 2% lower values in the Bm-UGT treated group , p = 0 . 0243 ) . The dramatic decrease observed in worm viability and fecundity after siRNA inhibition demonstrated that Bm-UGT is an essential protein for adult B . malayi survival . We next sought to evaluate the effects that UGT inhibitors had on adult worm survival . We tested two non-specific UGT inhibitors that act on multiple UGT isoforms for activity against B . malayi adult worms in vitro . Both of these agents , sulfinpyrazone and probenecid , are FDA-approved medications that are used to treat gout [17–20] . We incubated adult worms at various concentrations and found that both drugs killed filariae in vitro using worm motility as a metric for viability . For sulfinpyrazone , we observed a dose-response relationship for macrofilaricidal activity in vitro ( Fig 6A ) . The most rapid decline in adult worm motility occurred at 2500 μM . At this concentration , the area under the curve ( AUC ) was 3 . 6 and significantly different than the AUC of 31 . 6 for the control worms incubated with media alone ( p<0 . 0001 ) . Macrofilaricidal activity was also seen at 200 μM . The AUC at this concentration was 19 . 7 and significantly different ( p<0 . 0001 ) than the AUC for media control . For probenecid , we also observed a dose-response curve for macrofilaricidal activity in vitro ( Fig 6B ) . The greatest reduction in worm motility occurred at 5000 μM , which had an AUC of 8 . 1 ( p<0 . 0001 ) compared to the media control AUC of 24 . 2 . The lowest concentration that exhibited a significant effect on motility was 500 μM ( p = 0 . 0004 ) . Though worm motility at 250 μM was not significantly different over the course of the experiment , at day 7 there was a significant difference ( p = 0 . 0105 ) between the treatment group compared to the control . For both UGT inhibitors , there were doses ( >1000 μM sulfinpyrazone and 5000 μM for probenecid ) that resulted in worms exhibiting no movement early in the experiment . Drug treatment was stopped for these worms and there was no recovery throughout the course of the experiment . After testing the drugs on adult filariae , we tested for microfilaricidal effect . We observed modest microfilaricidal effects at the highest concentrations for both drugs ( Fig 6C and 6D ) . However , no clear microfilaricidal effect was demonstrated for probenecid at 500 μM and very little for sulfinpyrazone at 200 μM . While these drugs are FDA-approved , we wanted to determine whether the concentrations used were cytotoxic . Employing a lactate dehydrogenase ( LDH ) cytotoxicity assay with human embryonic kidney ( HEK ) cells , we did not detect any cytotoxicity at the concentrations that exhibited macrofilaricidal activity ( S1 Table ) . After observing the macrofilaricidal effect of sulfinpyrazone and probenecid , we decided to investigate whether synergy existed between these inhibitors and albendazole . We hypothesized that because UGTs are involved in drug detoxification [21] , inhibition of these enzymes may potentiate the effect of albendazole on filaria . In fact , one study showed that C . elegans treated with albendazole had upregulation of UGTs and metabolism of albendazole to albendazole-glucosides [22] . Because of this previous finding , we decided to incubate adult filaria with a sub-macrofilaricidal concentration of sulfinpyrazone ( 40 μM ) or probenecid ( 100 μM ) in combination with albendazole ( 10 μM ) . Mild macrofilaricidal activity was observed with the 10 μM of albendazole treatment . Treatment of adult filariae with sulfinpyrazone in combination with albendazole ( Fig 7A ) produced an AUC of 22 . 5 based on worm motility . This was significantly lower than the resultant AUCs from treatment with sulfinpyrazone ( 30 . 63 , p = 0 . 001 ) or albendazole ( 29 . 63 , p = 0 . 0007 ) . With the probenecid/albendazole combination ( Fig 7B ) , we observed a significant decrease in worm motility resulting in an AUC of 24 . 0 . This result was significantly different from a single treatment with either probenecid ( AUC = 30 . 88 , p = 0 . 0007 ) or albendazole ( AUC = 29 . 63 , p = 0 . 0022 ) . Further , no synergistic microfilaricidal effects were observed with albendazole and sub-optimal concentrations of sulfinpyrazone ( 40μM ) or probenecid ( 100 μM ) ( S2 Fig ) . A major obstacle for helminth vaccine development is the potential for individuals living in endemic countries to have pre-existing antigen-specific IgE and thus be at risk for developing allergic reactions when vaccinated with helminth antigens [23] . In this study we employed a luciferase immunoprecipitation system ( LIPS ) assay to determine whether individuals infected with filariae developed Bm-UGT-specific antibodies . Lysate containing Bm-UGT-luciferase fusion protein was incubated with serum from W . bancrofti infected individuals that were categorized as having asymptomatic microfilaremia ( n = 13 ) , chronic pathology ( lymphedema ) ( n = 9 ) , or tropical pulmonary eosinophilia ( n = 8 ) . All patients were untreated at the time of the blood draw . We also tested serum from individuals living in endemic areas that had no evidence of infection ( endemic normal , n = 5 ) . Healthy normal blood bank donor sera were used as negative controls ( n = 5 ) , while anti-Bm-UGT peptide antibodies raised in New Zealand rabbits served as positive controls . In addition , the naïve rabbit sera served as a negative control . The LIPS assay did not detect Bm-UGT-specific IgG ( Fig 8A ) or IgE ( Fig 8B ) in any of the serum samples from filaria infected or exposed patients . As expected , there was no specific IgG or IgE in serum from U . S . blood bank donors while the Bm-UGT peptide IgG antibodies recognized our fusion protein .
Infection studies conducted at FR3 and TRS were approved by their respective Animal Care and Use Committees , and protocols that enable receipt of filarial worms from FR3 and TRS for use at the Uniformed Services University of the Health Sciences ( USUHS ) were approved by the USUHS Animal Care and Use Committee . The generation of peptide antibodies by Genscript was on a protocol approved by the Genscript institutional animal care and use committee . Blood samples were obtained from patients and healthy volunteers who provided written consent under protocols approved by the NIAID’s Institutional Review board . All human subjects were adults . Female B . malayi adults used in this study were obtained from the NIH/NIAID Filariasis Research Reagent Resource Center ( FR3 ) and TRS Laboratories in Athens , Georgia , USA . The worms were cultured in Dulbecco’s Modified Eagle’s Medium ( Corning cellgro ) supplemented with 10% heat-inactivated fetal bovine serum ( Atlanta Biologicals ) , 100 units/mL of penicillin , 100 ug/mL of streptomycin , and 1% L-glutamine ( Sigma ) for 24 hrs at 37°C in 5% CO2 prior to siRNA treatment . Microfilariae were obtained from adult female worms cultured in vitro . Orthologs in other nematode species were identified in WormBase Parasite based on a BLAST query [24] against the Bm-UGT protein sequence ( Bm17378 ) . The following are the accession numbers of each ortholog as identified in WormBase Parasite: Brugia timori ( BTMF_0001026401 ) , Wuchereria bancrofti ( WBA_0000030501 ) , Brugia pahangi ( BPAG_0000208101 ) , Loa loa ( LOAG_03428 ) , Dirofilaria immitis ( nDi . 2 . 2 . 2 . t06727 ) , Litomosoides sigmodontis ( nLs . 2 . 1 . 2 . t00666-RA ) , Ancylostoma caninum ( ANCCAN_05977 ) , Anyclostoma duodenale ( ANCDUO_14383 ) , Dictyocaulus viviparous ( NDV . 1 . 0 . 1 . g111112 ) , Haemonchus contortus ( HCON_00121250 ) , Heligmosomoides polygyrus ( HPOL_0001615101 ) , Nippostrongylus brasiliensis ( NBR_0001252501 ) , Caenorhabditis elegans ( Y37E11AR ) , Strongyloides ratti ( SRAE_2000477000 ) , Strongyloides stercoralis ( SSTP_0001129400 ) , and Oesophagostomum dentatum ( OESDEN_03545 ) . Orthologs in selected mammals were identified in the National Center of Biotechnology Information ( NCBI ) databases based on a BLAST query against the Bm-UGT peptide sequence . The following are the orthologs selected for analyses: Homo sapiens ( NP_066307 ) , Canis lupus familiaris ( XP_005635657 ) , and Felis catus ( BAA2492 ) . The Bm-UGT sequence was initially analyzed for properties including signal peptide sequence , and potential transmembrane sequence using InterPro , SignalP4 . 1 ( PMID: 28451972 ) and TM servers [15] . Using the SWISS-MODEL homology modelling server [PMID: 29788355] , the iUGT sequence was used to search against the SWISS-MODEL template library using BLAST and HHBlits for structures that matched the target sequence . The model was visualized using PyMOL [25] and COOT [PMID: 20383002] , with residues 468–492 manually built using COOT . Based on sequence and structure identity , the co-factor binding domain was further analyzed , utilizing the inferred UDPGA-binding site of Bm-UGT mapped using the UGT 2B7 structure [PMID: 17442341] as a model and visualized using COOT [PMID: 20383002] . Using the BLOCK-iT™ RNAi Designer , we selected the top three Bm-UGT siRNA duplexes for gene silencing activity and specificity . The Bm-UGT siRNA and corresponding scrambled siRNA were synthesized by Life Technologies and purified by standard desalting methods . The 5’-3’ sequences of the Bm-UGT siRNA strands were as follows: For demonstration of siRNA uptake , adult female worms were incubated in 5 μM of 5’ Cy3-labeled Bm-UGT siRNA 3 ( Sigma Aldrich ) for 24 hrs . Adult worms incubated in media alone were used as a negative control . Both groups of worms were then stained with 10 μg/mL of DAPI ( Sigma-Aldrich ) in PBS . Fluorescent images were captured by a Nikon Eclipse E600 fluorescent microscope and converged using NIS-Elements software . For experiments to test ingestion of antibody , adult female worms were incubated with 100 μg of mouse Cy3-labeled IgG isotype control in 2 mL of culture media . The worms were imaged 24 hrs later using the TRITC filter on a Zeiss Axio Observer . A1 . siRNA inhibition of Bm-UGT in B . malayi adult female worms followed a protocol established by Aboobaker et al . with minor modifications [26] . siRNA inhibition in filarial worms has well-known variability and difficulty [27 , 28] . For this study , we analyzed data for experiments that received greater than 60% knockdown . For each timepoint , 5 adult female worms were soaked in an equal mixture of the Bm-UGT siRNAs at a final concentration of 5 μM in 850 μL of culture media in a 5000 MWCO Pur-A-Lyzer™ dialysis tube ( Sigma-Aldrich ) . This concentration of siRNA was shown in multiple studies to be sufficient at silencing gene expression [26 , 29–31] . The dialysis tubes were placed in 1 L beakers with 500 mL of culture media for 24 hrs at 37°C in 5% CO2 . Similarly , 5 adult female worms were soaked in media alone or scrambled siRNA ( 5 μM ) in dialysis tubes for each timepoint as experimental controls . After the 24-hr incubation , the worms for each group were carefully extracted from the dialysis tubes and individually placed into wells with 1 mL of media . The worms were evaluated at timepoints 1 , 3 , and 6 days post-incubation for transcript knockdown , worm motility , MTT reduction , and microfilariae release . Worms were visualized with a dissecting microscope by an observer blinded to treatment category . Motility of the adult female B . malayi worms was rated based on the following scale 4 = active movement , 3 = modest reduction in movement , 2 = severe reduction in movement , 1 = twitching , and 0 = no movement . Metabolic function of the adult female worms was assessed by reduction of ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) from Sigma using a protocol established by Comley et al [32] . For each group per timepoint , 2 worms were incubated in 0 . 5 mL of phosphate buffered solution ( PBS ) pH 7 . 4 with 0 . 5 mg/mL of MTT for 30 minutes at 37°C in 5% CO2 . The worms were then transferred into separate wells of a 96-well plate containing 200 μL of DMSO and incubated at room temperature for 1 hr . MTT reduction was quantified by absorbance relative to a DMSO blank at 570 nm using a Synergy HTX multi-mode plate reader ( BioTek ) . For each timepoint , adult worms were placed in new culture media 24 hrs prior to enumeration of microfilariae . After the overnight incubation , the worms were then removed for processing by the MTT reduction assay and RT-qPCR . The Mf in the well containing expended culture media ( 1 mL ) were counted under a light microscope at high magnification . For each condition , adult B . malayi female worms ( n = 3 ) were homogenized in TRIzol ( Thermo Fischer Scientific ) after three freeze/thaw cycles using Matrix D lysis tubes ( MP Biomedicals ) agitated by a FastPrep™-24 Biopulverizer ( MP Biomedicals ) for 7 minutes at 6 m/s . Chloroform was added to the homogenate , transferred to Phase Lock Gel tubes ( 5Prime ) , and phase separated at 11 , 900 g for 15 minutes at 4°C . The aqueous phase was collected and cold isopropanol was added to precipitate the RNA , which was then pelleted at 12 , 000 g for 1 hr and washed twice using 75% ethanol . The RNA pellet was resuspended in nuclease-free water and quantified using a NanoDrop 1000 ( Thermo Fischer Scientific ) . cDNA was prepared using Superscript IV ( Thermo Fischer Scientific ) as per the manufacturer’s protocol . The cDNA levels of Bm-UGT and B . malayi house-keeping gene gapdh were assessed in duplicate 20 uL reactions using 1 μL of 20X TaqMan™ gene expression assay ( Thermo Fischer ) , 1 μL of cDNA , and 18 μL of TaqMan™ gene expression master mix ( Applied Biosystems ) . PCR conditions were 2 min at 50°C , 10 min at 95°C , 40 cycles of 15 sec at 95°C , and 1 min at 60°C cycle of 50°C with a 7500 Real-Time PCR System ( Applied Biosystems ) . The primers used were as follows: Polyclonal anti-Bm-UGT peptide antibodies were generated in New Zealand rabbits by Genscript using Bm-UGT peptide sequences conjugated to keyhole limpet hemocyanin ( KLH ) . The peptide sequences used were as follows: CYEKDEHLIAEGRPN , DSTGSKLAKTVKIDC , and CGQIANFDPYGRKMS . Cysteines were added at either the N- or C-terminus to facilitate KLH conjugation . B . malayi adult worms ( n = 5 ) were incubated in 5 μM combination of Bm-UGT siRNA for 24 hrs using the previously mentioned method and transferred into individual wells with 1 mL of media . The adult worms were cultured for an additional 24 hrs and then homogenized in PBS ( pH 7 . 4 ) and 4 μL Halt™ Protease Inhibitor Cocktail ( Thermo Scientific ) using Matrix D lysis tubes ( MP Biomedicals ) agitated by a FastPrep™-24 Biopulverizer ( MP Biomedicals ) for 3 minutes at 4 m/s . Protein levels were quantified by the Bradford protein assay ( Bio-Rad ) . For immunoblot analysis , 10 μg of protein was separated on 10% Bis-Tris NuPAGE gel ( Invitrogen ) and blotted onto 0 . 2 μm nitrocellulose filter paper ( Bio-Rad ) . After blocking overnight in 5% bovine serum albumin ( BSA ) in tris-buffered saline with 0 . 1% Tween 20 ( TBS-T ) , the membrane was incubated with 1:4000 anti-UGT peptide antibodies ( Genscript ) and 1:1000 rabbit anti-β actin antibodies ( Abcam ) for 1 hr . Following this , the filter paper was washed with TBS-T and then incubated with 1:2000 horseradish peroxidase conjugated goat anti-rabbit IgG for 1 hr . The membrane subsequently washed and incubated in Chemiluminescent reagent , SuperSignal™ West Pico PLUS ( Thermo Scientific ) , to visual the bands . Sulfinpyrazone ( ChemCruz ) and probenecid ( Invitrogen , water soluble formulation ) , broadly acting UGT inhibitors , were evaluated for macrofilaricidal activity in vitro . Sulfinpyrazone was resuspended in 1X PBS ( pH 7 . 4 ) and 1% dimethylsulfoxide ( DMSO , v/v ) while probenecid was resuspended in deionized water . When testing sulfinpyrazone , adult B . malayi female worms were incubated in culture media with the drug for 8 days at concentrations of 2500 μM , 1000 μM , 200 μM , 40 μM , and 8 μM . For probenecid , adult female worms were incubated in culture media with the drug for 7 days at concentrations of 5000 μM , 500 μM , 250 μM , and 100 μM . Worms were transferred into new media with corresponding drug concentrations every day except day 4 . As a negative control , worms were incubated in culture media alone with a similar volume of vehicle . Worm motility was scored using the previously mentioned scale for the course of the experiment . Worms that were scored a zero stopped receiving UGT inhibitor treatment . For the albendazole synergy experiments , adult filariae were incubated in culture media with 40 μM of sulfinpyrazone or 100 μM of probenecid in combination with 10 μM of albendazole , which was resuspended in 1X PBS ( pH 7 . 4 ) and 1% DMSO ( v/v ) . The worms were scored for motility for 8 days and were transferred into new media with corresponding drug concentrations every day except day 4 . The above UGT inhibitors were evaluated for microfilaricidal activity in vitro . For each drug , experiments were performed in triplicate at a concentration of 2 x 104 Mf/mL in culture media . Viability was determined by quantifying the number of motile larvae from 100 randomly selected Mf per well . The concentrations used for sulfinpyrazone were 2500 μM and 200 μM while the concentrations used for probenecid were 5000 μM and 500 μM . As a negative control , larvae were incubated in culture media alone . We measured cytotoxicity of the UGT inhibitors using a Pierce LDH Cytotoxicity Assay Kit ( Thermo Scientific ) . HEK cells were seeded at 5 x 104 per well in DMEM ( Quality Biological ) with 10% Hyclone Cosmic Calf Serum ( Thermo Fischer ) , 200 μM of L-glutamine ( Quality Biological ) , and 50 μg/mL of gentamicin ( Quality Biological ) at 37°C in 5% CO2 . We then incubated the cells with various concentrations of the UGT inhibitors overnight . Following this , we transferred 50 μL of media from each well to a new 96-well plate and then added 50 μL of reaction buffer . We incubated the mixture for 30 minutes and then added 50 μL of stop solution . We measured absorbance at 490 nm and 680 nm . We employed the following controls: a spontaneous LDH activity control which was incubated with the vehicle only and a maximum LDH activity control which was incubated with nothing but later lysed prior to incubation with the reaction buffer . We calculated absorbance for each well by subtracting the 680 nm absorbance value ( background ) from the 490 nm absorbance value . We then calculated percent cytotoxicity using the following equation: %Cytotoxicity= ( UGTinhibitorLDHactivity−SpontaneousLDHactvity ) ( MaximumLDHactivity−SpontaneousLDHactivity ) Bm-UGT was expressed as a Renilla reniformis luciferase ( Ruc ) fusion protein by cloning the Bm-UGT coding sequence in pREN2 ( Genscript ) . The Bm-UGT signal sequence as predicted by signalP was removed prior to synthesis . Plasmid encoding the fusion protein was used to transformed TOP10 cells ( Thermo Fischer ) and plasmid DNA was obtained from colonies selected on kanamycin ( 50 μg/ml ) as per the manufacturer’s guidelines ( Qiagen Midi-Prep ) . 293F cells grown in 293 Freestyle Medium as suspension cultures were transfected with 30 μg of Bm-UGT-Ruc plasmid , at a final concentration of 1 μg per 1 x 106 cells ( Thermo Fischer Sceintific ) per mL , and cultured at 37°C with 8%CO2 on a rotary shaker at 125 rpm . After 72hrs , the cells were pelleted and sonicated in LIPS lysis buffer ( 20 mM Tris pH 7 . 5 , 100 mM NaCl , 5 mM MgCl2 , 1% TritonX-100 , 50% glycerol , protease inhibitors ( Mini from Roche ) ) . The lysate was centrifuged to remove cellular debris and supernatant containing the Bm-UGT-Ruc fusion proteins were stored at -80°C for later use . Antibody titers were measured using a luciferase immunoprecipitation system ( LIPS ) assay [33–35] . For IgG and IgE quantification , serum was diluted to 1:100 and 1:10 respectively in 50 μL of LIPS master mix ( 20 mM Tris pH 7 . 5 , 100 mM NaCl , 5mM MgCl2 , 1% Triton X-100 ) and mixed with 50 μL of the UGT-Ruc fusion containing 1 x 106 light units ( LU ) of protein in PBST ( PBS with 0 . 05% Tween-20 ) . The reaction mixture was incubated in a 96-well polypropylene plate for 10 minutes at room temperature and transferred to a 96-well high throughput screening filter plate ( Milipore ) containing 5 μL of a 50% suspension of Ultralink protein A/G ( Pierce ) or Ultralink anti-human IgE beads in PBS and incubated for an additional 15 minutes at room temperature . The plates were washed under vacuum with 200 μL of LIPS master 3x followed by PBS once . The relative light units ( RLU ) were measured with a Berthold LB 960 Centro microplate luminometer with 50 μL of coelenterzine solution ( Promega ) . For these experiments , samples were run in duplicate , and the calculated RLU was adjusted for the measured RLU of UGT fusion protein without serum . All human serum samples were obtained following written informed consent from all subjects using Institutional Review Board-approved protocols that have been registered ( NCT00001345 , NCT00090662 , NCT00342576 ) . Patients were grouped into clinical categories as previously detailed [36] . The siRNA and UGT inhibitor experiments were repeated two times under similar conditions . Data shown is from a single representative experiment . For the siRNA experiments , data was analyzed using one-way analysis of variance ( ANOVA ) or T-test by PRISM 7 . 0 . Following ANOVA , individual comparisons of mean values were performed using Tukey’s multiple comparisons test . For the UGT inhibitor experiments , we performed AUC analysis followed by one-way ANOVA to determine significance . Statistical significance between the experimental and control groups was designated as follows: * for p values <0 . 05 , ** for p values <0 . 01 , and *** for p values <0 . 001 .
UDP-glucuronosyltransferases ( UGT ) are enzymes important for detoxification of xenobiotics and homeostasis of endogenous molecules [21] . Specifically , these phase II enzymes increase the solubility of hydrophobic molecules by attaching sugar moieties such as glucuronic acid . Since this sugar molecule is negatively charged at physiological pH , anion efflux pumps are able to transport these molecules outside the cell [37] . In C . elegans , studies demonstrated that RNAi of detoxification enzymes results in lethality , sluggish movement , or impaired growth [38–40] . In addition , one study showed that glycosylation by phase II enzymes was important for the detoxification of albendazole in C . elegans [22] . While UGTs in helminths have not been studied extensively , there is evidence from intestinal helminths to suggest that these enzymes play a critical role in drug resistance [41–43] . In this study , we showed that B . malayi intestinal UGT expression could be silenced by specific siRNA . This knockdown caused a significant reduction in worm motility , fecundity , and metabolism . While these metrics alone do not determine worm survival , we are fairly confident these are appropriate surrogates . We observed several worms with a scored motility of zero early into the siRNA experiments that did not show any recovery over the course of the experiment . Thus , Bm-UGT appears essential for adult B . malayi worm survival . It is possible that the observed phenotypic changes occurred due to an imbalance in endogenous molecules . Past studies in mice and rats demonstrated that UGTs were critical for protection against free radicals , which if left unchecked could mediate damage to DNA , lipid membranes , and amino acids [44–47] . The reduction in microfilaria release was most likely due to decreased adult worm viability because Bm-UGT is not expressed in the Mf stage [13 , 14] . We suspect that targeting Bm-UGT in vivo would leave the worms not only susceptible to endogenous free radicals but also to those released by host immune cells . Interestingly , it should be noted that the Brugia intestinal UGT was predicted by InterPro to be localized to the plasma membrane , which would be a novel location for this family of enzymes that are typically found in the endoplasmic reticulum . The prediction software also determined that this protein has a large extracellular domain , which potentially makes it readily accessible to drugs or antibodies . Development of short-course macrofilaricidal agents would greatly enhance LF eradication efforts . Because the aim of this study was to evaluate Bm-UGT as a potential drug and vaccine target , we investigated the effects of non-specific UGT inhibitors on adult filariae . We found two UGT inhibitors that are also FDA-approved to treat gout [17–20] . Both drugs , sulfinpyrazone and probenecid , exhibited macrofilaricidal activity in vitro . For worms that were scored a zero for motility , we did not see any recovery after cessation of the UGT inhibitor treatment . The lowest effective concentration for sulfinpyrazone was 200 μM . A previous study investigating sulfinpyrazone showed a maximum concentration ( Cmax ) in humans of 79 . 9 μM for a 400 mg dose [48] . Given that the daily maximum recommended dose for humans is 800 mg , we believe a Cmax similar to our lowest effective sulfinpyrazone concentration may be achievable in humans [49] . Therefore , we speculate that this drug could serve as a novel therapeutic against adult filariae . Likewise , we believe that the same applies to probenecid , which demonstrated robust macrofilaricidal activity in vitro at 500 μM . We also observed a significant reduction in motility by day 7 at 250 μM suggesting that this concentration may be effective at killing adult worms if given over a longer time course . In the context of physiological relevance , one pharmacokinetic study showed the peak concentration in humans given a single 2 g oral dose of probenecid to be 148 . 6 μg/mL ( 520 . 7 μM ) with minimal adverse events [50] . Based on our in vitro data , this level could rapidly kill adult filarial worms . While the UGT inhibitors displayed macrofilaricidal activity , it is possible that probenecid and sulfinpyrazone may act on adult B . malayi worms through mechanisms independent of effects on Bm-UGT . In addition to inhibiting UGTs , probenecid and sulfinpyrazone also inhibit organic anion transporters ( OATs ) [51 , 52] and pannexins [53] . OATs function to transport negatively charged molecules and are likely important for adult filarial worm survival . Innexins , which are present in invertebrate organisms , are structurally very similar to pannexins and primarily function as membrane channels that communicate with the extracellular space [54] . Interestingly , probenecid has been shown to impair touch responses in C . elegans by inhibiting mechanosensitive innexin channels [55] . Elucidating the mechanisms by which probenecid and sulfinpyrazone kill adult filarial worms will be the focus of future studies . While we are interested in developing a drug that kills adult filarial worms , the absence of microfilaricidal activity also presents advantages . Current antifilarial therapeutics such as diethylcarbamazine ( DEC ) and ivermectin ( IVM ) are extremely effective at clearing microfilariae . However , their use is contraindicated in areas co-endemic for loiasis and onchocerciasis because rapid killing of microfilariae in these infections can lead to severe adverse outcomes [56] . Individuals with loiasis when treated with IVM or DEC have a significantly higher risk of experiencing severe neurologic events such as encephalopathy due to rapid Mf death in the vasculature [57 , 58] . Similarly , DEC can induce adverse systemic reactions such as skins lesions , fever , polyarthritis , and ocular reactions in patients with onchocerciasis as determined by Mf load [59 , 60] . Therefore , probenecid , which demonstrated macrofilaricidal but not microfilaricidal activity at 500 μM in vitro , may be an attractive LF treatment candidate in these co-endemic areas . Because UGTs are involved in drug metabolism , we suspected that there may be synergy between the UGT inhibitors and albendazole . A recent study demonstrated that overexpression of UGT-22 in C . elegans resulted in albendazole resistance [61] . This , coupled with an earlier study by Laing et al showing that upregulation of UGTs is associated with metabolism of albendazole into various glucuronide products [22] , suggests that UGTs may play a critical role in nematode metabolism of albendazole . In support of this hypothesis , our data demonstrate a synergistic effect of sub-macrofilaricidal concentrations with sulfinpyrazone or probenecid in combination with albendazole against adult filaria in vitro . These results suggest that combination therapy with albendazole and either probenecid or sulfinpyrazone may be highly effective in treating filarial infections in people . Future studies will determine whether Bm-UGT functions to metabolize albendazole in filarial worms and whether this metabolism is inhibited by UGT inhibitors . We also plan to test the efficacy of combination therapy in animal models of infection . As previously mentioned , these probenecid and sulfinpyrazone inhibitors are FDA-approved and have been shown to be safe in humans [62] , with probenecid characterized as a pregnancy category B drug . If these medicines demonstrate macrofilaricidal activity in vivo , translation into human use could occur quickly . In addition to being a potential drug target for filariasis , we postulate that Bm-UGT could serve as a vaccine candidate as well . One of the challenges in helminth vaccine development is the risk that the vaccine may induce an allergic response in endemic populations . Indeed , generalized urticaria was seen in several Brazilian patients immunized against Ancylostoma-secreted protein 2 during hookworm vaccine trials [23] . A solution to this obstacle is to identify “hidden antigens” which are not exposed to the immune system during natural infection yet are essential to worm survival and present in an anatomical location accessible to host antibodies [63 , 64] . In theory , these proteins would not elicit an IgE-mediated response , and , as postulated by Munn , these antigens may be especially vulnerable to the immune system due to a lack of evolutionary pressure to evade it [64] . There is evidence to support that the intestinal tract of nematodes contains hidden antigens . Studies have demonstrated the absence of pre-existing IgE in serum from endemic populations against hookworm intestinal antigens APR-1 and GST [8 , 9 , 65] . Furthermore , studies have shown that these antigens are protective in animal models [8 , 10 , 11] . There is also evidence of hidden antigens in H . contortus seen with the lack of an antibody response against H11 , a glycosylated intestinal protein [66] . In this study , we observed no detectable Bm-UGT-specific IgE in the serum from individuals infected with or exposed to lymphatic filariae , which suggests this antigen may be safe to administer as a vaccine candidate in endemic populations . However , due to the low numbers of patient samples tested , if vaccine work using Bm-UGT progresses , then future studies would need to evaluate potential for allergic responses by testing far larger numbers of individuals . After demonstrating that recombinant Bm-UGT would not induce an allergic response in endemic populations , we investigated whether adult B . malayi worms could ingest antibody as there is a degree of uncertainty about whether filarial worms use their intestine to feed . Only the adult and L4 stages of filariae have a fully formed intestinal tract , while the Mf , L2 , and L3 stages have an immature intestine inaccessible to nutrients [67] . Furthermore , past studies have already shown that Brugia worms are able to absorb nutrients such as nucleotides , amino acids , sugars , and vitamins through their cuticle , which calls into question the purpose of the intestinal tract [68] . Notwithstanding these findings , Attout et al . investigated the intestinal tract of L . sigmodontis and demonstrated that young adult worms ( 25–56 weeks post-infection ) ingested red blood cells [69] . This suggests that intestinal feeding may occur but only at the early adult stage . Another study showed that D . immitis can ingest labeled serum [70] . With our study , we were able to show that adult B . malayi worms can ingest Cy3-labeled IgG ( S1 Fig ) . This serves as clear evidence that circulating antibody can potentially access the intestine of adult filarial worms . However , not all the worms ingested the labeled antibody , which indicates that , at least during in vitro conditions , adult worms do not feed through the intestine continuously . Future studies will work towards recombinant expression of Bm-UGT in order to test its ability to induce protective immune responses in animal models of filariasis . On the basis of the phylogenetics analyses we conducted , we expect that Bm-UGT may also be essential in W . bancrofti and B . timori given the overall high homology shared between these species and B . malayi . Additionally , there is a high level of sequence homology ( > 70% ) between the B . malayi intestinal UGT and the orthologs found in D . immitis and L . loa and thus the UGT orthologs in these filaria species may also serve as novel therapeutic targets . Interestingly , we did not find an ortholog of Bm-UGT in O . volvulus and speculate this could be the result of evolutionary pressure to lose this gene . In summary , we believe that Bm-UGT is an essential intestinal protein in B . malayi adult worms that does not induce IgE antibodies in endemic populations . Importantly , we found that sulfinpyrazone and probenecid , two commercially available , FDA-approved medications in use for gout , exhibit strong macrofilaricidal activity in vitro at concentrations that are achievable in humans . This promising data warrants future investigation in animal models of Brugia infection as well as assessment of whether these UGT inhibitors exhibit macrofilaricidal activity in vitro against other filarial species . Furthermore , we demonstrated that probenecid and sulfinpyrazone may potentiate the effect of albendazole on filariae , suggesting that combination therapy may be an ideal approach to obtain macrofilaricidal effect . In terms of vaccine development , future studies will focus on recombinantly expressing Bm-UGT and then testing whether it is protective in animal models of filariasis . Finally , the results of this study suggest that the intestinal tract of filarial nematodes may serve as a rich source of essential proteins that can serve as important therapeutic targets . | Brugia malayi is a parasitic nematode and one of the causative agents of lymphatic filariasis , a disease that affects 70 million people worldwide . Currently , there are no effective therapeutics that kill adult filarial parasites when given as a short course . This limitation has hampered global eradication efforts . Studies have shown that the intestinal tract in nematodes can be effectively targeted by drugs and antibodies . Given this potential , we decided to investigate B . malayi intestinal UDP-glucuronosyltransferase as a potential therapeutic target . We determined that this protein is essential for B . malayi adult worm survival , as gene-expression knockdown rapidly decreased motility , fecundity , and microfilarial release . We also identified two FDA-approved UGT inhibitors that cause death of adult filariae in vitro . This is a critical finding due to the need for effective macrofilaricides and the potentially rapid translatability of these drugs for use in filaria-infected people . Finally , we showed that serum from filarial patients does not contain specific IgE to Bm-UGT and thus this protein would likely not induce allergic reaction if given as a vaccine antigen to endemic populations . |
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The myeloid-related proteins ( MRPs ) 8/14 are small proteins mainly produced by neutrophils , which have been reported to induce NO production in macrophages . On the other hand , Leishmania survives and multiplies within phagocytes by inactivating several of their microbicidal functions . Whereas MRPs are rapidly released during the innate immune response , their role in the regulation of Leishmaniasis is still unknown . In vitro experiments revealed that Leishmania infection alters MRP-induced signaling , leading to inhibition of macrophage functions ( NO , TNF-α ) . In contrast , MRP-primed cells showed normal signaling activation and NO production in response to Leishmania infection . Using a murine air-pouch model , we observed that infection with L . major induced leukocyte recruitment and MRP secretion comparable to LPS-treated mice . Depletion of MRPs significantly reduced these inflammatory events and augmented both parasite load and footpad swelling during the first 8 weeks post-infection , as also observed in MRP KO mice . On the contrary , mouse treatment with recombinant MRPs ( rMRPs ) had the opposite effect . Collectively , our results suggest that rapid secretion of MRPs by neutrophils at the site of infection may protect uninfected macrophages and favor a more efficient innate inflammatory response against Leishmania infection . In summary , our study reveals the critical role played by MRPs in the regulation of Leishmania infection and how this pathogen can subvert its action .
Myeloid-related proteins 8 and 14 ( MRPs 8/14 ) also known as S100A8 and S100A9 are small calcium binding cytoplasmic proteins secreted mainly by neutrophils and monocytes [1] , [2] . These proteins are formed by two Ca2+ binding domains separated by a hinge region [3] . Although these proteins exist as homodimers , a heterodimer ( MRP 8/14 ) is formed in the presence of calcium . Both proteins are expressed abundantly by neutrophils , being around 30 to 40% of their cytoplasmic proteins [4] . MRP 8 and 14 are not constitutively expressed by macrophages; however , expression of MRP 8 can be achieved in those cells by stimulation with LPS , IFN-γ , IL-1β and TNF-α . Interestingly , murine endothelial cells express both MRP 8 and MRP 14 following LPS stimulation [5] . Murine MRP 8 is chemotactic for neutrophils and monocytes , whereas human MRP 14 and the heterodimer MRP 8/14 are chemotactic for neutrophils , stimulate their adhesion to fibrinogen , and enhance monocyte transmigration across endothelial cells . It is also known that MRP 8 and 14 inhibit bacterial growth possibly by zinc chelation and by preventing bacterial adhesion to mucosal epithelial cells [6] . MRP 8 and 14 have been associated with a number of inflammatory diseases leading to the assumption that these molecules are involved in the body's defense against inflammation . Phagocytes expressing MRP 8 and 14 are found in a variety of inflammatory conditions , including rheumatoid arthritis , chronic bronchitis and inflammatory bowel disease [7] , [8] . Moreover , Tessier and collaborators [1] reported that in the murine air-pouch model , stimulation with LPS led to an abundant recruitment of neutrophils and subsequent secretion of MRPs [2] . We have previously reported that MRP 8 and 14 play an important role in the nitric oxide ( NO ) modulation; a key microbicidal function of macrophages [9] . This increase was linked with augmented expression of inducible nitric oxide synthase ( iNOS ) , at the gene and protein levels , concomitantly with ERK and JNK kinases phosphorylation and the rapid NF-κB nuclear translocation . These findings indicate that MRPs play an important role during inflammation . Although much is known about MRPs during inflammation and inflammatory diseases; little is known about the potential role of MRPs in Leishmaniasis . Leishmaniasis ( caused by parasites of the Leishmania genus ) , is a disease characterized by three main clinical manifestations; cutaneous Leishmaniasis , muco-cutaneous Leishmaniasis and the lethal if untreated visceral Leishmaniasis . Leishmania parasites of different species are able to abrogate the innate immune response in order to survive inside their host cell [10] . In regard of the role of MRPs in Leishmaniasis , only two reports have documented accumulation of macrophages expressing MRP 8 and 14 at the skin lesions of mice infected with L . major [11] , [12] . They also found , that amastigotes isolated from skin lesions presented MRP 8 and 14 adhered onto their surface . However , and despite these observations , the role of these proteins during Leishmania infection has not been investigated . Herein , we report the first study concerning the role of MRPs during Leishmania infection in a murine experimental model . More precisely , we found that MRP-primed macrophages infected by L . major exhibit antimicrobial activity , whereas unprimed L . major-infected cells were fully inactivated , showing no response to MRP stimulation . Using in vivo approaches , we further demonstrated that L . major's capacity to recruit inflammatory cells was accompanied by MRP secretion at the site of inoculation . The use of anti-MRP antibodies in addition to blocking Leishmania-induced leukocyte recruitment in the air-pouch also increased mice footpad swelling and parasite load . Similarly , MRP deficient mice were found more sensitive to develop footpad swelling . Importantly , use of recombinant MRPs ( rMRPs ) to treat infected footpads led to significantly reduced footpad swelling and lesion development as well as a reduced parasite load . Altogether , this study provides a clear demonstration that MRPs seem to play a critical role in the control of the progression of Leishmania infection by modulating the innate inflammatory and microbicidal responses .
The research involving animals in this work was carried out according with the regulations of the Canadian Council of Animal Care and approved by the McGill University Animal Care Committee ( AUP#4859 ) . BALB/c and C57Bl/6 mice were obtained from Charles River and Jackson Laboratory , respectively . MRP14 KO mice ( C57Bl/6 background ) were obtained from Dr . Philippe Tessier's laboratory at Laval University , QC . Canada . Immortalized murine bone marrow derived macrophages B10R cell line were grown at 37°C in 5% CO2 in Dulbecco's Modified Eagle medium ( DMEM ) supplemented with 10% heat inactivated FBS ( Invitrogen , Burlington ON , Canada ) and 100 U/ml penicillin 100 µg/ml streptomycin and 2 mM of L-glutamine ( Wisent , St . Bruno , QC , Canada ) . Leishmania promastigotes ( L . major A2 and L . major luciferase ) were grown and maintained at 25°C in SDM-79 culture medium supplemented with 10% FBS by bi-weekly passage . Macrophages were infected at a parasite-macrophage ratio 20∶1 with stationary phase promastigotes for the times specified in each figure legend . When cells were primed with 5 , 10 and 25 µg/ml of MRPs 8/14 heterodimer were used 1 hr before infection and remained throughout the infection time . All reagents if not indicated were obtained from Sigma Aldrich ( St-Louis MO , USA ) . Cloning expression , and purification of mouse MRP 8 and 14 ( S100A8/A9 ) were previously described [1] , [2] . Briefly , mouse S100A8 cDNA was cloned into the pET28a expression vector ( Novagen ) . Murine S100A9 cDNA was obtained by RT-PCR and cloned into the PET28a vector ( Dr . Philippe Tessier's laboratory , Laval University , QC . Canada ) . Recombinant protein expression was induced with 1 mM isopropyl-β-D-thiogalactoside in E . coli HMS174 for 16 hr at 16°C . After incubation , the bacteria were centrifugated and the pellet resuspended in PBS/NaCl ( 0 . 5 M ) /imidazole ( 1 mM ) and lysed by sonication . The pellet was centrifugated and the supernatant collected . Recombinant His-Tag proteins were purified using a nickel column; S100A8/A9 bound to the column were freed from their His-Tag by incubation with 10 U of biotinylated thrombin for 20 hr at RT . Finally the proteins were passed through a polymyxin B agarose column ( Pierce , Rockford , IL USA ) to remove endotoxins . The lysate , contamination by endotoxins was <1 pg/µg . The proteins were kept at −80°C until further use . B10R macrophages were plated in 12-well plates ( 0 . 5×106 cells/well , in triplicates ) . The next day , cells were pre-treated for 24 hr with 5 , 10 and 25 µg/ml of MRPs 8/14 and then infected with L . major ( 20∶1 ) for another 24 hr ( MRP-primed-infected ) ; or pre-infected with L . major for 24 hr and then stimulated with 5 , 10 and 25 µg/ml of MRPs for further 24 hr ( infected-MRP-stimulated ) . NO production was assessed by measuring the accumulation of nitrites in the cell culture medium using the colorimetric Griess reaction as previously described [13] . B10R macrophages were plated in 12-well plates ( 0 . 5×106 cells/well ) . Next day , cells were stimulated: MRPs alone , MRP-primed-infected or infected-MRP-stimulated . After the different times of stimulation ( indicated in the figure legend ) or infection , plates were centrifugated at 2500 rpm and 100 µl of supernatant were collected and added to TNF-sensitive L-929 fibroblasts [14] previously pleated in 96-well plates ( 3×105 cells/well/100 µl ) , in the following day 100 µl of B10R culture supernatant were added to the L929 cells making a 2-fold serial dilution . Actinomycin D ( final concentration of 2 µg/ml ) was added to each well and plates were incubated 18 hr at 37°C . Next day , live cells were stained with crystal violet ( 0 . 05% in 0 . 1% acetic acid solution ) for 10 minutes . After , plates were washed to remove excess of stain and 100 µl of 100% methanol were added to each well to elute stain from the cells . Plates were red at 595 nm . Data are expressed as unit of TNF referring to the dilution that induced 50% of L929 cell death . B10R macrophages ( 2×106 ) stimulated with MRPs alone ( 1 hr ) , MRP-primed ( 1 hr ) -infected ( 1 hr ) or infected ( 24 hr ) -MRP-stimulated ( 1 hr ) were washed three times with PBS to remove non-internalized parasites , and processed for nuclear extraction as previously described [15] , [16] . Briefly , macrophages were collected in 1 ml of cold PBS , centrifuged and pellets were resuspended in 400 µl of ice-cold buffer A ( 10 mM HEPES , 10 mM KCl , 0 . 1 mM EDTA , 0 . 1 mM EGTA , 1 mM DTT and 1 mM of PMSF ) and incubated 15 min on ice . 25 µl of IGEPAL 10% were added , and samples vortexed for 30 sec . Nuclear proteins were pelleted by centrifugation and resuspended in 50 µl of cold buffer C ( 20 mM HEPES , 400 mM NaCl 1 mM EDTA , 1 mM EGTA 1 mM DTT and 1 mM PMSF ) . Protein concentrations were determined by Bradford assay ( Bio-Rad , Hercules CA , USA ) . 6 µg of nuclear proteins were incubated for 20 min at room temperature with 1 µl of binding buffer ( 100 nM Hepes pH 7 . 9 , 8% v/v glycerol , 1% w/v Ficoll , 25 mM KCl , 1 mM DTT , 0 . 5 mM EDTA , 25 mM NaCl , and 1 µg/µl BSA ) and 200 ng/µl of poly ( dI-dC ) , 0 . 02% bromophenol blue and 1 µl of γ-P32labeled oligonucleotide containing a consensus sequence for AP-1 binding complexes ( 5′-CGTTTGATGACTCAGCCGGAA-3′ ) ( Santa Cruz Biotechnology Inc , Dallas , TX , USA ) , NF-κB ( 5′-AGTTGAGGGGACTTTCCCAGGC-3′ ) ( Santa Cruz Biotechnology Inc ) and STAT1 ( 5′-AAGTACTTTCAGTTTCATATTACTCTA-3′ ) . After incubation , DNA-protein complexes were resolved by electrophoresis in non-denaturing polyacrylamide gel 5% ( w/v ) . Subsequently gels were dried and autoradiographed . Competition assays were conducted by adding a 100-fold molar excess of homologous unlabeled AP-1 oligonucleotide , or the non-specific competitor sequence for SP-1 binding ( 5′-ATTCGAATCGGGGCGGGGCGAGC-3′ ) . B10R cells ( 1×106 ) stimulated with MRPs alone ( 30 min ) , MRP-primed ( 30 min ) - infected ( 1 hr ) or infected ( 1 hr ) -MRP-stimulated ( 30 min ) were washed 3 times with PBS and lysed with cold buffer ( 50 mM Tris-HCl pH 7 . 0 , 0 . 1 mM , 0 . 1 mM EGTA , 0 . 1% 2-mercaptoethanol , 1% NP-40 , 40 µg/ml aproptinin , 20 µg/ml of leupeptin 100 mM PMSF , and 20 mM NaVO4 ) . Proteins were dosed by Bradford ( Bio-Rad ) , and 30–60 µg of proteins were separated by SDS-PAGE , and transferred onto PVDF membranes ( GE healthcare , Piskataway NJ , USA ) . Membranes were blocked in 5% bovine serum albumin ( Wisent ) , washed and incubated ON with anti-phospho or total ERK , phospho or total -JNK , iNOS ( Cell signaling , Ipswich , MA , USA ) or β-actin . After washing , membranes were incubated 1 hr with α-rabbit or mouse HRP-conjugated antibody , and developed by autoradiography . To determine parasite survival inside B10R macrophages , cells were plated in 12-well plates ( 0 . 5×106cells/well ) and the following day they were infected with stationary phase L . major-LUC promastigotes ( 10∶1 ratio ) . After 6 hr of infection , the non-phagocytosed parasites were removed by washes with PBS , and samples were collected . For the second group of samples , fresh media was added and cells were incubated for another 18 hr . Adherent macrophages were collected and centrifuged 13 , 000 rpm×1 min . Pellets were lysed in 25 µl of 1× Cell Culture Lysis Reagent ( Promega , Fitchburg , WI , USA ) . 20 µl of lysate were mixed with 90 µl of Luciferase Assay Reagent ( Promega ) and luciferase counts were determined using a Mini Lumat LB 9506 luminometer ( EG&G ) . Air pouches were raised on the dorsum of 6 week-old BALB/c by s . c . injection of 3 ml of sterile air on days 0 and 3 . On day 6 , 1 ml of LPS ( 1 µg/ml ) or 5×106 parasites of Leishmania major in 1 ml of PBS were injected into the air pouches . At 6 hr , mice were sacrificed and air pouches were washed twice with 2 ml of PBS . Exudates were centrifuged at 1200 rpm for 5 min . Cells were counted with a hematocytometer . Characterization of leukocyte subpopulations migrated into the pouch space was performed by diff-quick staining of cytospins . In some experiments mice were injected i . p . with 4 mg of purified rabbit IgG anti-MRP8/14 16 hr before infection . To determine the concentration of MRP 8/14 ( S100A8/A9 ) in the air pouch , ELISAs were performed as previously described in [2] . Briefly , Costar high binding 96-well plates ( Corning Glass , Tewksbury MA , USA ) were coated overnight at 4°C with 100 µl of purified rabbit IgG against MRP8 or MRP14 , diluted in 1 µg/ml in 0 . 1 M of carbonate buffer , pH 9 . 6 . The wells were blocked with PBS/0 . 1% Tween 20/2% BSA for 30 min at room temperature . Then the samples and the standards ( 100 µl ) were added , and after 45-min period at room temperature , the plates were incubated with rat IgG ( 100 µl/well ) against MRP8 and MRP14 diluted in PBS/0 . 1% Tween 20/2% BSA for 45 minutes . To reveal the immune complex , 100 µl/well of peroxidase-conjugated goat-anti-rat was added and incubated for 45 minutes . Next 100 µl/well of 3 , 3′ , 5 , 5′-tetraamethylbenzidine substrate ( Research diagnostics , Las Vegas , NV , USA ) were added according to the manufacturer's instructions , and ODs were read at 500 nm . The lower limit of quantification was determined as 4 ng/ml for both MRP8 and MRP 14 , and 10 ng/ml for the heterodimer . All ELISAs were tested using excess amounts of the other S100 proteins and were shown to be specific under conditions reported in this work . L . major stationary phase promastigotes ( 5×106 in 50 µl of PBS ) were injected in mice's right hind footpad . Footpad thickness measurement was performed as previously described [17] for 10–12 weeks . For the group of MRP neutralization , anti-MRP 8/14 ( 4 µg/ml ) were injected i . p . 1 day after infection and then at days 3 , 6 , 9 , 12 , 15 , 18 and 21 after infection . After 8 weeks , mice were sacrificed and parasite load was measured by limiting dilution assay . For the group that received recombinant MRPs ( rMPR ) as treatment , mice were infected as previously described and then treated 3 times per week with 10 µg of the mix MRP8/14 in 50 µl of PBS , during the last four weeks of infection directly in the infected footpad . Thickness of the lesion was measured every week until the end of the infection and parasite load was measured as described below . Limiting dilution assay was done as previously described [18] , [19] with some modifications . Briefly , after 10–12 weeks of infection mice were sacrificed . The infected footpads were disinfected and inflamed area of the pad was excised , homogenized; extracted parasites were serially diluted in a 96 well plate in duplicate . After 8 days , the number of viable parasites was determined from the highest dilution using an inverted microscopy . Statistically significant differences were analyzed by ANOVA followed by Tukey test using the Graphpad Prism program ( version 5 . 0 ) . For limiting dilution and TNF , non-parametric Mann-Whitney or Kruskal-Wallis test was used . Values of P≤0 . 05 were considered statistically significant . All data are presented as mean ± SEM .
We have previously described that MRPs induce NO in murine macrophages [9] . Confirming and extending these data , we observed that increasing concentrations of MRPs 8/14 ( 5 , 10 and 25 µg/ml ) lead to NO synthesis by macrophages in a dose-dependent manner ( Figures 1A and 1B ) . Subsequent infection of MRP-primed macrophages with L . major did not affect NO production ( Figure 1A ) . However , when cells were first infected and then stimulated with MRPs , NO production was reduced by around 35% ( Figure 1B ) . To evaluate whether the effect of Leishmania infection was affecting iNOS protein levels , we performed western blotting . As expected , stimulation of macrophages with MRPs , led to an increase of iNOS expression ( Figure 1C ) , and in MRP-primed macrophages followed by Leishmania infection , we observed increased of iNOS expression , which was maximal when 25 µg/ml of MRPs were added . At the same concentration of MRPs , the expression of iNOS was reduced when the cells were infected prior to stimulation ( Figure 1C ) . These results revealed that Leishmania infection alters the capacity of MRPs to induce NO production by reducing iNOS expression . Tumor necrosis factor α ( TNF-α ) is a multifunctional cytokine produced primarily by monocytes and macrophages . It has been shown that this cytokine is essential for the control of Leishmania at early stages of infection [20] . Therefore , we were interested in investigating whether MRPs were able to induce TNF-α production in macrophages . We performed a time and dose-dependent experiment , stimulating the cells for 1 , 3 , 6 and 24 hr with 5 , 10 and 25 µg/ml of MRPs using TNF-sensitive L929 fibroblasts [21] . As shown in Figure 2A , the maximum peak of TNF-α production by MRP-stimulated macrophages occurred between 1 and 3 hr with 25 µg/ml of MRPs , decreasing thereafter . The time of 1 hr was chosen to evaluate the profile of TNF-α production during Leishmania infection . First , cells were primed for 1 hr with MRPs and then infected with L . major . Second , macrophages were infected with L . major overnight , followed by washes and stimulation with MRPs . As shown in Figure 2B , and similar to our NO data , MRP-primed macrophages subjected to L . major infection did not show altered capacity to produce TNF-α , however; the ability of L . major-infected cells to produce TNF-α in response to MRP stimulation was clearly reduced . MRP-priming conferred protection against Leishmania infection , as revealed by iNOS expression , NO and TNF-α production . Thus , we next evaluated whether this MRP-inducible microbicidal response correlated with an enhanced intracellular killing of the parasite [22] . To this end , macrophages were primed with various concentrations of MRPs prior to infection with a L . major strain expressing luciferase , then cells were collected at 6 and 24 hr post-infection . As shown in Figure 3A , at 6 hr post-infection , we observed a higher percentage of infection in the cells that were primed with 10 and 25 µg/ml of MRPs , compared to those that were not primed . However , after 24 hr of infection ( Figure 3B ) primed macrophages reduced the parasite load by 42% in a dose-dependent manner . Altogether these results could suggest that MRPs provide the cells with the ability to phagocytise and kill the parasites more efficiently that unprimed cells . As we observed that MRP stimulation increased the expression of iNOS , we further analyzed the signaling pathways involved in iNOS/NO production . We have previously reported that MRP-induced macrophage activation involves the participation of the ERK and JNK MAPKs [9] . Thus , it was critical to determine whether Leishmania could influence phosphorylation of these kinases in order to explain the incapacity of infected cells to respond to MRPs , knowing that Leishmania infection can interfere with signaling under the regulation of these kinases by activating host phosphatases [18] , [23] . As expected , phosphorylation of both ERK and JNK ( Figure 4 ) was observed in naive macrophages stimulated with MRPs . Phosphorylation of ERK and JNK was not altered in MRP-primed macrophages infected with L . major ( Figures 4A and 4C ) . On the other hand , MRP-inducible ERK and JNK phosphorylation was strongly inhibited in Leishmania-infected cells ( Figures 4B and 4D ) . To further characterize the activation of macrophage signaling after MRP stimulation , we investigated the nuclear translocation of transcription factors ( TFs ) involved in iNOS/NO production ( e . g . , NF-κB , STAT 1 and AP-1 ) by performing EMSA . A strong nuclear translocation of NF-κB ( Figures 5A and 5B ) and AP-1 ( Figures 5C and 5D ) occurred in response to MRPs stimulation . As shown in Figures 5A and 5C , MRP-primed macrophages showed translocation of NF-κB and AP-1 ( Figure 5A ) . Nonetheless , NF-κB and AP-1 MRP-induced translocation was inhibited when macrophages were first infected with Leishmania ( Figures 5B and 5D , respectively ) . In addition , it was possible to detect the p35 fragment ( Figure 5B ) that is a product of NF-κB degradation by Leishmania infection [24] . We also monitored the nuclear translocation of STAT; however , we did not observe any alteration of this TF in response to MRPs in either case ( data not shown ) . Whereas MRPs modulate the microbicidal functions of macrophages in vitro , their role in vivo is still unknown . Therefore , using an air-pouch model we attempted to monitor this innate inflammatory event . Previous reports from our laboratory using this model have demonstrated that inoculation of Leishmania promastigotes led to the recruitment of inflammatory leukocytes at sites of injection within hours and this was accompanied by the secretion of various chemokines [25] . In addition , Tessier and collaborators have previously described that injection of LPS into the air-pouch induced neutrophil accumulation and the subsequent secretion of MRPs , reaching a maximum peak at 6 hr post-stimulation [2] . In this set of experiments , BALB/c mice were infected in the air-pouch with 10×106 parasites for 6 hr . Afterwards , we evaluated the number of cells recruited and the secretion of MRPs . As shown in Figure 6A , Leishmania infection induced leukocyte recruitment comparable to LPS , neutrophils being around 80% of the total recruited leukocytes ( Figure S1 ) . In addition , Leishmania infection induced MRP 8/14 secretion by the recruited cells within the pouches ( Figure 6B ) . To further monitor the implication of MRP secretion in the Leishmania-induced inflammatory cell recruitment , we neutralized MRPs using anti-MRP antibodies prior to infection with L . major . As shown in Figures 6A and 6B the use of these antibodies led to a significant reduction in cell recruitment , concomitantly with an almost complete abrogation of MRP secretion . It is important to point out that although we could still observe that the majority of the cells recruited in the mice injected with neutralizing antibodies were neutrophils ( Figure S1 ) , the total amount of recruited cells was significantly lower in these mice , compared to mice that received PBS , LPS or L . major ( Figure 6A ) . In the murine model , cutaneous Leishmaniasis is caused by injection of L . major or L . mexicana directly in the footpad . This model has been widely used to measure progression of infection in resistant and susceptible mice under different circumstances and for further isolation of parasites [26] , [27] . To additionally investigate the role of MRPs during Leishmania infection , we monitored to which extend the neutralization of MRPs or the inoculation of recombinant MRPs would influence the progression of the infection in vivo . In a first set of experiments , we infected BALB/c mice and performed tri-weekly inoculation of MRP neutralizing antibodies for a period of 4 weeks . The progression of footpad thickening and development of lesion were followed over 8-weeks period . As shown in Figure 7A , mice that received anti-MRPs antibodies developed a significantly bigger footpad swelling during the first 8 weeks of infection comparatively to the untreated group . Significant differences were also detected between treated and control groups regarding the footpad parasitic load ( Figure 7A , bar graph ) . These data suggest that MRPs secreted in the infectious environment could play an important role in the immunological events controlling Leishmaniasis development during the initial weeks of the infection . To confirm the contribution of MRPs to the regulation of Leishmania infection , we tested whether recombinant MRP 8/14 ( rMRP8/14 ) injected in infected footpads could lead to reduce Leishmania-related pathologies in mice . As reported in Figure 7B , BALB/c mice which started to receive inoculation of rMRPs at 8 week post-infection over a 4-weeks period , showed a clear and significant reduction of their footpad swelling and parasitic load comparatively to the control group ( Figure 7B , bar graph ) . To further characterize the role of MRPs in the control of Leishmaniasis we used mice deficient for MRP14 that also fail to express MRP8 in peripheral and tissue leukocytes [28] . L . major infection caused a significantly greater pathology in MRP14 KO mice compared with its genetic background control ( Figure 8A ) , as well as higher parasite load in the footpad after 19 weeks of infection ( Figure 8B ) . This experiment was carried out for a longer period of time compared with the two previous experiments ( using anti-MRPs or rMRPs ) in order to observe the control of infection , as we used C57Bl/6 background mice . Although the parasite burden calculated by the limiting dilution assay was significantly lower in the C57BL/6 mice compared to the parasite burden from the BALB/c mice , we still observe that the absence of MRPs led to a higher parasite burden , even in resistant mice . This last set of experiments strongly suggests that MRPs play a significant role in the immunological mechanisms involved in the regulation of Leishmania infection . Moreover , these data unveil MRPs as potential therapeutic agents to treat Leishmaniasis .
MRP 8 and 14 also known as S100A8 and S100A9 belong to the S100 protein family , a large group of intracellular proteins associated with many cellular functions including contraction , motility , cell differentiation , calcium regulation among others [3] . In addition , the S100 proteins are also associated with different inflammatory diseases [29]–[32] . Recently , we and others have reported that MRP 8 and 14 can modulate macrophage functions including NO production [9] . Given that MRPs activate the macrophage signaling machinery and knowing that Leishmania parasites exert the opposite effect , we were interested in elucidating the role of MRP 8 and 14 during Leishmania infection both in vitro and in vivo . Our results clearly showed that MRP-primed Leishmania-infected murine macrophages were able to produce NO with the concomitant expression of iNOS . These events correlated with a more efficient killing of the parasites as demonstrated by the luciferase assay . NO plays a key role in the macrophage microbicidal functions and is essential for the control of Leishmania infection [22] . In addition , we also found that MRP-primed macrophages produced high levels of TNF-α and were able to phosphorylate ERK and JNK kinases . More importantly , we observed that this priming resulted in an increased nuclear translocation of NF-κB and AP-1 . This finding correlates with the fact that iNOS contains promoter binding sequences for these two transcription factors along with STAT1α [33] . The induction of MAPK phosphorylation and TFs nuclear translocation was observed very shortly after stimulation; the fact that MRPs are able to induce the NF-κB and the AP-1 pathways suggests that these TFs might act in synergy to enhance the expression of iNOS , resulting in high levels of NO and more efficient Leishmania killing . We have also reported that MRPs are recognized by Toll like receptor 4 ( TLR4 ) [9] . This is in line with the observation that NF-κB is strongly induced by MRPs , on the other hand , efficient induction of AP-1 might be due to the fact that ERK and JNK are up-stream activators of c-Jun and c-Fos which dimerize to form active AP-1 complexes [34] . Additionally , we observed that macrophages that were first infected and then stimulated with MRPs , did not have the capacity to respond in the same way as primed macrophages , since the levels of NO and TNF production as well as the phosphorylation of JNK and ERK and the nuclear translocation of TFs were substantially reduced . This suggests that the parasite is able to abrogate the activation of the macrophage signaling machinery induced by MRPs in order to survive inside the host . One of the main mechanisms adopted by the parasite to subvert the immune response is the rapid activation of host phosphatases [18] , [23] , [35] . This fact might explain the poor MAPK phosphorylation and TFs nuclear translocation observed in macrophages first infected and then stimulated with MRPs . Studies made by our group have shown that mouse infection with Leishmania parasites in the air-pouch model leads to neutrophil recruitment [25] , Here , we demonstrated that MRPs controlled neutrophil recruitment induced by Leishmania or LPS . However , the exact role of neutrophils during cutaneous Leishmaniasis is still controversial . For instance Lima et al . [36] showed that there is a massive infiltration of neutrophils soon after skin infection with L . major , they investigated in more detail the role of neutrophils in resistant C57BL/6 and susceptible BALB/c mice by depleting neutrophils with specific antibodies . They showed that neutrophil depletion in both susceptible and resistant mice accelerated parasite spreading and caused more severe footpad swelling . These data suggested that neutrophils are of crucial importance in early control of parasite infection . In contrast , a study made by Laskay et al . [37] showed that Leishmania uses neutrophils as an evasion strategy , since the parasite survives inside these cells and use them as “Trojan horses” to get access into the macrophages where it will survive and multiply . Later , the same group showed that Leishmania-infected neutrophils also are uptake by dendritic cells inhibiting early immune response against Leishmania in the tissue [38] . Some other reports have shown that depletion of neutrophils in BALB/c mice inhibited the IL-4 response and promoted partial resistance [39] . Using B10R macrophages we also observed significantly increase of parasite infection when cells were treated with MRPs , however , it did not reflect in the survival of the Leishmania within macrophages ( Figure 3 ) . More recently , Peters et al . [40] , [41] showed that depletion of neutrophils reduces the ability of the parasite to establish productive infections . Furthermore , they reported that the neutrophils are the initial host cell for a substantial fraction of parasites and that there is more control of the infection when the neutrophils are not present . Interactions between cellular populations have been pointed out as important to either the control or development of the disease , and one of the most important cell interactions is the one between neutrophils and macrophages . It has been demonstrated by some groups that interactions of apoptotic or necrotic neutrophils with macrophages may interfere with the outcome of the infection . Interaction of dead neutrophils with L . major-infected peritoneal macrophages isolated from BALB/c mice , led to an increase in parasite growth , a mechanism mediated by the TFG-β and PGE2 produced by macrophages; however , macrophages isolated from resistant C57BL/6 mice and co-cultured with the same dead neutrophils presented a good microbicidal activity , mediated by TNF-α , therefore controlling the infection [42] . Concurring with this , Afonso et al . demonstrated that phagocytosis of apoptotic neutrophils by L . amazonensis-infected macrophages led to an increase on the parasite burden; however , phagocytosis of necrotic neutrophils resulted in parasite killing in a NO-independent manner , but dependent on ROIs [43] . Later on , it was observed that elastase produced by neutrophils plays a key role in the control of the infection , since this molecule activates the microbicidal mechanisms of the L . major-infected macrophages in a TLR-4-dependent manner [44] . It has also been studied that , depending on the infecting Leishmania species , or even the specific strain , the interaction between neutrophils and macrophages can lead to resistance or susceptibility to the infection . For instance , Novais F . et al , demonstrated that L . braziliensis elimination depends on the interaction between neutrophils and macrophages , in a TNF-dependent mechanism [45] . The same effect was observed in the control of L . amazonensis , where it was shown that TNF-α , elastase and platelet activation factor , produced by neutrophils , were responsible for parasite killing . On the other hand , this study also showed that NO and ROIs were not involved in the clearance of the parasite , as has been observed with other Leishmania species [46]; we consider that , in both cases , it is also possible that neutrophils secrete MRPs , and this secretion helps to control the infection . Contrary to what was shown by Ribeiro et at . , it has been demonstrated that L . major-infected macrophages isolated from C57BL/6 resistant mice induce apoptosis of neutrophils , therefore favoring the propagation and survival of the parasite [47] . Our data are partially in agreement with some of the previous observation , since we clearly demonstrated that there is neutrophil recruitment at the site of infection in the air-pouch model; moreover , we showed that these neutrophils are able to secrete MRPs and that depletion of these MRPs significantly reduced the amount of recruited neutrophils and consequently MRP secretion . In addition , infection with L . major in the footpad of susceptible BALB/c mice and depletion of MRPs resulted in an increased parasite load and footpad swelling . These results strongly suggest that MRPs are important to control Leishmania infection . Strengthening this fact , when we treated mice with rMRPs directly in the footpad , we observed a significant reduction in size of lesion and parasite load . A potential mechanism underlying these events could be that injection of MRPs leads to an enhanced neutrophil recruitment , which in turn , can secrete more MRPs creating a positive feedback loop of constant secretion of MRPs , where it is possible that neutrophils are actually containing the progression of the infection , concomitant with the fact that these proteins present by themselves antimicrobial properties [6] . Additionally , we do not rule out the possibility that direct injection of MRPs also induced monocyte recruitment and as observed in our in vitro results which showed that MRP-primed macrophages are able to produce high levels of NO , being this responsible for the killing and more efficient control of the infection . However , whether the control of the infection in vivo is NO-mediated needs further investigation . In summary our data showed for the first time that MRP 8 and 14 play an important role in the control of Leishmania infection in vivo and in vitro and support the idea that they could have a potential role as therapeutic drugs . | Parasites of the Leishmania genus have developed multiple mechanisms to subvert the immune response . Among these mechanisms are the activation of host phosphatases and inactivation of cell signaling pathways , which in turn activate the immune response . On the other hand , it has been observed that the Myeloid Related Proteins ( MRPs ) 8 and 14 are potent activators of some components of the immune response . In this study , we evaluated the effect of MRPs 8 and 14 on the progression of cutaneous Leishmaniasis . To do so , we used immortalized macrophages and stimulated them with MRPs before or after infection with L . major . We observed that stimulating macrophages with MRPs prior to infection induced NO and TNF-α production , as well as phosphorylation of MAPKs and nuclear translocation of transcription factors NF-κB and AP-1 . However , when MRP stimulation was performed after infection , these effects where subverted . Moreover , using a murine model of cutaneous infection , we observed that depletion of MRPs caused increased parasite burden and bigger lesions . On the contrary , injection of recombinant MRPs directly into the lesion , considerably reduced lesion size and parasite burden . Our study suggests that MRPs could have a potential therapeutic use in the control of Leishmania infection . |
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The genus Salmonella contains two species , S . bongori and S . enterica . Compared to the well-studied S . enterica there is a marked lack of information regarding the genetic makeup and diversity of S . bongori . S . bongori has been found predominantly associated with cold-blooded animals , but it can infect humans . To define the phylogeny of this species , and compare it to S . enterica , we have sequenced 28 isolates representing most of the known diversity of S . bongori . This cross-species analysis allowed us to confidently differentiate ancestral functions from those acquired following speciation , which include both metabolic and virulence-associated capacities . We show that , although S . bongori inherited a basic set of Salmonella common virulence functions , it has subsequently elaborated on this in a different direction to S . enterica . It is an established feature of S . enterica evolution that the acquisition of the type III secretion systems ( T3SS-1 and T3SS-2 ) has been followed by the sequential acquisition of genes encoding secreted targets , termed effectors proteins . We show that this is also true of S . bongori , which has acquired an array of novel effector proteins ( sboA-L ) . All but two of these effectors have no significant S . enterica homologues and instead are highly similar to those found in enteropathogenic Escherichia coli ( EPEC ) . Remarkably , SboH is found to be a chimeric effector protein , encoded by a fusion of the T3SS-1 effector gene sopA and a gene highly similar to the EPEC effector nleH from enteropathogenic E . coli . We demonstrate that representatives of these new effectors are translocated and that SboH , similarly to NleH , blocks intrinsic apoptotic pathways while being targeted to the mitochondria by the SopA part of the fusion . This work suggests that S . bongori has inherited the ancestral Salmonella virulence gene set , but has adapted by incorporating virulence determinants that resemble those employed by EPEC .
Salmonella serovars are predominately pathogenic Enterobacteriaceae that are thought to have diverged from a common ancestor with Escherichia coli ∼100 million years ago [1] . The genus Salmonella currently comprises two species; S . bongori and S . enterica , with S . enterica being comprised of 6 subspecies enterica , salamae , arizonae , diarizonae , houtenae and indica [2] , [3] , [4] , [5] , [6] . These S . enterica subspecies are further subdivided into >2500 serovars . Although S . bongori have been reported to infect humans [7] , [8] , the species is predominantly associated with cold-blooded animals whereas serovars causing disease in humans and other warm-blooded animals mostly belong to S . enterica subspecies enterica . Since S . enterica incorporates clinically important pathogens , our knowledge about the genus Salmonella is heavily biased and there is a marked paucity of information relating to the genetic and phylogenetic makeup of S . bongori . Even though E . coli and Salmonella are estimated to have diverged millions of years ago , their genomes still display significant similarity including extensive regions of synteny . However , in common with other Enterobacteriaceae significant diversity has been driven by horizontal gene transfer on a background of gradual genome sequence drift [9] . Many of the genes which are unique to Salmonella serovars , compared to E . coli , are found on large discrete genomic islands that include prophage elements and specialised loci termed Salmonella pathogenicity islands ( SPIs ) [10] , [11] , [12] . These Salmonella-specific functions include many genes required for the full expression of virulence and some of these were acquired by S . enterica following the split from S . bongori . For example , S . enterica encodes two complete type III secretion systems encoded by SPI-1 ( T3SS-1 ) and SPI-2 ( T3SS-2 ) [13] , [14] , [15] , [16] , whereas S . bongori lacks SPI-2 , which is required for optimal replication within macrophages [15] , [17] , [18] . Several characteristics of S . bongori suggest that this species may , in evolutionary terms , lie somewhere between E . coli and S . enterica . Consequently , to prove this hypothesis we have studied multiple genotypic and phenotypic characteristics of S . bongori and compared these to S . enterica and other Enterobacteriaceae . In terms of genotype , we have determined a reference quality sequence of S . bongori 12419 , originally isolated from an African frog in 1972 ( Salmonella Reference Collection C strain SARC11 ) and prepared draft sequences of a globally and temporally diverse S . bongori collection including 21 representatives of the 23 known serovars ( SV ) . From our data we have been able to determine inter and intra-species phylogeny and have used this to differentiate ancestral and more recently acquired virulence and metabolic functions . These data show that S . bongori possesses only a basic set of ancestral Salmonella virulence functions and lacks several metabolic pathways that define S . enterica . Nevertheless , S . bongori has not remained functionally static; it has acquired a repertoire of 12 T3SS candidate effector proteins , 10 of which are not found in other salmonellae but are significantly similar to known effectors found in enteropathogenic Escherichia coli ( EPEC ) strains . We herein demonstrate that representatives of these effectors are translocated and that at least one of these effectors , S . bongori outer protein H ( SboH ) , is functionally related to the non-LEE encoded EPEC effector NleH1 .
To place S . bongori in the context of S . enterica we produced a phylogenetic tree using the concatenated MLST gene sequences ( as described in [19] ) from a selection of S . enterica Sequence types ( STs ) covering all of the subspecies of S . enterica . The STs for S . enterica were obtained from the S . enterica MLST website ( mlst . ucc . ie ) . The S . bongori MLST gene sequences were extracted from our sequenced strains ( described in Table S1 ) , and the EPEC MLST gene sequences were extracted from the genome sequence of strain E2348/69 ( Figure 1 ) . Despite the spatial , temporal and phenotypic diversity described within our collection , the S . bongori species forms a surprisingly tight cluster of sequence types ( STs ) clearly separated from the S . enterica subspecies ( Figure 1 ) . The S . bongori isolates in our collection fall into 20 STs , which include 11 novel Salmonella STs ( currently S . bongori-specific; Table S1 ) . In comparison , there were 1 , 419 STs identified as being part of S . enterica present in the MLST database as of the 3rd of May 2011 . To investigate the diversity and population structure of S . bongori we finished and fully annotated the genome of S . bongori 12419 ( also known as SARC11 [20] ) . We then used this genome as a reference to produce whole genome sequences for our collection of 27 further S . bongori isolates . Using the whole genome sequences we produced a phylogenetic tree using RAxML ( Figure 1 ) , following the removal of mobile genetic elements ( MGE; regions excluded from this analysis are listed in Table S2 ) . In order to determine the branch on which the root should be placed we also completed a separate mapping including S . enterica subspecies arizonae strain CDC346-86 ( S . arizonae; EMBL CP000880 ) strain in order to provide an outgroup . When using S . arizonae to locate the root for the S . bongori tree at least three phylogenetic clusters are evident , a feature that is supported by a clustering analysis performed using the program Bayesian Analysis of Population Structure [21] , [22] . One of these clusters appears to be basal to the other clusters , based on the position of the root . The clusters are separated by 15 , 948-22 , 398 SNPS ( Figure S1 ) . The level of SNP variation between the clusters is consistent with the level of SNP variation between two serovars of S . enterica . For example , 39 , 156 SNPs differentiate S . enterica subspecies enterica serovar Typhimurium ( S . Typhimurium ) strain SL1344 and S . enterica subspecies enterica serovar Enteritidis ( S . Enteritidis ) strain P125109 ( data not shown ) . Within S . bongori serotype does not appear to provide a meaningful indication of phylogenetic relationships within the population ( Figure 1 ) . This feature of the dataset may imply that there is frequent lateral gene transfer amongst S . bongori strains . It is also apparent from the genomic data that there is a larger difference in the shift in genome G+C content in S . enterica following the divergence of the salmonellae , compared with S . bongori ( Figure S2 ) . Considering S . enterica and S . bongori have been evolving over the same time period these differences are remarkable . Changes in G+C content over time are thought to reflect subtle differences in mutational bias as a consequence of different lifestyles [23] . The combined data in Figure 1 and Figure S2 suggest that there has been a greater increase in G+C content accompanying the specialisation of S . enterica subspecies into warm-blooded hosts . To obtain a comprehensive view of genetic flux over time we used data from the other available Salmonella enterica genome sequences , along with our 28 sequenced S . bongori isolates . To complement this analysis , we used a pan-Salmonella microarray [24] , which included S . bongori-specific probes , to look at gene presence/absence across the SARC collection where whole genome sequences are lacking ( see methods ) . First we focussed our analysis on virulence functions that unified or distinguished S . bongori from the other salmonellae ( summarised in Figure 2 ) . Functions discussed below are conserved amongst all 28 S . bongori strains we sequenced ( unless otherwise stated ) and are not isolate-specific . Of the 22 reported SPIs only SPI-1 , SPI-4 and SPI-9 are present in S . bongori 12419 with the same gene composition as those defined in S . enterica ( Summarised in Figure 2; Table 1 ) . Consistent with previous observations SPI-3 and SPI-5 are incomplete: SPI-3 exists as two independent insertions in S . bongori , SPI-3a and SPI-3b , that appear to have fused into a single element in S . enterica ( Figure 2; This study; [25] ) . SPI-5 has previously been shown to be a chimeric genomic island composed of two regions of markedly differing G+C content in S . enterica , region one carrying the T3SS-1 translocated effector genes sigE , sopB and pipD and region 2 encoding the T3SS-2 translocated effector gene pipB . S . bongori possesses region 1 only; there is no trace of the T3SS-2 effector gene encoded in region 2 . A significant distinguishing feature of S . bongori is the lack of SPI-2 [14] , [18] , [26] , [27] . The site occupied by SPI-2 in S . enterica ( alongside tRNA-valV ) carries a ∼20 kb genomic island in S . bongori encoding a novel type VI secretion system ( SPI-22; see below ) . The tetrathionate respiration ( ttr ) gene cluster which lies alongside SPI-2 in S . enterica is retained by S . bongori . All the T3SS-2 translocated effectors are absent from S . bongori with the exception of SlrP , which in S . enterica subspecies is known to be secreted by both T3SS-1 and T3SS-2 [28] . Conversely 10 of the 12 known T3SS-1 translocated effectors are almost entirely conserved between S . enterica subspecies and S . bongori and include those that stimulate proinflammatory responses , bind actin and are important for cellular invasion ( sipA , sipB , sipC , sopB , sopD and sopE2 ) . S . bongori also carries effectors that dampen down cytoskeletal rearrangements and host signalling responses by S . enterica subspecies including avrA ( reported to inhibit NF-kappa B [29] ) and sptP ( pseudogene ) . Although the S . bongori sopA gene is located at the same site as its orthologues within S . enterica it has been disrupted by an insertion which has generated a chimeric effector protein ( denoted SboH; see below ) . The S . bongori T3SS-1 translocated effector genes are found at exactly the same genomic loci as they are in S . enterica: carried on SPI-1 itself , SPI-5 or at identical sites in the chromosomal backbone ( Table 2 ) . This suggests that most T3SS-1 effectors were sequentially acquired prior to speciation , sopE and sspH1 being the only exceptions . The latter two effector proteins are sporadically distributed in S . enterica subspecies enterica isolates , and carried as cargo on phage [30] , [31] , consistent with them being more recent acquisitions . In addition to the lack of SPI-2 , S . bongori lacks the entirety of SPI-6 ( encoding a type VI secretion system ) , SPI-13 ( required for survival in chicken macrophages ) , SPI-14 ( encoding an electron transport system ) and SPI-16 ( bacteriophage remnant carrying genes associated with LPS modification ) making these islands unique to S . enterica ( This study;[10] , [32] , [33] ) . From the in silico analysis and microarray data it is evident that SPI-6 and SPI-16 are present in all S . enterica lineages whilst SPI-13 and SPI-14 are only sporadically distributed in S . enterica ( Table 1; This study [33] , [34] , [35] ) . S . bongori also lacks part of the centisome 54 island ( CS54 ) encoding shdB ratC and ratB which are associated with survival in macrophages and longterm shedding of bacteria from the host [36] . There are four distinct T6SSs currently described for Salmonella , encoded on SPI-6 , SPI-19 , SPI-20 and SPI-21 [37] , [38] . S . bongori lacks all four systems but carries a novel T6SS locus ( ∼20 kb in size ) which we have denoted SPI-22 ( Figure 3A ) . The T6SS genes carried on SPI-22 shares extensive similarity to the recently identified CTS2 T6SS locus of Citrobacter rodentium ICC168 [39] and the HSI-III locus of Pseudomonas aeruginosa strain PA01 known to be required for virulence ( Figures 3B & 3C ) [40] . SPI-22 encodes all of the core T6SS components including homologues of DotU and IcmF , necessary for secretion and membrane stabilisation of the T6SS apparatus , the ATPase ClpV , thought to provide energy to the system , as well as other essential functions associated with the T6SS apparatus including VgrG , Hcp and the Gp25-like protein ( Figure 3A ) [41] , [42] . Apparently in contrast to S . enterica , in the absence of SPI-2 S . bongori has significantly expanded its repertoire of T3SS-1 effector proteins . Most of these candidate effectors are novel within Salmonella but are related to non-locus of enterocyte effacement ( LEE ) encoded ( Nle ) effector proteins found in EPEC , enterohemorrhagic E . coli ( EHEC ) or C . rodentium [39] , [43] . These three enteric pathogens have a related infection strategy as they colonize the intestinal mucosa while causing attaching and effacing ( A/E ) lesions ( reviewed by [44] ) . Of the 12 candidate T3SS-1 effector proteins of S . bongori , SboD , SboE , SboF and SboG show significant sequence similarity to NleI/G ( Table 3 ) [45] . In addition SboC shares 57% amino acid identity with EspJ [46] . Only sboD and sboC genes have homologues in S . enterica subspecies: sboD is similar to an uncharacterized gene , STY1076 , that is carried as ‘cargo’ on the S . Typhi prophage 10 [10] , [47] and SboC shares 77% amino acid identity with the predicted product of SARI_00261 , present at the same locus in S . arizonae . S . bongori also harbours the first recognized Salmonella chimeric T3SS effector gene , sboH; a fusion of the 5′ 450 bps of Salmonella sopA to the 3′ 828 bps of a gene highly similar to the T3SS effector nleH1 from EPEC , EHEC and C . rodentium ( Figures S3 and S4 ) . The sboH gene is found at the same locus as the S . enterica sopA gene and so is likely to have been formed by the insertion and partial deletion of sopA by an nleH1 homologue . By homology the nleH1 portion of the gene is also incomplete , lacking the first 5′ 54 bps . The most obvious effect of this fusion is to replace the cognate export signal of NleH1 ( located in the N-terminal 19 amino acids [48] ) with the export signal and InvB chaperone-binding site of SopA ( located in the N-terminal 45 amino acids [49] ) ( Figure S3 ) . The loss of the sopA gene may be compensated for by the presence of two other sopA-related CDSs ( Table 3 ) : the product of sboA shares 89% amino acid identity over its full length with SopA , including the export signal , the chaperone binding domain , the invariant cysteine residue and other sites conserved in the C-terminus of this family of proteins ( Figure S5 [50] ) . The sequence conservation between SboB and SopA is limited to the N-terminal 130 amino acids ( Figure S3 ) . The remainder of the sequence of SboB is weakly similar to a number of proteins of unknown function from a range of organisms including: S . enterica subsp . arizonae ( SARI_00821 ) and S . enterica subsp . enterica serovar Kentucky ( SeKB_A1367: Genbank ABEI01000019 ) , other bacteria including Providencia and eukaryotic proteins including a protein of unknown function from Naegleria gruberi ( Amoeba; 38 . 4% identity [79 . 1% similarity] in 211 amino acid overlap ) . The remaining candidate effectors include SboI , SboJ , SboK and SboL , which all share similarity with leucine rich repeat effectors from S . enterica , such as SlrP , as well as Ipa invasion plasmid antigens from Shigella . Notably SboK is more similar to leucine rich repeat ( LRP ) effectors found in Edwardsiella and Yersinia spp . than SlrP . As in many other enteric pathogens , all of the novel S . bongori T3SS effector genes , except sboC , are found on intact or degenerate prophage or regions unique to S . bongori compared to other salmonellae . The exception being sboC which is found on a backbone region conserved only in S . arizonae ( Table 3 ) . To confirm that the candidate T3SS effector proteins could be translocated we selected representatives of all the classes we identified ( Table 3 ) and performed a fluorescence-based ß-lactamase translocation assay [51] . This confirmed that SboA ( SopA-like ) , SboH ( SopA - NleH1 chimera ) , the EspJ homologue SboC , the NleG-family effector SboD and the leucine rich repeat effector SboI were all efficiently translocated into host cells in a T3SS-1 dependent manner ( Figure 4A ) . Translocation of the effector SboI fused to four HA-tags ( HAx4 ) was also visualized by immunofluorescence microscopy of infected cells ( Figure 4B ) . No translocation was observed upon infection with S . bongori ΔinvA expressing SboI-HAx4 or S . bongori wild type expressing the house-keeping protein FabI fused to the HAx4-tag . FabI-HAx4 could be detected inside a few bacteria , which was also sporadically observed for SboI-HAx4 in wild type or ΔinvA strains ( data not shown ) . In contrast , upon infection with S . bongori wild type expressing SboI-HAx4 the effector showed cytoplasmic distribution throughout strongly infected cells , and was also found surrounding a fraction of the bacteria in a ring-like staining pattern , reminiscent of a vacuolar membrane . Since SboH is the first reported chimeric effector protein we wanted to confirm its function . The EPEC effector NleH1 was recently shown to inhibit apoptosis through a C-terminal interaction with Bax inhibitor 1 [52] . In order to determine if SboH possessed the anti-apoptotic activity of NleH1 , we transfected HeLa cells with pRK5-nleH1 , pRK5-sboH or a control plasmid pEGFP-N1 , treated with the pro-apoptotic compounds tunicamycin ( TUN ) or brefeldin A and quantified the number of transfected cells showing activation of the apoptosis executioner caspase-3 by immunofluorescence microscopy . SboH prevented activation of caspase-3 by both stimuli as efficiently as NleH1 ( Figure 5A & B ) . The immunofluorescence analysis of transfected cells indicated that NleH1 and SboH are targeted to different subcellular locations ( Figure 5A ) . Whereas NleH1 shows plasma membrane and perinuclear localization , SboH seemed to localize in discrete structures reminiscent of mitochondria . To substantiate this observation we stained transfected cells with the mitochondrial marker MitoTracker ( Figure 5C ) . This demonstrated that SboH almost exclusively co-localized with the mitochondria , whereas NleH1 did not localize to the mitochondria . To analyze the impact of SboH in S . bongori infection we performed a cell detachment assay as described by Hemrajani et al . [52] . This assay measures the loss of cells due to S . bongori infection without discriminating specific cell signaling pathways . The assay shows that a S . bongori ΔsboH mutant causes a moderate , but significant increase of 15% in cell loss ( p-value<0 . 001 ) compared to S . bongori wild type ( Figure 5D ) . Complementation of the S . bongori ΔsboH mutant with SboH restored levels of cell detachment to that of the wild-type . Taken together , our data suggest that SboH combines features of SopA , namely the mitochondrial targeting signal , with the capability of NleH1 to inhibit tunicamycin and brefeldin A induced apoptosis . During infection , most likely through its anti-apoptotic activity , SboH reduces bacterial cytotoxicity and host cell loss . We used genome sequence data to explore the distribution of metabolic pathways and their associated genes within the Salmonella serovars . As direct comparisons of the metabolic maps of S . Typhimurium and E . coli have been reported previously [53] we focused our analysis on comparing S . bongori to other Salmonella serovars building upon data from S . bongori 12419 ( Figure 2 ) . Arguably one of the most distant comparisons we could make across the salmonellae would be between S . bongori and the acutely pathogenic , human restricted , S . enterica subspecies enterica serovar Typhi ( S . Typhi ) . Within this comparison we found a surprisingly high degree of conservation . All of the thirty pathways known to be involved in the generation of precursor metabolites and energy for S . Typhi are present in S . bongori ( Table S4 ) . Of the 146 predicted biosynthetic pathways found in S . Typhi , including the biosynthesis of amino acids , carbohydrates , fatty acids and lipids , only 8 are missing from S . bongori . Equally , of the 78 degradative pathways carried by S . Typhi , S . bongori shares 72 and possesses only 3 unique pathways . The unique S . bongori metabolic capabilities ( compared to S . Typhi ) include the degradation of complex acid sugars D-galacturonate and L-idonate , which are sporadically distributed throughout the salmonellae . However , more restricted in its distribution is the capacity to degrade lactose . S . bongori encodes β-D-galactosidase ( lacZ ) and the lactose operon repressor ( lacI ) but is missing lacY ( the high affinity lactose permease ) which explains why although it is a non-lactose fermenter S . bongori gives a positive result on ortho-nitrophenyl-β-D-galactopyranoside ( ONPG ) medium . Most members of S . enterica subspecies enterica are phenotypically non-lactose fermenters and are unable to utilise ONPG . The only other salmonellae that appear to harbour the lac operon include S . enterica subspecies arizonae and diarizonae , but these subspecies also possess lacY . Comparing S . bongori to other Enterobacteriaceae which are non-lactose fermenters but are ONPG positive there is no strong conservation in genes or the site of insertion , suggesting independent acquisition events [54] , [55] . There is also evidence of metabolic streamlining in S . bongori . For example , like E . coli , S . bongori has lost the cob-pdu gene cluster ( S . bongori retains only fragments of the first and last gene of the cob-pdu cluster: cobT [SBG__1882] and pduX [SBG_1882A] ) and so lacks the capacity to anaerobically synthesise vitamin B12 ( cobalamine ) and to catabolise propanediol [56] . The cob-pdu gene cluster was thought to have been lost by many Enterobacteriaceae . It has been suggested that the cob-pdu gene cluster was subsequently reacquired by S . enterica , following its split from S . bongori [57] , [58] , [59] , where it has been shown to be important for survival in macrophages [60] a niche in which S . bongori cannot survive [61] . Conversely there is evidence that pathways conserved in S . bongori , E . coli and wider Enterobacteriaceae , have been lost and replaced in the warm blooded-host adapted serovars of S . enterica subspecies enterica by alternative pathways that are energetically more efficient producing more ATP/mol of substrate or have differing substrate specificities . S . bongori carries the same genes for L-tartrate or citrate fermention as those found in E . coli and most other enterics: the ttdABDT operon or citDEF . However , these have been either replaced ( only remnants of the ttd genes remain in members of subspecies enterica e . g . S . Typhi strain CT18 position 3230063 . . 3230191 and S . Enteritidis strain P125109 , SEN3049A ) or , in the case of citrate , augmented in S . enterica subspecies enterica by the acquisition of two operons encoding tartrate hydratase ( e . g . STM3350-3359 ) and a second citrate lyase gene cluster ( e . g . STM0052-STM0063 ) . Both of these clusters carry a dedicated Na+ translocating oxaloacetate decarboxylase to provide reducing power . Therefore unlike the pathways found in S . bongori and E . coli these new pathways do not require a co-substrate and are energetically more efficient , producing more ATP/mol of substrate [62] , [63] , [64] . Moreover mutations in these new gene clusters in S . Typhimurium can be found as attenuating in genome wide mouse mutagenesis studies [65] and our microarray analysis of SARC shows that all other S . enterica subspecies resemble S . bongori by possessing the ttd cluster but lacking the alternate tartrate and citrate dissimilatory operons ( data not shown ) . Looking more broadly across the Enterobacteriaceae , some Klebsiella pnenumoniae isolates also possesses a related second citrate lyase , sometimes located at the same site in the genome as that found in S . enterica subspecies enterica serovars . However , the gene makeup of this region in K . pnenumoniae differs slightly and we could not find this region at this site in other enterics we searched consistent with this region being sporadically acquired . There are other examples of lineage-specific metabolic streamlining which show a more sporadic phylogenetic distribution including the C-P lyase system ( phnA-P ) , able to breakdown a wide range of phosphonate compounds , and phosphonatase which is specific for 2-aminoethylphosphonate . Whilst S . enterica subspecies arizonae carries the entire cluster ( data not shown ) the S . bongori phn loci is degenerate , consisting of only phnOAB and remnants of phnP and phnN ( SBG_3727 and SBG_3728A , respectively ) . S . enterica subspecies enterica has also lost the majority of genes in this operon leaving only phnOAB [66] , but have acquired the phosphonatase system encoded by phnVUTSRWX [66] . The explanation for this replacement in members of subspecies enterica may lie in that fact that 2-aminoethylphosphonate is found in abundance in flagellates found in the digestive tracts of ruminants such as cattle , common hosts for members of subspecies enterica [67] . Our microarray data for this cluster also shows that genes phnVUTSRWX are only present in S . enterica subspecies enterica ( data not shown ) . The capacity to use allantoin as a sole nitrogen source under anaerobic conditions is also phylogenetically restricted . We have previously speculated that the acquisition of the allantoin gene cluster by S . enterica subsp . enterica was linked to differences in the sequential breakdown of purines by different hosts [68] , [69]: in fish , crustaceans and other invertebrates purines are broken down sequentially to ammonia and CO2 [70] , [71] , while genetic lesions in vertebrate species block purine catabolism at different steps leading to the accumulation of allantoin in most mammals ( including rodents and domesticated animals ) . Our current data shows that the genes encoding allantoin degradation in Salmonella are absent from S . bongori and restricted to S . enterica subspecies enterica and salamae only .
Our understanding of Salmonella evolution has been built largely on data from representative isolates of the relatively recently emerged Salmonella enterica subspecies enterica . S . bongori and S . enterica are thought to have diverged between 40-63 . 4 Myrs ago [72] and so comparing the genomes of these two distinct species provides a unique opportunity to understand the ancestral Salmonella and determine the evolutionary events that mark speciation and those that track different branch points in Salmonella evolution following this event . We have shown that a diverse set of S . bongori isolates form a tight cluster of sequence types that , when examined on a whole genome basis , appear to comprise at least three phylogenetic groups . The G+C content in S . bongori represents a midpoint between S . enterica subspecies enterica and E . coli ( Figure S2 ) , but the phylogenetic analysis with MLST data and whole genome sequences suggests that this may be an artifact of the host specialisation into warm blooded animals that took place in the evolution of S . enterica subspecies enterica . The variation across the S . bongori species , both within and between the phylogenetic groups , contrasts sharply with that observed across S . enterica , with a comparable amount of variation across the groups in S . bongori , to that which is found between two serotypes of S . enterica subspecies enterica . The apparent lack of variation is difficult to explain since even if S . bongori had been stably maintained for a long period within its current niche significant genome diversity would still be expected , even if this was largely neutral . This reduced level of apparent diversity could be a bias of sampling , yet the isolates sequenced in this study are from a wide range of sources and are globally and temporally diverse . Although the answer to this question is still equivocal it is possible that S . bongori has been through a recent evolutionary bottleneck . Despite their apparent evolutionary divergence , metabolic analysis showed that the biochemical maps of S . bongori and S . Typhi are very similar , suggesting that Salmonella serovars acquired many of the basic functions for an enteric lifestyle early in their evolution . This has been recently supported by the finding that the ability to use tetrathionate as an electron acceptor provides a competitive advantage to S . Typhimurium in the inflamed gut over normal flora [73] . The ttr cluster is conserved in S . bongori [74] . Where S . bongori did differ from S . enterica serovars , it generally most closely resembled E . coli and the wider Enterobacteriaceae , i . e . the presumed ancestral state . Whilst the independent acquisition of the lac operon by S . bongori is difficult to explain , for E . coli the acquisition of the lac operon may have facilitated metabolism of milk sugar and adaptation to the mammalian gut . Conversely the loss of this function from S . enterica subspecies enterica may be associated with its invasive lifestyle since recent evidence has shown that lacI expression interferes with the function of SPI-2 and attenuates virulence in macrophage [75] . This comparative analysis also highlighted metabolic traits that mark the evolution of S . enterica subspecies enterica including the differing abilities to ferment L-tartrate and citrate . These metabolic differences are already known to differentiate high and low pathogenicity Salmonella strains ( S . Paratyphi B and S . Paratyphi B variant Java ) [76] . Outside of Salmonella the ability to ferment citrate almost equally divides clinical Klebsiella pneumoniae biotypes into two groups and is thought to represent an adaptation to different nutrient conditions found within the host [77] . S . bongori possess a basic Salmonella virulence ‘tool kit’ consisting of SPI-1 , 3a , 3b , 4 , 5 and 9 . Although the S . bongori SPI-3 and SPI-5 have a different structure compared to those in S . enterica these SPIs are conserved across the salmonellae and could be considered part of the Salmonella core genome . Moreover , many of these core SPIs show significant regulatory and functional interplay between the functions they encode . For example the SPI-4 adhesin SiiE is required for efficient translocation of T3SS-1 effectors in S . Typhimurium [78] and SPI-4 , SPI-5 and SPI-1 genes are under joint control by the SirA/HilA global regulatory cascade [78] , [79] , [80] , [81] . From this it is tempting to speculate that these SPIs define one of the earliest virulence networks of Salmonella . Clearly there are multiple factors missing from S . bongori which limit its ability to cause disease in warm blooded animals demonstrated by experiments that have introduced SPI-2 into S . bongori [61] . The genome of S . bongori has not remained static since divergence; we see parallels with S . enterica serovars in the functions that have been acquired following divergence . For example both S . bongori and S . enterica have independently acquired different T6SSs . It is clear that the Salmonella genus as a whole includes representatives of each of the major T6SS phylogenetic groups , thus reinforcing the long-term importance of these systems . Also like S . enterica , S . bongori has sequentially acquired a range of T3SS-1 effector proteins many of which we have shown to be translocated . The S . bongori effectors have homologues in EPEC , EHEC and C . rodentium including EspJ; which in EPEC and EHEC prevents receptor mediated phagocytosis of opsonised cells [46] and so could be important for S . bongori in resisting phagocytosis . This strategy would be well in line with previous observations of the S . bongori life style as the bacteria are not able to sustain an intracellular life style in macrophages mainly due to absence of the SPI-2 T3SS-2 and its effectors [61] and likely also because of the lack of cob-pdu operon too [60] . We also functionally characterised the effector SboH and have shown that it inhibits apoptosis in a similar manner to its EPEC homologue NleH1 . Moreover , in infection SboH reduces bacterial cytotoxicity . In EPEC the anti-apoptotic activity of NleH1 has been proposed to sustain colonisation of the mucosal epithelium by reducing the ‘turn-over’ of surface enterocytes and associated any bacteria or microcolonies [52] . The acquisition of these effectors that most closely resemble those from pathogenic E . coli strains causing watery diarrhea suggests that following the split of S . bongori and S . enterica , S . bongori has adopted a specialised infection strategy which might in parts be more similar to the extracellular pathogenic E . coli than S . enterica . This infection strategy might be optimised to colonise cold-blooded reptiles , but still provides the basic armoury for S . bongori to emerge as an opportunistic pathogen of humans and animals .
The S . bongori exploited in this study included 28 isolates originating from between 1966-2004 , from the USA , Africa and Europe , from hosts including humans , frogs , pigeons and reptiles as well as environmental sources including the shell of a hen's egg , cheese , fishmeal and waste water ( see Table S1 ) . For bacterial cultures LB medium was inoculated and grown overnight at 37°C with each isolate . Genomic DNA was extracted from 1 ml of culture by using manufacturer's instructions ( Wizard Genomic DNA Purification kit from Promega ) . The genome of S . bongori strain 12419 was sequenced to approximately 11-fold coverage from pUC19 ( insert size 2 . 8–5 . 5 kb ) and pMAQ1b_SmaI ( insert size 5 . 5–6 . 0 kb ) genomic shotgun libraries using big-dye terminator chemistry on ABI3700 automated sequencers . End sequences from large insert BAC libraries in pBACe3 . 6 ( insert size 23–48 kb ) were used as a scaffold . All repeat regions were bridged by read-pairs or end-sequenced polymerase chain reaction ( PCR ) products . For all remaining S . bongori strains , tagged genomic library preparation and DNA sequencing ( with and without multiplexing ) was carried out as previously described [82] . Mapping of reads to the reference genome and SNP detection were carried out according to earlier described protocols [82] . De novo assemblies were performed by using Velvet v0 . 7 . 03 and their corresponding contigs were ordered using Abacas [82]; the resulting pseudomolecules were blasted against the reference genome to assess synteny as well as the existence of indels and novel regions . Details of mapping and assembly data output are given in Table S1 . Annotation and analysis was performed using Artemis and ACT [83] , [84] . Phylogenic analysis of Salmonella ( shown in Figure 1 ) was based on the 7 concatenated MLST loci sequences , from sequences generated in this study ( for the S . bongori isolates ) , those obtained from the Salmonella MLST Public Strains Database ( http://mlst . ucc . ie/mlst/dbs/Senterica for the S . enterica STs ) or obtained from genomic sequence ( for EPEC strain E2348/69 [43] ) . The S . bongori tree was produced using a whole genome alignment generated by mapping the S . bongori samples against the finished genome of strain 12419 . Trees were drawn using RAxML assuming a general time reversible site model with gamma correction [85] . In the case of the whole genome tree , phage and MGEs were removed prior to the production of the tree ( see Table S2 ) . Support for nodes was assessed by using bootstrapping ( x100 ) ; SNPs were reconstructed on the tree with parsimony using accelerated transformation . BAPS analysis was performed on the SNP alignments produced from the mapping alignment , using the BAPS individual mixture model [22] . Three independent iterations of BAPS were performed ( using an upper limit for the number of populations of 25 , 26 and 27 ) to obtain the most optimal partitioning of the sample . To infer the orthologous genes in each pair of genomes compared: Each CDS ( a ) from the genome ( A ) was searched , using FASTA , against the CDSs of the other genome ( B ) . If the top hit covered at least 80% of the length of both sequences with at least 30% identity , a reciprocal FASTA search of the top hit sequence ( b ) was launched against the CDSs of the first genome . If the reciprocal top hit was the same as the original query CDS then ( a ) and ( b ) are considered orthologous genes of ( A ) and ( B ) . In a second step , in order to validate the results , we performed a BLASTN and TBLASTX comparison between the 15 genomes , visualized using ACT [83] to curate ambiguous cases , for example , gene remnants ( pseudogenes ) , IS elements and phage-related CDSs , and to check for a syntenic relationship among the putative orthologs . A Pathway/Genome Database ( PGDB ) describing the metabolic pathways of S . Typhi was created in Pathway Tools v . 13 . 5 ( SRI International , California ) using the genome sequence and annotation associated with strain CT18 [10] . This PGDB underwent manual curation and currently comprises 200 predicted metabolic pathways and over 130 predicted transport reactions . To determine the differences in S . bongori relative to S . Typhi , we mapped orthologues onto the pathways and transport reactions , subsequently removing those missing functions and adding in the functions unique to S . bongori ( summarised in Figure 2 and listed in Table S4 ) . Primers , restriction enzymes and plasmids used to create S . bongori deletion mutants and to construct expression vectors of the putative S . bongori effector proteins are listed in Table S3 . Specific gene knockouts of the invA or sboH genes were generated in S . bongori 12419 as described previously [86] . To create non-polar mutations , the kanamycin resistance cassette was removed using plasmid pCP20 leaving a scar of 84 bp [86] , [87] . Deletions were confirmed by PCR and sequencing from the regions flanking the knockouts . To obtain the vectors encoding ß-lactamase ( TEM1 ) fusions all genes were PCR-amplified from S . bongori strain 12419 genomic DNA and PCR products digested and ligated into pCX340 [51] or pRK5 , respectively . If the KpnI restriction site of pCX340 was used a new ribosome binding site ( RBS ) was included in the forward primer . Sequence identity of the constructs was verified by DNA sequencing . The pCX340 derivative plasmids were named pICC522 ( fabI ) , pICC523 ( sboA ) , pICC524 ( sboC ) , pICC525 ( sboD ) , pICC526 ( sboI ) and pICC527 ( sboH ) , pICC611 ( sopB ) , pICC612 ( sopD ) . To create a plasmid allowing the C-terminal fusion of four HA-tags to the effectors , pCX340 was digested with EcoRI and XbaI to remove the tem1 gene . Subsequently an oligo cassette encoding four HA-tags was ligated into the vector to give the plasmid pICC613 . PCR products of sboI and fabI and pICC613 were digested and ligated as described for the vectors encoding TEM1 fusions to yield pICC614 ( pSboI-HAx4 ) and pICC615 ( pFabI-HAx4 ) . The plasmid pICC616 ( pSboH ) allowing the inducible expression of untagged SboH was constructed by ligation of the sboH PCR product in EcoRI and XbaI digested pCX340 and the transfection vector pRK5-SboH ( pICC548 ) by ligation of the PCR product into pRK5 ( Clontech ) . All pICC plasmids were used to transform S . bongori strain 12419 wild type and mutant strains by electroporation . The ß-lactamase ( TEM1 ) -translocation assay for the identification of translocated effector proteins was adapted from a protocol previously described [51] . To obtain a confluent cell layer 4 . 0×104 HeLa cells were seeded in 200 µL DMEM ( Sigma , 1000 mg/L glucose , supplemented with 10% fetal calf serum , Glutamax ( Invitrogen ) and MEM non-essential amino acids ( Sigma ) ) growth medium per well of a black wall/clear-flat bottom 96 well plate ( Becton Dickinson ) and cultured overnight . Prior to infection the medium was replaced with 150 µL fresh growth medium . Overnight LB broth cultures ( 6 µg/mL tetracycline ) of S . bongori strain 12419 or the ΔinvA mutant carrying the pICC plasmids were diluted 1∶30 in LB broth ( 6 µg/mL tetracycline ) and grown to an OD600 of 1 . 1–1 . 4 before protein expression was induced by addition of 1 mM Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) . The induced cultures were incubated until an OD600 of 1 . 8–2 . 2 was reached and diluted in Dulbecco's-PBS ( D-PBS , Sigma ) to a concentration of 3 . 75×108 bacteria/mL . To infect HeLa cells 20 µL bacterial dilution per well was added and the infection was synchronized by centrifugation ( 900 g , 5 min ) . After 1 h incubation at 37°C , 5% CO2 the cell supernatant was replaced with 100 µL Hanks' Buffered Salt Solution ( Gibco , supplemented with 20 mM HEPES , 3 mM Probenecid ( Sigma ) pH 7 . 4 designated HBSS-HP ) , and 20 µL freshly prepared CCF2-AM ß-lactamase substrate ( LiveBLAzer FRET-B/G Loading Kit , Invitrogen ) were added . After 1 h 45 min incubation at room temperature in the dark the cells were washed five times with HBSS-HP . Fluorescence emission at 450 nm and 520 nm was measured from the bottom using a Fluostar Optima plate reader ( excitation wavelength 410 nm , 10-nm band-pass ) . The translocation rate was calculated as recommended in the LiveBLAzer FRET-B/G Loading Kit manual . Briefly , emission values were first corrected by subtraction of the average background signals recorded for empty wells and the mean 450 nm/520 nm emission ratio of a triplicate of wells was calculated for each sample . The translocation rate is expressed as fold increase of the mean emission ratio 450/520 nm of each infected sample in relation to the mean emission ratio of uninfected cells . Expression of the TEM1 fusion proteins was controlled by Western blot using a mouse anti-ß-lactamase antibody ( QED Bioscience Inc; data not shown ) . HeLa cells ( 1 . 25×105 per 24-well plate well ) were seeded on coverslips and incubated in growth media overnight in a humidified atmosphere of 5% CO2 at 37 °C . S . bongori wild type or ΔinvA mutant containing pICC614 or pICC614 plasmids were grown , diluted and 50 µL dilution used for infection as described for the translocation assay . 1 h45 min –2 h post infection cells were washed three times with D-PBS , fixed with 3% paraformaldehyde ( PFA ) , treated with 50 mM NH4Cl in D-PBS , washed three times with D-PBS , permeabilised with 0 . 1% ( v/v ) Triton X-100 , washed three times with D-PBS and blocked with 2% ( w/v ) bovine serum albumin ( BSA ) and 2% ( v/v ) natural donkey serum in D-PBS for 1 h . The samples were stained with rabbit anti-Salmonella ( O:66 Statens Serum Institute ) and mouse anti-HA . 11 ( Covance ) primary antibodies followed by Rhodamine Red X ( RRX ) -conjugated donkey anti-rabbit IgG and DyLight 488-conjugated donkey anti-mouse IgG ( both Jackson ImmunoResearch ) antibodies . Nuclei were labelled with Hoechst 33342 dye and F-actin with AlexaFluor647 phalloidin ( Invitrogen ) . The coverslips were mounted using ProLong Gold antifade reagent ( Invitrogen ) and analysed on Zeiss Axio Imager Z1 or M1 immunofluorescence microscopes with Axiovision Rel 4 . 8 software . The experiments to compare the localization and anti-apoptotic activity of NleH1 and SboH were performed as described previously [52] , [88] . Briefly , transfected HeLa cells were treated with either 5 µg/ml tunicamycin ( TUN ) or 10 µg/ml brefeldin A ( BFA ) for 18 hours , or left untreated , prior to immunofluorescence microscopy processing . The cells were fixed in 3% PFA , washed with PBS , treated with 10 mM NH4Cl , permeabilized with 0 . 2% ( v/v ) Triton X-100 , washed with PBS and blocked with 1% ( v/v ) BSA in PBS for 1 h . Active caspase-3 and Myc-tagged effector proteins were detected using rabbit anti-cleaved caspase-3 ( Cell Signalling Technology ) , RRX-conjugated donkey anti-rabbit IgG ( Jackson ImmunoResearch ) and Fluorescein Isothiocyanate ( FITC ) -conjugated monoclonal mouse anti-Myc ( Sigma ) antibodies . Nuclei were labelled with the Hoechst 33342 reagent ( Invitrogen ) . Mitochondria were visualised using MitoTracker ( Invitrogen ) in accordance with manufacturer's guidelines before fixation . Samples were mounted and analysed by microscopy as described above . To determine the number of apoptotic cells 100 transfected cells were analysed in each repeat . Samples were tested in triplicate and experiments repeated a minimum of three times . HeLa cells ( 7 . 2×104 per 24-well plate well ) were cultured overnight . S . bongori wild type , ΔinvA , ΔsboH or ΔsboH pICC616 were grown for infection as described above . Prior infection 1 mL bacterial culture was harvested by centrifugation , resuspended in the same volume of cell culture medium and used to infect HeLa cells . After 1 h cells were treated with 200 µg/ml gentamicin and incubated for 4 h . A control sample was incubated with 1 µM staurosporine ( STS ) for 5 h in parallel to the infection . Cells were washed 5 times with PBS and then trypsinized for 10 min . Trypsin was inactivated by addition of 700 µL growth medium . Cells were counted on a Neubauer hemocytometer . All counts were compared with the level of uninfected , untreated cells and plotted as a percentage of cells lost . Statistical analysis was done using the GraphPad InStat Version 3 . 06 software . The one-way ANOVA Test using Bonferroni correction was used to determine significance of the observed differences ( p-values<0 . 001 ) . The annotated genome sequence of Salmonella bongori strain 12419 has been deposited in the public databases under the accession numbers FR877557 . The Illumina sequencing reads for all the sequences generated in this study have been deposited in the European Nucleotide Archive ( ENA ) under the accession numbers ERS002029- ERS002042 ( inclusive ) , ERS002044 , ERS004246 , ERS004249 , ERS004170 , ERS004173- ERS004176 ( inclusive ) , ERS004190- ERS004193 ( inclusive ) and ERS004196 . This is matched to strain names in Table S1 . Microarray data was submitted to ArrayExpress under accession number E-TABM-931 . | The bacterial genus Salmonella consists of two species: Salmonella enterica and Salmonella bongori . Salmonella are common causes of food poisoning in humans and can also cause more severe disease such as typhoid fever . Most of the Salmonella that cause disease in humans and animals are members of S . enterica . On the other hand S . bongori , is largely associated with reptiles but can cause disease in humans , albeit rarely . We have determined genomes for S . bongori isolates representing its known diversity . Using this , and existing genome information for a large number of different members of S . enterica , we were able to identify functions found in both species , and therefore likely to be ancestral , and differentiate them from those that have been more recently acquired . This information gives us more perspective on how pathogens evolve over the longer-term and allows us to identify functions that are associated exclusively with isolates that commonly cause disease in humans . Our analysis suggests that when S . bongori and S . enterica diverged they evolved to occupy very different niches . |
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Autophagy plays a crucial role in health and disease , regulating central cellular processes such as adaptive stress responses , differentiation , tissue development , and homeostasis . However , the role of autophagy in human physiology is poorly understood , highlighting a need for a model human organ system to assess the efficacy and safety of strategies to therapeutically modulate autophagy . As a complete , cyclically remodelled ( mini- ) organ , the organ culture of human scalp hair follicles ( HFs ) , which , after massive growth ( anagen ) , spontaneously enter into an apoptosis-driven organ involution ( catagen ) process , may provide such a model . Here , we reveal that in anagen , hair matrix keratinocytes ( MKs ) of organ-cultured HFs exhibit an active autophagic flux , as documented by evaluation of endogenous lipidated Light Chain 3B ( LC3B ) and sequestosome 1 ( SQSTM1/p62 ) proteins and the ultrastructural visualization of autophagosomes at all stages of the autophagy process . This autophagic flux is altered during catagen , and genetic inhibition of autophagy promotes catagen development . Conversely , an anti–hair loss product markedly enhances intrafollicular autophagy , leading to anagen prolongation . Collectively , our data reveal a novel role of autophagy in human hair growth . Moreover , we show that organ-cultured scalp HFs are an excellent preclinical research model for exploring the role of autophagy in human tissue physiology and for evaluating the efficacy and tissue toxicity of candidate autophagy-modulatory agents in a living human ( mini- ) organ .
In recent years , autophagy has emerged as a pivotal actor in adaptive responses to stress and starvation [1–3] and in tissue homeostasis [4] , cellular differentiation [5] , and ageing [6 , 7] . Key concepts of autophagy have arisen from molecular genetic experiments in a number of model organisms , including mammals , in vivo and ex vivo [8] . However , the role of autophagy in human organ physiology is as yet incompletely understood due to the lack of human model systems and the difficulty of experimental manipulation . For this , it would be helpful to have an easily tractable , clinically relevant human organ model at our disposal . Moreover , such human models would be useful to assess the efficacy and safety of the ever-increasing number of strategies that are being proposed to therapeutically modulate autophagy to treat various human diseases and to slow tissue ageing [9–12] . On this background , we have turned to a complete , cyclically remodelled human ( mini- ) organ , i . e . , terminal scalp hair follicles ( HFs ) [13] . Human HFs can be easily microdissected from excess tissue removed during plastic or hair transplantation surgery and organ cultured in a well-defined , supplemented , serum-free medium [14] . Under these conditions , growing ( anagen ) HFs continue to produce a pigmented hair shaft and will continue their spontaneous organ remodelling activity for many days ex vivo . The organ culture of human HFs has not only permitted major advances in translational hair research but have also permitted novel insights into human tissue physiology and pathology , spanning diverse fields including metabolism , cellular differentiation , chronobiology , cell cycle control , immunology , ( neuro ) endocrinology , toxicology , and pharmacology [15] . Therefore , the value of HF organ culture as a model for biomedical research extends far beyond its importance for dermatology alone . After years of massive growth activity ( anagen ) , human scalp HFs spontaneously enter into a rapid , apoptosis-driven organ involution process ( catagen ) [16] , following the dictates of an as yet insufficiently understood , organ-intrinsic “hair cycle clock” [17–19] . We hypothesized that late-stage anagen scalp HFs , whose hair matrix epithelium proliferates at a higher rate than most malignant tumors , despite being exposed to a number of stressors , are likely to come under increasing pressure to maintain tissue homeostasis and may require a substantial autophagic flux [20] to maintain their growth . That the HF can recover from massive toxicological insults , such as during chemotherapy-induced alopecia [21] , and that the antimalarial agent chloroquine ( CQ ) , a major autophagy inhibitor used in the clinic , can elicit adverse hair effects , such as change in hair color and hair loss [22] , also encouraged the concept that the HF may engage in autophagy as a fundamental adaptive mechanism against stress . Here , we have tested the hypotheses that organ-cultured human scalp HFs need to maintain a substantial autophagic flux in order to sustain anagen and that these ( mini- ) organs are well suited to study both the role of autophagy in human organ physiology ex vivo and to test candidate agents that modulate autophagy in a therapeutically desired manner under clinically relevant conditions . In the following , we report evidence that confirms both working hypotheses .
The yeast homologous autophagy-related protein 8 ( ATG8 ) , Light Chain 3 ( LC3 ) , and sequestosome 1 ( SQSTM1 , also known as p62 ) are well-documented markers to monitor autophagy by fluorescence microscopy [23] . Lipid conjugated LC3 proteins ( LC3-II ) are specifically recruited on the membrane of autophagosomes from the initial stages of autophagy [24] . Differing from a diffuse cytoplasmic signal of the unconjugated LC3 form ( LC3-I ) , lipidated LC3-II–containing autophagosomes appear as fluorescent dots when assessed by indirect immunofluorescence ( IF ) [23] . However , visualizing the endogenous LC3 protein in compact tissues can be quite challenging [23] . Therefore , as a first step in characterizing the role of autophagy in cycling human HFs , we first established a suitable indirect IF microscopy protocol to detect LC3 in acetone-fixed cryosections of organ-cultured anagen HFs . For this , we used a specific anti-LC3B antibody that has a higher affinity for the lipidated LC3B form ( S1 Fig ) . Confocal microscopy with anti-LC3B antibody/Alexa555 ( red ) demonstrated the presence of cells with distinct perinuclear fluorescent signal ( Fig 1III and 1IV ) , consistent with the recognized cellular localization of autophagosomes [23] . Interestingly , LC3B-positive dots were most prominently seen in keratinocytes of the proximal hair matrix below Auber’s line , the most rapidly proliferating compartment of the HF epithelium [25] , and in the precortical hair matrix ( Fig 1I and 1II ) , i . e . , the epithelial compartment where undifferentiated HF keratinocytes become committed to undergo terminal differentiation into the cells of the inner root sheath , hair shaft cortex , or medulla and begin synthesizing large quantities of specific hair keratins [26] . To further support that punctate fluorescent signals do indeed correspond to LC3B-containing autophagosomes , we conducted confocal microscopy analysis in anagen organ-cultured HFs that were treated for 4 h with CQ ( 10 μM ) with a vehicle control . Because CQ blocks autophagy at its late stage by inhibiting lysosomal function , CQ induces the accumulation of autophagolysosomes enclosing lipidated LC3B [27] . Compared with vehicle-treated samples , confocal images of CQ-treated HFs showed a significant increase in the number of LC3B-positive fluorescent dots ( red , Alexa555 ) , primarily in matrix keratinocytes ( MKs ) ( Fig 2A and 2B ) . Thus , our IF method is suitable to monitor endogenous autophagy in human organ-cultured HFs by indirect fluorescence microscopy . Moreover , the fact that LC3B-fluorescent dots increased upon CQ treatment indicates that the intrafollicular autophagic flux was active in MKs and that autophagolysosomal function was preserved in cultured human HFs [23] . Next , these IF results were independently investigated by transmission electron microscopy ( TEM ) . These ultrastructural analyses supported the presence of an active autophagic flux in the human HF matrix by showing diverse cytoplasmic double-membrane structures belonging to autophagic vacuoles at different stages of autophagosome biogenesis [28] ( Fig 2C and 2D ) . Indeed , we observed a putative phagophore sequestering a portion of the cytoplasm to form an autophagosome ( Fig 2CI ) . We also recognized several initial autophagic vacuoles ( AVi’s ) with visible bilayers separated by a narrower electron-lucent cleft , typical of autophagy ( Fig 2CII , 2CIII and 2CIVa ) . Predominantly , these AVi contained morphologically intact cytosol with ribosomes and organelles ( see as an example the mitochondria in Fig 2CIII ) , which is a common feature of nonselective autophagy [23] . In addition , we observed late/degradative autophagic vacuoles ( AVd’s ) characterized by a partially or completely degraded internal membrane and electron dense cytoplasmic material and/or organelles at various stages of degradation ( Fig 2CIVb ) . We also recognized a putative autophagolysosome , i . e . , degradative autophagic vacuole , that has fused with a lysosome , characterized by lamellar internal membranes ( Fig 2CV ) . Quantification of TEM images showed no accumulation of AVd structures ( Fig 2D ) , supporting the notion that the autophagic flux in the matrix of human anagen HFs ex vivo is actively occurring . Next , we asked whether key autophagy readout parameters change when human anagen scalp HFs spontaneously enter into catagen ex vivo [15] by comparing anagen VI HFs with HFs that showed morphological criteria of early or middle catagen stages , using a battery of previously defined objective classification criteria [29] . Confocal images revealed a marked reduction in the number of LC3B-positive fluorescent dots during the transition from anagen to catagen ( Fig 3A and 3B ) , suggesting a more prominent autophagic flux during the proliferative stage of the HF cycle . To probe this hypothesis , we compared the levels of SQSTM1 in anagen and catagen HFs . SQSTM1 serves as a link between LC3 and ubiquitinated substrates [30] . SQSTM1 and SQSTM1-bound polyubiquitinated proteins become incorporated into the completed autophagosome and are degraded in autolysosomes , thus serving as an index of autophagic degradation [23] . In line with a reduction in autophagy-dependent protein degradation during catagen induction , confocal images showed that SQSTM1 fluorescence signal ( green , Alexa488 ) was significantly higher in the matrix of catagen HFs , compared with the matrix of HFs in anagen VI ( Fig 3C and 3D ) . Further supporting our IF data , quantitative assessment of autophagy-related structures in TEM sections from anagen and catagen HFs showed a significantly higher number of autophagic vacuoles in anagen versus catagen HFs ( Fig 3E ) . These observations raised the possibility that autophagy may serve as a process that maintains and prolongs anagen . If true , manipulating intrafollicular autophagy would be of profound clinical interest , because the vast majority of patients with hair loss or undesired hair growth seen in clinical practice shows a premature shortening of anagen ( leading to effluvium/alopecia ) or retarded entry into catagen ( resulting in hirsutism/hypertrichosis ) [13 , 19] . As mentioned above , treatments with the autophagy inhibitor CQ present recognized deleterious effects on hair viability [22] . Nevertheless , CQ may eventually affect the hair cycle through autophagy-independent cytotoxic effects related to the ability of this drug to inhibit lysosomal function or to intercalate in DNA [31 , 32] . Therefore , to evaluate whether autophagy has a pro-anagen function , we adopted a molecular genetic approach by knocking down the autophagy-related gene 5 ( ATG5 ) with iRNA , a gene that plays a fundamental role in the early stages of autophagosome formation [33] . Anagen HFs from three diverse human individuals were transfected with pool small interfering RNA ( siRNA ) sequences against ATG5 ( siATG5 ) or with nontargeting scrambled oligonucleotides , using our previously described transfection method for gene silencing in human HFs [34 , 35] . Confirming the silencing was successful , 48 h after transfection , the ATG5-fluorescent signal was drastically reduced in siATG5-treated HFs compared with control HFs ( Fig 4A ) . Additionally , the number of LC3B-positive dots was significantly reduced in siATG5 HFs , demonstrating that ATG5 silencing was functionally deleterious to intrafollicular autophagy ( Fig 4B and 4C ) . Further confirming a decrease in autophagic degradation when ATG5 was silenced , SQSTM1 fluorescence levels were elevated in siATG5 HFs , compared with control HFs ( Fig 4D and 4E ) . We then morphologically and immunohistologically assessed the hair cycle stage of each HF 96 h after transfection with siRNA sequences against ATG5 or Control , as previously described [29] . Confirming that ATG5 silencing persisted at this time point , IF analysis showed a significant 80% reduction in ATG5-fluorescence signal of siATG5-treated HFs , compared with control HFs ( Fig 5A and 5B ) . While the majority of control HFs progressed through the anagen-catagen transformation relatively slowly and were mostly in early catagen stage , siATG5-transfected HFs involuted much more rapidly and reached the middle and late stages of catagen development ( Fig 5C and 5D ) with less than 10% of ATG5-silenced HFs having retained their characteristic anagen VI morphology . To validate these morphological analyses , we assessed the hair cycle stage in siATG5 and control HFs by measuring the number of proliferative and apoptotic MKs in the hair matrix tips , below the line that represents the widest part of the hair bulb ( Auber’s line ) [15 , 29] . Compared with control HFs , we observed a significant reduction in the percentage of MKs that were positive for the proliferation marker , Ki-67 ( red , Alexa555 ) , in siATG5-treated HFs , with a significantly higher percentage of apoptotic ( Terminal deoxynucleotidyl transferase dUTP Nick End Labeling [TUNEL]-positive ) cells ( green , Alexa488 ) ( Fig 5E–5G ) . Therefore , experimental autophagy inhibition prematurely terminates anagen and promotes apoptosis-driven development . The above findings imply that , conversely , up-regulating intrafollicular autophagy should prolong anagen . Notably , the principal ingredients ( core mix ) of an anti–hair loss product on the market contains Galeopsis segetum extract , biotin , and N1-methyspermidine , the latter of which is a metabolically stable analog of the well-recognized autophagy-promoting agent , spermidine [36–39] . We thus decided to test whether this core mix was able to induce autophagy and prolong anagen in organ-cultured HFs . Confocal images showed a marked increase of LC3B-positive fluorescent dots in HFs treated with the core mix , compared with vehicle-treated HFs ( Fig 6A and 6B ) . Supporting this , the core mix treatment significantly lowered SQSTM1-fluorescent signal compared with the vehicle , demonstrating that the increase in LC3B-fluorescent autophagosomes depended on an increased autophagic flux , thus enhanced autophagy-mediated degradation ( Fig 6C and 6D ) . Quantification of autophagic structures in TEM sections from HFs treated with core mix or a vehicle further validated an induction of a bona fide autophagic flux upon treatment ( Fig 6E ) . This result also suggests that the N1-methylspermidine retains the ability to induce autophagy , as its desmethylated analog . To validate this concept , we adopted an in vitro cellular assay to demonstrate the pro-autophagic function of spermidine [36] . We thus evaluated the levels of lipidated LC3B and SQSTM1 in human bone osteosarcoma epithelial U2OS cells treated with equimolar doses of N1-methyspermidine and spermidine . Consistent with the results published by Pietracola and coworkers [36] , spermidine treatment increased the levels of the lipidated LC3B-II form and stimulated autophagy-mediated degradation of SQSTM1 ( Fig 6F and 6G ) . In addition , N1-methylspermidine–related effects on both LC3B-II and SQSTM1 levels paralleled the differences observed with the natural spermidine version ( Fig 6F and 6G ) . Further indicating that both compounds can also induce autophagy in keratinocytes , these results were extended and confirmed in the human NCTC 2544 keratinocyte cell line ( S2 Fig ) . Next , we decided to validate that the autophagy-enhancing ability of the core mix corresponded to a pro-anagen effect . We thus assessed the hair cycle stage of anagen HFs from three diverse human donors after a 5-d treatment with the core mix or vehicle . Despite a substantial variability in HF cycling among the diverse donors frequently observed in human HF organ culture experiments in which multiple patients were used [15] , morphological evaluation showed that the treatment with the core mix increased the percentage of anagen HFs from all donors ( Fig 7A ) . Moreover , the autophagy-inducing mix significantly enhanced the relative percentage of Ki-67 proliferative cells while reducing apoptotic MKs in the hair matrix tips , below the Auber’s line ( S3 Fig ) . Notably , the anagen-promoting effects of the core mix was observed even in catagen-primed HFs . Indeed , the treatment of anagen VI HFs from a donor that was already primed to enter catagen , as indicated by the fact that all vehicle-treated HFs had transitioned into catagen at the end of organ culture , preserved a clear anagen VI morphology in 16% of core mix–treated HFs ( Fig 7A , donor 3 ) . To further validate that the observed promotion of anagen was related to an induction of autophagy , we repeated the treatment in HFs silenced for the ATG5 gene . Supporting our previous analysis , siControl HFs treated with the core mix had an increased percentage of anagen HFs compared with vehicle-treated HFs ( Fig 7B , siControl ) . In marked contrast , core mix treatment failed to promote anagen in ATG5-silenced HFs from two independent donors ( Fig 7B , siATG5 #1 and siATG5 #2 ) . Collectively , our results support a scenario in which intrafollicular autophagy plays a fundamental anagen-maintaining role in HFs .
The current study unveils a crucial new role of autophagy in human hair growth control , namely for maintaining the HF growing stage , anagen . Moreover , we show that organ-cultured scalp HFs are an excellent preclinical research model for exploring autophagy functions in human tissue physiology and for evaluating the efficacy and tissue toxicity of candidate autophagy-promoting and -inhibitory agents in a living human ( mini- ) organ . Specifically , we present the first evidence that anagen hair MKs exhibit an active autophagic flux ex vivo , as documented by the presence of several LC3B-fluorescent perinuclear dots ( Fig 1 ) , which increase upon CQ treatment ( Fig 2A and 2B ) , and by demonstrating that autophagosomes representing different stages of autophagy are present in hair MKs ( Fig 2C and 2D ) . We further show that the number of autophagosomes decreases during the spontaneous involution of this ( mini- ) organ ( catagen ) ( Fig 3 ) , suggesting that intrafollicular autophagy may be modulated by several factors that are also involved in the regulation of HF cycling [40] . Because fibroblast growth factor ( FGF ) signaling is an important positive regulator of autophagy in chondrocytes [41] and is also involved in hair cycle control [40] , FGFs are among the most plausible regulators of intrafollicular autophagy . For example , fibroblast growth factor FGF7 ( also known as keratinocyte growth factor [KGF] ) is predominantly expressed in anagen and protects human HF from cell death induced by UV irradiation and chemotherapeutic or cytotoxic agents [42] , while FGF5 signaling controls catagen development [40] . Such candidate regulators of HF autophagy can now be probed in HF organ culture , using the methods and readouts reported here . Interestingly , the molecular controls that govern the anagen-catagen transformation in human HFs include profound changes in intrafollicular peripheral clock activity , whereby clock silencing prolongs anagen [18] . Recently , a connection between the circadian clock and autophagy has been reported in many systems [43–47] . For example , turnover of the clock protein brain and muscle ARNT-Like 1 ( BMAL1 ) involves both proteasomal and autophagic activities [48] . As BMAL1 knock-down in human HF significantly prolongs anagen [18] , it is conceivable that autophagy in the anagen hair matrix may impact on the peripheral clock in human HFs . Because inhibiting autophagy by ATG5 silencing induces premature catagen and enhances hair MK apoptosis ( Figs 4 and 5 ) , autophagic flux in the anagen hair matrix appears to be important for anagen maintenance . That this is not only an ex vivo phenomenon but also clinically relevant is suggested by the fact that CQ can cause telogen effluvium in patients taking this antimalarial medication [22] , which is caused by premature catagen induction [13 , 19] . Conversely , the principal ingredients ( core mix ) of a nutraceutical product used to treat hair loss potently promotes autophagy in organ-cultured human scalp HFs ( Fig 6A–6E ) and promotes anagen ( Fig 7A and S3 Fig ) , but fails to do so in ATG5-silenced HFs ( Fig 7B ) . Taken together , our data support a scenario in which intrafollicular autophagy is eminently targetable for the therapeutic modulation of human hair growth . In line with this concept , another agent that positively regulates autophagy , caffeine [49 , 50] , is sold as a hair growth–promoting cosmeceutical and has been shown to also prolong anagen and stimulate the proliferation of hair MK [51] . Autophagy inducers , which are the focus of intense ongoing research efforts [52–55] , are therefore promising agents for the treatment of hair growth disorders and drug-induced hair loss phenomena characterized by premature catagen induction [56] . However , currently available chemical inducers of autophagy have limited specificity for the autophagic process and produce several autophagy-independent effects [55] that may also affect the HF cycle . Our genetically-modifiable HF model provides a suitable human test system for evaluating the efficacy and specificity of additional autophagy inducers in promoting hair growth . Notably , HFs are continuously exposed to multiple , potentially noxious stimuli , ranging from contact with pathogens and the skin microbiome , UV light , and other DNA-damaging and/or oxidative stress-inducing external pressures , including drugs ( many of which cause hair loss as an adverse effect ) and internal stressors such as inflammation , metabolic disorders , and ageing [57–61] . To cope with this plethora of stressors , the human HF has established complex but highly efficient stress-response and stress-management systems [21 , 62–67] . The current data suggest that autophagy , a recognized stress-adaptive mechanism [2] , is prominently enrolled into these intrafollicular stress-response/-management systems to such an extent that down-regulating autophagy does not permit human HFs to sustain their growth under conditions of stress ( such as organ culture ) . This working concept can be followed up in vivo by studying human scalp skin xenotransplants onto immunocompromised mice , in which longer-term cycling of human HFs can be studied [16] . Interestingly , autophagy has also been found to be functionally important in regulating interfollicular epidermis ( IFE ) physiology and stress responses [68–71] . Thus , future functional comparative studies between intrafollicular and interfollicular autophagy may reveal an even more prominent function of autophagy in skin protection and function . Our study also introduces and validates scalp HF organ culture as an instructive , clinically relevant preclinical tool for translational , autophagy-related studies in the human system ex vivo . This can now be used to evaluate the toxicity of candidate drugs with regard to how they affect autophagy in human ( mini- ) organs . For example , many drugs , including antidepressants , anticonvulsants , antihistamines , and anticancer agents , negatively impact on autophagy by reducing lysosomal function ( lysosomotropy ) [72] . High throughput assays developed for measuring this [72] can now be complemented in a second step by human HF organ culture as a much more clinically relevant drug toxicity–screening assay . Here , the efficacy and tissue toxicity of candidate autophagy-promoting and -inhibitory agents can also be assessed directly ex vivo . Perhaps most importantly , the model introduced here provides the autophagy research community with an excellent tool for exploring the as yet insufficiently understood signals that regulate autophagy in human epithelial tissues as well as the functional roles of autophagy in a human ( mini- ) organ under both physiological and experimentally induced pathological conditions ( e . g . , chemotherapy [73] , interferon-gamma treatment [64] , ultraviolet radiation [66] , and oxidative stress [63] ) .
Discarded human scalp HFs or skin samples were obtained with informed , written consent following the “Declaration of Helsinki Principles . ” Full-length HFs used for the all the experiments were received and stored with ethical and institutional approval from the University of Manchester . A full list of patient numbers , sex , and age information is provided in S1 Table . Human osteosarcoma U2OS cells were acquired from the American Type Culture Collection ( ATCC ) . Human NCTC 2544 keratinocytes were kindly provided by Dr . Barbara Marzani ( Giuliani s . p . a , Milan , Italy ) . U2OS and NCTC 2544 were grown in Dulbecco’s Modified Eagle Medium High Glucose and Roswell Park Memorial Institute ( RPMI ) 1640 medium , respectively , containing 4 . 5 g/L D-Glucose , 4mM L-glutamine , 10% fetal bovine serum ( FBS ) , and 0 . 25 mM sodium pyruvate . U2OS cells were adopted as an established excellent model cell system to evaluate pro-autophagic activity of natural products [36] . IF microscopy staining for localization and quantification of autophagy proteins LC3B , SQSTM1/p62 , and ATG5 in situ was performed with anti-LC3B ( D11 ) XP Rabbit mAb ( Cell Signaling Technology ) , anti-SQSTM1/p62 ( abcam ) , and anti-APG5L/ATG5 ( EPR1755 ) ( abcam ) antibodies . Briefly , 5-μm-thick cryosections were fixed with cold acetone ( −20 °C ) for 10 min . After several washes in PBS , primary antibodies were incubated overnight at 4 °C . Alexa Fluor 555-conjugated donkey anti-Rabbit and Alexa Fluor 488-conjugated donkey anti-Mouse ( Thermo Fisher ) were adopted as secondary antibodies . Confocal images of HF derma papilla and of the area surrounding were taken by using a Confocal Microscope NIKON A1 , the 20 × 0 . 25 and 60 × 1 . 40 numerical aperture objective lens . Quantitative immunohistomorphometry in defined reference area using standardized light exposure was performed with Image J ( NIH ) software , as described [29 , 64] . Microscopic hair cycle staging was performed as previously described , using Ki-67/TUNEL immunostaining and Masson Fontana histochemistry [15 , 17 , 29] . For the quantification of positive Ki-67– and TUNEL-positive cells , intramesenchymal TUNEL+ cells were excluded from our quantitative immunohistomorphometry analysis and only epithelial TUNEL-positive cells were counted . In fact , TUNEL-positive cells in the dermal papilla and connective tissue sheath is a well-recognized artifact of hair HF organ culture [15 , 29] , because these regions of the human HF mesenchyme do not show apoptosis under physiological conditions [16] . Hair matrix cells were identified based on morphology and position relative to the dermal papilla [15 , 29] . Similarly , the quantifications of LC3B- , SQSTM1- , and ATG5-fluorescent signals were performed only on epithelial HF cells—specifically , the hair matrix cells and precortical matrix—while the connective tissue sheath and dermal papilla were excluded from analysis . Human scalp HFs were fixed in a mixture of 2% glutaraldehyde and 2% paraformaldehyde in 0 . 1 M cacodylate buffer for 2 h at room temperature and then processed for TEM as described in the literature [47] . Briefly , the samples have been washed in cacodylate buffer ( 0 . 1 M , pH 7 . 4 ) , postfixed for 2 h with 1% osmium tetroxide in the same buffer , extensively washed again , and then incubated overnight in a 0 . 5% uranyl acetate aqueous solution in the dark . After washing , the sections have been dehydrated in a graded alcohol series , and after a final dehydration in 100% propylene oxide , they have been infiltrated with low viscosity Spurr resin overnight and polymerized for 48 h at 65 °C . Sections of about 70 nm were cut with a diamond knife ( DIATOME ) on a Leica EM UC6 ultramicrotome . Bright field TEM images have been collected with a Schottky field-emission gun FEI Tecnai G2 F20 ( FEI , USA ) transmission electron microscope operating at an acceleration voltage of 200 kV and equipped with a 2k × 2k Gatan Ultrascan ( Gatan , USA ) charge coupled device ( CCD ) . For quantitative assessment of autophagy-related structures in TEM sections [74] , we acquired images of both the cytoplasm and the autophagic structures at magnifications that allowed clear identification of compartments and delineation of profile boundaries . More than 20 fields were randomly recorded for each HF to quantify the number of AVs/field/follicle . Analysis and statistics were further performed on the average number of AVs per field from at least three independent HFs per condition . | Human scalp hair follicles ( HFs ) experience a massive growth for years , until they spontaneously enter into a rapid , apoptosis-driven organ involution process , orchestrated by an organ-intrinsic “hair cycle clock , ” the molecular control of which remains unclear . Human HFs maintain in vivo–like characteristics , even after being removed from the body , and spontaneously run through a fundamental organ-remodelling process , traversing through a stage of growth ( anagen ) and destruction ( catagen ) as a ( mini- ) organ model . Here , we exploit this unique remodelling ( mini- ) organ to unveil a crucial new role of autophagy in the growth of human HFs . We show that hair matrix keratinocytes exhibit an active autophagic flux ex vivo during anagen , which is altered after the transition to catagen . We find that genetic inhibition of follicular autophagy induces premature catagen and enhances hair matrix keratinocyte apoptosis , suggesting that autophagic flux in the anagen hair matrix is important for the maintenance of this stage . Indeed , we find that the principal ingredients of a product used to treat hair loss induces autophagy in organ-cultured human scalp HFs and promotes anagen . We conclude that organ-cultured human HFs are a suitable ( mini- ) organ system to study both the role of autophagy in human physiology ex vivo and to test candidate agents that modulate autophagy under clinically relevant conditions . |
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The innate immune response is essential for controlling West Nile virus ( WNV ) infection but how this response is propagated and regulates adaptive immunity in vivo are not defined . Herein , we show that IPS-1 , the central adaptor protein to RIG-I-like receptor ( RLR ) signaling , is essential for triggering of innate immunity and for effective development and regulation of adaptive immunity against pathogenic WNV . IPS-1−/− mice exhibited increased susceptibility to WNV infection marked by enhanced viral replication and dissemination with early viral entry into the CNS . Infection of cultured bone-marrow ( BM ) derived dendritic cells ( DCs ) , macrophages ( Macs ) , and primary cortical neurons showed that the IPS-1-dependent RLR signaling was essential for triggering IFN defenses and controlling virus replication in these key target cells of infection . Intriguingly , infected IPS-1−/− mice displayed uncontrolled inflammation that included elevated systemic type I IFN , proinflammatory cytokine and chemokine responses , increased numbers of inflammatory DCs , enhanced humoral responses marked by complete loss of virus neutralization activity , and increased numbers of virus-specific CD8+ T cells and non-specific immune cell proliferation in the periphery and in the CNS . This uncontrolled inflammatory response was associated with a lack of regulatory T cell expansion that normally occurs during acute WNV infection . Thus , the enhanced inflammatory response in the absence of IPS-1 was coupled with a failure to protect against WNV infection . Our data define an innate/adaptive immune interface mediated through IPS-1-dependent RLR signaling that regulates the quantity , quality , and balance of the immune response to WNV infection .
West Nile virus ( WNV ) is a neurotropic flavivirus and is an emerging public health threat . Infection with WNV now constitutes the leading cause of mosquito-borne and epidemic encephalitis in humans in the United States [1] . WNV is enveloped and contains a single strand positive sense RNA genome of approximately 11 kb in length that encodes three structural ( C , prM/M , and E ) and seven non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B , and NS5 ) . It cycles enzootically between birds and Culex mosquitoes , with humans infected as dead-end hosts . WNV infection has been modeled in inbred mice wherein infection and pathogenesis recapitulate many of the features of human infection ( reviewed in [2] ) . Following subcutaneous inoculation , WNV replicates in dendritic cells ( DCs ) at the portal of entry and in the draining lymph node . A primary viremia develops and virus spreads to visceral organs including the spleen , where further amplification occurs , leading to central nervous system ( CNS ) dissemination and encephalitis . In humans , WNV causes an acute febrile illness that can progress to severe and sometimes lethal neuroinvasive disease , especially in the elderly and immunocompromised [3] . However , healthy young adults are also afflicted with severe neurological disease [4] , [5] , [6] , indicating that virulence can occur independently of immune deficiencies or aging . Intracellular innate immune defenses and the actions of type I interferon ( IFN ) provide a first-line of defense against virus infection and are essential for the control of WNV replication , dissemination , and neurovirulence [7] . Innate antiviral immune defenses are triggered through the recognition of conserved pathogen associated molecular pattern ( PAMP ) motifs within viral products by intracellular pathogen recognition receptor ( PRR ) proteins in infected cells . PRR signaling directs downstream activation of latent transcription factors , including NF-κB , interferon regulatory factor ( IRF ) -3 and IRF-7 , in a cell type-specific manner to induce antiviral response programs that include expression of proinflammatory cytokines , chemokines , type I IFN , and interferon stimulated genes ( ISGs ) [7] , [8] , [9] , [10] . The ISG products induced through autocrine and paracrine actions of IFN confer antiviral activity by limiting virus replication and cell-to-cell virus spread . Modulation of IFN signaling has been identified as a virulence feature of pathogenic strains of WNV [11] , [12] . The RLRs , retinoic acid inducible gene-I ( RIG-I ) and melanoma differentiation antigen 5 ( MDA5 ) [13] , [14] , [15] , [16] , are PRRs that play critical roles in triggering immune defenses against RNA virus infection , including WNV . RIG-I and MDA5 are cytosolic RNA helicases that contain an amino terminal tandem caspase activation and recruitment domain ( CARD ) . Upon engaging RNA substrates , the RLRs undergo a conformational change and bind to the mitochondrial associated protein , interferon promoter stimulator-1 ( IPS-1 ) through a CARD-CARD interaction , leading to IPS-1-dependent signaling of IFN production and expression of immune response genes [17] , [18] . RLR signaling and IPS-1 function have an essential role in triggering IFN defenses during WNV infection of mouse embryo fibroblasts ( MEFs ) and human cell lines in vitro . Cells lacking either RIG-I or MDA5 were attenuated in their ability to generate an effective innate immune response to infection , whereas cells lacking both RIG-I and MDA5 or those deficient in IPS-1 alone were unable to respond to infection with WNV and related flaviviruses [19] , [20] , [21] , [22] . Recent studies examined the role of another class of pattern recognition receptors , Toll like receptor ( TLR ) 3 and TLR7 , and show that these receptors are also important PRRs of WNV infection , as they play a role in signaling IFN production and an inflammatory response upon viral ligand recognition [23] , [24] , [25] . TLR3 has been shown to contribute to both enhancement and protection of CNS inflammation and neurovirulence of WNV in vivo [23] , [24] , while TLR7-dependent signaling was shown to be essential for directing proper immune cell homing to sites of WNV infection during the adaptive immune response in vivo [25] . Type I IFN , a major product of PRR signaling , has been shown to link innate and adaptive immune responses . However , the specific PRR pathways that mediate this during acute WNV infection have not been delineated nor has the RLR pathway been evaluated in this context . The quantity and quality of the innate and adaptive immune responses after infection must be carefully regulated to avoid aberrant inflammation and immunopathogenesis . Regulatory T ( Treg ) cells and inflammatory dendritic cell ( DC ) subsets regulate inflammation during acute virus infection through T cell suppression and by modulating the trafficking and inflammatory cytokine production of immune cells into infected tissues [26] , [27] , [28] . Thus , the level of local and peripheral Treg cells , and the composition of local DC subsets that develop during WNV infection may determine immune control and WNV disease . Here , we assessed the role of RLR signaling and IPS-1 in WNV infection and immunity . Our studies define IPS-1 as an essential modulator of immunity in vivo and demonstrate that IPS-1-dependent signaling orchestrates an innate/adaptive immune interface that regulates immune responses to effectively control WNV infection .
WNV infection of primary embryonic fibroblasts recovered from RIG-I−/− mice revealed that RIG-I was important in eliciting innate antiviral immune defenses early during infection , whereas MDA5 was important for enhancing and sustaining this response [21] . We further evaluated WNV infection of RIG-I−/− or MDA5−/− mice and confirmed that RIG-I serves a dominant role among the RLRs for the acute induction of innate immune defenses and protection against WNV infection in vivo ( data not shown ) . Since the RLRs signal innate defenses through the IPS-1 adaptor protein [29] , we also examined the role of IPS-1 in protection against WNV infection upon a sub-lethal virus challenge of wild type and IPS-1−/− mice . IPS-1−/− mice were highly susceptible to WNV infection and exhibited 100% mortality with an average survival time ( AST ) of 7 . 3 days as compared to wild type mice ( 38 . 5% mortality with an AST of 13 . 2 days; p<0 . 0001; Fig 1A ) . Thus , RIG-I and IPS-1-dependent signaling are essential for protection against WNV infection . To define the role of IPS-1 in controlling WNV in vivo , wild type and IPS-1−/− mice were infected subcutaneously ( s . c . ) with 100 PFU of WN-TX and viral burden within peripheral tissues and the CNS was measured over time post-infection ( pi ) . IPS-1−/− mice exhibited increased viremia compared to wild type mice ( 45 . 7 fold enhancement at day 1 pi , P<0 . 05 ) throughout the course of infection ( Fig 1B ) . Similarly , viral loads in the spleen were elevated in the infected IPS-1−/− mice ( Fig 1C ) . WNV infection of IPS-1−/− mice displayed an expanded tissue tropism as infectious virus was found in the kidneys , a tissue that is not normally permissive to infection in wild type mice ( Fig 1D ) . WNV is typically detected in the CNS of wild type mice after s . c . challenge between 4 and 8 days pi [2] . Consistent with this time course , infected wild type mice exhibited detectable viral loads ( average viral titer of 101 . 8 pfu/gram of tissue ) in the brain by day 6 p . i . , although virus was not detected in the spinal cord ( Fig 1E and F ) . In contrast , WNV spread to the brain ( Fig 1E ) and spinal cord of IPS-1−/− mice ( Fig 1F ) by day 2 pi , with viral loads rising through day 6 pi . Together these results indicate that IPS-1 , likely through RLR signaling of innate immune defenses , limits WNV replication , viremia , and peripheral spread , and is essential for the control of viral invasion of the CNS . Myeloid cells , including tissue and lymphoid DC and macrophages ( Mφ ) , are among the first cells to encounter WNV during infection and thus function to restrict the spread of virus to distant tissues and the CNS [2] . To define the role of IPS-1 in controlling virus replication and innate immunity in myeloid cells , we analyzed WNV infection and host responses in primary bone marrow-derived DC and Mφ recovered from wild type and IPS-1−/− mice . DC and Mφ were infected at an MOI of 1 . 0 ( relative to viral plaque assay quantification of BHK-21 cells; see Methods ) and evaluated for virus replication , IFN induction , and innate immune triggering of ISG expression ( Fig 2 ) . IPS-1−/− DCs sustained significantly higher WNV replication at 36 and 48 hours pi compared to wild type infected cells ( Fig 2A ) . WNV infection of wild type DCs induced IFN-β secretion but this response was completely abolished in IPS-1−/− DCs ( Fig 2B ) . The lack of IFN-β induction in IPS-1−/− DCs correlated with a lack of ISG expression including RIG-I , MDA5 , and STAT-1 ( Fig 2C ) . In addition , expression of ISG54 and ISG49 , which are direct IRF-3 target genes [30] , [31] , were not induced during WNV infection of IPS-1−/− DCs ( Fig 2C ) . Moreover , ISG56 , another IRF-3 target gene [31] , was induced late during infection and to lower levels as compared to ISG54 and ISG49 in wild type , infected DCs . WNV infection of IPS-1−/− Mφ resulted in significantly higher virus replication between 24 and 48 hours pi as compared to infected wild type cells ( Fig 2D ) . Whereas wild type infected Mφ expressed IFN-β , this response was completely abolished in IPS-1−/− Mφ ( Fig 2F ) . We also observed a differential expression of ISGs and IRF-3-target genes within WNV-infected Mφ . RIG-I , MDA5 , and STAT-1 were not induced in IPS-1−/− Mφ , whereas , ISG56 , ISG49 , and PKR were expressed at reduced levels and with delayed kinetics . These data establish that IPS-1-dependent RLR signaling is the major innate immune signaling pathway that controls virus replication in conventional DCs and Mφ . Neurons represent the target cell of WNV infection in the CNS and their death after infection is a key factor in pathogenesis and neurological sequelae [32] , [33] . To define the role of RLR signaling in restricting virus replication in neurons , primary cortical neurons were generated from wild type and IPS-1−/− mice . Cells were infected at an MOI of 1 . 0 with WN-TX and virus yield , IFN-β induction , and ISG expression were evaluated . In the absence of IPS-1 , WNV replicated faster and to higher levels resulting in a 2 . 2 and 4 . 2-fold ( p<0 . 05 ) increase in viral production at 24 hrs and 48 pi , respectively as compared to infected wild type neuronal cells ( Fig 3A ) . This relatively modest virologic effect in neurons compared to that observed in IPS-1−/− DC and Mφ was expected , as IFN-α or -β pre-treatment only inhibits WNV infection in cortical neurons to a maximum of 5 to 8-fold [12] , suggesting that the IFN response is comparably less potent in neurons . IFN-β expression was induced to lower levels in IPS-1−/− neurons compared to wild type infected neurons at 24 ( 10-fold , p<0 . 05 ) and 36 hours pi ( 5-fold , p<0 . 05 ) despite the higher levels of virus replication ( Fig 3A and 3B ) . Expression of ISGs , ( including RIG-I and MDA5 ) and IRF-3 target genes ( including ISG56 and ISG49 ) followed this pattern and were dependent on IPS-1 for rapid and high level expression ( Fig 3C ) . The presence of IFN-β and ISG transcripts in IPS-1−/− cells at 48 hrs pi is consistent with the finding that TLR3 has an independent and subordinate role in triggering innate immune responses in cortical neurons at later time points after WNV infection [23] . These results demonstrate that the RLR signaling pathway controls virus replication and induces innate immune responses against WNV infection in cortical neurons . To determine the role of the RLR pathway in protection of neurons against WNV pathogenesis in vivo , we conducted histological analysis of brain tissue from wild type and IPS-1−/− mice infected with WN-TX ( Fig 4A ) . Analysis of brain sections from infected wild type mice revealed little or no inflammation or neuronal damage , with sparse and focal cell infiltrates restricted to the hippocampus and cerebral cortex on day 6 pi . By day 10 pi ( a timepoint in wild type mice in which peak virus replication in the CNS occurs [34] ) cellular infiltrates were present in the parenchyma and neuronal destruction was observed throughout the cortex and hippocampus . In contrast , brain sections from infected IPS-1−/− mice recovered on day 6 pi displayed extensive injury to neurons in the cortex and granular neurons of the hippocampus . Damaged neurons appeared pyknotic with vacuolation , degeneration and cell dropout . Somewhat surprisingly , we observed extensive inflammation in the brains from infected IPS-1−/− mice within the cortex , hippocampus , and cerebellum ( data not shown ) displaying prominent perivascular and parenchymal immune cell infiltrates . To evaluate the composition and antigen-specificity of the inflammatory cells within the brains of WNV-infected mice , lymphocytes were isolated from infected brains on day 6 pi and were characterized from pools ( n = 5 ) of wild type and IPS-1−/− infected mice . Brains from IPS-1−/− infected mice showed an 2 . 9-fold increase in the total number of immune cells as compared to wild type infected mice ( Fig 4B ) , and this was associated with an increase in absolute numbers of infiltrating CD4+ and CD8+ T cells ( Fig 4C ) . Among the brain CD8+ T cells isolated from IPS-1−/− mice , there was a remarkable 27-fold increase in the number of antigen-specific cells that expressed IFN-γ after treatment with an immundominant NS4B peptide ( Fig 4D ) [35] , [36] . Analysis of microglia/Mφ cells , based on relative surface expression of CD45 and CD11b [37] , revealed increased numbers of microglial cells ( CD45+lo/CD11b+ ) and infiltrating macrophages ( CD45+hi/CD11b+ ) within the brains of infected IPS-1−/− mice when compared to wild type mice ( Fig 4E ) . Similar findings were observed in the spinal cords from infected IPS-1−/− mice ( data not shown ) . Combined with the histological analysis , these results demonstrate that in the absence of IPS-1 , WNV infection induces a strong inflammatory response in the CNS . While this response is likely associated with increased viral loads , the failure of this increased inflammatory response to elicit protection or control CNS pathology , in the absence of IPS-1 , suggests a role for the RLR signaling pathway as a regulatory program that controls the quality of the inflammatory response to WNV infection . To further characterize how IPS-1 modulates the inflammatory response to WNV infection , we measured levels of systemic type I IFN , proinflammatory cytokines , and chemokines present in the serum of WNV-infected mice at 1 and 4 days pi . Paradoxically , a trend towards more rapid induction and increased levels of type I IFN were observed in the serum of IPS-1−/− mice compared to wild type mice ( Fig 5A ) . We note that in this case type I IFN was detected and quantified through a mouse-specific type I IFN bioassay , which does not differentiate between the IFN-α or -β species . This result is consistent with our recent studies showing that serum type I IFN levels accumulate during WNV infection in an IRF-7-dependent but IRF-3-independent manner [8] , [9] . In this case IFN-α species are likely accumulating through a TLR7-dependent signaling pathway involving plasmacytoid DCs , which do not require the RLR pathway for IFN production [38] . More recently , Town et al . assessed the role of TLR7 and MyD88−/− during WNV infection and found that mice lacking MyD88 produced elevated levels of systemic IFN during WNV infection [25] . Thus , during WNV infection systemic IFN is regulated through RLR-dependent and independent processes . Correspondingly , when compared to wild type mice , IPS-1−/− infected animals , which show higher viremia ( Fig 1B ) produced increased serum levels of proinflammatory cytokines and chemokines in response to WNV infection . Elevated levels of systemic IL-6 , TNF-α , CXCL10 , and IFN-γ were observed at 1 and/or 4 days pi in IPS-1−/− mice ( Fig 5B ) . Serum cytokine levels were also compared between uninfected wild type and IPS-1−/− mice and showed no differences in basal cytokine expression ( data not shown ) . These results demonstrate that in the absence of IPS-1 , greater proinflammatory cytokine and chemokine responses are induced during WNV infection . WNV-specific antibody responses are essential for suppressing viremia and virus dissemination and limiting lethal WNV infection [39] , [40] . To determine if a deficiency in IPS-1 modulated the quality and quantity of the humoral immune response , we characterized the antibody profile in sera during WNV infection . In wild type mice , neutralizing virus-specific IgM antibodies are typically detectable by day 4 pi with WNV and production of neutralizing virus-specific IgG antibodies follow between days 6 and 8 pi [40] . A time course analysis in wild type and IPS-1−/− infected mice showed that between 4 and 6 days pi , WNV-infected IPS-1−/− mice exhibited significantly higher levels of virus-specific IgM , IgG , and IgG subclasses as compared to infected wild type mice ( Table 1 ) . WNV-specific IgG1 antibodies were detected at low levels on day 6 pi in sera from wild type and IPS-1−/− mice . Additionally , we observed a ∼72 . 9-fold increase in WNV-specific IgG2a levels in infected IPS-1−/− as compared to wild type mice on day 6 pi and ∼2 . 2-fold increase on day 8 pi . Assessment of the virus-specific antibody responses through a PRNT assay revealed that neutralization titers in sera from wild type mice increased dramatically between 6 and 8 days pi . Sera from IPS-1−/− infected mice exhibited a modest increase in neutralization titer to 1∶1280 , despite having much higher levels of virus-specific antibodies . This difference translated into a serum neutralization index that was ∼39-fold lower on day 6 pi in the infected IPS-1−/− mice compared to wild type mice . These results demonstrate that the humoral responses in WNV-infected IPS-1−/− mice are distinct from responses in wild type infected mice . Thus , RLR signaling and IPS-1 actions likely contribute to regulatory processes that govern the levels , IgG class switching , and neutralizing capacity of antibodies generated in response to WNV infection . To characterize the immune parameters associating with the dysregulated inflammatory and humoral responses in WNV infected IPS-1−/− mice , we analyzed the immune cell composition in draining lymph node and spleen tissues . Wild type and IPS-1−/− mice were challenged with diluent alone or with WN-TX , and draining popliteal lymph node ( DLN ) and the spleen were harvested at 1 and 6 days pi , respectively . Analysis of the popliteal DLN provides insight into how IPS-1 modulates the inflammatory response immediately after infection whereas assessment of the spleen elucidates characteristics of the adaptive immune response prior to the infection endpoint . Immune cells were isolated from the popliteal DLN and were characterized by flow cytometry ( Fig 6 ) . Analysis of CD8α DC subsets , which are phenotypically the major antigen presenting cells within lymphoid tissues and are implicated in eliciting virus-specific CD8 T cell in response to acute WNV infection [41] , showed that infected wild type and IPS-1−/− mice exhibited similar increases in the numbers of CD8α+ and CD8α− DCs compared to mock-infected mice ( Fig 6A , B ) . However , a significant increase ( ∼3-fold; p<0 . 05 ) of a proinflammatory DC subset , characterized as CD11c+CD11bhiLy6C+ , was observed within the popliteal DLNs of IPS-1−/− infected mice ( Fig 6C ) . This DC subset is monocyte-derived and typically recruited to sites of acute inflammation where they propagate the inflammatory response [42] . We found that these DC subsets were not significantly expanded and showed no differences in their recruitment to the DLN in either wild type or IPS-1−/− infected mice at 12 hours pi ( data now shown ) . Thus , as early as 24 hours pi , an elevated cellular inflammatory response is initiated in the IPS-1−/− mice . In contrast , similar increases in plasmacytoid DCs were observed within infected wild type and IPS-1−/− infected mice ( Fig 6D ) , demonstrating that an absence of IPS-1 did not directly affect expansion and/or recruitment of this DC subset . Within the popliteal DLNs , mock-infected IPS-1−/− mice compared to wild type mice generally showed elevated numbers of B cells , CD4+ T cells ( p<0 . 05 ) , and CD8+ T cells ( Fig 6E , F , and G ) . These results suggest that IPS-1 contributes to the homeostasis of lymphocyte populations within LNs . WNV infection of wild type mice increased the number of B cells ( 3 . 4 fold ) , CD4+ T cells ( 3 . 1 fold ) , and CD8+ T cells ( 2 . 3 fold; p<0 . 05 ) in the DLN within 24 hours pi . Similar increases in B cells were observed upon infection of IPS-1−/− mice . However , the number of CD4+ and CD8+ T cells was reduced in the DLN after WNV infection of IPS-1−/− mice . Thus , in the absence of IPS-1 , WNV infection specifically increases the number of inflammatory Ly6c+ DCs but suppresses the overall expansion and/or recruitment of T cells in the DLN . We further analyzed the lymphocyte composition of the spleen on day 6 after WNV infection ( Fig 7 ) . Gross pathologic analysis revealed that WNV infection of IPS-1−/− mice results in massive splenomegaly whereas infection of wild type mice induces only a slight increase in spleen size ( Fig 7A ) . Cell analysis revealed increased numbers of total lymphocytes in the spleens of infected IPS-1−/− mice as compared to wild type mice ( Fig 7B ) . Regulatory T ( Treg ) cells have recently been shown to contribute to the dampening of inflammation and adaptive immune responses during acute virus infections [26] , [43] , [44] . Moreover , a reduction in the number of circulating Treg in mice leads to enhanced lethality after WNV infection [45] . A time course analysis of Tregs in wild type mice revealed that WNV infection results in a significant increase in the numbers of FoxP3+ T cells as compared to mock-infected mice beginning on day 4 and peaking by day 6 pi ( Fig 7C ) , indicating the expansion of Tregs during acute WNV infection . Despite their marked increase in viral load , the infected IPS-1−/− mice did not display an increase in the numbers of FoxP3+ T cells at any timepoint analyzed . Thus , IPS-1 signaling directly or indirectly impacts Treg proliferation and does so independently of viral load . We also observed that spleens from infected IPS-1−/− mice exhibited significantly increased numbers of CD8+ T cells in comparison to those from infected wild type mice , whereas the expansion of splenic CD4+ T cells in wild type and IPS-1−/− mice were not different ( Fig 7D ) . The spleens from WNV-infected IPS-1−/− mice showed significantly higher numbers ( 3 . 9-fold; p<0 . 05 ) of WNV-specific CD8+ T cells producing IFNγ . To further define the phenotype associated with WNV-induced splenomegaly in IPS-1−/− mice , we also assessed the numbers of NK cells and neutrophils . Spleens from infected IPS-1−/− mice contained greater numbers of NK cells ( CD4−CD8−NK1 . 1+cells ) , NKT cells ( CD4+/CD8+/NK1 . 1+ cells ) and neutrophils ( CD11b+Gr1+ cells ) ( Fig 7E ) . Although WNV-infected wild type mice infected displayed slight increases in the absolute numbers of these specific cell types , a deficiency of IPS-1 resulted in a more marked enhancement of these immune cell populations .
In this study , we establish a major role for RLR signaling in protection from WNV pathogenesis , and demonstrate that IPS-1 is critical for the control of WNV infection in vivo . Our studies indicate that IPS-1-dependent RLR signaling functions to establish balanced , effective , and protective innate and adaptive immune responses , and that IPS-1 links adaptive immune regulation with the innate immunity triggered by RLR signaling during WNV infection . In the absence of IPS-1 in vitro , innate immune defense programs of myeloid DCs and macrophages were ablated or severely attenuated . Moreover , in vivo analysis of infected IPS-1−/− mice showed altered IgG and IgM antibody responses with diminished virus neutralization activity . The inflammatory response to WNV infection is regulated by IPS-1-dependent processes such that a deficiency of IPS-1 resulted in elevated proinflammatory cytokine and chemokines and increased numbers of inflammatory DCs , antigen-specific T cells , natural killer cells , and neutrophils in lymphoid organs , and activated macrophage/microglial cells within the CNS . The dysregulated inflammatory response to WNV infection in IPS-1−/− mice was associated with a reduction in the numbers of Treg cells and their failure to expand during acute infection . These observations demonstrate the critical role of IPS-1 in mediating RLR signaling of innate antiviral immunity against WNV infection , and reveal novel features of IPS-1 function in regulating immune homeostasis , inflammation , and adaptive immunity to infection . Although infection of primary DCs , macrophages , and neuronal cells failed to induce type I IFN upon WNV infection , WNV-infected IPS-1−/− mice showed enhanced systemic type I IFN responses . This finding agrees with previous studies that indicate both IPS-1-dependent and -independent pathways contribute to the systemic type I IFN production in vivo [8] , [9] , [23] , [25] . Most importantly , the enhanced tissue tropism and rapid viral entry into the CNS observed in the IPS-1−/− mice is not affected by the elevated systemic IFN responses . This suggests that local tissue-specific and intracellular responses triggered by RLR-dependent signaling are more essential for reducing viral burden and dissemination . One possible explanation is that a distinct set of RLR-responsive genes function to control virus replication at the site of infection . This could explain , in part , the elevated levels of virus replication , enhanced tissue tropism and cell-to-cell spread in mice or cells deficient in IRF3 or IRF-7 , each of which are downstream transcription factors of RLR signaling [8] , [9] , [10] . Additionally , it is likely that pDCs , which are specialized dendritic cells for producing systemic type I IFN during a viral infection [46] , are likely contributing to the IFN responses observed during WNV infection . Studies by Silva et al . have shown that WNV triggers IFN induction in pDCs through a replication-independent manner [47] . Interestingly , within the DLN , we observed similar expansion of pDCs between wild type and IPS-1−/− infected mice , yet at the same timepoint ( 24 hours pi ) , elevated systemic type I IFN responses were observed in IPS-1−/− mice . This suggests two possibilities: 1 ) splenic pDCs or circulating pDCs are likely responding to the high levels virus in the serum from the IPS-1−/− infected mice to produce IFN at 24 hours pi and/or 2 ) various other cell types that express TLR3 and/or TLR7 are responding to WNV infection and contributing to systemic IFN responses . Taken together , these studies indicate that RLR signaling and the actions of IRF-3/7 are important in triggering IFN production and ISG expression to limit WNV replication and spread , and that TLR signaling may impart additional , RLR-independent defenses that regulate immunity against WNV infection . The production of and response to type-I IFN is a major linkage point between innate and adaptive immunity , as IFN-α and IFN-β sustain B cell activation and differentiation [48] , [49] , [50] , expand antigen-specific CD8+ T cells [51] , [52] , CD4+ T cells [53] , and activation of NK cells [54] . One of the most intriguing aspects of this study was the global alteration of the immune response elicited in the IPS-1−/− mice , indicating that RLR signaling couples innate immunity with regulation of the adaptive immune response . Infection of IPS-1−/− mice exhibited increased IgM and IgG WNV-specific antibodies , enhanced WNV-specific CD8+T cell response , and increased expansion of neutrophils , NK cells and NK-T cells . One trivial explanation for these differences is that there is an increased antigen load in the absence of IPS-1 and , as a result , enhanced virus-specific ( e . g . CD8+ T cells , IgG and IgM antibodies ) and nonspecific ( e . g . Neutrophils , NK cells ) responses . However , there are several key findings from this study that argue against these responses simply being attributed to higher antigen load: ( 1 ) In the absence of IPS-1 , the CD4 and CD8 T cells , which are protective against WNV infection [34] , [35] , [36] , [55] , [56] , [57] , [58] , were significantly enhanced in the peripheral and CNS compartments but failed protect against infection . One explanation for this observation is that the expansion and migration of CD4 and CD8 T cells to different tissues was itself uncontrolled , resulting in T cell-mediated pathology rather than T cell-mediated protection . ( 2 ) While the quantity of virus-specific IgM and IgG antibody responses were greatly enhanced in the absence of IPS-1 , there was a marked reduction in antibody quality in terms of neutralization capacity . In contrast deficiencies in TLR3 or MyD88 ( data not shown ) did not alter virus-specific antibody responses and neutralization capacities . Collectively , these findings suggest that RLR-dependent signaling coordinates effective antibody responses during WNV infection through as yet undefined pathway . ( 3 ) While systemic IFN responses provide a link between innate and adaptive immune responses , our studies suggest that the PRR signaling pathways ( RLR-dependent vs –independent ) and levels of IFN production in combination with production other proinflammatory cytokines or chemokines regulate the quantity and quality of the immune response during virus infection . Thus , in the absence of IPS-1 signaling , infected conventional DCs or Mφ , two integral cell types in establishing adaptive immunity , likely do not produce IFN or the normal array and level of proinflammatory cytokines/ chemokines . Instead , IFN and other mediators may be strictly produced by infected or bystander cells during WNV infection , occurring with altered kinetics and magnitude , through TLR-dependent pathways , such as TLR3 and/or TLR7 [23] , [25] . ( 4 ) In the absence of IPS-1 , the enhanced expansion of Ly6C+ “inflammatory” DCs failed to limit early WNV replication and dissemination . This inflammatory DC subset migrates to sites of infection , secretes pro-inflammatory cytokines , and promotes CD8+ T cell expansion during a secondary virus infection , suggesting it sustains the effector T cell response [59] . Our data indicate that Ly6C+ DC recruitment and/or expansion is governed by IPS-1-dependent events of RLR signaling . Thus , the aberrant recruitment/expansion of these inflammatory DCs may contribute to immunopathogenesis and limit development of an effective immune response to control WNV virus infection . ( 5 ) The lack of Treg expansion during WNV infection correlated with altered IFN levels , increased proinflammatory cytokines and chemokine levels , and an increased number and distribution of antigen-specific CD8+ T cells . These observations implicate an indirect or direct role for IPS-1 in regulating Treg levels during WNV infection , and provide evidence that links a lack of Treg expansion to immune dysregulation . While their importance in autoimmunity is established [60] , recent studies have implicated an integral role for Tregs in controlling inflammation and adaptive immune responses during acute virus infections [26] , [43] , [44] . During acute infection Tregs function to locally contain and control the immune response with the dual outcome of suppressing viral dissemination while decreasing the likelihood of immune-mediated pathology . In support of this model , infection studies with herpes simplex virus ( HSV ) and mouse hepatitis virus ( MHV ) , two well established models of viral encephalitis , have demonstrated the importance of Tregs in limiting proinflammatory cytokine and chemokine responses to protect the CNS and enhance survival [26] , [43] . Recent work also implicates Tregs in the control of WNV pathogenesis , wherein peripheral expansion of Tregs was associated with asymptomatic infection among WNV-infected blood donors but reduced Treg levels associated with WNV disease [45] . Furthermore , these studies revealed that the conditional depletion of Treg cells in mice results in enhancement of WNV virulence and expansion of antigen-specific CD8 T cells . Interestingly , from our studies , type I IFN does not appear to be the major contributor to Treg expansion during WNV infection , as Tregs failed to expand in the IPS-1−/− infected mice despite their elevated levels of systemic type IFN . These observations suggest that RLR signaling through IPS-1 provides essential signals that directly or indirectly impart the expansion of Tregs during WNV infection . We propose that IPS-1 coordinates an innate/adaptive immune interface wherein IPS-1- signaling after RLR engagement regulates the quantity , quality , and balance of the subsequent immune response . The integrity of the innate/adaptive immune interface is central to the eliminating virus but also restricting immunopathogenesis and inflammation during infection . RLR signaling is essential for triggering the innate immune response to RNA viruses that cause human disease , including the influenza viruses , respiratory syncytial virus and other paramyxoviruses , picornaviruses , reoviruses , flaviviruses , and hepatitis C virus [14] , [19] , [22] . Thus , in addition to WNV , IPS-1-dependent RLR signaling will likely have a broad impact for the control of inflammation , immune response quality , and viral disease .
BHK21 and L929 cells were maintained in Dulbecco's modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) , 2mM L-glutamine , 1 mM sodium pyruvate , antibiotic-antimycotic solution , and 1× nonessential amino acids ( complete DMEM ) . WNV strain TX 2002-HC ( WN-TX ) was isolated by as previously described [11] . Working stocks of WN-TX were generated by a single round of amplification on Vero-E6 ( ccl-81; ATCC ) cells , and supernatants were collected , aliquoted , and stored at −80°C . Virus stocks were titered by a standard plaque assay on BHK21 cells as previously described [40] . IPS-1−/− ( C57BL/6×129Sv/Ev ) and their wild type littermate control mice have been published [38] , [61] and were obtained as a generous gift from Dr . S . Akira ( Osaka University , Osaka , Japan ) . Mice were genotyped and bred under pathogen-free conditions in the animal facility at the University of Washington . Experiments were performed with approval from the University of Washington Institutional Animal Care and Use Committee . The methods for mice use and care were performed in accordance with the University of Washington Institutional Animal Care and Use Committee guidelines . Age-matched six to twelve week old mice were inoculated subcutaneously ( s . c . ) in the left rear footpad with 100 PFU of WN-TX in a 10 µl inoculum diluted in Hanks balanced salt solution ( HBSS ) supplemented with 1% heat-inactivated FBS . Mice were monitored daily for morbidity and mortality . For in vivo virus replication studies , infected mice were euthanized , bled , and perfused with 20 ml of phosphate-buffered saline ( PBS ) . Whole brain , spinal cord , kidney , and spleen were removed , weighed , homogenized in 500ul of PBS , and titered by plaque assay . Bone-marrow derived DC and Mφ were generated as described previously [9] . Briefly , bone marrow cells from wild type and congenic deficient mice were isolated and cultured for 7 days in either RPMI-1640 supplemented with granulocyte-macrophage-colony stimulating factor , and interleukin-4 ( Peprotech ) to generate myeloid DC or in DMEM supplemented with macrophage colony stimulating factor ( Peprotech ) to generate Mφ . On day 7 , DC or Mφ were infected with WN-TX at an MOI of 1 . 0 and at 12 , 24 , 36 , and 48 hours post-infection ( hpi ) , supernatants were collected for titration of viral burden by plaque assay on BHK21 cells and levels of IFN-β ( described below ) . Cells were collected in parallel for western blot analysis . Cortical neurons were isolated from 15-day-old embryonic mice and cultured as described previously [62] . On day 6 of culture , neurons were infected with WN-TX at an MOI of 1 . 0 and at 12 , 24 , 36 , and 48 hpi , supernatants were collected for virus titration by plaque assay on BHK21 cells and cells were collected for RNA analysis by RT-qPCR ( described below ) . Cells were lysed in modified RIPA buffer ( 10mM Tris [pH 7 . 5] , 150mM NaCl , 0 . 5% sodium deoxycholate , and 1% Triton X-100 ) supplemented with protease inhibitor cocktail ( Sigma ) and phosphatase inhibitor cocktail II ( Calbiochem ) . Protein extracts ( 25 µg ) were analyzed by immunoblotting as described previously [11] . The following primary antibodies were used to probe blots: mouse anti-WNV from the Center for Disease Control; rabbit anti-ISG56 , rabbit anti-ISG54 , rabbit anti-ISG49 , kindly provided by Dr . G . Sen; mouse anti-PKR from Santa Cruz; rabbit anti-RIG-I and rabbit anti-MDA5 from IBL; mouse anti-tubulin from Sigma; and rabbit anti-STAT-1 from Cell signaling . Secondary antibodies included peroxidase-conjugated goat anti-rabbit , goat anti-mouse , donkey anti-rabbit , and donkey anti-mouse were from Jackson Immunoresearch . For analysis of viremia , serum was separated ( BD Microtainer tube SST ) and RNA was extracted as previously described [8] . WNV RNA copy number was measured by RT-quantitative PCR ( RT-qPCR ) as previously described [63] . For cultured cells , total RNA was extracted using the RNeasy kit ( Qiagen ) , DNase treated ( Ambion ) and evaluated for ISG49 , ISG56 , IFN-β , RIG-I , and MDA5 RNA expression by one-step SYBR Green RT-qPCR . Specific primer sets for ISG-49 , ISG-56 , RIG-I , and IFN-β have been described previously [30] , [64] . Primer sets for MDA5 are: 5′-GTGGTCGAGCCAGAGCTGAT and 3′- TGTCTCATGTTCGATAACTCCTGAA . IFN-α and -β were measured in sera using a biological assay as previously described [65] . Briefly , L929 cells were seeded at 3×104 cells/well in a 96 well plate one day prior to the addition of interferon standards or experimental samples . Mouse sera ( diluted 1∶10 in L929 media ) were treated with UV light for 20 minutes to eliminate residual virus . Duplicate sera samples were then added to the 96-well plates in two-fold dilutions along with a murine IFN-β standard . The following day , EMCV challenge virus was added to the cells in 50 µl/well at an MOI of 5 . 0 . Twenty-four hours later , cytopathic effect was measured by a blinded scorer and IFN levels in the sera was calculated based on the IFN standard . IFN-β in cell culture supernatants was analyzed using mouse-specific ELISA kits from PBL Biomedical Laboratories according to the manufacturer's protocol . WNV-specific IgM , total IgG , IgG1 , and IgG2a levels were determined by an ELISA using purified recombinant E protein as previously described [55] . The neutralization titer of serum antibody was determined by using a previously described plaque reduction neutralization assay [40] . Briefly , sera samples from mock or WN-TX infected mice were diluted in DMEM followed by incubation at 56°C for 30 minutes to inactivate virus and complement factors . Sera were further diluted in two-fold increments and incubated with 100 PFU of WN-TX at 37°C for 1 hour . Standard plaque assays were performed on BHK21 cells and the dilution at which 50% of plaques were neutralized was determined by comparing the number of plaques formed from WNV-infected sera samples to mock infected sera samples . WNV infected sera were analyzed for the presence and levels of TNF-α , IFN-γ , CXCL10 ( IP-10 ) , and IL-6 by a mouse-specific cytokine/chemokine Milliplex ELISA ( Millipore ) . Mock-infected or WNV-infected mice were exsanguinated and perfused with PBS , 4% paraformaldehyde , pH 7 . 3 . Brains were embedded in paraffin and 10-µm sections were prepared and stained with hematoxylin and eosin ( H&E ) by the UW histology pathology laboratory . Sections were analyzed using a Nikon Eclipse E600 microscope ( UW Keck microscope facility ) . Draining lymph nodes from mice were isolated and digested with collagenase ( Roche ) and type I DNase in serum-free RPMI media at 37°C for 40 minutes with mechanical disruption . Cells were then incubated with RPMI media containing 10% FBS with EDTA and HEPES for 10 minutes at room temperature , pelleted , and resuspended in PBS containing 2% FBS and 0 . 1% sodium azide ( FACS Staining buffer ) . Splenocytes were isolated , washed , and re-suspended in RPMI 1640 containing 10% FBS before in vitro stimulation . Cells were washed twice before FACS staining . For isolation of CNS immune cells , mice were euthanized and perfused extensively with PBS to remove residual intravascular leukocytes . Brains and spinal cords from 5 mice per experimental group were isolated and pooled . Tissues were minced in RPMI media , triturated , and digested with Liberase ( Roche ) and type I DNase in serum-free RPMI media at 37°C for 45 min . Immune cells were isolated after gradient centrifugation from a 37/70% Percoll interface and washed twice with FACS staining buffer . Immune cells were stained with antibodies specific to CD11c , CD11b , B220 , CD3 , CD25 , CD4 , CD8 , NK1 . 1 , Gr-1 , siglec H , and CD45 ( all reagents from eBiosciences ) . Intracellular FoxP3 staining was performed as previously described [26] . Intracellular IFN-γ staining was performed on splenocytes and CNS immune cells as previous described [35] , [36] . Briefly , lymphocytes were stimulated with 1 µg/ml of the WNV NS4B peptide ( SSVWNATTAI ) for 4 h at 37°C . Cells were washed and stained for cell surface markers followed by permeabilization-fixation using the Cytofix-Cytoperm Kit ( BD-PharMingen ) and stained with a Pacific-Blue conjugated IFN-γ antibody ( eBiosciences ) at 4°C for 30 min , washed and analyzed by flow cytometry . Flow cytometry was performed on a BD LSRII machine using BD FACSDiva software . Cell analysis was performed on FlowJo ( v . 8 . 7 . 2 ) software . For in vitro studies and immune cell analysis an unpaired student T-test was used to determine statistical differences . For in vivo viral burden analysis , Mann-Whitney analysis was used to determine statistical differences . Kaplan-Meier survival curves were analyzed by the log-rank test . A p-value≤0 . 05 was considered significant . All data were analyzed using Prism software ( GraphPad Prism5 ) . | West Nile virus ( WNV ) is a mosquito-transmitted RNA virus that has emerged in the Western hemisphere and is now the leading cause of arboviral encephalitis in the United States . However , the virus/host interface that controls WNV pathogenesis is not well understood . Previous studies have established that the innate immune response and interferon ( IFN ) defenses are essential for controlling virus replication and dissemination . In this study , we assessed the importance of the RIG-I like receptor ( RLR ) signaling pathway in WNV pathogenesis through analysis of mice lacking IPS-1 , the central adaptor molecule of RLR signaling . Our studies revealed that IPS-1 is essential for protection against WNV infection and that it regulates processes that control virus replication and triggering of innate immune defenses . We found that IPS-1 plays an important role in establishing adaptive immunity through an innate/adaptive interface that elicits effective antibody responses and controls the expansion of regulatory T cells . Thus , RLRs are essential for pathogen recognition of WNV infection and their signaling programs help orchestrate immune response maturation , regulation of inflammation , and immune homeostasis that define the outcome of WNV infection . |
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Dendrite morphology , a neuron's anatomical fingerprint , is a neuroscientist's asset in unveiling organizational principles in the brain . However , the genetic program encoding the morphological identity of a single dendrite remains a mystery . In order to obtain a formal understanding of dendritic branching , we studied distributions of morphological parameters in a group of four individually identifiable neurons of the fly visual system . We found that parameters relating to the branching topology were similar throughout all cells . Only parameters relating to the area covered by the dendrite were cell type specific . With these areas , artificial dendrites were grown based on optimization principles minimizing the amount of wiring and maximizing synaptic democracy . Although the same branching rule was used for all cells , this yielded dendritic structures virtually indistinguishable from their real counterparts . From these principles we derived a fully-automated model-based neuron reconstruction procedure validating the artificial branching rule . In conclusion , we suggest that the genetic program implementing neuronal branching could be constant in all cells whereas the one responsible for the dendrite spanning field should be cell specific .
Dendrite morphology is the most prominent feature of nerve cells , typically used by neuroanatomists to discriminate and classify them [1] . These tree-like ramifications represent the input region of the neurons and fulfil the role of a complex computational unit [2]–[5] . Typically , dendritic arborizations are analyzed in a descriptive way , e . g . by enumerating local and global branching parameters [6]–[8] . Very little is known about the general rule leading to their distinct appearance partly due to the wide variety among different neurons . In insects , same neurons across individuals are rather invariant in their anatomy and constant in their function . Lobula plate tangential cells ( LPTCs ) of the fly visual system [9] are uniquely identifiable and are therefore ideal subjects for investigating the basic rule constraining dendrite formation . They integrate local motion information over an array of retinotopically arranged columnar elements [10] . Accordingly , their planar dendritic trees cover the area corresponding to their distinct primary receptive fields . In this paper we isolate potential fundamental principles which may lead to the morphological identity of individual LPTCs .
We studied inter-individual constancy and variability in four members of the LPTC group: the equatorial and the northern cell of the horizontal system ( HSE and HSN , Figure 1A ) , and two members of the vertical system ( VS2 and VS4 , Figure 1B ) , each of them represented by at least ten individuals from different flies . Two-photon image stacks were acquired from cells filled with fluorescent dye in the living blowfly , Calliphora vicina . Subsequently , the anatomy of each neuron was manually traced and described by a set of connected cylinders ( see detailed explanation on the reconstruction procedures in the Methods section ) . The idea was , in concordance with previous publications [6]–[8] , to use statistical distributions over morphological parameters thereby isolating key features of dendritic branching . Next to classical branching parameters on the “topological points” ( branching and termination points in the tree ) such as branching order and path lengths to the root , we parameterized the area covered by the dendritic tree , the so-called “dendrite spanning field” [11] . We defined spanning field by drawing a contour around the dendrite at a distance of 25 µm after orienting the reconstructed neuron along its axonal axis ( Figure 1C and 1D ) . Regarding branching-specific statistics ( Figure 1E–K ) , qualitative distinction was possible only by detailed examination of distributions of topological point density , path length to the root and branch order . Ratios between direct and path distances of the root ( Figure 1F ) followed a narrow distribution close to 1 in all cases for all topological points . Path length histograms ( Figure 1E ) therefore corresponded to the Sholl intersection diagram ( Figure 1L ) , a measure typically used to describe branching topology . On the other hand , parameters relating to the spanning field plainly reflected cell type specific differences: All four cells could be readily discriminated by eye by their relative position and the shape of their dendrite spanning fields ( Figure 1C and 1D , parameters see Figure 1M–R ) . Those differences were in conformity with the respective primary receptive field locations in the retinotopic arrangement . HS and VS spanning fields were easily distinguished by either their convexity index ( Figure 1O ) or the ratio of width against height ( Figure 1P ) . Finer differentiation of HSE against HSN and VS2 against VS4 was provided directly by their relative location to the axonal axis ( Figure 1N ) , and accordingly by their centre of mass ( Figure 1Q and 1R ) . We investigated the descriptive power of spanning field parameters versus branching parameters in a quantitative way ( Figure 2 ) . Spanning field related parameters readily grouped individual cells into their respective cell types as shown simply by plotting convexity index values against the contextual relative location off the axonal axis ( Figure 2A ) . On the other hand , even a highly-dimensional clustering analysis on the basis of parameterized shape fits of the distributions in Figure 1E–K ( see Figure S2 ) or subsets of these did not allow the separation of the real cells into their respective groups . Best clustering was obtained using path length , density and branching order distributions which separated HS from VS cells but not the members of the two families ( Figure 2B ) . In accordance to these findings we postulated that if the spanning area best determines neuronal appearance , the particularities in branching parameter distributions might be merely a consequence of the neuronal target zone . In order to identify the critical impact of spanning field shape on branching parameters , artificial dendrites were constructed covering the same region . Inside the contours of the original cells , random points were distributed following their respective density map . An iterative greedy algorithm was launched starting at the coordinates of the real dendrite root . At each step , a connection was added from the existing tree to one of the unconnected random points according to a cost function which kept house of both total amount of wiring and total path length from the root to each point [12] . The number of random points was set to match the resulting number of topological points with the original dendrites . Improved appearance and overall path distance to the root was achieved by a subsequent smoothing step along primary branches ( see Methods section ) . This resulted in artificial dendrites confined to the same area as the corresponding in-vivo dendrite reconstructions which were virtually indistinguishable from their real counterpart ( Figure 3A; see Figure S3 for a full overview and Video S1 depicting the constructing process ) . Interestingly , artificial dendrites also yielded quantitatively similar parameter distributions in all cases ( Figure 3B , compare with Figure 1E–L ) . The exact same branching rule can therefore account for all individual morphologies after constraining the spanning field shape alone . Consequently , one could consider that original raw fluorescent images containing a labelled neuron would correspond to a distribution of interconnected points within a spanning field . Then , if our assumptions about the branching rule are correct , one should be able to apply it to obtain the branching model directly from the image material . We therefore applied the same greedy algorithm describing our branching rule for artificial dendrites on structural points extracted from the raw data via image skeletonization . The results of such an attempt are shown at the examples of an HSE dendrite ( Figure 4A and 4B ) and a full VS2 cell ( Figure 4C and 4D ) . Faithful cylinder models of almost all branches could be retrieved in a fully automatic way from the image material after simply assigning manually a starting location at the dendrite root ( see detailed description of the procedure in the Methods section ) .
In summary , we claim that all cells analysed here follow the same branching rule , and that their morphological identifier is the shape of their dendrite spanning fields . This claim is supported by the presented branching statistics , the previously proposed branching rule [12] and its reapplication in a heuristic reconstruction algorithm . Early approaches to describing and reconstructing dendrite branching in general had failed to take into account a major functional constraint governing dendrites: their need to reach specific input locations . More recent attempts to constructing dendrite morphology in relation to their function and the location of their inputs had led to dendrite structures of low complexity and accuracy in spite of high computational costs [13] , [14] . However , circuitry and connectivity as well as simple wire packing issues are known to be determinants of dendrite morphology [15] , [16] . In addition , the specific organization and architecture of many parts of the brain helps to reduce wiring costs for the circuitry [17] , [18] . It is therefore not surprising that such constraints can be used to describe dendrite branching in LPTCs and other cells . Other planar space-filling cells , the cerebellar Purkinje cells , certainly follow a similar rule [19] . However , the suggested approach is not restricted to planar dendrites and future analysis will cover all different neuron arborizations to clarify the ubiquity of the suggested branching rule . At the example of LPTCs , the usefulness of the approach presented here can be put forward: LPTC electrophysiology was studied in great depth e . g . [20] and precise models , so-called compartmental models , including the detailed anatomical structure were designed and are continuously being improved [21]–[24] . Understanding LPTC branching , these constraints can be directly put in relation with the optic flow processing occurring within their circuitry [20] , [23] . Assuming generality of principles , even the function of cells , which have not yet been reconstructed , can be inferred based on the contours of their dendrites alone . Moreover , the fly is the model animal in which the molecular components that determine neural growth are currently being unveiled , mainly through genetic tools [25] , [26] . Our framework therefore allows a quantitative study of the impact of gene modifications far beyond basic statistics . In particular , molecular principles guiding neuronal self-avoidance during development [27] and others can now be put in relation with the branching constraints presented here . Eventually , studying molecular factors shaping dendritic spanning fields separately from a specific branching rule within should elucidate a fundamental organizational element in the brain , i . e . the neuron's branching structure .
Female blowflies ( C . vicina ) were dissected as described in [28] . In each subject either one or two different HS or VS cells were filled with a fluorescent dye ( Alexa 488 ) . Flies were viewed under a custom built two-photon microscope [29] , orienting the planar cells as orthogonal as possible in respect to the laser beam to minimize the amount of images in the Z-direction . In order to capture the entire expansion of the cells , 6 to 15 adjacent stacks ( 210 µm×210 µm area in XY x ∼30 in 2 µm Z-steps ) were taken from different XYZ positions with an overlap of about 10 percent ( Figure S1A ) . The image stacks were then transferred to Matlab ( Mathworks , Natick , MA ) and all further analysis was performed there in custom written software . Manual fine tuning of the original coordinates from the individual stacks was usually necessary to obtain a precise alignment in three dimensions . Maps of maximum intensity and corresponding depth were computed along the Z-axis . This reduction from 3D-data to two 2D images was sensible as there were no or very few 3D crossings of branches and all cells were planar . Based on these images cylinder models of the branching structure were obtained in a semi-automated way: interactive software allowed switched viewing of either Z-projection or an individual slice of an image stack ( Figure S1B ) . The widths of 2D rectangles connecting the end points were fitted by gauss functions to suggest a diameter for the cylinders ( Figure S1D ) . Z-values were attributed to each cylinder directly from the depth-map according to their 2D location . Quick tracing results ( 30 min ) were achievable working with maximum Z-projections alone , although slight movements of the living fly compromised the accuracy of the projection image ( Figure S1C ) . In order to achieve a higher accuracy , some manual corrections based on individual slices were necessary in all reconstruction steps . Taking advantage of the planar cell morphology allowed quicker reconstructions compared to other approaches [30]: detailed cell models with about 700 to 1600 compartments were obtained typically within around 2 hours . Jumps in the Z-axis were smoothed by use of linear interpolation . Internally and externally , the models were stored in the SWC format [31] . The reconstructions can be downloaded at: ( http://www . neuro . mpg . de/english/rd/scn/research/ModelFly_Project/downloads/ ) For simplification , the resulting generic directed graphs were transformed into strict binary trees by substituting multifurcations with several bifurcations after minimally shifting the branches on their parent cylinder . Region indices [32] ( soma ( 1 ) , axon ( 2 ) or dendrite ( 3 ) ) were manually attributed written to the SWC file . The somata in all cells consisted of a clearly separated bag-like structure that branched from the axon or dendrite . The last branch point ( very short branches were ignored ) before the soma was chosen to be the end of the dendrite and the beginning of the axon . The dendrite root was set to the primary branching point . Axonal parameters showed no trend to classify the cells ( data not shown ) . There was no obvious correlation between axonal and dendrite length measures . Hence , size normalization of the cells was discarded . Dendrite flattening was performed as a morphometric transform [33] ( Figure S1E ) . A distance isoline to any point on the dendrite was drawn at a 25 µm threshold to determine the dendrite spanning fields ( Figure S1F ) . This corresponds to performing a morphological dilation on the same points with a 25 µm radius disc . For most statistics , only the branching and termination points ( = topological points ) were selected as the carrier points for the topological complexity . A Voronoi segmentation was performed on these points in order to express space-filling density distribution ( Figure S1G , used in Figures 1K and 3B ) . The density value therefore describes the area in vicinity of a specific branching or termination point . All LPTC reconstructions were rotated in order for both the dendrite root and the furthest axon terminal tip to lie on the horizontal line building the axonal axis . In order to obtain a measure for the convexity of dendrites , the convex hull was drawn around all dendrite nodes . The surface ratio between the dendrite spanning field ( see above ) and this convex hull was chosen as a characteristic spanning field parameter , the convexity index ( Figure 1O ) . Centre of mass was calculated by taking the mean horizontal and vertical values of the line surrounding the dendrite spanning field ( Figure 1Q and 1R ) . Clustering ( Figure 2 ) was done on the three parameters which enabled a by eye discrimination of VS and HS cells in Figure 1: the branching order , the path length and the density . Their histograms were collapsed to three values ( mean , standard deviation and shape parameter ) by fitting them to a generalized extreme value distribution ( Figure S2 ) . After normalizing to weigh parameters equally , Euclidean distances between the different dendrites in the resulting 9 dimensional parameter space were clustered hierarchically using the single linkage algorithm and displayed as dendrograms . As an alternative , the principal components of the matrix containing the normalized scalar parameters for each tree were obtained and the trees observed in the corresponding reduced dimensionality plot: no better grouping was possible with this method ( data not shown ) . Boundary-corrected density maps of dendrite topological points were derived from real cell dendrites ( Figure S1H–L ) . Random points were distributed according to the obtained density maps ( Figure S1M ) . An extended greedy minimum spanning tree algorithm [12] was applied on these points starting at the root point of the original dendrite ( Figure S1N ) . The number of random points was increased until the resulting number of topological points in the artificial dendrites matched the original dendrites . XY-coordinates of points on longer branches were smoothed by Spline interpolation to result in realistic dendrites ( Figure S1O ) . Similar conclusions would arise if artificial dendrites were constructed on random points distributed entirely homogeneously ( data not shown ) . 3D image stacks from one HSE dendrite and a full VS2 cell were submitted to 2D anisotropic filtering , morphological closure and subsequent brightness level thresholding . After 3D skeletonization and sparsening the carrier points , the remaining points were submitted to the same greedy algorithm ( started at a user defined dendrite root location ) as used for obtaining artificial dendrites Quadratic diameter decay was mapped on the resulting trees [12] ( see Figure S1P ) . | Neural computation has been shown to be heavily dependent not only on the connectivity of single neurons but also on their specific dendritic shape—often used as a key feature for their classification . Still , very little is known about the constraints determining a neuron's morphological identity . In particular , one would like to understand what cells with the same or similar function share anatomically , what renders them different from others , and whether one can formalize this difference objectively . A large number of approaches have been proposed , trying to put dendritic morphology in a parametric frame . A central problem lies in the wide variety and variability of dendritic branching and function even within one narrow cell class . We addressed this problem by investigating functionally and anatomically highly conserved neurons in the fly brain , where each neuron can easily be individually identified in different animals . Our analysis shows that the pattern of dendritic branching is not unique in any particular cell , only the features of the area that the dendrites cover allow a clear classification . This leads to the conclusion that all fly dendrites share the same growth program but a neuron's dendritic field shape , its “anatomical receptive field” , is key to its specific identity . |
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Vascular leakage is one of the salient characteristics of severe dengue . Nonstructural protein 1 ( NS1 ) of dengue virus ( DENV ) can stimulate endothelial cells to secrete endothelial hyperpermeability factor , macrophage migration inhibitory factor ( MIF ) , and the glycocalyx degradation factor heparanase 1 ( HPA-1 ) . However , it is unclear whether MIF is directly involved in NS1-induced glycocalyx degradation . In this study , we observed that among NS1 , MIF and glycocalyx degradation-related molecules , the HPA-1 , metalloproteinase 9 ( MMP-9 ) and syndecan 1 ( CD138 ) serum levels were all increased in dengue patients , and only NS1 and MIF showed a positive correlation with the CD138 level in severe patients . To further characterize and clarify the relationship between MIF and CD138 , we used recombinant NS1 to stimulate human cells in vitro and challenge mice in vivo . Our tabulated results suggested that NS1 stimulation could induce human endothelial cells to secrete HPA-1 and immune cells to secrete MMP-9 , resulting in endothelial glycocalyx degradation and hyperpermeability . Moreover , HPA-1 , MMP-9 , and CD138 secretion after NS1 stimulation was blocked by MIF inhibitors or antibodies both in vitro and in mice . Taken together , these results suggest that MIF directly engages in dengue NS1-induced glycocalyx degradation and that targeting MIF may represent a possible therapeutic approach for preventing dengue-induced vascular leakage .
Dengue virus ( DENV ) is a flavivirus that infects approximately 390 million people and causes 500 , 000 infections requiring hospitalization every year , with an associated mortality rate of 2 . 5% [1] . DENV infection usually causes a flu-like illness , known as dengue fever ( DF ) , which is associated with high-grade fever and joint pain . Most dengue patients recover without hospitalization , but in some cases , patients develop potentially deadly complications called dengue hemorrhagic fever or dengue shock syndrome ( DHF/DSS ) . According to the latest guidelines from the World Health Organization ( WHO ) , dengue severity can be classified into dengue with or without warning signs and severe dengue . One of the main characteristics of DHF/DSS or severe dengue is plasma leakage . The increase in vascular permeability is the primary cause of plasma leakage , which finally causes hypotension and circulatory collapse . Because the mechanism underlying vascular hyperpermeability during DENV infection is not yet fully understood , and no specific approved treatments are available; only supporting treatments , such as fluid therapy , are available . An increase in endothelial permeability is frequently associated with the degradation of the endothelial glycocalyx [2 , 3] . Under normal physiological conditions , the glycocalyx acts as a barrier that controls numerous physiological processes , especially preventing the adhesion of leucocytes and platelets to the vessel walls [4 , 5] . Degradation of the endothelial glycocalyx correlates to several vascular pathologies , including sepsis [6 , 7] . Shedding of the endothelial glycocalyx is related to the activation of a heparan sulfate-specific heparanase , HPA-1 [5 , 8] . Activated HPA-1 enhances shedding of the transmembrane heparan sulfate proteoglycan syndecan-1 ( CD138 ) and elevates the level of CD138 in the bloodstream [7 , 9 , 10] . In addition , matrix metalloproteinases ( MMPs ) are capable of digesting many types of extracellular matrix , including the endothelial glycocalyx [11 , 12] . Glycocalyx degradation is strongly associated with severe plasma leakage in dengue patients [13 , 14] . However , the mechanisms causing glycocalyx degradation during DENV infection are not fully understood . Recently , DENV nonstructural protein 1 ( NS1 ) was found to play an important role in the pathogenesis of DENV-induced vascular leakage [15–17] . In addition , NS1 can induce the expression and activation of HPA-1 , leading to endothelial glycocalyx degradation and hyperpermeability [18] . In our previous study , we found that DENV NS1 can increase vascular permeability through macrophage migration inhibitory factor ( MIF ) -induced autophagy [19] . MIF is a chemokine-like inflammatory cytokine that binds to cell surface receptors ( CD74 and/or CXCR2/4/7 ) and activates downstream signals , such as MAPK/ERK , to modulate inflammatory and immune responses [20–25] . DENV infection can induce MIF secretion [26 , 27] , and the concentration of MIF is positively correlated with dengue severity [28] . Furthermore , DENV infection-induced disease was found to be less severe in MIF knockout ( Mif-/- ) mice than in normal mice [29] . However , it is unclear whether MIF is directly involved in NS1-induced glycocalyx degradation . To address this question , we studied the effects of NS1 on the secretion of MIF , HPA-1 , MMP-9 , and CD138 both in vitro and in vivo . We found that the levels of MIF , HPA-1 , MMP-9 , and CD138 were all increased in the serum of dengue patients . Similar results were found both in vitro and in vivo after recombinant NS1 challenge . Most importantly , the NS1-induced increases in HPA-1 , MMP-9 , and CD138 were all inhibited in the presence of MIF inhibitors or antibodies both in vitro and in vivo , indicating that NS1-induced MIF secretion may play an important role in the pathogenesis of DENV NS1-induced glycocalyx degradation and vascular leakage .
The concentrations of NS1 and glycocalyx degradation-related molecules in serum samples from healthy donors and dengue patients were measured by ELISA . The concentrations of NS1 and HPA-1 were increased in both dengue patients with warning signs and severe dengue patients ( Fig 1A and 1B ) . The concentrations of MMP-9 were also significantly elevated in dengue patients with warning signs but not in severe dengue patients ( Fig 1C ) . The concentrations of CD138 and MIF were significantly elevated in the serum of severe dengue patients compared to dengue patients with warning signs ( Fig 1D and 1E ) . To further elucidate the correlation between CD138 and the other molecules , the serum concentrations of NS1 , MIF , HPA-1 and MMP-9 in severe dengue patients were plotted against the concentration of CD138 ( Fig 2 ) . Only NS1 and MIF showed a positive correlation with CD138 in the sera of severe dengue patients ( Fig 2A and 2B ) . Additionally , the viral load of severe dengue patients did not show a significantly positive correlation with any factor mentioned above ( S1 Fig ) . These results suggest that NS1 and MIF may play important roles in CD138 shedding in severe dengue patients . According to a previous study , NS1 can induce endothelial cells to secrete HPA-1 to disrupt the endothelial glycocalyx , and this disruption is characterized by CD138 shedding [18] . To further investigate the underlying mechanism of NS1-induced HPA-1 secretion , human umbilical vein endothelial cells ( HUVECs ) were stimulated with NS1 for various durations . The results show that CD138 was significantly increased in cell culture medium after 24 h of NS1 treatment ( Fig 3A ) . To confirm that this effect was induced by NS1 , anti-NS1 monoclonal antibody ( mAb ) was used to block the effect of NS1 . Anti-NS1 mAb 2E8 , which can inhibit NS1-induced vascular leakage , was able to inhibit NS1-induced CD138 shedding ( Fig 3B ) [19] . In contrast , another anti-NS1 mAb ( DN5C6 ) , which was used as a negative control , failed to inhibit NS1-induced CD138 shedding from endothelial cells ( Fig 3B ) [19] . NS1 stimulation also increased the active HPA-1 level in endothelial cell lysates , which was abolished by mAb 2E8 but not control mouse IgG ( S2A Fig ) . To confirm that HPA-1 is involved in NS1-induced endothelial hyperpermeability and CD138 shedding , recombinant HPA-1 protein and the HPA-1 inhibitor OGT 2115 were used . Inoculating the mice with native but not heat-denatured recombinant HPA-1 directly induced vascular leakage ( S2B Fig ) . Furthermore , cotreatment with OGT 2115 attenuated NS1-induced endothelial hyperpermeability ( Fig 3C ) and reduced CD138 release to levels similar to those of the phosphate-buffered saline ( PBS ) control in vitro ( Fig 3D ) . In addition to HPA-1 , MIF is also capable of inducing endothelial hyperpermeability [19] . As a result , the MIF concentration in the conditioned medium obtained from NS1-stimulated HUVECs was measured . The result shows that 10 μg/ml NS1 was sufficient to induce MIF secretion ( S3A Fig ) . Furthermore , the conditioned medium obtained from NS1-stimulated HUVECs could induce endothelial hyperpermeability and CD138 shedding after incubation with another HUVEC monolayer ( S3B and S3C Fig ) . To clarify which protein mediates NS1-induced endothelial hyperpermeability and CD138 shedding , MIF-blocking antibodies , the HPA-1 inhibitor OGT 2115 and NS1-blocking antibodies were used . The results show that both the anti-MIF antibodies and OGT 2115 attenuated NS1-stimulated conditioned medium-induced HUVEC hyperpermeability and CD138 shedding ( S3D and S3E Fig ) . Interestingly , the NS1-blocking antibody 2E8 only partially diminished the conditioned medium-induced HUVEC hyperpermeability but not the conditioned medium-induced HUVEC CD138 shedding ( S3D and S3E Fig ) . Cotreatment with MIF inhibitors ( anti-MIF antibodies , ISO-1 , and p425 ) and NS1 also attenuated the NS1-induced HPA-1 secretion ( Fig 3E ) and CD138 shedding of endothelial cells ( Fig 3F ) . In addition , we also visualized the HPA-1 expression , CD138 deposition and sialic acid expression using immunofluorescence with anti-HPA antibodies , anti-CD138 antibodies and wheat germ agglutinin ( WGA ) lectin which can bind to sialic acids and other sugars such as N-acetylglucosamine . As shown in Fig 3G , NS1-induced HPA-1 expression , CD138 deposition and sialic acid degradation could also be rescued by MIF inhibition . However , the HPA-1 inhibitor OGT 2115 failed to affect MIF secretion , suggested that MIF is the upstream effector of HPA-1 ( S3F Fig ) . To further clarify this hypothesis , recombinant MIF was used . The results show that MIF increased CD138 shedding and the active HPA-1 level in HUVECs ( S4A and S4B Fig ) . The increased HPA-1 expression and CD138 deposition after MIF stimulation could also be observed by immunofluorescence ( S4C Fig ) . These results indicate that NS1 can induce the MIF-mediated secretion of active HPA-1 , leading to endothelial glycocalyx degradation . Since MMPs can degrade the endothelial glycocalyx and several MMPs are upregulated during DENV infection [30 , 31] , we speculated that MMPs are involved in NS1-induced glycocalyx degradation . Because a previous study has indicated that an increase in circulating MMP-9 levels is associated with dengue disease severity [32] , we first examined whether NS1 induces MMP-9 secretion . However , we found that NS1 barely induced MMP-9 secretion in HUVECs ( Fig 4A ) . Since MMPs are primarily secreted by leukocytes ( white blood cells , WBCs ) , including neutrophils and monocytes [33] , we tested whether NS1 could induce MMP secretion in human leukocytes , peripheral blood mononuclear cells ( PBMCs ) , and THP-1 human monocytes . We found that NS1 induced MMP-9 secretion in freshly isolated leukocytes after 3 h of stimulation ( Fig 4B ) , an effect that was attenuated by the NS1-blocking antibody 2E8 ( Fig 4C ) . Similarly , NS1 induced phorbol myristate acetate ( PMA ) -activated THP-1 cells to secrete MMP-9 after incubation for 3 h ( Fig 4D ) . However , neither MIF nor MMP-9 secretion was significantly induced in NS1-stimulated PBMCs compared to the controls ( S5A and S5B Fig ) . To obtain the secretion profile of MMPs , we used an MMP antibody array to analyze which MMPs were increased by NS1 in PMA-activated THP-1 cells and leukocytes . The results show that MMP-8 , MMP-9 , and TIMP-1 were increased in the culture medium of NS1-treated THP-1 cells and leukocytes ( Fig 4E ) . To confirm the activity of MMP-9 , cell culture medium from NS1-treated PMA-activated THP-1 cells and leukocytes were analyzed using a gelatin zymography assay , which showed that NS1 induced both THP-1 cells and leukocytes to secrete pro-MMP-9 and activated MMP-9 ( Fig 4F ) . To test whether the NS1-induced MMP-9 secretion of THP-1 cells causes endothelial hyperpermeability , the supernatant from NS1-treated THP-1 cells was incubated with HUVECs , and both permeability and CD138 shedding were examined . The results show that after 3 h of treatment , the supernatant from NS1-treated THP-1 cells increased endothelial permeability ( Fig 5A ) . This phenomenon was attenuated in the presence of the MMP-2/MMP-9 inhibitor SB-3CT and the MMP-9-specific inhibitor MMP-9 inhibitor I ( Fig 5B and 5C ) . The supernatant from untreated or PBS-treated THP-1 cells did not alter endothelial permeability ( Fig 5B and 5C ) . Similarly , the supernatant from NS1-treated THP-1 cells also induced CD138 shedding from HUVECs ( Fig 5D ) , and this effect was diminished by SB-3CT and MMP-9 inhibitor I ( Fig 5E and 5F ) . Similar results were found for the supernatant obtained from NS1-stimulated leukocytes ( S6 Fig ) . The NS1-blocking antibody 2E8 was used to block NS1 remaining in the supernatant , and it did not alter the endothelial permeability induced by the supernatant , showing that the effect of NS1 remaining in the supernatant is negligible ( S6 Fig ) . These results indicate that NS1 can induce MMP-9 secretion in leukocytes , leading to endothelial barrier dysfunction . As MIF is a crucial mediator of NS1-induced vascular leakage and an upstream regulator of MMP-9 [19 , 26 , 34 , 35] , we tested whether NS1-induced MMP-9 secretion in leukocytes is also MIF dependent . First , we wanted to confirm whether NS1 induces the secretion of MIF from leukocytes and THP-1 cells . Since a previous study has shown that NS1 increases the expression of IL-6 and IL-8 in PBMCs [16] , we also measured the concentrations of IL-6 and IL-8 after NS1 stimulation . The secretion of MIF from NS1-stimulated leukocytes steadily accumulated up to 2000 pg/ml ( S7A Fig ) . NS1 could also enhance the secretion of IL-6 and IL-8 from leukocytes , but the concentrations dropped rapidly after 3 h ( S7 B and S7C Fig ) . However , in THP-1 cells , NS1 only enhanced the secretion of MIF , not IL-6 or IL-8 ( S7D–S7F Fig ) . These results suggest that MIF is the major cytokine induced by the DENV NS1 stimulation of leukocytes and monocytes . To clarify whether NS1-induced MMP-9 secretion is mediated by MIF , the MIF inhibitor p425 and MIF short-hairpin RNA ( shRNA ) were used . The ELISA results show that inhibiting MIF with its inhibitor p425 abolished NS1-induced MMP-9 secretion , while p425 alone did not affect MMP-9 secretion ( Fig 6A ) . Next , we used shRNA to knockdown MIF expression in THP-1 cells . Western blot analysis showed that the expression of MIF was diminished by shMIF compared to the shLuc scrambled control ( Fig 6B ) . Furthermore , the knockdown of MIF decreased NS1-induced MMP-9 secretion from THP-1 cells ( Fig 6B ) , and the culture supernatant from shMIF THP-1 cells failed to increase endothelial permeability or CD138 shedding ( Fig 6C and 6D ) . We also knocked down MIF expression in HUVECs and measured the permeability under NS1 stimulation as a comparison . Consistent with our previous study , the knockdown of MIF in HUVECs diminished NS1-induced endothelial hyperpermeability ( S8A and S8B Fig ) . These results suggest that MIF acts on both endothelial cells and leukocytes to mediate NS1-induced endothelial hyperpermeability . To further confirm that NS1 can induce MIF , HPA-1 , MMP-9 and CD138 secretion in vivo , we injected 50 μg of NS1 into the tail veins of mice , and blood samples were collected every 24 h . The concentrations of NS1 , MIF , HPA-1 , and MMP-9 were measured by ELISA . The results show that the peak concentration of NS1 in the plasma of mice after injection was approximately 0 . 75 μg/ml , which falls in the range of NS1 circulating in the bloodstream of DENV-infected patients , estimated as 0 . 01–50 μg/ml [36] . The concentration of circulating NS1 in mice gradually decreased after the injection and was cleared from the plasma after 96 h ( Fig 7A ) . The MIF concentration increased 24 h after the injection , peaked at 72 h , and then dropped to basal levels after 96 h ( Fig 7A ) . The upregulation of HPA-1 occurred later than that of MIF , as it was significantly elevated after 48 h , but it also peaked at 72 h and then dropped to basal levels after 96 h ( Fig 7A ) . The secretion of MMP-9 did not increase until 72 h , and then it returned to basal levels at 96 h ( Fig 7A ) , exhibiting an increase over a relatively short period . To further investigate whether NS1 causes endothelial glycocalyx degradation in mice , the skin tissues of mice after two sequential subcutaneous injections of NS1 were fixed for immunohistochemical ( IHC ) staining . Costaining with the endothelial marker α-SMA revealed CD138 only in the samples with two injections of PBS , E or prM ( Fig 7B ) . After two sequential injections of NS1 , endothelial cells lost their CD138 staining ( Fig 7B ) . In addition , the intraperitoneal injection of NS1 significantly induced HPA-1 , MMP-9 , and CD138 secretion , and coinjection of ISO-1 significantly abolished the secretion of MMP-9 and CD138 but not HPA-1 found by the peritoneal lavage ( Fig 7C–7E ) . Furthermore , the inhibition of MIF and MMP-9 also attenuated NS1-induced vascular leakage in mice ( S9 Fig ) . These results suggest that MMP-9 induced by NS1-stimulated leukocytes may play an important role in endothelial glycocalyx degradation .
In this study , we first observed that the concentrations of NS1 , MIF , HPA-1 , MMP-9 and CD138 in the serum of dengue patients were increased . However , only the concentrations of NS1 and MIF showed a positive correlation with CD138 in severe dengue patients . Next , we showed that the DENV NS1 stimulation of endothelial cells and leukocytes could induce HPA-1 and MMP-9 secretion , respectively , causing endothelial glycocalyx degradation and hyperpermeability . Most importantly , both in vitro and in vivo data showed that dengue NS1-induced HPA-1 and MMP-9 secretion was MIF dependent . Therefore , these results suggest that MIF is a central modulator of both direct and indirect dengue NS1-induced endothelial glycocalyx degradation ( Fig 8 ) . Previously , Puerta-Guardo et al . showed that HPA-1 is involved in NS1-induced glycocalyx degradation and hyperpermeability [18] . However , MMPs were not discussed in the mechanism , even though they are the main enzymes that degrade endothelial glycocalyx [11 , 12] . It is known that DENV infection induces dendritic cells to secrete MMP-9 [31] . In this study , we further demonstrated that the NS1 stimulation of leukocytes but not endothelial cells nor PBMCs could induce MMP-9 secretion . It is known that DENV NS1 can induce neutrophil extracellular traps , which results in the release of tertiary granules containing MMP-9 [37 , 38] . A previous study has also shown that MIF can mediate the secretion of MMP-9 from neutrophils [39] . Since neutrophils are a major population of leukocytes , taken together , these results suggest that NS1-stimulated neutrophils may represent the main contributors to MMP-9 secretion in the blood . Therefore , even though neutrophils are not the primary target of DENV infection [40 , 41] , the secretion of MMP-9 from neutrophils induced by NS1 may also contribute to vascular leakage during DENV infection . Interestingly , although it has been shown in previous studies that the concentrations of MMPs are increased in dengue patients [13 , 32] and MMP-9 upregulation is positively correlated with the disease severity and vascular leakage of dengue [31 , 32 , 42 , 43] , we observed a significant increase in the serum level of MMP-9 only in dengue patients with warning signs , not in severe dengue patients . From the in vivo mouse study , we noticed that the secretion of MMP-9 occurred within a smaller time window than that of HPA-1 in mice after NS1 challenge . Therefore , it is possible that the discrepancy in the MMP-9 level in dengue patients between this and previous studies may be due to differences in the timing of sample collection . Because the specific day post-onset of symptoms that samples were collected was not available in the records of our dengue patients , we could not exclude the possibility of variation arising from different sampling times . Further study monitoring the sequential changes in the serum levels of MMP-9 and other glycocalyx-related molecules along with disease development is required to clarify their roles in dengue pathogenesis . From the results of the MMP antibody array , we also found that MMP-8 and tissue inhibitor of metalloproteinases 1 ( TIMP-1 ) were upregulated by NS1-stimulated leukocytes . TIMP-1 , which is a potent inhibitor of MMPs , can form a complex with pro-MMP-9 at a 1:1 stoichiometric relationship to inhibit its activation [44 , 45] . However , neutrophil elastase can inactivate TIMP-1 in the complex to free pro-MMP-9 , such that it can be activated by MMP-3 [46] . In addition , myeloperoxidase , which is most abundantly expressed by neutrophils , can also inactivate TIMP-1 via generating hypochlorous acid [47] . These possible mechanisms may explain why MMP-9 activity was not abrogated in the presence of TIMP-1 in NS1-stimulated leukocytes . A previous study has shown that NS1 can induce PBMCs to secrete IL-6 and IL-8 via Toll-like receptor 4 ( TLR4 ) , leading to vascular leakage [16] . However , in this study , we found that the secretion of IL-6 and IL-8 dropped rapidly after 3 h of NS1-stimulation in leukocytes ( S7B and S7C Fig ) . In contrast , MIF steadily accumulated in the supernatant of leukocyte cultures after NS1 treatment , and the concentration of MIF was higher than that of IL-6 and IL-8 ( S7 Fig ) . As NS1 needs at least 24 h to induce endothelial glycocalyx degradation ( Fig 3A ) , we speculated that IL-6 and IL-8 are not very involved in NS1-induced endothelial glycocalyx degradation . This speculation is consistent with a recent study performed by Glasner et al . , which found that DENV NS1 does not induce HMEC-1 human endothelial cells to secrete TNF-α , IL-6 or IL-8 and that blocking these cytokines does not affect DENV NS1-induced endothelial hyperpermeability [48] . On the other hand , the same study found that inhibition of HPA-1 prevents DENV NS1-induced endothelial hyperpermeability [48]; however , MIF was not measured . In our previous study and in this study , we demonstrated that NS1 induced HMEC-1 cells or HUMECs to secrete MIF , causing endothelial hyperpermeability [19] . In addition , we further demonstrated that both the secretion of HPA-1 and the shedding of CD138 induced by NS1-stimulation of endothelial cells are mediated by MIF . Due to MIF regulating the secretion of both MMP-9 and HPA-1 and because CD138 shedding was also directly affected by MIF signaling , MIF may be an upstream regulator of DENV NS1-induced glycocalyx degradation . However , the mechanism of how MIF causes HPA-1 and MMP-9 secretion is still unclear . A previous study has shown that MIF induces MMP-9 expression in macrophages via the MAPK pathway [35] . MIF is also known to activate NF-κB signaling through binding to CD74 [49] . Additionally , HPA-1 mRNA expression is elevated in an NF-κB-dependent manner during hypoxia [50] . Therefore , it is possible that MIF contributes to the secretion of HPA-1 and MMP-9 via the MAPK/NF-κB pathway . However , from our in vivo study , we also noticed that NS1-induced MMP-9 secretion and CD138 shedding were significantly attenuated by MIF inhibition , whereas the attenuation of HPA-1 secretion was not as significant . Serum samples from severe dengue patients also showed no linear relationship between the concentrations of MIF and HPA-1 ( S10 Fig ) . These results may suggest that in addition to MIF , other factors may participate in the regulation of HPA-1 secretion in vivo . Taken together , our results suggest that NS1 may contribute to vascular leakage through different mechanisms during DENV infection . DENV NS1 may bind to the TLR4 of leukocytes , inducing the secretion of cytokines and MMPs , or it may directly bind to endothelial cells , inducing the secretion of HPA-1 , both of which can cause glycocalyx degradation and subsequent vascular leakage . Consequently , NS1 may represent an important viral factor that causes vascular leakage and glycocalyx degradation during DENV infection . Indeed , antibodies against NS1 have been shown to be protective against DENV infection in mice [17 , 51 , 52] . Furthermore , MIF may represent the primary host factor that mediates NS1-induced glycocalyx degradation . Studies focusing on the development of neutralizing antibodies or small molecules against MIF may facilitate the development of drugs to prevent or treat severe dengue [53] .
The aim of this study was to clarify the mechanism of DENV infection-induced endothelial glycocalyx degradation . From analyzing clinical samples , we correlated glycocalyx degradation to MIF secretion . By applying the results from other studies , we hypothesized that HPA-1 or MMP-9 was involved in MIF-mediated glycocalyx degradation in dengue . This hypothesis was examined via in vitro experiments , which were carried out by recombinant NS1 stimulation , as it was indicated as an important effector in severe dengue . Since the interaction between different cell types is critical under physiological conditions , we assessed the DENV NS1-induced effects on both endothelial cells and leukocytes . To further elucidate the involvement of MMPs in this mechanism , MMP antibody array and gelatin zymography assays were performed . Subsequently , recombinant NS1 was injected into mice systemically or locally to confirm the involvement of MIF , HPA-1 and MMP-9 in NS1-induced endothelial glycocalyx degradation and hyperpermeability in vivo . All research involving adult participants has been approved by the Institutional Review Board of NCKUH ( IRB #B-ER-104-228 ) . Informed written consent was not obtained from patients because the demographic and clinical information for the patients were delinked prior to analysis . All animal studies were performed in accordance with the Guide for the Care and Use of Laboratory Animals ( The Chinese-Taipei Society of Laboratory Animal Sciences , 2010 ) and were approved by the Institutional Animal Care and Use Committee ( IACUC ) of NCKU under the number IACUC 105018 . In this study , serum samples were collected at Clinical Virology Laboratory of NCKUH from dengue patients in the acute stage ( days 0–7 after illness onset ) of the disease during a DENV outbreak in Tainan , Taiwan , in 2015 [54] . All dengue patient samples were screened via a rapid combo test for NS1 antigen and antibody detection and were assessed by qRT-PCR to quantify the DENV viral load . Patients were categorized as having dengue with warning signs or severe dengue according to the 2009 WHO criteria for dengue severity . The characteristics of these clinical samples are shown in S1 Table . In addition , 26 serum samples from healthy donors were included as the negative control . Two different commercialized recombinant DENV serotype 2 NS1 proteins were used: one was produced from mammalian HEK 293T cells ( The Native Antigen Company , Oxfordshire , UK ) , and the second was produced from drosophila S2 cells ( CTK biotech , San Diego , CA , USA ) . These proteins were tested for endotoxin concentration by the Limulus amebocyte lysate ( LAL ) assay using the LAL Chromogenic Endotoxin Kit ( Thermo Fisher Scientific , Waltham , MA , USA ) and were shown to be endotoxin-free ( <0 . 1 EU/ml ) . NS1 ( 20 μg/ml ) from HEK 293T cells was used for in vitro experiments , and NS1 ( 50 μg/mouse ) from S2 cells was used for the in vivo mice model . DENV E domain III and prM proteins were cloned from DENV serotype 2 ( strain PL046 ) using specific primers ( for E domain III , forward: 5’-CATATGCGTTGCATAGGAATATCAAA-3’ , reverse: 5’-CTCGAGTCCTCTGTCTACCATGGAGT-3’; and for prM , forward: 5’-CATATGTTCCATTTAACCACACGTAACG-3’ , reverse: 5’-CTCGAGTCTTTTCTCTCTTCTGTGTTCT-3’ ) . These proteins were cloned , expressed and purified from E . coli using Sepharose ( GE Healthcare , Chicago , IL , USA ) , chelated with 500 mM cobalt chloride , and then slowly dialyzed against PBS . Human MIF recombinant proteins were produced as previously described [26] . Briefly , human MIF proteins were cloned , expressed in E . coli , and purified by Sepharose ( GE Healthcare ) . Heparan sulfate and thrombin with protease activity were purchased from Sigma-Aldrich ( St . Louis , MO , USA ) and were used in several studies [55 , 56] . HUVECs ( Bioresource Collection and Research Center , Taiwan ) were cultured in EGM-2 ( Lonza , Basel , Switzerland ) , and THP-1 human monocytes ( Bioresource Collection and Research Center , Taiwan ) were cultured in Roswell Park Memorial Institute 1640 Medium ( RPMI 1640; Thermo Fisher Scientific ) . Medium used to grow both cell types was supplemented with 10% fetal bovine serum ( FBS; HyClone Laboratory , Logan , UT , USA ) , and cells were cultured at 37°C in a 5% CO2 atmosphere . Human leukocytes ( WBCs ) were isolated from the whole blood of healthy donors . After collecting the blood into EDTA-containing plasma tubes , the whole blood was centrifuged at 1000 g for 5 min . The buffy coat was then collected and treated with red blood cell lysis buffer ( Sigma-Aldrich , St . Louis , MO , USA ) . After one wash with PBS , the cells were cultured in serum-free RPMI 1640 at 37°C in a 5% CO2 atmosphere . Human PBMCs were isolated from the whole blood of healthy donors using Ficoll-Paque ( Sigma-Aldrich ) according to the manufacturer’s instructions . Briefly , blood was collected into EDTA-containing vacutainers ( BD , Franklin Lakes , NJ ) and transferred to the top layer of Ficoll-Paque . After centrifugation at 2500 g for 30 min , the PBMCs were collected and washed with RPMI 1640 twice , and then cultured in RPMI 1640 containing 10% FBS at 37°C in a 5% CO2 atmosphere . Stable MIF or Luc THP-1 knockdown THP-1 cells were generated as described in a previous study [57] . In brief , lentiviruses were generated from shRNA plasmids ( MIF: TRCN0000056818; Luc: TRCN0000072243; National RNAi Core Facility , Academia Sinica , Taipei , Taiwan ) , and pMD . G and pCMVDR8 . 91 were cotransfected into HEK 293T cells ( American Type Culture Collection , Manassas , VA , USA ) . THP-1 cells were infected with lentivirus and underwent selection in culture medium containing puromycin ( 1 μg/ml , Sigma-Aldrich ) . THP-1 cells were suspended in medium containing 5 ng/ml PMA ( Sigma-Aldrich ) . After 16 h , THP-1 cells were resuspended in fresh medium without PMA and incubated for another 8 h . NS1 ( 20 μg/ml ) was used to stimulate THP-1 cells , human leukocytes and PBMCs , and the resultant culture supernatants were collected at the indicated time points . A Transwell permeability assay was performed as described in a previous study [58] . HUVECs ( 2 x 105 ) were grown on a Transwell insert ( 0 . 4 μm; Corning Life Sciences , Corning , NY , USA ) until a monolayer formed . The upper chambers were reconstituted with 20 μg/ml NS1 , culture supernatant from NS1-activated THP-1 cells , or the inhibitor-containing medium . After 24 h , the upper chambers were reconstituted with 300 μl of serum-free media containing 4 . 5 μl of streptavidin-horseradish peroxidase ( HRP; R&D Systems , Inc . , Minneapolis , MN , USA ) . Next , 20 μl of medium in the lower chamber was collected 5 min after the addition of streptavidin-HRP and was assayed for HRP activity by the addition of 100 μl of 3 , 3' , 5 , 5'-tetramethylbenzidine ( TMB ) substrate ( R&D Systems ) . The color development at 450 nm was measured with a VersaMax microplate reader ( Molecular Devices , Sunnyvale , CA , USA ) . HUVECs were seeded as a monolayer onto a microscope cover glass slide and cultured under different conditions . After treatment for indicated time , the cells were fixed in 2% paraformaldehyde and then blocked with Superblock T20 ( PBS ) blocking buffer ( Thermo Fisher Scientific ) . To measure the integrity of the endothelial glycocalyx and the deposition of CD138 , the expression of sialic acid was stained with wheat germ agglutinin ( WGA ) lectin conjugated to FITC ( WGA-FITC , Genetex ) and the distribution of HPA-1 and CD138 was detected by anti-mouse-CD138 mAb ( BD , Franklin Lakes , NJ , USA ) or rabbit anti-HPA-1 polyclonal antibody ( GeneTex ) . Primary antibodies were incubated with the fixed monolayer overnight at 4°C , followed by incubation with Alexa 488-conjugated goat anti-mouse IgG secondary antibody , Alexa 594-conjugated goat anti-rabbit IgG secondary antibody ( Invitrogen , Carlsbad , CA , USA ) ( 1:500 diluted ) and Hoechst 33342 ( Invitrogen , Carlsbad , CA , USA ) ( 1:3 , 000 diluted ) for 1 h . Images were captured using a confocal microscope ( Olympus FluoView FV1000 , Melville , NY , USA ) . The human MMP antibody array ( Abcam ) was used according to the manufacturer’s instructions . Briefly , array membranes were incubated in equal quantities of the culture supernatant from PBS- or NS1-treated THP-1 cells or NS1-treated leukocytes for 24 h overnight at 4°C . After washing with commercial wash buffer , the membranes were incubated with biotin-conjugated anti-MMP antibodies , followed by HRP-conjugated streptavidin . Bound HRP-conjugated antibodies were detected using the Luminata Crescendo Western HRP substrate ( Merck Millipore , Darmstadt , Germany ) . MMP activity in the culture supernatant was assayed by gelatin zymography using 7 . 5% acrylamide gel containing gelatin [59] . Briefly , the culture supernatant of NS1-treated THP-1 cells or leukocytes was concentrated . Non-heat-concentrated culture medium samples were mixed with nonreducing sample dye and electrophoresed at 120 V for 90 min . The gels were subsequently renatured and developed before being stained with Coomassie blue to reveal the positions of active gelatinases ( clear bands ) against the undigested gelatin substrate in the gel . Mice were obtained from the animal center of NCKU . Before the injection of PBS or recombinant NS1 , blood from 8- to 12-week-old BALB/c mice was collected by orbital sinus sampling with 10% citrate . Next , the mice were intravenously injected with 50 μg of NS1 or 100 μl of PBS . After the intravenous injection , blood from the mice was immediately collected by orbital sinus sampling and every 24 h thereafter until 120 h after the injection . The plasma concentrations of NS1 , MIF , HPA-1 , and MMP were analyzed by ELISA . For the peritoneal challenge , 500 μl of PBS , 50 μg of NS1 , 50 μg of E or 50 μg of prM was injected intraperitoneally . After 24 h , the mice were sacrificed , and the abdominal cavities were washed with 5 ml of PBS . The resultant peritoneal lavage was collected , and the concentrations of MIF , HPA-1 and CD138 were quantified by ELISA . To further confirm that NS1 induced CD138 shedding in endothelial cells in mice , 50 μg of recombinant NS1 , E or prM protein or 50 μl of PBS was subcutaneously injected into 8- to 12-week-old BALB/c mice , followed by a second injection of an equal amount of recombinant proteins or PBS 24 h after the first injection at the same site . The mice were sacrificed 24 h after the second injection . The separated skin tissues were fixed in formalin overnight and embedded in paraffin for the preparation of a series of sections . After paraffin removal and antigen retrieval by citrate buffer , the tissue sections were blocked , and immunohistochemistry was performed using the Mouse/Rabbit HRP Detection System with DAB ( brown ) ( BioTnA Biotech , Kaohsiung , Taiwan ) . Hematoxylin was used as a counterstain . Anti-α-SMA antibody ( Arigo , Hsinchu City , Taiwan ) was used at 1:200 , and anti-CD138 antibody ( BD , Franklin Lakes , NJ ) was used at 1:100 . The resultant images were acquired using phase-contrast microscopy ( Olympus , Tokyo , Japan ) . The concentrations of MIF , HPA-1 , CD138 , MMP-9 , IL-6 and IL-8 in the serum or cell culture medium were measured using human MIF , HPA-1 , CD138 , MMP-9 , IL-6 and IL-8 ELISA kits ( R&D Systems ) following the manufacturer’s instructions . The concentrations of MIF , HPA-1 , MMP-9 and CD138 in the serum or peritoneal lavage fluid of mice were measured using mouse MIF , HPA-1 , MMP-9 and CD138 ELISA kits ( BlueGene Biotech , Shanghai , China ) . NS1 ELISA was carried out using paired anti-NS1 antibodies prepared in our laboratory and was quantified by the addition of 100 μl of 3 , 3' , 5 , 5'-tetramethylbenzidine ( TMB ) substrate ( R&D Systems ) . The patients’ sera data were expressed as the median ± interquartile range and tested if the values come from a Gaussian distribution by using D’Agostino and Pearson omnibus normality test . If the data meet Gaussian distribution , the significance of differences between each groups was analyzied using One-way ANOVA with Tukey’s method . If the data do not meet the assumptions of normality , they were analyzed with a non-parametric test by Kruskal-Wallis test . The in vitro and in vivo data are expressed as the mean ± standard deviation ( SD ) from more than three independent experiments . Student’s t-test was used to analyze the significance of differences between the test and control groups . One-way ANOVA with Kruskal-Wallis comparison test was used to analyze the significance of differences between multiple groups . All data were analyzed by GraphPad Prism 5 software . P values <0 . 05 were considered statistically significant . | DENV NS1 induces endothelial glycocalyx degradation and hyperpermeability via HPA-1 and MMP-9 activation in an MIF-dependent manner . |
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Naïve anti-viral CD8+ T cells ( TCD8+ ) are activated by the presence of peptide-MHC Class I complexes ( pMHC-I ) on the surface of professional antigen presenting cells ( pAPC ) . Increasing the number of pMHC-I in vivo can increase the number of responding TCD8+ . Antigen can be presented directly or indirectly ( cross presentation ) from virus-infected and uninfected cells , respectively . Here we determined the relative importance of these two antigen presenting pathways in mousepox , a natural disease of the mouse caused by the poxvirus , ectromelia ( ECTV ) . We demonstrated that ECTV infected several pAPC types ( macrophages , B cells , and dendritic cells ( DC ) , including DC subsets ) , which directly presented pMHC-I to naïve TCD8+ with similar efficiencies in vitro . We also provided evidence that these same cell-types presented antigen in vivo , as they form contacts with antigen-specific TCD8+ . Importantly , the number of pMHC-I on infected pAPC ( direct presentation ) vastly outnumbered those on uninfected cells ( cross presentation ) , where presentation only occurred in a specialized subset of DC . In addition , prior maturation of DC failed to enhance antigen presentation , but markedly inhibited ECTV infection of DC . These results suggest that direct antigen presentation is the dominant pathway in mice during mousepox . In a broader context , these findings indicate that if a virus infects a pAPC then the presentation by that cell is likely to dominate over cross presentation as the most effective mode of generating large quantities of pMHC-I is on the surface of pAPC that endogenously express antigens . Recent trends in vaccine design have focused upon the introduction of exogenous antigens into the MHC Class I processing pathway ( cross presentation ) in specific pAPC populations . However , use of a pantropic viral vector that targets pAPC to express antigen endogenously likely represents a more effective vaccine strategy than the targeting of exogenous antigen to a limiting pAPC subpopulation .
In the fight against virus invasion , TCD8+ play an essential role by killing virus-infected cells . Activation of these cells by professional antigen presenting cells ( pAPC ) is a vital step in generation of an effective adaptive immune response . pAPC are a heterogeneous population comprised of B cells , macrophages and dendritic cells ( DC ) , and specialized subsets of each of those populations . Numerous studies have examined the abilities of these populations and subpopulations to present pMHC-I following virus infection or immunization [1–6] . These studies have concluded that certain pAPC populations are specialized for particular functions , leading to multiple strategies targeting particular pAPC populations in vaccine design [7] . However , the extent to which pAPC populations provide sufficient pMHC-I for maximal generation of TCD8+ depends on factors such as viral tropism for pAPC populations [8] , interference with pMHC-I processing pathways [9] , or lysis of infected pAPC populations [10] . To date , previous studies have relied upon the semi-quantitative activation of T cells , measured either by initiation of proliferation or acquisition of effector functions such as cytokine production or lytic activity . Each measure of T cell activity is quantitative only in the sense that each T cell has undergone proliferation or displayed effector activity , but these activities are affected by many other factors , including the expression of costimulatory and adhesion molecules by TCD8+ or pAPC , the cytokine milieu and/or modulation of each of these factors by virus infection or pre-activation of the pAPC by other inflammatory stimuli [11] . Here we have quantitatively examined antigen presentation following infection with a poxvirus , the natural mouse pathogen ectromelia virus ( ECTV ) , which is pantropic for all pAPC populations examined . Our system allowed us to differentiate between presentation of endogenously synthesized antigen by multiple populations of infected pAPC ( direct presentation ) and presentation of antigen acquired by uninfected pAPC populations ( cross presentation ) . We have demonstrated that presentation of endogenously synthesized antigen results in much higher pMHC-I levels than acquisition of exogenous antigen and that , on a per cell basis , each infected pAPC population produces equivalent pMHC-I levels , irrespective of activation or maturation status . These data have important ramifications for rational vaccine design in that they indicate that a vaccine in which endogenous synthesis of the targeted antigen occurs within multiple pAPC populations is the most effective way to generate the greatest number of effective pMHC-I complexes which , in turn , results in an optimal antigen specific TCD8+ response .
To quantify ECTV infection and subsequent antigen presentation , we utilized a recombinant ECTV virus that encodes a fusion protein ( NP-S-EGFP ) consisting of the influenza nucleoprotein ( NP ) , an enhanced green fluorescent protein ( EGFP ) , and ovalbumin ( OVA ) residues 257–264 ( SIINFEKL ) [12] . This system allows us to identify ECTV-infected and uninfected cells based on the presence and absence of EGFP expression . Following injection with NP-S-EGFP i . d . , draining lymph nodes ( D-LN ) were harvested at 12 h . p . i . from naïve or ECTV-infected mice . A distinct EGFP+ cell population was observed ( Fig 1A ) . ECTV-infected cells were resident in the periphery of the D-LN , just below the sub-capsular sinus by 6 h . p . i ( Fig 1B ) . To assess whether the EGFP+ cells were infected by ECTV and were not uninfected cells that had engulfed dead or dying EGFP+ cells or EGFP+ cellular material , we conducted the following experiment . Splenocytes from C57BL/6 . SJL ( CD45 . 1+ ) mice were infected in vitro with ECTV NP-S-EGFP or wild type ( wt ) ECTV to allow expression of viral antigen and then treated with psoralen and UV-C-crosslinking to abolish further virus replication [13] ( S1A Fig ) . The infected and psoralen/UV treated cells were injected i . v into C57BL/6 ( CD45 . 2+ ) mice , and spleens subsequently assessed for the presence of recipient-derived EGFP+ cells . As a positive control , mice were directly infected i . v with a dose of NP-S-EGFP that was 30-fold lower than the number of infected splenocytes injected . We found EGFP+ cells in mice directly infected with ECTV NP-S-EGFP but not in naïve mice or mice immunized with either WT ECTV or a large excess of NP-S-EGFP-infected cells ( Fig 1C ) . Notably , infection of cells by ECTV in vivo was dependent on virus replication ( S1A Fig ) . These results demonstrate that EGFP+ cells resulted from ECTV infection , and not from internalization of EGFP+ material by uninfected cells . We isolated cells from the D-LN of mice infected with ECTV NP-S-EGFP or NP-EGFP ( which lacks the OVA257-264 SIINFEKL determinant ) 12 h . p . i . and stained with an antibody specific for Kb-SIINFEKL complexes [14] . Cells from mice inoculated with ECTV NP-EGFP did not show staining above background . Infected cells from ECTV NP-S-EGFP-infected mice expressed measurable levels of Kb-SIINFEKL complexes ( Fig 1D ) but none of the uninfected GFP- cells from mice infected with ECTV NP-S-EGFP displayed antibody staining ( Fig 1D ) . To ensure that antigen presentation in infected cells occurred via the conventional endogenous processing pathway , we measured antigen presentation following infection of mice lacking TAP1 , a vital component of this pathway . Mice lacking TAP1 did not display staining for Kb-SIINFEKL complexes above background levels ( Fig 1E ) . Collectively , these results indicate that this infection allows differentiation between virus-infected and uninfected cells in vivo and accurate quantification of specific peptide-MHC complexes on infected cells . To examine the pAPC ( DC , B cells and macrophages ) infected by ECTV , we injected vehicle , NP-EGFP , or NP-S-EGFP i . d . , and harvested D-LN at 24 h . p . i . We stained with a panel of antibodies to identify DC ( CD11c+ CD169- CD19- ) , B cells ( CD19+ CD11c- CD169- B220+ ) , and macrophages ( CD11b+ CD11c- CD19- CD169+ ) ( S1B Fig ) . A kinetic analysis indicated that CD169+ macrophages were the first pAPC to be infected , while CD19+ B cells and CD11c+ DC were infected by 12 h . p . i . ( S2A Fig ) . Therefore , at 24 h . p . i all major populations of pAPC were infected ( S2A Fig ) , allowing us to compare the efficiency of antigen presentation by each pAPC population . We compared the fluorescence produced from antigen-conjugated GFP in each pAPC population ( Fig 2B ) . B cells and macrophages expressed equivalent levels of antigen , but DC expressed significantly more ECTV-encoded antigen on a per cell basis ( Fig 2C , top panel ) . As above , we found that only infected pAPC stained for Kb-SIINFEKL . Staining of uninfected B cells , macrophages and DC was indistinguishable from cells isolated from mice infected with control ECTV-NP-GFP . We found higher levels of Kb-SIINFEKL complexes on the surface of DC than on the surface of B cells , and each was significantly higher than the levels observed on the surface of macrophages ( Fig 2C middle panel ) . The levels of Kb-SIINFEKL complexes increased with time after infection with NP-S-EGFP ( S2B Fig ) . Because DC express more ECTV antigen than B cells or macrophages ( Fig 2C , top panel ) we sought to ascertain the efficiency of antigen presentation in each pAPC population by equalizing for protein expression . Therefore , we calculated the efficiency of direct presentation as a ratio of Kb-SIINFEKL complexes per EGFP protein , which represents the formation of pMHC-I complexes as a function of the levels of the protein antigen from which the complexes were derived . DC and B cells were equally efficient at producing Kb-SIINFEKL complexes while macrophages were significantly less efficient ( Fig 2C , bottom panel ) . Although Kb-SIINFEKL complexes were only detected on the surface of infected pAPC populations , levels below the threshold of detection with the 25 . D1 . 16 antibody might still be capable of TCD8+ stimulation [14] . Therefore , we analyzed the ability of ECTV-infected and uninfected pAPC populations to activate naïve SIINFEKL-specific OT-I TCD8+ [15] . Mice were injected in the footpads with either NP-EGFP or NP-S-EGFP , and EGFP+ or uninfected EGFP- B cells , DC and macrophages were sorted from D-LN cell suspensions . Each cell population was co-cultured separately with naïve OT-I TCD8+ and TCD8+ proliferation was determined at 60 h post-culture . None of the pAPC populations purified from mice infected with control NP-EGFP , activated OT-I TCD8 above background ( Fig 2D ) . Only NP-S-EGFP-infected B cells and macrophages robustly activated naive OT-I TCD8+ , whereas uninfected B cells and macrophages did not stimulate naive OT-I TCD8+ ( Fig 2D ) . Notably , both ECTV-infected and uninfected DC were capable of activating naïve OT-I TCD8+ ( Fig 2D bottom panel ) . Thus , although Kb-SIINFEKL complexes were undetectable with antibody staining on EGFP- DC ( Fig 1D and 2B ) , these uninfected DC appear specialized ( compared to B cells and macrophages ) to express sufficient Kb-SIINFEKL complexes to stimulate the high affinity TCR on OT-I TCD8+ . Although we demonstrated antigen presentation by all infected pAPC populations , it was not clear whether all infected pAPC populations are located at sites at which naïve TCD8+ are activated . Therefore , we visualized the interaction of labeled naïve OT-I TCD8+ with virus-infected pAPC expressing cognate antigen . Recipient mice were injected with either NP-EGFP or NP-S-EGFP i . d . , and at 12 h . p . i ( Figs 3A and 3B ) or 24 h . p . i . ( Figs 3C–3J ) , D-LN were harvested for microscopic analysis . ECTV-infected cells were predominantly located at the periphery of the D-LN just below the sub-capsular sinus at early time points , with a few cells observable in the cortical region ( Figs 3A and 3B ) , as we [16] and others [17] have previously described following infection with the related poxvirus vaccinia virus ( VACV ) . However , in contrast to short–lived VACV infection , where the number of GFP+ cells is reduced over time [16] , following ECTV infection , EGFP+ cells were visualized 300 m from the periphery at 24 h . p . i , and the number of infected cells had increased significantly ( Figs 3C , 3E , 3G and 3I ) , mirroring our flow cytometry analyses ( S2A Fig ) . Notably , in D-LN infected with SIINFEKL-expressing virus ( NP-S-EGFP ) , the OT-I TCD8+ relocated into the peripheral regions of the D-LN ( Fig 3A ) , presumably , to interact with virus-infected cells . However , in D-LN infected with NP-EGFP ( Fig 3B ) , OT-I TCD8+ were restricted to the T cell zone . To determine the interaction of individual pAPC populations with naïve TCD8+ , cryosections were stained with anti-B220 ( B cells ) , anti-CD169 ( macrophages ) , anti-CD11c ( DC ) , or anti-CD103 ( migrating DC ) antibodies and visualized by fluorescence microscopy . As expected , we primarily observed B220+ cells in the B cell follicle region ( Fig 3C ) , CD169+ in the sub-capsular region ( Fig 3E ) , and CD11c+ or CD103+ cells in the cortical region of the D-LN ( Figs 3G and 3I ) . To visualize direct interaction between OT-I TCD8+ and ECTV-infected pAPC , we acquired and analyzed 3-dimensional high power images . When analyzing the images produced we considered that there would not be direct co-localization of cell surface stain with GFP , which is localized within the nucleus as it is attached to NP . In D-LN from mice infected with NP-S-EGFP , we visualized OT-I TCD8+ interacting with EGFP+CD169+ macrophages ( Fig 3F ) , EGFP+CD11c+ DC ( Fig 3H ) , EGFP+CD103+ DC ( Fig 3J ) and , surprisingly , EGFP+B220+ B cells ( Fig 3D ) within 24 h of infection . Therefore , the antigen presentation that we measured in vitro by each pAPC population in Fig 2 has the potential in vivo to induce the activation of naïve TCD8+ . DC are composed of different subpopulations that are proposed to be specialized to perform differing tasks during antigen presentation [5] . Several studies have reported a role for individual DC subsets in MHC class I mediated TCD8+ activation [1–3 , 5 , 6] . However , during a virus infection it is vital to account for viral tropism for individual DC subsets versus functional specialization of DC presenting viral antigen . We focused on the three major DC subsets in lymph node and spleen characterized as: CD8α+ CD11b- B220- ( hereafter CD8α+ DC ) , CD11b+ CD8α - B220- ( hereafter CD11b+ DC ) , and plasmacytoid B220+ CD11b- ( hereafter pDC ) . To determine whether there is specialization in MHC class I presentation by infected DC subsets , mice were injected with NP-EGFP or NP-S-EGFP i . d . , and D-LN were harvested at 24 h . p . i . Cells were stained to identify DC subsets and analyzed by flow cytometry . As NK cells , T cells and B cells share some DC markers and may alter antigen presentation [6] we stained with antibodies to identify NK cells , T cells and B cells , to exclude these lymphoid populations from our analysis . We found that GFP+ cells contained all DC subsets ( Fig 4A ) . We did not observe staining for Kb-SIINFEKL complexes on any uninfected cell population . The number of Kb-SIINFEKL complexes on the surface ( Fig 4B ) and efficiency with which these Kb-SIINFEKL complexes were generated from GFP-tagged antigen ( Fig 4C ) were , surprisingly , equivalent in each DC subset ( Figs 4B and 4C ) . This suggests that all DC subsets are equally capable of presenting endogenous antigen when infected . Because uninfected DC stimulated TCD8+ ( Fig 2D bottom panel ) we asked whether specific uninfected DC subsets were specialized to present antigen . We compared the ability of uninfected and ECTV-infected DC subsets to activate naive OT-I TCD8+ following a footpad injection with NP-S-EGFP . Twenty-four h . p . i . , D-LN cells were FACS-sorted for EGFP+ and EGFP- DC subsets . Isolated DC sub-populations were co-cultured with naïve OT-I TCD8+ , and 60 h later TCD8+ proliferation was determined . Infection with NP-EGFP did not induce proliferation of OT-I TCD8+ ( Fig 4D ) . Infected CD11b+ DC and pDC from mice infected with ECTV-NP-S-EGFP were highly efficient in stimulating naïve OT-I TCD8+ , but uninfected CD11b+ DC and pDC did not significantly prime TCD8+ ( Fig 4D ) . However , both ECTV-infected and uninfected CD8α + DC activated OT-I TCD8+ ( Fig 4D ) , indicating that the TCD8+ activation by uninfected DC in Fig 2D was mediated by cross presentation by CD8α + DC . The inflammatory milieu and expression of costimulatory molecules can also affect the efficiency of TCD8+ stimulation . Therefore , the inability of EGFP- CD11b+ DC and pDC isolated from NP-S-EGFP-infected D-LN to prime naïve OT-I TCD8+ could be attributed to the lack of or lower expression of co-stimulatory molecules , such as CD80 ( B7 . 1 ) and CD86 ( B7 . 2 ) , compared to EGFP+ DC . However , there was no significant difference in expression of CD86 between ECTV-infected and uninfected DC in any of the subsets examined and only minor changes in CD80 expression in the CD11b+ population ( Fig 4E ) . Therefore , ECTV infection of DC does not inhibit maturation and changes in costimulatory molecule expression induced by infection are unlikely to account for the differential ability of uninfected DC subsets to present antigen . Maturation of DC has been reported to enhance antigen presentation , and systemic in vivo activation of DC by TLR agonists such as LPS , CpG-B , and Poly I:C is reported to block cross presentation of viral antigen by uninfected cells [18] . However , TLR ligation inhibited influenza virus infection of DC in vitro [19] and markedly reduced in vivo viral loads following infection with the poxvirus VACV [20] , potentially reducing antigen presentation by infected cells . We asked whether TLR ligation and maturation of pAPC altered infection , antigen production or presentation . As expected , TLR treatment stimulated maturation of DC , following 12 hr CpG-B ( not shown ) or LPS treatment in vivo , as assessed by upregulation of MHC class II ( I-Ab ) , CD40 , CD80 and CD86 ( Fig 5A ) . This 12 hr pre-treatment with TLR ligands also inhibited proliferation of adoptively transferred CFDA-SE labeled OT-I TCD8+ following immunization with presentation incompetent β2m-/- cells that were infected in vitro for 6 hours with either NP-EGFP , NP-S-EGFP or were left uninfected . As β2m-/- cells lack MHC class I and therefore cannot present antigen , this indicates that TCD8+ priming in this system via cross-presentation is inhibited by systemic TLR ligation ( Fig 5B ) . In contrast , the majority of OT-I TCD8+ in untreated mice that received β2m-/- cells infected with NP-S-EGFP proliferated ( Fig 5B ) . Therefore , presentation of ECTV-derived antigen by uninfected pAPC was inhibited by TLR agonist treatment in vivo . We next assessed whether TLR agonists affected ECTV infection of pAPC or direct antigen presentation by infected pAPC . Mice were injected with CpG-B , Poly I:C , or LPS , and then infected with either NP-EGFP or NP-S-EGFP . Presentation of antigen by infected pAPC was quantified 12 h . p . i . by flow cytometry . In vivo treatment with TLR agonists resulted in an approximate 70% reduction in the numbers of ECTV-infected DC ( Fig 5C ) and other pAPC ( not shown ) , indicating that DC maturation dramatically reduces virus infection . This inhibition of ECTV infection of DC extended across all the sub-populations examined ( Fig 5C , top panels ) , but GFP fluorescence in the infected population was not altered by TLR ligation ( not shown ) . Examination of antigen presentation by the remaining 30% of infected DC revealed that infected mature DC were able to directly present antigen with the same efficiency as DC that were not exposed to TLR agonists ( Fig 5C , lower panels ) , suggesting that DC maturation did not enhance direct presentation in vivo . To reconcile our findings with those describing a role for DC maturation in enhanced antigen presentation [21 , 22] , and no effect of TLR ligation upon virus infection in vitro [18] , we isolated DC from mice , treated with LPS or CpG-B for 12 h , and infected with NP-S-EGFP . TLR ligation prior to virus infection did not inhibit ECTV infectivity of DC or DC subsets in vitro ( Fig 5D , top panel ) , regardless of MOI ( Fig 5E , top panel ) . TLR ligation also did not enhance direct antigen presentation ( Fig 5D , bottom panel ) even when DC were infected at various MOI ( Fig 5E , bottom panel ) . However , at the highest MOI , overall direct presentation was significantly lower , presumably due to ECTV-induced cell death ( Fig 5E , bottom panel ) . Our data above indicate that during ECTV infection only CD8α + DC can present antigen when uninfected . To test the importance of this pathway for induction of antigen-specific TCD8+ we infected wild-type or Batf3-/- mice with NP-S-EGFP . Batf3-/- mice lack CD8α + DC and have a significant defect in cross presentation [23] . At 2 d . p . i . no proliferation of adoptively transferred OT-1 TCD8+ was observed in the spleen following infection with either NP-SEGFP or control NP-EGFP ( not shown ) . We did observe proliferation of OT-1 in the D-LN after infection with NP-S-EGFP ( Fig 6A ) , but the proliferation observed was equivalent in wild-type and Batf3-/- mice , indicating that CD8α + DC are dispensable for initiation of an OVA-specific TCD8+ response . To extend our observation beyond an OVA-specific response and beyond the use of the highly sensitive OT-1 TCR TCD8+ we examined the functional activation of TCD8+ specific for native ECTV encoded epitopes within the B8R , M1L , A3L , A8R , and E7R viral proteins . Seven days after infection , the frequency ( Fig 6B ) and numbers ( Fig 6C ) of TCD8+ producing IFN-γ in response to the B8R and A8R epitopes were equivalent in wild-type and Batf3-/- mice . However , responses to the M1L , A3L , and E7R epitopes were reduced in Batf3-/- mice ( Figs 6B and6C ) , indicating that presentation by CD8α + DC may be required for maximal presentation of some determinants .
Vaccines aimed at inducing protective TCD8+ responses have the promise of targeting invariant intracellular proteins that can be used to clear the pathogens encoding the antigens when antibody responses are ineffective . Recent studies have indicated that pAPC , and particularly DC , subpopulations are specialized to induce T cell responses via different antigen presentation pathways . Recent vaccine strategies have specifically targeted exogenous antigen to particular DC populations , often along with ligands known to induce DC maturation , in an attempt to increase the efficacy of TCD8+ priming [7] . However , our work reveals that for vaccines aimed at inducing protective TCD8+ , targeting only individual pAPC populations , particularly with exogenous antigens , may drastically reduce the presentation of peptide-MHC complexes in vivo , irrespective of DC maturation . In particular , our results indicate that the number of peptide MHC complexes generated from endogenous sources dramatically outnumbers those produced from exogenous sources . Indeed , peptide-MHC complexes produced from exogenous sources were below the level of detection using our specific antibody ( >100 complexes per cell [14] ) even when mice were immunized with 3 x 107 infected cells expressing large quantities of viral protein . Therefore it is clear that if a virus infects a pAPC more peptide-MHC complexes are likely to be produced than if these cells remain uninfected , even if targeted exogenously . This finding may have been hidden by the experimental use of mismatched human virus/murine target combinations where virus tropism is diverted away from pAPC , which are often a Trojan Horse when infected that allow transmission of numerous viruses . The use of the ECTV system reveals that during a fulminant natural infection , direct presentation likely predominates during induction of protective TCD8+ . Increasing the number of pMHC-I on the surface of an APC in vitro causes activation induced cell death and allows survival of only low affinity TCD8+ [24] . In contrast , increasing the number of pMHC-I in vivo can increase the number of TCD8+ primed [25 , 26] up to a certain point [27] , and does not reduce the affinity of the responding TCD8+ . Therefore , a vaccine vector that produces a larger number of cell surface pMHC-I will produce more effective TCD8+ . The TCD8+ response to the poxvirus VACV is initiated following antigen presentation by infected APC [16 , 20 , 28] . Here we demonstrate that the number of pMHC-I presented by infected pAPC vastly outnumbers the number of complexes presented by uninfected pAPC , even when the antigen is readily available for presentation by both infected and uninfected cells . Therefore , our findings show that the most efficient way to induce a strong TCD8+ response is to utilize a vaccine in which endogenous expression of antigen within pAPC is optimized . Here we found that uninfected CD8α+ DC were able to present exogenously derived viral antigen . Previous studies have implicated CD8 α+ DC in the presentation of all viral antigen [1] , but these studies may reflect preferential infection of certain DC subpopulations by viruses [29] , or exclusive presentation of exogenous antigen as pAPC are not infected [2 , 3] . In addition , it has been proposed that some pAPC populations are specialized to present peptides on MHC Class I while other populations are specialized to present on MHC Class II [5] . Support for this hypothesis comes from gene array analysis describing a paucity of expression of components of the MHC Class I processing pathway in DC populations that did not present exogenous antigen [5] . Importantly , these studies only examined presentation of exogenous antigen . Virtually all nucleated cells express both MHC Class I and the machinery required to present peptide-MHC complexes derived from endogenous antigens . Specialization of pAPC populations to avoid such presentation would furnish viruses and intracellular bacteria with a location in which they could replicate with relative indifference to the action of the adaptive immune system . Therefore , it is logical that all infected pAPC will present pMHC-I derived from endogenous antigens , and this is indeed what we observe . We examined the relative efficiency of presentation of endogenous antigens to reveal that DC do not appear to be more efficient at presenting endogenous antigens than B cells , although both appear to be better than macrophages ( Fig 2C ) . There is no specialization within DC subpopulations , a pronounced difference from the presentation of exogenous antigens , which CD8α+ DCs are substantially superior at presenting [2] . This lack of specialization by DC populations is at odds with the gene array data indicating differential expression of MHC Class I processing machinery [5 , 30] . However , the supply of antigenic peptide , rather than the expression of any processing components , is limiting in MHC Class I presentation [31] . Therefore , the rate of antigen production and degradation controls the efficiency and amplitude of antigen presentation in infected cells . In the system examined here , DC ( of all subsets ) produce more fluorescent antigen than other pAPC , and so present a higher number of peptide-MHC complexes per cell . Peptides are generated from endogenous short-lived proteins , termed Defective Ribosomal Products ( DRiP ) or Rapidly Degraded Proteins ( RDPs ) [22 , 32] much more efficiently than from long-lived proteins , which are the substrates for cross presentation [33] . DRiP/RDP are unlikely to be correctly folded and therefore may not be fluorescent in our system . Our calculations of the relative efficiency of antigen presentation are made with the assumption that the proportion of newly synthesized protein within the RDP fraction is equal between pAPC populations . There are no publications that indicate the contrary . DC that were ECTV-infected following TLR agonist treatment directly presented antigen at equivalent levels to untreated DC , demonstrating that DC maturation does not enhance antigen presentation and so likely does not affect the supply of antigenic peptide . Systemic TLR ligation did block cross presentation , as previously published [18] , but it also reduced ECTV infection by around 70% demonstrating that , as with VACV infection , TLR ligation fails to differentiate between antigen presentation by infected and uninfected pAPC [20] . Pre-treatment of DC with TLR ligands rendered DC resistant to influenza virus infection in vitro [19] . However , we did not observe a decrease in virus infectivity when DC were treated with TLR agonists in vitro , regardless of MOI . It is possible that this may reflect an overall reduction in DC infectability that is a byproduct of the DC isolation procedure upon infectability with ECTV , but this is unavoidable . Nonetheless , we did not observe an inhibition of infection by TLR treatment in vitro . Thus , systemic TLR ligation may reduce the infectability of pAPC populations via an indirect mechanism , such as the relocalization of DC populations , alteration in virus drainage to reduce cellular exposure to virus , or inhibition of virus replication through induction of innate antiviral pathways . Using current methodology , it has not been possible to differentiate between infection of DC in the periphery or in the D-LN . However , at early time points following ECTV infection i . d , the ECTV-infected cells in the D-LN were found predominantly below the sub-capsular sinus , and phenotypic analysis showed that these infected cells were CD169+ macrophages . Infection of macrophages found within or below the sub-capsular sinus has been previously reported with VACV and vesicular stomatitis virus infection [16 , 17 , 34] . Our kinetic studies of ECTV infection revealed that macrophages were probably the first pAPC to be infected by 6 h . p . i . , while B cells and DC were infected by 12 hours post-ECTV infection ( S2 Fig ) . These findings suggest that virus drained from the site of infection into the D-LN and subsequently infected DC , although we cannot exclude the possibility that ECTV-infected DC migrated from the site of infection into the D-LN at later time points [35] . Although it was expected that only certain infected pAPC populations interact with naïve TCD8+ we readily identified naïve TCD8+ interacting with all of the pAPC populations that are presenting antigen . The interaction of macrophages and DC with TCD8+ during a poxvirus infection has been previously described [16 , 17] . Previous reports also showed that recently triggered antigen-specific TCD8+ relocated to the peripheral regions in an area termed the “peripheral inter-follicular region” [17] . This region was just below the LN sub-capsular sinus , and TCD8+ were shown to interact with DC found in this macrophage-rich region of the LN . Interaction with infected macrophages may induce an intermediate activation phenotype [17] . The rapid decline in GFP+ cells following VACV infection indicates that this non-native virus infection rapidly kills the cells that it infects and inefficiently infects other cells in the D-LN , which contributes to our inability to purify significant numbers of VACV-infected cells [16 , 36] . All ECTV-infected pAPC populations ( including infected B cells ) purified from infected mice were able to trigger in vitro proliferation of naïve TCD8+ , and interact with naïve TCD8+ in vivo . The interaction of infected B cells and naïve TCD8+ observed is surprising , the separation between the T cell zone and the B cell follicle within secondary lymphoid organs is carefully regulated by tightly controlled chemokine gradients . However , poxviruses , including ECTV , encode chemokine-binding proteins [37] that likely alter the balance of local chemokines in infected LN . Such an alteration in local chemokine gradient could allow interaction of TCD8+ with infected B cells . Notably , very few TCD8+ were visualized in the B cell follicles but were mainly distributed in the cortical region and marginal zones of the LN . This suggests that ECTV-infected B cells may have migrated to the inter-follicular regions where they interacted with antigen-specific TCD8+ . Our ongoing efforts seek to understand the impact of ECTV-mediated changes in local chemokine gradients on the role of B cells in induction of ECTV-specific TCD8+ and TCD4+ . Overall , our results are of importance for both vaccine design and to appreciate the basic mechanisms responsible for induction of a TCD8+ response to a fulminant widespread virus infection . In a vaccine the most effective way to induce large numbers of antigen-specific TCD8+ appears to be expression of antigen endogenously within pAPC populations , as the number of peptide-MHC complexes generated from endogenous antigens far exceeds those produced from exogenous sources . Specific DC populations did not display enhanced presentation capabilities , and prior induction of a TCD8+ response did not enhance antigen presentation on a cellular level . Our data indicate that a viral vector that effectively infects multiple pAPC populations and induces an inflammatory state via expression of natural pattern recognition receptor ligands may induce an optimal protective TCD8+ response . In terms of the basic mechanisms responsible for induction of a TCD8+ response it appears that a widespread natural infection may primarily use direct presentation by infected pAPC to prime naïve TCD8+ . The predominance of the use of cross presentation in the literature may be a byproduct of the study of human viruses in the mouse or of viruses that specifically avoid infection , even if unproductive , of pAPC populations .
C57BL/6 mice were purchased from Charles River Laboratories . Beta 2-microglobulin ( β2m-/- ) [38] , OT-I [15] , TAP1-/- [39] were from Jackson and were bred and housed in the specific-pathogen-free animal facility at the Hershey Medical Center . The Penn State College of Medicine Institutional Animal Care and Use Committee approved all studies . Recombinant ECTV ( Moscow strain ) encoded a fusion protein consisting of the influenza virus A/NT60 nucleoprotein ( NP ) affixed to the NH2-terminus of enhanced green fluorescent protein ( EGFP ) [12] . Ovalbumin ( OVA ) residues 257–264 ( SIINFEKL ) were inserted between the NP and EGFP to produce NP-S-EGFP . A control virus that lacks SIINFEKL peptide is denoted as NP-EGFP . Replication of each recombinant virus in vitro and in vivo is similar to wild-type ECTV . Mice were immunized with 106 plaque-forming units ( PFU ) of rECTV intravenously ( i . v . ) , intraperitoneally ( i . p . ) , intradermally ( i . d ) in the ear pinnae , or footpad injection . For in vitro studies , cells were infected with ECTV at a multiplicity of infection ( MOI ) of 0 . 1 , 1 or 10 , depending on the experiment . In vivo , mice were injected i . v . , and in vitro , splenocytes were treated with 15 μg/ml of Escherichia coli 055:B5 lipopolysaccharide ( LPS ) ( Sigma-Aldrich ) , 20 μg/ml of CpG-B oligonucleotides 1826 ( Invivogen ) and 20 μg/ml of Polyinosinic:polycytidylic acid ( Poly I:C ) ( Sigma-Aldrich ) dissolved in phosphate buffered saline ( PBS ) . Spleens and lymph nodes were harvested from OT-I . SJL mice and cells incubated with anti-CD8α beads , and TCD8+ were positively selected using an AutoMACS sorter ( Miltenyi Biotech ) . To assess TCD8+ proliferation , Carboxyfluorescein diacetate , succinimidyl ester ( CFDA-SE ) ( Invitrogen ) labeled OTI . SJL TCD8α+ cells were adoptively transferred into mice on day minus 3 by i . v . injection into the tail vein . On day 3 , TCD8+ cell proliferation was determined by dilution of CFDA-SE fluorescence using flow cytometry . For visualization , TCD8+ were labeled with 5 μM CellTracker Orange CMTMR ( 5- ( and-6 ) - ( 4-chloromethyl ) benzoyl ) amino ) tetramethylrhodamine ( Invitrogen ) and adoptively transferred into mice . Twenty four hours later , the mice were infected with rECTV , and the draining lymph nodes ( D-LN ) were harvested and frozen . Cryostat sections ( 30 μm ) were cut and fixed in 4% paraformaldehyde . Cryostat sections were incubated with Fab donkey anti-mouse IgG ( Jackson ImmunoResearch ) then stained with directly labeled APC-conjugated anti-CD11c ( N418 ) ( eBiosciences ) or Alexa-647 conjugated anti-B220/CD45R ( RA3-6B2 ) ( eBiosciences ) antibodies . Staining with the unlabeled primary antibodies anti-CD103 ( BioLegend ) or anti-CD169 ( 3D6 . 112 ) ( Serotec ) was revealed by staining with Cy-5 conjugated F ( ab ) 2 donkey anti-rat IgG ( Jackson ImmunoResearch ) . Staining was visualized using an Olympus 1X81 deconvolution microscope and Slidebook 5 . 0 digital microscope . Antibodies to the following molecules were purchased from eBioscience unless otherwise stated: MHC class II ( I-Ab ) ( 25-9-17 ) , CD11c ( N418 ) , CD45 . 1 ( A20 ) , CD80 ( 16-10A1 ) , CD45R/B220 ( RA3-6B2 ) , CD19 ( ID3 ) , NK1 . 1 ( PK136 ) , CD90 . 2 ( 53-2 . 1 ) , CD11b ( M1/70 ) , CD8α ( 53-6 . 7 ) , Streptavidin , CD86 ( GL1 ) ( BD Biosciences ) , CD40 ( 3/23 ) ( BD Biosciences ) , CD169 ( 3D6 . 112 ) ( Serotec ) , and 25-D1 . 16 ( grown , purified and labeled in house ) . β2m-/- , STBKM-1 fibroblast cells or C57BL/6 . SJL lymphoid cells were infected with ECTV at an MOI = 10 for 6 hours , then treated with psoralen and UV-C light ( 254 nm ) for 1 hour , as previously described [13] . The mice were then administered LPS i . v . on day 0 , then 12 hours later injected i . p . with UV-treated/gamma-irradiated β2m-/- cells that were infected with NP-EGFP or NP-S-EGFP . Using a MoFlo XDP cell sorter , popliteal lymph node cells were sorted for EGFP+ or EGFP- pAPC: Macrophages ( CD11c-CD19-B220-CD11b+CD169+ ) , B cells ( CD11c-CD11b-CD169-CD19+B220+ ) , DC ( CD19-NK1 . 1-CD90-CD11c+ ) , and DC subsets ( CD8α+B220-CD11b- , CD11b+CD8α-B220- , B220+CD11b-CD8α- ) . Cells were co-cultured with OTI . SJL TCD8+ at 1:8 DC:T cell ratio for 60 hours , then proliferation of OTI . SJL TCD8+ measured by flow cytometry . To prevent T cell infection by ECTV 50 μM Vistide/Cidofovir ( Gilead ) was added . Spleens were harvested from B6 and Batf3-/- mice at 7 days post infection ( d . p . i . ) with ECTV , and cells stimulated for 5 hrs with 1 μg/mL of ECTV-specific peptide ( B8R20-27 ( TSYKFESV ) , M1L424-438 ( KSIIIPFIAYFVLMH ) , A3L270-277 ( KSYNYMLL ) , A8R189-196 ( ITYRFYLI ) and E7R130-137 ( STLNFNNL ) ) or no peptide in the presence of 10 μg/mL of brefeldin A . After stimulation , cells were washed , fixed in 2% paraformaldehyde and permeabilized prior to staining intracellularly for IFN-γ . Net frequencies and numbers of epitope-specific TCD8+ were calculated by subtracting the no peptide background response . | To induce a protective cell type ( CD8+ T cells ) following virus infection , it is necessary to present degraded fragments of viral protein in complex with self molecules on the surface of so-called antigen presenting cells ( APC ) . This process can occur in infected or uninfected APC and has been studied and quantified extensively in experimental setups in the lab . However , the extent to which presentation by infected or uninfected cells contribute to the induction of a protective CD8+ T cell response has not been studied extensively during a natural infection in a mouse model . Here we use a natural mouse virus to examine importantly , quantify , the contribution of presentation of the fragments of viral protein by infected or uninfected cells . We find that the presentation by infected cells dwarfs that seen by uninfected cells . The importance of this work lies in the fact that , if infected cells present way more antigen than uninfected cells , successful vaccine design should utilize this observation to make a vaccine where infected cells expressing virus proteins are the prevalent mode of induction of CD8+ T cells . |
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Many different intestinal parasite species can co-occur in the same population . However , classic diagnostic tools can only frame a particular group of intestinal parasite species . Hence , one or two tests do not suffice to provide a complete picture of infecting parasite species in a given population . The present study investigated intestinal parasitic infections in Beira , Mozambique , i . e . in the informal settlement of Inhamudima . Diagnostic accuracy of five classical microscopy techniques and real-time PCR for the detection of a broad spectrum of parasites was compared . A cross-sectional population-based survey was performed . One stool sample per participant ( n = 303 ) was examined by direct smear , formal-ether concentration ( FEC ) , Kato smear , Baermann method , coproculture and real-time PCR . We found that virtually all people ( 96% ) harbored at least one helminth , and that almost half ( 49% ) harbored three helminths or more . Remarkably , Strongyloides stercoralis infections were widespread with a prevalence of 48% , and Ancylostoma spp . prevalence was higher than that of Necator americanus ( 25% versus 15% ) , the hookworm species that is often assumed to prevail in East-Africa . Among the microscopic techniques , FEC was able to detect the broadest spectrum of parasite species . However , FEC also missed a considerable number of infections , notably S . stercoralis , Schistosoma mansoni and G . intestinalis . PCR outperformed microscopy in terms of sensitivity and range of parasite species detected . We showed intestinal parasites—especially helminths—to be omnipresent in Inhamudima , Beira . However , it is a challenge to achieve high diagnostic sensitivity for all species . Classical techniques such as FEC are useful for the detection of some intestinal helminth species , but they lack sensitivity for other parasite species . PCR can detect intestinal parasites more accurately but is generally not feasible in resource-poor settings , at least not in peripheral labs . Hence , there is a need for a more field-friendly , sensitive approach for on-the-spot diagnosis of parasitic infections .
Intestinal parasitic infections are among the most prevalent infections in humans in low- and middle-income countries . They can be largely categorized into two groups , i . e . helminthic and protozoan infections . Intestinal parasitic infections can cause significant morbidity . Especially children—who are generally more prone to heavy worm burdens—suffer from the sequelae of intestinal parasitic infections , such as diarrhea , malabsorption and anemia [1;2] . The most important intestinal helminths , both in terms of abundance and disease burden , are soil-transmitted helminths ( STHs ) such as hookworms , Ascaris lumbricoides , and Trichuris trichiura [3] . It is estimated that STHs infect more than two billion people or more than a third of the world’s population [4] . Also , the Schistosoma spp . blood flukes are of great public health importance , with more than 250 million people infected worldwide [5;6] , and an estimated global disease burden of 4 . 0 million disability-adjusted life years ( DALYs ) [7] . In comparison to helminth infections , less is known about intestinal protozoan infections . They have been associated with persistent diarrhea in developing countries [8–10] , and can cause severe morbidity , especially in immunocompromised individuals [11] . Hundreds of millions of people may be affected by intestinal protozoa annually [12;13] . Yet , there are no reliable estimates of the global burden of disease [14–16] . This lack of knowledge is due to the fact that intestinal protozoa are difficult to diagnose . Also , some diagnostic techniques cannot distinguish pathogenic from non-pathogenic species ( i . e . Entamoeba histolytica versus the other Entamoeba spp . ) . For some species there is no consensus on their pathogenicity ( e . g . Blastocystis ) , while for others , disease only develops in certain infected individuals but not in all ( e . g . Giardia intestinalis ) . Loss of microscopic skills in many clinical laboratories and the general lack of awareness on protozoon infections further add to these difficulties . The diagnosis of intestinal parasites typically relies on the microscopic detection of egg , larval , trophozoite , cyst , and/or oocyst life stages in human feces samples [17;18] . The sensitivity of stool microscopy is generally low , and for a reliable diagnosis it is important to choose the appropriate microscopic technique [19] . For example , relatively simple techniques such as the direct smear are known to detect high A . lumbricoides loads while underestimating the presence of other helminths such as Schistosoma mansoni [20] . Ideally , the technique with the highest diagnostic accuracy for the parasite of interest should be selected . In practice however , this is difficult to achieve since many different parasite species may occur in a given population , or even in a single individual , and resources are generally limited in countries where most of these infections are endemic , so not all appropriate microscopic techniques can be used . In the past decade , alternative diagnostic procedures have become available , such as the detection of parasite DNA in stool samples using real-time PCR [21] . The disadvantage of PCR , however , is that—in contrast to microscopy—it needs a high-tech laboratory , which is even more of a challenge for diagnostic laboratories within endemic countries . Relatively little is known about the distribution of intestinal parasites in Mozambique [22;23] . The present study was initiated because a local hospital noticed many cases of diarrhea in one of the informal settlements ( ‘bairro’ ) in Beira , Mozambique . Given the sanitary conditions in the study area , intestinal parasites were suspected to be the cause of these complaints . However , diagnostic methods that were being used in the hospital at that time were not adequate to detect these infections . Hence , the aim of this study was 1 ) to investigate which intestinal parasite species are most common in this area , and 2 ) to compare diagnostic accuracy between different microscopic techniques and real-time PCR for these intestinal parasitic infections . Five commonly used microscopic techniques were applied and evaluated , i . e . direct smear , formal-ether concentration ( FEC ) , Kato smear , Baermann method , and coproculture , for the detection of a uniquely broad spectrum of intestinal parasites: from intestinal helminths like Strongyloides stercoralis , Ancylostoma spp . , Necator americanus , A . lumbricoides , T . trichiura and Schistosoma spp . blood flukes , to pathogenic intestinal protozoa such as G . intestinalis , E . histolytica , the coccidium Cystoisospora belli and the microsporidia Enterocytozoon bieneusi and Encephalitozoon spp . Microscopy and real-time PCR results were compared to one another and to composite reference standards ( CRSs ) .
Approval to perform the study was obtained from the Beira Committee of Medical Ethics , Mozambique and the study proposal was filed by the Committee of Medical Ethics of the Leiden University Medical Centre ( reference number CI5 . 151/NV/ib ) . Prior to the study , written informed consent was obtained from the head of participating households . Individuals who were infected according to microscopy were offered treatment following standard clinical practice at the local hospital . Samples were anonymized for further data analysis . The study was performed in Inhamudima ( E34 . 86° , S19 . 84° ) , an informal settlement in the city of Beira , Mozambique , and was conducted on request of the local hospital and faculty of medicine . The area of Inhamudima is frequently flooded and is not connected to a sewage system . The rainy season lasts from October to March . The study was performed between June and August 2007 . A geographical map of this area was prepared and households and roads were annotated . In order to obtain a random and geographically evenly distributed sample of households and a logistically feasible sample size , a grid with 75 x 75 meter quadrants was superimposed on this map and the household that was closest to each of the intersections was selected . In this way , all participants of in total 63 households were approached to participate . In the field , these houses were located using handheld GPS devices . Infants ( younger than one year ) and people who did not provide sufficient fecal material for all procedures were excluded from the study . Fig 1 shows that 303 out of the 399 individuals that had given informed consent provided sufficient fecal material for inclusion into the study ( i . e . participation rate of 76% ) . Initially , urine samples were also collected for detection of Schistosoma haematobium ( by urine filtration on one 10ml urine sample ) . Because of the relatively low numbers of S . haematobium cases however , collection of urine samples was stopped to focus on the diagnosis of intestinal parasites . Fecal samples were collected from all participating household members on a door-to-door basis , 0-18h after production of the samples , and examined in Beira within 24h after collection . Three well trained microscopists performed the laboratory procedures , and on average not more than eight stool samples were processed per day to ensure high quality microscopic results . Multiple approaches were used for the detection of cysts and oocysts of the protozoa , and eggs and larvae of the helminths ( Table 1 ) . Microscopic techniques included direct smear , FEC , Kato thick smear , Baermann method , and charcoal plate coproculture [17] . For the direct smear , ~2mg of feces was mixed with normal saline on a microscopy slide and examined for helminth eggs . Another ~2mg of feces was mixed with a drop of iodine and examined for protozoan cysts [17] . For FEC , the fecal parasite concentrator ( FPC , Evergreen ) was used . One gram of fecal material was thoroughly mixed with 8 ml of 10% formalin . An FPC strainer with 15 ml tube was attached to the tube containing this mixture . After having filtered the suspension into the empty tube , 3 ml of ether was added to the filtrate . This mixture was then shaken vigorously for 1 minute and centrifuged at 500 x g for 2 minutes . A thick , unstained wet mount of the sediment was used for the detection of helminth eggs and larvae . For protozoan cysts , a thin , iodine-stained wet mount of the sediment was used . The Kato smear—also known as Kato-Katz smear—consisted of a single slide of fecal material [18;29;30] . A 25 mg template was placed on the microscopy slide and filled with sieved ( ~300 μm pore size ) fecal material . Upon removal of the template , the sample was covered with a cellophane slip soaked with glycerol and water ( 1:1 ) . The sample was flattened by pressing it onto an even surface , and examined 30–60 minutes after preparation . For the Baermann method , fecal material ( ~4g ) was placed on a layer of 2 hydrophilic gauze bandages . The gauze was folded into a pouch by attaching the four perforated corners of the gauze to a stick . Subsequently , the pouch was placed in a 50 ml tube filled with tap water for 3h in such a way that the pouch lightly touched the water . Most of the water was decanted and the remaining sediment was left to stand for 2 hours before being examined for nematode larvae . For coproculture , the classical charcoal culture procedure was used [31] . Approximately 2g of fecal material was homogenized , mixed 1:1 with vermiculite , and placed on a filter paper on a plastic platform in a petri dish . Tap water was added to wet the filter paper and the petri dish was covered . After incubation at room temperature for 7 days , the water was collected in a tube and left standing for 2h . The sediment was examined for nematode larvae . For the Baermann method as well as for coproculture , two microscopy slides were prepared , each with 100μl of the sediment . A drop of iodine was added if moving larvae were detected , enabling identification and quantification of the larvae . In Beira , an aliquot ( ~1g ) of each stool sample was sieved and mixed with 3 volumes of 96% ethanol for preservation and shipment to Leiden , the Netherlands [32] . Here , the samples were stored at -20°C until detection and quantification of parasite DNA loads by real-time PCR . DNA isolation , amplification and detection were performed blinded to previous microscopic results . For DNA isolation , 250μl of feces suspension was centrifuged and the pellet was washed with phosphate-buffered saline , resuspended in 200μl of 2% polyvinylpolypyrolidone ( Sigma ) and heated for 10 minutes at 100°C [32;33] . After sodiumdodecyl sulfate-proteinase K treatment ( 2h at 55°C ) , DNA was isolated using QIAamp Spin Columns/Mini Kit ( Qiagen , Germany ) . In each sample , a fixed amount of Phocine Herpes Virus 1 was included within the isolation lysis buffer as an internal control [34] . In total , 10 PCR targets were included and 5 μl DNA was used in each real-time PCR . Amplification generally comprised of 15’ at 95°C followed by 50 cycles of 15” at 95°C , 30” at 60°C , and 30” at 72°C . Parasite-specific primers and probes were used for amplification of sequences , according to previously published protocols . Hookworm DNA ( Ancylostoma spp . and N . americanus ) was detected by one multiplex PCR described by Verweij et al . [35] . Ascaris lumbricoides [36] and S . stercoralis [37] DNA was detected in separate PCRs instead of in a multiplex format combined with other helminth targets . Schistosomal DNA was detected in an additional PCR as described by Obeng et al . [38–41] . Protozoa ( E . histolytica , G . intestinalis , and C . parvum/C . hominis ) DNA was detected by multiplex HGC-PCR [42] . Microsporidial ( E . bieneusi and Encephalitozoon spp . ) DNA was detected in another multiplex PCR described by Verweij et al . [43] . Negative and positive control samples were included in each PCR run . The PCR output from this system consisted of a cycle-threshold ( Ct ) value , representing the amplification cycle in which the level of fluorescent signal exceeded the background fluorescence . Hence , low Ct values correspond to high parasite-specific DNA loads in the sample tested , and vice versa . The maximum Ct value was set at 50 indicating that DNA was not detected in the sample after 50 cycles of amplification . The Ct values of the internal Phocine Herpes Virus 1 control were within the expected range ( Ct value between 30 and 33 ) for all samples , indicating that there was no evidence of inhibition of amplification in any of these samples . IBM SPSS 22 . 0 ( IBM Corp . ) and Microsoft Excel 14 . 0 ( Microsoft Corp . ) were used for statistical analyses . GraphPad Prism 5 ( GraphPad Software , Inc . ) was used to prepare graphs . There is no gold standard for the detection of individual intestinal parasite species . Although microscopic techniques are known to lack sensitivity , they are , just as the PCR , supposed to be 100% specific [44] . We therefore combined the results of several diagnostic methods into a composite reference standard ( CRS ) [45;46] . The CRS was defined in such a way that it was negative if none of the diagnostic methods detected the parasite of interest , and positive if one or more methods detected the parasite . Table 1 shows how the CRS was defined for the different parasite species . Infection prevalence was based on the CRS unless stated otherwise , and 95% Wald confidence intervals were calculated for this parameter . For sensitivities of the different diagnostic methods , the Wilson score method without continuity correction was used to calculate 95% confidence intervals [47] . Differences between test sensitivities were considered statistically significant if there was no overlap of their confidence intervals . The independent samples Mann-Whitney U test was used to determine whether differences in Ct values between microscopy-positives and -negatives were statistically significant .
The study population ( n = 303 ) consisted of 144 ( 48% ) males and 159 ( 52% ) females with a median age of 17 years ( range 1 to 72 ) . These people were derived from 63 households . Per household 1 to 11 subjects participated ( median of 4 subjects ) . STH infections were widespread with a prevalence of 93% , 56% , 48% and 38% for T . trichiura , A . lumbricoides , S . stercoralis and hookworm , respectively ( Fig 2A ) . PCR indicated that Ancylostoma spp . was the most abundant hookworm: 25% of the population ( 75/303 ) harbored Ancylostoma spp . while N . americanus was detected in 15% ( 46/303 ) of the population . Mixed Ancylostoma spp . and N . americanus infections were observed in 5% ( 15/303 ) of the population . The prevalence of S . mansoni was 10 . 9% . Within the population of Inhamudima , 96% ( 292/303 ) of individuals were found to harbor at least one of the following helminths in their stool sample: S . stercoralis , Ancylostoma spp . , N . americanus , A . lumbricoides , T . trichiura , S . mansoni , and 49% ( 147/303 ) of the total population harbored three or more different helminth species ( Fig 3 ) . Up to five helminth infections were found in one individual . The prevalence of infections with pathogenic protozoa is shown in Fig 2B . The pathogenic protozoan Cystoisospora belli was not detected in this population . Giardia intestinalis was the most common pathogenic protozoan with a prevalence of 37% ( 113/302 ) . Microsporidia were found in 9% ( 28/301 ) and mainly consisted of E . bieneusi infections ( 27/28 cases ) . Cryptosporidium parvum/C . hominis was found by PCR in 2% ( 6/302 ) of the study population . All showed a Ct value higher than 30 ( median Ct 37 . 6 ) and none of these 6 overlapped with the 3 samples positive in the Ziehl-Neelsen staining ( legend f , Table 1 ) . The prevalence of E . histolytica complex spp . infections was 10% ( 30/303 ) based on microscopy . However , PCR showed that only 10% ( 3/30 ) of these infections involved E . histolytica , i . e . the pathogenic species . Combining the findings of pathogenic protozoa with the detection of helminths , 98% ( 294/300 ) of the tested inhabitants of Inhamudima were found to be infected with at least one intestinal parasite species . The prevalence of non-pathogenic protozoa varied between 4% and 34% for the different species , with a prevalence of 34% ( 102/303 ) for Entamoeba coli , 26% ( 80/303 ) for Endolimax nana , 21% ( 63/302 ) for Blastocystis , 9% ( 26/303 ) for Entamoeba hartmanni , 8% ( 24/303 ) for Chilomastix mesnili and a prevalence of 4% ( 13/303 ) for Iodamoeba bütschlii . Diagnostic sensitivity was estimated for the different microscopic techniques and for each of the parasite species ( Fig 4 ) . FEC and Kato smear had the highest sensitivities for the detection of each of the helminths , except for S . stercoralis . The direct smear was inferior to these two methods for the detection of S . stercoralis , hookworm , T . trichiura and S . mansoni . Similarly , the direct smear was inferior to FEC for the detection of G . intestinalis . The Baermann method and coproculture showed the highest sensitivities for S . stercoralis ( 48% and 57% , respectively ) , while the direct smear and FEC showed equally low sensitivities ( 18% and 25% , respectively ) . The sensitivity of coproculture for the detection of S . stercoralis and hookworm ( 57% and 77% , respectively ) tended to be higher than the sensitivity of the other microscopic techniques ( up to 48% and 69% , respectively ) . Moreover , the sensitivity of helminth detection increased upon combination of multiple microscopic methods . The sensitivity of PCR for the detection of each of the parasite species tested was higher than that of any of the microscopic techniques applied ( Fig 4 ) . This difference was statistically significant for all microscopic methods used for S . stercoralis , hookworm , S . mansoni , and G . intestinalis , and for the direct smear for the detection of A . lumbricoides . For some species , the sensitivity of the best microscopic technique was manifold lower than that of PCR . For example , the estimated sensitivity for the detection of S . mansoni was 12% for Kato smear versus 97% for the detection of Schistosoma DNA in feces via PCR . For G . intestinalis , sensitivity of FEC was 21% while that of PCR was 99% . In addition , in only one of the three PCR-positive E . histolytica samples , E . histolytica complex spp . cysts could be detected by microscopy ( FEC ) . Fig 5 shows that , for each parasite species , PCR-positive but microscopy-negative samples had significantly lower DNA loads ( i . e . higher Ct values ) than PCR-positive samples that were also microscopy-positive . Although observed less frequently , some microscopy-positive samples could not be confirmed by PCR . In most of these samples , only few parasites were detected by microscopy ( see Table 2 ) .
We demonstrate that intestinal helminth and protozoan infections and co-infections are widespread in Inhamudima , Beira . We showed that classical techniques such as FEC are useful for the detection of some intestinal helminths such as hookworm , T . trichiura and A . lumbricoides . However , they lack the sensitivity to reliably characterize the wide range of intestinal parasites that may coexist in a population or individual . PCR can detect intestinal parasites more accurately but in most endemic areas it is not ( yet ) possible to perform this technique , at least not in the more peripheral laboratories . So , until a more field-friendly approach becomes available , infection levels of intestinal parasites—and polyparasitism—are best approximated by combining multiple and relatively simple microscopic techniques . | In populations living in adverse conditions due to poverty , a wide variety of intestinal parasite infections can be observed . These infections are usually diagnosed by stool microscopy , but can be easily missed if the procedures used are inaccurate or performed in a suboptimal way . In the present study , we investigated the prevalence of intestinal parasite infections in an informal settlement in Beira , Mozambique . We also compared the diagnostic performance of five different microscopic techniques and real-time PCR for the detection of DNA of five helminth and five protozoa species . For this purpose , a single stool sample was collected from members of 63 households , evenly distributed within the settlement . Parasitic infections were found to be highly abundant in the 303 examined samples . Virtually all individuals were found to be infected with at least one helminth species and a majority of the stools showed multiple parasites . None of the classical microscopic techniques was suitable to detect all different pathogenic species . Hence , the outcome of several microscopy procedures had to be combined to get a complete picture . We found PCR to be the most accurate diagnostic approach , even in this hyper-endemic setting . However , DNA detection is a high-tech technology , generally not applicable in resource-poor settings . |
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Granulomas are complex lung lesions that are the hallmark of tuberculosis ( TB ) . Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment . Three fluoroquinolones ( FQs ) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin ( MXF ) , levofloxacin ( LVX ) or gatifloxacin ( GFX ) . To date , insufficient data are available to support selection of one FQ over another , or to show that these drugs are clinically equivalent . To predict the efficacy of MXF , LVX and GFX at a single granuloma level , we integrate computational modeling with experimental datasets into a single mechanistic framework , GranSim . GranSim is a hybrid agent-based computational model that simulates granuloma formation and function , FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data . We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load , sterilization rates , early bactericidal activity and efficacy under non-compliance and treatment interruption . GranSim reproduces in vivo plasma pharmacokinetics , spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs . We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio . We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum . This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation . MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario . We conclude that MXF has a small but potentially clinically significant advantage over LVX , as well as LVX over GFX . We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB .
Tuberculosis ( TB ) , caused by Mycobacterium tuberculosis ( Mtb ) , is a global public health threat killing 1 . 5 million people annually [1] . Despite our arsenal of anti-TB antibiotics , effective treatment remains a challenge , requiring at least 6 months of combination therapy with up to four antibiotics . One obstacle to refining TB treatment is complex granuloma structures that develop in patient lungs following infection . Granulomas are dense collections of host immune cells , bacteria and dead host cell debris ( caseum ) ; and can be cellular ( without caseum ) , caseous , fibrotic or suppurative ( containing neutrophils in the core ) [2] . Granulomas isolate Mtb , enhance Mtb replication and provide a potential barrier for antibiotic penetration [3 , 4] . Fluoroquinolones ( FQs ) are a class of antibiotics typically used as second-line agents against multi-drug resistant TB ( MDR-TB ) [5] , or as preventive therapy for MDR-TB contacts [6 , 7] . One of three FQs is used in MDR-TB treatment: moxifloxacin ( MXF ) , levofloxacin ( LVX ) or gatifloxacin ( GFX ) . The choice of one FQ over another is essentially motivated by availability , cost and national guidelines . The WHO recommends use of LVX over MXF , and MXF over GFX [5] . In the absence of comparative clinical trials other than early bactericidal activity [8] , there are not sufficient data to declare that treatment with one FQ results in superior clinical outcome . Identifying the best FQ will require careful study of antibiotic dynamics and activity in granulomas . Recent studies have characterized pharmacokinetic ( PK ) and pharmacodynamic ( PD ) metrics of MXF , LVX and GFX ( Table 1 ) . The variety of mixed and conflicting data make it unclear whether one FQ is optimal . For example , PK metrics alone indicate: LVX and GFX have higher plasma exposure ( area under the concentration curve ( AUC ) ) , and MXF has higher concentrations in epithelial lung fluid or alveolar macrophages [9 , 10] . Examining PD metrics , GFX has lower MIC against intracellular Mtb , MXF and GFX have equivalent MICs against Mtb grown in liquid culture , MXF has higher bactericidal activity compared to LVX , and MXF and GFX can prevent resistance at lower concentrations than LVX [11–14] . According to clinical metrics , LVX has higher early bactericidal activity ( EBA , daily decrease in sputum bacterial burden ) ( day 0 to 2 ) , all three FQs have equivalent extended EBA ( day 2 to 7 ) , and MXF and LVX perform similarly on sputum culture conversion after 3 months , time to sputum culture conversion , and treatment success rate [8 , 15–18] . Based on these existing data it is not clear whether one FQ should be preferred for treatment of TB . The ability of an antibiotic to successfully treat TB depends on complex interactions along its path from dose , to plasma , to granuloma to bacterium [19] . Antibiotic concentrations in the blood determine how much antibiotic is available for distribution into granulomas . Antibiotics diffuse from blood vessels into lung tissue and granulomas , where spatial distribution is affected by uptake into host-cells , binding to caseum , and location of functional blood vessels . Once an antibiotic reaches bacteria , it must penetrate the bacterial cell wall and reach the molecular target in sufficient concentration to kill . Complicating things further is inter-individual host variability in plasma pharmacokinetics and lung pathology ( lesion type ) that must be considered when predicting antibiotic efficacy in TB . Both experimental and computational studies can be useful to identify antibiotic regimens that will effectively treat TB . Experiments can quantify antibiotic concentrations , spatial distributions , in vitro activity and in vivo efficacy in animal models or as part of background regimens in humans ( Table 1 ) . Computational approaches can combine datasets from multiple experimental systems , interpolate between experimental data points , and screen large numbers of treatment regimens more time- and cost-effectively [20–22] . Here we take a systems pharmacology approach , integrating state-of-the-art experimental and computational methods to predict FQ efficacy and compare FQs . We present a spatio-temporal computational model of granuloma formation , function and treatment that is calibrated to an exhaustive experimental dataset . Data include FQ dynamics in blood plasma , spatial and temporal distribution in granulomas , and activity in vitro . Our hybrid computational model , GranSim , tracks events at multiple spatial scales ( molecular , cellular and tissue ) and time scales ( seconds to months ) . Our systems pharmacology approach provides a unique format for predicting , at a single granuloma level , the potential effects of these three different FQs .
To predict and compare FQ efficacy in granulomas we use our mechanistic computational model , GranSim ( Fig 1 ) [23–26] . GranSim is a spatio-temporal model of granuloma formation and function that incorporates macrophage and T cell recruitment , migration and interaction; secretion and diffusion of chemokines and cytokines; Mtb growth and phagocytosis; and caseation . In the context of these in silico granulomas , GranSim simulates antibiotic plasma PK , tissue PK and PD [20–22] . GranSim is implemented as in [20–22] with three updates made in this work: 1 ) inclusion of fluoroquinolone dynamics ( previous versions include only isoniazid and rifampin ) ; 2 ) dynamic representation of cellular uptake of antibiotics ( previous versions assume pseudo-steady state ) ; and 3 ) antibiotic binding to caseum and normal lung tissue ( previous versions approximate binding by using effective diffusivity parameters ) . These changes were necessary for model calibration to experimental FQ data . We estimate GranSim parameters by calibrating to plasma PK , tissue PK , and PD data from in vitro and rabbit studies performed in this work , and from human studies described in the literature ( Table 2 ) . The plasma PK model within GranSim reproduces rabbit plasma concentrations of FQs ( Fig 2 ) . GranSim captures temporal concentration measurements in homogenized cellular and caseous rabbit granulomas ( Fig 3 ) , as well as qualitative differences in the spatial distribution of the FQs ( Fig 4 ) . PD parameters reproduce in vitro dose response curves specific to different bacterial subpopulations ( intracellular , extracellular replicating or extracellular non-replicating ) ( Figure in S1 Fig ) . Parameters used for simulations are listed in Table 3 . Interesting comparisons between the FQs emerge from the calibrated model . Plasma PK suggest higher peak concentrations of LVX and GFX ( Fig 2 ) , and faster inter-compartmental clearance for MXF ( Table 3 ) . Contrary to plasma PK , data from homogenized granulomas reveal higher MXF peak concentrations compared to GFX and LVX ( Fig 3 ) . This finding that MXF peak concentrations are lower in plasma and higher in granulomas compared to GFX and LVX highlights the need for more detailed PK studies in granulomas . Spatial distribution of FQs in rabbit and simulated granulomas reveal: poor penetration of GFX and MXF into caseum compared to LVX , GFX accumulation in cellular areas immediately surrounding caseum , and more evenly distributed MXF accumulation in cellular areas of granulomas ( Fig 4 ) . These spatial data suggest that average concentrations in homogenized granulomas might not represent antibiotic dynamics at specific locations where bacteria reside , e . g . caseum . PD parameter values show that MXF and GFX have similar C50 values ( concentration where 50% of maximum activity is achieved ) , while LVX has higher C50 values against both intracellular and extracellular Mtb . Model parameters also provide insight into the mechanisms behind FQ spatial distribution and function . Tissue PK parameter values indicate that: higher penetration of LVX into caseum is due to higher effective diffusivity in granulomas and slightly lower binding to caseum; accumulation of MXF in cellular areas is due to higher uptake into host cells; and GFX accumulation around caseum is due to slightly faster binding to caseum compared to MXF . PD parameter values indicate that MXF , LVX and GFX all have steep dose response curves , suggesting that these drugs would have very little effect at sub-C50 concentrations . Integration of multiple datasets ensures that our computational model captures spatial and temporal in vivo dynamics , while being consistent with in vitro and literature observations . Predictions for FQ efficacy in granulomas are now possible . For example , is better penetration of LVX into the caseum able to overcome its higher C50 values ( i . e . are spatial granuloma PK differences relevant in the context of PD differences ) ? Is the higher concentration of MXF in granulomas able to overcome its poor relative penetration into caseum ( i . e . are spatial granuloma PK differences relevant in the context of temporal PK differences ) ? We use our systems pharmacology model to address these questions . Before predicting FQ efficacy we explore how human vs . rabbit plasma PK affects the distribution of FQs in granulomas . There are notable differences in FQ plasma PK between rabbits and humans . To predict if human plasma PK would affect the observed spatial distribution of FQs within granulomas , we simulate treatment using human plasma PK parameters that we fit to existing data [9] . Parameter differences between rabbit and human data suggest faster absorption and slower clearance of all three FQs in humans compared to rabbits ( Table 3 ) . Spatial distributions in simulated granulomas are similar when using human or rabbit plasma PK parameters ( Figure in S3 Fig ) . Temporal differences in granulomas reflect differences in plasma PK between rabbits and humans , most notably more antibiotic accumulation within granulomas at 24 hours post dose with human plasma PK compared to rabbit plasma PK . This is a result of slower plasma clearance in humans ( Table 3 ) , resulting in slower movement of FQs out of the lung tissue into blood following peak concentrations . These results indicate that the qualitative spatial distribution of FQs is relatively insensitive to plasma PK . Taken together , our simulations suggests that the rabbit model provides an accurate representation of FQ spatial distribution within human granulomas if the granulomas are similar . To compare FQ efficacy , we simulate 6 months of daily therapy with each FQ in a collection of 210 in silico granulomas , starting at day 380 post-infection , using human plasma PK parameters . We use the following metrics to quantify FQ efficacy: bacterial load per granuloma during and after treatment , percentage of granulomas sterilized and time to sterilization . We also characterize FQ treatment in terms of immune responses within granulomas . Bacterial load per granuloma during and after treatment is similar for MXF and LVX ( Fig 5A ) , with higher bacterial load following GFX treatment . For all three FQs , treatment responses comprise a sharp initial decline followed by a slower decline . This biphasic response is widely observed during TB therapy [28] . Evaluating the response of specific bacterial subpopulations to treatment reveals that MXF sterilizes the intracellular bacterial subpopulation more quickly than LVX and GFX ( Fig 5B ) . The extracellular replicating subpopulation is effectively sterilized ( falling below an average of 1 bacterium per granuloma ) within 10 to 13 days by all three FQs ( Fig 5C ) . The non-replicating subpopulation ( residing in the caseum ) shows a very slow decline for all three FQs ( Fig 5D ) , and is responsible for the second phase in the biphasic kill curve in Fig 5A . Besides FQ effects on bacterial load within granulomas , we can also track immunological changes within granulomas during treatment . Upon infection , macrophages in the lung are activated in response to inflammatory cytokines ( TNF-α , IFN-γ ) and/or the presence of bacteria [29–31] . Furthermore , computational and non-human primate studies have shown that a balance between concentrations of inflammatory cytokines ( e . g . TNF-α ) and anti-inflammatory cytokines ( e . g . IL-10 ) is an important determinant in controlling bacterial growth in granulomas while limiting tissue damage [23 , 32] . The number of activated macrophages and the ratio of TNF-α concentration to IL-10 concentration in our simulation is therefore used as two metrics of inflammation . The inflammation metrics reflect the predicted bacterial differences between the FQs . MXF sterilizes the intracellular population more quickly than LVX and GFX , thereby eliminating infected macrophages , which are important drivers of host inflammation . As a result , numbers of activated macrophages and ratio of TNF-α concentration to IL-10 concentration both decline more quickly during MXF treatment compared to LVX and GFX ( Fig 5E and 5F ) . Other metrics of inflammation ( e . g . number of activated cytotoxic or IFNγ-producing T cells ) show little differences between FQs . These results suggest that bacterial killing by immune mechanisms continue to play a role during GFX and LVX treatment , and are less prominent during MXF treatment . The FQ concentration experienced by each of the bacterial subpopulations ( Fig 6 ) reveals the cause for the observed bacterial load dynamics . Intracellular bacteria are exposed to concentrations above C50 , BI for more than half of the dosing period for MXF , but not LVX or GFX ( Fig 6A ) . Extracellular replicating bacteria are exposed to FQ concentrations well in excess of their effective concentrations throughout most of the dosing period ( Fig 6B ) . We predict that non-replicating subpopulations see concentrations ~3-fold lower than their effective concentrations ( Fig 6C ) . Time to sterilization and percentage of granulomas sterilized are captured by Kaplan-Meier curves ( Fig 7 ) . Our simulations predict that MXF and LVX sterilize significantly higher percentages of granulomas compared to GFX . Based on these data we conclude that the three FQs are similar , with MXF having a slight advantage over LVX ( faster intracellular killing ) , and LVX a slight advantage over GFX ( more granulomas sterilized ) . To determine the effect of parameter uncertainty on our efficacy predictions , we quantify the effect of individual parameters on model outputs , as described in Sensitivity Analysis in Methods and in [33] . Briefly , we simultaneously sample all antibiotic parameters in ranges spanning values for the three FQs ( Table in S2 Table ) , and calculate partial rank correlation coefficients ( PRCCs ) between each parameter and model output ( Table in S3 Table ) . Consistent with previous results [20] , PRCCs reveal that plasma PK parameters ( plasma clearance rate constant , CL ) , tissue PK parameters ( cellular accumulation ratio , a , and permeability coefficient , PC ) and PD parameters ( maximum intracellular activity , Emax , BI , and C50 for intracellular Mtb , C50 , BI ) are drivers of infection and inflammation in the model . Outputs driven by these parameters include: bacterial load , macrophage activation , T cell activation , and TNF-α and IL-10 production . There is relatively low uncertainty in the plasma and tissue PK parameters , since they are estimated from calibration to multiple in vitro and in vivo data sets . PRCCs indicate that uncertainty in intracellular PD parameters influences model outputs , while extracellular and non-replicating PD parameters do not ( Table in S2 Table ) . This result is expected based on the concentration profiles in Fig 6 . Intracellular bacteria are exposed to antibiotic concentrations close to the measured ranges for C50 , BI , and therefore uncertainty in these values would affect predicted efficacy . I . e . if the in vivo C50 , BI values are significantly higher or lower than the in vitro measured values , the intracellular bacterial population would have a more or less significant role , respectively , in the long-term bacterial response to treatment in our simulations . It is not currently possible to directly measure intracellular PD parameters in vivo , and we therefore rely on in vitro measurements . The importance of these parameters in our efficacy predictions highlight the need for controlled in vivo efficacy studies that would allow for indirect estimation of PD parameters through calibration of bacterial loads to per-granuloma experimental data . Nonetheless , unless one FQ’s PD parameters are more sensitive to in vitro conditions than the others , our conclusion of MXF having an advantage over LVX and GFX should still hold . EBA , defined as the daily decrease in sputum bacterial burden measured in colony forming units ( CFU ) , is frequently used to assess the efficacy of a single drug in the first 7 or 14 days of treatment in clinical trials [34] . Here , we introduce a new term: ‘in silico EBA’ , which is defined as the daily decrease in simulated bacterial burden per granuloma . In silico EBA is also calculated for individual bacterial subpopulations , e . g . the ‘in silico intracellular EBA’ is the daily decrease in simulated intracellular bacteria per granuloma . Compared to clinical EBA measured from sputum [8] , in silico EBA is lower for all three FQs ( Table 4 ) . This is expected since our model tracks all bacteria in granulomas , whereas sputum samples contain a subpopulation of bacteria that may not fully represent the population in granulomas . This discrepancy between Mtb found in sputum vs . granulomas has been implicated in the poor ability of clinical sputum EBA to predict sterilization and long-term treatment outcomes [35] . In silico intracellular and extracellular replicating EBA more closely resemble clinical sputum EBA , compared to in silico non-replicating EBA , suggesting that intracellular and extracellular replicating subpopulations could be enriched in sputum . This is in agreement with human data showing high proportions of intracellular bacteria in sputum [36] . In silico intracellular EBA confirms that MXF is more efficacious than GFX and LVX . All three FQs have similar EBA against extracellular and non-replicating bacteria . With any TB treatment there is a risk of patient non-compliance ( inconsistent dosing throughout the treatment period ) or treatment interruption ( incomplete treatment ) , often arising from side effects and long treatment duration [37] . Our results show that both bacterial load and host immune responses decline more quickly during MXF treatment , compared to LVX and GFX treatment ( Fig 5 ) . This raises the question: is MXF treatment more sensitive to non-compliance or treatment interruption due to lower immune responses ? Or , is the bacterial population sufficiently controlled by the antibiotic such that the lower inflammatory response does not affect infection control , even during non-compliance or treatment interruption ? We predict the efficacy of each FQ during non-compliance by simulating 6 months of daily therapy with random skipping of 20% of the doses . This threshold is chosen because it is commonly used to define patients as ‘compliant’ in clinical trials [38] . Bacterial load after treatment shows increases under non-compliance conditions for GFX , MXF and LVX therapy , relative to full compliance ( Fig 8 ) . However , bacterial loads during non-compliant MXF and LVX treatment is lower than fully compliant GFX treatment . Kaplan-Meier curves comparing each FQ in the compliant vs . non-compliant scenario show noticeable , but statistically insignificant , differences for LVX and MXF ( Fig 9 ) . However , percentage of granulomas sterilized during non-compliant conditions for MXF ( 18% ) and LVX ( 20% ) treatments is still higher than during fully compliant GFX treatment ( 13% ) . These small differences at the granuloma level could manifest as clinically significant if we consider that each patient likely has multiple granulomas [2 , 39 , 40] . For example , if a single granuloma has a 5% probability of failing treatment , a person with 10 granulomas has a 40% probability of failing treatment . Studies in non-human primates indicate they have on average 46 granulomas [41] , so this is a significant factor . We predict the efficacy of each FQ under treatment interruption by simulating 10 or 70 days of daily treatment , after which we stop treatment for the rest of the 6-month period . We choose 10 days based on clinical studies suggesting that treatment interruptions start increasing around 2 weeks of treatment [42] , and our earlier results suggest that differences in immune response and bacterial load is most pronounced at this time . We choose 70 days for late interruption simulation time because at that point the immunological differences between FQs have largely disappeared ( Fig 5E and 5F ) . Bacterial load shows sharp increases following interruption of GFX and LVX treatment , and a return to the pre-treatment trajectory for all bacterial subpopulations ( Fig 10A–10D ) . In contrast , while interruption of MXF treatment results in an increase in all bacterial subpopulations , the increase is slower and the infection follows a slowed trajectory compared to pre-treatment . We can visualize the opposing forces of infection and immune responses by defining an immune score ( a collective metric of immune response ) and an infection score ( a collective metric of infection severity ) . The immune score is defined as: ( Activated macrophages* + Activated IFN-γ-producing T cells* + Activated cytotoxic T cells* + concentration of free TNF-α * ) /4; where ‘*’ indicates that the value is normalized to its value at the start of treatment . Similarly , the infection score is defined as: ( Infected macrophages * + Chronically Infected macrophages * + Extracellular Replicating Mtb* + Extracellular Nonreplicating Mtb* ) /4 . Tracking the immune and infection scores throughout treatment ( Fig 10E and 10F ) indicates that although the immune response is weaker during MXF treatment , it is sufficient to control the significantly reduced bacterial population . If treatment is interrupted after 70 days , the immune score and infection score remain stable following MXF and LVX interruption , while GFX interruption results in a slight increase in infection score ( Table in S4 Fig ) . Based on our in silico predictions that MXF-treated granulomas have lower bacterial loads and lower levels of treatment failure during non-compliance and treatment interruption , MXF is recommended over LVX and GFX in patients deemed at high risk of non-compliance or treatment interruption .
There are a number of new anti-TB antibiotics and antibiotic regimens in development and in various stages of clinical testing [43 , 44] . To maximize the useful lifespan of new and existing antibiotics , we need to optimize their implementation . Efficacy of any antibiotic depends on a combination of factors ( Table 1 ) , and in a nonlinear and often non-intuitive way . The complexity stems from granuloma pathology , host dynamics , pathogen interactions and drug properties [4] . Systems pharmacology approaches that combine host , pathogen and antibiotic dynamics are ideal tools to study these complexities in a single model system , and are valuable in identifying promising treatment regimens to advance to animal and clinical studies . We use a systems pharmacology approach to compare efficacy of three FQs in TB granulomas , concluding that MXF has a small but potentially clinically significant advantage over LVX , and LVX over GFX . MXF outperforms LVX and GFX in terms of total bacterial load , EBA and efficacy during non-compliance and treatment interruption . MXF and LVX each outperform GFX in terms of time to granuloma sterilization as well as percentage of granulomas sterilized . We would therefore recommend MXF over LVX , and LVX over GFX . Our predictions are currently being tested in rabbit models of TB , and will inform future studies in non-human primates . These studies will also be used to refine estimates of important in vivo intracellular PD parameters for future simulations . Our results could help guide FQ selection for MDR treatment as well as for future clinical trials for drug sensitive TB treatment . In addition to recommendations for future treatment and trials , our work also provides insight into clinical trial results . Recent phase III clinical trials explored the possibility of using MXF or GFX to shorten the 6-month treatment regimens prescribed for drug-sensitive TB [45–47] . All three trials failed to show non-inferiority compared to the standard 6-month regimen . In contrast , preclinical results in mouse models of TB showed FQs can improve cure rates [48 , 49] or bactericidal activity over shorter time scales [50 , 51] . Phase II clinical studies showed a larger decline in sputum bacterial load when FQs are substituted into the standard regimen [18 , 52 , 53] , but the 8 week time points evaluated in these phase II studies did not predict long term outcomes such as sterilization and recurrence that appeared in the phase III studies [45] . Our computational approach could explain why MXF and GFX failed to improve treatment outcomes in these 4-month regimens . In previous studies we found that granulomas that fail to sterilize with INH and RIF treatment contain mostly intracellular and non-replicating Mtb [20] . Our results here indicate that GFX and MXF would be unable to sterilize the non-replicating bacterial subpopulation . Taken together , these results suggest that MXF and GFX would be complementary to INH or RIF in the tested 4-month regimens by targeting the intracellular populations not eliminated by INH or RIF . However , the non-replicating populations that survive INH or RIF in the 4-month regimens would still persist in the context of MXF or GFX . Our results therefore predict that non-inferiority of these 4-month regimens could be due to non-replicating bacteria that persist throughout therapy , contributing to subsequent relapse . Toward the goal of optimizing TB treatment regimens that rely on multiple drugs , in future simulations we will explore the performance of these FQs in combination with INH and/or RIF and other anti-TB antibiotics . Combination therapy presents a number of challenges that can be studied using computational approaches . The ability of our method to track responses of different bacterial subpopulations to treatment will allow us to design and optimize combination therapies that effectively target all bacterial subpopulations . One particular challenge we can address is predicting the risk of ‘effective monotherapy’ . Effective monotherapy occurs when spatial or temporal windows of monotherapy arise , even under combination therapy , due to PK differences between the antibiotics given and could lead to inadvertent selection of drug resistant bacteria [19 , 54 , 55] . Beyond windows of effective monotherapy , optimizing multi-drug therapy is complicated by drug-drug interactions . The inherent properties of antibiotics ( e . g . how they are metabolized ) can result in complex networks of interaction ( synergy/antagonism ) [56 , 57] that also influence the selection of drug resistant bacteria [58] . While such detailed interaction networks are not currently available for Mtb , work in Mycobacterium marinum [57] could inform future optimization studies . We predict that FQ concentrations inside granulomas must be at least 3-fold higher than those simulated here to eliminate the non-replicating bacterial population . Indeed simulations with higher doses predict granuloma sterilization within 20 days ( data not shown ) . These doses would likely result in toxicity [59] . Targeted or inhaled drug delivery strategies could be used to increase the concentration within granulomas while lowering systemic distribution and therefore toxicity [22 , 60] . However , PK of antibiotics after release from such targeted delivery vehicles determines the feasibility of such approaches . For example , we previously predicted that INH is suitable for inhaled delivery , but RIF’s PK would require unrealistically high carrier loadings [22] . It is difficult to anticipate whether inhaled delivery would be a feasible alternative to oral dosing for FQs based on their plasma and tissue PK parameters derived here . Future studies would have to systematically explore the delivery vehicle parameter space to answer this question . EBA is a treatment outcome metric commonly used to compare antibiotic efficacy in early clinical trials , but the EBA has a poor ability to predict sterilization and long-term treatment outcomes [35 , 61] . The inability of bacterial load measurements in sputum to capture bacterial dynamics inside granulomas is supported by our model results that show lower in silico EBA compared to clinical sputum EBA . Systems pharmacology approaches could help extend the impact of EBA studies by predicting underlying bacterial dynamics . As with all computational models , the necessary assumptions made in our model place limitations on the predictions presented here . Due to the complexity of the system , the model has a large number of parameters that are known with varying degrees of certainty . When possible , we increase our confidence in parameter values by fitting to multiple experimental datasets ( e . g . LCMS and MALDI-MSI to estimate diffusivity ) and by including inter-individual variation in the calibration and prediction simulations . In vivo efficacy studies can assess the effect of these limitations on our current predictions , and help lessen these effects for future predictions . Systems pharmacology provides a platform to integrate sometimes-conflicting experimental data with computational modeling to further our understanding of PK/PD interactions in an in vivo setting . It also allows us to narrow the design space of combinations of drugs to better determine optimal treatments for patients . Here we have focused on FQs , but the approach can be applied in any setting of drug distributions in specific tissues . In TB , multiple drug regimens are used for long periods of time and thus the next necessary step to increasing our understanding is to perform virtual clinical trials that will allow us to predict the right combinations of drugs to shorten treatments .
All animal studies were carried out in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health , with approval from the Institutional Animal Care and Use Committee of the New Jersey Medical School , Newark , NJ , and the National Institute of Allergy and Infectious Diseases ( National Institutes of Health ) , Bethesda , MD . The experimental data we obtained for model calibration is outlined here , and detailed below . In vitro pharmacodynamic data comprise dose response curves in liquid culture media [27] as well as in Mtb-infected bone marrow derived macrophages from C57Bl/6 mice . In vitro pharmacokinetic data comprise drug uptake in human THP-1 cells and caseum binding . In vivo pharmacokinetic studies in rabbits include: temporal LC/MS-MS of antibiotic concentrations in plasma and homogenized granulomas between 0 . 5 and 24 hrs following dosing of each FQ; and MALDI-MSI ( matrix-assisted laser desorption ionization—mass spectrometry imaging ) providing semi-quantitative images of antibiotic spatial distribution within granulomas . Parameters used in the model are known with varying degrees of certainty depending on the process that the parameters describe and if this process is experimentally measurable . Estimation of specific parameters is discussed in more detail below , but briefly we take a combination of the following approaches: A summary of experimental data and how they are integrated into our computational framework is given in Table 2 . To quantify the influence of individual antibiotic parameters on model outcomes , we perform a sensitivity analysis ( SA ) . We sample all antibiotic parameters simultaneously and uniformly using Latin hypercube sampling ( LHS ) [33 , 69 , 70] . Parameters and ranges used for SA are listed in Table in S2 Table . Parameters were sampled 400 times , and each set of parameters was simulated in six unique granulomas . Simulations consisted of daily treatment between 380 and 410 days post infection with a dose of 400 mg/kg . Model sensitivity is quantified using Partial Rank Correlation Coefficients ( PRCC ) between model parameters and model outputs . Model outputs are averaged over the six granulomas tested . A p-value < 0 . 01 is considered significant for PRCC . | Tuberculosis ( TB ) is caused by infection with the bacterium Mycobacterium tuberculosis ( Mtb ) and kills 1 . 5 million people each year . TB requires at least 6 months of treatment with up to four drugs , and is characterized by formation of granulomas in patient lungs . Granulomas are spherical collections of host cells and bacteria . Fluoroquinolones ( FQs ) are a class of drug that could help shorten TB treatment . Three FQs that are used to treat TB are: moxifloxacin ( MXF ) , levofloxacin ( LVX ) or gatifloxacin ( GFX ) . To date , it is unclear if one FQ is better than the others at treating TB , in part because little is known about how these drugs distribute and work inside the lung granulomas . We use computer simulations of Mtb infection and FQ treatment within granulomas to predict which FQ is better and why . Our computer model is calibrated to multiple experimental data sets . We compare the three FQs by multiple metrics , and predict that MXF is better than LVX and GFX because it kills bacteria more quickly , and it works better when patients miss doses . However , all three FQs are unable to kill a part of the bacterial population living in the center of granulomas . Our results can now inform future experimental studies . |
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Recognition of viral RNA structures by the intracytosolic RNA helicase RIG-I triggers induction of innate immunity . Efficient induction requires RIG-I ubiquitination by the E3 ligase TRIM25 , its interaction with the mitochondria-bound MAVS protein , recruitment of TRAF3 , IRF3- and NF-κB-kinases and transcription of Interferon ( IFN ) . In addition , IRF3 alone induces some of the Interferon-Stimulated Genes ( ISGs ) , referred to as early ISGs . Infection of hepatocytes with Hepatitis C virus ( HCV ) results in poor production of IFN despite recognition of the viral RNA by RIG-I but can lead to induction of early ISGs . HCV was shown to inhibit IFN production by cleaving MAVS through its NS3/4A protease and by controlling cellular translation through activation of PKR , an eIF2α-kinase containing dsRNA-binding domains ( DRBD ) . Here , we have identified a third mode of control of IFN induction by HCV . Using HCVcc and the Huh7 . 25 . CD81 cells , we found that HCV controls RIG-I ubiquitination through the di-ubiquitine-like protein ISG15 , one of the early ISGs . A transcriptome analysis performed on Huh7 . 25 . CD81 cells silenced or not for PKR and infected with JFH1 revealed that HCV infection leads to induction of 49 PKR-dependent genes , including ISG15 and several early ISGs . Silencing experiments revealed that this novel PKR-dependent pathway involves MAVS , TRAF3 and IRF3 but not RIG-I , and that it does not induce IFN . Use of PKR inhibitors showed that this pathway requires the DRBD but not the kinase activity of PKR . We then demonstrated that PKR interacts with HCV RNA and MAVS prior to RIG-I . In conclusion , HCV recruits PKR early in infection as a sensor to trigger induction of several IRF3-dependent genes . Among those , ISG15 acts to negatively control the RIG-I/MAVS pathway , at the level of RIG-I ubiquitination . These data give novel insights in the machinery involved in the early events of innate immune response .
IFN induction in response to several RNA viruses involves the intracytosolic pathogen recognition receptor ( PRR ) CARD-containing DexD/H RNA helicase RIG-I . Following its binding to viral RNA , RIG-I undergoes a change in its conformation through Lys63-type ubiquitination by the E3 ligase TRIM25 . This allows its N-terminal CARD domain to interact with the CARD domain of the mitochondria-bound adapter MAVS [1] , [2] . MAVS then interacts with TRAF3 to further recruit downstream IRF3 and NF-κB-activating kinases , that stimulate the IFNβ promoter in a cooperative manner . In addition , IRF3 stimulates directly the promoters of some interferon-induced genes ( early ISGs ) while NF-κB stimulates that of inflammatory cytokines [3] . The RNA of Hepatitis C virus ( HCV ) has an intrinsic ability to trigger IFNβ induction through RIG-I [4] , [5] , [6] . Yet HCV is a poor IFN inducer . One reason for this comes from the ability of its NS3 protease to cleave MAVS [7] . Another relates to the ability of HCV to trigger activation of the dsRNA-dependent eIF2α kinase PKR [8] , [9] which leads to inhibition of IFN expression through general control of translation while the viral genome can be translated from its eIF2α-insensitive IRES structure [8] . HCV infection can trigger important intrahepatic synthesis of several IFN-induced genes ( ISGs ) in patients [10] , [11] and in animal models of infection in chimpanzees [12] . Expression of ISGs can be explained at least in part by the ability of HCV to activate the IFN-producing pDCs in the liver through cell-to-cell contact with HCV-infected cells [13] . Intriguingly , despite the recognized antiviral activity of a number of these ISGs , their high expression paradoxically represents a negative predictive marker for the response of these patients to standard combination IFN/ribavirin therapy [14] , [15] , [16] . The ubiquitine-like protein ISG15 is among the ISGs which are the most highly induced by HCV [16] and was recently shown to act as a pro-HCV agent [17] . Interestingly , ISG15 was also shown to control RIG-I activity through ISGylation [18] . Here , we show that HCV controls IFN induction at the level of RIG-I ubiquitination through the ubiquitine-like protein ISG15 , one of the early ISGs . Use of small interfering RNA ( siRNA ) targeting to compare the effect of ISG15 to that of PKR on IFN induction and HCV replication led to the unexpected finding that HCV infection triggers induction of ISG15 and other ISGs by using PKR as an adapter through its N terminal dsRNA binding domain . This recruits a signaling pathway which involves MAVS , TRAF3 and IRF3 but not RIG-I . Altogether , our results present a novel mechanism by which HCV uses PKR and ISG15 to attenuate the innate immune response .
We recently reported that the HCV permissive Huh7 . 25 . CD81 cells [19] that we used to identify the pro-HCV action of PKR , did not induce IFN in response to HCV infection , unless after ectopic expression of TRIM25 [8] . We started this study by investigating at which level this defect could occur . A P358L substitution in the endogenous TRIM25 of these cells , revealed by sequence analysis , proved to have no incidence of the ability of TRIM25 to participate in the IFN induction process . Indeed , ectopic expression of a TRIM25 P358L construct was as efficient as a TRIM25wt construct to increase IFN induction in the Huh7 . 25 . CD81 cells , after infection with Sendai virus ( SeV ) ( Figure 1A ) . Like some other members of the TRIM family , TRIM25 is localized in both the cytosol and nucleus and is induced upon IFN treatment [20] . No specific difference between the cellular localization of TRIM25 was observed in the Huh7 . 25 . CD81 cells when compared to Huh7 cells or Huh7 . 5 cells , which rules out a role for a cellular mislocalization in its inability to participate in IFN induction ( Figure 1B ) . TRIM25 was also efficiently induced by IFN ( Figure 1B and Figure S1 ) . We assayed whether increasing TRIM25 upon IFN treatment could mimic the effect of its ectopic expression and restore IFN induction in response to HCV infection . However , this resulted only in a poor stimulation of an IFNβ promoter ( 3 to 5-fold ) , in contrast to its effect upon SeV infection ( 230-fold ) ( Figure 1C ) . Similarly , HCV infection at higher m . o . i , as an attempt to favour recognition of RIG-I by the viral RNA , only modestly increased IFN induction ( Figure 1D ) . TRIM25 plays an essential role in IFN induction through RIG-I ubiquitination [1] . We then analysed whether this step was affected by HCV infection in the Huh7 . 25 . CD81 cells . The results showed that , in contrast to SeV infection used as control , HCV infection could not trigger RIG-I ubiquitination , unless the cells are supplied with ectopic TRIM25 ( Figure 1E ) . Thus , HCV infection appears to mediate a control on IFN induction through regulation of RIG-I ubiquitination . Inhibition of the function of TRIM25 or RIG-I ubiquitination has been suggested to occur via the small ubiquitin-like protein ISG15 and the process of ISGylation [18] , [21] . We then analysed whether ISG15 was involved in the control of RIG-I ubiquitination upon HCV infection . For this , we chose a transient transfection approach using siRNAs targeting ISG15 in the Huh7 . 25 . CD81 cells . Indeed , this resulted in a strong ubiquitination of RIG-I at 9 hrs and 12 hrs post-HCV infection , which was equivalent to that observed in cells supplied with ectopic TRIM25 ( Figure 2A ) . A similar result was obtained after JFH1 infection in the Huh7 cells , used as another HCV-permissive cell line ( Figure S2 ) . Thus , ISG15 can control RIG-I ubiquitination in different cells infected by HCV . We next investigated whether ISGylation was involved in this process . Absence of detection of RIG-I ubiquitination after HCV infection of the Huh7 . 25 . CD81 cells precludes direct analysis of the effect of ISG15 on RIG-I . We used an IFNβ-luc reporter assay instead , as it proved to be sensitive enough to detect some IFN induction in response to JFH1 infection in those cells ( see Figure 1D ) . We found that IFN induction increased when cells were transfected with siRNAs targeting ISG15 while it decreased in cells overexpressing IGS15 ( Figure 2B ) . Expression of ISG15 in the presence of the E1 , E2 and E3 ligases involved in ISGylation ( respectively Ube1L , UbcH8 and HERC5 ) [22] further inhibits IFNβ induction ( Figure 2B ) . Similar results were observed upon infection with Sendai virus ( Figure S3 ) . The ISGylation process is strictly dependent on the presence of the E1 ligase Ube1L [23] . Indeed , enhanced IFN promoter activity has been observed in Ube1L−/− cells in response to NDV [18] . In accord with this , depletion of endogenous Ube1L from the Huh7 . 25 . CD81 cells ( Figure S4 ) , as such or after ectopic expression of ISG15 , UbcH8 and HERC5 , resulted in an increase in IFNβ induction after infection with HCV ( Figure 2B ) . We then analysed the effect of siISG15 on IFNβ induction after infecting the cells with HCV up to 72 hours , in order to pass through the 24 hr time-point where the signaling pathway leading to the transcription of this gene is expected to stop because of the NS3/4A-mediated cleavage of MAVS [8] . The results show that , whereas IFNβ transcription was indeed strongly inhibited after 24 hr in the control cells , it still occurred significantly in the cells expressing siRNA ISG15 ( Figure 2C ) . Previous data have shown a positive role for ISG15 on HCV production [24] , [25] . In accord with this , silencing of ISG15 resulted in clear inhibition of HCV RNA expression with however no significant consequence on the ability of the virions produced to re-infect fresh cells ( Figure 2D ) . Analysis of expression of MAVS and NS3 , as well as the expression of the core protein as another example of viral protein , then showed that the depletion of ISG15 both decreased and delayed the expression of the viral proteins as compared to the siRNA control cells and that this was correlated by a delay in the NS3/4A-mediated cleavage of MAVS ( Figure 2E ) . These results show that ISG15 controls the process of IFN induction during HCV infection by interfering with RIG-I ubiquitination through an ISGylation process and by boosting efficient accumulation of NS3 , among other viral proteins , thus favouring its negative control on IFN induction by cleavage of MAVS . ISG15 ( [24] , [25] and this study ) and PKR [8] , [9] emerge as two ISGs with pro-HCV activities , instead of playing an antiviral role . We then assayed the effect of a combined depletion of PKR and ISG15 on HCV replication and IFN expression in the Huh7 . 25 . CD81 cells . As shown in Figure 2 D and B , siRNAs targeting ISG15 were sufficient both to inhibit HCV replication ( Figure 3A ) and to increase IFNβ expression , either measured by RTqPCR ( Figure 3B ) or by using an IFNβ-luciferase reporter assay ( Figure 3C ) . Very limited additional effect was observed in the concomitant presence of siRNAs targeting PKR . ( Figure 3B ) . Interestingly , we noticed that expression of luciferase from the IFNβ promoter increased throughout the first 18 hours of HCV infection in the siISG15 cells ( Figure 3C ) . This was intriguing as it should have been inhibited after 12 hours of HCV infection through the eIF2α kinase activity of PKR and its control on translation [8] . We therefore analysed whether the state of PKR activation ( phosphorylation ) was dependent on the expression of ISG15 . For this , the Huh7 . 25 . CD81 cells were transfected either with siRNAs targeting ISG15 or with a plasmid expressing an HA-ISG15 construct and PKR phosphorylation was analysed as described previously [8] . The results showed that depletion of ISG15 inhibits PKR activation in the HCV-infected cells , while its overexpression stimulates it ( Figure 3D and Figure S5 ) . Therefore these data reveal that , in addition to negatively controlling RIG-I ubiquitination , ISG15 can also positively control PKR activity . The conjugation of both effects results in an efficient control of IFN induction during HCV infection . The Huh7 . 25 . CD81 cells express ISG15 at significant basal levels . This situation was not surprising as various cellular systems can also express some of the ISGs at basal level . Expression of ISG15 was approximately 2- and 5-fold higher in the Huh7 . 25 . CD81 cells than in the Huh7 . 5 or Huh7 cells ( data not shown ) . Intriguingly however , we noticed that ISG15 expression was increased in response to HCV infection ( see Figure 2E ) . To investigate this further , we simply re-used the RNAs prepared for the experiment shown in Figure 3B and performed a quantitative kinetics analysis . The results confirmed that HCV can trigger induction of ISG15 ( Figure 4A ) . Unexpectedly , analysis of the RNA extracted from the cells treated with siRNAs targeting PKR , revealed that ISG15 RNA expression was strongly repressed when PKR was silenced ( Figure 4A ) . This surprising result was confirmed by analysing induction of ISG56 , another early ISG [26] , both at the level of its endogenous RNA ( Figure 4B ) or by using an ISG56-luciferase vector ( Figure 4C ) . In the latter case , a strong increase of the reporter expression in the cells treated with siRNAs targeting ISG15 , was similar to the situation observed for IFNβ RNA ( Figure 2B and 2C ) . This can be related to activation of the RIG-I pathway , which can function when ISG15 is absent . These data suggest that HCV may use PKR to activate gene transcription . Importantly , this phenomenon was specific to HCV as infection with Sendai virus resulted in a similar induction of ISG15 and ISG56 , regardless of PKR ( Figure 4D and Figure S6 ) . We then examined whether overexpression of PKR could boost induction of ISG15 during HCV infection and how this would affect HCV replication and IFN induction , in relation to the pro-HCV action of ISG15 . Huh7 . 25 . CD81 cells were transfected with a plasmid expressing PKR alone or in presence of siRNAs targeting ISG15 , before being infected with HCV over 48 hours . Overexpression of PKR increased the ability of HCV to induce ISG15 and concomitantly , led to an increase in HCV RNA expression . The latter increase was abolished when ISG15 was silenced , thus showing that the PKR-dependent increase in HCV expression is mediated by ISG15 ( Figure 4E ) . However , while the cells silenced for ISG15 are able to induce IFN in response to HCV infection , as shown in Figure 3B , they are unable to do so when PKR is overexpressed . This suggests that PKR may also interfere with the process of IFN induction , independently of ISG15 , a possibility that remains to be explored . A role for PKR in gene induction in response to HCV infection has not been described before . Additional information was therefore obtained through a transcriptome analysis of 2165 genes in the Huh7 . 25 . CD81 cells treated with control siRNAs or siRNAs targeting PKR and infected with HCV for 12 hrs . Out of the most significant 422 genes that were identified , 99 were unmodified or barely modified and 33 were down-regulated , while 290 genes were found to be up-regulated by HCV infection ( data not shown ) . Among those , HCV infection triggered up-regulation of 49 genes which are directly dependent on PKR expression ( Table 1 ) . Forty percent of these genes ( 20 ) belong to the family of the ISGs , with ISG15 among the most induced genes ( Table 1 ) . In the reciprocal situation , only 17 genes depended on PKR for their down-regulation by HCV infection , with no link to a particular family of genes and limited variation both in number and intensity ( Table S1 ) . Thus , induction of ISGs upon HCV infection may occur through a novel signaling pathway that involves PKR . Infection with RNA viruses or transient transfection with dsRNA can directly and rapidly induce early ISGs , such as ISG15 , through IRF3 , after activation of the RIG-I/MAVS pathway and recruitment of TRAF3 , an essential adapter which recruits the downstream IRF3 kinases TBK1/IKKε . We have shown that the RIG-I pathway was not operative during HCV infection in the Huh7 . 25 . CD81 cells , precisely due to the presence of ISG15 . To determine how ISG15 induction through PKR relates to or differs from the RIG-I/MAVS pathway , the Huh7 . 25 . CD81 cells were treated with siRNAs aimed at targeting separately PKR , RIG-I , MAVS , TRAF3 and IRF3 ( Figure S7 ) and infected with HCV . The results clearly showed that induction of ISG15 in response to HCV infection depends on PKR , MAVS , TRAF3 and IRF3 but not on RIG-I ( Figure 5A ) . The participation of IRF3 was further confirmed by immunofluorescence studies which showed its nuclear translocation at 6 hours post-infection ( Figure S8 ) . ISG15 , as well as ISG56 , was also clearly induced in response to HCV infection in two other HCV permissive cell lines , such as Huh7 and Huh7 . 5 cells , and this induction was abrogated in presence of siRNAs targeting PKR ( Figure 5B and Figure S9 ) . Importantly , since Huh7 . 5 cells express a non-functional RIG-I/MAVS pathway due to a mutation in RIG-I , result with these cells supports the notion that the ability of HCV to trigger induction of ISGs through PKR is independent of RIG-I . To have more insights on this novel PKR signaling pathway , PKR was immunoprecipitated at early times points following infection of Huh7 . 25 . CD81 cells with HCV and the immunocomplexes were analysed for the presence of MAVS , TRAF3 and RIG-I . Both MAVS and TRAF3 , but not RIG-I , associate with PKR in a time dependent manner , beginning at 2 hrs post-infection ( Figure 5C ) . Strikingly , these associations were abrogated by the cell-permeable peptide PRI which is analogous to the first dsRNA binding domain ( DRBD ) of PKR [8] , while unaffected by C16 , a chemical compound which inhibits the catalytic activity of PKR ( Figure 5D ) . In line with this , PRI but not C16 , abrogated the ability of HCV to induce ISG15 ( Figure 5E ) . The same result was obtained for induction of ISG56 ( Figure S10 ) . We then used human primary hepatocytes ( HHP ) to determine whether HCV was also able to induce ISGs through PKR in a more physiological cellular model . A follow-up of the infection over a period of 96 hours showed that JFH1 was replicating correctly in those cells as well as leading to induction of ISG15 ( 10-fold ) and to some induction of IFNβ ( 2 . 5-fold ) . These cells were infected with JFH1 for 8 hours in the absence or presence of PRI , making convenient use of the cell-penetrating ability of this peptide . Longer period of treatment with PRI were not investigated for practical reasons ( see Materials and Methods ) . The results showed that PRI was significantly inhibiting the induction of ISG15 while it had no effect on that of IFNβ ( Figure 5F ) . Altogether , these data demonstrate that HCV triggers induction of early ISGs through MAVS and TRAF3 by using PKR as an adapter protein . The ability of HCV to control activation of the RIG-I/MAVS pathway after induction of ISG15 through a novel PKR/MAVS pathway suggests that PKR has the possibility to bind MAVS prior to RIG-I . To determine this , we established the kinetics of these interactions , after treating the Huh7 . 25 . CD81 cells with siRNAs targeting ISG15 prior to HCV infection . This was necessary in view of the negative control of ISG15 on RIG-I . MAVS was immunoprecipitated from the cell extracts at different times post-infection and the presence of PKR and RIG-I was examined in the immunocomplexes , as well as that of TRAF3 , used as marker of activation of the MAVS signaling pathway . As expected , only PKR was able to associate with MAVS and TRAF3 in the control cells ( Figure 6A ) whereas both PKR , RIG-I and TRAF3 were found in the immunocomplexes in the absence of ISG15 ( Figure 6B ) . The PKR/MAVS association took place at 4 hrs post-infection in the control cells but was observed 2 hrs earlier in the ISG15-depleted cells . Whether ISG15 plays a role in the regulation of the PKR/MAVS association remains to be determined . However , the presence of TRAF3 in association with MAVS at 2 hrs post-infection in the control cells ( Figure 6A ) correlates with its association with PKR ( Figure 5C ) which indicates that the MAVS pathway can be activated through PKR as soon as 2 hrs post infection . In ISG15 knock-down cells , the RIG-I/MAVS association occurred later at 6 hrs post-infection with an increase in TRAF3 association at 9–12 hrs post infection . Altogether , these data revealed that HCV infection triggers an earlier interaction of MAVS with PKR than with RIG-I . Finally , we asked whether PKR was able to associate with HCV RNA and how this association can be compared to that of RIG-I . PKR and RIG-I were immunoprecipitated at 2 , 4 and 6 hrs post-infection and the presence of HCV RNA was analysed in the complexes . The results showed that PKR associates with HCV RNA with best efficiency at 2 hrs post-infection . Importantly , this association was strongly inhibited in presence of PRI , thus confirming the importance of PKR DRBD in the process . In contrast , the association of HCV RNA with RIG-I was detected only at 6 hrs post-infection . Interestingly , the association between RIG-I and HCV RNA was not affected by PRI , which rules out the possibility that the initial formation of a complex between PKR and HCV RNA was a pre-requisite for the subsequent binding of RIG-I to HCV RNA . Immunoprecipitation of PKR at 1 , 2 , 4 and 6 hrs post-infection , in presence of an inhibitor of ribonucleases also did not lead to detection of RIG-I in the complexes ( Figure S11 ) . Association of HCV RNA with eIF2α , used as negative control , was not significant , thus showing the specificity of the assay ( Figure 6C ) . Whether a direct interaction of PKR with HCV RNA represents the initial event leading to the MAVS-dependent induction of early ISGs remains now to be characterized . Altogether , these data reveal an earlier mobilization of PKR than RIG-I in response to HCV infection which leads to activation of a MAVS-dependent signaling pathway .
Hepatitis C virus can attenuate IFN induction at multiple levels in infected hepatocytes , such as through the NS3/4A-mediated MAVS cleavage [7] , [27] and by using the eIF2α kinase PKR to control IFN and ISG expression at the translational level [8] , [9] . Here , we have identified another process by which HCV controls IFN induction at the level of RIG-I ubiquitination through ISG15 and an ISGylation process . Importantly , we have shown that ISG15 is rapidly induced , among other ISGs , in response to HCV infection , through a novel signaling pathway that involves PKR , MAVS , TRAF3 and IRF3 but not RIG-I . In this pathway , PKR is not used for its kinase function but rather as an adapter protein with its dsRNA binding domain ( DRBD ) playing an essential role in this mechanism ( Figure 7 ) . By transcriptome analysis , we showed that HCV induces a number of ISGs in the HCV-permissive Huh7 . 25 . CD81 cells and we confirmed the induction of two of these , ISG15 and ISG56 , in other HCV-permissive cells , such as Huh7 . 5 and Huh7 cells . In addition , induction of ISG15 by HCV in a PKR-dependent manner was confirmed in human primary hepatocytes . The ability of HCV to trigger high expression levels of ISG15 and ISG56 , as well as other ISGs , has previously been reported in models of HCV-infected chimpanzees [10] , [12] , [28] and in HCV-infected patients [14] , [15] , [16] . Induction of ISGs thus represents a general propriety of the response of the cells to HCV . In addition to this , natural variations in intra-hepatic levels of ISG15 in vivo may increase the susceptibility of some patients to HCV infection . The ability of HCV to control RIG-I activity through ISG15 is important to note in view of several reports which highlight the importance of a role for ISG15 in the maintenance of HCV in livers [15] , [16] or in the control of HCV replication in cell cultures [17] , [25] . Our data provide an explanation for the presence of ISGs at high expression levels in HCV-infected patients [14] , [15] , [16] and in models of HCV-infected chimpanzees [10] , [12] , [28] in the absence of , or with poor IFN expression . The 15 Kda ISG15 , or Interferon Stimulated Gene 15 [29] , also known as ubiquitin cross reactive protein ( UCRP ) [30] , can be conjugated ( ISGylation ) to more than 150 cellular protein targets [31] through the coordinated action of three E1 , E2 and E3-conjugating enzymes , in a process similar but not identical to ubiquitination . While both ubiquitin and ISG15 can use the same E2 enzyme UbcH8 , Ube1L functions as a specific E1 enzyme for ISG15 , in spite of its 45% identity with Ube1 , the E1 enzyme for ubiquitin [32] . The major E3 ligase for human ISG15 is HERC5 [33] . Interestingly , RIG-I was identified as a target for ISG15 , among other IFN-induced proteins or proteins involved in IFN action [31] . However , its activity appears to be negatively controlled by ISG15 and the ISGylation process , either as shown previously after cotransfection with the ISG15 and the ISG15-conjugating enzymes [18] or as shown here , in a model of infection with HCV . Indeed , ISG15 is now emerging as playing a proviral role in case of HCV infection . Several reports now highlight the importance of a role for ISG15 in the control of HCV replication in cell cultures [17] , [25] as well as in the maintenance of HCV in livers and pinpoint ISG15 as among the predictor genes of non-response to IFN therapy [14] , [15] , [16] . At present , we do not know at which level ISGylation regulates IFN induction in response to HCV infection . An HCV-mediated increase of ISG15 would favour preferential binding of ISG15 over that of ubiquitin to the E2 enzyme UbcH8 and hence enhance the spatio-temporal availability of UbcH8-ISG15 for HERC5 over that of UbcH8-ubiquitin for TRIM25 . It may also lead to inhibition of TRIM25 , through autoISGylation [21] , [34] , which would decrease its ability to ubiquitinate RIG-I . We showed that overexpression of HERC5 together with Ube1L , UbcH8 and ISG15 was increasing the ability of ISG15 to inhibit IFN induction by HCV ( Figure 2B ) . All three enzymes Ube1L , UbcH8 and HERC5 belong to the family of genes induced by IFN and it has been reported that ISGylation is optimum in a context of IFN treatment [18] , [35] . Therefore , it is tempting to speculate that elevated levels of ISG15 in some HCV-infected patients would bring the most favourable context for the virus when those patients are under IFN therapy . This would be in accord with the clinical data showing that HCV-induced high expression of ISG act as a negative predictive marker for response to IFN therapy . It is doubtful that viruses with high IFN-inducing efficiency , such as Sendai virus may control RIG-I through ISG15 and PKR . However , viruses that avoid inducing IFN may have use of the PKR pathway . A good example might be that of Hepatitis B Virus ( HBV ) [36] , [37] , [38] . PKR expression was previously reported to be elevated in HCC liver from chronically HBV infected patients [39] and a relationship between PKR and IFN induction during HBV infection would be important to evaluate . At present , we have established that HCV RNA interacts with PKR as soon as 2 hours post-infection . This interaction occurs prior the interaction of HCV RNA with RIG-I , which suggests that PKR may rapidly detect structures containing the incoming HCV RNA genome . Indeed , PKR has been reported to bind the dsRNA domains III and IV of HCV IRES [40] in addition to its ability to also bind 5′ triphosphorylated ss or dsRNA structures [41] . Whether PKR behaves as a pathogen recognition receptor for HCV RNA , like RIG-I , remains to be clarified . It is however clear that , in contrast to RIG-I , PKR acts here in favour of the pathogen rather than in favour of the host defense . We have established that the HCV RNA/PKR interaction depends on the first DRBD present at the N terminus of PKR and is independent on its kinase activity . The ability of PKR to serve as adapter in signaling pathways is not a total surprise since it has been previously shown to activate NF-κB through interaction of its C terminus with members of the TRAF family , such as TRAF5 and TRAF6 [42] . PKR contains also TRAF interacting motif in its N terminus [42] and an association between TRAF3 and PKR has been reported upon cotransfection in 293T cells [43] . Intriguingly , PKR was previously reported to participate in the induction of IFNβ , in association with MAVS , through activation of NF-κB or ATF-2 but not or partially IRF3; however these studies were not performed in the absence of RIG-I [44] , [45] , [46] . The mode of interaction between PKR , TRAF3 and MAVS , independently of RIG-I , and how it leads to a preferential induction of ISGs and not of IFNβ in response to HCV infection in contrast with the RIG-I/MAVS pathway remains to be determined . Based on our data , we propose now to divide the innate response to acute HCV infection into two phases: an early acute phase in which PKR is activated and a late acute phase that depends on RIG-I , the early phase controlling activation of the late phase . It is now essential to progress towards the generation of specific pharmaceutical inhibitors targeting PKR in order to abrogate the early acute phase to the benefit of the RIG-I-driven late phase . In a more general view , care should now be taken in the choice of compounds designed to be used as immune adjuvants , such as to be devoid of activation of the early acute PKR phase . This will ensure their efficiency as to activate properly the innate immune response through the late acute RIG-I phase .
The culture of Huh7 , Huh7 . 5 , Huh7 . 25 . CD81 cells , the preparation of Sendai virus stocks ( ≈2000 HAU/ml ) and of HCV JFH1 stocks ( ≈6 . 104 FFU/mL and ≈6 . 106 FFU/mL ) was as described [8] , [47] . Preparation and cultures of human primary hepatocytes was as described [48] . Of note , the ability of the Huh7 . 25 . CD81 cells to induce IFN in response to SeV without prior IFN treatment ( 40-fold ) was not observed in our previous study [8] . The ability of Sendai virus to induce IFN is related to the presence of copyback DI ( Defective Interfering ) genomes [49] . The higher IFN inducing ability of the novel Sendai virus stock may have come from an important accumulation of these copyback DI genomes , during its growth in chicken eggs . The C16 compound [50] and the cell-permeable PRI peptide [51] were provided by Jacques Hugon . These drugs were applied ( 200 nM for C16 and 30 mM for PRI ) one hour before the end of the 2 hr- incubation time with JFH1 and re-added to the medium after washing the cells with phosphate buffered saline ( PBS ) . Note that PRI loses its effect very rapidly , probably through degradation in the cells , and requires to be added every hour to the cells until the end of treatment . TRIM25 was cloned from the IFN-treated Huh7 . 25CD81 cells ( 500 U/ml IFN-α2a; Cellsciences ) after RT-PCR using the forward: 5′-ATGGCAGAGCTGTGCCCCCT-3′ and reverse 5′-CTACTTGGGGGAGCAGATGG-3′ primers . The pcDNA3 . 1 ( + ) vector expressing 5′HA tagged-TRIM25 ( provided by D . Garcin; University of Geneva , Switzerland ) was used to generate the TRIM25 P358L construct by site-directed mutagenesis . The IFNβ-firefly luciferase ( pGL2-IFNβ ) and pRL-TK Renilla-luciferase reporter plasmids were described previously [8] . The pGL3 luciferase reporter construct containing the −3 to −654 nucleotides of the ISG56 promoter was provided by N . Grandvaux [52] . The Myc-HIS-Ubiquitin construct was provided by R . Kopito ( Stanford University , CA ) . ISG15 was cloned from IFN-treated Huh7 cells using the forward: 5′- GGATCCCATGGGCTGGGACCTGACGGTG-3′ and reverse 5′-CTCGAGCTCCGCCCGCCAGGCTCTGT-3′ primers and inserted into the pcDNA3 . 1 ( + ) HA vector . The Ube1L , UbcH8 and HERC5 constructs were kindly provided by Jon M . Huibregtse [35] . The pcDNA1/AMP vector expressing PKR has been described previously [53] . The siRNAs directed against PKR , MAVS , RIG-I , TRAF3 and IRF3 which were used for the experiment described in figure 5A correspond to pools of siRNA ( Smartpool ) obtained from Dharmacon Research , Inc . ( Lafayette , CO ) , as well as siRNAs directed against Ube1L used in Figure 2B . Control ( scrambled ) siRNA and siRNA directed against PKR or ISG15 , used in all other experiments , were chemically synthesized by Dharmacon ( scrambled and PKR ) and by EUROFINS MWG Operon ( ISG15 ) ( Text S1 ) . The siRNAs ( final concentration 25 nM or 50 nM ) were transfected for 48 h using jetPRIME reagent according to the manufacturer's instructions ( PolyPlus transfection TM ) before transfection with other plasmids or before infection . Mab to ISG15 ( clone 2 . 1 ) was a kind gift of E . Borden [54] . Mab to PKR was produced from the murine 71/10 hybridoma ( Agro-bio; Fr ) with kind permission of A . G . Hovanessian [55] . Other antibodies were as follows: anti-mouse IgG ( Santa Cruz ) , anti-TRAF3 ( Santa Cruz ) , pThr451-PKR ( Alexis ) , MAVS ( Alexis ) , anti-actin ( Sigma ) , anti-pSer10-Histone H3 ( Millipore ) , anti-HCV NS3 ( Chemicon ) , anti-HCV core ( Thermo scientific ) , anti-RIG-I ( Alexis Biochemical Inc . ) , anti-TRIM25 ( 6105710; BD Bioscience ) , anti-IRF3 ( Santa Cruz ) , anti-HA ( 12CA5; Roche ) and anti-Myc ( Santa Cruz ) . Huh7 . 25 . CD81 cells ( 80 , 000 cells/well; 24-well plates ) were transfected with 40 ng of pRL-TK Renilla-luciferase reporter ( Promega ) and 150 ng of either pGL2-IFNβ-Firefly luciferase reporter or pISG56-luciferase reporter and processed for dual-luciferase reporter assay as reported previously [8] . Total cellular RNA was extracted using the TRIZOL reagent ( Invitrogen ) . HCV RNA was quantified by one-step RTqPCR . Reverse-transcription , amplification and real-time detection of PCR products were performed with 5 µl total RNA samples , using the SuperScript III Platinum one-step RTqPCR kit ( Invitrogen ) and an AbiPrism 7700 machine . For the sequence of the different primers , see Text S1 . The results were normalized to the amount of cellular endogenous GAPDH RNA using the GAPDH control kit from EuroGentec . Copies number of HCV RNA may vary due to internal calibration and depending on the preparation of the viral stocks . All m . o . i were calculated using the titers expressed in FFU/ml . The IFNβ , ISG15 , ISG56 , Ube1L and GAPDH amplicons were quantified by a two-step RTqPCR assay as described [8] . Cellular RNA was extracted and purified from the cells using RNAeasy mini kit ( QIAGEN K . K . , Tokyo , Japan ) . Comprehensive DNA microarray analysis was performed with 3D-Gene Human Oligo chip25k with 2-color fluorescence method by New Frontiers Research Laboratories , Toray Industries Inc , Kamakura , Japan as previously described [56] . In brief , each sample was hybridized with 3D-Gene chip . Hybridization signals were scanned using Scan Array Express ( PerkinElmer , Waltham , MA ) . The scanned image was analyzed using GenePix Pro ( MDS Analytical Technologies , Sunnyvale , CA ) . All the analyzed data were scaled by global normalization . Cells were washed once with PBS and scraped into lysis buffer 1 ( 50 mM TRIS-HCl [pH 7 . 5] , 140 mM NaCl , 5% glycerol , 1% CHAPS ) that contained phosphatase and protease inhibitors ( Complete , Roche Applied Science ) . The protein concentration was determined by the Bradford method . For immunoprecipitation , lysates were incubated at 4°C overnight with the primary antibodies as indicated and then in the presence of A/G-agarose beads ( Santa Cruz Biotechnology ) for 60 minutes . The beads were washed three times , and the precipitated proteins were extracted at 70°C using NuPAGE LDS sample buffer . Protein electrophoresis was performed on NuPAGE 4–12% Bis TRIS gels ( Invitrogen ) . Proteins were transferred onto nitrocellulose membranes ( Biorad ) , and probed with specific antibodies . Fluorescent immunoblot images were acquired and quantified by using an Odyssey scanner and the Odyssey 3 . 1 software ( Li-Cor Biosciences ) as described previously [8] . For detection of ISG15 , cells were lysed in RIPA buffer ( 50 mM TRIS-HCl [pH 8 . 0]; 200 mM NaCl; 1% NP-40; 0 . 5% Sodium Deoxycholate; 0 . 05% SDS; 2 mM EDTA ) and protein electrophoresis was performed on 4–20% polyacrylamide gels ( PIERCE ) . Pellets from cells washed in ice-cold phosphate-buffered saline ( PBS ) were lysed in ice-cold cytoplasmic buffer ( 10 mM TRIS [pH 8 . 0] , 5 mM EDTA , 0 . 5 mM EGTA , 0 . 25% Triton X-100 ) containing phosphatase and protease inhibitors . The suspension was centrifuged for 30 seconds at 14 , 000 g and the supernatant ( cytoplasmic fraction ) was transferred into microcentrifuge tubes . The nuclear pellet was resuspended in Urea buffer ( 8 M Urea , 10 mM TRIS [pH 7 , 4] , 1 mM EDTA , 1 mM dithiothreitol ) containing phosphatase and protease inhibitors , homogenized by vortex and boiled for 10 minutes . The protein concentration was determined by the Bradford method . Huh7 . 25 . CD81 cells were transfected for 48 hrs with 5 µg of Myc-His-Ubiquitin expression plasmid using jetPRIME reagent . The cells were then washed in ice-cold PBS containing 20 mM N-ethylmaleimide ( Sigma-Aldrich ) , harvested directly in Gua8 buffer ( 6 M guanidine-HCl , 300 mM NaCl , 50 mM Na2HPO4 , 50 mM NaH2PO4 [pH 8 . 0] ) , briefly sonicated , and centrifuged at 14 , 000 g for 15 min at 4°C . 1/10th of the lysate was subjected to precipitation with 10% trichloroacetic acid for protein analysis in whole cell extracts . The rest of the lysate was incubated for 2 hrs with 20 µl ( packed volume ) of Talon resin Ni-affinity beads ( Clontech ) on a rotating wheel . Bound proteins were washed four times in Gua8 buffer , three times in Urea 6 . 3 buffer ( 8 M Urea , 10 mM TRIS , 0 . 1 M Na2HPO4 , 20 mM Imidazole [pH 6 . 3] ) , and three times in cold PBS , after which they were eluted by boiling in NuPAGE LDS sample buffer . Electrophoresis was performed on 4–12% of acrylamide NuPAGE gels ( Invitrogen ) . Huh7 . 25 . CD81 cells were incubated for 10 min in their culture medium containing 1/10 volume ( Vol ) of a crosslinking solution ( 11% Formaldehyde , 0 . 1 M NaCl , 1 mM Na-EDTA-[pH 8] , 0 . 5 mM Na-EGTA-[pH 8] , 50 mM HEPES [pH 8] ) . The reaction was stopped by addition of a solution of 0 . 125 M glycine in PBS [pH 8] at room temperature ( RT ) . The cells were washed three times in ice-cold PBS containing 1000 U/ml of RNAse inhibitor ( Promega ) , scraped in PBS and dispatched into three sets containing ½ ( set 1 ) , ¼ ( set 2 ) and ¼ ( set 3 ) of the cell suspension . The three sets were centrifuged for 30 seconds at 14 , 000 g and 4°C and the cell pellets were lysed into lysis buffer 1 containing phosphatase/protease and RNAse inhibitors ( Promega ) for sets 1 and 2 or into TRIZOL reagent for set 3 . Cell lysates from sets 1 and 2 were then incubated at 4°C , first overnight with the appropriate primary antibodies and for 60 minutes in the presence of A/G-agarose beads ( Santa Cruz Biotechnology ) . After the incubation period , the beads were washed four times with buffer 1 . Set 1 ( HCV RNA bound to immunocomplexes ) and set 3 ( input HCV RNA ) were submitted to TRIZOL treatment and HCV RNA was quantified by one-step RTqPCR as described previously . The immunoprecipitated proteins from set 2 were extracted at 70°C using NuPAGE LDS sample buffer and analysed by immunoblot after electrophoresis on 4–12% of acrylamide NuPAGE gels ( Invitrogen ) . | Hepatitis C Virus ( HCV ) is a poor interferon ( IFN ) inducer , despite recognition of its RNA by the cytosolic RNA helicase RIG-I . This is due in part through cleavage of MAVS , a downstream adapter of RIG-I , by the HCV NS3/4A protease and through activation of the eIF2α-kinase PKR to control IFN translation . Here , we show that HCV also inhibits RIG-I activation through the ubiquitin-like protein ISG15 and that HCV triggers rapid induction of 49 genes , including ISG15 , through a novel signaling pathway that precedes RIG-I and involves PKR as an adapter to recruit MAVS . Hence , we propose to divide the acute response to HCV infection into one early ( PKR ) and one late ( RIG-I ) phase , with the former controlling the latter . Furthermore , these data emphazise the need to check compounds designed as immune adjuvants for activation of the early acute phase before using them to sustain innate immunity . |
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Buruli ulcer is an infectious disease involving the skin , caused by Mycobacterium ulcerans . This disease is associated with areas where the water is slow-flowing or stagnant . However , the exact mechanism of transmission of the bacillus and the development of the disease through human activities is unknown . A case-control study to identify Buruli ulcer risk factors in Cameroon compared case-patients with community-matched controls on one hand and family-matched controls on the other hand . Risk factors identified by the community-matched study ( including 163 pairs ) were: having a low level of education , swamp wading , wearing short , lower-body clothing while farming , living near a cocoa plantation or woods , using adhesive bandages when hurt , and using mosquito coils . Protective factors were: using bed nets , washing clothes , and using leaves as traditional treatment or rubbing alcohol when hurt . The family-matched study ( including 118 pairs ) corroborated the significance of education level , use of bed nets , and treatment with leaves . Covering limbs during farming activities is confirmed as a protective factor guarding against Buruli ulcer disease , but newly identified factors including wound treatment and use of bed nets may provide new insight into the unknown mode of transmission of M . ulcerans or the development of the disease .
Buruli ulcer ( BU ) is an infectious disease involving the skin , caused by Mycobacterium ulcerans , characterized by a painless nodule , papule , plaque or edema , evolving into a painless ulcer with undermined edges , often leading to disabling sequelae [1] . BU has been reported from 30 countries in Africa , the Americas , Asia and the Western Pacific , mainly in tropical and subtropical regions [2] , [3] . The epidemiologic pattern is defined by the presence of confined foci where BU is endemic [1] , [3] , with prevalence ranging from a few cases to up to 22% in given communities [4] . The preventive and therapeutic tools for reducing the impact of this disease are still very limited [5] , [6] . In Cameroon , BU was first described in 1969 in 47 patients in a well confined area located in the neighborhood of the villages of Ayos and Akonolinga ( “Province du Centre” ) , in the valley of the Nyong river [7] . The Nyong river basin in this area is characteristically known for its swampy banks ( Figure 1 ) . Cocoa and coffee farming was the main resource activity until the international pricing crisis of the 1990s . Known in this area as “Atom” , this disease did not arouse particular interest among public health professionals until the beginning of this century , when BU was “rediscovered” [8] . A cross-sectional study in the Nyong river basin in 2001 identified 436 patients with active or inactive BU , giving an estimated prevalence of 0 . 44% [8] . It is unclear whether BU has re-emerged or if cases had been undiagnosed due to the fear of stigmatization [9]–[11] . Buruli ulcers are associated with areas where the water is slow flowing or stagnant [1] , [12]–[15] . Ecologic transformations have been frequently associated with occurrence or increase in BU incidence [16] . Nevertheless , the exact mechanism of transmission of the mycobacterium and the development of the disease through water-related human activities is unknown . Previous trauma at the lesion site has been recognized as a route of infection [17] . More recently , insects have been suggested to be involved in transmission of M . ulcerans , either through bites or by contamination of a previous trauma site [18] . This hypothesis is supported by experimental evidence showing that M . ulcerans can be transmitted to laboratory mice by the bite of aquatic bugs ( Naucoridae ) infected with this organism [19] . Few case-control studies have been published [4] , [12] , [20]–[24] and none of these concern Cameroon . We conducted a case-control investigation in Cameroon seeking for environmental and behavioral risk factors for BU , with two categories of controls: i ) an age- and community-matched control and ii ) a family-matched control .
A double-matched case-control study was designed in the two health districts of Cameroon where BU is endemic , i . e . Akonolinga and Ayos . A probable case of BU was defined as a patient presenting with active or inactive BU [1] in one of the two BU treatment centers in the area . The clinical diagnosis of BU was made by trained and specialized health practitioners in charge of the BU treatment centers . A confirmed case was defined as a probable case with evidence of M . ulcerans infection , indicated by a Ziehl-Neelsen test for acid-fast bacilli in smears of lesion exudates [25] , a positive polymerase chain reaction ( PCR ) [26] or both . Laboratory analyses were done at the mycobacteria reference laboratory in Centre Pasteur du Cameroun , Yaoundé , Cameroon . An eligible control was defined as a person who had no signs or symptoms of active or inactive BU . One age- and village-matched control was selected . A control child for child case-patients attending primary school was randomly sampled within the same classroom . Controls for other children and adult case-patients were randomly sampled within the village . An unaffected member of the family of each patient was enrolled as a family-matched control ( formally: the nearest brother/sister in age ) . No family-matched control was enrolled when the patient was a single child or when his/her siblings lived out of the study area . Study enrollment was voluntary . Written informed consent was obtained from case-patients and control subjects or from their parents or guardians . All BU case-patients had received or were currently receiving free treatment for BU in one of the two BU treatment centers . The study protocol was approved by the National Ethics Committee and the Cameroon Ministry of Public Health . The sample size for matched case-control studies [27] was evaluated 168 pairs of one case patient/one control ( control case ratio 1 , odds ratio ≥2 , power ( 1-β ) 0 . 8 , significance level ( α ) 0 . 05 , correlation of exposure between pairs in the case-control set ( ϕ ) 0 . 2 , calculated using the SAMPSI_MCC Stata ( Stata Corporation ) module [28] ) In February and March 2006 , study personnel administered two standard questionnaires to participants concerning demographic , environmental and behavioral risk factors ( see supporting information file ) . The first questionnaire concerned familial items ( e . g . house characteristics and environment ) and was given to each case-patient and his/her matched community control . The second questionnaire concerned individual items ( e . g . activity and personal exposure to water ) and were responded to by all case-patients and controls . All questions were close-ended . Questionnaires were verbally administered in French and/or in Ewondo ( the local language ) . Both languages are regularly spoken irrespective to educational level of inhabitants . Case-patients were interviewed about their habits the year before onset of symptoms; controls were interviewed about their habits the year before the interview . Community-matched and family-matched case-control studies were analyzed independently . Univariate and multivariate conditional logistic regressions were used to assess the link between variables and BU within the matched group of one case-patient/one control , using the R software ( The R Core Team [29] , “clogit” function , “survival” library ) . Following the univariate analysis , variables that attained a p-value<0 . 10 were retained for multivariable analysis . A procedure using backward and forward selection based on the Likelihood Ratio Test ( LRT ) was used to obtain the final model . The same initial multivariate model , excluding familial items , was used for the intra-familial case-control study , followed by the same algorithm , based on the LRT , for selection of variables .
Among the 163 probable cases , 111 ( 68% ) were confirmed by a positive PCR . Six additional probable cases ( 4% ) were confirmed by a single positive Ziehl-Neelsen test . The remaining probable case-patients had not been sampled for BU confirmation when symptoms were present and no sampling could be done at the time of the study as the lesions had healed . No significant difference was observed between probable cases and confirmed cases in terms of demographic data , type of first lesion , and localization of lesion ( Table 1 ) . Probable cases and confirmed cases were combined for the main analyses of the study and supplementary analyses using confirmed case-control pairs was done to corroborate results obtained from the whole dataset . The median age of all recruited patients was 14 years ( range: 1 to 78 years ) . However , male case-patients were generally younger than female case-patients ( median age: 12 and 19 , respectively; p<0 . 01 , nonparametric K-sample test for equality of medians ) . When interviewed , 25 patients had contracture deformities or scars and three had had an amputation . The first BU lesions in most cases occurred on the leg ( 92/159 , 58% , data missing for 4 cases ) or arm ( 57/159 , 36% ) . Initial lesions occurred less frequently on the trunk ( 7/159 , 4% ) and head ( 3/159 , 2% ) . More patients had lesions on a distal extremity ( from the elbow to the hand and from the knee to the foot , 103/159 , 65% ) than on a proximal extremity , trunk or head ( 56/159 , 35% , Fisher exact test: p<0 . 01 ) . When first BU lesions appeared on a limb , it was more frequently on the lower limbs than upper limbs ( 92 and 57 , respectively; Fisher exact test: p = 0 . 01 ) and more frequently on the left side than on the right side ( 92 and 57 , respectively; Fisher exact test: p = 0 . 01 ) . There was no significant difference in this distribution of lesions associated with the sex or the age ( <10 compared to >10 years ) of case-patients . Most case-patients ( 127/163 , 76% ) did not declare an association between the occurrence of their first lesion and a particular event . Nevertheless , 16/163 ( 10% ) associated it with an injury , 15/163 ( 9% ) with an insect bite and 8/163 ( 5% ) with another event . While all case-patients had been treated in a hospital , as recruitment was hospital-based , 80/162 had been treated in parallel or consecutively by a traditional practitioner .
Case-control study limitations are undoubtedly applicable to this study . The study could not be done on incident cases due to the low incidence of BU in the area . Cases that were at early stages of infection could not be included which could possibly induce a bias . Memory bias may have occurred as onset of BU symptoms could have happened a long time before the study . Beliefs about BU may have modified participants' responses; nevertheless , this study confirms that most people do not have any knowledge of the origin of the disease or think it is due to witchcraft , like in other BU endemic countries [9] , [10] . Also , interviewers were not blinded to the disease status of participants . The proportion of confirmed BU cases in Cameroon is increasing , especially since the PCR technique was implemented in the Centre Pasteur du Cameroun , Yaoundé . Nevertheless , the statistical power of the analysis is lower if we restrict the study to confirmed cases . The similar characteristics of confirmed and unconfirmed cases give us confidence to combine these two subpopulations , as well as the high sensitivity of the clinical diagnostic found elsewhere [21] . The presence of misclassification ( false positive cases ) is not excluded . Possible misclassification of BU-free patients as case-patients reduces the statistical power of the study and may introduce bias [30] . Nevertheless , we confirmed the major factors identified on the whole dataset when analyses were done on the sub-sample of confirmed case-control pairs . We matched case-patients with controls from their villages of residence , but did not use the nearest-neighbor method in order to avoid overmatching . Overmatching is evident in the family case-control study . Results from both analyses are not independent as the case-samples were the same . Nevertheless , the identification of a risk factor in both family and community case-control studies provides additional support for that risk factor . Our study design could not investigate the influence of age on BU . The majority of case-patients are children under 15 years of age , as described in other publications [1] . We confirm the prevalence of BU lesions on the extremities , especially on the legs [8] , [12] , [15] , [31] . A study carried out in Ghana in 1989 reported that the left leg was more frequently affected than the right leg in adults [31] , but this asymmetrical distribution was not confirmed in a more recent study [32] or in Cameroon [8] . We found an unequal right-left distribution in favor of the left side . We found no association between this asymmetrical distribution and sex or age; thus , we cannot make assumptions about the differential behavior within these subpopulations regarding exposure to BU infection . This is the largest case-control study using face-to-face questionnaire ever published for BU . Nevertheless , one should address the possible lack of power of the analysis to identify factors weakly related to the transmission or development of this disease . Indeed , some factors not determined to be risk factors in this study deserve comment . BCG vaccination is known to be effective against leprosy [33] . Though univariate analysis in this study indicates that it is a protective factor for BU , multivariate analysis assessing confounding factors does not confirm this finding , similar to previous reports [21] , [24] . A higher risk for BU in BCG-vaccinated patients ≥5 years of age was recently observed in a case-control study on 2 , 399 case files [23] , but we did not observe this in our study . Though fetching water has been suggested to be a risk factor for BU [34] , we see no evidence of this in our study . Unlike previous reports [15] , [23] , our findings do not suggest that use of unprotected water sources is a significant risk factor . A low education level of the subject ( <secondary ) was observed as a significant risk factor for BU in both the case-/community- and case-/familial-matched control studies . All pairs of children <12 years old having a <secondary level of education , this effect is observed only from the teenagers and the adults records . This factor was not linked to e . g . farming activities , bath or swimming activities or wound treatment . We found that living near a cocoa plantation or woods is a risk factor for BU in this area . The people of this area made their livings from cocoa plantations until the major crisis of the mid-1990s . Many study participants noted that cocoa farmers developed food crop plantations near the Nyong river following this crisis . This development was associated with profound ecologic upheaval . Further studies including geographic information systems should be conducted , but these ecologic changes might be related to the re-emergence of BU in the area . BU endemicity in this area is associated with the presence of the Nyong swamp . It is extremely difficult to determine risky behaviors for BU infection with more precision , as being exposed to water bodies are part of the daily routine of a majority of inhabitants of this area . Many variables linked to exposure to water , and especially to the river Nyong swamp , are significantly associated with cases of BU in univariate community-matched and family-matched analyses . Nevertheless , we observed many colinearities . The final multivariate models only show that i ) wading in the Nyong river swamp ( community-matched analysis ) and swimming in the Nyong river ( family-matched analysis ) are risk factors , and ii ) fishing in the Nyong river ( family-matched analysis ) is a protective factor . Wading in a river or stream has been identified as a risk factor in Ghana [21] and indirectly in Benin as swamp water is a primary water source [23] . Swimming was found to be a significant risk factor in Ghana [20] . Community-matched analysis did not indicate that fishing in the Nyong river , implying daily exposure to the Nyong river and its swamp , is an independent risk factor . Additionally , family-matched analysis indicated that it is a protective factor . Fishing activities have never been found to be independent risk factors for BU . We hypothesize that heavily exposed populations acquire protection against M . ulcerans , or at least against the M . ulcerans-driven pathogenic processes . Marsollier et al . [35] showed that prior exposure to bites from M . ulcerans–free aquatic insect predators confers some protection against M . ulcerans infection . The same hypothesis is applicable to the protective effects of washing clothes , which also implies daily exposure to water . Wound-treatment practices seem to substantially influence BU in the community-matched and the family-matched case-control studies . Use of rubbing alcohol can prevent infection of the trauma site , but the protective effect of leaves compared to adhesive bandages should be confirmed . Antiseptic or astringent active principles ( e . g . tannins , flavonoids ) in leaves traditionally used in these areas possibly explain this protective effect . The pharmacologic properties of leaves used traditionally in Cameroon should be investigated further . We found that wearing long clothing during farming activities is protective against BU , as in Ghana and the Ivory Coast , [12] , [21] . Note nevertheless that the wide confidence intervals obtained for this variable reflects a small amount of discordant pairs . This result may not be robust . This finding suggests that long periods of skin exposure facilitate infection . It is unusual to be bare chested in Cameroon , corroborated by the smaller number of observed lesions on the trunk than that observed in other countries [32] . This finding is consistent with both prevailing hypotheses that insect vectors and penetrating injuries are potential modes of BU transmission for M . ulcerans [21] . The use of bed nets is a strongly associated protective factor for BU in our study , but not in Ghana [21] . In Cameroon , bed nets are principally used to prevent malaria which is endemic in the whole country . The cost of bed nets does still not afford its universal use , and families generally do not own bed nets for the whole members of the family . Infection was less frequent among those using bed nets than those not using them , even within families . This intra-familial confirmation invalidates the hypothesis of a confounding effect linked , for example , to household location , access to bed nets or socio-economic status . The choice of the people who sleep under a bed net within a family was not investigated in this study . The impact of bed nets , especially pyrethroid-impregnated bed nets , on personal protection against the malaria mosquito is uncontroversial . Unfortunately , the questionnaire did not explore whether or not these bed nets were impregnated with insecticide . This observation is consistent with the recent detection of M . ulcerans by PCR in a small proportion of mosquitoes trapped in a BU-endemic area in Victoria , Australia [36] , [37] . Pyrethroid impregnated bed nets also protect from other insects , including day-flying insects , crawling insects , head lice , chicken ticks or bedbugs [38]–[40] . Water bugs ( genera Naucoridae and Dyplonichus ) , which are suspected to be a possible vector of M . ulcerans [18] , [19] are flying insects but there common ecological area is not households . The impact of bed nets on bites from these sylvatic insects is thus less evident . Our study supports the hypothesis implicating domestic or peridomestic insects ( e . g . mosquitoes ) in the transmission of M . ulcerans .
Our findings are consistent with both major hypotheses of M . ulcerans transmission , i . e . insect bites and/or contamination following or accompanying trauma . Treatment practices following trauma were highly significant , supporting the hypothesis involving contamination of a trauma site . However , the use of bed nets , which we propose to be a protective factor , favors the hypothesis involving an insect vector . A specific study should be undertaken to confirm these risk factors for two reasons . First , it may yield information about the mode of transmission . Second , measures to control these risks should be easy to implement to protect inhabitants from BU and other diseases . This study confirms that wading in the swamp and wearing short clothing during farming activities are risk factors for BU . Public health messages about these risk factors can now be provided to local populations . Nevertheless , providing information , ending stigmatization , focusing on early detection and prompt treatment still make up the best public health strategy to reduce BU burden until the mode of transmission of M . ulcerans and the following development of the disease is more clearly understood . | Buruli ulcer ( BU ) is a neglected tropical infectious disease caused by Mycobacterium ulcerans . While BU is associated with areas where the water is slow-flowing or stagnant , the exact mechanism of transmission of the bacillus is unknown , impairing efficient control programs . Two hypotheses are proposed in the literature: previous trauma at the lesion site , and transmission through aquatic insect bites . Using results from a face-to-face questionnaire , our study compared characteristics from Cameroonian patients with Buruli ulcer to people without Buruli ulcer . This latter group of people was chosen within the community or within the family of case patients . The statistical analysis confirmed some well-known factors associated with the presence of BU , such as wearing short lower-body clothing while farming , but it showed that the use of bed nets and the treatment of wounds with leaves is less frequent in case patients . These newly identified factors may provide new insight into the mode of transmission of M . ulcerans . The implication of domestic or peridomestic insects , suggested by the influence of the use of bed nets , should be confirmed in specific studies . |
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Various studies showed that chemotherapy can control schistosomiasis morbidity , but association of measures ( water supply , sewage disposal and increase of socioeconomic conditions ) is necessary for transmission control . A survey dealing with socioeconomic conditions , snail survey , contact with natural waters , and clinical and stool examinations was undertaken at an endemic area in the State of Minas Gerais , Brazil . The methodology used was the same for both evaluations ( 1981 and 2005 ) . Four hundred and seventy-five out of 1 , 474 individuals studied in 1981 could be contacted . From these , 358 were submitted to stool examination , and 231 of them were clinically examined . Patients eliminating S . mansoni eggs in their stools were treated . The results showed that the prevalence rate in Comercinho , a municipality of the State of Minas Gerais , Brazil , was substantially reduced to 70 . 4% and 1 . 7% in 1981 and 2005 , respectively , as well as the frequency of the hepatosplenic form ( 7% to 1 . 3% ) after five treatments effectuated between 1981 and 1992 . No other new case of this form was detected from 1981 onwards . Another important aspect to be considered was the improvement of people's living standard that occurred in the region after more than two decades' efforts ( better housing , professional skill and adequate basic sanitation ) . The control of morbidity and very significant decrease of schistosomiasis transmission in an area until then considered as hyperendemic was possible by means of association of successive specific treatments of the local population , together with the construction of privies , water supply in the houses and improvement of socioeconomic conditions .
Schistosomiasis is a social disease , found at poor rural regions and periphery of cities , with a precarious socioeconomic development , where the inhabitants have frequent contact with contaminated waters , as well no available of adequate sewerage system . WHO considers schistosomiasis as the second only to malaria in socioeconomic importance worldwide , and the third more frequent parasitic disease Public Health importance . [1] The main necessary step recommended for reduction of schistosomiasis morbidity is the treatment of individuals living in endemic areas [2] . The program for national control of schistosomiasis was launched in Brazil in 1975 , by SUCAM ( “Superintendência de Campanhas de Saúde Pública” ) – Ministry of Public Health , by means of the “Programa Especial de Controle da Esquistossomose ( PECE ) ” ( Special Program for Schistosomiasis Control ) , which directed its activities for the chemotherapeutic treatment with oxamniquine in large scale ( more than 13 million people were treated ) . As molluscicide , niclosamide was used , but in lower scale and irregular manner . Sanitation , safe water supply and health education were also measures adopted , but with less frequency [3] . Various early studies demonstrated that improvement of the sanitary conditions and treatment of positive patients contribute to reduce morbidity and prevalence of the disease [4]–[11] . A study carried out in Comercinho/MG , Brazil , in 1974 , clearly shows these facts . In that year , a staff from the Laboratory of Schistosomiasis , Research Center René Rachou/FIOCRUZ , under the leadership of one of the authors ( NK ) , performed the first survey on schistosomiasis mansoni ( prevalence of 70 . 4% ) . Census of the population , mapping of the town , clinical and stool examinations of the patients infected with S . mansoni were performed . However , the treatment of the local population could not be administered as it was intended , due to the appearance of lethal cases in Brazil with the use of hycanthone , the antischistosomal drug of choice at that occasion . After the discovery of a novel drug – oxamniquine – the researchers came back to Comercinho in 1981 . On that year , besides the above mentioned measures , other ones were taken , such as: identification of the intermediate host , socioeconomic survey , research on contact with natural waters , clinical examinations of the population , and intradermal reaction for this group , besides specific treatment of infected patients [12] . In 1984 and 1986 , the individuals that presented S . mansoni eggs in the feces , detected by means of examination of the local residents performed in the preceding year , were once more treated . In 1988 a new re-evaluation was undertaken according to the same methodology [13] . In 1992 , Rocha and Katz [14] re-examined the conditions of the area after five treatments with oxamniquine ( from 1981 to 1991 ) . From that date onwards the Prefecture of the town was in charge of the program for the control of schistosomiasis , and the treatments continued to be administered at the local Public Health Center by local physicians and technicians ( horizontalization of the Program for Schistosomiasis Control ) . In 2005 , a new clinical-epidemiological survey of the population living in the area in 1981 was carried out , focused on the following priorities: identification of the intermediate host; parasitological , clinical and socioeconomic evaluations of the population and evolution of contact with natural waters . In the present paper we compare the results of the last evaluation ( 2005 ) with data related to the inhabitants of the region in 1981 , when people were treated with antischistosomal drug for the first time .
Comercinho is a little town located at the Northeast of the State of Minas Gerais , macro-region of Jequitinhonha , Brazil , at a distance of 714 Km from the capital of the state . In 2005 , the population was estimated in 10 . 181 inhabitants and 3 . 340 of them were living in the urban area , where this study was done . The urban center has three public buildings pertaining to the Prefecture ( 1 for odontological attendance , the other ones for the “Programa de Saúde da Família ( PSF ) ” – Program for Family Health – and for the “Programa de Controle da Esquistossomose - PCE” – Program for the Control of Schistosomiasis . COPASA ( “Companhia de Saneamento de Minas Gerais” ) is responsible for the water supply and sewerage system . The household waste is daily collected in all the urban area , and the solid residues are deposited in a landfill situated 2 Km far from the urban center . The patients were informed about this new study , and a signed written informed consent was obtained from all patients ( including from parents/guardians for all the 7–14-year old children ) before admission to the study . This study was approved by the Ethical Committee for Human Research of the Research Center René Rachou/FIOCRUZ ( 02/2006-CEPSH/CPqRR ) , and by the Ethical Committee for Human Research of the Santa Casa Hospital , in Belo Horizonte/MG ( Statement n° 016/2006 ) . The inhabitants that participated in the study performed in 1981 , and were still living in the area , were interviewed by the technicians of the “Programa de Saúde da Família ( PSF ) ” . They answered a socioeconomic questionnaire , and they were also invited to be submitted to clinical and stool examinations . Collection of snails was performed within the urban area ( Sapê and Areia brooks ) . The snails were sent to the Mollusc Room at the Research Center René Rachou/FIOCRUZ in order to be identified and evaluated in relation to S . mansoni infection , by means of light exposure and by crushing between two glass plates . The staff of the “Programa de Saúde da Família ( PSF ) ” visited the housings of the participants in the study performed in 1981 , and the interview was held with the owner or user of the housing . The socioeconomic survey considered the following items: a ) insertion of the family head into the productive system; b ) individual occupation; c ) working place; d ) place of birth; e ) type of housing; f ) source of water supply . These topics were assessed according to the same parameters described by Costa in 1983 [12] . The patients received a recipient to collect the feces , identified with the same plot number attributed to them in 1981 . A stool sample was collected for preparation of two slides by the Kato-Katz method [15] . The eggs were counted , and an arithmetic average of the number of eggs per gram of feces was considered as an individual result . In order to know the number of eggs per gram of feces ( epg ) in the community the geometric average was used . The positive patients ( eliminating S . mansoni eggs in the feces ) received chemotherapeutic treatment for schistosomiasis and/or other helminthiases . Seven hundred fifty-nine school children ( 7 to 14-year-old ) were examined for evaluation of the current situation related to schistosomiasis . Clinical examination was performed by means of anamnesis and abdominal palpation . The clinical classification adopted was: type I ( intestinal ) , type II ( hepatointestinal ) or type III ( hepatosplenic ) [16] . The patients clinically examined answered a questionnaire on contact with natural waters , for evaluation of the frequency and reason for contact . The data were grouped as follows: washing clothes , fetching water , bathing , leisure ( swimming and/or fishing ) , professional activities ( watering garden , removing sand and crossing the stream ) . The chi-square test was used for comparison of frequency distribution ( 1981–2005 ) . The test was performed with 95% confidence , using the program SPSS version 11 . 5 .
One hundred and sixteen out of the 181 captured snails were found to be alive , and were identified as Biomphalaria glabrata . None of the alive snails examined were infected with Schistosoma mansoni . In 2005 , 475 out of 1 . 474 individuals that have participated in the study carried out in 1981 could be contacted . Stool and clinical examinations were performed in 1 . 329 and 836 , and in 358 and 231 individuals , in 1981 and 2005 , respectively . Table 1 shows comparison of the socioeconomic survey obtained in 1981 and 2005 . As can be seen in Table 1 , significant improvements were attained , such as substantial increase in the number of housings with safe water supply provided by the Public Service ( from 33 . 7% in 1981 to 96% in 2005 ) . Waste disposal using cess-pits or flush toilets was increased from 71 . 7% to 97 . 6% , provided by the population . In 1981 , only 34 . 2% of the housings were classified as type A ( considered of better quality ) , and 97 . 6% in 2005 . The proportion related to the heads of the households considered as skilled workers showed also a significant increase ( 6 . 6% to 22 . 8% ) ( Figure 1 ) . The school-children were used as indicators of the current situation of schistosomiasis mansoni in 2005 . Thus , the prevalence rate estimated in 759 school-children was 1% for S . mansoni , 1 . 7% for hookworms , and 0 . 4% for Ascaris lumbricoides . Table 2 shows the data related to the distribution of indicators for schistosomiasis mansoni in the re-evaluated group . The infection rate decreased dramatically ( from 70 . 4% in 1981 to 1 . 7% in 2005 ) . Infection rate in the age of group ( 30–40 years ) in 1981 was 69 . 2% in 130 persons; in 2005 it was 4 . 1% in 98 persons . The geometric average of eggs per gram of feces was 334 epg and 172 epg , in 1981 and 2005 , respectively . The patients presenting more than 500 epg in 1981 were 36 . 6% , whereas only one case could be detected in 2005 . Table 3 shows the results obtained with clinical evaluation in the studied years . The clinical form Type I was detected in 67 . 9% and 95 . 2% of the patients in 1981 and 2005 , respectively . The clinical forms Type II and III were observed in 25% and 3 . 5% , and 6 . 8% and 1 . 3% , in 1981–2005 , respectively . Among the signs and symptoms evaluated , abdominal pain and diarrhoea were the most reported at both study periods . Blood in the stools was present in 50% of the studied population in 1981 , but no similar case was reported in 2005 . Table 4 shows the results on contact with natural waters in Comercinho . In 1981 , the daily contact with natural waters was reported by 62% of the population , whereas in 2005 by just 25% . Also , a marked decrease related to the use of natural waters was detected , when the bi-weekly or less frequency was considered ( 51 . 9% in 2005 , and 13 . 7% in 1981 ) . As far as the reasons for contact with natural waters were concerned , in 1981 , 21% and 3 . 7% were due to leisure and professional activities , and in 2005 these activities were reported by 27 . 2% and 44 . 4% of the population .
In the last two decades , studies performed in Brazil [5] , [6] , [9] , [10] , [12] , [13] , [17]–[19] , [32] , and in other countries [20]–[27] , have described the distribution of infection , reasons and frequency of contact with natural waters , as well as other parameters related to schistosomiasis mansoni . In the present study , we studied a population from about 1 , 400 individuals that participated in a survey carried out in 1981 , comparing the results obtained with the current ones . Nevertheless , only 358 patients were re-examined , the other ones could not be observed , since some of them moved from the town or refused to participate . Comercinho/MG , Brazil , was considered as a hyperendemic area in 1981 ( 70 . 4% ) , but turned into a low endemic area ( 1 . 7% ) in 2005 . Administration of various treatments and quality of intervention measures produced an appreciable decrease in the prevalence of the disease ( 97 . 6% ) . The geometric average of the number of eggs per gram of feces obtained was 172 epg , lower than that reported by Costa in 1983 [12] ( 334 epg ) , or a little higher than the average found by Cury in 1991 [13] ( 105 epg ) , both of them also in Comercinho . It is note worthy that this actual average was obtained taking into account only 6 cases related to S . mansoni eggs discharged in the feces . Various studies in different regions demonstrated that the intensity of infection varies very much , and that reinfection after treatments in endemic areas show a lower number of eggs in the feces , when compared to the number detected pre-treatment [28] , [29] . The rate of splenomegaly was 1 . 3% in adults in 2005 , lower than that detected at the beginning of the project ( 6 . 8% ) . This fact may be connected with various treatments administered to the population along of 25 years . In fact , according to Kloetzel ( 1967 ) and Bina ( 1977 ) [28] , [30] , after specific treatment for schistosomiasis , even when reinfection occurs , it can be observed that the splenomegaly rate decreases significantly , and no new cases among the treated patients could be found . In Comercinho , no new case of hepatosplenomegaly could be detected after more than two decades of surveillance . The reasons and frequency for natural water contact frequently occurs in association with the socioeconomic standard of the population living in endemic areas , and depend on their needs and cultural habits . In Comercinho in 2005 , the main reasons for contact with natural waters pointed to professional activities , such as: watering vegetable-garden or farming , removing sand , crossing a brook , etc . ( 44 . 4% ) . The decrease of the related daily contact may be directly connected with the increase in the number of households with water supply . However , it was not possible to correlate directly the contact with natural waters to infection with S . mansoni , in the last survey , since only three positive patients mentioned contact with natural waters , with a biweekly frequency . Costa et al . ( 1987 ) [33] reported that the main risk factors responsible for splenomegaly in Comercinho were: absence of piped water , daily contact with natural waters and unskilled workers . Scott et al . ( 2003 ) [24] showed that many aspects , such as frequency , duration or time of contact , have influence on the infection rate . Certainly , the supply of safe water at town level diminished the incidence of schistosomiasis , since the existence of piped water in the housings reduced considerably the frequency and duration of contact with natural waters . In 231 patients clinically examined in 2005 , 95 . 2% presented intestinal clinical form of the disease , 3 . 5% showed hepatointestinal and 1 . 3% hepato-splenic forms , whereas at the beginning of the study , the percentages were 67 . 9% , 25 . 3% and 6 . 8% , respectively . The reversal of hepatomegaly and splenomegaly was deemed as significant . The importance of treatment and provision of sanitation for decrease of prevalence and morbidity control was previously emphasized . In Capitão Andrade , a small town in the State of Minas Gerais , Brazil , Conceição & Pereira ( 2002 ) [9] noticed that over a 21-year-period , from 1973 to 1994 , the prevalence decreased ( 60 . 8% in 1973; 32 . 2% in 1984 , and 27 . 3% in 1994 ) , whereas the evolution profile of the clinical forms was found to be satisfactory ( unaltered in 76 . 7% , clinical progression in 8 . 4% and regression in 14 . 9% ) . The reduction of both prevalence and severity of S . mansoni infection were ascribed to the treatment with oxamniquine administered in all infected individuals in 1984 , as well as to provision of piped water in the housings . In 2003 , those authors re-evaluated the area , and observed that the prevalence has also decreased ( 19 . 4% ) in relation to the preceding years , as well as the hepato-splenomegaly ( 5 . 8% in 1973 , 2 . 8% in 1984 , 2 . 3% in 1994 and 1 . 3% in 2003 ) . They observed that in spite of the significant reduction in the prevalence of infection without treatment at the initial phase ( 1973–1974 ) , followed by a specific treatment with oxamniquine in 1984–1994 , the rate of the severe forms and prevalence remained very high throughout the period 1994–2003 . During this time , people continued to receive treatment , but there were no improvements related to either basic sanitation or potable water supply , only sanitary education was strengthen . Thus , these facts led to the supposition that the high prevalence and severity of the clinical forms may have occurred due to reinfection [10] . In our laboratory , a study was devised to be carried out in Ravena , a district of Sabará , State of Minas Gerais , Brazil , in 1980 . Initially , the prevalence of schistosomiasis in Ravena was 36 . 7% , with an infection intensity of 229 epg ( geometric average related to positive individuals ) . No cases of hepato-splenic form could be detected . A specific treatment with oxamniquine in large scale was provided ( every four years , three treatments ) to patients discharging eggs in the feces . In 1992 , the local population was re-examined . When the study was initiated , 90% of the housings received safe water supply . The number of housings with an appropriate waste disposal also increased ( from 17% to 36% ) . In 1992 , the prevalence in the population decreased to 11 . 5% , and the average of eggs was 60 . 3 [34] , [35] . Recently , the same area was re-examined , i . e . , 27 years after the first clinical-epidemiological survey [11] . In this last survey , the prevalence was 2 . 5% , with an average of 21 epg . In the age group of 0–14 years old the positivity rate was 0 . 75 , whereas in 1980 this rate was 11 . 6% . Besides , 95% of the housings disposed of safe water supply , and more than 80% had appropriate waste disposal either by means of sewerage system , flush toilets or cess-pits . From 1990 onwards , the population was treated by a physician at the local health center , based on the results of stool examinations and by spontaneous plea . The living standard related to water contact in Ravena was modified throughout the years , since the majority of the population is no more in the habit of using natural contaminated water . In the last survey in Comercinho , 96% of the houses visited disposed of safe water supply by means of the public system , 97 . 6% had flush toilets or cess-pits for waste disposal , and 97 . 6% of the housings were classified as being of better quality ( Type A ) . The composition of a schistosomiasis control program varies according to two approaches: 1 . Control of morbidity , aiming at reducing the number of severe form of the disease; 2 . Control of transmission , by interrupting the evolutive cycle of the parasite . In the first case , the control of morbidity is specially undertaken by using chemotherapy , whereas the control of transmission requires treatment , safe water supply and appropriate waste disposal , environmental sanitation , and health education [2] , [31] . Currently , in Comercinho , low prevalence rate regarding the population in general and in previously treated individuals , low frequency of cases with hepatosplenic form , have clearly proved that control measures in association can led to interruption or significant decrease of transmission . At least , this clearly happens in Ravena and Comercinho . Finally , due to the effectiveness of the measures above mentioned , it is quite clear that the Brazilian Government should adopted the association of control measures mentioned in this study in order to attain schistosomiasis transmission control in the country . | A clinical-epidemiological reevaluation on schistosomiasis mansoni was performed in 2005 , in the urban area of a little town , Comercinho , MG , specifically focusing on the inhabitants of the same area in 1981 , when a first survey and treatment with oxamniquine were carried out . The surveys included: identification of the intermediary host , census , mapping of the city , socioeconomic survey , stool examination , clinical examination , research dealing with contact with natural waters , and treatment of the positive cases . From a population of 1 , 474 people studied in 1981 , 358 were submitted to stool examination , and 231 were clinically examined . From 1981 to 1992 five specific treatments were performed with oxamniquine and the last one with praziquantel . The results obtained were compared and demonstrated that the prevalence in Comercinho decreased significantly ( 70 . 4% to 1 . 7% ) , as well as the hepatosplenic form ( 7% to 1 . 3% ) in 1981 and 2005 , respectively . Significant improvement in the life quality ( improvement in the housing , professional qualification and basic sanitation ) were observed and must be considered important for the schistosomiasis control . |
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Hantaviruses are zoonotic viruses transmitted to humans by persistently infected rodents , giving rise to serious outbreaks of hemorrhagic fever with renal syndrome ( HFRS ) or of hantavirus pulmonary syndrome ( HPS ) , depending on the virus , which are associated with high case fatality rates . There is only limited knowledge about the organization of the viral particles and in particular , about the hantavirus membrane fusion glycoprotein Gc , the function of which is essential for virus entry . We describe here the X-ray structures of Gc from Hantaan virus , the type species hantavirus and responsible for HFRS , both in its neutral pH , monomeric pre-fusion conformation , and in its acidic pH , trimeric post-fusion form . The structures confirm the prediction that Gc is a class II fusion protein , containing the characteristic β-sheet rich domains termed I , II and III as initially identified in the fusion proteins of arboviruses such as alpha- and flaviviruses . The structures also show a number of features of Gc that are distinct from arbovirus class II proteins . In particular , hantavirus Gc inserts residues from three different loops into the target membrane to drive fusion , as confirmed functionally by structure-guided mutagenesis on the HPS-inducing Andes virus , instead of having a single “fusion loop” . We further show that the membrane interacting region of Gc becomes structured only at acidic pH via a set of polar and electrostatic interactions . Furthermore , the structure reveals that hantavirus Gc has an additional N-terminal “tail” that is crucial in stabilizing the post-fusion trimer , accompanying the swapping of domain III in the quaternary arrangement of the trimer as compared to the standard class II fusion proteins . The mechanistic understandings derived from these data are likely to provide a unique handle for devising treatments against these human pathogens .
Hantaviruses are a small group of zoonotic viruses of rodents , bats and insectivores such as moles and shrews [1] . They are often transmitted to humans by persistently infected rodents , causing serious outbreaks of pulmonary syndrome or of hemorrhagic disease with renal syndrome [2 , 3] . The case fatality rates can reach 50% , for instance in the case of the “Sin Nombre” hantavirus outbreak in the 1990s in the four-corners area in the US [4] . The name hantavirus derives from the prototype virus , Hantaan virus , which was discovered in the early 1950s during the Korean war , when troops stationed by the Hantaan river developed hemorrhagic manifestations [5] . Outbreaks of hantavirus disease of varying severity have occurred periodically in the last decades throughout the Americas [6 , 7] and in Europe and Asia [8 , 9] . It is therefore important to understand the structural organization of hantavirus particles as a step forward in attempts to devise curative or preventative strategies . Hantaviruses constitute one of five genera forming the Bunyaviridae family of enveloped RNA viruses , which have a genome composed of three segments of single-stranded RNA of negative polarity [10] . The bunyavirus proteins involved in genome replication–the large ( L ) polymerase and the nucleocapsid ( N ) protein , encoded in the large and small genome segment , respectively–are very similar to their counterparts in other families of segmented negative-strand ( sns ) RNA viruses , such as the Arenaviridae or the Orthomyxoviruses [11] . In contrast , the envelope glycoproteins , which derive from a polyprotein precursor encoded in the medium ( M ) size genomic segment , are totally unrelated . Whereas the other snsRNA virus families display class I membrane fusion proteins characterized by a central alpha-helical coiled-coil in their post-fusion form , the bunyavirus envelope proteins have properties of class II enveloped viruses [12]; i . e . , they are β-sheet-rich proteins ( as predicted from their amino acid sequences ) such as those found in the icosahedrally symmetric flaviviruses and alphaviruses . The latter , like viruses in the Bunyaviridae family other than the hantaviruses , are transmitted to vertebrates ( or to plants in the case of tospoviruses ) by insect or tick vectors , and are accordingly termed arthropod-borne viruses or “arboviruses” . In class II viruses , the membrane fusion protein is the second in a tandem of proteins–Gn and Gc in the bunyaviruses—encoded sequentially within a single precursor polyprotein . They heterodimerize in the ER and are then transported to the site of budding , which is the Golgi apparatus for a number of bunyaviruses [13] , although some hantaviruses were reported to bud at the plasma membrane [14 , 15] . The bunyavirus Gc glycoproteins were predicted to adopt a “class II” fold using proteomic computational analyses [16] , and Gc from Andes virus ( a hantavirus endemic in South America ) was modeled using the flavivirus E protein as template [17] , thereby predicting the location of the “fusion loop” , which was later functionally confirmed as important for fusion by site directed mutagenesis [18] . Similarly , for La Crosse virus in the Orthobunyavirus genus , mutagenesis of the predicted fusion loop region—deduced by comparison to the alphavirus fusion glycoprotein E1—confirmed the importance of this region for entry [19] . More recently , the determination of the crystal structure of Gc of the Rift Valley Fever virus [20] , a bunyavirus in the Phlebovirus genus , demonstrated that it indeed has the typical fold of class II membrane fusion proteins , providing a conclusive proof . These observations are compatible with the icosahedral organization of the phleboviral particles observed by electron microscopy [21 , 22] . Extrapolation to the other genera should be done with caution , however , since the sequence similarity is very low and not easily detectable by current methods . In the case of the Flaviviridae family , for instance , where viruses outside the Flavivirus genus were also expected to have class II fusion proteins , the prediction was proven wrong when the corresponding crystal structures were determined , i . e . the E2 protein from the pestiviruses [23 , 24] and from the hepatitis C virus [25 , 26] . Structural studies on members of the Bunyaviridae family are relatively limited , providing the low-resolution overall arrangement of spikes at the virus surface , but not the organization of the individual proteins . Differently to the icosahedral organization observed in virions of the Phlebovirus [21 , 22] and Orthobunyavirus [27] genera , hantavirus particles were shown to be pleomorphic , with some virions showing a roughly spherical and others an elongated aspect [28] . Cryo-electron tomography and sub-tomogram averaging revealed that the Gn/Gc spikes are arranged with apparent 4-fold symmetry [29 , 30] on a curved square lattice , incompatible with icosahedral symmetry . The resulting array of hantavirus spikes does not cover the whole surface of the observed particles . The organization of the Gn/Gc subunits within the spikes was interpreted as a ( GnGc ) 4 hetero-octamer . A recent study described the crystal structure of the Gn ectodomain of Puumala hantavirus , which was docked on the spike using the 3D reconstruction of Tula hantavirus extended to 16Å resolution , placing Gn at an exposed site on the spike , distal to the viral membrane [31] . Here , we report the crystal structure of the ectodomain of Gc from Hantaan virus in a monomeric pre-fusion conformation and in its trimeric post-fusion form , validating its prediction as a class II fusion protein , albeit presenting a number of hantavirus-specific features . Combined with structure-guided functional studies on the related Andes virus , we show that hantavirus Gc has a multi-partite membrane interaction surface , with residues outside the cd loop ( which is the fusion loop in standard class II proteins ) also being important for membrane insertion and fusion . We further show that Gc requires the formation of a carboxylate-carboxylic acid hydrogen bond , which can form only at acidic pH , for structuring the membrane interacting region . In addition , we show that hantavirus Gc has an N-terminal segment ( the “N tail” ) which is absent in other class II proteins and which is functionally involved in trimerization to form the stable post-fusion form . Further analysis also identifies a conserved “cysteine” signature in the amino acid sequence of Gc giving rise to a disulfide bonding pattern conserved in the Nairovirus , Orthobunyavirus and Tospovirus genera , but notably different from the phlebovirus Gc .
We expressed the recombinant ectodomain of Gc ( rGc ) from Hantaan virus strain 76–118 ( UniProtKB/SwissProt accession: P08668 . 1 ) in Drosophila S2 cells as described in Materials and Methods . It behaved in solution as a monomer , both at neutral and acidic pH , as assessed by size exclusion chromatography ( SEC ) and multi-angle static light scattering ( MALS ) ( S1A Fig ) . Electron micrographs of negatively stained samples of purified rGc showed a thin rod-like molecule of ~140Å in length ( see below ) . Despite multiple trials , rGc failed to crystallize on its own , and we therefore screened a human single-chain variable domain ( scFv ) antibody fragment library , which in principle was hantavirus-naïve [32] , to identify potential binders that could act as crystallization chaperones . We thus identified scFv A5 , which is very close to its germ line ( Table 1 ) and which interacted with rGc as monitored by ELISA ( see Methods ) . Further analysis by size-exclusion chromatography ( SEC ) together with multi-angle light scattering ( MALS ) , showed that scFv A5 forms a 1:1 complex with rGc ( S1B Fig ) . We obtained crystals diffracting to 3Å resolution of the A5/rGc complex at pH 7 . 5 , after limited proteolysis of the complex with trypsin . This treatment removed a C-terminal segment of rGc and the purification tags of both , A5 and rGc . We determined the X-ray structure by a combination of molecular replacement with the variable domains of an antibody and the anomalous scattering from a samarium derivative ( see Materials and Methods ) . The final electron density map was clear for amino acids ( aa ) 16–414 of rGc ( out of residues 1–457 in the intact Gc ectodomain , Fig 1A ) , with internal breaks at loops 84–91 and 108–132 ( Fig 1B ) . These disordered segments map to the tip of domain II , at the membrane interacting region , as discussed below . All A5 residues were clearly resolved in density , except for the linker connecting light and heavy chains in the scFv construct . An atomic model of the complex was built into the electron density with the program Coot and refined with Phenix . refine to an R factor of 22% and free R factor of 27% ( S1 Table ) . The scFv makes an important contribution to the packing contacts in the crystal . The antibody buries about 1 , 000 Å2 of the accessible surface of rGc , with 60% of the contacts made by the light chain ( which is un-mutated with respect to its germ line , see Table 1 ) . In spite of the relatively large buried antibody/antigen surface , affinity measurements by surface plasmon resonance ( SPR ) indicated an A5:rGc binding constant in the micromolar range , in line with the absence of affinity maturation of the human scFv library ( S1C and S1D Fig ) . The structure shows that Gc displays a typical class II fusion protein fold ( Fig 1 ) , first observed in the ectodomain of the flavivirus E [34] and alphavirus E1 [35] fusion proteins , and more recently in the rubella virus E1 glycoprotein [36] and in phlebovirus Gc [20]–the only other genus of the Bunyaviridae family for which a Gc structure is available . The class II fold features a central β-sandwich domain ( termed domain I ) made of eight β-strands labeled B0 through I0 , connected sequentially with up-and-down topology and arranged in two antiparallel β-sheets , the “inner” B0I0H0G0 and the “outer” C0D0E0F0 sheets apposed against each other ( the names of the β-sheets refer to their orientation in the post-fusion trimer ) . Like flavivirus E and phlebovirus Gc , hantavirus Gc has an additional short N-terminal β-strand , A0 , which starts at residue 18 and runs parallel to strand C0 at the edge of the outer sheet . The N-terminal segment , upstream of strand A0 and which contains a number of residues strictly conserved across the Hantavirus genus ( Fig 1D ) , is disordered in the crystals . The segments connecting β-strands D0 to E0 in the outer sheet and H0 to I0 in the inner sheet are very long and elaborated . They make up domain II ( yellow in the Figures ) , which is composed of 13 β-strands ( labeled a through l ) and a couple of short helices ( η1 and α2 , Fig 1 ) . Strand j’ , unique to hantavirus Gc and inserted between helix α2 and strand k ( Fig 1D ) carries the single Gc N-linked glycosylation site at Asn280 ( Fig 1B ) , which is strictly conserved across hantaviruses ( Fig 1D ) . Domain II has an elongated shape with two subdomains , a central , domain I-proximal open β-barrel made of β-strands klaefgj’ , and a distal “tip”—a β-sandwich between the bdc β-sheet and the ij β-hairpin , which projects the cd loop ( which is the fusion loop in the standard arbovirus class II proteins ) at its distal end . This region corresponds to the disordered Gc tip in the crystal structure , stabilized in part by the scFv A5 ( Fig 1B ) . Finally , after strand I0 , domain I connects via a 12 residue linker ( cyan in Fig 1 ) to domain III ( blue ) , which has an immunoglobulin superfamily C2 subtype fold [37] composed of β-strands A through G ( Fig 1B and 1D ) . Hantavirus Gc has a total of 26 cysteine residues in the ectodomain , 24 of which are present in the crystallized fragment , making 12 disulfide bonds . The surface area buried at the interface between domains I and III is very small , and is stabilized in the crystals by a cobalt-hexamine ion ( Fig 1B ) , which was identified in crystal optimization screens to improve the diffraction of the crystals . Although Gn was shown to be displayed prominently on the hantavirus spikes , and some of the epitopes of neutralizing antibodies were mapped there [31] , a number of hantavirus neutralizing antibodies target Gc as well . The only neutralization escape mutant mapping to Gc reported in the literature corresponds to an S287F change in Puumala hantavirus Gc and confers escape from neutralizing Mab 1C9 [38] , which maps to the k strand on one side of domain II ( Fig 1C ) . There are also data on Gc residues that affect binding of neutralizing antibodies for various other hantaviruses . Although often such epitopes are conformational and cannot be mimicked by peptides , the epitope of the neutralizing murine Mabs 3G1 and 3D8 [39] against Hantaan virus have been respectively mapped by peptide scanning to residues 96–105 [40] and 242–248 [41] . The 3G1 epitope thus maps to the bc loop and largely overlaps with that of scFv A5 , whereas the 3D8 epitope maps to the i strand , at the opposite side ( Fig 1C ) . The epitope of a human antibody against Hantaan virus , Y5 , was also identified by peptide scanning , and mapped to two discontinuous segments of the Gc polypeptide , 268–276 and 307–315 [42] , which correspond , respectively , to the region around helix α2 in domain II and to the end of strand I0 and the linker between domains I and III ( see Fig 1C and 1D ) . These two segments are far apart on the Gc monomer , but may be located more closely in a multimeric arrangement of Gc on the hantavirus spike . Although the docking of Gn appeared to be clear in the reported 16Å resolution electron density map of Tula hantavirus [31] and allowed to understand how the Gn epitopes are exposed on the spike , docking rGc is less clear in the same map and will need to await higher resolution data and/or a crystal structure of a Gn/Gc heterodimer to unambiguously assess where the Gc epitopes lie on the spike . In particular , as the epitopes map toward the Gc tip , which is partially disordered in the crystals , the conformation of this flexible region may be affected by Gn-Gc interactions on the spike . We investigated the behavior of rGc in interaction with lipids , and found that it binds liposomes at acidic pH as detected by flotation on sucrose gradients and by SPR on a matrix with immobilized liposomes ( see Materials & Methods ) ( S2A Fig ) . The SPR measurements also detected binding to liposomes at pH 7 . 4 ( S2B Fig ) , although to a lower extent than at pH 5 . 5 . We found that the interaction required the presence of cholesterol in the liposomes ( Fig 2A ) , in agreement with recent studies [43] . Addition of the scFv A5 interfered with lipid binding ( Fig 2A ) , in line with its epitope lying close to the membrane interacting region of Gc ( Fig 1 ) . Because the A5 epitope overlaps with that of the neutralizing Mab 3G1 , our data suggest that the neutralization mechanism of antibodies targeting this region involves blocking membrane insertion of Gc . Electron microscopy showed that the Gc proteoliposomes display radial projections with a shorter and thicker aspect than the observed overall shape of monomeric rGc in solution ( Fig 2B ) . The rGc projections on the liposomes are very similar in size and shape to the trimeric projections made by class II viral fusion proteins in their post-fusion conformation on liposomes [36 , 44 , 45] . This similarity suggested that rGc had adopted its predicted post-fusion conformation , as recently evidenced for Andes virus Gc by sucrose sedimentation in an in vitro system [46] . Because attempts to crystallize the membrane inserted form of rGc failed , we introduced a mutation in the predicted fusion loop , which caused rosette formation upon concentration after detergent solubilization ( in line with the model for insertion of trimeric post-fusion class II proteins into membranes , reviewed in [47 , 48] ) . The fusion loop mutation was inspired by a recent report showing that the trimeric post-fusion form of the flavivirus class II fusion protein E could be crystallized in its post-fusion , trimeric form in the absence of detergent by replacing Trp 101 by histidine , as this residue is prominently exposed at the membrane facing-end of the post-fusion trimer [49] . We accordingly substituted Trp115 , predicted to be at the tip of the cd loop in hantavirus Gc [18] by histidine . The rGc W115H mutant indeed crystallized under mildly acidic conditions ( pH 6 . 5 ) in the rhombohedral space group R3 , and the crystals diffracted to 1 . 6Å resolution . We determined the crystal structure of acid-pH rGc by molecular replacement using the individual domains of Gc , and refined the atomic model to 1 . 6 Å resolution to an R factor of 14% ( free R factor 17% ) ( S1 Table ) . A single Gc protomer ( or trimer subunit ) is present in the asymmetric unit of the crystals , packing about a crystallographic 3-fold axis and adopting the characteristic class II post-fusion form ( Fig 2C ) . As in the other class II proteins [36 , 50–53] , the post-fusion form shows a drastic re-orientation of domain III such that it packs laterally against the domain I/II junction of both , the same and the adjacent subunits in the trimer ( Fig 2C ) . This relocation of domain III is in line with recent data showing that exogenous domain III can block the fusion process [54] by binding to the domain I/II inner trimer core and interfering with the necessary translocation of domain III to reach the post-fusion hairpin conformation , as had been shown earlier for alpha- and flaviviruses [55] . The Gc structures show that during the pre- to post-fusion transition , domain II hinges by 26 degrees about the domain I/II junction , thereby bringing the domain II tips of the three protomers into contact at the trimer tip ( Fig 2D ) . The A5 epitope remains accessible on the trimer , and modeling shows that three scFvs can bind simultaneously to one trimer ( S2C Fig ) . The observed inhibition of trimer insertion is likely due to the scFv protruding further at the tip of the trimer than the fusion loop itself , helping the complex to remain in solution . The buried area per Gc subunit in the trimer is 2290 Å2 , and the residues at the interface are mostly hydrophilic and conserved ( S3 Fig ) . In contrast to the neutral pH form , the tip of domain II , ( the cd loop but also the neighboring parts of the bc and ij loops ) displayed clear electron density and allowed tracing the polypeptide chain with no breaks . As expected , the side chain of Trp115 ( which is His115 in the mutant used for the crystals ) is exposed at the very tip of domain II , where it would be expected to insert into membranes . In a previous study , Trp115 was indeed shown to be essential for fusion activity of Andes hantavirus [18] . That study also showed that Asn118 in the cd loop was essential for membrane fusion . The low pH structure now shows that the Asn118 side chain makes a crucial set of hydrogen bond interactions with the peptide backbone of the cd loop and its main chain with the ij loop ( Fig 2E ) . This key structuring-role of the domain II tip explains the strict conservation of Asn118 across hantaviruses ( Fig 1D ) and its functional importance for fusion . In addition to the role of Asn118 in structuring the fusion loop , we observed that the disorder at the tip of domain II in the monomeric , pre-fusion form of Gc begins at residue Asp108 in β-strand c , within a strictly conserved 106-EXD-108 amino acid motif ( where X is any amino acid ) ( Fig 1D ) . In the post-fusion structure , the side-chain of Glu106 is connected via hydrogen-bonds to the indole ring of the strictly conserved Trp98 and to the Asp108 side chain ( Fig 3A , left panel ) , making a relatively short ( 2 . 6 Å distance ) carboxylate—carboxylic acid hydrogen bond . The pK of the amino acids in the latter interaction is therefore shifted in the structure , which was obtained at pH 6 . 5 ( see S1 Table ) ( the normal pK of glutamic and aspartic acid is 4 . 2 and 3 . 8 , respectively ) , such that a proton is present in between . Of note , in crystals of rGc also at pH 6 . 5 obtained in the presence of KCl at concentrations above 200 mM , Gc was in its post-fusion form but the tip was disordered ( S4 Fig ) . The crystal-packing environment was not responsible for the observed disorder , as the crystals were isomorphous , having the same symmetry and the same cell parameters , and diffracting to high resolution ( around 1 . 4 Å ) . Inspection of the structure further showed that a K+ ion from the crystallization conditions becomes trapped near the central 3-fold axis of the rGc trimer , coordinated by the side chain hydroxyl group of Tyr105 together with the main-chain carbonyl oxygens of Phe240 , Gly242 of the same subunit and of Asp259 from a neighboring protomer , as well as an immobilized water molecule ( Fig 3B ) . Tyr105 directly precedes the di-carboxylate 106-EXD-108 motif , and as it adjusts its orientation to chelate the K+ ion , it alters the main chain such that Glu106 is pulled away from the interaction with Asp108 , becoming de-protonated and now accepting hydrogen bonds from the imidazole ring of His104 , while still maintaining the interaction with the indole ring of Trp98 ( Fig 3A , middle panel ) . These results therefore indicate that formation of the carboxylate-carboxylic acid hydrogen bond is essential to the organization of the tip of domain II , and that the reason why this region is disordered in the neutral pH structure is that this interaction cannot form ( Fig 3A , right panel ) . This disordered region in Gc at neutral pH is in line with the altered mobility of Gc in SEC at the two pH values measured , with the monomer at neutral pH displaying a larger Stokes radius for the same molecular mass ( S1A Fig ) . We tested the functional relevance of these interactions in the Andes hantavirus system , for which it was shown that expression of the M genomic segment ( i . e . , coding for wild type Gn and Gc proteins ) in cells induces syncytium formation when treated at low pH . We thus introduced each of the following Gc substitutions: E106A , E106Q , D108A , D108N and W98A into Andes virus Gc using this plasmid , and compared syncytium formation by the mutants and by the wild type protein . Although the level of Gn and Gc that reached the cell surface was similar to wild type ( S5 Fig ) , there was no syncytium induced by the mutants except for D108N ( Fig 3C ) , which can still make a hydrogen bond with the Glu106 side chain ( Fig 3A , left panel ) . The reverse situation , in the E106Q mutant , is not viable , indicating that the Glu106 side chain is essential in this process , perhaps because of its dual interaction with Asp108 and Trp98 ( Fig 3A , left panel ) . We confirmed these results by introducing the same mutations into a system of SIV particles pseudotyped with the Andes virus glycoproteins , which allows visualization of entry by expression of a fluorescent reporter gene [56] . Again , only the D108N mutant was as efficient as wild type for entry ( Fig 3D ) , whereas none of the other mutants was , in spite of being present in similar amounts as wild type Gn and Gc on these particles ( S5 Fig ) . The requirement for a glutamic acid to be protonated in order to organize the structure of the domain II tip only upon acidification is unique to hantavirus Gc , as it has not been described so far for any other membrane fusion protein . An important difference with the post-fusion structures of the arbovirus class II proteins is that in the quaternary organization of hantavirus Gc , domain III takes the place occupied in the other trimers by its counterpart from a neighboring protomer . This had been observed previously in the structure of the rubella virus E1 glycoprotein in its post-fusion conformation ( Fig 4A ) , the only other non-arbovirus viral class II protein of known structure [36] . In this altered quaternary organization of the Gc trimer , the strictly conserved glycan chain of Gc at Asn280 in domain II fits snugly into a groove at the subunit interface , with the glycan making a number of inter-chain hydrogen bonds with hydrophilic amino acids at the domain III surface from the adjacent subunit ( Fig 4B ) . Ablation of this glycosylation site in Hantaan virus Gc was shown to give rise to a Gn/Gc glycoprotein complex that was able to reach the cell surface [57] , but could not induce syncytium of transfected cells upon low pH treatment [58] . These observations are in line with the added trimer stability provided by the glycan contacts . The domain III swap is accompanied by the N tail and by strand A0 ( residues 19–21 in the pre-fusion form ) , which switches from its parallel interaction with strand B0 to run antiparallel ( residues 14–19 in the post-fusion form ) to C0 of the neighboring subunit in the trimer ( Fig 2F ) , thereby augmenting the inner sheet and providing an extensive pattern of inter-subunit main chain hydrogen bonds . Other inter-chain interactions between strictly conserved residues involving the Gc N tail include hydrogen bonds and salt bridges between His14 and Asp174 in β-strand G0 and between Asp11 and His399 in domain III , as well as Trp9 packing against Pro321 and disulfide 9 ( Cys322-Cys352 ) ( Fig 4C ) , also in domain III . The domain I/III linker ( residues 310–321 ) runs along the A0 strand , making several antiparallel β-sheet interactions with it ( Fig 4C ) . At the very N-terminal end , residues 1 through 5 ( which are variable in sequence across the hantaviruses , Fig 1D ) project into solvent and are disordered . As the N tail is not present in the other class II proteins , but in hantaviruses a number of its residues are strictly conserved ( Fig 1D ) and are seen in the structure to make a network of interactions , we turned to the Andes virus system to functionally test some of these residues . We chose to substitute Asp11 , Thr12 and His14 by alanine , and also His14 by tyrosine to see if a bigger side chain could functionally substitute for histidine . These mutants were all expressed correctly and reached the cell surface ( S5 Fig ) , but syncytium formation and cell entry by the corresponding pseudotyped SIV particles was abrogated ( Fig 4D and 4E ) . In order to further explore whether trimerisation of the mutants is impaired , we made virion-like particles ( VLPs ) of Andes virus [60] harboring the mutations D11A and H14Y in Gc . We harvested VLPs from cells transfected with wild type Gn/Gc or with wild type Gn and the mutant Gc , which secreted VLPs at similar levels ( S5 Fig ) . We analyzed the VLPs after low pH treatment followed by detergent solubilization for Gc trimer formation by sedimentation on a sucrose gradient . The amount of mutant Gc trimer formation was similar to the wild type VLPs ( Fig 4F ) , indicating that trimerization is not impaired . We therefore analyzed the stability of the resulting acid-induced mutant Gc trimers by trypsin digestion of the VLPs . We observed that , contrary to wild type Gc , the low pH treated D11A and H14Y mutants were not resistant to proteolytic degradation ( Fig 4G ) . In parallel , we found that the alanine substitution of Tyr88 , Trp115 or Phe250 , which are located at the tip of Gc domain II and which are also impaired in fusion ( see below ) but for which the side chains are not involved in inter-protomer contacts in the trimer , resulted in trypsin-resistant trimers as wild type Gc , serving as a positive control . These data indicate that the inter-subunit interactions of the Gc N tail are important to confer sufficient stability of the Gc post-fusion trimer in order to be fusion active . The hantavirus Gc ij loop is longer than the corresponding loop in standard class II fusion proteins , and it places Phe250 at the tip of the trimer , along with Trp115 and Pro123 in the cd loop and Tyr88 in the bc loop ( Fig 5A ) . To understand whether these residues are involved in the functional targeting of the host cell membrane , we performed alanine substitutions and examined the corresponding mutants in the context of Andes hantavirus with the tools described above . We examined these mutants alongside the W115A mutant characterized previously [18] . As W115A , the Y88A , P123A and F250A mutants were properly expressed and the corresponding Gn/Gc complexes trafficked to the plasma membrane of transfected cells ( S5 Fig ) . Except for the P123A mutant , which behaved as wild type and served a positive control , the Y88A and F250A mutants were impaired in syncytia formation , and did not support entry of the pseudotyped particles into cells ( Fig 5E and 5F ) , similar to the previous results with the W115A mutant . We also analyzed the interaction of VLPs generated with the same mutants with fluorescently labeled liposomes using a sucrose gradient . When VLPs and liposomes incubated at pH 7 were run on the gradient , the liposomes were found by fluorescence floating on top of the gradient , while Gc was recovered from the bottom fractions ( Fig 5G ) . But when the VLPs were incubated with liposomes at pH 5 . 5 , each of the single mutants was recovered in the top fractions of the gradient , as was wild type ( Fig 5G ) , indicating that substitution by alanine of a single residue at the tip of domain II was not sufficient to abolish the interaction with membranes necessary to float with the liposomes . We also tested VLPs containing double mutations ( which produced VLPs in similar yields as wild type , S5 Fig ) : whereas Y88A/W115A still partially floated with the liposomes , W115A/F250A remained in the bottom fractions at acidic pH . But neither of them mediated low pH-induced syncytia formation , as expected from the single substitution mutants ( Fig 5E ) . In order to identify the actual step at which substitution of W115A and F250A block the membrane fusion process , we tested whether these single or double mutants still underwent acid-induced Gc trimerization , since it was shown previously that Gc can trimerize in the absence of membrane insertion [46] . As expected , sedimentation in a sucrose gradient of acid-treated VLPs including Gc mutants W115A and W115A/F250A revealed that , independently of the membrane inserting activity , these mutants underwent homotrimerization as did wild type Gc ( Fig 5H ) . This result is in line with the structure of the post-fusion trimer , in which the side chains of these residues are not involved in trimer contacts but are exposed at the membrane-interacting side of the trimer ( Fig 5A ) . To further investigate the stage at which fusion is blocked with these mutants , we incubated pyrene-labeled VLPs [61] bearing wild type Gc or the mutants with liposomes , in order to monitor lipid mixing . Acidification resulted in decrease of the fluorescence of the pyrene excimer within 20 sec in the case of wild type Gc , reflecting lipid mixing ( Fig 5I ) during fusion . In contrast , the VLPs carrying the Gc double mutant W115A/F250A , which do not insert into target membranes as monitored by liposome flotation , displayed no signal for lipid mixing , corroborating the assay ( Fig 5I ) . When we ran this experiment with VLPs containing the Gc fusion inactive single substitution mutants Y88A , W115A or F250A , we could still detect lipid mixing upon low pH incubation with the liposomes ( Fig 5I ) , indicating that these mutants led to an incomplete fusion process , most likely arrested at the hemifusion stage , as they do not induce full fusion ( Fig 5E and 5F ) . Indeed , if only the labeled lipids of the outer leaflet become diluted during hemifusion , then the expectation is to obtain a lower lipid mixing signal , as observed– , provided that there is no lipid flipping from inner to outer leaflets of the membrane during the time frame of the experiment . The fact that the mutants did not induce full fusion ( Fig 5E and 5F ) indicates that the observed lipid mixing was due to hemifusion with negligible lipid flipping under the conditions of the experiment . These results therefore indicate that the Gc single mutants do not insert stably enough into the membrane to induce full fusion , but they still can induce lipid mixing . We conclude from these results that the longer ij loop observed in hantavirus Gc , as well as the bc loop with Tyr88 projecting into the membrane , have functional implications in engaging the target membrane such that full fusion can proceed . In contrast to the arbovirus class II proteins , which appear to have a single fusion loop–the cd loop–in hantaviruses the membrane interacting surface is multipartite . Amino acid sequence alignment of hantavirus Gc with its counterparts from viruses of the various genera of the Bunyaviridae family allowed the identification of a motif that systematically identifies Gc glycoproteins from four out of the five genera , leaving out the phleboviruses . The alignment used to extract this motif is displayed in Fig 6 , and includes four of the disulfide bonds that stabilize the tip of domain II as well as one of the disulfides in domain I ( the one stapling together β-strands E0 and F0 ) . This alignment allows the prediction of the connectivity of the additional cysteines in the other genera ( S8 Fig ) . Two features appear important: as hantavirus Gc , the other genera also have N-terminal extensions , which are quite large ( as in orthobunyavirus Gc ) , and lack the N-terminal disulfide bond stapling β-strand A0 and C0 as in the other class II fusion proteins featuring an A0 strand ( i . e . , phlebovirus Gc , flavivirus E and the cellular fusion protein EFF-1 ) . Such a disulfide bond would be incompatible with the rearrangements of strand A0 that are necessary to have a swapped domain III in the post-fusion form , and one prediction therefore is that the phlebovirus Gc will have the “standard” class II quaternary arrangement , whereas in all the other genera the post-fusion form of Gc is likely to feature a swapped domain III . Similarly , the ij loop is also longer in the Nairovirus , Orthobunyavirus and Tospovirus genera than in the standard class II proteins , and is likely to contribute to the membrane interacting surface ( see Fig 5A , 5B and 5C ) . In contrast , the carboxylate—carboxylic acid hydrogen bond in strand c , which structures the tip of domain II , appears as a hantavirus-specific feature , as these residues are not conserved across these bunyavirus genera .
Membrane fusion is a critical step in entry for any enveloped virus , and in hantaviruses it is mediated by the highly conserved glycoprotein Gc . Because of this conservation , the features identified in the structures of Hantaan virus Gc in its pre- and post-fusion forms can be applied to all members of the Hantavirus genus . We have taken advantage of this conservation to use the molecular tools developed to test the glycoproteins of Andes virus for function . By combining structural and functional data , one important aspect that we have discovered is the multi-partite nature of the membrane interaction surface of hantavirus Gc as well as the key role played by the N tail for fusion . These results set hantavirus Gc—and by extension , also Gc from the three more closely related genera in the Bunyaviridae family , see below–apart from the more standard class II fusion proteins observed in the flaviviruses and phleboviruses . The latter have an A0 strand ( contrary to alphavirus E1 , which lacks strand A0 ) but it is locked by a disulfide bond to the C0 strand of the same polypeptide chain , prohibiting a conformational rearrangement similar to the domain III swap , which is accompanied by the N tail ( Fig 2F ) . The various loops interacting with the target membrane in hantavirus Gc are reminiscent of the bi-partite membrane contacting region of class III fusion proteins [65] , with two fusion loops—which may also require elements from the C-terminal , membrane proximal region to engage the target membranes and induce full fusion . A more extensive membrane binding region was previously observed in the class II fusion protein E1 of rubella virus , which features an insertion within the cd loop to make two short α-helices and an additional β-strand ( c’ ) running parallel to strand c , such that the bdc β-sheet at the tip of domain II becomes a four-stranded bdcc’ β-sheet [36] . This results in two fusion loops , cc’ and c’d , projecting toward the target membrane , and a Ca2+ site in between the two loops that is essential for function [66] . An analogous role appears to be played by Asn118 in hantavirus Gc , by bridging two loops in the membrane interacting region ( Fig 2E ) . A further similarity with the rubella virus post-fusion E1 trimer is the swapped domain III , although in E1 there is neither A0 strand nor N tail to accompany this conformational change , and the transition is not yet understood in the absence of a structure of E1 in its pre-fusion form . The mechanistic implications derived from the structures of the hantavirus Gc can be extended to other bunyaviruses , further broadening the scope of this work . Indeed , our comparison of hantavirus Gc with its counterparts of the other genera indicates that the hantavirus and nairovirus Gc proteins are closer to each other than they are to Gc from other bunyaviruses , and that Asn118 , which plays a key structuring role within the cd loop and the interaction with the ij loop ( Fig 2E ) is conserved across the two genera ( Fig 6 and S8 Fig ) . We also note the conservation in nairoviruses of Trp9 and His14 of the N tail , which are involved in the network of interactions illustrated in Fig 4C , supporting the notion that a similar domain III swap may occur in nairoviruses as well . As nairoviruses require the additional cleavage of preGc into mature Gc at the N-terminal end , at an “RKPL” site corresponding to a non-standard subtilase SKI-1 like protease [67] , it is possible that the cleavage is necessary to release the N tail such that the fusogenic conformational change can take place ( the RKPL sequence is about 40 residues upstream the first residue displayed in the alignment of the S8A Fig ) . The sequence conservation pattern further shows that orthobunyavirus and tospovirus Gc proteins are also closer to each other than they are to the others ( S8B Fig ) , and feature an insertion in the ij loop , which becomes even longer ( Fig 6 ) . The amino acid alignment indicates that a dibasic motif downstream the disulphide 4 in the ij loop ( highlighted in a cyan background in Fig 6 ) will face an acidic “EE” motif in the cd loop ( highlighted in the same Figure ) at the location of Asn118 in hanta- and nairoviruses , suggesting that a different set of polar/electrostatic interactions may replace the network formed by Asn118 at the tip of domain II in Gc of these two genera . Our results on hantavirus Gc therefore reveal a number of unanticipated aspects of the bunyaviruses in general . In particular , they introduce an evolutionary hierarchy for the five genera based on the Gc gene , which appear to display a deep branching site between phleboviruses and the others , the latter then splitting into two branches each , giving rise to hantaviruses and nairoviruses on the one hand , and to orthobunyaviruses and tospoviruses on the other , with mechanistic similarities for the membrane fusion process accompanying this diversification . We thus propose to group these fusion proteins as a sub-class within class II , having by specificity the fact of presenting a multipartite membrane targeting region , together with an N tail involved in the fusogenic conformational rearrangement . Additional structure-function studies on the Gc orthologs from each of these branches will further nail down the practical implications of the identified evolutionary trends , opening the possibility of unveiling common vulnerability sites on Gc to be targeted by broad-spectrum anti-bunyavirus compounds to help treat patients against these deadly pathogens .
In order to obtain milligrams amount of soluble Hantaan virus Gc , we inserted a synthetic gene codon-optimized for expression in Drosophila cells , into a modified pMT/BiP plasmid ( Invitrogen ) . This initial construct , termed pMT-Gn/rGc , included the full length Gn followed by the Gc ectodomain , i . e . lacking the transmembrane and cytoplasmatic domains . To help in the purification we included two strep-tag sequences separated by a ( GGGS ) 3 linker and preceded by an enterokinase cleave site . As this construct produced only small amounts of soluble rGc , most likely because it is retained within the cells , either by Gn and/or because the fusion loop of Gc interacts with cellular membranes , we removed Gn from the construct to make pMT-rGc . As mentioned in the main text , we also introduced the W115H mutation in this plasmid ( pMT-rGc-W115H ) . Similarly , scFv A5 , identified as binding to rGc by screening the Griffin library using helper phage KM13 [68] , was cloned into the modified pMT/BiP plasmid followed by a double strep-tag ( pMT-scFvA5 ) . These plasmids were used separately to obtain stable transfectants of Drosophila S2 cells together with the pCoPuro plasmid ( ratio 1:20 ) for puromycin selection . The stable cell lines were selected and maintained in serum-free Insect-Xpress medium containing 7 μg/ml puromycin . Cultures of 1–3 liters were grown in spinner flasks in Insect-Xpress medium supplemented with 1% penicillin/streptomycin antibiotics to about 1 x 107 cells/mL , and the protein expression was induced with 4 μM CdCl2 . After 5 days , the S2 media supernatant was concentrated to 40 ml and supplemented with 10 μg/mL avidin and 0 . 1M Tris-HCl pH 8 . 0 , centrifuged 30 minutes at 20 , 000 g and purified by strep-tactin affinity chromatography and gel filtration . The yields were about 5–10 mg/L for rGc and rGc-W115H and 10–15 mg/L for scFv A5 . For crystallization , rGc was incubated on ice for 30 minutes with a 1 . 2 molar excess of scFv A5 with the pH adjusted to 8 . 5 by adding TrisHCl pH 8 . 5 to a final concentration of 100 mM . The mixture was digested with trypsin ( mass ratio of 1:100 ) , for 1 hour at 37°C , stopping the reaction by adding 1 mM of PMSF and cooling on ice . The digest was loaded to a gel filtration Superdex 75 16/60 column in 10 mM Tris HCl pH 8 . 0 , 150 mM NaCl , and the fractions of the peak corresponding to the complex were pooled and concentrated in the same buffer for crystallization trials . The rGc-W115H construct was used to obtain the post-fusion form without adding detergent , as explained in the main text . Digestion with enterokinase ( New England Biolabs ) after Strep-tactin affinity purification to remove the Strep-tag for crystallization showed an immediate cleavage but overtime a second , shorter resistant fragment accumulated overnight at 4°C . Only this second enterokinase resistant fragment , in which rGc appeared to lose the stem region , resulted in crystals . Inspection of the stem region indeed indicates several lysine residues where enterokinase could cleave . In the final protocol , the enterokinase treatment was allowed to proceed overnight at 4°C , and then the digest was submitted to gel filtration on a Superdex 200 16/60 column in 10 mM Tris HCl pH 8 . 0 , 150 mM NaCl . The protein was then buffer exchanged and concentrated in 20 mM Bis-Tris pH 6 . 1 , 150 mM NaCl , using a Vivaspin centricon , to a final concentration of 5–10 mg/ml . We determined the structure of the rGc/scFv A5 complex by a combination of molecular replacement ( MR ) and single-wavelength anomalous dispersion ( SAD ) . Native crystals were grown in 60 mM Na-HEPES pH 7 . 5 , 40 mM hexamine cobalt chloride salt , 13 . 5% ( w/v ) PEG 4K , 7 . 4% ( v/v ) 2-propanol , and 1% ( v/v ) glycerol and cryo-protected in the same solution supplemented with 22% ( v/v ) PEG 400 . The crystals used for the heavy atom derivative were grown in presence of 60 mM Na-HEPES pH 7 . 5 , 20 mM hexamine cobalt chloride salt , 13% ( w/v ) PEG 4K , 7 . 4% ( v/v ) 2-propanol , and 1% ( v/v ) glycerol . They were subsequently soaked for 18h in mother liquor supplemented with 2 mM samarium and cryo-cooled using 22% ( v/v ) PEG 400 as cryo-protectant before plunging into liquid nitrogen . In order to optimize the anomalous signal we collected a highly redundant dataset ( S1 Table ) , merging three independent datasets collected from a single crystal using a kappa goniometer ( kappa = 10° , 0° , -10° ) using an “inverse beam” strategy . The final SAD dataset could be processed up to a resolution of 3 . 65 Å and showed a usable ( CCanom > 0 . 3 ) anomalous signal up to 4 . 5 Å . To determine the phases , we first modelled an scFvA5 molecule using the RosettaAntibody3 program [69] through the ROSIE interface [70] . We then used the resulting model as MR template for PHASER [71] with the native dataset at 3 Å resolution . We obtained a single solution with a translation function Z-score of 7 . 4 in which the scFv molecules are packed together back to back around a two-fold axis , with the CDR loops exposed to solvent , leaving enough space to accommodate one Gc molecule . The initial MR phases were used to identify a set of heavy atom sites in the Sm-SAD dataset using the HySS ( Hybrid Substructure Search ) module of the PHENIX package [72] , and we used them to calculate SAD phases with PHASER . The combined MR and SAD phases were improved by density modification using RESOLVE [73] . All these steps were done automatically using the AutoSol wizard [74] in PHENIX . The final map was of enough quality to start manual model building , and the extended and corrected model was used as input model in a new MR-SAD cycle . With this iterative procedure we built a model that comprises full domains I and III , and a large part of domain II . We used domains I and III as MR templates to determine the structure of the rGc-W115H trimer at 1 . 6 Å resolution and build a complete model using this dataset . We then used the structure of the high-resolution post-fusion form as a guide to finish the model building and to generate restraints for refinement of the complex rGc/scFvA5 . The crystals of the rGc-W105H trimer were grown in 100 mM Na-MES pH 6 . 5 , 10 . 8% ( w/v ) PEG 8K , and 7% ( v/v ) glycerol and were cryo-protected in the same solution supplemented with 20% ( v/v ) glycerol . As described above , the structure was solved by MR using the partial models of domains I and III from the rGc/scFvA5 crystals . We also identified conditions of rGc-W105H trimer crystal growth in the presence of 500 mM KCl , which showed no density for the fusion loop region . We then carried out a detailed study of the effect of KCl by growing crystals of rGc-W105H in the presence of increasing concentrations of KCl . We collected datasets in the presence of 100 mM KCl , ( resolution 1 . 8 Å ) , 200 mM KCl ( 1 . 7 Å ) , 300 mM KCl ( 1 . 6 Å ) , 500 mM ( 1 . 5 Å ) , and 600 mM KCl ( 1 . 4 Å ) , where the values in parentheses are the resolution limits in each case . The structures of these crystals were determined by molecular replacement using as a model rGc-W105H . We refined the structures using phenix . refine and the final model validated with Molprobity [75] and CheckMyMetal [76] . The statistics of all crystals and refinement is provided in the S1 Table . Liposomes were prepared fresh each time by the freeze-thaw and extrusion method [77] . DOPC ( 1 , 2-dioleoyl-sn-glycero-3-phosphocholine ) , DOPE ( 1 , 2-dioleoyl-sn-glycero-3-phosphoethanolamine ) , sphingomyelin ( from bovine brain ) , cholesterol ( from ovine wool ) , PC ( phosphocholine , from egg yolk ) and PE ( phosphoethanolamine , prepared from egg phosphatidylcholine by transphosphatidylation ) were purchased from Avanti Polar Lipids . Type 1 , 2 , 3 and 4 liposomes were made using DOPC alone , DOPC/cholesterol ( 1/1 ) , DOPC/DOPE/sphingomyelin/cholesterol ( 1/1/1/3 ) and PC/PE/sphingomyelin/cholesterol ( 1/1/1/1 . 5 ) , respectively . Samples of rGc were negatively stained with phosphotungstic acid and screened with a Tecnai G2 Spirit Biotwin microscope 5 ( FEI , USA ) operating at an accelerating voltage of 120 kV . To obtain the rGc/liposomes pictures , 200 ng of rGc were added to 10 ul of a solution of 1 mM of type 3 liposomes in 20 mM MES pH 5 . 5 , 150 mM NaCl ( MN 5 . 5 ) . After 1 minute incubation at room temperature , the sample was spotted onto carbon coated glow discharged grids , contrasted with 2% phosphotungstic acid and screened in a TecnaiG2 Spirit Biotwin microscope operating at an accelerating voltage of 120 kV . Images were recorded using a 4Kx4K camera Eagle ( FEI , USA ) and the TIA software ( FEI , USA ) . All experiments were performed on a Biacore T200 instrument ( GEHealthcare ) equilibrated at 25°C in two different running buffers: PBS pH 7 . 4 , and MN pH 5 . 5 ( 20 mM MES pH 5 . 5 , 150 mM NaCl ) . We used the expression plasmid pI . 18/GPC for the expression of the full length GPC coding region ( encompassing both Gn and Gc in the M genomic segment ) of Andes virus strain CHI-7913 [56] . Site-directed mutations were generated by DNA synthesis and sub-cloning into pI . 18/GPC using intrinsic restriction sites . For expression , 293FT cells ( Invitrogen ) ( 3 . 6×106 ) grown in 100 mm plates were calcium-transfected with 8–20 μg of DNA and 48 h later , cell surface proteins were labelled with biotin in order to separate the biotinylated ( surface proteins ) from non-biotinylated ( intracellular proteins ) fractions using a cell surface protein isolation kit ( Pierce ) . For protein detection by western blot , primary antibodies anti-Gc monoclonal antibody ( MAb ) 2H4/F6 [78] or anti-β-actin MAb ( Sigma ) were used at 1:2 , 500 and subsequently detected with an anti-mouse immunoglobulin horseradish peroxidase conjugate ( Thermo Fisher Scientific ) 1:5 , 000 and a chemiluminescent substrate ( SuperSignal WestPico , Pierce ) . This assay was performed as previously described [18] . Briefly , Vero E6 cells ( ATCC ) seeded into 16 well chamber slides were transfected with the pI . 18/GPC wt or the different mutant constructs using lipofectamine 2000 ( Invitrogen ) . The DNA amounts were adjusted to obtain similar levels of Gc at the cell surface; the cells were accordingly transfected with plasmid DNA ranging between 0 . 5–1 . 5 μg . 48 h later , the cells were incubated in E-MEM ( pH 5 . 5 ) at 37°C for 5 min , subsequently washed , and the incubation continued for 3 h at 37°C in E-MEM ( pH 7 . 2 ) . The cell cytoplasm was then stained for one hour with 1 μM of CellTracker CMFDA ( Molecular Probes ) and cells were next fixed for 20 min with 4% paraformaldehyde . For immunofluorescence labelling , cells were permeabilized with 0 . 1% Triton X-100 in PBS and Gc labelled using the MAb 2H4/F6 1:500 and secondary antibody anti-mouse immunoglobulin conjugated to Alexa555 1:500 ( Invitrogen ) . Finally , nuclei were stained for 5 min with DAPI 1 ng/μL and samples examined under a fluorescence microscope ( BMAX51 , Olympus ) . The fusion index of Gc-expressing cells was calculated using the formula: 1- [number of cells/number of nuclei] . Approximately 200 nuclei per field were counted ( 20X magnification ) and five fields used to calculate the fusion index for each sample ( n = 2 ) of two independent experiments . VLPs were harvested from supernatants of 293FT cells transfected with the pI . 18/GPC wt or the different mutant constructs at 48 h post-transfection as previously established [60] . Subsequently , VLPs were concentrated by ultracentrifugation for one hour at 135 , 000 g and detected by reducing SDS PAGE and western blot using anti-Gc MAb as described above . The presence of VLPs was further corroborated by negative-stain electron microscopy using phosphotungstic acid 2% pH 7 . 4 ( FEI Tecnai 12 Transmission Electron Microscope , Philips ) . Simian immunodeficiency virus ( SIV ) vectors pseudotyped with ANDV Gn and wild type or mutant Gc were prepared as previously described [56] . Briefly , 293FT were transfected with the following plasmids: pSIV3+ [79] , pGAE1 . 0 [80] ( kindly provided by Jean-Luc Darlix , INSERM , ENS-Lyon , France ) and pI . 18/GPC wild type or the different mutant constructs . At 72 h post-transfection , pseudotyped vectors released into the supernatant were harvested , concentrated by ultracentrifugation at 135 , 000 g and used to transduce Vero E6 cells ( ATCC , CR-1586 ) . 72 h post-inoculation , cells were trypsinized and GFP expression assessed by flow cytometry ( FACScan , Becton Dickinson ) . Transduction titers were calculated using the percentage of GFP positive cells , counting at least 10 , 000 cells of each condition . The coflotation of viral particles with liposomes was performed as previously established [46] . Briefly , VLPs or GPC-pseudotyped SIV vectors prepared from wild type or mutant pI . 18/GPC construct were incubated with 200 mM 1 , 6-diphenyl-1 , 3 , 5-hexatriene ( DPH ) -labelled liposomes ( Type 4 ) for 30 min at pH 7 . 4 or 5 . 5 . The VLP-liposome mixture was then added to the bottom and adjusted to 25% ( w/v ) sucrose . Additional sucrose steps of 15% and 5% were then over-layered . After centrifugation for 2 h at 300 , 000 g , liposomes were detected by the fluorescence emission of DPH ( λex = 230 nm; λem = 320 nm ) and VLPs by western blot using anti-Gc MAb 2H4/F6 . Acid-induced Gc homotrimerization was tested as established before [46] . VLPs or GPC-pseudotyped SIV vectors prepared from wt or mutant pI . 18/GPC constructs were incubated for 30 min at 37°C at the indicated pHs to allow multimerization changes . Next , Triton X-100 1% ( v/v ) was added to allow the extraction of the membrane glycoproteins from the viral particle . The extracted glycoproteins were then added to the top of a sucrose gradient ( 7–15%; w/v ) and centrifuged at 150 , 000 g for 16 h . Finally , fractions were collected and the presence of Gc was analyzed by western blot using MAbs anti-Gc 2H4/F6 . The stability of the Gc homotrimer was studied by analyzing its resistance to trypsin digestion as determined before [46] . VLPs were incubated at pH 5 . 5 for 30 min at 37°C to allow Gc multimerization changes or pH 7 . 4 as a digestion control . Next , the VLPs were digested with 500 μg/ml of TCPK trypsin ( Sigma ) for 30 min , stopping the reaction by the addition of sample buffer and heating to 95°C for 10 min . The extent of Gc digestion was determined by western blot , using an anti-Gc MAb . The trypsin resistance of wild type and mutant Gc was quantified by dividing the densitometry values of the digested Gc signal by the undigested Gc assay input control for each mutant , using the Fiji software [81] . The average value and standard derivation of 3 experiments was calculated and a Student’s t test was performed for statistical evaluation: *** , P < 0 . 00025; ** , P < 0 . 0025; * , P < 0 . 025 . For the lipid mixing assay VLPs were metabolically pyrene-labeled by supplementing the producing cell’s media with 25 μg/ml of 1-pyrenehexadecanoic acid ( Molecular Probes ) . Labeled VLPs were mixed with liposomes and lipid mixing was monitored by the decrease in pyrene excimer fluorescence generated by the dilution of the pyrene-labeled phospholipids with the unlabeled phospholipids in the liposome membrane in a continuously stirred fluorimeter cuvette at 37°C . Fluorescence was recorded continuously at 480 nm using a Varian Eclypse Fluorescence Spectrophotometer ( Agilent Technologies ) at an excitation wavelength of 343 nm using a 10-nm slit width for excitation and emission . After a stable base line was established , the pH of the solution was lowered to 5 . 5 for reaction initiation ( time = 0 ) . The base line excimer value was defined as 0% lipid mixing and the maximal extent of excimer dilution was defined by the addition of detergent Triton X-100 after lipid mixing of each condition concluded . All relevant data are within the paper and its Supporting Information files . The coordinate files of the structures described in this manuscript have been submitted to the Protein Data Bank , and the corresponding accession codes are listed in the S1 Table . | Hantaviruses belong to the Bunyaviridae family of enveloped viruses . This family englobes in total five established genera: Tospovirus ( infecting plants ) , and Phlebovirus , Orthobunyavirus , Nairovirus and Hantavirus infecting animals , some of which cause serious disease in humans . An important characteristic of the hantaviruses is that they are not transmitted to humans by arthropod vectors , as those from the other genera , but by direct exposure to excretions from infected small mammals . As all enveloped viruses , they require the activity of a membrane fusogenic protein , Gc , for entry into their target cells . Our structural analysis of the hantavirus fusion protein Gc led to the identification of a conserved pattern of cysteines involved in disulfide bonds stabilizing the Gc fold . This motif is matched exclusively by all of the available bunyavirus Gc sequences in the database , with the notable exception of phlebovirus Gc , which appears closer in structure to the fusion proteins of other families of arthropod-borne viruses , such as the flaviviruses and alphaviruses . This analysis further suggests mechanistic similarities with hantaviruses in the fusion mechanism of viruses in the remaining three most closely related bunyavirus genera , which we propose belong to a new separate sub-class of fusion proteins with a multipartite membrane targeting region . |
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Yaws is a treponemal infection that was almost eradicated fifty years ago; however , the disease has re-emerged in a number of countries including Ghana . A single-dose of intramuscular benzathine penicillin has been the mainstay of treatment for yaws . However , intramuscular injections are painful and pose safety and logistical constraints in the poor areas where yaws occurs . A single center randomized control trial ( RCT ) carried out in Papua New Guinea in 2012 demonstrated the efficacy of a single-dose of oral azithromycin for the treatment of yaws . In this study , we also compared the efficacy of a single oral dose of azithromycin as an alternative to intramuscular benzathine penicillin for the treatment of the disease in another geographic setting . We conducted an open-label , randomized non-inferiority trial in three neighboring yaws-endemic districts in Southern Ghana . Children aged 1–15 years with yaws lesions were assigned to receive either 30mg/kg of oral azithromycin or 50 , 000 units/kg of intramuscular benzathine penicillin . The primary end point was clinical cure rate , defined as a complete or partial resolution of lesions 3 weeks after treatment . The secondary endpoint was serological cure , defined as at least a 4-fold decline in baseline RPR titre 6 months after treatment . Non- inferiority of azithromycin treatment was determined if the upper bound limit of a 2 sided 95% CI was less than 10% . The mean age of participants was 9 . 5 years ( S . D . 3 . 1 , range: 1–15 years ) , 247 ( 70% ) were males . The clinical cure rates were 98 . 2% ( 95% CI: 96 . 2–100 ) in the azithromycin group and 96 . 9% ( 95% CI: 94 . 1–99 . 6 ) in the benzathine penicillin group . The serological cure rates at 6 months were 57 . 4% ( 95% CI: 49 . 9–64 . 9 ) in the azithromycin group and 49 . 1% ( 95% CI: 41 . 2–56 . 9 ) in the benzathine penicillin group , thus achieving the specified criteria for non-inferiority . A single oral dose of azithromycin , at a dosage of 30mg/kg , was non-inferior to a single dose of intramuscular benzathine penicillin for the treatment of early yaws among Ghanaian patients , and provides additional support for the WHO policy for use of oral azithromycin for the eradication of yaws in resource-poor settings . Pan African Clinical Trials Registry PACTR2013030005181 http://www . pactr . org/
Yaws is a relapsing non-venereal treponematosis caused by Treponema pallidum subspecies pertenue . The disease mainly affects the skin , but if untreated , can also affect bone , joints and cartilage . Yaws may persist for many years as a chronic infection , and late stage disease may lead to crippling disfigurement [1 , 2] . The bacterium that causes yaws is closely related to T . pallidum ssp . pallidum , the causative organism of venereal syphilis , however T . pallidum ssp . pertenue is thought to be less virulent [3] . Transmission of yaws occurs from person to person through direct skin to skin contact , involving transfer of infectious exudates from the early skin lesions of infected individuals to micro- or macro-abrasions of the skin of siblings/playmates . [4–7] . Yaws was previously widespread throughout the tropics but a global eradication campaign between 1952 and 1964 resulted in a 95% reduction in disease prevalence worldwide [8] . Following this initial success , yaws control was integrated into national primary healthcare systems . Unfortunately , this integration resulted in a weakening of yaws surveillance in many countries and the re-emergence of the disease by the 1970s [9] . Several reports have recently documented a resurgence of yaws in parts of West and Central Africa , South East Asia and several Western Pacific Islands [10–12] . Yaws is an important public health problem in Ghana and affects the poor rural communities . The disease is reported in all the ten regions of the country . A prevalence study in three purposively selected districts in Southern Ghana in 2008 showed an overall prevalence of 1 . 92% among children in primary schools; however individual school prevalence rates ranged from 0% to a high of 19 . 5% [13] . Previous mass treatment campaigns to eradicate yaws in Ghana were based on the use of a single dose intramuscular ( IM ) injection of long-acting penicillin . The advantages of single-dose treatment with penicillin are its low cost , adherence and the absence of antimicrobial resistance despite extensive use . However , treatment with intramuscular penicillin has several disadvantages such as pain , anaphylaxis , and potential for transmission of other blood-borne infections . Azithromycin has previously been shown to be an effective agent in the treatment of venereal syphilis [14]and is also the cornerstone of the strategy for the elimination of trachoma [15] . Azithromycin offers a number of advantages as an agent for the treatment of yaws , including its low cost , oral route of administration , excellent safety profile and negligible risk of anaphylaxis . As such , the drug is well suited for use in community mass treatment campaigns for yaws eradication [16] . In 2012 , a single centre study conducted in Papua New Guinea , showed that a single oral dose of 30mg/kg azithromycin was non-inferior to a single injection of benzathine penicillin in the treatment of the disease , Clinical cure rate was 85 . 4% ( 95% CI: 78 . 2–90 . 6 ) in patients treated with azithromycin compared to 86 . 5% ( 95% CI: 79 . 5–91 . 5 ) in those treated with benzathine penicillin [17] . These findings led to recommendations incorporated into the WHO’s Morges yaws eradication strategy [18] . Here , we report a similar study carried out in Ghana , West Africa in order to confirm the efficacy and suitability of azithromycin for treatment of yaws in Sub-Saharan Africa .
This trial was conducted according to the principles of the Declaration of Helsinki . The study was approved by the Ghana Health Service Ethical Review Committee ( Ref: GHS-ERC: 13/11/10 ) . Written informed consent was obtained from parents/guardians of all participants; those aged 12 years and above also signed an assent certificate . The study was conducted between 25th May 2011 and 31st December 2012 in three neighbouring yaws endemic districts in southern Ghana: Ga South , Awutu Senya and West Akim districts ( Fig 1 ) . The three study districts have a total population of 787 , 747 with a distribution of 316 , 091 , 274 , 584 and 197 , 072 respectively . Children under the age of 15 years , known to bear the bulk of yaws infections , represent an estimated 38 . 3% of these populations . There are more than 600 rural communities in these three districts . The districts have a total of 8 government and private hospitals , 10 government health centres , 81 private health facilities made up of small clinics and maternity homes and 16 Community Based Health Planning and Services ( CHPS ) compounds . Only 20% of these health facilities are located in the rural parts of the district where yaws occurs . The doctor population ratio in the study area is 1:15 , 754 . The main occupation of the people in the study area is subsistence farming . There are a total of 1750 primary schools and kindergartens in the study area with a total enrollment of 174 , 536 made up of 51% males and 49% females . Yaws surveillance in the study area is mainly by passive detection at the health facilities . Based on routine reports of presentation of clinical cases to health facilities and a small number of active case searches in schools , the yaws case notification rate has been estimated to be between 87–241 per 100 , 000 people . Between 2009 and 2011 , 2674 clinical cases of yaws were reported in the 3 study districts combined . Participants were recruited based on clinical suspicion by health workers at home or in school , and all subjects with suspected yaws were assessed for eligibility . Clinical examination involved inspection of the skin and scalp for signs of early yaws lesions . A suspected case of primary yaws was defined as an ulcer with raised edges and a dirty crusty base or a papilloma that appeared as a firm yellowish skin lesion with a dark tip , on any part of the body . A suspected case of secondary yaws was defined as any of the following skin lesions: multiple ulcerative or papillomatous skin lesions; a palmar or plantar hyperkeratosis or a macular , papular or maculopapular skin lesion . A confirmed case of yaws was defined as a suspected case with a positive TPHA test and an RPR titre of at least 1:4 . Photographs of the lesions were taken before treatment and subsequently at follow-up review visits . A 5mL sample of venous blood was collected from those with lesions clinically suggestive of yaws and analyzed at the National Public Health Reference Laboratory in Accra by qualitative Treponema pallidum haemaglutination assay ( TPHA ) testing ( Debens Diagnostics Ltd . , Ipswich , UK . ) . In parallel , a second set of frozen sera were sent to the Komfo Anokye Teaching Hospital serology laboratory in Kumasi for qualitative and quantitative rapid plasma reagin ( RPR ) testing ( Immutrep RPR test kit , Omega Diagnostics , Alva UK ) . Inclusion criteria for enrolment were individuals aged 1–15 years with suspected primary or secondary yaws , a reactive TPHA test and a RPR titre of at least 1:4 . Exclusion criteria were a negative qualitative TPHA test , a baseline RPR titre of less than 1:4 , allergy to penicillin and/or a macrolide antibiotic , a medical condition that would impair drug absorption , recent ingestion of a broad spectrum antibiotic ( 30 days prior to the day of randomisation ) and patients who were not willing to give informed consent or who would not be available for follow up visits . In this study azithromycin manufactured by Pfizer in the strength of 250mg tablets was supplied by Gokals Laborex , Ghana . Benzathine penicillin , 1 . 2 million units per vial , manufactured by Troge Medicals , Hamburg was supplied by Ghana Health Service Central Medical Stores . Participants who met the inclusion criteria were randomized to receive treatment with either a single dose oral azithromycin administered at a dose of 30mg/kg ( maximum of 2g ) or a single dose of benzathine penicillin administered as an intramuscular injection of 1 . 2 million units for subjects 10-15years , and 0 . 6 million units for those below 10 years of age . Participants in both arms were directly observed receiving treatment and for two hours thereafter . The allocation sequence was based on a computer generated block randomization scheme , stratified according to district . Participants were allocated in a ratio of 1:1 to treatment with azithromycin or penicillin . Treatment allocation was concealed from investigators through the use of sequentially numbered , opaque , sealed envelopes that were kept in a safe and were opened at the point of treatment by the treatment team . Owing to the obvious differences between the mode of administration of the two drugs , investigators and study participants could not be blinded to the treatment received , however individuals assessing study outcomes were masked to treatment allocation . Serological tests were conducted in separate laboratories by laboratory technicians masked to clinical data . Participants were followed up at 3 weeks , 3 months and 6 months . Skin lesions were re-examined at 3 weeks post treatment . At 3 months and 6 months , skin lesions were examined and blood collected for repeat quantitative RPR testing . Individuals with lesions that had not healed at follow-up were re-treated with benzathine penicillin . Health workers and community-based surveillance volunteers monitored and documented all adverse events up to 72 hours after treatment . Parents and teachers also were counselled on possible adverse events after the field team had departed and the need to report to the nearest health facility if necessary . Adverse events that occurred within 2 hours of treatment were documented and managed by the field teams . Our primary outcome was clinical cure defined as a total or partial resolution of yaws skin lesions 3 weeks after treatment . The secondary outcome was serological cure defined as at least a 4-fold drop in baseline RPR titre within 6 months of treatment . Treatment was considered to have failed if there was no resolution of yaws skin lesions ( complete or partial ) 3 weeks after treatment . This trial was designed to assess if azithromycin was non-inferior to benzathine penicillin for the treatment of yaws . With an expected efficacy of penicillin of 95% , a type 1 error of 0 . 05 , and a non-inferiority margin of 10% and assuming that 10% would be lost to follow-up , a sample size of 310 children ( 155 per arm ) would give a statistical power of 90% to test the hypothesis . Analysis of the primary endpoint of clinical cure was estimated by the two-sided 95% confidence interval for the difference in cure rates between the penicillin and azithromycin groups . Secondary outcome analyses were done using similar methods . Subgroup analyses were performed with stratification by baseline RPR titre , household exposure to yaws and stage of clinical yaws . A two-sided test at a significance level of 0 . 05 was used in the comparison of baseline characteristics of the two treatment groups . All statistical analyses for this study were carried out in STATA 11 . 1 ( Statacorp , Texas , USA ) . The per-protocol ( PP ) analysis included all subjects who completed all study procedures at 6 months . The intention-to-treat ( ITT ) analysis included all eligible participants who were randomised and treated . Individuals with missing data were considered treatment failures for the purposes of the intention-to-treat analysis .
The trial profile is shown in Fig 2 . From May 2011 to December 2012 , four hundred and three subjects with suspected primary or secondary yaws lesions were assessed for eligibility; 50 were found to be ineligible ( 39 were either TPHA negative or had a RPR titre below 1:4 , 11 declined to participate ) . Therefore 353 eligible participants were randomly assigned to receive either a single-dose oral azithromycin or a single intramuscular injection of long-acting penicillin . Of the 353 subjects randomised , 25 participants ( 7 . 0% ) were lost to follow up . Six participants in the azithromycin group relocated and 1 refused to continue participation in the study . In the penicillin group , 15 participants relocated , 2 refused to continue participation and 1 patient died 5 months after treatment , a verbal autopsy concluded cause of death as malaria and National Yaws Eradication Programme was notified . The remaining 328 participants ( 169 in the azithromycin group and 159 in the penicillin group ) completed the study and were analysed in the per protocol analysis . Demographic and clinical characteristics did not vary between the two treatments groups ( Table 1 ) . The mean age of study participants across both groups was 9 . 5 years ( SD: 3 . 1 , range: 1 to 15 years ) ; 274 ( 70% ) were male . Primary yaws was present in 187 cases ( 53% ) , 13 participants ( 3 . 7% ) had fever at presentation , 17 ( 4 . 8% ) had arthralgia , and 33 ( 9 . 4% ) had one or more other skin lesions in addition to those of yaws . One hundred and fifty five ( 43 . 9% ) participants had a baseline RPR titre between 1:4 and 1:16 , and 198 ( 56 . 1% ) had titres between 1:32 and 1:128 . One hundred and seventy one participants ( 48 . 6% ) lived in houses with at least one other individual who had been diagnosed with active yaws within the past one month . The most frequent clinical lesions were ulcers ( 167 , 47 . 3% ) followed by papillomas ( 101 , 28 . 6% ) , hyperkeratosis of the palms and soles ( 25 , 7 . 1% ) , while the rest were macules , papules and maculopapular lesions . Three individuals had ulcers with sabre tibia . Similar cure rates were recorded between the two treatment groups ( Table 2 ) . For the primary outcome of clinical cure defined as complete or partial healing of yaws lesion , 166 out of 169 participants ( 98 . 2% ) in the azithromycin group and 155 out of 159 participants ( 96 . 9% ) in the penicillin group exhibited complete or partial resolution ( Figs 3 and 4 ) ) 3 weeks after treatment ( risk difference: -1 . 3% , ( -4 . 7 to 2 . 0 ) . For the secondary outcome ( serological cure ) 97 out of 169 ( 57 . 4% ) participants in the azithromycin group showed a 4- fold or greater decline in baseline RPR titres by 6 months after treatment compared to 78 out of 159 participants ( 49 . 1% ) in the penicillin group , ( risk difference:-8 . 3 ( -19 . 1 to 2 . 4 ) . Azithromycin therefore met the criteria for non-inferiority in both the primary and secondary outcomes . Three ( 1 . 8% ) participants in the azithromycin group and 4 ( 2 . 5% ) in the penicillin group with ulcerative yaws lesions did not resolve 3 weeks after treatment and were classified clinically as “treatment failures” . All participants considered as “treatment failures” were re-treated with intramuscular penicillin and all ulcers subsequently resolved within 2 weeks . Five subjects with ulcerative lesions which had healed at 3 weeks and had achieved serological cure were found to have recurred during the 3 month follow up period . Of these 3 ( 1 . 7% ) occurred in the azithromycin group and 2 ( 1 . 3% ) in the penicillin group . However due to the fact that subjects had achieved serological cure , lesions were likely of a different aetiology . Azithromycin treatment also proved to be non-inferior to penicillin therapy in subgroup analyses of primary outcome ( clinical cure at 3 weeks ) by clinical stage of yaws , baseline RPR titre and household exposure ( Table 3 ) . Analysis of serological cure by baseline RPR titres are shown in Table 4 . Cure rates were low ( 41 . 8% in the azithromycin group and 31 . 7% in the penicillin group ) in patients with low RPR titres of 1:4–1:16 compared to higher cure rates in patients with high RPR titres of 1:32–1:128 ( 69 . 5% in the azithromycin group and 60 . 4% in the penicillin group ) . No serious adverse effects related to the treatment drugs were reported in this trial . Minor adverse effects were reported by 4 participants ( 2 . 4% ) in the azithromycin group , most commonly gastrointestinal upset , and 8 participants ( 5% ) in the penicillin group , most commonly pain at the injection site .
This study shows that a single oral dose of azithromycin given at a dosage of 30mg/kg is non-inferior to a single intramuscular dose of benzathine penicillin in the treatment of yaws in children in Ghana . Indeed the cure rate for the primary outcome at 3 weeks was slightly in favour of azithromycin . For the primary outcome of clinical cure , 166 out of 169 participants ( 98 . 2% ) in the azithromycin group and 155 out of 159 participants ( 96 . 9% ) in the penicillin group showed a complete or partial resolution of yaws lesions 3 weeks after treatment . Our results were consistent with a previous study conducted in Papua New Guinea ( 17 ) . We also showed that azithromycin was non-inferior to IM penicillin in all subgroup analyses , confirming the robustness of this conclusion . Azithromycin was well tolerated by participants , with no serious adverse events reported after treatment . The recorded side effects were , in keeping with the known side effects profile of the drug , namely mild to moderate and most commonly gastrointestinal in nature . Serological cure rates at 6 months were higher in the former study in Papua New Guinea ( 88% ) compared to this study ( 57 . 4% ) , due to enrolment of patients with higher baseline RPR titres ( ≥1:16 ) in the PNG study . In this trial , 42 . 6% of participants treated with azithromycin and 50 . 9% of those treated with penicillin who were clinically cured did not achieve a decline in baseline RPR titres by at least 2 dilutions ( 4-fold ) by 6 months after treatment . Among patients treated with azithromycin 5 . 3% did not achieve a fall in RPR titre , 38 . 1% achieved a 2 fold fall , 2 . 9% showed a 2 fold increase and 2 . 4% had a 4 fold increase in RPR titre 6 months after treatment . In the penicillin group 12 . 7% did not show a fall in RPR titre , 33 . 5% showed a 2-fold falling titre , 11 . 8% showed a 2 fold increase in titre and 0 . 6% showed a 4 fold increase in titre 6 months after treatment . Unfortunately , it is clinically and serologically impossible to distinguish treatment failure and relapse from re-infection . Since this study was conducted in endemic communities where exposure to antibiotics is uncommon , it is possible that many of those cases that showed a 4-fold increase in titre reflect re-infection rather than treatment failure . Increase in RPR titre could also be related to performing of initial RPR titre in the early stage in infection before titre reached its peak . The 2-fold increases and decreases in titre that were recorded here largely reflect the relative insensitivity of the quantitative RPR test . The availability of an orally effective treatment for yaws is key if the goal of eradication of yaws is to be attained . In this trial we demonstrate the efficacy of azithromycin in the treatment of yaws in a second yaws-endemic region far removed from the initial trial site in Papua New Guinea , confirming its place in the WHO yaws eradication strategy [19] . Effective treatment of yaws involves treatment of whole communities . Azithromycin is well suited to administration by community health volunteers , even in the poorly-resourced rural communities where yaws occurs . A recent study has demonstrated the impact of a single round of mass treatment with azithromycin in reducing transmission of yaws in Lihir Island , Papua New Guinea [20] . Azithromycin treatment failure among patients with syphilis , caused by a closely-related treponeme T . pallidum ssp . pallidum , has been widely reported in high-resource settings where overuse of antibiotics is common . Treatment failure has been associated with a single amino acid mutation at positions 2058 and 2059 in the 23S rRNAgene [21] , which prevents binding with the bacterial 50S ribosomal subunit . In contrast , azithromycin resistance among T . pallidum ssp . pertenue strains has yet to be documented . Although populations in yaws endemic areas are typically not exposed to excessive antibiotic usage , there is a clear need to strengthen surveillance systems and closely investigate possible treatment failures for evidence of emergence of azithromycin resistance . There are a number of limitations to this study . Most notable is the inability to mask treatment assignments . We tried to mitigate this by having outcomes evaluated by independent assessors who were blinded to treatment allocation . We could not determine whether lesions that did not heal at 3 weeks post treatment were true treatment failures , since neither dark field microscopy nor PCR of lesion exudates was performed . As mentioned above , we were also unable to distinguish treatment failure from re-infection . Enrolment of patients with low RPR titres ( 1:4–1:8 ) may have influenced serological cure in this study , it was impossible to observe a 4-fold drop in RPR in patients with low titres . Finally , participants were followed up for only 6 months , after which no serological or clinical data were collected . This randomized controlled trial has clearly demonstrated that a single oral 30mg/kg dose of azithromycin is non inferior to a single dose of IM benzathine penicillin for the treatment of early yaws in Ghana . There was no significant difference in cure rates between patients treated with azithromycin and those treated with injection benzathine penicillin . Oral treatment with azithromycin overcomes the logistical and operational problems of using intramuscular penicillin in mass treatment campaigns . Our findings lend additional support for the use of a single dose azithromycin as the preferred regimen in yaws eradication programs . | Yaws is a tropical infection caused by a bacterium closely related to that which causes syphilis . It is transmitted from person to person through skin to skin contact and often causes papillomatous and ulcerative skin lesions , usually in young children . Without treatment , it can lead to deformities and disabilities . In the past , treatment of cases and their contacts and mass treatment of whole communities has been conducted using single doses of long acting penicillin . This treatment is inexpensive and does not pose a problem with adherence . However , the injections are painful and make it difficult to gain the cooperation of children . In addition , it requires trained health workers to safely administer treatment in poor-resource settings where yaws commonly occurs . In this study one group of children aged 1–15 years with clinically and serologically confirmed yaws received the standard treatment of a single injection of benzathine penicillin . A second group of children were treated with a single dose of oral azithromycin . The children were followed up at 3 weeks to assess healing of lesions , and at 3 and 6 months respectively to monitor serological indicators of infection . Our conclusion is that single dose oral azithromycin is as effective as a single injection of benzathine penicillin for the treatment of early yaws in Ghana and confirms the findings of a previous study undertaken in Papua New Guinea . |
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Understanding the cellular mechanisms that ensure an appropriate innate immune response against viral pathogens is an important challenge of biomedical research . In vitro studies have shown that natural killer ( NK ) cells purified from healthy donors can kill heterologous cell lines or autologous CD4+ T cell blasts exogenously infected with several strains of HIV-1 . However , it is not known whether the deleterious effects of high HIV-1 viremia interferes with the NK cell-mediated cytolysis of autologous , endogenously HIV-1-infected CD4+ T cells . Here , we stimulate primary CD4+ T cells , purified ex vivo from HIV-1-infected viremic patients , with PHA and rIL2 ( with or without rIL-7 ) . This experimental procedure allows for the significant expansion and isolation of endogenously infected CD4+ T cell blasts detected by intracellular staining of p24 HIV-1 core antigen . We show that , subsequent to the selective down-modulation of MHC class-I ( MHC-I ) molecules , HIV-1-infected p24pos blasts become partially susceptible to lysis by rIL-2-activated NK cells , while uninfected p24neg blasts are spared from killing . This NK cell-mediated killing occurs mainly through the NKG2D activation pathway . However , the degree of NK cell cytolytic activity against autologous , endogenously HIV-1-infected CD4+ T cell blasts that down-modulate HLA-A and –B alleles and against heterologous MHC-Ineg cell lines is particularly low . This phenomenon is associated with the defective surface expression and engagement of natural cytotoxicity receptors ( NCRs ) and with the high frequency of the anergic CD56neg/CD16pos subsets of highly dysfunctional NK cells from HIV-1-infected viremic patients . Collectively , our data demonstrate that the chronic viral replication of HIV-1 in infected individuals results in several phenotypic and functional aberrancies that interfere with the NK cell-mediated killing of autologous p24pos blasts derived from primary T cells .
Natural killer ( NK ) cells are important effectors of innate immune responses and are capable of providing cellular immunity against tumor-transformed and virally-infected cells , without prior antigen sensitization [1] , [2] . Among the several NK cell effector-functions , spontaneous killing of non-self targets was the first to be described and is the reason they were named “natural killer” cells [3] . NK cell cytolytic machinery is modulated by a delicate balance between opposing signals delivered by two heterogeneous families of inhibitory and activating NK cell receptors . Under physiological conditions , cytotoxicity against normal autologous cells is blocked by the specific recognition of different MHC class –I ( MHC-I ) molecules by inhibitory NK cell receptors ( iNKRs ) . Interactions between iNKRs and MHC-I , tolerance to self , and determination of the extent of cytolytic activity are achieved through a complex process that educates NK cells to ensure self-recognition [4] . Diminution or absence of expression of HLA-I alleles on a cell surface following viral infection or tumor transformation results in the reduced engagement of iNKRs and allows activating NK receptors and co-receptors to trigger NK cell-mediated cytolysis [5] . Several studies have already described numerous aberrancies of NK cell phenotype and function in chronically HIV-1 infected patients with high levels of ongoing viral replication . These abnormalities include aberrant expression and function of several iNKRs and natural cytotoxicity receptors ( NCRs ) , markedly impaired cytolytic activity against tumor cell targets , defective production of important antiviral cytokines [6] , [7] and defective interactions with autologous dendritic cells ( DCs ) [8] . All of these phenotypic and functional perturbations are particularly pronounced in an unusual CD56neg/CD16pos ( CD56neg ) NK cell subset that is preferentially expanded in HIV-1 infected viremic patients [9] , [10] , [11] . Because the frequency of peripheral blood CD4+ T cells that harbor replication-competent virus is extremely low in HIV-1 infected patients [12] , [13] , it remains to be determined whether highly dysfunctional NK cells from patients with high levels of ongoing viral replication are able to eliminate autologous and endogenously HIV-1 infected CD4+ T cells . In order to further understand the direct effects of HIV-1 on CD4+ T cells and other cell types , several models of in vitro infection with different HIV-1 strains have been developed . Through these experimental methods , several reports show that HIV-1 selectively down-modulates HLA-A and -B alleles in both cell lines and CD4+ T cell-derived blasts [14] , [15] , [16] . It has been also demonstrated that the ability of NK cells from healthy donors to kill autologous and exogenously infected CD4+ T cell blasts is influenced by modulation by the exogenous virus of ligands for inhibitory and activating NK cell receptors on these primary T cell blasts [15] , [17] . Even though these approaches using in vitro infection have significantly contributed to understanding the cellular interactions between NK cells and autologous HIV-1 infected CD4+ T cell blasts , it is still unclear what role , if any , NK cells obtained from HIV-1 infected viremic patients play in the clearance of endogenously infected autologous CD4+ T cells ex vivo . In the present study , we describe the killing of endogenously infected CD4+ T cell blasts by autologous NK cells from HIV-1 infected viremic individuals . In addition , we describe several mechanisms involved in the regulation of this NK cell-mediated killing . Finally , we characterize phenotypically the endogenously HIV-1 infected CD4+ T cell blasts used as targets in this experimental system .
It has been reported previously that activation in vitro with different stimuli of either total PBMCs or purified CD4+ T cells from HIV-1 infected individuals induces viral expression and replication [18] , [19] , [20] , [21] . The number of HIV-1 virions produced by these endogenously infected activated blasts was detected through real-time PCR , while the amount of p24 HIV-1 core antigen released in culture supernatant was determined by ELISA . Using an experimental approach similar to that for activating primary T cells in vitro ( Figure S1 ) , we sought to isolate and characterize these productively infected cells starting from highly enriched CD4+ T cells purified from HIV-1 infected viremic patients . As shown in Figure 1A , 5–7 days of stimulation with phytohemoagglutinin ( PHA ) and recombinant IL-2 ( rIL2 ) were required to observe a variable but consistent percentage of endogenously HIV-1 infected CD4+ T cell blasts , characterized by the presence of intracellular viral p24 core antigen . In order to determine the peak of maximal expansion of these infected cells , we tested the amount of p24 antigen in CD4+ T cell blasts every 3 days for 3 weeks . Within our cohort of HIV-1 infected viremic donors , the highest percentages of p24pos expression in activated CD4+ T cell blasts were detected , on average , after 12 days of activation ( median: 9 . 43%; SD = ±6 . 6 ) and started to decrease progressively after this time point ( Figure 1B ) . Of the other several stimuli used to expand HIV-1 infected CD4+ T cell blasts , only PHA plus rIL-2 and rIL-7 achieved similar and sometimes better results after 12 days in culture compared to stimulation with PHA and rIL-2 ( Figure S2 ) . We then analyzed whether p24pos CD4+ T cell blasts were able to proliferate during the period of maximal expansion . As expected , the ability of unfractionated CD4+ T cell blasts from HIV-1 infected patients to undergo proliferation was significantly lower compared to that of unfractionated blasts from healthy donors ( Figure 2A ) . Even though the positive expression of Ki67 nuclear antigen by p24pos fractions of CD4+ T cell blasts indicated that these endogenously infected cells were able to enter the cell cycle ( Figure 2B ) , it has been shown both in vitro and ex vivo that they are arrested in G2/M stage and do not complete the cell cycle [22] , [23] . In order to correlate the kinetics of expansion of these endogenously infected blasts with cell proliferation and CD4 expression , we analyzed the dilution of the vital dye carboxyfluorescein diacetate succinimidyl ester ( CFSE ) in CD4+ T cell blasts using a multicolor flow cytometric approach . After 12 days of stimulation , a subset of proliferating CFSE-labeled blasts showed active intracellular viral replication ( p24pos cells ) with a simultaneous down-modulation of cell surface CD4 ( Figure 2C ) . Therefore , the loss of CD4 is associated with a productive infection in either endogenously ( Figure 3A ) or exogenously infected CD4+ T cell-derived blasts [17] , [24] , [25] . We also visualized the intracellular HIV-1 p24 core antigens in endogenously infected CD4+ T cell blasts by fluorescence microscopy ( Figure 2D ) . It has also been reported that HIV-1 infection in vitro results in a selective down-modulation of MHC-I molecules in cell lines and in exogenously infected primary CD4+ T cells [14] , [15] , [16] . In order to determine whether this phenomenon also occurs in endogenously infected CD4+ T cell blasts expanded from HIV-1 infected individuals , we analyzed the expression of classic and non-classic HLA molecules on p24pos and p24neg blasts . We found that surface levels of HLA-A and -B alleles , calculated as mean fluorescence intensity , were significantly down modulated on p24pos/CD4neg blasts compared to p24neg/CD4pos blasts ( p = 0 . 004 for HLA-A alleles; p = 0 . 018 for HLA-BW4 and –BW6 alleles ) . In contrast , the expression of HLA-C and HLA-E molecules did not substantially differ between p24neg and p24neg blasts ( Figures 3B–C and Table 1 ) .
In the present study we demonstrate the ability of NK cells from HIV-1 infected viremic patients to kill endogenously HIV-1-infected autologous CD4+ T cell blasts derived from viremic patients . We show that , subsequent to the selective down-modulation of MHC-I molecules , infected p24pos blasts become partially susceptible to lysis by rIL-2 activated NK cells , while p24neg blasts are spared from killing . This NK cell-mediated killing occurs mainly through the NKG2D activation pathway . However , decreased NK cell expressions of NCRs contribute to the low level of NK cell cytolytic activity . In addition , the unusually high frequency of dysfunctional CD56neg NK cell subsets among HIV-1 infected viremic patients was shown to strongly correlate with the low degree of NK cell cytolytic responses against infected p24pos cell blasts . The ability of NK cells to kill autologous HIV-1 infected target cells mainly through the NKG2D pathway has been previously demonstrated in vitro with exogenously infected CD4+ T cell blasts from healthy donors [15] , [17] . In contrast , we examined NK cells from HIV-1 viremic individuals and autologous target cells that were expanded from endogenously infected CD4+ T cells . Given the reported abnormalities in phenotype and functions of NK cells from HIV-infected viremic individuals [26] , [27] , [28] , the present study was designed to determine the function of NK cells in killing HIV-1 infected target cells under conditions that more closely mimic the in vivo situation in HIV-infected individuals . Our experimental system relied on rIL-2 activated NK cells instead of freshly purified NK cells . We previously reported that prolonged activation with rIL-2 did not restore phenotype and function of highly dysfunctional NK cells from HIV-1 infected viremic individuals . Only CD56 expression was recovered upon activation with rIL-2 after 3 weeks of culture . Despite the reversion of the CD56neg to a CD56pos phenotype , rIL-2 stimulated CD56neg-derived NK cell populations still expressed very high percentages of iNKRs and extremely low levels of NCRs , similar to results from experiments performed with freshly purified CD56neg NK cell subsets . Even after 21 days of activation with rIL-2 the cytolytic potential of these highly dysfunctional NK cells from HIV-1 infected viremic patients did not improve and remained significantly lower compared to that of NK cells from uninfected individuals[7] , [8] , [11] . Given the fact that stimulation with rIL-2 does not substantially reverse the pathologic characteristics of freshly purified NK cells from HIV-1 infected viremic donors , we designed an experimental system using rIL-2 activated NK cells . This yielded NK cells as effector cells against autologous and endogenously HIV-1 infected CD4+ T blasts at the day of their maximal expansion . Circulating CD4+ T cells from HIV-infected individuals harbor very low frequencies of replication-competent virus [12] , [13] . This has made it very difficult to adequately characterize endogenously infected CD4+ T cells from HIV-infected individuals . Several studies showed that activation in vitro with several and multiple stimuli enhanced viral replication in CD4+ T cells from HIV-1 infected patients . The in vitro induced replication of HIV-1 was measured by the release of p24 HIV-1 core antigen in culture-supernatant [18] , [19] , [20] , [21] . Using a similar approach for cell activation , we stimulated freshly purified CD4+ T cells from HIV-1 infected viremic patients in order to expand and isolate these endogenously infected T cell blasts detected by intracellular p24 staining . Activation with PHA plus rIL2 ( with or without rIL-7 ) was the most effective in expanding a population of endogenously infected CD4+ T cell blasts . The peak maximal expansion of HIV-1 productively infected blasts was reached , on average , 12 days after activation and the rate of expansion of p24pos cell blasts differed among samples from the 30 HIV-1 infected viremic patients analyzed in the present study . The reasons for such heterogeneous results are unclear and many variables such as cellular or soluble suppressive factors , different numbers of circulating latently HIV-1 infected cells , different viral strains , culture conditions or other factors could contribute to this variability . Further investigations are needed to extensively characterize the kinetics of HIV-1 in endogenously infected cells and the replication cycle of these p24pos/CD4neg cell blasts . Our aim in the present study was restricted to an examination of the interactions between endogenously HIV-1 infected CD4+ T cell blasts and autologous NK cells from HIV-1 infected viremic patients . In the preparation of target cells , we separated infected from uninfected cells on the basis of the lack of expression of CD4 together with intracellular expression of p24 on certain cells ( infected ) and the expression of CD4 and lack of intracellular expression of p24 on other cells ( uninfected ) . It has been reported that HIV-1 is able to down-modulate the expression of CD4 on T cell surfaces , a phenomenon induced either by Nef , which enhances the internalization and degradation of CD4 , or by Vpu and Env , which interfere with the transport of newly synthesized CD4 to cell surface [24] , [25] . Even though the physiological relevance of CD4 down-regulation is not fully understood , the absence of CD4 on cell surfaces represents another marker of cells productively infected with HIV-1 . We confirmed that endogenously infected CD4+ T cell blasts harboring replication-competent virus down-modulate CD4 expression and , on the basis of surface levels of this molecule , we were able to separate infected p24pos from uninfected p24neg T cell blasts . In line with results previously reported with HIV-1 infection in vitro [15] , endogenously HIV-1 infected CD4+ T cell blasts selectively down-modulated HLA-A and –B alleles while the expression of HLA-C and HLA-E molecules was conserved . The selective down-regulation of these MHC-I molecules should render p24pos/CD4neg cell blasts susceptible to NK cell-mediated killing . In fact , although the surface levels of HLA-C and -E may still protect infected cell blasts from the cytolysis exerted by autologous NK cells expressing iNKRs specific for these conserved alleles of MHC-I [14] , [29] , this is not the case for NK cells that express iNKRs specific for HLA-A and –B [15] . We show that the degree of NK cell-mediated lysis of p24pos/CD4neg blasts was significantly higher compared with that of p24neg/CD4pos blasts . Moreover , masking experiments highlighted the important role of the selective down-modulation of HLA-I molecules , because only the complete blocking of all MHC-I alleles rendered infected p24pos and uninfected p24neg cell blasts equally susceptible to NK cell-mediated lysis . Other studies reported that conserved or even up-regulated levels of HLA-E on HIV-1 infected cells are able to inhibit NK cell-mediated cytolysis of HIV-1 infected cells through binding to its specific inhibitory receptor NKG2A [14] , [30] . In our study , we used two different mAbs ( 3D12 and 4D12 ) in order to detect the surface levels of HLA-E . Despite the fact that there was some variability among different donors , we detected no significant differences in the high levels of HLA-E expression between HIV-1 infected and uninfected CD4+ T cell blasts from HIV-1 infected viremic patients . Moreover , given that the frequency of the NKG2Apos NK cell subset is greatly decreased in chronic HIV-1 infected viremic patients compared to that of healthy donors [7] , [31] , it is unlikely that the interaction between HLA-E and NKG2A can explain the decreased NK cell mediated killing of HIV-1 infected blasts . In fact , our masking experiments demonstrated that the complete blocking of NKG2A did not increase NK cell cytolysis of autologous p24pos blasts ( data not shown ) . The reason for the discrepancy in the role of NKG2A/HLA-E interactions between these previous studies and our data may be the fact that the effector cells used in those previous studies were heterologous NK cell lines expressing high levels of NKG2A against HLA-E transfected target cell lines . As mentioned above , the engagement of activating NK cell receptors should be able to trigger the cytolytic activity in NK cells expressing iNKRs specific for HLA-A and -B and lacking iNKRs for HLA-C and-E . In this regard , NK cell-mediated killing of infected p24pos/CD4neg cell blasts was found to be mainly NKG2D-dependent . These results are in line with the highly conserved expression of NKG2D on NK cells from HIV-1 infected viremic individuals and with the relatively high percentages of p24pos blasts expressing NKG2D ligands . The direct effect of HIV-1 on the positive or negative modulation of NKG2D ligands on the surfaces of primary CD4+ T cells infected in vitro with HIV-1 is controversial [17] , [32] . We show that masking the binding of NKG2D to its ligands clearly resulted in a marked reduction of the NK cell-mediated lysis of infected blasts . These results suggest that the NKG2D ligands , expressed at high levels in p24pos/CD4neg blasts , play an important role in NK cell-mediated killing of autologous infected cells . Several groups previously reported that HIV-1 viremia affects several functions of NK cells and dramatically influences their phenotype [26] , [27] , [28] . Interestingly , the surface expression and activation pathway of NKG2D are among the few NK cell characteristics spared from the deleterious effects of HIV-1 infection . In order to understand better how HIV-1 affects NK cell cytolytic responses , it would be important for future investigations to address the molecular mechanism ( s ) underlying the resistance of NKG2D , compared to other important activating and inhibitory NK receptor pathways , to the dysfunction associated with HIV viremia . Although defective in HLA-A and –B expression , p24pos/CD4neg blasts remain still poorly sensitive to killing exerted by autologous NK cells . This is partly the result of inhibitory interactions between iNKRs and conserved HLA-C molecules , as demonstrated by the relatively low levels of killing of both infected and uninfected autologous blasts even in the presence of anti-MHC-I mAbs which completely block the interactions between MHC-I molecules and iNKRs . The relatively low degree of NK cell cytolytic activity against endogenously HIV-1 infected CD4+ T cell blasts might be secondary to aberrancies in NK cell triggering through important activating receptors other than NKG2D . This concept is further supported by the finding that NK cells from HIV-1 infected viremic patients were markedly impaired , compared to that from healthy donors , in their ability to kill highly susceptible target cells such as K562 and 221 tumor cell lines that do not express MHC-I molecules . Moreover , if we compare these experimental data with our previously reported results obtained with HIV-1 infection in vitro [17] , the degree of killing of autologous , exogenously HIV-1 infected CD4+ cell blasts by NK cells from healthy donors appears to be markedly higher compared to killing by of highly dysfunctional NK cells obtained from HIV-1 infected viremic individuals . In this context , the low levels of NKp46 and NKp30 on NK cells from HIV-infected individuals significantly correlated with NK cell-mediated killing of MHC-Ineg K562 and 221 cell lines ( data not shown ) and of endogenously HIV-1 infected autologous CD4+ T cell blasts . These data suggest that the negative effect of HIV-1 viremia on NKp46 and NKp30 expression interfere with the NK cell lysis of endogenously HIV-1 infected autologous CD4+ T cell blasts . HIV-1 infected autologous CD4+ T cell blasts . Our finding of the negative contribution of NCRs in the killing of endogenously infected targets in HIV-infected viremic individuals differs from the findings of a previous study in which we described that NKG2D was important in NK lysis of infected targets , but that NCRs played no demonstrable role [17] . This discrepancy may result from the fact that the study in question used NK cells from normal individuals and target cells that were infected in vitro with several viral strains , whereas the present study employed NK cells from HIV-infected viremic individuals and endogenously infected target cells . It is well known that HIV-1 viremia induces a CD4+ T cell depletion that leads to immunodeficiency and correlates with disease progression . However , it has also been reported that the majority of CD4+ T cells dying during the infection are not productively infected with HIV-1[33] . One possible explanation is that these uninfected CD4+ T cells are eliminated through a mechanism not directly linked to viral replication . It has been demonstrated both in vitro[17] and ex vivo ( Figure 5B ) that HIV-1 replication can modulate the expression of ligands for NKp46 , NKp30 and NKp44 on uninfected p24neg/CD4pos T cell blasts . In particular , an highly conserved motif of HIV-1 gp41 envelope protein can induce the expression of NKp44 ligand on uninfected CD4+ T cell blasts and render these cells susceptible to NK cell-mediated killing via NKp44 activation pathway[34] . A recent report showed that is possible to prevent the expression of NKp44 ligand on CD4+ T cells , thus providing new insight for both preventive and therapeutic HIV-1 vaccine strategies[35] . In conclusion , the present study shows that NK cells from HIV-1 infected viremic patients display a variable although generally low ability to lyse endogenously HIV-1 infected autologous CD4+ T cell blasts derived from peripheral blood . The selective down-modulation of HLA-A and -B molecules makes p24pos/CD4neg cell blasts susceptible , at least in part , to autologous NK cell-mediated lysis mainly through the NKG2D activation pathway . Several other factors including the decreased NK cell expression of NCRs , low levels of NCR-specific ligands on p24pos CD4+ T cell blasts and the high frequency of the dysfunctional CD56neg NK cell subset also contribute to the low levels of NK cell-mediated killing of HIV-1 endogenously infected autologous CD4+ T cell blasts . In fact , the defective killing through the NCR activation pathways and the presence at very high levels of a markedly anergic CD56neg NK cell population substantially impair the ability of NK cell to kill endogenously HIV-1 infected autologous CD4+ T cell blasts . Understanding the mechanisms by which HIV-1 is able to negatively modulate the expression and function of NCRs on NK cell and of their ligands on HIV-1 infected CD4+ T cells will certainly give us new insights for improving the NK cell-mediated lysis of infected cells and for enforcing the innate immune control of HIV-1 infection .
Thirty HIV-1 infected viremic individuals were studied . The median CD4+ T cell count was 373 cell per ml ( SD = ±193 ) and the median viremia was 32 , 677 HIV-1 RNA copies ( SD = ±80 , 734 ) per ml of plasma as detected by an ultrasensitive branched DNA ( bDNA ) assay ( Chiron ) with a lower limit of detection of 50 copies per ml . Patients were either naïve to antiretroviral therpay ( ART ) or had formerly been receiving ART , but were not receiving therapy at the time of the study . Leukapheresis was conducted in accordance with protocols approved by the Institutional Review Boards ( IRBs ) of the University of Toronto , Ontario , Canada and the National Institute of Allergy and Infectious Diseases ( NIAID ) , National Institutes of Health ( NIH ) , Bethesda , Maryland , USA . Each patient signed a consent form that was approved by the above IRBs . As negative controls , cells from 30 healthy donors seronegative for HIV-1 were obtained by apheresis generously provided by the Transfusion Medicine Department of the Mark O . Hatfied Clinical Research Center of the NIH as a part of IRB approved clinical studies . PBMCs were obtained from leukapheresis packs by Ficoll-Hypaque density gradient centrigugation ( LSM , MP Biomedicals ) . CD4+ T cells and NK cells were freshly isolated by negative selection ( Stem Cell Technologies ) according to the protocol provided by the manufacturer . The purity of CD3+/CD4+ T cells was ≥97% . Purified NK cells contained ≤ 3% contamination with other PBMC subsets , as determined by expression of CD3 , TCR-a/b , TCR-g/d , CD19 or CD14 . In order to expand CD4+ T cell blasts productively and endogenously infected with HIV-1 , we activated freshly purified CD4+ T cells ( 2×106/ml ) with different stimuli , as shown in Figure S1 . Briefly , cells were cultured with RPMI medium 1640 supplemented with antibiotics ( Gibco ) and FCS ( HyClone ) as previously described[7] and stimulated with phytohemoagglutinin ( PHA ) ( Sigma-Aldrich ) at 3 µg/ml for 24 hours plus recombinant IL-2 ( rIL-2 ) ( Roche ) at 50 IU/ml with or without recombinant IL-7 ( rIL-7 ) ( R&D Systems ) at 10ng/ml for 21 days . We also activated freshly purified CD4+ T cells with rIL-7 with or without rIL-2 or with soluble anti-CD28 mAbs at 5 µg/ml on tissue culture plates coated with anti-CD3 mAbs at 10 µg/ml ( BD-Pharmingen ) for 21 days in the presence of rIL-2 . Freshly purified NK cells were activated in vitro for 12 days with rIL-2 at 200 IU/ml at 2*106/ml . The following panel of anti-human monoclonal antibodies ( mAbs ) were used in this study: mAbs 289 ( IgG2a anti-CD3 ) , C218 and A6-220 ( IgG1and IgM anti-CD56 , respectively ) , KD1 ( IgG2a anti-CD16 ) , AZZ20 and F252 ( IgG1 and IgM anti-NKp30 , respectively ) , BAB281 and KL247 ( IgG1 and IgM anti-NKp46 , respectively ) , Z231 and KS38 ( IgG1 and IgM anti-NKp44 , respectively ) , ON72 and Bat221 ( IgG1 anti-NKG2D ) , MA127 and ON56 ( IgG1 and IgG2b anti-NTBA , respectively ) , pp35 and Co54 ( IgG1 and IgM anti-2B4 , respectively ) , Ma152 and CER1 ( IgG1 and IgM anti-NKp80 , respectively ) , KRA236 and F5 ( IgG1 and IgM anti-DNAM-1 , respectively ) , L14 ( IgG2a anti-Nectin 2 ) , L95 ( IgG1 anti-poliovirus receptor ) , Z270 ( IgG1 anti-NKG2A ) , Y9 ( IgM anti-CD94 ) , EB6 ( IgG1 anti-p58 . 1/KIR2DL1 ) , Gl183 ( IgG1 anti p58 . 2/KIR2DL2 ) , Z276 ( IgG1 anti-p70/KIR3DL1 ) , F278 ( IgG1 anti-LIR-1ILT2 ) and A6 . 136 ( anti-MHC class I molecules , IgM ) . FITC- , PE- or APC-labeled anti-CD3 , anti-CD4 , anti-CD8 , anti-TCRα/β , anti-TCRγ/δ , anti-CD14 , anti-CD19 , anti-CD56 , anti-MICA/B and anti-CD48 mAbs were purchased from BD Biosciences . Soluble fusion proteins for NKp30 , NKp46 , NKp44 and NKG2D with the Fc portion of human IgG and anti-human ULBP-1 , -2 and -3 mAbs were purchased from R&D Systems . PE-labeled anti-human Fc fragment mAb was purchased from Jackson ImmunoResearch Laboratories . FITC- and PE- anti human anti-HIV-1 p24 mAb ( clone KC57 ) used for intracellular flow cytometry staining was purchased from Coulter Clone . PE-labeled anti-HLA-A mAbs were purchased from Lab Vision Corporation . Anti-human HLA-C mAb ( clone L31 ) was kindly provided by Dr . Patrizio Giacomini ( Regina Elena Cancer Institute , Rome , Italy ) and used in flow cytometry as previously described[36] . Anti-human HLA-Bw4 ( clone 116 . 5 . 28 ) and HLA-BW6 ( clone 126 . 39 ) mAbs were kindly provided by Dr . Keith Gelsthorpe ( National Blood Transfusion Service , Sheffield , UK ) . Anti-human HLA-E mAbs ( clones 3D12 and 4D12 ) were kindly provided by Dr . Dan Gerarthy ( Fred Hutchinson Cancer Research , Seattle , WA , USA ) . For one- , two- or three-color cytofluorimetric analysis ( FACS Calibur , BD ) , cells were stained with the appropriate FITC- , PE- or APC-labeled mAbs . For indirect staining , cells were stained with appropriate unlabeled mAbs followed by FITC- or PE-conjugated isotype-specific goat anti-mouse second reagent ( Southern Biotechnology Associates ) . Second appropriate anti-isotypic mAbs stained with FITC and/or PE and/or APC were used as negative controls . For intracellular staining , samples were fixed and permeabilized by cytofix/cytoperm solution and washed with perm-wash solution 1X ( BD-Pharmigen ) according to the protocol provided from the manufacturer . The data were analyzed using FlowJo software ( Tree Star Inc . ) . The percentages of HIV-1 infected CD4+ T cell blasts were detected by intracellular flow cytometry with an anti-p24 core virus antigen mAb . Cells undergo cell cycling were evaluated by detecting the intra-nuclear expression of Ki67 ( BD-Pharmigen ) . CD4-derived T cell blast proliferation was detected by 3[H]thymidine uptake assay ( 16 hours ) . Cellular proliferation was also evaluated by dilution of the vital dye CFSE ( Molecular Probes ) according to the supplier's instructions . After 10–12 days of activation with PHA and rIL-2 , unfractionated CD4+ T cell blasts were permeabilized by cytofix/cytoperm solution and stained with an PE-labeled anti-p24 HIV-1 core antigen mAb ( Coulter Clone ) followed by a biotin conjugated mouse anti R-PE mAb ( BD ) . Infected p24pos CD4+ T cell blasts were detected by a Pacific Blue fluorescent-dye conjugate of streptavidin ( Molecular probes ) according to the supplier's instructions . Fluorescent cells were then washed in PBS , suspended in medium , and sealed on the slides with cover slips . Images were collected on a Leica TCS-NT/SP confocal microscope ( Leica ) using a 63x oil immersion objective NA 1 . 32 . Pacific Blue was excited using an Argon laser at 364 nm . DIC ( differential interference contrast ) images were collected simultaneously with the fluorescence images using the transmitted light detector . Images were processed using Leica TCS-NT/SP software ( version 1 . 6 . 587 ) , Imaris 3 . 3 . 2 ( Bitplane AG ) , and Adobe Photoshop 7 . 0 ( Adobe systems ) . In line with the timeframe of maximal expansion of p24pos/CD4neg blasts , after 12 days of stimulation we removed by negative selection all contaminant cells from CD4+ T cell blast cultures ( MACS , Milteny Biotec ) . As a result , we obtained a highly purified population of CD4+ T cell-derived blasts containing ≤5% contamination of other lymphocyte subsets ( TCRg/d+ , CD8+ , CD56+ , CD16+ and CD19+ cells ) . HIV-1 infected CD4+ T cell blasts were then separated from uninfected blasts on the basis of CD4 surface expression through magnetic microbeads conjugated with an anti-human CD4 mAb ( MACS , Milteny Biotec ) , according to the protocol provided by the manufacturer . The purities of fractions of uninfected CD4pos and infected CD4neg blasts , as assessed by intracellular staining with HIV-1 p24 core antigens , was ≥97% and ≥70% , respectively . p24neg/CD4pos and p24pos/CD4neg cell blasts were then used as target cells against autologous rIL-2 activated NK cells in a 4-hour 51Cr release assay as described previously[37] . Saturating concentration ( 10 µg/ml ) of specific mAbs blocking NK cell receptors or MHC-I molecules were added for the masking experiments . The NK cell:T cell blast ratio was 10∶1 ( Figure S1 ) . Polyclonal NK cells were also tested in a 4-hour 51Cr release assay against MHC-Ineg erythroleukemia K562 and MHC-Ineg B-EBV cell line 721 . 221 ( thereafter termed 221 ) . E/T ratios are indicated in the figures . 51Cr release cytolytic assay were performed on cells from 15 HIV-1 infected viremic patients . Immune response distributions between healthy donors and HIV-1 infected viremic patients were compared using the Mann-Whitney test . The phenotypic and functional differences between p24pos and p24neg blasts from HIV-1 infected individuals were evaluated using the Wilcoxon signed ranks test . The functional differences between NK cell-mediated baseline lysis and lysis in masking experiments were evaluated using the Wilcoxon signed ranks test . All p-values are 2-sided and unadjusted . All statistical associations between different immune parameters were determined by the Spearman rank test for correlation . To estimate the time of maximal infection , the mean outcome of infected p24pos over time was modeled as a polynomial function of time and estimated using least squares . The maximum of this function was then determined and a bootstrap procedure was used to provide a confidence interval ≥95% for the maximum . | Natural killer ( NK ) cells represent an important line of defense against viral infections . In vitro studies with exogenously infected CD4+ T cell blasts from healthy donors have demonstrated that NK cells can kill autologous HIV-1-infected target cells . However , the ability of NK cells from HIV-1-infected viremic patients to kill autologous , endogenously infected CD4+ T cells had never been examined and remains uncertain . Given the reported abnormalities in phenotype and functions of NK cells from HIV-infected viremic individuals , we determined the function of NK cells in killing HIV-1-infected target cells under conditions that more closely mimic the in vivo environment in HIV-infected individuals . We show that NK cells from HIV-1-infected viremic patients display a variable although generally low ability to selectively eliminate autologous and endogenously HIV-1-infected CD4+ T cell blasts expanded ex vivo from peripheral blood . Various factors , including the markedly defective engagement of important NK cell activation pathways and high frequencies of the pathologic CD56neg/CD16pos NK cell subset in HIV-1-infected viremic patients , influenced NK cell–mediated cytolysis of endogenously infected CD4+ T cell blasts . |
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Centriole positioning is a key step in establishment and propagation of cell geometry , but the mechanism of this positioning is unknown . The ability of pre-existing centrioles to induce formation of new centrioles at a defined angle relative to themselves suggests they may have the capacity to transmit spatial information to their daughters . Using three-dimensional computer-aided analysis of cell morphology in Chlamydomonas , we identify six genes required for centriole positioning relative to overall cell polarity , four of which have known sequences . We show that the distal portion of the centriole is critical for positioning , and that the centriole positions the nucleus rather than vice versa . We obtain evidence that the daughter centriole is unable to respond to normal positioning cues and relies on the mother for positional information . Our results represent a clear example of “cytotaxis” as defined by Sonneborn , and suggest that centrioles can play a key function in propagation of cellular geometry from one generation to the next . The genes documented here that are required for proper centriole positioning may represent a new class of ciliary disease genes , defects in which would be expected to cause disorganized ciliary position and impaired function .
A fundamental question in cell biology is how cell geometry is established and maintained [1–4] . Cell geometry refers to the characteristic positioning of organelles within the cell body in order for a cell to be able to carry out its specified function . Despite the importance of cell geometry in tissue organization and cell function , the mechanis-tic origins of cell geometry remain a mystery . Further compounding the mystery is the fact that , as demonstrated by the classic experiments of Beisson and Sonneborn [5] , cell organization can be propagated through cell division , alleviating the need for cells to re-establish their infrastructure after each round of mitosis , and potentially allowing a coherent organization to be maintained across developing tissue during proliferative growth . Many organelles take part in this elaborate cellular patterning . One organelle that is often found in specific subcellular locations is the centriole . Centrioles are non–membrane-bound organelles composed of nine triplet microtubule blades arranged around a central cartwheel structure . Centrioles are found as a pair , composed of a mother and a daughter , which is duplicated during each cell cycle . Mother centrioles are so-called because they were assembled in a previous cell cycle to the daughter centriole . Mother centrioles have unique ultrastructural modifications [6] and are decorated with a number of molecules not found on daughter centrioles . Centrioles have two main functions in the cell . First , centrioles together with pericentriolar material comprise the centrosome , the major microtubule-organizing center of the cell . Indeed , centrioles are the highly stable , core nucleating centers for the centrosome , providing it with persisting structural integrity [7] and attaching it to cytoplasmic microtubules during G1 [8] . Second , centrioles serve as basal bodies to nucleate the assembly of cilia . In order to carry out these functions in the cell , centrioles often need to be specifically localized . Although originally named for their centralized location , centrioles are repositioned to more peripheral sites during cell-state transitions such as wound healing , cell migration , and cell growth [9–11] . The importance of centriole positioning for development and physiology is perhaps most clearly illustrated in situations involving cilia , which are assembled from centrioles . The problem of ciliary positioning is 2-fold . First , centrioles must migrate to the proper region on the cell surface where they will dock and assemble cilia . Second , once centrioles reach the cell surface , they must become properly oriented so as to create a proper directional stroke in the case of motile cilia , or so they are oriented to participate in signaling as in the case of a primary cilium . Perturbation in either step of ciliary positioning has severely deleterious effects in humans [12] . For example , inability of centrioles to properly migrate prior to ciliary assembly has recently been linked to Meckel-Gruber syndrome [13] . Additionally , proper orientation of cilia via centriole positioning towards the posterior of embryonic node cells is critical for establishing left–right asymmetry during mammalian development [14] . Centrioles must also be properly positioned when they serve as basal bodies in multiciliated cells such as in the tracheal epithelium . Centriole orientation , and the resulting proper alignment of respiratory cilia , is required for effective mucus clearing in the airway [15] . In all cases in which cilia act either to drive fluid flow or act as sensors , it is important that they be placed on the appropriate region of the cell surface; for example , in cells lining a duct , the cilia would have to face the lumen of the duct , which requires specific positioning of centrioles on a limited patch of cell surface . It is clear that centriole positioning is critical in many aspects of cell behavior , especially in placing a cilium that will interact with the extracellular environment . Centriole position may also serve a function in intracellular events . As centrioles are anchored to the cytoskeleton during G1 , they may act as a set of stable “handles” by which the centrosome can be repositioned to orient the cytoskeleton , cilia , and perhaps , other cellular structures as well . Moreover , the process of centriole duplication provides an ideal mechanism to transmit cell geometry across generations . Although both planar cell polarity [16 , 17] and apical/basal cues [18 , 19] can influence centriole position , the mechanism by which centrioles are positioned , and the degree to which their positioning is self-propagating , is currently unknown . The unicellular alga Chlamydomonas reinhardtii provides an ideal genetic system in which to study centriole positioning . Each pair of centrioles , composed of a mother and a daughter , must relocate from the apical cell surface to the spindle poles during mitosis . After division , centrioles return to the apical pole where they nucleate the assembly of two cilia ( called flagella in this organism ) . Chlamydomonas centrioles and cilia are structurally similar to those of vertebrates , with the vast majority of centriolar and ciliary proteins conserved between humans and Chlamydomonas . Chlamydomonas cells also have reproducible chiral cell geometry with many characteristically positioned structures [20] ( illustrated in Figure 1A and 1B ) , facilitating quantification of geometric relationships within the cell . Given the importance of cilia positioning in animal tissues , and the high conservation of the ciliary apparatus components between Chlamydomonas and animals , we feel that this unicellular alga is an excellent gene-discovery platform for analyzing cilia-placement mechanisms that may turn out to be important in human ciliary diseases . Using Chlamydomonas cells , we identified mutants with defects in centriole positioning . Combining genetic analysis , three-dimensional ( 3D ) imaging , and a novel algorithm for quantifying cellular geometry , we demonstrate that the mother centriole guides the daughter centriole to the proper subcellular location . Specifically , in mutants in which mother and daughter centrioles are separated , only mother centrioles localize properly . We further show that in mutants in which the centrioles are detached from the nucleus , the nucleus becomes randomly positioned , whereas the mother centrioles retain correct positioning , indicating that normally , the mother centriole plays a role in properly positioning the nucleus and not vice versa . These data indicate that the mother centriole may act as a node to coordinate the positioning of many subcellular structures .
To initiate a genetic analysis of the mechanism of centriole positioning and its impact on cell geometry , we began with a screen based on Chlamydomonas phototaxis . Chlamydomonas cells phototax using a light-sensing organelle called the eyespot . Cells rotate while swimming , sweeping out a 360° path , looking for light . When the eyespot detects light , it signals to the flagella via calcium signaling , inducing the cell to turn towards the light [21] . We predicted that cells with aberrantly placed centrioles , and therefore , aberrantly placed flagella , would lack the geometric relationship between the eyespot and the flagella that is required for phototaxis , and would be revealed in a screen for phototaxis defects . We screened 10 , 000 insertionally mutagenized lines for defects in phototaxis using an assay similar to previously described techniques [22–24] . Phototaxis-defective lines were visually rescreened by differential interference contrast ( DIC ) microscopy to identify mutants with defective cell morphology . Screen details are listed in Figure S1 . Centriole positioning mutants were identified as those whose flagella are displaced from the apical pole of the cell ( the usual position of centrioles in G1 in Chlamydomonas ) and were verified using a 3D computer-aided image analysis strategy as follows . We defined the long axis of the cell using the center of mass of the pyrenoid ( Figure 1E , yellow circle ) , a starch-storage structure that is located basally , and the cellular center of mass ( Figure 1E , purple circle ) . We then marked the centrioles ( Figure 1E , white cylinders ) , and using the long axis to construct a spherical coordinate system , we determined the angle by which each centriole was displaced off the long axis of the cell ( θcentriole , Figure 1E ) . θcentriole represents the zenith angle in a spherical coordinate system and is by definition between 0° and 180° . We were unable to measure the azimuth angle φ due to a lack of a visible reference point . We identified 13 mutants , which we termed askew ( asq ) , in which centrioles are mispositioned as judged by θcentriole . For example , asq1 cells have a mean θcentriole of 42 . 3 ± 21 . 3° ( Figure 1G , n = 54; all reported angles are the mean ± standard deviation ) . asq2 cells have a mean θcentriole of 61 . 7 ± 32 . 3° ( Figure 1H , n = 71 ) . These values differ significantly ( one-tailed t-test , asq1: p < 5 . 4 e−10 , asq2: p < 9 . 8 e−17 ) from wild-type ( wt ) cells , which have a mean θcentriole of 20 . 5 ± 9 . 0° ( Figure 1F , n = 62 ) . The average angle in wt is non-zero because the two centrioles are on either side of the apical-most point , and hence displaced off the long axis . In asq cells , the angles tend to be restricted to the apical half of the cell due to the occlusion of the basal portion by other cellular structures . The basal portion and some of the apical portion of Chlamydomonas cells contain chloroplast . We measured the position of the chloroplast by using the same long-axis assignment described above . We then marked each plastid nucleoid ( Figure 1I , green circles , visualized using DAPI , and Figure 2A , left ) and determined the angle each nucleoid was displaced off the long axis of the cell . wt cells have a mean θchloroplast of 112 . 1 ± 36 . 0° ( Figure 1J , n = 181 ) . The pyrenoid center of mass is defined as 180° in all of our θ measurements because it is used as one of the points to define the long axis . The outer bounds of the pyrenoid span the basal part of the cell ( Figure 1B ) . As was the case with the centrioles measurements , we calculate the zenith angle θ in standard spherical coordinates , which by convention can only vary between 0° and 180° . Thus , the bounds of the pyrenoid will both be less than 180° . The mean pyrenoid boundary in wt cells is 139 . 0 ± 14 . 4° ( Figure 1J , yellow-shaded region , n = 90 ) . The region of the cell that is occupied by the chloroplast and pyrenoid is thus complimentary to the region in which asq centrioles can be found , consistent with the notion that in asq mutants , centrioles are randomly distributed over the accessible part of the cell cortex . asq mutants can be subdivided into two classes based on the pairwise association of centrioles . Normally , mother and daughter centrioles are held together by a system of connecting fibers . The asq1 mutant represents a class of mutants ( containing 9/13 asq mutants ) in which mother and daughter centrioles are attached to each other as in wt , but are randomly localized together on the cell surface ( Figures 1C , 2B , and 2C ) . The asq2 mutant represents a second class ( containing 4/13 asq mutants ) in which the mother and daughter centrioles are independently positioned on the cell surface ( Figures 1D , 2D , and 2E ) . In asq2 cells , some centrioles appear at the correct apical location ( Figures 2E and S5B ) , whereas other centrioles can occupy atypical positions ( Figure 2D and 2E ) . In addition to centriole positioning defects , asq2 cells also have variable numbers of centrioles , and therefore make variable numbers of flagella ( Figure 3B and 3C ) . In contrast to wt cells , which always have two flagella ( Figure 3A and 3D , black bars ) , asq2 cells can have from zero to seven centrioles per cell ( Figure 3D and Table S1 ) . Other Chlamydomonas mutants with a similar variability in centriole number have been previously identified [25–27] and are referred to as vfl ( variable flagellar number ) mutants because the variable number of centrioles nucleates the assembly of variable numbers of flagella ( Figure 3D ) when the centrioles become basal bodies . These mutant phenotypes are thought to result from defective centriole segregation [28] and from defects in centriole mother–daughter cohesion [25 , 29] . The similarity between the variable flagellar number phenotypes of asq2 and the vfl mutants raised the possibility that the vfl mutants might also share the centriole positioning phenotype . We therefore tested vfl2 and vfl3 for defects in centriole positioning and found when analyzed using our computational strategy that these mutants have centriole positioning defects comparable with those of asq2 . vfl2 cells have a mean θcentriole of 55 . 2 ± 28 . 8° ( Figure 3E , n = 64 ) and vfl3 cells have a mean θcentriole of 59 . 4 ± 35 . 2° ( Figure 3F , n = 90 ) . Genetic mapping studies show that asq2 is not an allele of any of the previously described VFL genes ( unpublished data ) . Using these mutants , we can begin to ask which component of the centrosome responds to polarity cues during positioning . The centrosome is composed of a mother centriole , a daughter centriole , and pericentriolar material , and is attached to the nucleus . In Chlamydomonas , these structures are spatially distinct but connected by fibers . Mother–daughter pairs are linked by striated fibers and connected to the nucleus by rhizoplasts [28 , 30] in Chlamydomonas and by Hook/Sun domain proteins in other organisms [31 , 32] . In principle , any of these components ( the mother centriole , the daughter centriole , or the nucleus ) could localize the others in response to polarity cues . We first tested whether the mother centriole can localize the daughter or vice-versa . Previous studies have demonstrated that the vfl mutants result in dissociation of mothers from daughters and/or centrioles from the nucleus [28 , 29] . Using electron microscopy ( EM ) , we verified that mother and daughter centrioles are likewise disconnected in asq2 cells ( Figure 4B ) . In wt cells , electron-dense fibers connect mother and daughter centrioles ( Figure 4A , arrow ) . In contrast , asq2 cells lack these connecting fibers ( Figure 4B , arrow ) , confirming a loss of mother–daughter connections . These mutants therefore allow us to test which of these structures is able to localize properly when detached from the others . Visual examination of asq2 and vfl mutants suggested to us that the centriole distribution can be interpreted as a mixture of two populations: a population of correctly positioned centrioles ( Figures 2E and S5B ) and a population of randomly positioned centrioles ( Figure 2D and 2E ) . On the basis of these observations and the known inherent disparity in maturation state between centrioles in each cell , we propose a model in which centriole maturity affects positioning . We considered a model in which the mother centriole is necessary for positioning the daughter centriole ( Figure 4C ) . In accordance with this model , in the asq1 class of mutants , the mother centriole can no longer respond to the cell polarity cue , and the mother–daughter pairs end up randomly localized . In the asq2 class , the mother and daughter centrioles would be detached from each other , resulting in a population of properly positioned mother centrioles and a population of misplaced daughter centrioles . Because mother and daughter centrioles are no longer connected , centrioles will not segregate properly following mitosis , resulting in cells with variable numbers of centrioles . The key prediction of this model is that the mother centrioles in asq2 cells should be properly localized , whereas the daughter centrioles should be improperly localized ( Figure 4C ) . To test the prediction that mother centrioles are correctly positioned whereas daughters are mislocalized , we must be able to differentiate mother and daughter centrioles in 3D microscopy images . Mother centrioles have ultrastructural modifications that are lacking on daughter centrioles and are visible by EM , but serial section EM is not suitable for analyzing large numbers of cells . In order to be able to distinguish mothers and daughters in a more high-throughput manner , we employed a genetic strategy to render mother and daughter centrioles distinguishable by light microscopy . To do this , we took advantage of the uni1 mutant in which flagella are formed predominantly by mother centrioles [33] ( see flagellar distribution in Table S1 ) . We then tested whether mother centrioles localize to the proper position at the apical pole by measuring the θcentriole ( Figure 1E ) for all flagellated ( mother ) centrioles in asq2uni1 double-mutant cells . If mother centrioles can respond to polarity cues , they should account for the properly positioned centrioles sometimes seen in asq2 mutants , hence the mean θcentriole of flagellated centrioles in asq2uni1 cells should be smaller and less variable than that of asq2 cells ( Figure 5C ) . Indeed , we find that asq2uni1 cells have a mean θcentriole of 32 . 4 ± 13 . 1° ( Figure 5D , green lines , n = 60 ) , which is significantly ( one-tailed t-test , p < 2 . 02 e−10 ) smaller than the mean θcentriole for asq2 cells ( Figures 1H and 5D , grey lines ) . The mean θcentriole for flagellated centrioles in asq2uni1 cells is slightly higher than wt ( Figure 1F , mean θcentriole = 20 . 5 ± 9 . 0° ) and uni1 ( Figure S2A , mean θcentriole = 20 . 4 ± 8 . 5° ) , but this is expected because the uni1 phenotype is incompletely penetrant , such that some daughter centrioles still bear flagella in uni1 mutants ( Table S1 ) . So as not to rely solely on the pyrenoid and cellular center of mass measurements , we employed an alternative measure of geometry based on distance measurements . We measured the 3D through-space distance between flagellated centrioles in asq2uni1 cells . If mother centrioles localize to the same subcellular site , then the distance between flagellated centrioles should be relatively low in the double mutant , especially when compared to that of asq2 cells in which both mother and daughter centrioles have flagella ( Figure 5A , right ) . In contrast , if mother centrioles are randomly localized , then the interflagellar distance in asq2uni1 double-mutant cells should be at least as large as in asq2 cells and just as variable ( Figure 5A , left ) . We find that in asq2uni1 double mutants , the interflagellar distance is significantly smaller ( Figure 5B , blue bars , mean = 0 . 89 μm ± 0 . 04 standard error of the mean [S . E . M . ] , n = 85 ) than that of asq2 cells ( Figure 5B , yellow bars , mean = 1 . 48 μm ± 0 . 09 S . E . M . , n = 88 ) and less variable , confirming that mother centrioles cluster in the same subcellular location . An alternative explanation for these data is that the uni1 mutation acts as a suppressor of the centriole segregation and/or positioning phenotype in asq2 cells . Centriole number in asq2uni1 cells ( Figure S3A , mean centriole number = 1 . 67 ± 1 . 25 , n = 317 ) is indistinguishable ( one-tailed t-test p < 0 . 3 ) from that of asq2 cells ( Figure S4 , asq2 mean centriole number = 1 . 72 ± 1 . 27 , n = 440 ) , indicating that uni1 does not suppress the centriole segregation defect . Furthermore , uni1 does not act as a suppressor of centriole positioning defects , because intercentriolar distance is similar in asq2 ( mean = 1 . 39 ± 0 . 94 , n = 168 ) and asq2uni1 ( mean = 1 . 42 ± 1 . 12 , n = 174 ) cells ( Figure S3B , one-tailed t-test , p > 0 . 39 ) . The 3D immunofluorescence imaging of asq2uni1 cells demonstrates that the mother and daughter centrioles remain detached in the double mutant just as in the asq2 single mutant , demonstrating that the uni1 mutation does not simply behave as a suppressor , either of the mother–daughter detachment phenotype or of the centriole mispositioning phenotype of the asq2 mutation . Indeed , mother centrioles properly localize to the apical pole ( Figure 5E , flagellated centrioles , white arrow ) , whereas disconnected daughter centrioles can wander to atypical sites ( Figure 5E , unflagellated centriole , blue arrow ) . These observations confirm that mother centrioles are competent to be properly positioned and normally play an instructive role in leading the daughter centriole to the correct subcellular location . We therefore conclude that in asq2 cells , centriole positioning is intact , because mothers can find the proper subcellular location , but daughters are mispositioned because they are detached from their mother . Mother centrioles guide daughters to the correct subcellular position , but does the mother centriole play a role in instructing the position of other organelles ? In a wt Chlamydomonas cell , the centrioles sit atop the nucleus and are attached to it by centrin-containing fibers called rhizoplasts [30] ( Figure 6A ) . This juxtaposition suggests that centriole and nuclear positioning could be intimately linked . In most cell types , there tends to be a correlation between nuclear and centrosomal position . In asq mutant cells , the nucleus seems to be mispositioned along with the centrioles ( Figure 6B ) , suggesting that centrioles position the nucleus or vice versa . A recent study has suggested that nuclear reorientation affects the position of the centrosome during cell migration in mammalian cells [9] . However , it has also been demonstrated that centrosomes are able to reach the cell cortex during Drosophila development without the aid of the nucleus [34] . To help address the controversy over who positions whom , nucleus or centrosome , we wanted to determine whether the nucleus could be impacting the localization of the mother centriole . To test directly whether nuclear positioning has a causal impact on centriole position , we made use of the vfl2 mutant in Chlamydomonas that has a mutation in centrin [35] , a protein component of the rhizoplast . vfl2 cells lack the centrin-based rhizoplast structure that connects the centrioles to the nucleus [28] . As shown in Figure 6D , vfl2 centrioles have increased variability in positioning , but , like asq2 , the mother centrioles remain properly localized at the apical pole as determined in vfl2uni mutants . We quantified nuclear position ( θnucleus ) in vfl2uni1 , uni1 , and wt cells in a manner similar to the determination of θcentriole . We determined the long axis of the cell using the same method described above , but instead of marking each centriole , we obtained the nuclear center of mass and measured how much this point was shifted off the long axis of the cell . In wt cells , the mean angle θnucleus is 15 . 5 ± 8 . 1° ( Figure 6E , n = 62 ) . This value is similar to that of uni1 cells ( Figure S2B , θnucleus = 14 . 3 ± 5 . 6° , n = 40 ) . In vfl2uni1 cells , in which the nucleus has been uncoupled from the centrioles , the θnucleus is much more variable and the mean θnucleus ( mean θnucleus = 25 . 0 ± 11 . 8° , Figure 6F , n = 49 ) is significantly higher ( one-tailed t-test , p < 2 . 9 e−6 ) , indicating that the nucleus is free to visit a wider range of positions once detached from the centrioles ( Figure 6C ) . In contrast to the variable nuclear position , we find that , as in asq2uni1 , in vfl2uni1 cells , flagellated mother centrioles are properly localized , whereas the position of daughters is randomized ( Figure 6D , vfl2uni1 θcentriole [orange lines] , vfl2 θcentriole [grey lines] ) . vfl2uni1 cells have a mean θcentriole that is not statistically different ( one-tailed t-test , p > 0 . 03 ) from wt or uni1 , indicating that the mother centrioles can be correctly positioned despite the variable position of the nucleus . We further tested whether the nucleus dictates centriole position , by measuring the correlation of nuclear position to that of centriole position on a cell-by-cell basis . In vfl2uni cells , θcentriole for flagellated centrioles does not correlate with θnucleus ( Figure 6H , n = 49 , correlation coefficient of 0 . 10 ) . When we compare the mean θcentriole of cells with a correctly positioned nucleus ( θnucleus is less that one standard deviation from the mean θnucleus for wt cells ) to the mean θcentriole of the cells with an incorrectly positioned nucleus ( θnucleus is more than one standard deviation from the wt mean ) , the values do not differ significantly ( one-tailed t-test , p > 0 . 33 , Figure 6H , inset ) . These data indicate that the position of the nucleus has no obligatory impact on the position of centrioles in the cell and that correct centriole positioning in Chlamydomonas cells does not require attachment to the nucleus . Conversely , because the nucleus is mispositioned with the centrioles in asq mutant cells ( Figure 6B ) , we wondered whether centrioles are involved in positioning the nucleus . In a population of wt cells , the θcentriole correlates with θnucleus ( correlation coefficient = 0 . 63 , Figure 6G , n = 62 ) . The fact that centriole position is unaltered and nuclear position randomized in a mutant that detaches centrioles from the nucleus , together with the fact that centriole position and nuclear position are correlated with each other when the centrioles are attached to the nucleus by the rhizoplast , suggests that centrioles dictate the position of the nucleus rather than vice versa . Recent studies in migrating cell lines demonstrated that nuclear reorientation is important in positioning the centrosome towards the leading edge of the cell [9] . However , these studies only measured translational position of the centrosome and therefore cannot rule out a model in which rotation of the centrosome drives nuclear movement rather than vice versa . It would be interesting to repeat those experiments in cells lacking the nucleus–centrosome connections . In addition to the nucleus , we also found that the rootlet microtubules ( acetylated microtubule bundles involved in cleavage furrow placement in Chlamydomonas cells ) are mispositioned along with centrioles in asq mutants . We found that rootlets were co-localized with centrioles in 27/27 cells ( representative image shown in Figure S4B ) . Additionally , the contractile vacuoles are also mispositioned with centrioles in asq mutants ( DIC image shown in Figure 3B and 3C , immunofluorescence images shown in Figure S4 ) . To measure the position of the contractile vacuole , we fixed cells and incubated them with an antibody against FMG-1 ( a flagellar membrane glycoprotein [36] ) that binds to protein in the flagellar membrane as well as in other membrane-bound structures , including the contractile vacuoles ( Figure S4C , inset ) . The distance between the contractile vacuole and the centrioles does not differ significantly between wt cells ( mean = 0 . 52 ± 0 . 07 μm , Figure S4C ) and cells in which centrioles are misplaced as in asq1 ( mean distance = 0 . 49 ± 0 . 07 μm , wt compared to asq1 , p < 0 . 06 , Figure S4D ) , asq2 ( mean distance = 0 . 49 ± 0 . 08 μm , wt compared to asq2 , p < 0 . 04 , Figure S4E ) , or bld2 cells ( mean distance = 0 . 53 ± 0 . 07 μm , bld1 compared to bld2 , p < 0 . 02 , bld2 compared to wt , p < 0 . 31 , Figure S4F ) . We conclude that both rootlets and contractile vacuoles remain co-localized with centrioles even when centrioles are displaced , suggesting that centrioles may play a role in positioning these structures . Strictly speaking , because we do not have mutations that separate contractile vacuoles or rootlets from centrioles , we cannot definitively conclude whether the centrioles position these structures , or vice versa . However , we do note that in asq2uni1 double mutants , rootlets can be seen associated with misplaced daughter centrioles in cells in which the mother centrioles have properly localized at the anterior pole ( e . g . , Figure 4E ) , suggesting that at least in this mutant , mother centrioles respond properly to the cell polarity cue , whereas the rootlets can be misplaced . The differential ability of the mother versus the daughter to respond to the polarity cue , despite no difference in their rootlet associations , tends to suggest that the mother , rather than the rootlets , is the primary responder to the polarity cue , although more complex models remain possible . We also note that although the nucleus , rootlets , and contractile vacuole appear to co-localize with misplaced centrioles , this is not true of other structures , such as the pyrenoid or eyespot . The data therefore suggest that centrioles may influence the geometry of a specific subset of cellular structures , with other structures being independently oriented by a cell polarity system upstream of normal centriole positioning . To begin to analyze which part of the mother centriole is responsible for positioning , we took advantage of known Chlamydomonas mutants with defects in centriole assembly , bld2 and bld10 . bld2 cells have a mutation in epsilon tubulin [37] and are missing the B- and C-tubule of each of the nine triplet microtubule blades that normally comprise the centriole ( compare Figure 7A and 7B ) . As a result , bld2 centrioles have nine short , singlet microtubules and are lacking portions of the distal end . bld10 cells , which are defective in the production of the centriole cartwheel-localized protein Bld10p , are missing all centriole microtubules and have at most just the most proximal portions of the centriolar structure [38] . Because bld2 and bld10 cells both lack flagella , we first determined the centriole positioning phenotype of bld1 cells , which also lack flagella but have a structurally normal centriole . bld1 cells have a mutation in the gene that encodes IFT52 [39] . These cells have centrioles that are structurally identical to wt cells , but due to a defect in a component of intraflagellar transport , they are unable to make flagella ( Figure 7A ) . We found that bld1 cells have a mean θcentriole of 19 . 8 ± 8 . 0° ( Figure 7G ) , similar to wt and demonstrating that assembly of flagella is not necessary for proper centriole positioning . To determine whether the distal portion of the centriole is necessary for positioning , we measured the θcentriole for bld2 and bld10 cells and compared it to θcentriole for bld1 cells . bld2 cells have a mean θcentriole of 45 . 9 ± 26 . 9° ( Figure 7H ) , and bld10 cells have a mean θcentriole of 40 . 2 ± 30 . 8° ( Figure 7I ) . These values differ significantly from those of bld1 cells ( bld2: one-tailed t-test , p < 5 . 4 e−8 , bld10: one-tailed t-test , p < 3 . 1 e−5 ) , which indicates that the distal portion of the centriole may be necessary for positioning . One potential explanation for the mispositioning of centrioles in bld2 and bld10 cells is that the centrioles are not actually attached to the cell surface . In many bld2 and bld10 cells ( Figure 7E and 7F , respectively ) , centrioles appear in the cell interior and not at the apical membrane as in bld1 cells ( Figure 6D ) and wt cells ( Figure 2A ) . Therefore , structures at the distal ends of centrioles such as the transition fibers ( Figure 7A ) may be responsible for properly positioning the mother centriole by docking the centriole onto the cell surface .
These data highlight a set of gene products required for proper centriole positioning ( Table 1 ) , which will serve as a starting point for a molecular dissection of the centriole positioning pathway . Moreover , the data support a model in which the mother centriole plays a role in establishing cell geometry . Particularly , the mother centriole leads the daughter to the proper location . Additionally , the centrioles position the nucleus and may position the rootlet microtubules and contractile vacuoles . Using the uni1 mutation , we were able to distinguish between mature and immature centrioles in asq2 and vfl2 cells and determine their subcellular locations . One intriguing possibility is that at least some of the mispositioned unflagellated centrioles in asq2uni1 and vfl2uni1 cells are de novo–assembled centrioles , which are known to form in vfl mutants [40] . Because de novo–assembled centrioles are perhaps the most immature form of centrioles , this possibility would not invalidate our model that centriole maturity affects positioning . In fact , our model only presumes that mature centrioles can find their way to the proper subcellular site , whereas immature centrioles ( which could include both templated daughter and de novo–assembled centrioles ) cannot . An alternative model to explain centriole positioning is that there are only two slots for centrioles to dock into at the correct apical location , such that any cell with more than two centrioles would have more centrioles than could dock into these slots , and the extra centrioles would be mispositioned by default ( an equivalent model for the case of ciliates was proposed [1] ) . Although cells with three or more centrioles per cell occur in vfl2 and asq2 populations ( e . g . , Figure S3A ) , those cells represent a small fraction of the population and hence would not account for the large increase in θcentriole on average . Furthermore , a strong prediction of this model is that any cell with only one or two centrioles should have properly positioned centrioles because the two slots could accommodate these centrioles . However , we often observe cells with one or two centrioles that are clearly not at the correct position ( Figures 2D and S5A ) , and conversely , we also see cells with more than two centrioles in which centrioles are clustered near the apical pole . Competition for a limited number of docking sites alone cannot explain these data . Therefore , although there may be specific docking sites on the cell surface , these sites alone are not sufficient to drive correct centriole positioning . There may in fact be a two-component system involving a specialized region at the cortex at which competent centrioles could dock . Although we therefore do not think that saturation of a small , discrete set of docking sites can explain our data , our results are in no way inconsistent with the idea that a defined subregion of the cortex is set aside as a docking region . Indeed , just such a docking zone has been shown to exist in surf clam [41] and the marine worm Chaetopterus [42] , in which it plays a key role in spindle attachment . A similar region exists in ascidians , known as the centrosome-attracting body , which plays a key role in asymmetric cell division during early embryogenesis [43] . The mother centriole could be interpreting a global polarity cue and tracking to a specialized cortical region , where it would be able to read out aspects of cell polarity to the position of other cellular structures . Alternatively , the mother centriole could itself be the mark to establish aspects of cell polarity . In Caenorhabditis elegans embryos , the paternally contributed centrosome is the early symmetry-breaking mark that induces a local change in the cortex and thereby establishes the anterior-posterior axis [44] . A similar role for centrioles in cell polarity is supported by the observation that bld2 and bld10 cells are often more round than are wt cells ( compare cell shape in Figure 7E and 7F to Figure 2A ) , perhaps indicating a perturbation in global cell polarity . Because centrioles do not appear docked onto the cell surface in bld2 and bld10 cells , the centriole may require its distal portion not only for positioning , but also for exerting its effect on cell polarity . The mother centriole has structural appendages in the subdistal region that may couple centriole position and orientation with cell geometry through the cytoskeletal network . A model in which the mother centriole can impact and propagate local cell geometry is appealing in light of experiments in ciliates [5 , 45 , 46] and vertebrate ciliated tissues [47] that demonstrate that ciliary orientation is dictated and propagated by a heritable local mark . These prior experiments demonstrated that a heritable mark exists , but were not able to reveal the identity of this mark because they could not dissociate the cellular components from one another . For instance in Paramecium , thousands of cilia are arranged into rows , with each cilium arising from a cortical unit . If rows of cilia are inverted from their normal orientation , the inverted orientation can propagate during cell division [5] . However , each cortical unit contains not only a cilium and centrioles , but also kinetodesmal fibers , trichocysts , striated bands , infraciliary lattice fibers , the “fork/bone node” [48] , and an apparently self-duplicating oriented structure called the “post” [49] . Because inversion of rows simultaneously inverts the orientation of all of these other structures [50] , it is not possible to determine which of the substructures within the cortical unit serves as a coordinating local signal to orient the other structures during formation of new cortical units in cell division . The difficulty in interpreting the results of ciliate micromanipulation studies arises because such procedures leave the interactions between centrioles and other cortical structures intact , making it impossible to say who is positioning whom . In contrast , genetic manipulation using Chlamydomonas mutants allowed us to separate mother and daughter centrioles from each other and from other oriented structures , permitting us to determine that the local signal responsible for inheritance of orientation appears to be the mother centriole . The differential potential of older versus more recently assembled structures has also been documented in higher organisms . Recent studies in Drosophila male germline [51] have shown that the mother centrosome behaves differently from the daughter centrosome during asymmetric cell division . Specifically , the mother centrosome is always inherited by the stem cell , whereas the daughter centrosome is inherited by the differentiating cell . The mother centriole may therefore be playing a similar role to the results described here in impacting aspects of cell geometry in metazoans . The fibrous connections between organelles have been intensively characterized in Chlamydomonas , but similar physical connections exist in vertebrate cells , for example between the mother and daughter centrioles and between centrioles and the nucleus [52 , 53] , indicating that the mother centriole has the potential to coordinate cell geometry in a broad range of organisms . Although Drosophila can develop without centrioles [54] , there is a clear requirement of centrioles in ciliated cells . Flies lacking centrioles are sterile and uncoordinated , indicating that sperm and potentially asymmetric cell divisions are perturbed . In this context , the role of centriole positioning may be in properly placing a cilium . Ciliary positioning is critical in higher vertebrates , for example in the establishment of left–right asymmetry [14] and in effective mucus clearing in the airway [15] , where coordinated rotational orientation of the basal bodies is necessary to drive coherent flow of fluid across the epithelial surface . Abnormalities in cilia positioning due to defects in centriole migration have been observed in human patients [52] , indicating that defects in centriole positioning may represent a specific class of ciliary disease . Because spindles can form in the absence of centrioles by a centrosome-independent pathway , there may be a similar fail-safe pathway for organizing other aspects of cell geometry . The centriole is unique among cellular structures in its complexity , chirality , stability , and templated replication , and these features make it an ideal hub around which to organize and propagate particular aspects of cellular geometry . In particular , the fact that a mother centriole can not only produce a daughter , but instruct the daughter centriole concerning the correct positioning within the cell provides a potential basis for the phenomenon of “cytotaxis” [2] as the ability of a pre-existing cellular structure to determine the position or organization of newly formed cellular structure during cell replication . Our results have implications for the general problem of organelle positioning and cell geometry . The ability of the mother centriole to position the daughter and to orient the nucleus suggests that a complete understanding of organelle positioning will require analysis not only of individual organelles , but also of the pairwise mechanical linkages that may exist among distinct organelles .
C . reinhardtii cells were grown and maintained in Tris-acetate-phosphate ( TAP ) media [55] . To generate insertional mutants to screen for phototaxis defects , the cell wall-less strain CC-849 , cw10 was electroporated [56] with linearized plasmid DNA containing the aph7 gene , which confers resistance to hygromycin [57] . Strains were backcrossed to a wt strain of the opposite mating type ( CC-125 ) , and tetrads were dissected as previously described [55] . Double-mutant strains were constructed by crossing the pertinent single mutants and choosing spores from NPD tetrads that showed a non-wt phenotype . Cells were fixed with Lugol's iodine solution to maintain robust cell geometry and prevent flagellar shearing and allowed to adhere to polylysine-coated coverslips . Cells were permeabilized with methanol and blocked with 5% BSA , 1% coldwater fish gelatin and 10% normal goat serum in PBS . Cells were then incubated in primary antibodies followed by secondary antibodies ( Jackson ImmunoResearch , http://www . jacksonimmuno . com ) diluted in 20% block , with six washes of 20% block in between . Cells were incubated with DAPI ( diluted 1 μg/ml in water ) and mounted in Vectashield mounting media on microscope slides . Slides were imaged using a 100× lens ( numerical aperture [n . a . ] = 1 . 4 ) on a Deltavision deconvolution microscope with an air condenser for DIC imaging . Images were processed and manipulated using Softworx image processing software . Cells were fixed and stained as described above . For asq analysis , cells were labeled with DAPI and antibodies against centrin ( diluted 1:100; a generous gift from J . Salisbury ) , acetylated tubulin ( diluted 1:100; Sigma , http://www . sigmaaldrich . com ) , and Bld10p ( diluted 1:100; a generous gift from M . Hirono ) , which together allow unambiguous identification of centrioles . A 3D stack through each cell was generated and used in the asq analysis . Using Softworx software , the center of mass of the nucleus , pyrenoid , and cell were defined . The center of mass was determined by obtaining the centroid , approximated by the midpoint of the three orthogonal edges of a bounding box containing the structure of interest and whose edges were parallel to the x- , y- , and z-axes of the 3D image . The appropriate structure for each specific θ measurement ( e . g . , the centrioles for θcentriole ) were also marked . These coordinates were entered into a PERL script to calculate θ . Comparison of means was performed using a one-tailed Student t-test in Excel . Unless indicated , error is shown as the standard deviation of the mean . For measuring correlation of datasets , the Pearson correlation coefficient was used .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession numbers for the genes discussed in this paper are BLD1 ( AF397450 ) , BLD10 ( AB116368 ) , BLD2 ( AF502577 ) , VFL2 ( AW773019 ) , and VFL3 ( AAQ95706 ) . | Cells are not just homogenous bags of enzymes , but instead have a precise and complex internal architecture . However , the mechanisms that define this architecture remain unclear . How do different organelles find their proper location within the cell ? We have begun to address this question for one particular organelle , the centriole , using a genetic approach . Our approach relies on the fact that centrioles are required for the assembly of cilia and flagella , which are used for swimming . We studied the unicellular green alga Chlamydomonas , which use flagella to swim towards a light source . We screened for mutants that could not swim towards light , and found a set of mutants in which the centrioles and flagella are displaced from their normal location within the cell . Using these mutants , we have obtained evidence that centrioles play a role in positioning other structures within the cell , such as the nucleus . We also found that in these cells , which contain two centrioles differing in age , the older centriole plays a role in positioning the newer centriole , suggesting that cells may have a way to propagate spatial patterns from one generation to the next . |
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Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances . Yet , the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood . Here we predict computationally and analytically that any organism evolving to maximize growth rate , ATP production , or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical nonoptimal states . The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all . We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity . Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways , shedding new light on microbial evolution , robustness , and versatility for the execution of specific biochemical tasks . In particular , the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function .
A fundamental problem in systems biology is to understand how living cells adjust the usage pattern of their components to respond and adapt to specific genetic , epigenetic , and environmental conditions . In complex metabolic networks of single-cell organisms , there is mounting evidence in the experimental [1]–[6] and modeling [7]–[14] literature that a surprisingly small part of the network can carry all metabolic functions required for growth in a given environment , whereas the remaining part is potentially necessary only under alternative conditions [15] . The mechanisms governing this behavior are clearly important for understanding systemic properties of cellular metabolism , such as mutational robustness , but have not received full attention . This is partly because current modeling approaches are mainly focused on predicting whole-cell phenotypic characteristics without resolving the underlying biochemical activity . These approaches are typically based on optimization principles , and hence , by their nature , do not capture processes involving non-optimal states , such as the temporary activation of latent pathways during adaptive evolution towards an optimal state [16] , [17] . To provide mechanistic insight into such behaviors , here we study the metabolic system of single-cell organisms under optimal and non-optimal conditions in terms of the number of active reactions ( those that are actually used ) . We implement our study within a flux balance-based framework [18]–[23] . Figure 1 illustrates key aspects of our analysis using the example of Escherichia coli . For any typical non-optimal state ( Figure 1B ) , all the reactions in the metabolic network are active , except for those that are necessarily inactive due either to mass balance constraints or environmental conditions ( e . g . , nutrient limitation ) . In contrast , a large number of additional reactions are predicted to become inactive for any metabolic flux distribution maximizing the growth rate ( Figure 1A ) . This spontaneous reaction silencing effect , in which optimization causes massive reaction inactivation , is observed in all four organisms analyzed in this study , H . pylori , S . aureus , E . coli , and S . cerevisiae , which have genomes and metabolic networks of increasing size and complexity ( Materials and Methods ) . Our analysis reveals two mechanisms responsible for this effect: ( 1 ) irreversibility of a large number of reactions , which under intracellular physiological conditions [14] is shared by more than 62% of all metabolic reactions in the organisms we analyze ( Table 1 and Note 1 ) ; and ( 2 ) cascade of inactivity triggered by the irreversibility , which propagates through the metabolic network due to stoichiometric and flux balance constraints . We provide experimental evidence of this phenomenon and explore applications to data interpretation by analyzing intracellular flux and gene activity data available in the literature . The drastic difference between optimal and non-optimal behavior is a general phenomenon that we predict not only for the maximization of growth , but also for the optimization of any typical objective function that is linear in metabolic fluxes , such as the production rate of a metabolic compound . Interestingly , we find that the resulting number of active reactions in optimal states is fairly constant across the four organisms analyzed , despite the significant differences in their biochemistry and in the number of available reactions . In glucose media , this number is ∼300 and approaches the minimum required for growth , indicating that optimization tends to drive the metabolism surprisingly close to the onset of cellular growth . This reduced number of active reactions is approximately the same for any typical objective function under the same growth conditions . We suggest that these findings will have implications for the targeted improvement of cellular properties [24] . Recent work predicts that the knockout of specific enzyme-coding genes can enhance metabolic performance and even rescue otherwise nonviable strains [25] . The possibility of such knockouts bears on the issue of whether the inactivation of the corresponding enzyme-catalyzed reactions would bring the whole-cell metabolic state close to the target objective . Thus , our identification of a cascading mechanism for inducing optimal reaction activity for arbitrary objective functions provides a natural set of candidate genetic interventions for the knockout-based enhancement of metabolic function [25] .
We model cellular metabolism as a network of metabolites connected through reaction and transport fluxes . The state of the system is represented by the vector v = ( v1 , … , vN ) T of these fluxes , including the fluxes of n internal and transport reactions , as well as nex exchange fluxes for modeling media conditions . Under the constraints imposed by stoichiometry , reaction irreversibility , substrate availability , and the assumption of steady-state conditions , the state of the system is restricted to a feasible solution space ( Materials and Methods ) . Within this framework , we first consider the number of active reactions in a typical non-optimal state v∈M . We can prove that , with the exception of the reactions that are inactive for all v∈M , all the metabolic reactions are active for almost all v∈M , i . e . , for any typical state chosen randomly from M ( Text S1 , Section 1 ) . Accordingly , the number n+ ( v ) of active reactions in a typical non-optimal state is constant , i . e . , ( 1 ) The reactions that are inactive for all states are so either due to mass balance or environmental conditions , and can be identified numerically using flux coupling [26] and flux variability analysis [9] . We now turn to the maximization of growth rate , which is often hypothesized in flux balance-based approaches and found to be consistent with observation in adaptive evolution experiments [31]–[34] . Performing numerical optimization in glucose minimal media ( Materials and Methods ) , we find that the number of active reactions in such optimal states is reduced by 21%–50% compared to typical non-optimal states , as indicated in the middle bars of Figure 2 . Interestingly , the absolute number of active reactions under maximum growth is ∼300 and appears to be fairly independent of the organism and network size for the cases analyzed . We observe that the minimum number of reactions required merely to sustain positive growth [7] , [8] is only a few reactions smaller than the number of reactions used in growth-maximizing states ( bottom bars , Figure 2 ) . This implies that surprisingly small metabolic adjustment or genetic modification is sufficient for an optimally growing organism to stop growing completely , which reveals a robust-yet-subtle tendency in cellular metabolism: while the large number of inactive reactions offers tremendous mutational and environmental robustness[52] , the system is very sensitive if limited only to the set of reactions optimally active . The flip side of this prediction is that significant increase in growth can result from very few mutations , as observed recently in adaptive evolution experiments [35] . We now turn to mechanisms underlying the observed reaction silencing , which is spread over a wide range of metabolic subsystems ( see Figure 1 for E . coli ) . The phenomenon is caused by growth maximization via reaction irreversibility and cascading of inactivity . Although we have focused so far on maximizing the biomass production rate , the true nature of the fitness function driving evolution is far from clear [44]–[47] . Organisms under different conditions may optimize different objective functions , such as ATP production or nutrient uptake , or not optimize a simple function at all . In particular , some metabolic behaviors , such as the selection between respiration and fermentation in yeast , cannot be explained by growth maximization [48] . Other behaviors may be systematically different in situations mimicking natural environments [49] . Moreover , various alternative target objectives can be conceived and used in metabolic engineering applications . We emphasize , however , that while specific numbers may differ in each case , all the arguments leading to Eqs . ( 2 ) – ( 4 ) are general and apply to any objective function that is linear in metabolic fluxes . To obtain further insights , we now study the number of active reactions in organisms optimizing a typical linear objective function by means of random uniform sampling . We first note the fact ( proved in Text S1 , Section 4 ) that with probability one under uniform sampling , the optimal solution set Mopt consists of a single point , which must be a “corner” of M , termed an extreme point in the linear programming literature . In this case , dopt = 0 , and Eq . ( 2 ) becomes ( 5 ) With the additional requirement that the organism show positive growth , we uniformly sample these extreme points , which represent all distinct optimal states for typical linear objective functions . We find that the number of active reactions in typical optimal states is narrowly distributed around that in growth-maximizing states , as shown in Figure 4 . This implies that various results on growth maximization extend to the optimization of typical objective functions . In particular , we see that a typical optimal state is surprisingly close to the onset of cellular growth ( estimated and shown as dashed vertical lines in Figure 4 ) . Despite the closeness , however , the organism maintains high versatility , which we define as the number of distinct functions that can be optimized under growth conditions . To demonstrate this , consider the H . pylori model , which has 392 reactions that can be active , among which at least 302 must be active to sustain growth ( Table 3 ) . While only a few more than 302 active reactions are sufficient to optimize any objective function , the number of combinations for choosing them can be quite large . For example , there are combinations for choosing , say , 5 extra reactions to be active . Moreover , this number increases quickly with the network size: S . cerevisiae , for example , has less than 2 . 5 times more reactions than H . pylori , but about 500 times more combinations ( ) . Our results help explain previous experimental observations . Analyzing the 22 intracellular fluxes determined by Schmidt et al . [50] for the central metabolism of E . coli in both aerobic and anaerobic conditions , we find that about 45% of the fluxes are smaller than 10% of the glucose uptake rate ( Table 4 ) . However , less than 19% of the reversible fluxes and more than 60% of the irreversible fluxes are found to be in this group ( Fisher exact test , one-sided p<0 . 008 ) . For the 44 fluxes in the S . cerevisiae metabolism experimentally measured by Daran-Lapujade et al . [51] , less than 8% of the reversible fluxes and more than 42% of the irreversible fluxes are zero ( Table 5; Fisher exact test , one-sided p<10−7 ) . This higher probability for reduced fluxes in irreversible reactions is consistent with our theory and simulation results ( Table 6 ) combined with the assumption that the system operates close to an optimal state . For the E . coli data , this assumption is justified by the work of Burgard & Maranas [44] , where a framework for inferring metabolic objective functions was used to show that the organisms are mainly ( but not completely ) driven by the maximization of biomass production . The S . cerevisiae data was also found to be consistent with the fluxes computed under the assumption of maximum growth [52] . Additional evidence for our results is derived from the inspection of 18 intracellular fluxes experimentally determined by Emmerling et al . [53] for both wild-type E . coli and a gene-deficient strain not exposed to adaptive evolution . It has been shown [21] that while the wild-type fluxes can be approximately described by the optimization of biomass production , the genetically perturbed strain operates sub-optimally . We consider the fluxes that are more than 10% ( of the glucose uptake rate ) larger in the gene-deficient mutant than in the wild-type strain . This group comprises less than 27% of the reversible fluxes but more than 45% of the irreversible fluxes ( Table 7; Fisher exact test , one-sided p<0 . 12 ) . This correlation indicates that irreversible fluxes tend to be larger in non-optimal metabolic states , consistently with our predictions . Altogether , our results offer an explanation for the temporary activation of latent pathways observed in adaptive evolution experiments after environmental [16] or genetic perturbations [17] . These initially inactive pathways are transiently activated after a perturbation , but subsequently inactivated again after adaptive evolution . We hypothesize that transient suboptimal states are the leading cause of latent pathway activation . Since we predict that a large number of reactions are inactive in optimal states but active in typical non-optimal states , many reactions are expected to show temporary activation if we assume that the state following the perturbation is suboptimal and both the pre-perturbation and post-adaptation states are near optimality . To demonstrate this computationally for the E . coli model , we consider the idealized scenario where the perturbation to the growth-maximizing wild type is caused by a reaction knockout and the initial response of the metabolic network—before the perturbed strain evolves to a new growth-maximizing state—is well approximated by the method of minimization of metabolic adjustment ( MOMA ) [21] . MOMA assumes that the perturbed organisms minimize the square-sum deviation of its flux distribution from the wild-type distribution ( under the constraints imposed by the perturbation ) . Figure 5A shows the distribution of the number of active reactions for single-reaction knockouts that alter the flux distribution but allow positive MOMA-predicted growth . While the distribution is spread around 400–500 for the suboptimal states in the initial response , it is sharply peaked around 300 for the optimal states at the endpoint of the evolution , which is consistent with our results on random sampling of the extreme points ( Figure 4 ) . We thus predict that the initial number of active reactions for the unperturbed wild-type strain ( which is 297 , as shown by a dashed vertical line in Figure 5A ) typically increases to more than 400 following the perturbation , and then decays back to a number close to 300 after adaptive evolution in the given environment , as illustrated schematically in Figure 5B . A neat implication of our analysis is that the active reactions in the early post-perturbation state includes much more than just a superposition of the reactions that are active in the pre- and post-perturbation optimal states , thus explaining the pronounced burst in gene expression changes observed to accompany media changes and gene knockouts [16] , [17] . For example , for E . coli in glucose minimal medium , temporary activation is predicted for the Entner-Doudoroff pathway after pgi knockout and for the glyoxylate bypass after tpi knockout , in agreement with recent flux measurements in adaptive evolution experiments [17] . Another potential application of our results is to explain previous experimental evidence that antagonistic pleiotropy is important in adaptive evolution [54] , as they indicate that increasing fitness in a single environment requires inactivation of many reactions through regulation and mutation of associated genes , which is likely to cause a decrease of fitness in some other environments [15] .
All the stoichiometric data for the in silico metabolic systems used in our study are available at http://gcrg . ucsd . edu/In_Silico_Organisms . For H . pylori 26695 [77] , we used a medium with unlimited amount of water and protons , and limited amount of oxygen ( 5 mmol/g DW-h ) , L-alanine , D-alanine , L-arginine , L-histidine , L-isoleucine , L-leucine , L-methionine , L-valine , glucose , Iron ( II and III ) , phosphate , sulfate , pimelate , and thiamine ( 20 mmol/g DW-h ) . For S . aureus N315 [78] , we used a medium with limited amount of glucose , L-arginine , cytosine , and nicotinate ( 100 mmol/g DW-h ) , and unlimited amount of iron ( II ) , protons , water , oxygen , phospate , sulfate , and thiamin . For E . coli K12 MG1655 [75] , we used a medium with limited amount of glucose ( 10 mmol/g DW-h ) and oxygen ( 20 mmol/g DW-h ) , and unlimited amount of carbon dioxide , iron ( II ) , protons , water , potassium , sodium , ammonia , phospate , and sulfate . For S . cerevisiae S288C [76] , we used a medium with limited amount of glucose ( 10 mmol/g DW-h ) , oxygen ( 20 mmol/g DW-h ) , and ammonia ( 100 mmol/g DW-h ) , and unlimited amount of water , protons , phosphate , carbon dioxide , potassium , and sulfate . The flux through the ATP maintenance reaction was set to 7 . 6 mmol/g DW-h for E . coli and 1 mmol/g DW-h for S . aureus and S . cerevisiae . Under steady-state conditions , a cellular metabolic state is represented by a solution of a homogeneous linear equation describing the mass balance condition , ( 6 ) where S is the m×N stoichiometric matrix and is the vector of metabolic fluxes . The components of v = ( v1 , … , vN ) T include the fluxes of n internal and transport reactions as well as nex exchange fluxes , which model the transport of metabolites across the system boundary . Constraints of the form vi≤βi imposed on the exchange fluxes are used to define the maximum uptake rates of substrates in the medium . Additional constraints of the form vi≥0 arise for the reactions that are irreversible . Assuming that the cell's operation is mainly limited by the availability of substrates in the medium , we impose no other constraints on the internal reaction fluxes , except for the ATP maintenance flux for S . aureus , E . coli , and S . cerevisiae ( see Strains and media section above ) . The set of all flux vectors v satisfying the above constraints defines the feasible solution space , representing the capability of the metabolic network as a system . The flux balance analysis ( FBA ) [18]–[20] , [22] , [23] used in this study is based on the maximization of a metabolic objective function cTv within the feasible solution space M , which is formulated as a linear programming problem: ( 7 ) We set αi = −∞ if vi is unbounded below and βi = ∞ if vi is unbounded above . For a given objective function , we numerically determine an optimal flux distribution for this problem using an implementation of the simplex method [43] . In the particular case of growth maximization , the objective vector c is taken to be parallel to the biomass flux , which is modeled as an effective reaction that converts metabolites into biomass . To find a set of reactions from which none can be removed without forcing zero growth , we start with the set of all reactions and recursively reduce it until no further reduction is possible . At each recursive step , we first compute how much the maximum growth rate would be reduced if each reaction were removed from the set individually . Then , we choose a reaction that causes the least change in the maximum growth rate , and remove it from the set . We repeat this step until the maximum growth rate becomes zero . The set of reactions we have just before we remove the last reaction is a desired minimal reaction set . Note that , since the algorithm is not exhaustive , the number of reactions in this set is an upper bound and approximation for the minimum number of reactions required to sustain positive growth . | Cellular growth and other integrated metabolic functions are manifestations of the coordinated interconversion of a large number of chemical compounds . But what is the relation between such whole-cell behaviors and the activity pattern of the individual biochemical reactions ? In this study , we have used flux balance-based methods and reconstructed networks of Helicobacter pylori , Staphylococcus aureus , Escherichia coli , and Saccharomyces cerevisiae to show that a cell seeking to optimize a metabolic objective , such as growth , has a tendency to spontaneously inactivate a significant number of its metabolic reactions , while all such reactions are recruited for use in typical suboptimal states . The mechanisms governing this behavior not only provide insights into why numerous genes can often be disabled without affecting optimal growth but also lay a foundation for the recently proposed synthetic rescue of metabolic function in which the performance of suboptimally operating cells can be enhanced by disabling specific metabolic reactions . Our findings also offer explanation for another experimentally observed behavior , in which some inactive reactions are temporarily activated following a genetic or environmental perturbation . The latter is of utmost importance given that nonoptimal and transient metabolic behaviors are arguably common in natural environments . |
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Synaptic communication is a dynamic process that is key to the regulation of neuronal excitability and information processing in the brain . To date , however , the molecular signals controlling synaptic dynamics have been poorly understood . Membrane-derived bioactive phospholipids are potential candidates to control short-term tuning of synaptic signaling , a plastic event essential for information processing at both the cellular and neuronal network levels in the brain . Here , we showed that phospholipids affect excitatory and inhibitory neurotransmission by different degrees , loci , and mechanisms of action . Signaling triggered by lysophosphatidic acid ( LPA ) evoked rapid and reversible depression of excitatory and inhibitory postsynaptic currents . At excitatory synapses , LPA-induced depression depended on LPA1/Gαi/o-protein/phospholipase C/myosin light chain kinase cascade at the presynaptic site . LPA increased myosin light chain phosphorylation , which is known to trigger actomyosin contraction , and reduced the number of synaptic vesicles docked to active zones in excitatory boutons . At inhibitory synapses , postsynaptic LPA signaling led to dephosphorylation , and internalization of the GABAAγ2 subunit through the LPA1/Gα12/13-protein/RhoA/Rho kinase/calcineurin pathway . However , LPA-induced depression of GABAergic transmission was correlated with an endocytosis-independent reduction of GABAA receptors , possibly by GABAAγ2 dephosphorylation and subsequent increased lateral diffusion . Furthermore , endogenous LPA signaling , mainly via LPA1 , mediated activity-dependent inhibitory depression in a model of experimental synaptic plasticity . Finally , LPA signaling , most likely restraining the excitatory drive incoming to motoneurons , regulated performance of motor output commands , a basic brain processing task . We propose that lysophospholipids serve as potential local messengers that tune synaptic strength to precedent activity of the neuron .
Activity-dependent plasticity of neuronal networks refers to the adaptive changes in their properties in response to external and internal stimuli . In a prominent form of central nervous system ( CNS ) plasticity , synaptic strength results in an increase ( potentiation ) or decrease ( depression ) of transmission efficacy , depending on the neuron’s precedent activity ( activity-dependent synaptic plasticity ) . Short-lived processes that modify synaptic strength occur in practically all types of synapses [1] , and short-term synaptic plasticity is essential in regulating neuronal excitability and is central to information processing at both cellular and neuronal network levels [2] . Homeostatic adjustment of synaptic weights counteracts neuronal rate disturbances that affect self-tuning neuronal activity within a dynamic range via Ca2+-dependent sensors [3] . The number of receptors in the surface membrane and at synaptic sites , and the size of the readily releasable pool ( RRP ) of synaptic vesicles ( SVs ) , are important determinants of synaptic strength , short-term plasticity , and intersynaptic crosstalk [4–8] . Unmasking the feedback mechanisms that are believed to sense neuron activity and adjust synaptic strength ( i . e . , activity-dependent , coupled messenger synthesis and/or release ) would help to explain how circuits adapt during synaptic homeostasis , experience-dependent plasticity , and/or synaptic dysfunctions that underlie cognitive decline in many neurological diseases . The ligand-gated ionotropic channels—A-type GABAA receptors ( GABAARs ) and AMPA-type glutamate receptors ( AMPARs ) —mediate fast synaptic transmission at the vast majority of inhibitory and excitatory synapses , respectively , in the mammalian brain [4 , 5 , 9] . Cell surface stability of receptors is further regulated by post-translational phosphorylation , palmitoylation , and/or ubiquitination . In particular , AMPAR and GABAAR phosphorylation modulates the receptor’s biophysical properties and membrane trafficking . Hence , the coordinated activity of kinases and phosphatases plays a pivotal role in controlling synaptic strength and neuronal excitability . Key residues within the intracellular domains of diverse AMPAR and GABAAR subunits are targeted by a number of kinases , including protein kinases A and C , calcium/calmodulin-dependent kinase II , and tyrosine kinases of the Src family . Generally , phosphorylation stabilizes the receptor on the surface and , conversely , dephosphorylation appears to be important for receptor endocytosis [4 , 9] . Lysophosphatidic acid ( LPA ) is a strong candidate to function as a local messenger that rapidly affects synaptic strength . A membrane-derived bioactive phospholipid that affects all biological systems , LPA is an important intermediary in lipid metabolism and has a vital role in de novo biosynthesis of membrane phospholipids [10] . The nervous system is markedly modulated by LPA signaling . LPA , autotaxin ( the main LPA-synthesizing enzyme ) , and many subtypes of LPA-specific G-protein-coupled receptors ( LPA1–6 ) are enriched in the brain [10–12] . Downstream signaling cascades mediating LPA signaling include mitogen-activated protein kinase ( MAPK ) activation , adenylyl cyclase inhibition or activation , phospholipase C ( PLC ) activation/Ca2+ mobilization and/or protein kinase C ( PKC ) activation , arachidonic acid release , Akt/PKB activation , and the activation of small GTPase RhoA and subsequent Rho kinase ( ROCK ) stimulation [10] . Many subtypes of LPA receptors ( LPARs ) are expressed in the brain; in particular , LPA1 is highly expressed and is the most prevalent receptor subtype in both the embryonic and adult brains [13–15] . Accordingly , LPA targets all CNS cell types to modulate developmental processes including neurogenesis , migration , differentiation , and morphological and functional changes [10] . However , little is known about how LPA signaling influences neuron physiology and neuronal connectivity or integrates incoming synaptic drive . Presynaptic LPA2 at glutamatergic synapses mediates neuronal network hyperexcitability in an epileptic mouse model [16] . In addition , LPA1-deficient mice manifest alterations in managing diverse neurotransmitters [17–20] . Endogenous ROCK activity , an intracellular partner in LPA signaling , is necessary to maintain afferent AMPAergic and GABAAergic synaptic strength in motoneurons [8] . As a conventional link in synaptic plasticity , activity-dependent LPA production occurs downstream of noxious activation of glutamate receptors in models of neuropathic pain [21] . However , whether LPA signaling is actually able to modulate synaptic strength and mediate activity-dependent synaptic plasticity remains unresolved . The aim of this study was to investigate whether LPA regulates synaptic strength and plasticity of motoneuron excitatory and inhibitory synapses . Here , we show that LPA—mainly via LPA1—induced rapid and reversible depression in synaptic strength ( short-term depression [STD] ) , and operated as an autocrine messenger mediating activity-dependent STD at inhibitory synapses . At glutamatergic synapses , presynaptic LPA signaling reduced the size of the RRP of SVs . At GABAergic synapses , postsynaptic LPA action mediated dephosphorylation and endocytosis-dependent internalization of the GABAAγ2 subunit . Strikingly , LPA signaling regulated the performance of motor output commands in vivo . Therefore , LPA seems to have important implications for synaptic plasticity , pathology , and information processing in the brain .
To explore a possible role of LPA in shaping the normal motor output of the HN , it was necessary to determine the predominant isotype of its main target receptors expressed in this motor nucleus . Assessment of the expression levels of mRNAs for LPA1–6 receptors in microdissected HN from neonatal ( P7 ) rats revealed that lpa1 mRNA was 1 . 5 to 12 . 5 times more abundant than lpa2–6 transcripts ( Fig 1A ) . Subsequently , confocal analysis of double immunolabeled HN from P7 pups showed LPA1-immunoreactive ( ir ) puncta , patches , and fiber-like structures colocalizing with SMI32-positive HMN perikarya and dendrite-like structures ( Fig 1B–1D ) . Three-dimensional reconstructions agreed with a cytoplasmic and membrane localization of LPA1 in perikarya and main dendritic branches of HMNs ( Fig 1E and 1F ) . Triple immunofluorescence for LPA1 , SMI32 , and the vesicular glutamate ( VGLUT2 ) or GABA ( VGAT ) transporters as synaptic markers confirmed that LPA1-ir puncta were colocalizing with excitatory ( VGLUT2-ir ) or inhibitory ( VGAT-ir ) presynaptic structures ( Fig 1G , 1H , 1J , and 1K ) . Both excitatory and inhibitory inputs were also found apposed to SMI32-ir neuropil or somata coexpressing LPA1 ( Fig 1H and 1I ) . Although LPA1 expression in other neural cell types is not excluded , this expression pattern supports pre and/or postsynaptic roles of LPA1 at the main excitatory and inhibitory inputs on HMNs , suggesting a potential contribution of LPA to motoneuron physiology . Next , we investigated the functional effects of LPA on glutamatergic and GABAergic synaptic currents by whole-cell patch-clamp recordings of HMNs ( slices from P6–P9 rats ) . Electrical stimulation of the ventrolateral reticular formation ( VLRF ) evoked postsynaptic currents ( ePSCs ) in HMNs ( Fig 2A ) . The AMPAR- or GABAAR-mediated components of ePSCs ( excitatory [eEPSCsAMPA] or inhibitory postsynaptic currents [eIPSCsGABAA] , respectively ) were isolated and recorded as described in S1 Text . The two major species of LPA ( approximately 70% ) found in the brain [26] , monounsaturated ( 18:1 , or LPA ) and saturated ( 18:0 , or s-LPA ) , were used in this study . While LPA activates LPA1–3 , s-LPA has high affinity for LPA1/2 , but is a comparatively poor agonist against LPA3 [27] . Unless stated otherwise , LPA was used at a similar concentration ( 2 . 5 μM ) to that found in serum ( 1–5 μM ) [28] . In general , unsaturated LPAs are more potent than s-LPA in activating LPARs and inducing biological activities [29] . Accordingly , a higher concentration was used for s-LPA ( 40 μM ) than for LPA ( 2 . 5 μM ) to achieve a similar effect on neurotransmission . Both phospholipids , added for 10 min to the bath solution , strongly attenuated the amplitude of eEPSCsAMPA and eIPSCsGABAA ( Fig 2B ) . The effects were reversed after 10 min of washing . Thus , LPA modulated rapidly and reversibly the strength of AMPAR- and GABAAR-mediated synaptic transmission in motoneurons . The tested dose ( 2 . 5 μM ) of LPA had a proportionately higher effect on inhibitory than on excitatory inputs ( Fig 2B and 2C ) . Further , differential sensitivity to LPA was studied by applying various concentrations , ranging from 1 nM to 20 μM . After subtracting vehicle-induced changes ( S1 Text ) , an effect on both currents was detectable at concentrations as low as 10 nM and increased with LPA concentration to a similar maximum reduction in both currents ( approximately 70% ) at 10–20 μM ( Fig 2C ) . Dose-response relationships were well fitted ( p < 0 . 001; r2 > 0 . 99 ) by biphasic ( two slopes ) five-parameter logistic equations , suggesting that LPA affects synaptic neurotransmission by multiple mechanisms . It remains to be determined whether this is the consequence of the recruitment of diverse isoreceptors and/or downstream signaling pathways . In any case , from the nanomolar to first-order micromolar range , LPA diminished inhibitory inputs ( IC50 = 1 . 0 ± 0 . 17 μM ) in greater proportions ( p < 0 . 001 , Kolmogorov-Smirnov test ) than excitatory ones ( IC50 = 1 . 8 ± 0 . 08 μM ) , but at higher concentrations , LPA affected both synaptic systems similarly ( Fig 2C ) . As in our previously published study [8] , a combined electrophysiological analysis was performed to identify the LPA synaptic site of action . LPA signaling on AMPAR-mediated transmission is likely not attributable to changes in postsynaptic sensitivity to glutamate . LPA did not alter the amplitude in both the miniature quantal EPSCsAMPA ( mEPSCsAMPA ) and postsynaptic currents evoked by exogenous glutamate pulses ( S1 Text; S1 Fig ) . For that reason , we sought evidence for a presynaptic mechanism by recording spontaneous AMPAergic synaptic currents under facilitated spontaneous glutamate release ( sEPSCsAMPA ) . In this condition , synaptic activity was a mixture of action potential-dependent and -independent events . After LPA treatment , the sEPSCsAMPA amplitude , but not frequency ( 10 . 8 ± 1 . 0 Hz , p = 0 . 761 ) , reversibly decreased to a value similar to that recorded for mEPSCsAMPA in control condition ( before: 36 . 0 ± 3 . 8 pA; LPA: 24 . 0 ± 2 . 0 pA; Fig 3A–3C ) . This agrees with a LPA-induced full inhibition of action potential-dependent events . In addition , we evaluated eEPSCsAMPA facilitation using paired-pulse and repetitive afferent stimulation protocols as in our previously published study [8] . Under repetitive stimulation , a change in the amount of facilitation is considered to be attributable to a presynaptic change in the release probability of neurotransmitter quanta [1] . In the control condition , paired-pulse stimulation displayed a strong facilitation of eEPSCsAMPA over the entire range of interstimulus intervals tested , but this was more pronounced at shorter interstimulus intervals ( Fig 3D; S2 Fig ) . Facilitated PPR ( paired-pulse ratio ) showed a marked and reversible increase at 25 ms and 50 ms intervals after application of either s- or LPA ( abbreviated as s-/LPA; Fig 3D; S2 Fig ) . On average , LPA and s-LPA increased the magnitude of PPR by 12 . 8% and 29 . 3% at 25 ms , respectively . The finding that LPA also reversibly potentiated the facilitation index of eEPSCsAMPA under repeated VLRF stimulation provided additional evidence in support of these outcomes ( S1 Text; S3 Fig ) . At this point in our study , the attenuation of eEPSCsAMPA induced by LPA was related to a reduction in the glutamate release probability , which is believed to be determined by the number of fusion-competent SVs or the size of the RRP of SVs [6 , 7] . This idea was further strengthened by a subsequent analysis of eEPSCsAMPA amplitude using the minimal stimulation paradigm , designed to stimulate only one fiber and a single or small number of release sites . As in our previous study [8] , the intensity of the stimulation was set to elicit eEPSCsAMPA with 30% to 40% failure ( Fig 3E ) . In this context , LPA treatment evoked a significant reduction of the mean amplitude of eEPSCsAMPA elicited by minimal stimulation and an enhancement of the eEPSCsAMPA failure rate ( Fig 3E; S4 Fig ) . The presynaptic action of LPA on glutamatergic inputs is further supported because LPA1-ir puncta colocalize with Munc13-1 , a presynaptic active zone ( a . z . ) marker [30] , in VGLUT2-containing boutons ( Fig 3F and 3G ) . The LPA1 association with a region of the presynaptic membrane compromised in the fusion of SVs supports that LPA signaling has a direct relationship with the machinery involved in the regulation of neurotransmitter release . The qRT-PCR and immunohistochemical studies , together with additional pharmacological tests ( S1 Text: S5 Fig; S6 Fig ) , robustly point to LPA1 as a pivotal LPAR affecting glutamatergic synapses . In this context , injection of a small interfering RNA ( siRNA ) against lpa1 ( siRNAlpa1; 2 μg/2 μl ) into the fourth ventricle efficiently reduced LPA1 expression in the brain stem ( Fig 4A; S1 Text; S7 Fig ) . siRNAlpa1 robustly diminished , but did not fully avoid , ( s- ) LPA-induced alterations on eEPSCsAMPA amplitude and PPR relative to the administration of control noninterfering siRNA ( cRNA; 2 μg/2 μl ) or vehicle ( RNase-free phosphate buffered saline; 2 μl ) ( Fig 4A–4D ) . Whether the remaining response of eEPSCsAMPA to ( s- ) LPA could be due to residual LPA1 expression or to recruitment of compensatory mechanisms—e . g . , via up-regulated LPA3 in response to LPA1 knockdown—remains to be elucidated . LPA1 couples with and activates three G proteins: Gα12/13 , Gαi/o , and Gαq/11 [10] . Previous findings [8] and pharmacological data ( S1 Text ) did not support Gα12/13 involvement . Alternatively , preincubation for 2 h with the Gαi/o specific inhibitor pertussis toxin ( PTX ) , but not with the noncatalytic B oligomer of PTX ( bPTX ) , prevented ( s- ) LPA-induced STD and PPR increase ( Fig 4E , 4G , and 4H; S8A , S8D , and S8E Fig ) . Cascade downstream of lysophospholipids included PLC activation; the PLC inhibitor U73122 , but not its inactive analog U73343 , reversed—to a control-like state—the changes in amplitude and PPR provoked by ( s- ) LPA ( Fig 4F–4H; S8B , S8D , and S8E Fig ) . Finally , the Gαq/11 inhibitor YM-254890 did not interfere with s-LPA effects on eEPSCsAMPA ( S8C–S8E Fig ) . Altogether , these findings indicate that LPA signaling controls excitatory inputs via presynaptic Gαi/o-protein-coupled LPA1 and PLC ( Fig 4I ) . LPA induces smooth muscle contraction in a PLC-dependent , ROCK-independent manner that involves myosin light chain ( MLC ) phosphorylation by MLC kinase ( MLCK ) [31] . These findings point to MLCK as a potential kinase mediating the presynaptic action of LPA on excitatory neurotransmission . Accordingly , LPA increased the p-MLC:MLC ratio in the HN relative to aCSF-incubated brain stem slices , which was fully prevented by coincubation with the specific MLCK inhibitor ML-7 ( Fig 5A and 5B ) . In concordance , though ML-7 per se did not alter the amplitude of eEPSCsAMPA , as we also recently reported [8] , it fully suppressed LPA-induced alterations on eEPSCsAMPA amplitude and PPR ( Fig 5C–5F ) . This further supports MLCK as a main molecular substrate activated by LPA signaling within excitatory presynaptic terminals . MLC phosphorylation stimulates actomyosin interactions [32] , and presynaptic Ca2+ concentration regulates MLCK activity and modulates the RRP size in the calyx of the Held synapse [33] . Therefore , LPA signaling , through its modulatory control on MLCK and the actomyosin cytoskeleton , might regulate clustering and spatial distribution of SVs within excitatory ( S-type , spherical SVs-containing ) boutons ( S1 Text ) . Electron microscopy analysis , performed as in our previous study [8] , showed that , in a MLCK-dependent way , LPA noticeably reduced the number of SVs near the a . z . in S-type boutons attached to HMNs , compared to control conditions ( Fig 5G–5L; S1 Text ) . In addition , LPA induced a drop ( −20 . 2 ± 6 . 3% ) in the SV population morphologically docked to ( i . e . , in contact with ) the a . z . , which corresponds to the release-ready neurotransmitter quanta [34] that was prevented by coaddition of ML-7 ( Fig 5M and 5N ) . These outcomes robustly support that LPA signaling regulates the size of the RRP of SVs in S-type boutons by a MLCK-dependent mechanism . Together , these data strongly suggest that the depression of synaptic strength induced by LPA treatment is dependent on a reduction in the probability of release from excitatory glutamatergic terminals . This effect is attributable , at least in part , to a reduction in the size of the RRP of SVs . Our results reaffirm that LPA signaling modulates excitatory synaptic transmission through mechanisms modulating the presynaptic component of the synapse . Next , we explored whether LPA modulates GABAergic and glutamatergic synapses by similar mechanisms of action . Amplitude , but not frequency , of miniature quantal IPSCsGABAA ( mIPSCsGABAA ) recorded in HMNs was reduced by LPA , in agreement with a postsynaptic site of action ( Fig 6A; S9 Fig ) . The molecular cascade downstream of LPA is also distinct , since LPA-induced alterations on mIPSCsGABAA were reversed by the ROCK inhibitor H1152 ( Fig 6A; S9 Fig ) . H1152 also returned ( s- ) LPA-induced changes in eIPSCGABAA amplitude to a control-like state ( S10A and S10B Fig ) . In support of a non-presynaptic action of s-LPA on eIPSCsGABAA , the mean PPR remained similar to the control condition in the presence of s-LPA or s-LPA plus H1152 ( S10C and S10D Fig ) . Colocalization in HMNs of LPA1-ir with the postsynaptic marker gephyrin , a clustering protein for GABAARs [35] , strengthened the evidence of a postsynaptic site of action for LPA ( Fig 6B ) . Postsynaptic action and the molecular signaling underlying LPA-induced modulation of GABAAergic system were assessed in primary cultures of spinal motoneurons ( SMNs ) ( S1 Text; S11 Fig ) . The mean amplitude of inward GABAAR-mediated current evoked by exogenous GABA pulses ( −4 . 13 ± 0 . 98 nA; n = 8 SMNs ) was robustly reduced by s-LPA ( −62 . 5 ± 10 . 1% , p < 0 . 001 , one-way ANOVA for repeated measures ( RM-ANOVA ) ) , in a ROCK-dependent way ( s-LPA+H1152: −3 . 23 ± 0 . 49 nA , p = 0 . 345 ) ( Fig 6C ) . In addition , we observed that s-LPA activated the small GTP-binding protein RhoA , the major ROCK activator , in SMNs . This was evidenced by an s-LPA-induced increase ( +78 . 3 ± 25 . 7%; p < 0 . 05 , one-way ANOVA on Ranks ) in the membrane ( M ) :cytosolic ( C ) ratio of RhoA expression relative to the control condition ( Fig 6D ) . Supplementary data support LPA signaling as the activator for the RhoA/ROCK pathway in motoneurons ( S1 Text; S12 Fig ) . Furthermore , pretreatment with siRNAlpa1 prevented the effects of ( s- ) LPA on GABAAR-mediated currents compared to cRNA-treated SMNs , providing conclusive evidence of postsynaptic LPA1 involvement ( Fig 6E and 6F; S1 Text; S13 Fig ) . Phosphorylation of serine 327 on the GABAAγ2 subunit ( pGABAAγ2 ) regulates GABAAR clustering and synaptic strength at inhibitory synapses [36 , 37] . Therefore , we investigated whether LPA1-ROCK signaling regulates phosphorylation of GABAAγ2 . Contrary to expectations of a direct interaction between ROCK and GABAAγ2 , s-LPA induced a robust reduction ( −83 . 3 ± 5 . 2% ) of the pGABAAγ2:GABAAγ2 ratio in SMNs that was prevented by coaddition of H1152 ( +1 . 6 ± 6 . 0% ) ( Fig 6G ) . This was also observed in the HN ( S1 Text; S14 Fig ) . Strikingly , direct binding of the phosphatase calcineurin ( CaN ) to GABAAγ2 subunits dephosphorylates Ser327 [37 , 38] , which leads to a reduction in inhibitory postsynaptic current amplitude [37] . Therefore , recruitment of CaN ( also named Ca2+/calmodulin-dependent phosphatase 2B ) , was proposed as a potential link between LPA1-ROCK signaling and GABAAγ2 dephosphorylation . Preincubation of SMNs with CaN autoinhibitory peptide ( Cap; 50 μM ) also prevented ( +4 . 8 ± 16 . 5% ) s-LPA from inducing a reduction in pGABAAγ2:GABAAγ2 ratio ( Fig 6G ) . Expression of GABAAγ2 remained unchanged regardless of treatment ( Fig 6G ) . s-LPA also had no effect on the GABA-evoked currents in SMNs pretreated with Cap for 30 min ( Cap: 2 . 2 ± 0 . 3 nA; Cap+s-LPA: 2 . 1 ± 0 . 3 nA; n = 5 SMNs ) ( Fig 6H ) . s-LPA-induced alterations in mIPSCsGABAA and eIPSCsGABAA in HMNs were also CaN-dependent ( S1 Text; S15 Fig ) . Additionally , CaN activity strongly increased in SMNs after incubation with s-LPA , but not with s-LPA plus H1152 or H1152 alone ( Fig 6I ) . Altogether , these data show that ( s- ) LPA , specifically acting through postsynaptic LPA1-RhoA/ROCK-CaN signaling pathway , regulate GABAAR-mediated neurotransmission , by a mechanism involving dephosphorylation of GABAAγ2 subunit at Ser327 . It is generally accepted that dephosphorylation appears to be important for receptor endocytosis [4 , 9] . As a next step , we investigated whether LPA-triggered dephosphorylation was accompanied by further subunit internalization . We found that s-LPA ( 15 min ) led to a strong reduction ( −99 . 9 ± 0 . 01% ) in the amount of GABAAγ2 allocated in M fraction in SMN cultures . A proportional increase ( +109 . 4 ± 14 . 1% ) in the quantity of GABAAγ2 was observed in the C fraction relative to total GABAAγ2 ( Fig 7A ) . These outcomes suggest a translocation of at least this subunit from the SMN membrane to the cytosol triggered by s-LPA . The s-LPA-induced translocation was prevented by coincubation with either the ROCK inhibitor H1152 or the CaN inhibitor Cap ( Fig 7A ) . GABAAγ2 compartmentalization in SMNs was maintained after treatment with H1152 or Cap per se ( Fig 7A ) . To explore whether internalization is actually required for LPA-induced GABAAergic STD , and given that GABAAR endocytosis is dynamin-dependent [39] , we added the dynamin inhibitor dynasore to the bath to block GABAAR endocytosis . Dynasore ( 80 μM for 30 min ) fully prevented both a reduction in the GABAAγ2 M:T ratio and an increase in the C:T ratio induced by s-LPA , which was not altered by vehicle ( −84 . 5 ± 5 . 8% ) . Dynasore per se did not modify GABAAγ2 location ( −6 . 4 ± 18 . 8% ) relative to the vehicle condition ( 100 . 0 ± 36 . 7% ) ( Fig 7B ) . Interestingly , electrophysiological recordings showed that preincubation with dynasore had no effect on s-LPA-induced changes in GABA-evoked currents ( −48 . 1 ± 8 . 7%; n = 4 SMNs ) ( Fig 7C ) . These outcomes support that GABAAγ2 internalization by endocytosis is not required for the attenuation in GABAAergic neurotransmission induced by LPA signaling . CaN-dependent dephosphorylation of Ser327 at the GABAAγ2 subunit is involved in the increase of lateral diffusion and cluster dispersal of surface GABAARs in the dendrites of cultured hippocampal neurons [36 , 40] . Therefore , we investigated whether s-LPA-induced STD under endocytosis inhibition conditions would involve GABAAR cluster disarrangement . Double immunolabelling for GABAAγ2 and the postsynaptic scaffolding protein , gephyrin , confirmed GABAAγ2-ir clusters at the surface of SMNs , most of them colocalized with gephyrin-ir clusters ( Fig 7D ) . In consonance with phospholipid-evoked GABAAR internalization , treatment with s-LPA ( 10 min ) reduced mean fluorescence intensity , but not area , per cluster for these two postsynaptic proteins ( Fig 7E–7G ) . However , the size of surface GABAAγ2-ir clusters increased in parallel with a reduction in fluorescence when s-LPA was added after pretreatment with dynasore ( Fig 7E–7G ) . This agrees with s-LPA-induced lateral diffusion and cluster dispersal of GABAARs . In addition , the mean area of GABAAγ2-associated clusters of gephyrin was unaltered , but fluorescence was reduced by s-LPA under endocytosis inhibition ( Fig 7E–7G ) . These results are compatible with s-LPA-induced disorganization of GABAAR clusters that concludes in receptor internalization . Effects under the presence of dynasore support that this GABAAR disarrangement might involve previous lateral diffusion and cluster dispersal of surface GABAARs like that reported previously for cultured hippocampal neurons [36 , 40] . In summary , our data highlight a pathway by which , via recruitment of RhoA/ROCK signaling , postsynaptic LPA1 evokes CaN-dependent dephosphorylation at Ser327 of the GABAAγ2 subunit , which is followed by GABAAR cluster dispersion and its concomitant translocation from the plasma membrane to the cytosol ( Fig 7H ) . The latter does not seem to be required for the reduction in GABAAergic synaptic strength triggered by LPA . Phospholipid-induced synaptic strength depression seems to be mainly supported by GABAAγ2 dephosphorylation and subsequent GABAAR cluster dispersal . Next , the role of LPA signaling in short-term , activity-dependent synaptic plasticity was explored . N-methyl-D-aspartate receptor ( NMDAR ) activation causes a rapid , local , surface dispersal of synaptic GABAARs leading to an inhibitory synaptic depression [36 , 37] . We directly examined the role of LPA1-mediated signaling in NMDAR-induced STD of GABAAergic signaling in SMNs . In cRNA-treated SMNs , perfusion of glutamate and glycine ( Glut/Gly ) for 4 min caused a rapid and reversible depression in GABA-induced current ( −59 . 6 ± 5 . 3% , p < 0 . 001 ) in the presence of TTX , d-tubocurarine , strychnine and NBQX . This activity-dependent plastic event was absent in SMNs precultured with siRNAlpa1 ( −15 . 2 ± 8 . 7%; Fig 8A ) , in untreated cells under zero extracellular Ca2+ ( −9 . 3 ± 11 . 5%; n = 6 SMNs ) , or in the presence of APV ( −5 . 4 ± 13 . 9%; n = 6 SMNs ) , demonstrating Ca2+- and NMDAR-dependence . LPA1 knockdown reduced by approximately 40% the magnitude of activity-dependent STD at inhibitory synapses . From an extrapolation of these values to the dose-response curve in Fig 2C , it could be indirectly estimated that local concentrations of phospholipids achieved in response to those levels of motoneuron activity were first order micromolar , assuming all synthesized and released phospholipids were the monounsaturated form of LPA ( 18:1 ) . Glut/Gly also caused a drastic decrease in the pGABAAγ2:GABAAγ2 ratio in untreated or cRNA-incubated SMNs , which was prevented by siRNAlpa1 ( Fig 8B ) . Altogether , these data indicate that NMDAR-driven GABA-current depression was spike-independent and essential to extracellular Ca2+ entry via NMDARs and LPA1 activation , which downstream induces Ser327GABAAγ2 dephosphorylation . Findings from activity-dependent synaptic plasticity experiments agree with the notion that motoneurons are potential sources for Ca2+-dependent , spike-independent synthesis and release of lysophospholipids , which in turn might stimulate autocrine signaling pathways ( to modulate inhibitory synapses ) , at least by way of the LPA1 receptor . These outcomes also strongly point to lysophospholipids as paracrine retrograde messengers that act on presynaptic LPA1 to regulate excitatory synapses; however , further research is needed to confirm this possibility . Finally , physiological involvement of LPA signaling in performance of motor output commands was investigated . In vivo , most HMNs exhibit rhythmic inspiratory-related bursting discharges driven by glutamatergic brain stem afferents , mainly acting on AMPARs , with little or no contribution of inhibitory inputs [22 , 23] . We began by analyzing the level and pattern of expression of the LPA1 receptor within the HN of the adult rat . qRT-PCR analysis showed that disparity between lpa1 and lpa2–6 transcripts in the HN was even more accentuated in adults than at the neonatal stage ( Fig 9A ) . Interestingly , mRNA and protein levels for LPA1 at adulthood were approximately 150% and 140% , respectively , higher than in neonatal animals ( Fig 9A and 9B ) . These results suggest a gain in relevance of LPA1-mediated signaling in the HN during postnatal development , supporting previous observations in the murine brain [41] . Immunohistochemistry revealed LPA1-ir puncta-like structures all along the HN ( Fig 9C ) and colocalization between VGLUT2- and LPA1-ir puncta ( Fig 9D , 9E , and 9H ) . A high proportion of VGLUT2-ir inputs ( 47 . 9 ± 3 . 4%; n = 55 HMNs ) apposed to the perikarya of SMI32-identified HMNs were colocalizing with LPA1-ir puncta ( Fig 9E and 9F ) . This also supposed an increase of approximately 150% during postnatal maturation . LPA1-ir appeared to border and colocalize with SMI32-ir structures ( Fig 9G ) , supporting cytoplasmic and membrane location of LPA1 in adult HMNs . Therefore , the molecular machinery to support a role of LPA1 in modulating excitatory neurotransmission is also present in adults . Additionally , in vivo decerebrated rats maintain respiratory activity [22 , 23] . To look for a role of LPA signaling in processing motoneuron inspiratory activity , LPA1/3 inhibitors VPC 32179 ( 0 . 5 mM ) , VPC 32183 ( 1 mM ) , and Ki16425 ( 2 mM ) or its vehicle ( 10% DMSO ) were microiontophoretically applied to antidromically-identified HMNs subjected to unitary extracellular recordings ( Fig 9I ) . The effect of these drugs on the unitary basal firing inspiratory-related activity of HMNs in basal conditions ( end-tidal CO2 = 4 . 8%–5 . 2% ) was evaluated . The time course of the mean firing rate averaged over the duration of the inspiratory burst ( mFR/burst ) was measured by applying increasing currents ( −20 to −140 nA , 30 s duration ) through the drug barrels ( Fig 9J–9L ) . A current-dependent increase in the mFR/burst of HMNs was observed for all drugs but not when current was applied to the vehicle solution ( Fig 9J–9L ) . In summary , these data point to a physiological role for LPA signaling in motor output performance by restraining the inspiratory-related activity driven by glutamatergic inputs to HMNs .
The present study showed that bioactive membrane-derived phospholipids evoke rapid and reversible synaptic depression and mediate activity-dependent synaptic plasticity , mainly via LPA1 . Phospholipids likely operate as local messengers in activity-dependent GABAergic STD in a Ca2+-dependent , spike-independent manner . Strikingly , at physiological concentrations of nanomolar to first order micromolar , LPA has a greater effect on inhibitory than excitatory inputs . Finally , LPA signaling regulates brain-elemental processing tasks such as performance of motor output commands . These data open a new scenario in which the membrane-phospholipid metabolism actively participates in controlling synaptic strength , and then affects neuronal excitability in physiological and pathological states . Important determinants of synaptic strength , short-term plasticity and intersynaptic crosstalk mainly involve fine-tuning of the number of neurotransmitter receptors and the RRP size of SVs [4 , 8] . LPA depresses the main excitatory and inhibitory synaptic systems , affecting both by different degrees , loci , and mechanisms of action . At glutamatergic synapses , and by way of presynaptic Gαi/o-protein-coupled LPA1 and PLC-MLCK activation , LPA results in MLC phosphorylation , which might stimulate the actomyosin contractile apparatus [32] to reduce the bulk of the RRP of SVs ( Fig 10 ) . Depletion of some RRP of SVs usually underlies short-term forms of synaptic depression [1 , 2] . Ultrastructural correlates for LPA-induced STD further supported that functional synaptic changes are partly explained by a reduction in the size of the RRP of SVs . Changes in the actin cytoskeleton are a prerequisite for exocytosis , enabling docking and fusion of SVs with the plasmalemma [32] . As in our results , LPA-dependent contraction of smooth muscle cells involves activation of PLC and MLCK , followed by MLC phosphorylation [31] that promotes actomyosin interactions [32] . In this context , a physical relationship between p-MLC and glutamatergic synapses on adult and neonatal motoneurons has been recently reported [42] . At the calyx of Held synapse , MLCK controls the size of the fast-releasing pool of SVs [43] . In addition , ROCK regulates p-MLC levels via MLCK inhibition to maintain basal RRP ordering of SVs at excitatory inputs [8 , 42] . Therefore , presynaptic LPA-dependent and ROCK signaling seem to converge onto a common molecular mechanism , namely MLC phosphorylation and size of the RRP at excitatory synapses . It is interesting , then , that the ROCK inhibitor did not actually enhance LPA-induced depression of AMPAR currents . These outcomes suggest that the antagonistic functional actions of ROCK and LPA1-signaling , converging on MLCK , results in a push–pull mechanism that regulates the size of the RRP of SVs at excitatory synapses . At GABAergic synapses , LPA dephosphorylates Ser327 of GABAAγ2 subunits and favors GABAAγ2 internalization via postsynaptic Gα12/13-coupled LPA1/RhoA/ROCK signaling and subsequent CaN activation ( Fig 10 ) . The cell surface stability of GABAARs is regulated by post-translational modifications such as phosphorylation . GABAAR phosphorylation is involved in the modulation of receptor biophysical properties and membrane trafficking [44] . Phosphorylation stabilizes the GABAAR on the surface and , conversely , dephosphorylation is important for receptor endocytosis [4] . NMDAR activation causes GABAAR cluster dispersal and lateral diffusion by CaN activation and dephosphorylation of Ser327GABAAγ2 [36 , 40] , leading to long-term depression at CA1 inhibitory synapses [37] . Dispersal could involve receptor clustering at clathrin-coated sites at the plasmalemma , which invaginate and pinch off to form clathrin-coated vesicles . Internalized receptors are then either subject to rapid recycling or are targeted for lysosomal degradation [4] . Our results indicated that the LPA1-RhoA/ROCK-CaN pathway dephosphorylates the GABAAγ2 subunit , which undergoes lateral diffusion , dispersal of clusters , and subsequent endocytosis ( Fig 10 ) . However , endocytosis does not seem to be crucial for LPA-induced functional depression at GABAAergic neurotransmission , which seemed to be mainly supported by GABAAγ2 dephosphorylation and subsequent clusters dispersal of surface GABAARs . The kinetic recovery suggests rapid replenishment of the synaptic GABAAR content , given that re-establishment of inhibitory synaptic strength occurred with 7 to 10 min washing after LPA-induced depression . The coordinated action of kinases and phosphatases , downstream of LPA1-triggered signaling , then plays a pivotal role in controlling neuronal excitability by modulation of GABAAγ2 phosphorylation and receptor recycling . The present results seem controversial in relation to our previous findings demonstrating a presynaptic role for endogenous baseline ROCK activity in the regulation of AMPAergic and GABAAergic neurotransmission [8]; here , we describe that ROCK also acts postsynaptically to mediate LPA-induced depression of the GABAAergic transmission . Whether presynaptic baseline ROCK activity in inhibitory inputs depends on membrane-derived bioactive lipid mediators , such as LPA and/or sphingosine 1-phosphate , remains to be elucidated . Nevertheless , at glutamatergic synapses , ROCK activity is likely independent of LPA1/3 signaling , because inhibitors of these receptors did not mimic AMPAergic STD induced by ROCK inhibition . However , we cannot discard the involvement of another LPAR in maintaining baseline ROCK activity in the synaptic terminals . Interestingly , although presynaptic ROCK is active in our experimental conditions [8] , postsynaptic endogenous activity of ROCK , if any , is even below the level required to reveal its impact on synaptic strength and membrane properties [8] of motoneurons . This could be explained by the differential expression of ROCK isoforms at the two compartments , ROCKα in the postsynaptic site and ROCKβ in the presynaptic one , and/or the lower concentration of ROCKα in motoneurons relative to synaptic structures [8] . Anyway , data suggest that when motoneuron activity is low , presynaptic ROCK activity maintains inhibitory synaptic strength by stabilizing the size of the RRP of SVs . However , after exogenous addition of LPA or when motoneuron activity rises , and subsequent coupled LPA synthesis and/or release occurs , postsynaptic LPA1 stimulates ROCK . This leads to deinhibition by GABAAγ2 dephosphorylation and receptor endocytosis . In the rat , the highest LPA concentration in tissue is found in the brain [12] . Cultured cortical neurons produce LPA at nanomolar concentrations [45] , but LPA levels increase up to 10 μM after injury , trauma , or hemorrhage involving blood–brain barrier damage [46] . Here , physiological concentrations ( nanomolar to first order micromolar ) of LPA affected GABAergic to a greater degree than glutamatergic inputs , achieving maximal and similar affectation at 10 μM . Thus , it is possible that LPA signaling maintains neuronal excitability around a dynamic range , promoting deinhibition at low levels of neuronal activity and depressing excitatory inputs when activity increases , perhaps as part of a homeostatic mechanism that prevents excitotoxicity . Any candidate for coupling synaptic strength to neuronal activity must be regulated by activity at the postsynaptic site . Interestingly , noxious stimulation of primary afferent neurons induces LPA production in the dorsal horn in a glutamate-dependent manner [21] . Here , LPA signaling , mainly via LPA1 , was essential in STD of inhibitory inputs triggered by precedent activity of the neuron . Autocrine LPA signaling was essential for NMDAR-driven GABA-current depression , which depends on extracellular Ca2+ entry passing through NMDARs . Activity-dependent synaptic plasticity occurred independently of the generation of action potentials at the postsynaptic neuron . Postsynaptic [Ca2+] increase and LPA signaling dependence for activity-dependent STD in cultured motoneurons strongly support that this cell type is a potential source for activity-dependent LPA synthesis and/or release . Despite the apparent lack of endogenous LPA signaling affecting synaptic strength in our in vitro model , local iontophoretic application of three LPA1/3 inhibitors increased , in a dose-dependent manner , the baseline inspiratory-related activity of HMNs in the adult rat . This rhythmic inspiratory-related bursting discharge of HMNs is driven mainly by glutamatergic brain stem afferences , with little or no contribution of inhibitory inputs [22 , 47] . There is an apparent gain in relevance of LPA1-mediated signaling in the HN during postnatal development , to the detriment of LPA2–6-triggered pathways , as well as excitatory inputs apposed to adult HMNs express LPA1 . Taken together , these findings support that phospholipids , most likely activating LPA1 at glutamatergic synapses , controlled physiological inspiratory-related activity of HMNs , presumably by restraining their AMPAergic input drive [22] . Thus , endogenous LPA signaling physiologically contributes in the performance of normal patterns of motor output commands in adult animals . Alterations in phospholipid homeostasis affect various pathological conditions , thus attracting increased diagnostic and pharmacological interest [48] . The exquisite balance between excitatory and inhibitory inputs is critical for the proper functioning of the brain , and its imbalance leads to the cognitive impairment associated with neurodegenerative diseases and metabolic syndromes related to obesity , dyslipidemia , lipodystrophy , insulin resistance , and alcoholism [49–51] . In particular , LPA production and/or autotaxin are increased in obesity-associated metabolic diseases [52] , induced hypercholesterolemia [53] , congenital lipodystrophy [54] , as well as in ethanol-fed mice [55] and in patients with Alzheimer disease [56] or multiple sclerosis [57] . In addition , phospholipids uptake in mammalian cells depends on their activation status , a critical support for cellular incorporation of nutrition-derived fatty acids . Imported phospholipids are utilized for production of bioactive lipids , such as LPA [58] , and thereby modify synaptic transmission . Therefore , we can point to LPA as a promising candidate in coupling brain function , by modulating synaptic strength and plasticity , to the metabolic condition of the organism across physiological and pathological states .
Brain stem coronal sections ( 30 μm thick ) and SMNs were processed by immunohistochemistry against vesicular glutamate ( VGLUT2 ) , GABA ( VGAT ) transporters , GABAAγ2 subunit , gephyrin and/or Munc13-1 as synapse-related markers , LPA1 , and/or the nonphosphorylated form of neurofilament H ( SMI32 ) as a motoneuron marker , following standard protocols . Brain stem slices ( 300 μm thick ) incubated for 10 min ( approximately 22°C ) , with aCSF alone , 0 . 2% DMSO ( vehicle ) or with various drug treatments were immediately fixed and processed for electron microscopy analysis . Ultrathin sections ( 70–80 nm thick ) were analyzed at high magnification ( 43 , 000x ) . Only boutons , contacting with motoneurons at the level of the nucleolus , evidencing at least an a . z . were included in this study [8] . Neonatal rats ( P4 ) received an acute injection of siRNAlpa1 , or nontargeting siRNA ( cRNA ) , ( 2 μg/rat ) in 2 μl of RNase-free PBS into the fourth ventricle . The target sequence for the siRNAlpa1 was UCAUUGUGCUUGGUGCCUU . A group of animals was infused with 2 μl of RNase-free PBS ( vehicle ) as an additional control . Primary cultures of SMNs were incubated with 2 . 5 μl of either cRNA or siRNAlpa1 ( each 100 μM ) for 72 h at 37°C . Cells were then collected for qRT-PCR analyses or used for electrophysiological studies . Total RNA was extracted from the HN or cultured SMNs using TRIzol , and 0 . 5 μg of RNA was used for cDNA synthesis with iScript cDNA synthesis . The PCR primers were as indicated in S2 Table . Total protein was extracted from microdissected HNs , NSC34 cells , and membrane and cytosol fractions of NSC34 cells and SMNs . Membranes were blotted with specific antibodies against GABAAγ2 , pSer327GABAAγ2 , LPA1 , p-MLC , MLC , or RhoA . Membranes were also probed with anti-α1-tubulin or anti-β-actin antibodies as control for the total amount of protein contained in each well . Data are expressed as the mean ± standard error of the mean ( SEM ) . The number of analyzed specimens per experimental condition is indicated in figure legends or in the result section . Data were obtained from at least three animals per experimental condition . In ROCK activity , western blotting and qRT-PCR experiments , each individual assay was performed by using tissue samples collected from at least six animals per experimental condition . Quantitative data from ROCK and CaN activity assays , western blot , and qRT-PCR represent the average of , at least , three independent experiments . Applied statistical tests per experimental condition are indicated in figure legends or in results . Post hoc Holm Sidak or Dunn tests were applied for ANOVA for repeated measures or on Ranks , respectively . In all cases , the minimum significance level was set at p < 0 . 05 . | Neuronal networks are modules of synaptic connectivity that underlie all brain functions , from simple reflexes to complex cognitive processes . Synaptic plasticity allows these networks to adapt to changing external and internal environments . Membrane-derived bioactive phospholipids are potential candidates to control short-term synaptic plasticity . We demonstrate that lysophosphatidic acid ( LPA ) , an important intermediary in lipid metabolism , depresses the main excitatory and inhibitory synaptic systems by different mechanisms . LPA depresses inhibitory synaptic transmission by reducing the number of postsynaptic receptors at inhibitory synapses; whereas it depresses excitatory synaptic transmission by decreasing the size of the ready-to-use synaptic vesicle pool at excitatory terminals . Finally , we demonstrate that LPA signaling contributes to the performance of motor output commands in adult animals . Our data documents that synaptic strength and neuronal activity are modulated by products of membrane phospholipid metabolism , which suggests that bioactive phospholipids are candidates in coupling brain function to the metabolic status of the organism . |
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Innate lymphoid cells ( ILCs ) are severely depleted during chronic HIV-1 infection by unclear mechanisms . We report here that human ILC1s comprising of CD4+ and CD4- subpopulations were present in various human lymphoid organs but with different transcription programs and functions . Importantly , CD4+ ILC1s expressed HIV-1 co-receptors and were productively infected by HIV-1 in vitro and in vivo . Furthermore , chronic HIV-1 infection activated and depleted both CD4+ and CD4- ILC1s , and impaired their cytokine production activity . Highly active antiretroviral ( HAART ) therapy in HIV-1 patients efficiently rescued the ILC1 numbers and reduced their activation , but failed to restore their functionality . We also found that blocking type-I interferon ( IFN-I ) signaling during HIV-1 infection in vivo in humanized mice prevented HIV-1 induced depletion or apoptosis of ILC1 cells . Therefore , we have identified the CD4+ ILC1 cells as a new target population for HIV-1 infection , and revealed that IFN-I contributes to the depletion of ILC1s during HIV-1 infection .
Innate lymphoid cells ( ILCs ) represent a novel family of cellular subsets that produce large amounts of T cell-associated cytokines in response to innate stimulation in the absence of antigens [1 , 2] . Based on the expression of specific transcription factors , cell surface markers and signature cytokines [1 , 3 , 4] , ILCs can be divided into three groups . Group 1 ILCs ( ILC1s ) have been defined as lineage-CD127+CD117- cells and can produce interferon ( IFN ) -γ and depend on T-bet for their functions [5] . Group 2 ILCs ( ILC2s ) are a population of lineage-CD127+CRTH2+ cells that preferentially produce IL-5 and IL-13 and require GATA3 for differentiation [6] . Group 3 ILCs ( ILC3s ) are lineage-CD127+CD117+cells that have the potential to produce IL-17 and/or IL-22 , and are dependent on RORγt [3 , 7] . An increasing number of studies have indicated that ILCs represent a heterogeneous family of cells [8–10] . ILC1s were recently divided into CD4+CD8- , CD4-CD8+ and CD4-CD8- cell populations , and ILC3s comprise CD62L+ naïve cells and HLA-DR+ ILC3 subsets [8] . These novel ILC subsets still need to be explored with regard to their functionality and clinical significance in humans . ILCs have emerged as central players in homeostatic and inflammatory conditions . In particular , changes in the number of ILCs have been found to be associated with the pathogenesis and progression of a number of human diseases including chronic infections and inflammatory diseases [1 , 3 , 11–13] . For example , IFN-γ production by intraepithelial ILC1s promotes inflammation in mouse models of colitis , and blocking of IFN-γ reduces disease severity [12] . In addition , ILC1s may also contribute to human inflammatory bowel diseases , as their numbers have been found to be higher than normal in patients with Crohn’s disease [5 , 12] . Changes in the number and function of ILCs have also been documented during HIV-1 or SIV infection . Further , it has been reported that SIV infection results in persistent loss of IL-17-producing ILCs , especially in the jejunum [14] . NKp44+ ILC3s are also rapidly depleted in the intestinal mucosa during acute SIV infection [15] . In HIV-1-infected patients , too , ILCs are found to be severely depleted [16–18] . We have previously demonstrated that in HIV-1/SIV infection , ILC3s are depleted through plasmacytoid dendritic cell ( pDC ) activation and CD95-mediated apoptosis [17] or TLR signaling [19] . However , it is not clear whether HIV-1 influences ILCs through infection and how ILCs are depleted , especially ILC1s , during HIV-1 infection . In this study , we first showed that tissue ILC1s , as reported previously in the case of human peripheral blood mononuclear cells ( PBMCs ) [20] , consist of CD4+ , CD8+ and CD4-CD8- cells , three populations that widely exist in various lymph organs in human . In addition , we found that CD4+ ILC1s exhibit significant differences from CD4- ILC1s with regard to their phenotype , cytokine production and expression profile of transcriptional factors . Thus , we have identified a previously unknown CD4+ ILC1 population that serves as a target for HIV-1 productive infection . We showed that HIV-1 can infect , activate and preferentially deplete these CD4+ ILC1s . Our data was also indicative of the pathogenic effect of sustained type I interferon ( IFN-I ) signaling during HIV-1 infection , including depletion of ILC1s .
It was recently reported that ILC1s in human peripheral blood contain CD4+ , CD8+ and CD4-CD8- subpopulations [20]; however , it is unclear whether these cell populations are present in human lymphoid organs . Here , we investigated the distribution of each ILC1 subpopulation in various human lymph organs . By gating on live human CD45+ cells that were negative for lineage-specific surface markers of B cells ( CD19 and CD20 ) , T cells ( CD3 ) , conventional natural killer ( NK ) cells ( CD16 ) , monocytes and dendritic cells ( CD14 , CD11c and CD123 ) , and surface markers of hematopoietic precursors ( CD34 ) , ILC2 cells ( CRTH2 ) as well as ILC3 cells ( CD117 ) , we identified ILC1s as hCD45+Lin-CD117-CRTH2-CD127+CD56- cells ( S1A Fig ) . Similar to the results of a previous study [20] , we found that ILC1s comprise of CD4+CD8- , CD4-CD8+ and CD4-CD8- subpopulations ( S1A Fig ) . All the ILC1 subsets don’t express the T cell marker TCRαβ , TCRγδ and NK cell marker CD94 which excludes T cell and NK cell contamination; while they express CD5 ( S1B Fig ) . More importantly , we found that the all the three ILC1 subsets , including CD4+ ILC1s , were all present in various human lymphoid organs including the spleen , bone marrow , large intestine , small intestine and liver perfusion ( Fig 1A ) . Further analysis indicated that CD45+ cells constituted 0 . 019%–0 . 818% of the total ILC1 cells ( Fig 1B ) and CD4+ ILC1s constituted 2 . 35%–39 . 2% of the total ILC1s in different organs ( Fig 1C ) . We further investigated the expression of transcriptional factors such as T-bet and eomesodermin ( Eomes ) in the three ILC1 subsets in human peripheral blood ( Fig 1D ) . We found that CD4+ and CD4-CD8- ILC1s expressed lower levels of T-bet than CD8+ ILC1s ( Fig 1E , left ) . In addition , CD4+ ILC1s also expressed lower levels of Eomes than CD4- ILC1 subsets in the blood ( Fig 1E , right ) . We also examined the expression of T-bet and Eomes in ILC1 subsets from various human lymphoid organs by flow cytometry ( S2A Fig ) . We found that in most tissues that we examined , CD4+ ILC1s expressed lower levels of T-bet than CD8+ or CD4-CD8- ILC1s . Notably , the expression levels of T-bet were significantly lower in all ILC1 subsets from the small intestine than in the corresponding subsets from the other organs . This indicates that ILC1s present in the small intestine may have a unique function or activity ( S2B Fig ) . CD4+ ILC1s also expressed lower levels of Eomes than CD4- ILC1 subsets in the blood , spleen , bone marrow and liver perfusion . However , the opposite phenomenon was observed in the large and small intestine , where CD4+ ILC1s expressed higher levels of Eomes than CD4- ILC1s ( S2C Fig ) . These data suggest that the transcriptional factor profiles of ILC1s differ according to subsets and tissue types . In particular , CD4+ ILC1s are characterized by lower expression of T-bet and Eomes transcriptional factors in human peripheral blood . With regards to phenotypic characteristics , CD4+ ILC1s in peripheral blood expressed CD45RA , the NK cell-related molecule CD161 , the chemokine receptors CCR6 and CXCR3 , death receptor CD95 and adhesive molecule CD11a , which indicate an immature phenotype . However , peripheral blood CD4+ ILC1s did not express the integrin CD103; activation markers CD69 , CD38 and HLA-DR; proliferation marker Ki67; and death molecules DR5 , caspase 1 and caspase 3 . Moreover , they expressed low levels of the ILC progenitor marker IL-1R1 ( S3 Fig ) . However , there was no significant difference in the expression of most of the molecules between CD4+ and CD4- ILC1 subsets from peripheral blood . As for functionality , we evaluated cytokine production by peripheral blood ILC1 subsets after PMA/ionomycin or IL-12/IL-18 stimulation ( Fig 1F and 1G ) . We found that CD4+ ILC1s produce more TNF-α and lower levels of IFN-γ than CD8+ and CD4-CD8- ILC1s under PMA/ionomycin stimulation ( Fig 1F ) . Under IL-12/IL-18 stimulation , CD4+ ILC1s also produced lower levels of IFN-γ but similar levels of TNF-α than CD8+ and CD4-CD8- ILC1s ( Fig 1G ) . These ILC1 subsets produced no detectable IFN-γ and TNF-α without stimulation . These data indicate that peripheral blood ILC1 subsets are characterized by functional heterogeneity , and that CD4+ ILC1s preferentially produce TNF-α in response to stimulation , as opposed to CD4- ILC1 subsets , which produce lower levels of TNF-α . Taken together , these comprehensive analyses indicate that CD4+ and CD4- ILC1s exist in various human lymphoid tissues , and their relative numbers , transcription and functionality depend on the subsets and the tissues . In particular , CD4+ ILC1s display relatively unique expression of transcriptional factors , immune phenotypes , and cytokine production in relation to CD4- ILC1s . Since a significant proportion of ILC1s express CD4 , the receptor for HIV-1 infection , we investigated whether HIV-1 can infect CD4+ ILC1s . First , we examined the expression of the HIV-1 co-receptors CCR5 and CXCR4 on ILC1s by flow cytometry . Both CCR5 and CXCR4 were expressed on CD4+ ILC1s from human PBMCs and the spleen of humanized mice ( Fig 2A ) . CD4- ILC1s also expressed comparable levels of CCR5 and CXCR4 ( Fig 2A ) . Further analyses indicated that 12% of human CD4+ ILC1s express CCR5 , while 60% express CXCR4 ( Fig 2B ) . The expression of CCR5 and CXCR4 was also detected on CD4+ ILC1s in lymphoid organs , including the spleen , peripheral lymph node and bone marrow , and peripheral blood from humanized mice , but the expression level was slightly lower than that in human PBMCs ( Fig 2B ) . We then examined whether HIV-1 can infect human CD4+ ILC1s . We infected resting human PBMCs with the CXCR4 tropic virus NL4-3 and the CXCR4 and CCR5 dual-tropic virus R3A in vitro . Productive infection by HIV-1 was detected by staining of the HIV-1 protein p24 in ILC1s ( Fig 2C ) and in CD3+ T cells ( control cells ) ( S4A Fig ) . We found that HIV-1 p24 protein was detected in 2 . 2% of ILC1s after R3A infection and in 3% of ILC1s after NL4-3 infection ( Fig 2D ) , which was comparable to the p24 levels in CD3+ T cells ( S4A Fig ) . A neutralizing monoclonal antibody ( Clone CH31 ) specific to the CD4 binding site [21] blocked both R3A and NL4-3 infection by 90% ( Fig 2C and 2D and S4 Fig ) . We also found that HIV-1 infection down-regulated CD4 expression in ILC1s ( Fig 2C ) , as observed in T cells ( S4 Fig ) . Interestingly , when PBMCs were activated by PHA ( Fig 2E and 2F ) , both ILC1s and T cells were infected at higher levels by HIV-1 in vitro ( Fig 2E and 2F and S5A and S5B Fig ) . These results indicate that HIV-1 can productively infect ILC1s via the CD4 receptor . We also examined whether HIV-1 also infected ILC1s in vivo in human patients and in humanized mice . We purified CD4+ ILC1s from HIV-1-infected patients and determined the cell-associated HIV-1 DNA level by real-time PCR . On average , we detected 800 copies of cell-associated HIV-1 DNA in one million CD4+ ILC1s ( Fig 2G ) . As controls , 3200 copies of HIV-1 DNA were detected in one million CD4+ T cells , while no HIV-1 DNA was detected in CD8+ T cells ( Fig 2G ) . HIV-1 can effectively infect and replicate in vivo in humanized NOD-Rag2-/-γc-/- ( NRG-hu ) mice transplanted with human CD34+ hematopoietic stem cells [22] , a highly relevant model for studying HIV-1 induced pathology in vivo [23] . We therefore investigated whether CD4+ ILC1s could be directly infected by HIV-1 in vivo in humanized mice . At 3 weeks after R3A infection , 4 . 9% of ILC1s expressed p24 , while 2 . 7% of CD3 T cells were positive for p24 ( Fig 2H , left ) . To exclude the possibility that the p24 protein detected here was from virions taken into cells by endocytosis , we infected humanized mice with an engineered R3A reporter virus which expresses the mouse CD24 gene in the Vpr ORF [24] . We found that 8% of ILC1s expressed the mouse CD24 protein ( Fig 2H , right ) . Taken together , our results show that HIV-1 can productively infect CD4+ ILC1s both in vitro and in vivo . We next investigated whether HIV-1 infection also activates ILC1s in patients . We analyzed the expression of CD38 and Ki-67 in ILC1s ( Fig 3A and S6 Fig ) . Both CD4+ and CD4- ILC1s expressed higher levels of CD38 and Ki67 in HIV-1-infected patients than in the healthy control ( HC ) subjects , while highly active antiretroviral therapy ( HAART ) reduced the activation and proliferation of both CD4+ and CD4- ILC1s ( Fig 3B ) . As expected , HIV-1 also activated CD8 T cells in HIV-infected patients , and that the activation level was significantly decreased after HAART ( Fig 3C and 3D ) . Further , the percentage of Ki67-expressing CD4+ ILC1s , but not CD4- ILC1s , was found to positively correlate with the plasma HIV-1 viral load ( Fig 3E and 3F ) . In contrast , Ki-67 expression in CD8 T cells was not correlated with plasma HIV-1 load in these patients ( Fig 3G ) . These data indicate that HIV-1 infection activated both CD4+ and CD4- ILC1s . In particular , the activation of CD4+ ILC1s , the HIV-1 target population , was positively correlated with the HIV-1 viral load . We next investigated whether HIV-1 infection depletes ILC1s in vivo . Compared to the HCs , ILC1s in CD45+ cells were significantly reduced in the peripheral blood of patients with chronic HIV-1 infection ( Fig 4A and 4B ) , and HAART partially reversed the reduction of total ILC1s ( Fig 4A and 4B ) . Further analysis indicated that the percentage of both CD4+ and CD4- ILC1s in total CD45+ cells was lower in patients with HIV-1 infection than in the HC subjects , while only the CD4+ ILC1s but not CD4- ILC1s were significantly rescued by HAART ( Fig 4C ) . The absolute cell counts of total ILC1s and CD4+ and CD4- ILC1s were found to be largely reduced in patients with chronic HIV-1 infection as compared to those of HC subjects; and HAART successfully recovered the absolute cell counts of total ILC1s and CD4+ ILC1s but not CD4- ILC1s ( Fig 4D ) . Correlation analysis indicated that the percentage of peripheral CD4+ ILC1s was negatively correlated with the plasma HIV-1 viral load ( Fig 4E ) and positively correlated with the CD4/CD8 ratio in the HIV-1-infected subjects ( S7 Fig ) . We further examined whether ILC1s in the gut were also depleted by HIV-1 infection in humans , which is the key lymphoid organ in HIV-1-associated pathogenesis . As shown in Fig 4F , CD4+ ILC1s were significantly depleted in the large intestine in HIV-1-infected patients as compared to the HC donors . The summarized data also showed that the percentage of total ILC1s within CD45+ cells was significantly decreased in the large intestine in patients with HIV-1 infection ( Fig 4G ) . Further analysis indicated that the percentage of both CD4+ and CD4- ILC1s was reduced during chronic HIV-1 infection ( Fig 4H ) . Importantly , when gated on ILC1 populations , the percentage of CD4+ ILC1s was largely decreased and the percentage of CD4- ILC1s was increased accordingly , which indicates that the CD4+ ILC1s were preferentially depleted ( Fig 4I ) . These data indicate that CD4+ ILC1s from both peripheral blood and large intestine are preferentially depleted during chronic HIV-1 infection . ILC1s can produce large amounts of Th1-associated cytokines in response to innate stimulation . We next analyzed whether persistent HIV-1 infection affected the cytokine production ability of ILC1s . As shown in Fig 5A , IFN-γ and TNF-α production by both CD4+ and CD4- ILC1 subsets induced by PMA/ionomycin stimulation were significantly lower in HIV-1-infected patients than in HCs . Similar phenomena were also observed when the ILC1s were stimulated by IL-12 and IL-18 ( Fig 5B ) . HAART failed to rescue the function of ILC1 subsets , with the exception that IFN-γ production was rescued by HAART after IL-12 and IL-18 stimulation ( Fig 5A and 5B ) . We thus conclude that chronic HIV-1 infection impaired the ability of the remaining ILC1s , including CD4+ ILC1s , to produce cytokines . We next examined how HIV-1 infection leads to ILC1 depletion . We discovered that chronic HIV-1 infection significantly up-regulated active caspase-3 expression in both CD4+ and CD4- ILC1s ( Fig 6A and 6B ) . In contrast , caspase1 was not significantly up-regulated in CD4+ ILC1s ( and only slightly increased in CD4- ILC1s ) of patients with HIV-1 infection as compared to the HC subjects ( S8A and S8B Fig ) . HAART could significantly decrease the expression of active caspase-3 in both CD4+ and CD4- ILC1s ( Fig 6A and 6B ) , correlated with rescued ILC1s . These findings indicate that HIV-1 infection leads to depletion of ILC1 subsets via apoptosis-dependent mechanisms . We further investigated whether the Fas/FasL pathway was involved in the apoptosis of ILC1s ( up-regulation of active caspase-3 ) , as reported in ILC3s in our previous study [17] . We found that expression of CD95 was significantly up-regulated in both CD4+ and CD4- ILC1s from patients with chronic HIV-1 infection compared with HC subjects ( Fig 6C and 6D ) . HAART decreased the expression of CD95 in CD4+ but not CD4- ILC1s ( Fig 6C and 6D ) . In contrast , the expression of death receptor 5 ( DR5 ) was not up-regulated in ILC1 subsets in patients with chronic HIV-1 infection ( S8C and S8D Fig ) . Notably , the expression of caspase-3 and CD95 was also up-regulated in CD8+ T cells in HIV-1-infected patients as compared to HC subjects ( S9A and S9B Fig ) . We then investigated whether the Fas/FasL pathway is involved in the apoptosis of ILC1 subsets . After in vitro stimulation with the anti-CD95 agonist antibody , both CD4+ and CD4- ILC1s from HIV-1-infected patients displayed higher levels of active caspase-3 expression than those from HCs ( Fig 6E ) . Accordingly , the number of live CD4+ and CD4- ILC1s was significantly reduced after treatment with the anti-CD95 agonist antibody as compared to the IgG control in HIV-1-infected patients but not in the HC subjects ( Fig 6F ) . Thus , the number of live CD4+ and CD4- ILC1s in HIV-1-infected patients was markedly less than that in HC subjects in response to in vitro stimulation with the same anti-CD95 agonist antibody ( Fig 6F ) . This indicates that ILC1 subsets from HIV-1-infected patients are more sensitive to Fas/FasL signaling than those from HC subjects . We conclude that the Fas/FasL pathway is actively involved in the apoptosis of ILC1 subsets in patients with chronic HIV-1 infection . Sustained IFN-I signaling has been reported to be correlated with and contribute to SIV and HIV-1-induced immune pathogenesis [25–27] . We have proved that depletion of pDCs or blocking IFN-I signaling prevents HIV-1-induced T cell and ILC3 depletion in vivo [17 , 26 , 28] . We thus investigated whether IFN-I signaling also contributes to HIV-1-induced ILC1 depletion in vivo . We treated HIV-1-infected humanized mice with the anti-IFNAR1 mAb [26] from week 6 through week 10 after infection . At 10–12 weeks after infection , we terminated the mice and measured ILC1 number and phenotype in each group . We found that blockade of IFN-I signaling with the anti-IFNAR1 mAb rescued both CD4+ and CD4- ILC1s cells in percentages ( Fig 7A–7C ) and in numbers ( Fig 7D ) as compared to the isotype IgG control group . In addition , we found that blocking the IFN-I pathway significantly decreased CD95 expression on CD4+ ILC1s in humanized mice with persistent HIV-1 infection ( Fig 7E and 7F ) . We further cultured PBMCs from HIV-1-infected patient ex vivo in the absence or presence of pDC-depleting 15B mAbs conjugated with the SAP toxin ( immune toxin 15B-sap ) or the anti-IFNα/β receptor blocking antibody . We observed significant downregulation of both CD95 and active caspase-3 expression in CD4+ ILC1s from HIV-1-infected patients cultured in vitro in the presence of the immune toxin 15B-sap or anti-IFN-α/β receptor antibodies as compared to the IgG control ( Fig 7G ) . Therefore , depletion of pDCs or blockade of IFNAR1 both prevents HIV-1 induced ILC-1 depletion in vitro ( Fig 7H ) . These data indicate that IFN-I signaling contributes to ILC1 depletion during chronic HIV-1 infection . Since HAART fails to restore ILC function in HIV-1-infected patients , we therefore investigated whether blocking IFN-I signaling combined with combined antiretroviral therapy ( cART ) in vivo can rescue the function of ILC1s in HIV-1 infected humanized mice . We treated HIV-1 infected mice with cART at 4 weeks post infection ( wpi ) . As reported [26] , 3 week after cART , the infected humanized mice received α-IFNAR1 mAb treatment for 3 weeks from 7 to 10wpi . The function of ILC1 was analyzed at 12wpi . Interestingly , we found that cART alone restored IFN-γ and TNF-α production by splenic ILC1s under PMA/Ionomycin stimulation ( S10B Fig ) . IFN-I blockade in combination with cART did not further increase IFN-γ and TNF-α production by splenic ILC1s ( S10B Fig ) . The result is different from human studies which indicated that HAART cannot rescue ILC1 function ( Fig 5 ) . One possible reason for the differences is that we started cART treatment in humanized mice at early infection phase ( 4 weeks post HIV-1 infection ) , while in HIV-1 infected patients HAART is usually initialized years after infection at chronic phase of the infection included in our study . Further studies are needed to unveil the effect of IFN-I signaling on ILC1 function .
Our study investigates the heterogeneity of ILC1s in human lymphoid organs , and provides the first piece of evidence to show that HIV-1 can directly infect CD4+ ILC1s and lead to their activation , depletion and functional impairment in vivo in humans and in humanized mice . Successful HAART rescued the number of CD4+ ILC1s but not cytokine production activity via the inhibition of Fas/FasL-mediated apoptosis of ILC1s . This study , therefore , is the first to identify CD4+ ILC1s as important HIV-1 target cells , and may serve as a novel target of HIV-1 therapies aimed at human immune reconstitution . Through a comprehensive analysis of lymphocytes from human spleen , bone marrow , large intestine , small intestine and liver , we found that human ILC1s consist of CD4+ , CD8+ and CD4-CD8- populations and that all these populations are widely present in all lymphoid organs , which has not been described in previous studies [8 , 9 , 20] . We also observed that CD4+ ILC1s expressed immature phenotypes and lower levels of Th1-associated transcriptional factor T-bet and Eomes than CD4- ILC1s and higher level of TNF-α in response to stimulation . It Is not clear where these CD4+ ILC1s are developed and how the immature ILC1 subsets traffic to various lymphoid tissues . A recent study based on mass cytometry- and t-SNE-based analysis showed ILC1s were undetectable across different human tissues [10] . However , in a re-analysis on CyTOF dataset , ILC1s are clearly clustered in lymphoid tissues [29] . Another report also suggested that ILC1s reported in previous studies may be attributable to CD5+ T cell contamination [30] . However , CD5 is also expressed and functions independently of T cells [31] . Indeed , ILC1s express high levels of CD5 in our study and previous report [32] . Therefore , the use of CD5 with CD4 or CD8 in ILCs without confirming surface CD3 or TCR expression does not definitively identify CD4+ and CD8+ T cells [20] . Furthermore , human patients with RAG1 deficiency , who lack T cells , are characterized by the presence of circulating ILC1s at frequencies comparable to those of ILC2s and ILC3s [33] . ILC1s have also been cloned under T-cell-promoting conditions , and have been detected in inflamed intestinal tissues of patients suffering from Crohn’s diseases [5 , 34] . Taken together , our data provide a comprehensive description of the heterogeneity of CD4+ and CD4- ILC1s ( not T cells ) across various lymphoid tissues in humans . The identification of CD4 expression on ILC1s led to the question of whether this population can be infected by HIV-1 . Our results clearly showed that CD4+ ILC1s also express CCR5 and CXCR4 and can be productively infected by HIV-1 both in vitro and in vivo . The relative infection and replication of HIV-1 in CD4+ ILC1s is comparable to that in CD4 T cells . Interestingly , PHA activation of PBMC enhanced HIV infection in both CD4+ ILC1 and T cells . These results indicate that CD4+ ILC1s are HIV-1 target cells and possibly support HIV-1 persistence in patients with chronic HIV-1 infection . Therefore , we identified CD4+ ILC1s as a new target for HIV-1 infection . Further studies to identify whether CD4+ ILC1s serve as an HIV-1 reservoir in HIV patients during HAART will be important for developing strategies for HIV-1 treatment . It has been reported that HIV-1 infection leads to depletion of all ILC subsets , including ILC1s , in circulation [16 , 17 , 19] and lymphoid organs [18] . We discovered here that HIV-1 infection also depleted ILC1s in the large intestine of patients . Unlike the results of a previous study [16] , we found that HAART can rescue the number of peripheral ILC1s in HIV-1-infected patients . This discrepancy could be explained by the difference in the cohorts enrolled in the two studies . Differences in the time of HAART onset may lead to differences in immune reconstitution [35 , 36] , which may then affect the restoration of the number of ILC1s . Our results indicate that HIV-1 infection depletes ILC1s both in circulation and in lymphoid organs . Of particular note , we found that CD4+ ILC1s were preferentially depleted within the total ILC1 population , which indicates that they are more sensitive to HIV-1-induced apoptosis . The mechanism underlying HIV-1-induced depletion of ILC1s is poorly defined . We have reported previously that HIV-1 infection induces depletion of ILC3s via Fas/FasL signaling in a pDC/IFN-I-dependent manner [17] . In the present study , we found that the depletion of ILC1s was also associated with cell apoptosis mediated by the Fas/FasL pathway during HIV-1 infection . We therefore tested the pDC/IFN-I axis in humanized mice with HIV-1 infection . Our data clearly showed that blocking IFN-I signaling with an antibody against IFNAR1 prevented HIV-1-induced depletion of ILC1s in vivo in humanized mice . Furthermore , blocking IFN-I signaling or depletion of pDCs during in vitro culture of PBMCs from HIV-1 infected patients also significantly reduced ILC1 apoptosis and rescued their number . We thus have demonstrated that pDC and IFN-I signaling plays a critical role in ILC1 depletion during chronic HIV-1 infection . Our data demonstrate that HIV-1 infection not only depletes ILC1s but also leads to their activation and functional impairment , as indicated by the significant decrease observed in their production of cytokines , including IFN-γ and TNF-α . Interestingly , HAART rescues ILC1s in number but fails to recover their function of cytokine production in HIV-1-infected patients . In HIV-1-infected humanized mice , however , we found that HAART starting during early phase of infection ( 4wpi ) rescued both ILC1 number and functions in IFN-γ and TNF-α production . This differential effect of HAART on ILC1 function may be due to different treatment time in patients and in humanized mice . For patients in the study , HAART was usually initialized years after HIV-1 infection at chronic infection phase; while HAART was given at early phase of HIV-1 infection ( 4 weeks ) for humanized mice in the study . Indeed , our findings are similar to a previous report in which antiretroviral therapy initialized during acute infection could preserve ILCs in patients [16] . These data also indicate that depletion of ILC1s and their functional impairment may be mediated by various mechanisms during short acute and long chronic infection . We have recently found that pDC depletion or blockade of IFN-I signaling could significantly reduce residual immune activation and restore anti-HIV immunity in HIV-1-infected humanized mice without or with cART [26] . Future studies should focus on the differential mechanisms underlying cell depletion and functional impairment of ILC1 subsets , and determine whether HAART combined with IFN-I blockade can restore ILC1 function in chronic HIV-1 infection in human patients . In summary , we identified subset- and tissue-dependent heterogeneity of ILC1s and provided evidence to show that CD4+ ILC1s are a novel target for HIV-1 infection . Further , we demonstrated that IFN-I signaling contributes to the depletion of ILC1s , at least partly through the Fas/FasL pathway during HIV-1 infection . These new findings , therefore , extend our earlier findings which show that sustained pDC activation and IFN-I production contributes to HIV-1 pathogenesis . Therefore , blockade of the pDC/IFN-I axis will be a novel therapeutic stratagem to reverse HIV-1-induced pathogenesis , including ILC1 depletion and impairment .
Approval for animal work was obtained from the University of North Carolina Institutional Animal Care and Use Committee ( IACUC ID: 14–100 ) . The study protocol on human samples was approved by the Institutional Review Board and the Ethics Committee of Beijing 302 Hospital in China . The written informed consent was obtained from each subject . All samples were anonymized in the study . Human tissue samples , including the spleen , small intestine , large intestine , bone marrow and liver perfusion , used in this study were obtained from adult donors who had undergone liver transplantation as healthy controls . Gut mucosa from HIV-1-infected patients were obtained for pathological diagnosis . Written informed consent was obtained from each donor . Complete RPMI media were used for all cell isolation experiments . Human fetal livers and thymuses ( gestational age 16 to 20 weeks ) were obtained from medically indicated or elective termination of pregnancies through a non-profit intermediary working with outpatient clinics ( Advanced Bioscience Resources , Alameda , CA ) . Written informed consent from the maternal donor was obtained in all cases under regulations governing the clinic . All animal studies were conducted following NIH guidelines for housing and care of laboratory animals . The project was reviewed by the University’s Office of Human Research Ethics , which determined that this submission does not constitute human subjects research as defined under federal regulations [45 CFR 46 . 102 ( d or f ) and 21 CFR 56 . 102 ( c ) ( e ) ( l ) ] . Thirty HIV-1-infected HAART-naïve individuals and 12 HIV-1-infected patients who underwent successful HAART were enrolled in our study ( S1 Table ) . The majority of these individuals had been infected with HIV-1 via sexual transmission , while a few subjects were paid blood donors . Twenty-six uninfected subjects were employed as healthy controls ( HCs ) . The study protocol was approved by the Ethics Committee of Beijing 302 Hospital , and written informed consent was obtained from each subject . Immune cells from human samples were isolated according to previously reported protocols . In brief , peripheral blood mononuclear cells ( PBMCs ) and bone marrow cells were isolated by Ficoll-Hypaque density gradient centrifugation of heparinized blood of enrolled subjects . The spleen was first ground on ice , after which the cells were collected and filtered . The liver perfusion was directly filtered and concentrated by centrifugation ( 750 g , 15 min , 20°C ) , and was layered onto the Ficoll gradient . The small intestine and large intestine were first finely minced using scalpels , and were then incubated with 0 . 8 mg/mL collagenase type IV ( Worthington-Biochemical ) and DNase I ( Roche ) for 1 h before they were filtered through a 70-mm strainer . The filtered cells were collected and isolated in a similar manner to PBMCs . Upon isolation , all the cells were cryopreserved in 90% fetal calf serum plus 10% DMSO for subsequent assay . We constructed NRG-hu mice using a previously reported method [22] . Briefly , human CD34+ cells were isolated from 16- to 20-week-old fetal liver tissues ( Advanced Bioscience Resources , Alameda , CA ) . The tissues were digested with liver digest medium ( Invitrogen , Frederick , MD ) . The suspension was filtered through a 70-μm cell strainer ( BD Falcon , Lincoln Park , NJ ) and centrifuged for 5 min to isolate mononuclear cells by Ficoll gradient centrifugation . After selection with the CD34+ magnetic-activated cell sorting ( MACS ) kit , CD34+ hematopoietic stem cells were injected into the liver of each irradiated ( 300 rad ) 2- to 6-day-old NRG mouse ( 0 . 5 × 106/mouse ) . More than 95% of the humanized mice were stably reconstituted with human leukocytes in the blood ( 60%–90% at 12–14 weeks ) . The level of engraftment was similar in each cohort . All the mice were housed at the University of North Carolina at Chapel Hill . Total leukocytes were isolated from the spleen of humanized mice as previously described [22] . Lymphoid tissues , including red blood cells , were lysed with the ACK buffer , and the leukocytes were stained and fixed with 1% formaldehyde before FACS analysis . The total cell number was quantified by Guava Easycytes with the Guava Express software . An R5-tropic strain of HIV-1 , JR-CSF ( NIH AIDS reagents program , Cat# 2708 ) , was used for inducing persistent HIV-1 infection . Viruses were generated by transfection of 293T cells ( SIGMA-ALORICH , Cat# 12022001-1VL ) . R3A-HSA was constructed by replacing the vpr gene with mouse heat stable antigen ( HSA; CD24 ) as reported previously . Humanized mice with stable human leukocyte reconstitution were infected with JR-CSF or R3A-HSA at a dose of 10 ng p24/mouse , through an intra-orbital injection . Humanized mice infected with mock-transfected 293T cell culture supernatant were used as control groups . For acute HIV-1 infection , viral genomic RNA present in the plasma was measured by real-time PCR ( ABI Applied Biosystem ) . An X4 and R5 dual-tropic strain of HIV-1 , R3B/Av1v2 , was used for the in vitro experiment . Fresh PBMCs were incubated with the infectious HIV-R3A stock , NL4-3 stock or mock stock with or without the neutralizing monoclonal antibody ( Clone CH31 ) for 2 h at 37°C . Then , the cells were incubated in complete RPMI 1640 medium at a density of 2 × 106 cells/ml in the presence of IL-2 ( 50 IU/ml ) and IL-7 ( 20ng/ml ) for an additional 3 days . Alternatively , fresh PBMCs were activated with phytohemagglutinin ( PHA , 5 μg/ml ) or medium in the presence of IL-2 ( 50 IU/ml ) and IL-7 ( 20 ng/ml ) for 24 hours . Then the cells were incubated with the infectious NL4-3 stock or mock stock for an additional 4 days . Intracellular p24 expression on ILC1 subsets or CD3+ T cells was determined by flow cytometry as described above . An anti-IFNAR1 blocking antibody was developed as per our recent report [26] . Briefly , the human IFNAR1 expression cell line 293T was first incubated with the supernatant of the hybridoma and then incubated with the PE-labeled goat anti-mouse IgG secondary antibody . Then , an IFN-I reporter cell line 293T stably transfected with a mouse A2 promoter-driven EGFP was used to screen antibody clones that could block human IFNAR1 signaling . Humanized mice with HIV-1 infection were treated intraperitoneally with anti-IFNAR1 blocking antibodies from 7 to 10 weeks post-infection twice a week at a dose of 400 μg/mouse at the first treatment and 200 μg/mouse for the following treatments . The same dose of mouse isotype IgG2a control was used in all the experiments . Alternatively , the HIV-1-infected mice were treated with combination antiretroviral therapy ( cART ) as reported [26] . HIV-1 infected , cART treated mice were treated i . p . with IFNAR1 blocking antibodies from 7 to 10 wpi twice a week with 400 μg/mouse at the first injection and 200 μg/mouse for the following treatments . A same dose of mouse isotype IgG2a control was use in all experiments . Flurochrome-conjugated antibodies or regents obtained from Biolegend , BD Bioscience , eBioscience and R&D Systems were used in the study . Live/dead fixable violet dead cell dye ( LD7 ) was purchased from Molecular Probes ( Eugene , OR ) . For humanized mice , live human leukocytes ( Y7-mCD45-hCD45+ ) were analyzed for ILC1 subsets and other cell subsets or phenotypes with CyAn FACS ( Dako , Beckman Coulter , Denmark ) . The data were analyzed with the Summit Software . For human PBMCs and various tissue-derived lymphocytes , dead cells were excluded using the fixable viability dye eFluor 450 ( eBioscience ) . The remaining live CD45+ cells were analyzed for phenotypic expression with FACS CANTO II , and the data obtained were further analyzed with the FlowJo software ( TreeStar , San Carlos , CA ) . Cytokines , including IL-2 , IL-12 and IL-18 , were purchased from PeproTech ( Rocky Hill , NJ ) . For surface marker staining , leukocytes were incubated with antibodies on ice for 30 min and then washed and fixed for further analysis . For staining of HIV-1 gag p24 , transcriptional factors , Ki67 and the apoptotic marker active caspase-3 , the cells were stained with the surface marker first , and then permeabilized using a Cytofix/Cytoperm kit ( BD Bioscience ) and stained for intracellular protein . Alternatively , fresh cells were mixed with caspase-1 for 2 h for caspase-1 staining and were then subjected to surface staining . For intracellular cytokine detection , freshly isolated cells were stimulated for 6 h by culturing with PMA ( 50 ng/ml , Sigma ) and ionomycin ( 1 μM , Merck ) in the presence of BFA ( 1 μM ) . Alternatively , the cells were incubated with IL-12 ( 20 ng/ml ) plus IL-18 ( 20 ng/ml ) for 12 h , followed by Golgi-stop for an additional 6 h . The cells were then collected for surface marker staining; this was followed by cell permeabilization and intracellular cytokine staining . For CD107a staining , the cells were incubated with anti-CD107a antibodies from the onset of stimulation . Then , the cells were further incubated with BFA for an additional 6 h . Freshly isolated PBMCs from HC and HIV-1-infected patients were enriched for ILCs by depletion of CD3+ T cells , CD14+ monocytes and CD19+ B cells using microbeads ( Miltenyi Biotech , Germany ) . Then , the enriched cells were sorted on a FACSAria II ( BD Biosciences ) . CD4+ ILC1s were isolated by sorting on live cells , singlets , scatter , and lineage-CD56-CD127+CD4+ cells ( lineage including CD3 , CD14 , CD16 , CD19 , CD34 , CD11c , CD123 , CD117 and CRTH2 ) . CD4+ and CD8+ T cells were directly sorted from PBMCs . Then , nucleic acid was extracted by sorting CD4+ ILC1s , CD4+ T cells and CD8+ T cells using the DNAeasy minikit ( Qiagen ) to measure total cell-associated HIV-1 DNA . HIV-1 DNA was quantified by real-time PCR according to our previous protocol . DNA from serial dilutions of ACH2 cells , which contain 1 copy of the HIV-1 genome per cell , was used to generate a standard curve . Frozen PBMCs from HCs and HIV-1-infected patients were thawed and cultured in complete RPMI ( RPMI 1640 containing 10% heat-inactivated fetal bovine serum , 2 mM l-glutamine , 100 U/ml penicillin and 100 mg/ml streptomycin sulfate ) ( Cellgro , Manassas , VA ) with IL-12 ( 10 ng/ml ) , IL-18 ( 10 ng/ml ) and IL-2 ( 50 IU/ml ) for 12 h . Then , the cells were collected to perform in vitro assays . The cells were incubated in the presence of plate-bound anti-CD95 monoclonal antibody or isotype control antibody ( 5 μg/ml , clone CH11 , Millipore ) for an additional 24 h . Alternatively , the cells were incubated with 15B mAb conjugated with the toxin sap ( 15B-sap , 8 ng/ml ) to deplete pDCs or with anti-IFN-α/β receptor antibodies ( 10 μg/ml , Millipore ) to block IFN-I signaling for an additional 72 h . Then , the cells were harvested , and the number of live cells was counted and stained for active caspase-3 and/or CD95 expression by ILC1 subsets . Data were analyzed using GraphPad Prism software version 5 . 0 ( GraphPad software; San Diego , CA , USA ) . The data represent the mean ± s . e . m values . One-way ANOVA was used for primary comparisons between different groups , and the result was represented by the overall p value . Secondary comparisons between any two different cohorts of mice or patients were performed using a two-tailed unpaired Student’s t-test . Correlations between variables were evaluated using the Spearman rank-correlation test . Results were considered significant at p values <0 . 05 . | Innate lymphoid cells ( ILCs ) , including ILC1 , ILC2 and ILC3 populations , represent a novel cellular family of the immune system and have potentials to produce large amounts of T cell-associated cytokines in response to innate stimulation in the absence of specific antigen stimulation . ILCs have emerged as central players in homeostatic and inflammatory conditions , and correlated with the pathogenesis and progression of multiple human diseases . It is reported that ILCs are depleted in HIV-1 infected patients . However , it is not clear whether HIV-1 can infect ILCs and how ILCs are depleted during HIV-1 infection . Here , we find that ILC1s consist CD4+ and CD4- subsets and both are present in various human lymphoid organs . We show that HIV-1 can directly infect CD4+ ILC1s . HIV-1 infection leads to activation , depletion and functional impairment of ILC1s in humans and in humanized mice in vivo . Blocking IFN-I signaling prevents HIV-1-induced apoptosis of ILC1s both in vitro and in humanized mice in vivo . Our study reveals the CD4+ ILC1 population as a new target for HIV-1 infection and identifies an IFN-I mediated mechanism of ILC1 depletion during chronic HIV-1 infection . |
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On its own , a single cell cannot exert more than a microscopic influence on its immediate surroundings . However , via strength in numbers and the expression of cooperative phenotypes , such cells can enormously impact their environments . Simple cooperative phenotypes appear to abound in the microbial world , but explaining their evolution is challenging because they are often subject to exploitation by rapidly growing , non-cooperative cell lines . Population spatial structure may be critical for this problem because it influences the extent of interaction between cooperative and non-cooperative individuals . It is difficult for cooperative cells to succeed in competition if they become mixed with non-cooperative cells , which can exploit the public good without themselves paying a cost . However , if cooperative cells are segregated in space and preferentially interact with each other , they may prevail . Here we use a multi-agent computational model to study the origin of spatial structure within growing cell groups . Our simulations reveal that the spatial distribution of genetic lineages within these groups is linked to a small number of physical and biological parameters , including cell growth rate , nutrient availability , and nutrient diffusivity . Realistic changes in these parameters qualitatively alter the emergent structure of cell groups , and thereby determine whether cells with cooperative phenotypes can locally and globally outcompete exploitative cells . We argue that cooperative and exploitative cell lineages will spontaneously segregate in space under a wide range of conditions and , therefore , that cellular cooperation may evolve more readily than naively expected .
Many cell phenotypes alter the growth and division of nearby cells by changing local resource availability [1]–[4] . Some of these phenotypes promote the survival and reproduction of others , and thus qualify as a simple form of cooperation . A cell may be considered cooperative , for example , if it secretes enzymes that free nutrients which neighboring cells can use . The efficiency with which a cell group processes environmental resources or exploits a host often depends on such publicly beneficial cell phenotypes . For instance , many microbial infections and cancerous tumors derive their pathogenicity in part from the cooperative secretion of digestive enzymes by their constituent cells [5]–[8] . How cooperative cell phenotypes evolve therefore presents an important question , one that is particularly challenging because any genetic variants that exploit others' cooperation – without themselves paying a cost – can potentially invade and increase in frequency . In light of this problem , social evolution theory has been developed to understand the evolutionary trajectories of cooperative traits [9] , but this framework has only recently been applied to unicellular systems [4] , [10]–[12] . The critical prediction is that preferential interaction among genetically related individuals increases the propensity for cooperative phenotypes to evolve . Variation among individual cells is a common feature of many cell groups: microbial biofilms are often composed of multiple strains or species [13] , [14] , and cancerous tumors can consist of many different genetic lineages [15] , [16] . The majority of work on cooperative cell phenotypes assumes relatively well mixed interactions among different genetic variants in standing or shaken liquid culture [17]–[21] . This kind of environment does not reflect the natural condition of most cell groups , in which cells are typically constrained in space and influence each other in a distance-dependent manner . These spatial relationships may be paramount to understanding the evolution of cellular cooperation [22] . When different cell lineages are segregated in space , those expressing cooperative phenotypes are more likely to benefit others of their own kind [23]–[25] . When different cell lineages are mixed together , on the other hand , cells that exploit the resources of others can thrive [17]–[20] . Local populations of bacterial and cancer cells are often established by groups of progenitors that proliferate into larger clusters . Experiments with bacterial colonies on agar have revealed that expanding cell groups can segregate into sectors that are each dominated by a single genetic lineage [26] , [27] . This observation has been used predominantly to motivate new population genetic models [28]–[30] . When only cells on the periphery of an expanding group can access nutrients and reproduce , the group's effective population size is reduced . As a result , neutral or even mildly deleterious alleles can spread by genetic drift along the advancing front . Because they are constrained in space , genetic lineages that manage to proliferate along the population's leading edge become physically separated into zones composed of clonal or closely related individuals . By promoting interaction between individuals of the same genotype , the spontaneous segregation of different genetic lineages in space may also influence social evolution within cell groups [23] , [24] . In the present paper , we use a generalized mechanistic model to define the physical and biological factors that govern cell group spatial structure , and we explore the potential connection between genetic drift along the fronts of expanding cell groups and the evolution of social phenotypes .
We began with simulations in which the environment surrounding cell groups was altered by increasing or decreasing growth substrate concentration . These in silico experiments were initiated with equal numbers of randomly distributed red and blue cells , which did not differ in any way other than their color . The two neutral color markers were used to judge whether cell lineages remain randomly mixed or become spatially segregated as cell groups expand . Environmental substrate availability was decreased from saturating to sparse across multiple simulations , and we observed three different regimes in cell group structure: Our next goal was to describe why environmental substrate concentration affects lineage assortment in expanding cell groups . Under limited growth substrate availability , the majority of cell growth and division occurs along a group's advancing front in an active layer whose depth depends on substrate penetration ( Figure 3 ) . Previous work has hinted that active layer depth is a critical factor influencing cell group surface structure [39] , [40] , and we therefore hypothesized that it is not substrate concentration in particular , but more generally the depth of a cell group's active layer that controls cell lineage segregation . Because segregation increased as growth substrate supply decreased in our preliminary simulations , we predicted that thinner active layers would lead to stronger lineage segregation in expanding cell groups . Active layer depth is not solely a function of bulk growth substrate concentration . For example , higher substrate diffusivity increases active layer depth by allowing substrate to enter further into the cell group before being depleted . Faster cell growth rates , on the other hand , decrease active layer depth by raising the rate of substrate consumption at the cell group's outer surface . If we are correct that active layer depth is the underlying determinant of lineage segregation , all of the physical and biological factors that control active layer depth should also influence lineage segregation in cell groups . Using an analytical technique from chemical engineering ( Methods ) , we combined the factors that influence active layer depth into a dimensionless number , δ , which has the following form for our system: ( 1 ) Here , Gbulk is the bulk liquid concentration of growth substrate , DG is the growth substrate diffusion coefficient , Y is the yield with which cells convert substrate to biomass , μmax is the maximum specific cell growth rate , ρ is the cell biomass density , and h is the height of the diffusion boundary layer ( Figure 3 ) . The smaller the value of δ , the thinner the cell group's active layer . We performed three new sets of simulations to test the hypothesis that active layer depth controls cell lineage segregation . Within each set , we varied active layer depth ( δ ) by altering only one parameter from Equation 1: maximum cell growth rate ( μmax ) , bulk growth substrate concentration ( Gbulk ) , or growth substrate diffusivity ( DG ) . At the end of each simulation , we calculated the segregation index . Our hypothesis makes two key predictions: 1 ) cell lineage segregation should be inversely related to δ , a proxy for active layer depth . 2 ) The relationship between cell lineage segregation and δ should be independent of which parameter from Equation 1 is altered . The results are shown in Figure 4 and support both predictions . Lineage segregation within cell groups declines with increasing δ , regardless of how δ is altered . Using the dimensionless number δ renders our results independent of the exact values of Gbulk , DG , Y , μmax , ρ , and h used to run simulations . It is the relative magnitudes of these parameters in combination that ultimately matter . How does active layer depth influence cell lineage segregation ? When growth substrate penetrates through most of a cell group before being depleted , all cells grow and divide , pushing each other into a homogeneous mixture . As active layer depth decreases below the total thickness of a cell group , however , cells that happen to fall below a critical distance from the group's front can no longer contribute to population expansion . Decreasing active layer depth thus reduces the cell group's effective population size , rendering it more susceptible to genetic drift along its advancing front . Because the cells are constrained in space , reductions in genetic diversity along the group's leading edge lead to localized clusters of individuals that all descend from a common progenitor [30] . This phenomenon – often referred to as sectoring or gene surfing [28]–[30] – has been observed in agar colonies of Paenibacillus dendritiformis [26] , Escherichia coli and Saccharomyces cerevisiae [27] . Reducing active layer depth even further yields an additional qualitative shift in cell group structure: the expanding population becomes sensitive to small irregularities along its leading edge . Cells in the peaks of surface irregularities retain access to substrate and grow into tower projections , while cells in the troughs of surface irregularities lose access to substrate and cease growing . This process is related to viscous fingering at the interface of two fluids [39] , [41] , and it is known to generate rough surface structure along the leading edges of growing biofilms , bacterial colonies on agar [34] , [35] , [40] , and moving fronts in general [36] . From a biological perspective , our analysis predicts that such surface roughness is accompanied by abrupt genetic lineage segregation along the front of an expanding population . The spatial assortment of cell lineages is potentially critical for traits that affect the reproduction of other individuals in the population . It is increasingly recognized that cells express many such social phenotypes [4] , [12] , which are often involved in nutrient acquisition and pathogenesis [42]–[45] . A common example is the secretion of extracellular enzymes or nutrient-chelating molecules . Cells that synthesize these substances must forgo a fraction of their reproductive capacity [17]–[19] , but if enough cells participate , all can gain a net benefit ( to the detriment of their host , in the case of pathogens ) . In many cases the evolution of simple cooperative phenotypes depends on three factors: 1 ) c , the cost incurred by cooperative individuals 2 ) b , the benefit gained by the receivers of cooperative behavior , and 3 ) r , the correlation between genotypes of givers and receivers of cooperation . Cooperation is predicted to evolve when rb>c , a condition known as Hamilton's Rule [9] . The cost and benefit factors are measured in terms of reproductive fitness . When cooperation is genetically determined , relatedness may be thought of as the degree to which the benefits of cooperation are preferentially distributed to other cooperative individuals . The segregation index depicted in Figures 2 and 4 is equivalent to a form of the relatedness coefficient in Hamilton's Rule: both measure the degree of biased interaction among relatives ( here , physical proximity amounts to biased interaction ) . As such , our segregation index forms a bridge between social evolution theory and the emergence of lineage segregation in cell groups , allowing us to extend our prediction from the previous section . Because thin active layer conditions generate lineage segregation , we predict that decreasing active layer depth will promote interaction among clonemates ( increasing r in Hamilton's Rule ) and favor the evolution of cooperation [9] , [12] , [23] . Positive spatial assortment of related cells does not guarantee that cooperation will be favored , however , as the same segregation that allows cooperators to preferentially interact also increases the strength of competition between them [24] . We tested our prediction by implementing a cooperative phenotype in our model framework and competing cooperative cells against exploitative cells that devote all resources to growth . Cooperative individuals secrete a diffusible compound that benefits all other cells in the local area ( we will refer to the compound as an extracellular enzyme ) . Local availability of the secreted enzyme increases cell growth rate by a fold factor B , but only after the enzyme's concentration passes a threshold value , τ . Cooperative cells constitutively secrete the enzyme and incur a fold decrease in growth rate of C x RE , where C is a cost scaling factor and RE is the enzyme production rate . In our main analysis , B = 3 , C = 0 . 3 , and RE ranges from 0 to 2 . We derived these values from experimental data on elastase , a secreted enzyme and virulence factor of the bacterial pathogen Pseudomonas aeruginosa [19] , [46] . We asked whether a cooperative cell line , which pays a cost to produce a diffusible , publicly beneficial compound , could outcompete an exploitative cell line that invests all of its resources into growth . Each competition simulation began with a randomly distributed 1∶1 mixed monolayer of the two cell types , and cell groups were grown to a maximum height of 100 µm . We then calculated the evolutionary fitness of the cooperative cell line , relative to that of the exploitative cell line ( Methods ) . This competition pairing was repeated over a range of extracellular enzyme production rates on the part of cooperative cells . The higher the enzyme production rate , the more rapidly cells accrue its benefit , but the larger the cost suffered by cooperative cells . Finally , all competition pairings were repeated across three active layer depth conditions ( δ = 10 , 2 , 1 ) , representing the three cell group structure regimes described in Figure 1 . Figure 5 summarizes the results of our competition simulations . When active layers are thick ( δ = 10 ) , leading to well mixed cell lineages , the extracellular enzyme is homogenously distributed through cell groups . The non-cooperative cell line is therefore able to consistently exploit and outcompete the cooperative cell line ( Figure 5A ) . This result is consistent with numerous observations that exploitative mutants outcompete enzyme-secreting bacteria when they are inoculated together in liquid culture , in which cell lineages largely remain mixed [17]–[20] . When active layer depth is decreased ( δ = 2 ) , there is a narrow range of extracellular enzyme production rates at which cooperative cells outcompete exploitative cells ( Figure 5B ) . The critical difference is that cooperative cells and exploitative cells no longer remain well mixed; rather , they segregate into clonal regions . As a result , the benefit of extracellular enzyme released by cooperative cells accrues asymmetrically to other cooperative cells . The range of enzyme production rates at which cooperative cells prevail is narrow , however , because the benefits of lineage segregation ( increasing r in Hamilton's Rule ) can be outweighed by the cost of higher extracellular enzyme production ( increasing c in Hamilton's Rule ) . Further decreasing active layer depth ( δ = 1 ) leads to the growth of spatially isolated , clonal cell towers . Under these conditions , the benefits of a cooperative secreted enzyme are distributed even more asymmetrically to other cooperative cells . Consistent with our predictions , this allows cooperative cells to outcompete exploitative cells over a larger range of enzyme production rates ( Figure 5C ) . We also noted the sizable variation between simulation runs when δ = 1 , particularly if extracellular enzyme production rates were low ( Figure 5C , enzyme production rate = 0 , 0 . 25 , 0 . 5 ) . This variation reflects a founder effect; it manifests most strongly when there is no or little difference between the competitive abilities of cooperative and exploitative cell lines , rendering the outcome of each simulation subject to chance events that determine which cells seed the few tower structures that emerge from an expanding cell group . Our results show that thin active layer conditions allow cells expressing cooperative phenotypes to outcompete exploitative cells within a single cell group . To better account for the long-term evolution of a metapopulation comprising many cell groups , we performed an invasion analysis to determine whether a novel cooperative mutant can spread through a metapopulation otherwise containing only exploitative cells ( Supporting Information , Text S1 ) . We also examined the reciprocal case to determine if a rare exploitative mutant can invade a metapopulation otherwise containing only cooperative cells [32] , [33] . We found that cooperation can invade under a large swath of parameter space , but only under thin active layer conditions that promote lineage segregation can cooperative cells eliminate exploitative cell types on a metapopulation scale ( Supporting Information , Figure S2 ) . The results of both our local competition and invasion analyses are robust to the cost/benefit ratio of cooperation , with one partial exception when cells invest very heavily into an expensive cooperative phenotype ( Supporting Information , Figure S3 ) . Our study indicates that an order of magnitude change in nutrient availability , nutrient diffusivity , cell metabolic efficiency , cell growth rate , or biomass density can shift cell groups from a regime of lineage mixing to a regime of pronounced lineage segregation . The number δ defined in Equation 1 relates these parameters to the depth of a cell group's active layer , which governs how cell lineages become spatially assorted over time . Thick active layers promote lineage mixing , while decreasing active layer depth generates increasingly strong lineage segregation . Cell lineage segregation , in turn , favors the evolution of cooperative phenotypes . Previous work performed with bacteria in liquid planktonic culture has concluded that cooperative cell phenotypes cannot be selectively favored within a single population also containing exploitative cells [17] , [19] , [20] . Our study shows that this conclusion will not always hold because cooperative cells can spontaneously segregate from exploitative cells when they are constrained in space . Our results also imply that , given realistic parameters for a cooperative cell phenotype , the benefits of preferential interaction between cooperators can outweigh the costs of increased competition between related cells that are clustered together in space [24] . Like all models , ours uses simplifying assumptions . We deliberately omit some physical processes , such as shear stress , that may be applied to cell groups in the real world [47] . Our simulations also do not consider active cell motility , which in reality could influence cell group structure and evolution . We have additionally assumed that cell phenotypes of interest , such as extracellular enzyme secretion , are expressed constitutively or not at all . In nature , the expression of many social phenotypes is adjusted in response to environmental cues [48]–[50] . Though these simplifications should be assessed theoretically and empirically , they were critical in allowing us to identify basic physical and biological parameters that control cell group structure and evolution . In summary , our model suggests that clusters of genetically related cells can emerge quite easily in spatially constrained cell groups , even when cells possess no mechanism for actively gathering with clonemates . Lineage segregation allows cooperative cells to outcompete exploitative cells , and accordingly we predict that localized cooperation will evolve more readily in cell groups than suggested by models and experiments that only consider liquid environments .
We simulate cell groups using an individual-based model described in detail previously [31] . Simulation parameters are listed in Table S1 ( Supporting Information ) . Cell growth is a function of the local microenvironment , namely the concentrations of solutes such as growth substrate ( G ) and extracellular enzyme ( E ) ( Supporting Information , Table S2 ) . The uptake of growth substrate by each cell is considered when calculating the spatial gradients of substrate concentration . We achieve this by solving a reaction-diffusion equation , where r is a growth rate expression: ( 2 ) Following the common assumption that reaction-diffusion is much faster than cell growth and division [31] , our simulations proceed according to the following steps: The individual-based simulation framework was written in the Java programming language , and its related numerical methods are detailed elsewhere [31] . Briefly , they include the Euler method to grow cells at each iteration , a hard-sphere collision detection method to identify pushing events between neighboring agents , and the FAS multigrid to solve reaction-diffusion equations to steady state [51] . The 3D images in Figure S1 where rendered using POV-Ray . All other figures were prepared using Matlab ( the Mathworks , Inc . ) . The computations in this paper were run on the Odyssey cluster supported by the Harvard University FAS Research Computing Group . To obtain the segregation index for a cell group at a single point in time , we first identify every actively growing cell . These M cells are indexed by Ai: A1 , A2 , … , AM . To measure segregation with respect to a single focal cell Ai , we identify all other individuals within a distance of 10 cell lengths . The N cells in this neighborhood are indexed by aj: a1 , a2 , … , aN . We define a genetic identity function , g ( aj ) : ( 3 ) and a metabolic activity function , m ( aj ) : ( 4 ) where [G] is the local concentration of growth substrate , and KG is the half-saturation constant for cell growth rate . Segregation with respect to a focal cell , s ( Ai ) , is calculated as the mean product of the g and m functions for every cell in its neighborhood: ( 5 ) Finally , we define the segregation index for the entire cell group as the mean value of s ( Ai ) across all metabolically active cells: ( 6 ) Our segregation index measures the degree to which co-localized , metabolically active cells are clonally related to each other . The index is equal to a form of the relatedness coefficient from social evolution theory under the following assumptions: 1 ) A cell expressing the cooperative phenotype equally benefits all other individuals within a 10 cell-length radius; 2 ) Each cell within range of receiving cooperative benefits makes a contribution to mean relatedness proportional to its growth rate; 3 ) Cell groups are seeded randomly from a large population pool . The dimensionless number , δ , is a proxy for the depth to which growth substrate penetrates into a cell group before being depleted by cell metabolic activity . δ is derived by non-dimensionalizing Equation 2 . We normalize growth substrate concentration by its bulk liquid concentration , , and local biomass by cell biomass density , x = X/ρ . We then normalize the space coordinates by the height of the boundary layer , h . The steady state , dimensionless version of Equation 2 becomes: ( 7 ) Note that the factor multiplying the Laplacian of , , is the square of δ as defined in the main text . δ is also the inverse of the Thiele modulus [52] , a number commonly used in chemical engineering to quantify the activity of solid catalysts . We calculate the competitive fitness of each cell line as the mean number of rounds of cell division per unit time that each achieves over the course of a simulation: ( 8 ) where NS , t is the number of cells of strain S present within the cell group at time t . The relative fitness of a strain S1 in local competition with another strain S2 is defined as: . | Cooperation is a fundamental and widespread phenomenon in nature , yet explaining the evolution of cooperation is difficult . Natural selection typically favors individuals that maximize their own reproduction , so how is it that many diverse organisms , from bacteria to humans , have evolved to help others at a cost to themselves ? Research has shown that cooperation can most readily evolve when cooperative individuals preferentially help each other , but this leaves open another critical question: How do cooperators achieve selective interaction with one another ? We focus on this question in the context of unicellular organisms , such as bacteria , which exhibit simple forms of cooperation that play roles in nutrient acquisition and pathogenesis . We use a realistic simulation framework to model large cell groups , and observe that cell lines can spontaneously segregate from each other in space as the group expands . Finally , we demonstrate that lineage segregation allows cooperative cell types to preferentially benefit each other , thereby favoring the evolution of cooperation . |
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Paroxysmal nocturnal hemoglobinuria ( PNH ) is an acquired clonal blood disorder characterized by hemolysis and a high risk of thrombosis , that is due to a deficiency in several cell surface proteins that prevent complement activation . Its origin has been traced to a somatic mutation in the PIG-A gene within hematopoietic stem cells ( HSC ) . However , to date the question of how this mutant clone expands in size to contribute significantly to hematopoiesis remains under debate . One hypothesis posits the existence of a selective advantage of PIG-A mutated cells due to an immune mediated attack on normal HSC , but the evidence supporting this hypothesis is inconclusive . An alternative ( and simpler ) explanation attributes clonal expansion to neutral drift , in which case selection neither favours nor inhibits expansion of PIG-A mutated HSC . Here we examine the implications of the neutral drift model by numerically evolving a Markov chain for the probabilities of all possible outcomes , and investigate the possible occurrence and evolution , within this framework , of multiple independently arising clones within the HSC pool . Predictions of the model agree well with the known incidence of the disease and average age at diagnosis . Notwithstanding the slight difference in clonal expansion rates between our results and those reported in the literature , our model results lead to a relative stability of clone size when averaging multiple cases , in accord with what has been observed in human trials . The probability of a patient harbouring a second clone in the HSC pool was found to be extremely low ( ~10-8 ) . Thus our results suggest that in clinical cases of PNH where two independent clones of mutant cells are observed , only one of those is likely to have originated in the HSC pool .
Paroxysmal nocturnal hemoglobinuria ( PNH ) is an acquired disorder of hematopoietic stem cells ( HSC ) due to a somatic mutation in the PIG-A gene [1 , 2] . Loss of function or hypofunction mutations in this gene result in loss of or reduced ability to synthesize the glycosylphosphatidylinositol ( GPI ) anchor . As a consequence , many cell surface proteins that need this anchor to attach to the plasma membrane are no longer available or only available in reduced numbers on the cell [3 , 4] . Some of these proteins such as CD55 and CD59 are essential for the protection of red blood cells from complement mediated lysis . As a consequence , erythrocytes that lack CD55 and CD59 undergo intravascular hemolysis leading to anemia , hemoglobinuria , iron deficiency and fatigue . Scavenging of nitric oxide by free plasma hemoglobin results in endothelial and platelet dysfunction leading to the high risk of venous and arterial thrombosis associated with this disease . Additional symptoms related to nitric oxide depletion include abdominal pain , esophageal pain , chronic kidney disease and erectile dysfunction . PNH is a rare condition and many practitioners ( mostly those working outside of large hospital facilities ) have yet to encounter a single patient with this disease . Although the discovery of somatic mutations in the PIG-A gene ( which resides on the X chromosome ) provided a very elegant explanation of how an acquired mutation in a single gene could lead to the disease phenotype [1] , this does not explain how the mutant clone expands . It has been shown that ( i ) the mutation rate in PIG-A deficient cells is normal [5] , ( ii ) PIG-A deficient cells are not more resistant to apoptosis than normal cells [6] , ( iii ) the replication rate of mutated cells is normal [7] and PIG-A deficient cells do not have a proliferative advantage over normal cells [8] . Perhaps it was natural for investigators in the field to assume from the outset that there must be a selective fitness advantage of PIG-A mutated cells and consequently to investigate possible causes for some benefit that enables clonal expansion . It has been proposed that the selective advantage of mutated cells is extrinsic to them and due to an immune mediated attack on normal cells [9] . Some evidence in support of this hypothesis exists [10–13] , but this hypothesis is unable to explain the following observations: ( i ) PIG-A is ubiquitously expressed in the body–why should the immune attack presumably against the GPI anchor be restricted to the normal HSC population ? ( ii ) Immunosuppressive therapy does not lead to the elimination of the mutant cells and expansion of the normal HSC with return to normal hematopoiesis . ( iii ) A significant fraction of patients with PNH undergo ‘spontaneous’ extinction of the clone [14] . A second hypothesis has been proposed , which postulates that additional mutations in one or more genes that confer a fitness advantage to the PIG-A mutant cells may occur . Indeed , several case reports are available including two patients with a mutation in HMGA2 [15] , one patient with a concomitant JAK2V617F mutation [16] , a mutant N-RAS [17] and more recently a patient with PNH and concomitant BCR-ABL in the same cell population was also reported [18] . However , these cases appear to be the exception and not the rule , and their description in our opinion requires specific explanations other than the one responsible for the general origin: clonal expansion and possible elimination of PNH phenotype clones . We have previously shown that given the normal mutation rate in PNH cells , it would be rare for a patient to have a second mutation in a PIG-A mutated stem cell that would provide the fitness advantage necessary for clonal expansion [19 , 20] . Moreover , to date there is no evidence of a fitness advantage for the PIG-A mutated cells even though recent data suggests that some could have additional mutations ( that are typically seen in myelodysplastic syndrome or leukemia ) present [8 , 21] . Finally , deep sequencing of patients with PNH clones reveals that a significant fraction do not have additional mutations present apart from that in PIG-A [21] . Given these observations , we proposed that perhaps clonal expansion in PNH is simply due to neutral drift , given i ) that cells with GPI deficiency do not seem to have any evolutionary advantage over normal cells and ii ) the limited data in support of any benefit with immunosuppressive therapy ( in the absence of concomitant aplastic anemia ) [22] . Interestingly , in some patients with PNH more than one clone is detected–one with complete deficiency of GPI anchored proteins ( so called PNH III cells ) and another with partial deficiency of GPI anchored proteins ( PNH II cells ) [3 , 23] and this has been confirmed by sequencing [21] ( for historical reasons , normal cells are referred to as PNH I cells ) . Clearly , these two mutant populations arise due to independent mutations in the PIG-A gene in different cells leading to these phenotypes . In this work , we use evolutionary principles and stochastic dynamics modelling of the HSC pool to determine the incidence of the disease in populations , estimate clone size and average age at diagnosis , and also address the question of multiple PIG-A mutated clones in patients with this disease . These results strengthen and extend the ones found by Dingli et al . [22] , who first tested this hypothesis , by the use of a Markov chain formulation rather than traditional simulations . We provide exact solutions for the probabilities in the state space and for the first time , an estimate of the rate of clonal expansion in this disease .
Depending on the total number of mutated stem cells in an individual we can assign different diagnoses: when at least 20% of the total stem cell pool has a PIG-A mutation the individual is defined as having clinical PNH . Cases where the PIG-A clone ( s ) consist of <20% of the cell population are considered to be subclinical ( or latent ) PNH [24] . The probability for an individual to develop clinical PNH increases with age according to the curves shown in Fig 1A , although this risk actually decreases briefly during the period of ontogenic growth due to the corresponding growth of the stem cell pool [25] . Indeed , the limited data available suggests that PNH is quite rare in children [26 , 27] and generally occurs in the context of bone marrow failure . While classical hemolytic PNH represents about 10% of pediatric patients with a PIG-A mutant population , data from the International PNH registry suggests that perhaps half of adults with PNH have classical hemolytic disease [28] . We find that the probability of a patient having clinical PNH with two independent clones arising from the HSC pool is approximately 103 times smaller than the same probability of diagnosis with a single clone ( Fig 1A ) . Furthermore , we estimate that patients who have 3 or more distinct PNH clones contributing to hematopoiesis occur with a probability that is another 2 orders of magnitude lower ( Fig 1A ) . This implies that approximately only 1 in 1000 cases of clinical PNH would host more than a single mutant clone that arose in the stem cell compartment . Note that these numbers result from a model dealing only with stem cell dynamics . Thus , this does not preclude the occurrence of mutations farther downstream among progenitor cells ( which are present in larger numbers than HSC and also divide faster [19 , 29] ) . Moreover , PIG-A mutations occurring in early progenitors will also remain contributing to hematopoiesis for years before any eventual wash-out [30 , 31] . Thus , divergent PIG-A mutations found in mature cells are more likely to have originated at later stages of differentiation [19] than to originate in independent mutations occurring in the active stem cell population . Using population age distribution data obtained in 2010 by the United States Census Bureau , we estimate the prevalence of clinical PNH ( weighted sum of census data and clonal existence probabilities ) for both mono- and multiclonal cases in the USA ( Fig 1B and 1C ) . We calculate an expected prevalence of 1 . 76 cases per 105 citizens for any diagnosis of clinical PNH ( mono- or multiclonal ) , which is similar to what has been reported in a well-defined population by Hill et al . [32] . The expected number of patients with biclonal disease arising at the level of the HSC is determined at 1 . 29 per 108 individuals . For the US population , this would amount to approximately 3000 patients with a single clone and 2 patients with biclonal disease , respectively . The number of individuals in the population with a subclinical ( <20% ) PIG-A mutated clone is estimated to be much higher , at 6 . 0 per 104 for monoclonal and 1 . 9 per 107 for biclonal cases , which amounts to respectively 184 , 495 and 60 individuals in the US . The first mutated cell in the HSC pool can occur quite early in an individual’s life , as shown in Fig 2A . The probability of harboring a mutant cell in the stem cell population grows one order of magnitude from age 20 ( ~2×10-3 ) to age 100 ( ~2×10-2 ) . Though these values may seem quite high , it is important to note that in the neutral drift hypothesis , the second line of defense against PNH is the significant low likelihood of clonal expansion , a fact that is illustrated well by comparing the probability of occurrence of a clone ( which is quite common in healthy people [33] ) with the probability of having clinical PNH . For example , in an individual of age 60 , the probability of having acquired a mutant clone is 1×10-2 , while the probability of having clinical PNH is 2×10-5 , three orders of magnitude smaller . The average ages of clonal occurrence are projected at 41 and 54 years for mono- and biclonal ( stem cell ) cases respectively ( Fig 2B ) . In general , it appears that , on average , most clones arrive only after adulthood is reached and the hematopoietic stem cell pool has reached its maximal size . The average age at diagnosis–in our model we take this as the time at which the total number of mutated HSCs reaches 20%–is found to be 49 years , and is quite similar to what has been reported from the International PNH registry [28 , 34] . Because some investigators define clinical PNH at a lower threshold , especially in the presence of aplastic anemia , we also calculated the average age when 10% of the HSC pool is composed of PIG-A mutant cells , and obtained a mean arrival time of 44 years . As mentioned above , the lack of a selective advantage makes it difficult for the mutated clone to expand , since at each replication event it is equally likely to decrease in size as it is to expand ( if one neglects the low probability of mutation ) [35–37] . Over time the size probability distribution widens , adding more emphasis on larger clones while smaller clones become less probable ( since they are more likely to go extinct ) . Thus , in cases where two separate clones are simultaneously present , the first that occurred is likely to be larger and therefore less likely to resolve than the second . From a mathematical perspective , this behavior can be ascribed to the fact that the all-normal state ( absence of mutants ) and all-mutant state ( complete takeover by mutants—fixation ) are ( in the absence of mutations ) absorbing states of the evolutionary dynamics . An important consequence of the all-normal absorbing state is that most clones which arise in a population go extinct before reaching a significant size . We find that approximately 83% of all clones that appeared in our in-silico population resolved , and in most of these cases clonal extinction would have occurred soon after the clone’s arrival , so that the individuals at stake would never have been diagnosed with PNH as their clone would have been very small . On the other hand , extrapolating these simulations to the entire hematopoietic tree clearly suggests that the massive cell turnover ( with mutation ) that occurs normally in hematopoiesis explains why finding a PIG-A mutant cell population with sensitive sequencing or flow cytometry in a healthy individual is not unexpected [33] . If a clone does manage to increase in size , the likelihood of spontaneous clonal extinction becomes less pronounced . In Fig 3A we show the probability distribution of clonal size measured at 3 different times after disease diagnosis . The variance of this distribution increases not only over time–as the clone has more time to expand or diminish–but also as the clone increases in size due to the frequency dependence of this “random walk” . In particular , the closer the clone size comes to comprising 50% of the SC pool , the larger this variance will be . One consequence of this behavior is that the distributions shown in Fig 3A are skewed to the right . Note , however , that despite the changing shape of the size distribution , the mean clone size ( the average of this distribution ) does not change over time . This result implies that in a cohort of diagnosed patients , we expect the average of their clone sizes to remain stable despite individual expansions or recessions . Using the census data , the average clone size m in individuals in the US population with at least one mutant HSC is estimated in our model to be at 3 . 4% of the total pool . However , the average clone size in those suffering from clinical PNH ( m≥20% ) is much larger , found to be 31 . 1% , or 19 . 6% if this threshold is instead taken at m≥10% . Of course , in individual patients , clone size can be quite high and be the predominant contributor to hematopoiesis . Whenever the number of mutant stem cells reaches the threshold of clinical PNH , it is nevertheless possible for the disease to disappear due to the stochastic nature of the clonal dynamics . We calculated the probability that a recently diagnosed case of clinical PNH ( assuming the SC pool is ≈20% mutated at diagnosis ) becomes subclinical again . The result ( Fig 3B ) shows that over time it is more likely for the disease to recede than to persist , although it would take at least 2–10 years for significantly smaller clone sizes ( <15% or <10% ) to be reached . The probability of the clone becoming truly extinct only becomes realistic after 20–50 years , and in reality clinically detectable extinction will depend on the assay that is used to determine the presence or absence of the clone . It should be evident that more sensitive flow cytometry based assays will be able to detect the clone , even when the ‘old’ standard Ham’s test becomes negative . Note that these results only represent the likelihood of disease reduction under neutral dynamics , and as such do not exclude the possibility of a more fit clone arising which can advantageously compete with the PIG-A clone and lead to disease loss [38] . We compared our predictions of average clone size and patient age at diagnosis with data from the International PNH Registry [28] . Our predicted mean clone size of 31 . 1% ( standard deviation: 32 . 6 ) and mean age at diagnosis of 48 . 6 ( standard deviation 52 . 8 ) seem to fit nicely with the registry data for patients with AA-PNH syndrome ( categorized as also suffering from aplastic anaemia ) which shows a mean clone size of 28 . 3% ( standard deviation 32 . 8% ) and 43 . 2% of patients diagnosed between 30 and 59 years of age . However , other categories presented much greater average clone sizes ( though similar ages at diagnosis ) . Our prediction of the number of patients with clinical PNH in the general US population is slightly lower than what Hill et al [36] reported . However , we note that our modeling takes into account the age structure of the population in the United States , whereas the patients observed by Hill et al . were from Great Britain , which may have a different age distribution . Furthermore , our prevalence estimates depend on a strict definition of clinical PNH ( clone size >20% ) , whereas in clinical practice , the transition from subclinical to clinical disease may be less abrupt . We also compared our findings on clonal expansion rate with measurements from Araten et al . [7] , who reported a ≥5% size increase per year in 12 out of 36 patients . Most of the other patients experienced either a reduction or no change at all , though the authors did not specify these amounts quantitatively . The study found no significant expansion or reduction ( ≈0% ) when calculating the mean over all patients , which nicely fits our neutral drift model . Our model projected the fraction of patients that would experience a ≥5% increase after 1 year to be between 5% and 10% depending on the size of the initial clone , which is lower than their observed 33% . This discrepancy could be due to several factors including the relatively small size of their patient cohort and the fact that our model does not include the contribution of progenitor compartments to the overall size of the PNH population . If the progenitor cell population is contributing significantly to hematopoiesis , then more fluctuations in clone size would be expected due to the shorter lifetime of these cells [30] . Our modeling predicts that after 10 years from diagnosis , the probability that the clone is small enough not to be associated with clinical PNH is upwards of 30% ( Fig 3B ) . This is also comparable to what Hillmen et al [14] reported in their cohort of patients who survived for more than 10 years from diagnosis ( 12 out of 35 patients ) .
The appearance of mutations in HSC and their fate over time is an important clinical problem , since many diseases such as myelodysplastic syndromes and several leukemias ( e . g . chronic myeloid leukemia , some subtypes of acute myeloid leukemia ) arise due to mutations within the HSC . Landmark studies in PNH have shown that it is an acquired clonal HSC disorder [39] with very interesting dynamic properties , including an uncanny probability of spontaneous clonal extinction [14] . The mechanism of clonal expansion in PNH has been a source of great debate and several hypotheses have been proposed to explain it , such as a selective advantage of the mutant cells due to an immune attack on normal HSC ( extrinsic advantage ) , or the presence of a second mutation that grants a fitness advantage ( intrinsic advantage ) . Some evidence for either hypothesis exists , but both also suffer from deficiencies as described in the Introduction . In particular , immunosuppressive therapy does not return hematopoiesis to normal and there is no reduction in the size of the PIG-A mutant clone once the presumed selective advantage is eliminated . It is also difficult to see how a cell can acquire multiple mutations sequentially in the absence of genomic instability , which has not been observed in PNH [5] . We have proposed that the PIG-A mutant cells generally possess no fitness advantage ( or disadvantage ) , and that clonal expansion is simply a consequence of neutral drift within the ( small ) active HSC pool that maintains hematopoiesis [22] . This hypothesis leads to the simplest of explanations of PNH , and our stochastic modeling suggests that this may be the case–at least in some patients–since we are able to predict the incidence and prevalence of the disease , average age at diagnosis , average clone size and the probability of clonal extinction purely from first principles with results quite similar to what has been reported in the literature . Although it is difficult to deliver conclusive proof of our hypothesis , the close parallel between our predictions and clinical reality provides considerable support for it . It has been reported that in at least one patient , that PNH clonal extinction was concomitant with the appearance of a new population of cells that harbored mutations in genes such as STK36 that can potentially provide a fitness advantage to the non-PIG-A mutant cells [38] . Our model makes no assumptions about the possibility of more fit clones arising , but merely gives the likelihood of the disease disappearing entirely on its own through neutral drift . Moreover , while the ongoing dynamics within the normal hematopoietic stem cell group put this population at continued risk of accumulating new mutations–some of which could lead to a fitness advantage as proposed by Babushok et al [38]–the presence of such mutations by itself does not imply that the PNH clone resolved due to takeover of hematopoiesis by a new population with mutations that are often found in patients with myeloid disorders . As discussed elsewhere [40] the presence of such mutations by itself does not necessarily mean that the mutant cells with a normal PIG-A gene have a fitness advantage since the mutant gene ( e . g . STK36 ) may or may not be a ‘driver’ mutation . This point is further highlighted by the fact that in the case reported by Bubashok et al [38] , PNH clonal reduction occurred over the course of 12 years while our estimate for the general population under neutral drift is that on average , 8–10 years are required for the PNH clone to reach ~15% . Neutral drift may come as a surprise for many in the field of hematology and oncology who are accustomed to associate malignant clonal expansions in cancer with some form of selective advantage . Nevertheless , it is not uncommon for mutations in populations to expand by neutral drift , as suggested by Kimura many years ago [41] . In fact , the recent cancer genomics data explosion suggests that most mutations found in tumours are examples of neutral drift and are labeled passenger mutations , since they provide no fitness advantage to the tumour [42] . Of course , mutations that increase the fitness of malignant clones clearly exist . Perhaps the main difference in PNH is that neutral drift could be the main mechanism of clonal expansion . Not only is it the simplest explanation , but it also provides a very elegant correlation of genotype with phenotype , a feat that is much more difficult to achieve in malignant tumours due to the complex mutational landscape that they harbor . In conclusion , we present a stochastic model of HSC dynamics which studies mutations in the PIG-A gene that leads to the PNH phenotype . Our predictions based on an in silico model of Markovian stochastic dynamics enable us to determine from first principles the incidence of the disease in a population , the average clone size and the probability of clonal extinction , with results similar to what is observed in clinical practice . We also find that in a neutral drift model the probability of multiple PNH clones arising separately in the HSC pool is exceptionally small , a result which suggests that in clinical cases where differing clones are observed , all but one of the clones are likely to have emerged in later stages of differentiation . We propose that PNH is perhaps the first disease where neutral drift alone may be responsible for clonal expansion leading to a clinical problem .
The model describes a system of non-interacting HSC that undergo cell division and differentiation . The size NSC of this stem cell pool changes during ontogenic growth from about 20 cells at birth , to approximately 400 cells by adulthood [43] , following the growth curve determined by Dingli et al [25 , 44] . A stochastic evolution of the system is modelled in discrete-time steps , where at each time step a division and subsequent differentiation event are performed ( so-called birth-death process ) . This is done by randomly selecting for replication a single cell from the stem cell pool , which generates two daughter cells . Since we consider only neutral dynamics , all cells possess the same fitness , meaning each cell ( whether it is normal or PIG-A mutated ) has the same probability of being selected . Following replication , a new cell is randomly selected from the total population ( now of size NSC+1 ) for differentiation , removing it from the stem cell pool . Thus NSC remains unchanged and this neutral evolution model can be considered a special case of the well-known Moran process [45] . When a normal cell replicates there is a probability μ that one of the daughter cells acquires a mutation , introducing a potential seed for the development of a mutated clone in the population . The cells in the active HSC compartment , replicate slowly , at a rate of approximately once per year [46] . The conversion between number of cell replications and biological time is done in the following way: When the number of replication events equals the cell population size , then one year has passed . Thus , in adulthood , if ≈400 replications within the HSC pool occurred , a year would have passed . The increase of NSC during ontogenic growth follows an predetermined growth curve derived before [25 , 44] and is implemented by including additional divisions without successive differentiations at fixed times ( 2 week intervals ) to match the expected growth rate . Thus , while the Moran-like dynamics are respected for all division-differentiation events , they are periodically interrupted during this phase to model the increase of the population . While this model of neutral dynamics has already been introduced and studied by Dingli et al . [22] , the current approach ( described below ) allows for a more robust probing of the system from which new insights can be obtained . The discrete time-evolution of this system is well described by a Markov chain in which the state space is represented by the number of mutants present in the population . To predict the stochastic evolution we numerically evolve the master equation Pm[x+1]=∑kpm , k × Pk[x] where Pmx is the probability of finding the HSC cell population in state m ( with m being the number of mutated cells in the population ) at time step x , and pm , k is the probability to go from state k to state m in a single time step . Starting from a mutant free population leads to the initial conditions P00 = 1 ( the probability of 0 mutants existing at time t = 0 is 1 ) and Pm>00 = 0 ( the probability of more than 0 mutants existing at time t = 0 is 0 ) . The transition probabilities are found by considering all changes that may occur when performing a division and differentiation event . For example , an event in which a normal cell divides without acquiring a mutation and a normal cell differentiates results in a transition into the same state ( one normal cell is added and one is removed ) , and occurs with a probability 1-mNSC1-μ1-mNSC+1 . The total probability of staying in the same state ( i . e . the transition m→m ) is found by summing this term with all other events that result in a same state transition; in particular , if a mutant cell is selected in both division and differentiation steps–probability mNSCm+1NSC+1 , and if a normal cell divides but acquires a mutation and a mutant cell differentiates–probability 1-mNSCμm+1NSC+1 . In a similar manner we can obtain all nonzero elements of the transition matrix to obtain: {pm , m−1=m−1NSC ( 1−mNSC+1 ) + ( 1−m−1NSC ) μ ( 1−mNSC+1 ) pm , m=mNSCm+1NSC+1+ ( 1−mNSC ) μm+1NSC+1+ ( 1−mNSC ) ( 1−μ ) ( 1−mNSC+1 ) pm , m+1= ( 1−m+1NSC ) ( 1−μ ) m+1NSC+1 Note that for the “division-only” events occurring sporadically during ontogenic growth a simpler transition matrix is used in which no differentiation takes place ( see S1 Text ) . It is clear that only transitions between “nearest-neighbours” in the state space are possible , so that pm , k = 0 if m-k>1 . An interesting property of this system can be seen from that fact that if we neglect the possibility of a new clone arising ( that is , neglecting the possibility of mutation so that μ = 0 ) , the transition probabilities to move up or down from a state m in a single time step are identical: pm+1 , m=pm−1 , m=NSCm−m2NSC ( NSC+1 ) This symmetry implies the system will perform a random walk reminiscent of Brownian motion , the main difference being that the probability to move away ( up or down ) from a current state is not independent of m , instead adhering to the quadratic function given above , with a maximum value at m = NSC/2 , reflecting the frequency dependence of the evolutionary dynamics . This means that the closer the mutant population gets to this maximum , the more “volatile” it becomes . It is also worth noting that while the above transition matrix holds–as mentioned earlier–only in the absence of fitness differences between cell types , it can easily be extended to cases where one type is more likely to divide or differentiate . The probability of a cell selected for division ( or differentiation ) being a mutant then not only depends on the size m of the mutant clone as m/NHSC , but also on the clone’s relative fitness rPIGA [47] P{dividing cell∈mutants}=rPIGAmrPIGAm+rhealthy ( NHSC−m ) Where rhealthy is the fitness factor of the non-mutated HSC . It is also immediately clear that taking rPIGA = rhealthy reduces the expression to the selection-free case treated in this work . The entire set of transition probabilities in the presence of selection are given in the appendix . Evolving the basic Markov chain described above provides no method for tracking the evolution of multiple clones that arise from separate mutational events , as only a single PIG-A mutant population is considered . Thus , in order to distinguish clonally unrelated subpopulations , we extend the model by expanding the state space to account for multiple mutations . In the following , we take advantage of previous estimates which show that one can safely ignore two mutations in the same stem cell [19 , 20] to limit our state space to account for a maximum of two different clones . This considerably simplifies the associated computations , as the state space scales as ~Ni , with N being the cell population size and i the maximum number of independent clones . As a result , the state space is divided into three separate histories , as shown in Fig 1 , corresponding to states with different pasts . For example , an evolutionary history in which a mutation occurred but the resulting clone eventually died should correspond to a different state than one where no mutation occurred in the first place . Thus , the master equation is now altered to include transitions to different histories: Pm1 , m2i[x+1]=∑m1' , m2' , jpm1 , m2 , m1' , m2'i , j× Pm1' , m2'j[x] where any state is now characterized by the clone sizes m1 and m2 , and the appropriate history i . The tensor elements pm1 , m2 , m1' , m2'i , j represent transitions from state m1' , m2' to ( m1 , m2 ) and history j to i , and are found in an identical manner as before , though now more transitions are possible ( Fig 4 ) . The probabilities obtained by evolving the master equation also allow us to calculate the average clone size m in individuals of a given population whose clone size is within a chosen range . For example , in “clinical PNH” patients ( defined as having a clone which involves at least 20% of the HSC pool ) we are only interested in individuals whose number of PIG-A mutated cells ranges between 20% and 100% . The expression to evaluate is then: m=∑y=1100∑m=m0NSCm wy , mqy∑y=1100∑m=m0NSCwy , mqy , where wy , m is the probability of an individual of age y having a clone of size m ( wy , m = ∑i , nPm , niy which results from evolving the Markov chain ) , and qy is the fraction of individuals of age y in the population based on the 2010 US census [United States Census Bureau . Single Years of Age and Sex: 2010 . https://www . census . gov . Accessed 13 June 2017 . ] , where we take individuals up to 100 years of age . The terms in the numerator form the average when all possible sizes 0 , … , NSC are considered , while the denominator normalizes the result if we are interested in a particular range , e . g . m0→Nsc ( it is easy to see that when m0 = 0 the denominator becomes 1 ) . | The mechanisms leading to expansion of HSC with mutations in the PIG-A gene that leads to the PNH phenotype remains unclear . Data so far suggests there is no intrinsic fitness advantage of the mutant cells compared to normal cells . Assuming neutral drift within the HSC compartment , we determined from first principles the incidence of the disease in a population , the average clone size in patients , the probability of clonal extinction , the likelihood of several separate clones coexisting in the HSC pool , and the expected expansion rate of a mutant clone . Our results are similar to what is observed in clinical practice . We also find that in such a model the probability of multiple PNH clones arising independently in the HSC pool is exceptionally small . This suggests that in clinical cases where more than one distinct clone is observed , all but one of the clones are likely to have emerged in cells that are downstream of the HSC population . We propose that PNH is perhaps the first disease where neutral drift alone may be responsible for clonal expansion leading to a clinical problem . |
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A proportion of all immunocompetent patients treated for visceral leishmaniasis ( VL ) are known to relapse; however , the risk factors for relapse are not well understood . With the support of the Rajendra Memorial Research Institute ( RMRI ) , Médecins Sans Frontières ( MSF ) implemented a program in Bihar , India , using intravenous liposomal amphotericin B ( Ambisome ) as a first-line treatment for VL . The aim of this study was to identify risk factors for VL relapse by examining the characteristics of immunocompetent patients who relapsed following this regimen . This is an observational retrospective cohort study of all VL patients treated by the MSF program from July 2007 to August 2012 . Intravenous Ambisome was administered to 8749 patients with VL in four doses of 5 mg/kg ( for a total dose of 20 mg/kg ) over 4–10 days , depending on the severity of disease . Out of 8588 patients not known to be HIV-positive , 8537 ( 99 . 4% ) were discharged as initial cures , 24 ( 0 . 3% ) defaulted , and 27 ( 0 . 3% ) died during or immediately after treatment . In total , 1 . 4% ( n = 119 ) of the initial cured patients re-attended the programme with parasitologically confirmed VL relapse , with a median time to relapse of 10 . 1 months . Male sex , age <5 years and ≥45 years , a decrease in spleen size at time of discharge of ≤0 . 5 cm/day , and a shorter duration of symptoms prior to seeking treatment were significantly associated with relapse . Spleen size at admission , hemoglobin level , nutritional status , and previous history of relapse were not associated with relapse . This is the largest cohort of VL patients treated with Ambisome worldwide . The risk factors for relapse included male sex , age <5 and ≥45 years , a smaller decrease in splenomegaly at discharge , and a shorter duration of symptoms prior to seeking treatment . The majority of relapses in this cohort occurred 6–12 months following treatment , suggesting that a 1-year follow-up is appropriate in future studies .
Visceral leishmaniasis ( VL ) is a neglected tropical disease that results in the loss of an estimated 1 million disability-adjusted life years annually in South East Asia [1]; it is typically fatal if untreated . VL predominantly affects the poorest strata of society and those with limited access to care [2] . The incidence is estimated to be between 146 , 700 and 282 , 800 cases per year [3] . Fifty percent of VL cases worldwide occur in India , and up to 90% of these in the state of Bihar . Although complete parasite clearance is rarely achieved , it is thought that patients with competent immune systems who are successfully treated develop an effective lifelong cellular immune response that suppresses residual parasite growth [4] . High relapse rates in HIV-positive patients have been previously described [5] , [6] . However , in nearly all studies assessing treatment effectiveness , a proportion of immunocompetent patients relapse following treatment despite negative end-of-treatment test-of-cure results . Typically , these relapses occur within 6 months of initial treatment with later recurrence considered rare [7] . Very little is known regarding the characteristics of immunocompetent patients with VL who relapse [8] , [9] , particularly in the Indian context . The aim of this observational retrospective cohort study was to identify risk factors for relapse in immunocompetent patients who had been treated with 20 mg/kg Ambisome for their primary episode of VL . With the permission of the State Health Society of Bihar and the support of the specialist VL research institute the Rajendra Memorial Research Institute ( RMRI ) , Médecins Sans Frontières ( MSF ) has been treating patients with VL in Vaishali district since 2007 , in coordination with the National Vector Borne Disease Control Programme of India . Between July 2007 and August 2012 , a total of 8749 patients diagnosed with VL were treated with intravenous 20 mg/kg liposomal amphotericin B ( Ambisome; Gilead Pharmaceuticals , Foster City , CA , USA ) . This regimen has been shown to have a 6-month cure rate of 98% in the Indian context [10] . Ambisome is a brand name for Liposomal Amphotericin B . There are a number of preparations of Liposomal amphotericin B available on the market; however due to the lack of standard and widely applicable regulations or guidance for liposomal technology , it is important that this specific preparation be named . At time of publication , none of the rival preparations have undergone peer reviewed non-inferiority studies against Ambisome nor received stringent regulatory approval for use in VL . It is for this reason that MSF and the WHO currently only use Ambisome rather than other preparations . However it is urgent that clear regulatory guidelines for endemic countries be established by a normative setting organisation like the WHO and other existing formulations be formally evaluated [11] . Using the data routinely collected from the MSF program , we determined the demographic and clinical characteristics of 119 immunocompetent patients who presented back to the program with parasitologically confirmed VL relapse . We then identified possible risk factors for relapse by comparing these patients to the 8418 patients who were discharged as cured and were not known to have relapsed .
This analysis met the Médecins Sans Frontières Institutional Ethics Review Committee's criteria for a study involving the analysis of routinely collected program data . Although a new treatment in the Indian setting , the programme utilised a recognised treatment for VL and was run in coordination with the State Health Society through a memorandum of understanding , which is the usual procedure for NGOs operating in this context . All electronic data were analysed anonymously .
Male patients had higher odds of relapsing ( unadjusted odds ratio [uOR] 1 . 8; 95% CI 1 . 2–2 . 6 ) compared with female patients . Patients aged <5 years ( uOR 3 . 6; 95% CI 1 . 8–7 . 2 ) and ≥45 years ( uOR 2 . 2; 95% CI 1 . 2–1 . 4 ) were more likely to relapse than patients aged ≥15 to <30 years . Factors not associated with relapse in the univariate analysis ( p>0 . 05 ) included: caste; living in an area where MSF was conducting information , education , and communication activities as well as supporting the primary health center; a history of previous relapse; location of treatment administration ( treatment camp or primary health center/hospital ) ; and season or year of treatment . According to the univariate analysis , patients reporting a shorter duration of symptoms prior to seeking treatment had higher odds of relapsing . Compared with the baseline group of patients who received treatment ≤4 weeks after developing symptoms ( also known as time to presentation ) , the odds of relapse progressively decreased as the duration of symptoms prior to seeking treatment increased , from 0 . 6 ( 95% CI 0 . 4–0 . 97 ) for those presenting >4 to ≤8 weeks after symptoms occurred to 0 . 4 ( 95% CI 0 . 2–0 . 8 ) for those presenting >8 weeks after symptoms occurred . Additionally , patients who exhibited a decrease in spleen size of ≤0 . 5 cm/day by the time of discharge appeared to have higher odds of relapse ( 1 . 7; 95% CI 1 . 1–2 . 5 ) compared with those who exhibited a decrease in spleen size of >0 . 5 cm/day . No other clinical factors were significantly associated with risk of relapse ( Table 2 ) . Notably , nutritional status , spleen size and Hb level upon admission , and duration of treatment were not predictive of relapse . A multivariate logistic regression model was developed for those variables that were shown to be significant by univariate analysis ( p<0 . 05 ) ( Table 3 ) . Variables that were justified a priori or were associated with relapse in other studies [8] , [9] were also included in the multivariate analysis . These additional variables , which included nutritional status and spleen size and Hb levels upon admission , were added step-wise to the model . However , the variables associated with relapse as determined by the univariate analysis remained significant in the multivariate analysis , and those that were non-significant in the univariate analysis did not attain significance in the multivariate analysis .
This cohort represents the largest number of patients with VL treated with Ambisome both worldwide and on the Indian subcontinent to date . Although based only in one district , this program has treated an estimated 5 . 8% of all reported VL cases in India between 2008 and 2011 [14] . The present study is also the only India-based study that specifically examines risk factors for and characteristics of relapse in immunocompetent patients , and describes the distribution of VL relapses >6 months after treatment . It is , therefore , of particular interest considering the move towards lower dose Ambisome as the first-line therapy for VL on the Indian subcontinent [7] . A strength of this study is the robust database that has been maintained throughout the program and has minimal missing data . A limitation is that all patients were not followed up prospectively to determine relapse status , and as such the identification of relapses depended on patients returning to the programme for assessment if their symptoms recurred . This may result in an underestimation of the number of relapses to the 20 mg/kg regimen . There are limited data available regarding VL relapses in immunocompetent patients , and risk factors for relapse appear to vary from country to country . A retrospective study of 300 VL patients treated with meglutamine antimoniate in Georgia between 2002 and 2004 identified 21 cases of relapse . Among these cases , age <1 year , time to treatment of >90 days , and Hb levels of <6 g/dL were associated with relapse [8] . However , it is unclear whether these patients were tested for HIV . No association between relapse and spleen size nor sex was observed . A more recent study examined patient characteristics and drug regimens associated with VL relapse in South Sudan between 1999 and 2007 . The treatment records for 166 patients with VL who presented with relapses were compared with the treatment records for 7924 primary VL patients who did not re-attend with relapse [9] . This study found that larger spleen size upon admission and at the time of discharge were strongly associated with relapse , as was treatment with a short-course combination treatment ( 17 days sodium stibogluconate/paromomycin vs 30 days sodium stibogluconate ) . Age , sex , nutritional status , mobility , and treatment complications were not significantly associated with relapse . The main limitation was missing data , which resulted in the inclusion of only 26 . 7% ( 166/621 ) of the relapses in the analysis . Additionally , HIV testing was not performed for the relapse patients , although the authors considered this unlikely to be a factor for relapse in this group , as the estimated prevalence of co-infection was only 0 . 5% . Our results suggest that age <5 and ≥45 , male sex , a decrease in spleen size of ≤0 . 5 cm/day at discharge , and a short duration of symptoms prior to seeking treatment are risk factors for VL relapse in immunocompetent patients in India . Younger patients may be particularly susceptible to relapse due to the lack of a mature immune system [8] . The increased number of male patients presenting with relapse may be explained by the possibility of limited access to care for females . Indeed , in an analysis of the overall field outcomes of the MSF program in Bihar , the proportion of females admitted to the program progressively decreased in older age groups [Submitted to PLOS NTD] . We are unable to explain the strong inverse correlation that the present study revealed between the duration of symptoms prior to seeking treatment ( time to presentation ) and relapse . Indeed , this correlation is contrary to a priori knowledge , which would predict an association between a longer duration of illness and a more severe clinical presentation , and therefore more serious outcomes . However , other indicators of prolonged illness , such as low Hb level , poor nutritional status and increased splenomegaly at time of admission , were also not associated with relapse . It is possible that , in the Indian context , a rapid presentation to the healthcare provider could itself be an independent indicator of more severe illness or poorer immune status . This association needs to be correlated with other programmes' outcomes and warrants further investigation . In India , the synthetic phospholipid derivative hexdecylphosphocholine ( miltefosine ) is currently recommended by the Indian National Programme as the first line treatment for VL . It is a 28-day oral treatment but its use is limited by teratogenicity , which restricts its use in pregnant and lactating women and requires 3–5 months of contraceptive cover for women of childbearing age [15] . Although miltefosine initially showed promising efficacy and tolerability , recent studies in India [16] , [17] and Nepal [13] have demonstrated relapse rates in immunocompetent patients of between 6 . 8% to 10 . 8% at 6 months respectively , and up to 20 . 0% at 12 months in Nepal . Over the past decade , several studies have examined liposomal preparations of amphotericin B . For doses of Ambisome ≥10 mg/kg , the efficacy at 6 months is >95% [11] . Following a pivotal phase III study published in 2010 [18] , the WHO expert committee on leishmaniasis adopted a single 10 mg/kg dose regimen as the recommended first-line treatment for VL in South East Asia [7] , a strategy that has yet to be introduced by the Indian National Programme . The results from the present study suggest that following treatment with 20 mg/kg Ambisome , risk factors for VL relapse include male sex , age <5 years and ≥45 years , a slower decrease in splenomegaly during treatment , and a shorter duration of symptoms prior to seeking treatment . It also indicates that when using this regimen the majority of relapses occur 6–12 months post-treatment . Recent evidence from another study in Nepal suggests that a significant number of patients relapse 6–12 months post-treatment with miltefosine [13] . Given the move towards treating VL patients with a single 10 mg/kg dose of Ambisome or short-course combination therapies , and the aim of elimination in the Indian subcontinent , we suggest that a 1-year follow-up is essential and should be recommended for all VL treatments . | Visceral leishmaniasis ( VL ) , also known as Kala azar , accounts for the second-highest burden of parasitic disease worldwide . Fifty percent of VL cases worldwide occur in India , and up to 90% of these in the state of Bihar . Up to 10% of VL patients relapse within 6 months of treatment , particularly if co-infected with HIV . Limited data are available regarding relapse in patients with intact immune systems . Between 2007–2012 , with support of the Rajendra Memorial Research Institute ( RMRI ) , Médecins Sans Frontières ( MSF ) treated VL patients in Bihar with 20 mg/kg liposomal amphotericin B ( Ambisome ) . Here we identify risk factors for relapse in immunocompetent patients by comparing the demographics and clinical characteristics of 119 patients testing negative for HIV who experienced parasitologically-confirmed VL relapse against those of the remaining 8418 patients not known to have relapsed . Male sex , age <5 years and ≥45 years , shorter duration of symptoms prior to seeking treatment , and a smaller reduction in spleen size by time of discharge were all risk factors for relapse . The majority of relapses occurred 6–12 months post-treatment with Ambisome . The conventional period of follow-up is 6 months . Considering the aim of elimination in the Indian subcontinent , this data suggests that 1-year follow-up is necessary . |
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Asexual spores ( conidia ) are the infectious propagules of many pathogenic fungi , and the capacity to sense the host environment and trigger conidial germination is a key pathogenicity determinant . Germination of conidia requires the de novo establishment of a polarised growth axis and consequent germ tube extension . The molecular mechanisms that control polarisation during germination are poorly understood . In the dimorphic human pathogenic fungus Penicillium marneffei , conidia germinate to produce one of two cell types that have very different fates in response to an environmental cue . At 25 °C , conidia germinate to produce the saprophytic cell type , septate , multinucleate hyphae that have the capacity to undergo asexual development . At 37 °C , conidia germinate to produce the pathogenic cell type , arthroconidiating hyphae that liberate uninucleate yeast cells . This study shows that the p21-activated kinase pakA is an essential component of the polarity establishment machinery during conidial germination and polarised growth of yeast cells at 37 °C but is not required for germination or polarised growth at 25 °C . Analysis shows that the heterotrimeric G protein α subunit GasC and the CDC42 orthologue CflA lie upstream of PakA for germination at both temperatures , while the Ras orthologue RasA only functions at 25 °C . These findings suggest that although some proteins that regulate the establishment of polarised growth in budding yeast are conserved in filamentous fungi , the circuitry and downstream effectors are differentially regulated to give rise to distinct cell types .
The generation of an axis of cell polarity is central to the activity of many cells and the establishment of a wide variety of cell morphologies . It relies on the ability to mark different cellular regions by specific protein localisation . The establishment of polarised growth requires selection of a site to which proteins and components of the cytoskeleton are recruited . Growth is then directed specifically to this site via targeted cellular trafficking and concomitant cell wall deposition . Fungi are small eukaryotes that exhibit highly polarised growth patterns and provide excellent models for the study of the molecular mechanisms underlying cell polarity . Saccharomyces cerevisiae establishes a polarised axis of growth during the processes of budding cell division , schmoo formation during mating , and pseudohyphal growth . Under conditions of nitrogen starvation , S . cerevisiae diploid cells undergo pseudohyphal growth , a morphological switch that involves changes in cell shape and division [1] . Pseudohyphal growth requires the initiation of polarised growth for cellular elongation and is under the control of two signaling pathways: a cyclic adenosine monophosphate ( cAMP ) /protein kinase A ( PKA ) pathway and a mitogen-activated protein kinase ( MAPK ) cascade . The cAMP/PKA pathway is activated by a glucose/sucrose sensitive receptor Gpr1p , which activates the G protein Gpa2p ( α subunit ) , which in turn is inhibited by the novel kelch-Gβ subunits Gpb1/2p , a third subunit Gpg1p , and a negative regulator Rgs2p [2–8] . The Gpa2-Gpb1/2 complex regulates the cAMP/PKA pathway directly in association with the Ras2p GTPase and the RasGAP neurofibromin homologues Ira1/2p [6–7 , 9] . Ras2p can also activate the MAPK cascade by activating the guanine nucleotide exchange factor Cdc24p , which catalyzes the guanosine diphosphate ( GDP ) to guanosine triphosphate ( GTP ) exchange of the Rho GTPase Cdc42p [1 , 10 , 11] . GTP-bound Cdc42p is required to initiate actin polarisation and recruits and activates additional proteins required for polarised growth such as septins , myosins , and the p21-activated kinase ( PAK ) Ste20p ( reviewed in [12] ) . In turn , Ste20p activates the MAPK cascade by phosphorylating Ste11p ( MAPKKK ) ; Ste11p phosphorylates Ste7p ( MAPKK ) , which then activates the Kss1p MAPK [13–15] . Filamentous fungi exhibit a highly polarised axis during vegetative hyphal growth , asexual ( conidiation ) and sexual ( mating ) development , and initiation of polarised filamentous growth during the germination of asexual ( conidia ) and sexual ( ascospores ) spores . Spherical conidia germinate under favorable environmental conditions by initially growing isotropically . This growth is followed by the establishment of a de novo axis of polarised growth to allow a germ tube to emerge [16–18] . Conidial germination is a central aspect of fungal cell propagation , initiating the formation of the extensive radiating hyphal network necessary for colonization of substrates from a dormant conidium . Conidia are also the infectious propagules of many pathogenic fungi , and germination of conidia in the lung or leaves of potential hosts is likely to be a key pathogenicity determinant . Studies in the filamentous fungus Aspergillus nidulans have shown that the cAMP-PKA and Ras pathways play a role [18–20] . However , the molecular mechanisms governing germination of fungal conidia are not well understood in any system . Penicillium marneffei is an opportunistic human pathogen with a thermally regulated dimorphic switch . At 25 °C , in the saprophytic growth phase , conidia germinate to produce highly polarised , septate , branched , multinucleate hyphae . Conidia can also germinate at 37 °C to produce polarised arthroconidiating hyphae , in which nuclear division and septation are coupled , double septa are laid down , and fragmentation occurs along this plane to liberate uninucleate yeast cells that consequently divide by fission [21] . The yeast cells are the pathogenic growth form and multiple yeast cells are observed in the pulmonary alveolar macrophages of infected individuals . P . marneffei infection is thought to occur through inhalation of conidia , which bind to the laminin in the broncholalvelolar epithelia . Conidia are then ingested by pulmonary alveolar macrophages and germinate , generating the uninucleate yeast cells [21] . Therefore in P . marneffei , conidial germination can lead to two very different morphological programs in response to different temperatures . Some of the core components regulating polarised growth establishment in S . cerevisiae are conserved during polarity establishment in germinating conidia of filamentous fungi . The P . marneffei Gpa2p homologue , encoded by gasC , is required during conidial germination to produce hyphae . Deletion of gasC results in delayed germination , whereas expression of a dominant activating allele shows a significantly accelerated germination rate [22] . Likewise , the P . marneffei Ras homologue , RasA , is required for conidial germination where expression of either a dominant negative or activated allele results in a germination delay and conidia with abnormal isotropic growth [23] . Expression of either a dominant negative or constitutively active allele of the P . marneffei CDC42 homologue ( cflA ) results in a decrease or increase in the rate of germination at 25 °C , respectively [24] . The role of GasC , RasA , and CflA during conidial germination in P . marneffei suggests that the core components regulating polarised growth establishment in S . cerevisiae may be conserved during polarity establishment in germinating conidia of filamentous fungi . To investigate if any of the downstream effectors of this core pathway are also conserved in function , a PAK STE20 homologue ( designated pakA ) was identified and deleted in P . marneffei . Characterization of pakA in P . marneffei has shown that this gene is essential for conidial germination at 37 °C and for polarised growth of yeast cells and is downstream of both a heterotrimeric G protein and Cdc42 pathway . In contrast , PakA plays only a minor role during germination of conidia at 25 °C and is not required for polarised growth of hyphae . Germination in this case is controlled by a heterotrimeric G protein–Ras-Cdc42 pathway . These data suggest that although some proteins that regulate the establishment of polarised growth in budding yeast and filamentous fungi may be conserved , the downstream effectors are likely to be different or regulated differently to give rise to the distinct cell types in these two modes of growth .
An A . nidulans sequence was identified from the genome sequence ( http://www . broad . mit . edu/annotation/genome/aspergillus_group/MultiHome . html ) with strong homology to Candida albicans Cst20p ( 67% identity , 81% similarity ) and other PAKs , including S . cerevisiae Ste20p ( 71% identity , 81% similarity , accession number AAA35039 ) . Primers were designed to amplify the sequence encoding the conserved kinase domain and a PCR product was generated using A . nidulans genomic DNA . The A . nidulans STE20 homologous sequence was used to screen a P . marneffei genomic library at low stringency . Five positive clones were identified , which fell into two classes based on restriction enzyme digestion patterns . A 6 . 4 kb NotI/BglII hybridizing fragment from one of these classes was subcloned into NotI/BamHI digested pBluescript II SK+ ( pKB5751 ) . Sequencing revealed strong sequence homology to STE20-like PAKs from Magnaporthe grisea ( 78% identity , 89% similarity , accession number AAP93639 ) , Ustilago maydis ( 74% identity , 83% similarity , accession number AAM97788 ) , and S . cerevisiae ( 72% identity , 83% similarity ) . The gene within this clone was subsequently named pakA . The pakA open reading frame spans 2407 bp and contains seven exons and six introns . The predicted protein is 642 amino acids in length and contains a Cdc42/Rac interactive binding ( CRIB ) domain at positions 98–115 , a predicted kinase domain at 361–612 , and a Gβ binding domain at 619–629 . Preliminary analysis of the second class of clones revealed that the gene within these clones has strong sequence homology to CLA4-like PAKs . RNA was isolated from vegetative hyphae grown for 2 d in liquid medium at 25 °C , asexual development ( conidiation ) cultures grown for 4 d on solid medium at 25 °C , and yeast cells grown for 8 d in liquid medium at 37 °C . A pakA transcript was detected under all conditions . The amount of pakA transcript was approximately equivalent under all conditions when compared with the histone H3 control ( unpublished data ) . A pakA construct , which deleted from −425 to +2030 of pakA , was linearised and transformed into P . marneffei strain SPM4 ( niaD1 pyrG1 ) and pyrG+ transformants selected . PyrG+ transformants were screened by genomic Southern blotting and one strain was isolated that possessed a restriction pattern consistent with replacement of pakA by pyrG at the genomic locus . The deletion strain was plated on medium containing 5-fluoroorotic acid ( 5-FOA ) to generate a ΔpakA pyrG strain ( Materials and Methods ) . This strain was cotransformed with plasmids containing pakA+ and pyrG+ genes and co-transformants confirmed by Southern blot analysis . The transformants contained from 1–7 copies of pakA . In S . cerevisiae , the H345G mutation in the conserved CRIB domain of Ste20p results in a mutant protein that shows reduced interaction with the upstream activator Cdc42p ( GTPase ) in a two-hybrid assay and loss of correct localisation to sites of polarised growth [25] . The phenotype of the STE20H345G strain is almost equivalent to the null , indicating that the interaction of Cdc42p and Ste20p is essential for Ste20p function [25] . The equivalent mutation was generated in P . marneffei pakA ( pakAH108G ) by inverse PCR ( Materials and Methods ) . The pakAH108G construct was co-transformed with pyrG+ into the ΔpakA pyrG− strain and transformants selected for PyrG+ . Co-transformation was confirmed by Southern blot analysis of genomic DNA and four representative transformants with copy numbers ranging from 3 to 12 were selected for further analysis . The gfp::pakA and gfp::pakAH108G fusion constructs were generated and co-transformed with the pyrG+ gene into the ΔpakA pyrG strain to investigate the localisation of PakA and to assess whether the pakAH108G mutation affects PakA localisation . Transformants were selected for PyrG+ and confirmed by Southern blot analysis of genomic DNA . Four transformants of each genotype were selected for further analysis and had copy numbers ranging from 4 to 9 for gfp::pakA and from 2 to 20 for gfp::pakAH108G . The gfp::pakA and gfp::pakAH108G strains were grown on agar-coated slides for 2 and 4 d at 25 °C and 37 °C , respectively . At 25 °C , the GFP::PakA fusion protein was concentrated at the hyphal apex ( Figure 1A ) and localised as spots along the subapical cells of the hyphae ( Figure 1B ) . In contrast , the GFP::PakAH108G fusion protein was visible as diffuse fluorescence in the cytoplasm but not concentrated at the hyphal apex or as spots along the hyphae ( Figure 1A and 1B ) . At 37 °C , the GFP::PakA fusion protein was concentrated at the apex of arthroconidiating hyphae , although this fluorescence was less intense than that visualized in the same transformants at 25 °C . The GFP::PakAH108G fusion protein was not observed at the apex of arthroconidiating hyphae ( unpublished data ) . To investigate whether the GFP::PakA fusion protein co-localises with actin at the hyphal apex and at nascent septation sites , immunostaining using mouse anti-actin and rabbit anti-GFP antibodies was performed on two of the gfp::pakA and two of the gfp::pakAH108G strains at both 25 °C and 37 °C . At 25 °C , actin is localised as cortical actin spots along the hyphae and is concentrated at nascent septation sites and the hyphal apex ( Figure 1C–1F ) . The GFP::PakA fusion protein co-localised with actin at all of these locations ( Figure 1C–1F ) . The GFP::PakAH108G fusion protein also co-localised with actin at cortical actin patches , nascent septation sites , and the hyphal apex , in addition to showing diffuse staining throughout the cytoplasm ( Figure 1C–1F ) . At 37 °C , actin is also localised as cortical patches along the arthroconidiating hyphae , concentrated at nascent septation sites and the apex of arthroconidiating hyphae ( Figure S1 ) . The GFP::PakA and GFP::PakAH108G fusion proteins co-localised with actin at all of these sites ( Figure S1 ) . At 25 °C , P . marneffei colonies are comprised of highly polarised vegetative hyphae growing on the agar surface and bearing asexual structures ( conidiophores ) that appear green due to the presence of pigmented asexual spores ( conidia ) on the conidiophores . After 5 d growth at 25 °C , surface hyphae are visible in the wild-type strain ( Figure 2A ) . The ΔpakA pakA+ strains appeared wild-type after 5 d at 25° C , whereas both the ΔpakA and ΔpakA pakAH108G strains showed a reduction in growth ( Figure 2A ) . Despite the initial reduction in growth , after 10 d all strains were producing conidia . P . marneffei colonies are yeast-like at 37 °C and ΔpakA pakA+ strains were indistinguishable from the wild type when grown for 4 d at 37 °C ( Figure 2B ) . In contrast , the ΔpakA and ΔpakA pakAH108G strains displayed reduced growth at 37 °C ( Figure 2B ) . Both the ΔpakA and ΔpakA pakAH108G strains exhibit a delay in growth after 5 d at 25 °C ( Figure 2A ) . One explanation for this difference could be a delay in the germination of conidia . The kinetics of germination were measured at 25 °C in the wild-type ( pakA+ ) , ΔpakA , ΔpakA pakA+ , and two ΔpakA pakAH108G strains by counting the number of ungerminated versus germinated conidia ( conidia with a visible germ tube ) in a population of 100 in three independent experiments after 8 , 12 , or 15 h incubation in liquid media ( Table S1 ) . The complemented ΔpakA strain ( ΔpakA pakA+ ) is indistinguishable from wild type ( Figure 3A and 3B ) . The ΔpakA and ΔpakA pakAH108G strains show a minor delay in germination at 25 °C ( Figure 3A and 3B ) . To investigate if the deletion of pakA or the pakAH108G mutation results in aberrant hyphal morphology or asexual development at 25 °C the wild-type , ΔpakA , ΔpakA pakA+ , and ΔpakA pakAH108G strains were grown on agar-coated slides for 2 or 4 d at 25 °C , stained with calcofluor ( to observe cell walls ) or 4′6-diamidino-2-phenylindole ( DAPI; to observe nuclei ) , and examined microscopically . After 2 d at 25 °C , wild-type P . marneffei grows as septate , branched hyphae of which subapical cells are predominately uninucleate and apical cells are multinucleate . The ΔpakA , ΔpakA pakA+ , and ΔpakA pakAH108G strains were indistinguishable from wild type after 2 d , with normal morphology , septation , branching , and nuclear index . After 4 d at 25 °C , wild-type P . marneffei begins to undergo asexual development , with the production of a specialized stalk from which differentiated cells are produced sequentially in a budding fashion: metulae bud from the stalk , phialides bud from metulae , and uninucleate conidia bud from phialides . The ΔpakA , ΔpakA pakA+ , and ΔpakA pakAH108G strains produced conidiophores with wild-type morphology ( unpublished data ) . In contrast to the wild-type and ΔpakA pakA+ strains , the ΔpakA and ΔpakA pakAH108G strains displayed reduced growth rates at 37 °C ( Figure 2B ) . To assess if the basis of this difference is because pakA is required during the germination of conidia or during yeast morphogenesis and growth at 37 °C , the wild-type ( pakA+ ) , ΔpakA , ΔpakA pakA+ , and ΔpakA pakAH108G strains were inoculated on agar-coated slides and incubated for 4 d at 37 °C . It was immediately apparent that the ΔpakA and ΔpakA pakAH108G strains possessed a severe germination defect and almost all of the conidia remained ungerminated . The germination kinetics were measured by counting the number of ungerminated versus germinated conidia ( conidia with a visible germ tube ) in a population of 100 in three independent experiments after 8 , 12 , 15 , or 20 h in liquid medium ( Table S2 ) . Germination is slower and germlings appear fatter at 37 °C compared with 25 °C ( Figure 3A and 3C ) . In contrast to wild-type and the ΔpakA pakA+ strains , both the ΔpakA strain and the ΔpakA pakAH108G strains showed a severe defect in germination ( Figure 3C and 3D ) . Despite the majority of conidia remaining ungerminated in the ΔpakA and ΔpakA pakAH108G strains , a small proportion do germinate , and it is presumably these cells that go on to establish the colony . Growth of the pakA strains in liquid medium showed that the small proportion of conidia that germinate go on to form arthroconidiating hyphae that fragment at septation sites to liberate uninucleate yeast cells , just like the wild type . The wild-type ( pakA+ ) , ΔpakA , ΔpakA pakA+ , and ΔpakA pakAH108G strains were grown in liquid brain heart infusion ( BHI ) for 6 d at 37 °C and cells were stained with calcofluor or DAPI and observed microscopically ( Figure 4 ) . In wild type , the culture consists of a mixture of fragmented arthroconidiating hyphae and uninucleate yeast cells . The ΔpakA strain produced swollen arthroconidiating hyphae and yeast cells with increased septation ( Figure 4 ) but normal nuclear index . These defects were complemented when the strain was transformed with pakA+ . The ΔpakA pakAH108G strains also produced swollen arthroconidiating hyphae and yeast cells , but the phenotype was more severe than the ΔpakA strain , with very few yeast cells produced and—in contrast to the ΔpakA strain and wild type—a decrease in the septation index ( Figure 4 ) . Therefore , the pakAH108G allele has an inhibitory activity on septation . P . marneffei infection occurs through the inhalation of conidia . The conidia are ingested by host pulmonary alveolar macrophages where they germinate into unicellular yeast cells that divide by fission . Multiple yeast cells are seen in the pulmonary alveolar macrophages and peripheral blood mononuclear cells of infected individuals [21] . To investigate if perturbing conidial germination will influence pathogenicity , the ability of the mutant pakA strains to germinate into pathogenic yeast cells was investigated . LPS activated J774 murine macrophages were infected with no conidia or with conidia of the wild-type ( pakA+ ) , ΔpakA , ΔpakA pakAH108G , or ΔpakA pakA+ strains ( Materials and Methods ) . After 24 h , numerous yeast cells dividing by fission were observed in macrophages infected with wild-type ( pakA+ ) or ΔpakA pakA+ conidia ( Figure 5 ) . Only 16 . 3 ± 2 . 50% or 21 . 5 ± 2 . 07% of wild-type ( pakA+ ) or ΔpakA pakA+ conidia , respectively , remained ungerminated in infected macrophages . In contrast , conidia of the ΔpakA or ΔpakA pakAH108G strains remained predominately ungerminated in infected macrophages ( Figure 5 ) . 60 . 0 ± 3 . 80% of ΔpakA conidia and 66 . 1 ± 7 . 16% of ΔpakA pakAH108G conidia remained ungerminated in macrophages 24 h post infection . This suggests that PakA is required for conidial germination during infection of host macrophages . The temperature-dependent regulation of conidial germination could be the result of the presence of a temperature-specific factor on which PakA depends or the result of a change in the thermostability of a complex in which PakA operates . To distinguish between these two possibilities , wild-type and ΔpakA conidia were incubated in liquid medium at different temperatures ranging from 25 °C to 37 °C , in 2 °C increments , and germination rates were measured ( Materials and Methods ) . In contrast to the wild type , the ΔpakA strain showed a gradual decrease in the percentage of germination as the temperature increased , indicating that there is no critical temperature during the switch ( Figure 3E ) . This result supports the latter hypothesis and suggests PakA-dependent thermosensitivity . To investigate if ΔpakA conidia at 37 °C are waiting for the signal to germinate or have aborted , ΔpakA conidia were incubated at 37 °C for 20 h , then at 25 °C for 20 h . The majority of conidia germinated upon switching to 25 °C , indicating that after 20 h incubation at 37 °C , the ΔpakA conidia remained viable ( unpublished data ) . To identify other factors involved in the pakA-dependent differences in germination at 25 °C and 37 °C , conidial germination was analyzed in detail at both temperatures in strains carrying mutations in cflA ( CDC42 orthologue ) , gasC ( GPA2 orthologue ) , and rasA ( RAS2 orthologue ) , which have been shown previously to affect germination at 25 °C but which had not been characterized at 37 °C [22–24] . The role of CflA , GasC , and RasA in germination at 37 °C was characterized by assessing the percentage of germinated conidia after 8 , 12 , 15 , and 20 h at both 25 °C and 37 °C in two strains of each genotype ( Tables S1 and S2 ) . Two-level nested ANOVA was performed on the data for each time point at both 25 °C and 37 °C to test if germination rates differed significantly between genotypes and also between transformants of the same genotype ( Materials and Methods ) . ANOVA showed there was a significant difference between genotypes in all time points except 0 h . In a few instances , there was variation within transformants of the same genotype ( Tables S1 and S2 ) . Strains expressing the dominant negative cflAD120A allele showed delayed germination at both 25 °C and 37 °C ( Figure 6A and 6C ) . Dominant activated cflAG14V strains displayed accelerated germination at both 25 °C and 37 °C ( Figure 6B and 6D ) . These results indicate that active CflA promotes conidial germination at both 25 °C and 37 °C . Likewise , the dominant interfering gasCG207R strains showed delayed germination at both 25 °C and 37 °C ( Figure 7A and 7C ) . The dominant activated gasCG45R strains showed accelerated germination at 25 °C and 37 °C , suggesting that , like CflA , GasC is required for conidial germination at both 25 °C and 37 °C ( Figure 7B and 7D ) . Both the dominant activated ( rasAG19V ) and dominant negative ( rasAD125A ) rasA strains showed a decrease in germination at 25 °C ( Table S1 ) . In contrast , both the dominant negative and dominant activated strains showed wild type germination patterns at 37 °C , suggesting that RasA is required for conidial germination at 25 °C but not at 37 °C ( Table S2 ) . To investigate any genetic interaction between pakA and cflA , double mutants were generated ( ΔpakA cflA+ , ΔpakA cflAD120A , ΔpakA cflAG14V , ΔpakA pakAH108G cflA+ , ΔpakA pakAH108G cflAD120A , and ΔpakA pakAH108G cflAG14V ) and germination was characterized by assessing the percentage of germinated conidia after 8 , 12 , 15 , and 20 h at both 25 °C and 37 °C ( Tables S1 and S2 ) . It should be noted that multiple copy integrants may result in significant overexpression . Two strains of each genotype were assessed and a single representative strain is shown in Figure 6 . ANOVA was performed on the data for each time point at both 25 °C and 37 °C to test if germination rates differed significantly between genotypes and also between transformants of the same genotype ( Materials and Methods ) . ANOVA showed there was a significant difference between genotypes at all time points except 0 h . In a few instances , there was variation within transformants of the same genotype ( Tables S1 and S2 ) . The control ΔpakA cflA+ and ΔpakA pakAH108G cflA+ strains showed germination patterns at 25 °C and 37 °C that were indistinguishable from the parental ΔpakA and ΔpakA pakAH108G strains . At 25 °C , the ΔpakA cflAD120A and ΔpakA pakAH108G cflAD120A strains displayed delayed conidial germination at rates similar to the single cflAD120A mutant strains ( Figure 6A ) . At 37 °C , the ΔpakA cflAD120A and ΔpakA pakAH108G cflAD120A strains displayed dramatically reduced germination like the single ΔpakA and ΔpakA pakAH108G strains ( Figure 6C ) . In contrast to the accelerated germination observed in cflAG14V mutants at 25 °C , which is much faster than wild-type ( Figure 6B ) , the ΔpakA cflAG14V and ΔpakA pakAH108G cflAG14V double mutants display slower than wild-type germination at 25 °C , similar to the ΔpakA and ΔpakA pakAH108G single mutant strains ( Figure 6B ) . This suggests that the accelerated germination observed in cflAG14V strains at 25 °C requires active PakA and an interaction of CflA and PakA via the PakA CRIB domain . Likewise at 37 °C , in contrast to the accelerated germination observed in cflAG14V mutants , the ΔpakA cflAG14V and ΔpakA pakAH108G cflAG14V double mutants display dramatically reduced germination at rates equivalent to the ΔpakA and ΔpakA pakAH108G single mutant strains ( Figure 6D ) . The inability of the cflAG14V dominant activated allele to suppress the reduced germination phenotype of the ΔpakA and ΔpakA pakAH108G mutants suggests that at 37 °C , CflA acts upstream of PakA during germination and that an interaction between CflA and the CRIB domain of PakA is required for germination to proceed . P . marneffei RasA operates upstream of CflA at both 25 °C and 37 °C [23] . The genetic interaction of pakA and rasA was investigated by generating ΔpakA rasA+ , ΔpakA rasAD125A , and ΔpakA rasAG19V double mutants . At 25 °C , the ΔpakA rasA+ strains showed wild-type germination patterns , whereas the ΔpakA rasAD125A and ΔpakA rasAG19V mutants showed delayed germination similar to the single rasAD125A and rasAG19V mutants ( Table S1 ) . At 37 °C , the ΔpakA rasA+ , ΔpakA rasAD125A , and ΔpakA rasAG19V double mutants showed severely reduced germination similar to the ΔpakA mutant ( Table S2 ) . To investigate the genetic interaction between pakA and gasC , double mutants were generated ( ΔpakA gasC+ , ΔpakA gasCG207R , and ΔpakA gasCG45R ) . Germination was characterized in five strains of each double mutant genotype by assessing the percentage of germinated conidia after 8 , 12 , 15 , and 20 h at both 25 °C and 37 °C ( Tables S1 and S2 ) . ANOVA was performed on the data for each time point at both 25 °C and 37 °C ( Materials and Methods ) and revealed there was a significant difference between genotypes at all time points except 0 h . In a few instances , there was variation within transformants of the same genotype ( Tables S1 and S2 ) . ΔpakA gasC+ strains were indistinguishable from ΔpakA . At 25 °C , ΔpakA gasCG207R strains have delayed germination , which is slower than both the gasCG207R single mutant strains and the ΔpakA mutant ( Figure 7A ) . At 37 °C ΔpakA gasCG207R strains have a severe reduction in germination similar to ΔpakA ( Figure 7C ) . In contrast to the accelerated germination of the gasCG45R single mutant strains and the delayed germination of the ΔpakA mutant , the ΔpakA gasCG45R double mutant strains show wild-type germination at 25 °C ( Figure 7B ) . In addition , ΔpakA gasCG45R double mutant strains show germination rates at 37 °C , which are lower than those of wild-type and the gasCG45R mutants but higher than that of the ΔpakA mutant ( Figure 7D ) . This indicates that expression of the gasCG45R mutant allele partially suppresses the germination defects of ΔpakA and suggests that GasC regulates two pathways during germination , one of which is independent of PakA .
The establishment of an axis of polarised growth is orchestrated by the asymmetric distribution of cellular components through the localisation and activation of proteins required for growth . Some of the core components regulating polarised growth establishment in S . cerevisiae are conserved both in the genome and functionally during polarity establishment in more complex organisms . However , the question remains as to how multi-cellular organisms generate the greater diversity of distinct cell types with the same set of core components . Unlike small eukaryotes like fungi , larger eukaryotes such as flies and mammals often have an increased number of factors involved in polarity establishment with significant redundancy [26–28] . One possible mechanism is to alter the activity of the key establishment proteins in different cell types , while another is to differentially regulate the effector proteins . In the dimorphic pathogen P . marneffei , the germination of conidia gives rise to two different developmental pathways and cell types . The regulation of conidial germination by CflA ( CDC42 orthologue ) and GasC ( GPA2 orthologue ) at both 25 °C and 37 °C suggests that the core components regulating polarised growth establishment in S . cerevisiae may be conserved during polarity establishment in germinating conidia of filamentous fungi . However , mutations in pakA , a potential downstream effector of cflA , result in a dramatic reduction in the rates of germination at 37 °C but not 25 °C . This suggests that in P . marneffei conserved polarity establishment proteins regulate germination , but the downstream effectors are differentially regulated to give rise to distinct cell types . The results suggest a model in which , at 25 °C , GasC activates two pathways regulating conidial germination ( Figure 8 ) . In one pathway , RasA activates CflA , which activates PakA—by association via the CRIB domain—and a proposed additional effector to establish polarised hyphal growth ( Figure 8 ) . At 37 °C , GasC also activates two pathways regulating conidial germination . In one pathway , similar to 25 °C , CflA activates PakA via the CRIB domain , and this interaction is required for PakA function . Unlike 25 °C , RasA does not activate CflA , PakA plays a crucial role during the establishment of polarised arthroconidiating hyphal growth , and no additional effector is required ( Figure 8 ) . gasC encodes a heterotrimeric guanine nucleotide-binding ( G-protein ) α-subunit with homology to S . cerevisiae GPA2 . Gpa2p is required for the initiation of filamentous growth ( pseudohyphal growth ) in response to nitrogen starvation via the activation of the cAMP-PKA pathway [2 , 3] . In P . marneffei , expression of dominant activated gasCG45R and dominant interfering gasCG207R alleles at both 25 °C and 37 °C results in accelerated or delayed conidial germination , respectively , and suggests that GasC acts as a general upstream activator initiating filamentous growth . This study suggests that GasC activates two pathways regulating germination at both 25 °C and 37 °C , one of which is dependent on PakA ( Figure 8 ) . In contrast to the accelerated germination of gasCG45R strains and the slightly delayed germination of ΔpakA , ΔpakA gasCG45R double mutant strains display wild-type germination at 25 °C . A reduction in the germination rates suggests that PakA is acting downstream of GasC at 25 °C . However , as germination of the ΔpakA gasCG45R strains is higher than ΔpakA , GasC must also regulate an additional PakA-independent pathway activating germination at 25 °C . This hypothesis is supported by the germination rates observed in the ΔpakA gasCG207R strains , which are lower than both the single mutant strains , suggesting these mutations have an additive effect . Likewise , at 37 °C , partial suppression of the ΔpakA germination defect by expression of the gasCG45R allele indicates that GasC acts as an upstream regulator of two pathways activating conidial germination , one of which is dependent on PakA . The activation of two pathways regulating the initiation of filamentous growth in P . marneffei differs from S . cerevisiae . In S . cerevisiae , the Gpa2p regulation of pseudohyphal growth occurs via the activation of the cAMP-PKA pathway , which is independent of the Ste20p/MAPK cascade [3] . The pseudohyphal growth defect of a gpa2 mutant can be suppressed by addition of cAMP or overexpression of the dominant activated RAS2G19V allele . However , the pseudohyphal defect cannot be suppressed by the overexpression of the dominant active STE11–4 allele and has no effect on expression of a reporter gene ( FRE-lacZ ) , which is known to be regulated by the MAP kinase cascade [3] . These results together suggest that , unlike P . marneffei GasC , S . cerevisiae , Gpa2p regulates the cAMP pathway but not the Ste20p-MAPK cascade , thus regulating the initiation of filamentous growth . In S . cerevisiae Cdc42p localises and activates Ste20p ( reviewed in [12] ) . Therefore P . marneffei PakA is a potential downstream effector of the CflA ( Cdc42p orthologue ) and may be involved in similar processes . Like cflAD120A strains , which show delayed germination at 37 °C , the ΔpakA mutant exhibits a dramatic reduction in conidial germination at 37 °C , suggesting that PakA is crucial during the establishment of polarised growth to give rise to arthroconidiating hyphae . However , unlike CflA , PakA plays only a minor role during germination at 25 °C , as strains expressing the dominant negative cflAD120A allele show a severe delay in germination compared with only a slight delay in conidial germination for the ΔpakA and ΔpakA pakAH108G strains . These results suggest that PakA acts downstream of CflA at both 25 °C and 37 °C in P . marneffei . This hypothesis is also supported by the observation that the accelerated germination seen in cflAG14V strains is abrogated by the ΔpakA at both 25 °C and 37 °C in ΔpakA cflAG14V strains . PAKs contain a conserved N-terminal CRIB domain and a C-terminal kinase domain ( reviewed in [27] ) . The CRIB domain of S . cerevisiae Ste20p negatively inhibits the kinase domain preventing signaling [25] . This autoinhibition is relieved by interaction of the CRIB domain with Cdc42p , and this interaction is also necessary for localisation of Ste20p to sites of polarised growth [25] . The H345G mutation in the Ste20p CRIB domain shows reduced interaction with Cdc42p in a two-hybrid assay and the loss of correct localisation to sites of polarised growth . The phenotype of the STE20H345G strain is almost equivalent to the null , indicating that the interaction of Cdc42p and Ste20p is essential for Ste20p function ( and for relief of autoinhibition of the kinase domain ) [25] . The H108G CRIB domain mutation in P . marneffei is equivalent to the H345G of S . cerevisiae and was found to have a similar effect . The H108G mutation resulted in reduced localisation of PakA to sites of polarised growth and a phenotype equivalent to the deletion mutant . Compared with the accelerated germination of cflAG14V single mutant strains at both 25 °C and 37 °C , the ΔpakA pakAH108G cflAG14V mutant strains exhibited reduced germination , suggesting that the interaction of CflA and PakA via the CRIB domain is required during conidial germination at both 25 °C and 37 °C . The interaction of CflA and PakA is also required during polarised growth of yeast cells , as the ΔpakA pakAH108G strains showed a swollen , abnormal yeast morphology at 37 °C , similar to the deletion strain . In S . cerevisiae , the CRIB domain is essential for pseudohyphal growth but dispensable for G protein–mediated pheromone signaling [11] . In P . marneffei the CRIB domain of PakA is also required for the initiation of filamentous growth , but unlike S . cerevisiae the initiation of filamentous growth in P . marneffei by PakA is partially dependent on G-protein signaling . The minor role played by PakA during germination and hyphal growth at 25 °C suggests that another CflA effector , possibly a Cla4p orthologue , is required for these processes . The genomes of A . nidulans , M . grisea , U . maydis , Coprinopsis cinerea , Neurospora crassa , and C . albicans encode two PAKs , one with homology to Ste20p and the other to Cla4p ( http://www . broad . mit . edu/annotation/fgi/ ) . In addition to the CRIB and kinase domains of Ste20p orthologues , Cla4p homologues have a pleckstrin homology domain . Cla4p in S . cerevisiae is required for septation but does not play a role in pseudohyphal growth . However , deletion of CLA4 in the yeasts Yarrowia lipolytica and C . albicans blocks filament formation , and the cla4 deletion mutant of the plant pathogen U . maydis is unable to form filaments during infection [29–32] . cflA has been previously shown to play a pivotal role during hyphal morphogenesis with mutations resulting in grossly aberrant hyphae [24] . It was therefore expected that the ΔpakA strain may have a similar phenotype , albeit less severe , as CflA is proposed to interact and activate numerous effector proteins . The lack of a ΔpakA hyphal phenotype suggests that PakA is not required for hyphal growth . Like P . marneffei , deletion of the Ste20p homologues in C . albicans and U . maydis does not result in defects in hyphal morphology [33 , 34] . However , the co-localisation of the GFP::PakA fusion protein with actin and the localisation to the same cellular locations as CflA at nascent septation sites and to the hyphal apex suggests a role during polarised growth of hyphae [23] . In S . cerevisiae , Ste20p directly phosphorylates Bni1p , a component of the polarisome [35] . The polarisome is a protein complex , which contains Bni1p , Spa2p , Pea2p , and Bud6p , that promotes polarised morphogenesis during filamentous growth [35 , 36] . The Bni1p homologue , SepA , plays a conserved role in polarised growth in A . nidulans [37] . However , A . nidulans lacks a Pea2p homologue and the Spa2 homologue , SpaA , is only partially conserved in sequence and function , indicating that the polarisome in filamentous fungi likely consists of a modified set of components with different contributions to polarisome function [36] . The implication is that in P . marneffei , in addition to specific developmental roles , pakA and pakB play complementary , and possibly overlapping , roles in the establishment of polarised growth during conidial germination and in the maintenance of an axis of polarisation during hyphal growth . How the two PAKs coordinately regulate different aspects of development of multi-cellular fungi still remains unclear . The analysis of a CLA4 orthologue from P . marneffei may resolve many of these issues .
P . marneffei genomic DNA and RNA was isolated as previously described [38 , 39] . Southern and northern blotting was performed with Amersham Hybond N+ membrane according to the manufacturer's instructions . Filters were hybridized using [α−32P]dATP-labeled probes by standard methods [40] . Primers L18 ( 5′-TGATCCCACAAAACTTTACT-3′ ) and L19 ( 5′-GCTCGTTTCTCAGGGTCCAC-3′ ) were used to amplify the A . nidulans genomic sequence encoding the conserved kinase domain of the STE20 homologue . The PCR product was sequenced and used to screen a P . marneffei genomic library ( constructed in λGEM-11 ) at low stringency ( 50% formamide , 2 x SSC , 37 °C ) . A 6 . 4 kb NotI/BglII hybridizing fragment from a positively hybridizing clone was subcloned into NotI/BamHI digested pBluescript II SK+ ( pKB5751 ) . Sequencing was performed by the Australian Genome Research Facility and analyzed using Sequencher 3 . 1 . 1 ( Gene Codes Corporation ) . The Genbank accession number of the P . marneffei pakA gene is AY621630 . A pakA deletion construct ( pKB5792 ) was generated by replacing the 2 . 5 kb EcoRV/ClaI fragment of pKB5751 with the 2 . 5 kb SmaI/ClaI fragment containing the pyrG+ selectable marker . This resulted in pyrG+ flanked by 2 . 6 kb of 5′ and 1 . 1 kb of 3′ pakA sequence , and deleted from −425 to +2030 . Inverse PCR using the mutagenic primers N30 ( 5′-ACATGAGTAACACCGACAGGG-3′ ) and N32 ( 5′-TGGATACGACAATCAGACTGG-3′ ) was used to introduce the H108G mutation into pakA generating pKB5908 . The integrity of the construct was confirmed by sequencing . The gfp::pakA and gfp::pakAH108G constructs were generated by ligating a BamHI/XbaI fragment from pKB5751 ( pakA ) and pKB5908 ( pakAH108G ) into pALX196 ( gpdA ( p ) ::gfp ) . Strains used in this study are shown in Table 1 . The ΔpakA strain ( ΔpakA::pyrG+ ) was generated by transforming the strain SPM4 with linearised pKB5792 and selecting for pyrG+ . Transformation was performed using the previously described protoplast method [38] . The ΔpakA pyrG− strain was isolated by plating the ΔpakA strain ( ΔpakA::pyrG+ ) on medium containing 1 mg/mL−1 5-FOA supplemented with 10 mM γ-amino butyric acid ( GABA ) and 5 mM uracil to select for the loss of the pyrG marker . A 5-FOA resistant sector was isolated that had a restriction pattern consistent with loss of pyrG at the pakA locus . The strain is unable to grow in the absence of 5 mM uracil . P . marneffei FRR2161 , SPM4 , cflAD120A , cflAG14V , rasAD125A , rasAG19V , gasCG207R , and gasCG45R have been previously described [22–24 , 38] . All other strains listed in Table 1 were generated by cotransformation of the ΔpakA pyrG or ΔpakA strain with plasmids containing the appropriate mutant allele and either pAB4342 ( pyrG+ ) or pMT1612 ( barA+ ) as selectable markers . Southern blot analysis was used to confirm cotransformation and to determine the plasmid copy number . At 25 °C strains were grown on A . nidulans minimal medium ( ANM ) supplemented with 1% glucose and 10 mM GABA or on synthetic dextrose ( SD ) medium supplemented with 10 mM ammonium sulphate ( [NH4]2SO4 ) as a sole nitrogen source [41 , 42] . At 37 °C , strains were grown on BHI medium or on SD medium supplemented with 10 mM ( NH4 ) 2SO4 . J774 murine macrophages ( 1 × 105 ) were seeded into each well of a 6-well microtitre tray containing one sterile coverslip and 2 mL of complete Dulbecco's Modified Eagle Medium ( complete DMEM: DMEM , 10% fetal bovine serum , 2 mM L-glutamine and penicillin-streptomycin ) . Macrophages were incubated at 37 °C for 24 h before activation with 0 . 1μg/mL−1 lipopolysaccharide ( LPS ) from E . coli ( Sigma ) . Macrophages were incubated a further 24 h at 37 °C and washed 3 times in phosphate buffered saline , and 2 mL of complete DMEM medium containing 1 × 106 conidia was added . A control lacking conidia was also performed . Macrophages were incubated for 2 h at 37 °C ( to allow conidia to be engulfed ) , washed once in phosphate buffered saline ( to remove free conidia ) , and incubated a further day at 37 °C . Macrophages were fixed in 4% paraformaldehyde and stained with 1 mg/mL−1 fluorescent brightener 28 ( calcofluor , CAL ) to observe fungal cell walls . The numbers of germinated conidia was measured microscopically by counting the numbers of germinated conidia ( conidia with a visible germ tube or yeast cells ) in a population of approximately 100 . Three independent experiments were performed . Mean and standard error of the mean values were calculated using GraphPad Prism3 . P . marneffei strains were grown on slides covered with a thin layer of solid medium , with one end resting in liquid medium [38] . Wild-type ( pakA+ ) , ΔpakA , ΔpakA pakA+ , and ΔpakA pakAH108G strains were grown on ANM medium supplemented with GABA at 25 °C for 2 or 4 d . At 37 °C , strains were grown on BHI medium for 4 d or in liquid BHI medium for 6 d . Immunofluorescence microscopy for examination of the actin cytoskeleton was performed using standard protocols [43] . Double staining was performed with the mouse C4 monoclonal anti-actin ( Chemicon International ) and rabbit anti-GFP polyclonal primary antibodies , as well as ALEXA 488 rabbit anti-mouse ( Molecular Probes ) and ALEXA 594 anti-rabbit ( Molecular Probes ) secondary antibodies . Single immunostaining controls and a minus primary antibody control were also performed . The gfp::pakA and gfp::pakAH108G strains were grown on agar-coated slides containing ANM plus GABA for 2 d or SD with 5 mM ammonium tartrate ( NH4T ) for 4 d at 37 °C . Slides were examined using differential interference contrast ( DIC ) and epifluorescence optics for GFP , antibody fluorescence , cell wall staining with fluorescent brightener 28 ( calcofluor , CAL ) , or nuclear staining with DAPI and viewed on a Reichart Jung Polyvar II microscope . Images were captured using a SPOT CCD camera ( Diagnostic Instruments ) and processed in Adobe Photoshop . Approximately 106 spores were inoculated into 300 μL of SD plus 10 mM ( NH4 ) 2SO4 and incubated for 8 , 12 , or 15 h at 25 °C or for 8 , 12 , 15 , or 20 h at 37 °C . The rates of germination were measured microscopically by counting the numbers of germinating conidia ( conidia with a visible germ tube ) in a population of 100 . Three independent experiments were performed . Mean and standard error of the mean values were calculated using GraphPad Prism3 . Two-level nested ANOVA was performed on the data for each time point at both 25 °C and 37 °C to test if germination rates differed significantly between genotypes and between transformants of the same genotype . ANOVA simultaneously tests two null hypotheses; there is no difference between the means of the data sets from all genotypes and there is no difference between the means of the data sets between transformants of the same genotype . The generation of two F-statistics and probability values allow rejection or acceptance of these null hypothesis at a 99% confidence . Values with an asterisk in Tables S1 and S2 showed significant differences between transformants of the same genotype . To investigate the differential regulation of conidial germination at 25 °C and 37 °C , wild-type and ΔpakA conidia were incubated in liquid medium at different temperatures ranging from 25 °C to 37 °C . The 20-h incubation was performed in a gradient thermocycler using 2 °C temperature increments from 25 °C to 37 °C . The media was then transferred to a microtitre tray and the rates of germination were measured microscopically by counting the numbers of germinating conidia ( conidia with a visible germ tube ) in a population of 100 . Three independent experiments were performed . Mean and standard error of the mean values were calculated using GraphPad Prism3 . | Many fungal infections are initiated by the entry of dormant fungal spores into their host . Once inside the host these dormant spores must reactivate ( germinate ) for the infection to proceed . Productive infections necessitate that the fungus grow and divide within the host , which makes understanding the mechanisms that control germination crucial to developing preventative or prophylactic treatments for fungal infections . The molecular mechanisms that control spore germination are poorly understood and studies of the opportunistic fungal pathogen Penicillium marneffei have shown that a group of highly conserved signalling and cell polarity factors , known as small GTPases , play important roles in germination and other aspects of morphogenesis . In this study we have shown that a downstream target of these small GTPases , a p21-activated kinase plays a crucial role in germination at the host temperature of 37 °C but not at 25 °C . This is the first component of germination , which shows temperature-dependent effects and provides insights into the different mechanisms used by fungal pathogens to infect their host or to grow saprophytically in non-host environments . |
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Ocular herpes simplex virus infection can cause a blinding CD4+ T cell orchestrated immuno-inflammatory lesion in the cornea called Stromal Keratitis ( SK ) . A key to controlling the severity of SK lesions is to suppress the activity of T cells that orchestrate lesions and enhance the representation of regulatory cells that inhibit effector cell function . In this report we show that a single administration of TCDD ( 2 , 3 , 7 , 8- Tetrachlorodibenzo-p-dioxin ) , a non-physiological ligand for the AhR receptor , was an effective means of reducing the severity of SK lesions . It acted by causing apoptosis of Foxp3- CD4+ T cells but had no effect on Foxp3+ CD4+ Tregs . TCDD also decreased the proliferation of Foxp3- CD4+ T cells . The consequence was an increase in the ratio of Tregs to T effectors which likely accounted for the reduced inflammatory responses . In addition , in vitro studies revealed that TCDD addition to anti-CD3/CD28 stimulated naïve CD4+ T cells caused a significant induction of Tregs , but inhibited the differentiation of Th1 and Th17 cells . Since a single TCDD administration given after the disease process had been initiated generated long lasting anti-inflammatory effects , the approach holds promise as a therapeutic means of controlling virus induced inflammatory lesions .
Ocular infection with herpes simplex virus ( HSV ) can result in a chronic immuno-inflammatory reaction in the cornea which represents a common cause of human blindness [1] , [2] . The pathogenesis of stromal keratitis ( SK ) involves numerous events , but studies in murine SK models indicate that lesions are mainly orchestrated by CD4+ T cells that recognize virus derived peptides , or perhaps altered self proteins unmasked in the damaged cornea [1]–[4] . The severity of SK can be influenced by the balance of CD4+ effector T cells and Foxp3+ regulatory T cells ( Treg ) [5] , [6] . Procedures that change this balance represent a promising approach for therapy . This has been achieved either by adoptive transfer with Treg populations [6] or the repeated administration of reagents that can cause naïve CD4+ T cells to convert to become Treg [7] , [8] . From a therapeutic angle , procedures that could shift the balance of T effectors and Treg after a single drug administration would represent a convenient maneuver . Recent evidence from studies to control autoimmunity and graft-versus-host disease indicate that the objective might be achieved by the administration of stable agonists of the aryl hydrocarbon receptor ( AhR ) [9]–[11] . The AhR is a cytosolic transcription factor that can be activated by different ligands . These include the physiological ligand tryptophan photoproduct 6-formylindolo ( 2 , 3-b ) carbazole ( FICZ ) , and synthetic molecules such as 2 , 3 , 7 , 8- tetrachlorodibenzo-p-dioxin ( TCDD ) [10] , [12] . Signaling through the AhR has consequences that include changes in innate cell function , as well as some modulatory effects on several aspects of T cell immunity [13] , [14] . For example , Weiner and colleagues showed that TCDD administration could suppress the induction of experimental autoimmune encephalomyelitis ( EAE ) , an effect attributed to a reduction of proinflammatory T cells along with the expansion of Treg [9] . By a similar mechanism , TCDD had suppressive effects in an autoimmune diabetes model [15] . Similarly , the administration of TCDD prior to the induction of colitis led to reduced lesions along with an increase in the Treg population [16] . In graft versus host disease ( GVHD ) too , the reduced lesions in TCDD treated animals was attributed to the expansion of adaptive Tregs that suppressed allospecific cytotoxic T cell generation [11] , [17] . Modulating AhR by TCDD has also been shown to control the differentiation of Type 1 regulatory T cells ( Tr1 ) in vitro , which produce IL-10 and are instrumental in the prevention of tissue inflammation , autoimmunity as well as GVHD [18] . Although AhR ligation can result in reduced inflammatory lesions , in some circumstances lesions may be exacerbated . This was noted in the Weiner studies when the physiological ligand FICZ , rather than TCDD , was used for treatment [9] . In this study administration of FICZ boosted Th17 differentiation and increased the severity of EAE . Proinflammatory effects of AhR activation were also noted in a model of rheumatoid arthritis [19] , where synoviocytes were exposed to different concentrations of TCDD and shown to produce inflammatory cytokines . Additional proinflammatory effects of AhR ligation were also associated with pulmonary neutrophilia [20] , [21] , as well as with the induction and expansion of IL-17+ secreting CD4+ T cells ( Th17 ) that expressed high levels of AhR receptors [22] , [23] . Currently , it is not clear why AhR activation causes either an increased , or a reduced effect on inflammatory reactions , but the stability of the ligand used for AhR stimulation is one suspected explanation [24] . Accordingly , TCDD is a non-degradable high affinity ligand for AhR and most studies using this ligand report inhibitory effects on inflammatory reactions [24] , [25] . The effects of AhR agonists have not been evaluated in microbe induced inflammatory lesions . In this report , we show that a single administration of the stable AhR ligand TCDD was highly effective at suppressing the severity of ocular immuno-inflammatory lesions caused by HSV . The outcome was attributed to inhibitory effects on inflammatory IFN-γ+ secreting CD4+ T cells ( Th1 ) and Th17 cells . However , since Foxp3+ regulatory T cell numbers remained unchanged by the treatment , the balance between T effectors and Tregs favored the latter population . TCDD was also shown to cause apoptosis ex vivo of Foxp3- CD4+ T cells and could cause some naïve T cells to convert to Foxp3+ CD4+ T cells . Since a single TCDD administration given after the disease process had been initiated generated long lasting anti-inflammatory effects , the approach holds promise as a therapeutic means of controlling virus induced inflammatory lesions .
To evaluate the role of AhR engagement on the outcome of ocular HSV infection , mice were given a single intraperitoneal ( IP ) administration of TCDD on day 1 post-infection ( pi ) , and the effect on the severity of ocular lesions was compared to untreated controls . All treated animals developed significantly reduced lesions compared to controls , but around 40% of the animals developed clinical signs typical of herpes encephalitis before the end of the 15 day observation period and had to be terminated ( Figure 1A–D ) . Ocular viral loads were also increased in the TCDD treated group ( Figure 1E ) . Accordingly , the drug was judged to be effective but would not be recommended for use when virus is present and actively replicating in the cornea . In other experiments , the physiological AhR ligand FICZ was administered daily starting at day 1 pi . This drug was without significant effects on lesion severity ( Figure 1F ) , and none of the treated animals developed herpetic encephalitis ( data not shown ) . In additional experiments , TCDD administration was begun on day 5 pi , a time when levels of replicating virus in the cornea were barely detectable and inflammatory lesions start to become evident [3] . This treatment procedure resulted in significantly reduced lesion severity , as well as the extent of corneal neovascularization , compared to untreated infected controls ( Figure 2A–D ) , and none of the treated animals developed encephalitis . The treatment procedure delayed the time of lesion appearance and average severity scores were significantly less at most time points over a 15 day observation period . For example , on day 12 pi , whereas 10 of 12 eyes from untreated animals had lesion scores of 3 or above , only 2 of 14 eyes in the treated group had lesions of such severity ( Figure 2B ) . An example of comparative severity of control and treated animals is shown in the histological sections in Figure 2E . In additional experiments terminated on day 28 pi , the pattern of results was similar with treated animals showing significantly diminished lesions compared to untreated controls ( Figure 2F ) . In conclusion , ligation of the AhR with a single administration of TCDD given 5 days after virus infection significantly diminished HSV induced immunopathology . To measure the effect of TCDD treatment on the cellular composition of SK lesions collagen digested corneas were analyzed by FACS and compared to controls at day 15 pi . The combination of three independent experiments is shown in Figure 3A–D . As shown in Figure 3D , the average number per cornea of neutrophils and CD4+ T cells was reduced in the treated group by 2 . 03 fold and 4 . 7 fold respectively when compared to untreated controls . In separate experiments of the same design , pools of corneas were processed to quantify mRNA of selected cytokines ( IL-1β , TNF-α , IL-6 , IFN-γ , and IL-17 ) and chemokines ( CCL20 , CXCL9 , CXCL10 , and CXCL11 ) by quantitative real time PCR ( Q-RTPCR ) . As shown in Figure 3C , the consequence of TCDD treatment was a reduction in the levels of several proinflammatory cytokines and chemokines . However , levels of the cytokine IL-10 was increased to 1 . 4 fold in samples from treated compared to controls . Taken together , our results show AhR ligation by TCDD significantly reduced the total cellular infiltration of CD4+ T cells and neutrophils , as well as the amount of proinflammatory cytokines and chemokines . To measure the consequences of TCDD treatment on the T cell subset composition of SK lesions at day 15 pi , pools of corneas from treated and control animals were collagen digested to recover the T cell population . Part of the pool was stimulated in vitro for 4 hours with PMA and ionomycin to enumerate cells that were either IFN-γ or IL-17 producers . The other fraction was used to enumerate Foxp3+ CD4+ T cells . In the experiment shown , there was an average 12 . 3 fold reduction of Th1 cells and a 9 . 4 fold reduction of Th17 cells in treated compared to control corneas . The numbers of Foxp3+ T cells were almost identical in corneal pools from treated and control animals ( Figure 4C ) . Two additional experiments provided a similar pattern of results . Taken together , our results show that a consequence of TCDD treatment was to increase the ratio of total numbers of Foxp3+ CD4+T cells to both , Th1 and Th17 cells ( Figure 4D ) . Parallel studies of a similar design were performed with T cells isolated from the draining lymph nodes ( DLN ) and spleen collected from the same animals used for the corneal studies . The results shown in Figure 5A–D demonstrate that Th1 and Th17 cell frequencies and total numbers per organ were significantly reduced in TCDD recipients when compared to controls . However the frequencies of Foxp3+ Tregs , compared as a fraction of total CD4+ T cells , were increased in treated animals when compared to controls . Additionally , when the ratio of total numbers of Treg per T effectors was compared to controls , a significant increase in the number of Treg per Th1 or Th17 cells in the TCDD treated mice was evident ( Figure 5E ) . To compare levels of IFN-γ and IL-17 produced by CD4+ T cells from infected and treated or untreated mice , sorted CD4+ T cells were isolated from DLN on day 10 pi and stimulated in vitro with PMA and ionomycin . When comparing the number of IFN-γ and IL-17 secreting CD4+ T cells by ICCS , averages were reduced for both in TCDD treated animals ( Figure 5G ) . Similarly IFN-γ secreting levels measured by ELISA were reduced 2 . 9 fold as a consequence of TCDD treatment ( Figure 5H ) . Results from the previous section indicated that there was a shift in the balance between Tregs and T effectors towards Tregs , as well as a reduction in the production of proinflammatory cytokines . To further determine how TCDD could change the balance of Treg to T effectors , naïve splenocytes from DO11 . 10RAG2-/- ( 98% naïve CD4+ T cells ) animals were stimulated in vitro with plate bound anti-CD3 and anti-CD28 , in the presence of IL-2 . Cultures were either untreated or treated with graded amounts of TCDD ( from 0 . 1 µM to 0 . 25 µM ) . Cultures with 0 . 25 µM of TCDD significantly triggered the conversion of approximately 6 . 2% of CD4+ T cells into Foxp3+ CD4+ T cells , as compared to 0 . 3% in the untreated controls ( Figure 6B ) . Other cultures were TCR stimulated in a cytokine cocktail reported to induce either Th1 or Th17 cells in the additional presence of different doses of TCDD . The outcome was a significant decrease in both Th1 and Th17 cell induction ( Figure 6C–D ) with the highest TCDD dose studied ( 0 . 6 µM ) causing a disappearance of the majority CD4+ T cells from the cultures ( data not shown ) . Taken together , our results indicate that activation of AhR signaling by TCDD can induce some CD4+ T cells to become Foxp3+ , but it is inhibitory to the generation of IFN-γ+ CD4+ and IL-17+ CD4+ T cells . To determine if TCDD had differential effects on Foxp3+ and Foxp3- CD4+ T cell proliferation , Foxp3-GFP mice were infected and some treated with TCDD on day 5 pi . After an injection of 5-Bromo-2-deoxyuridine ( BrdU ) on day 8 pi , experiments were terminated on day 9 pi and proliferation of both Foxp3-CD4+ and Foxp3+ cells was detected by BrdU incorporation . Our results show that TCDD treatment significantly reduced the proliferative response of the Foxp3- CD4+ T cell population in both corneas and lymphoid tissue , but was without significant inhibitory effects on the Foxp3+ CD4+ T cell population . Instead , the effect of TCDD on Foxp3+ cells in the cornea was to cause a modest increase in proliferation ( Figure 7A–B ) . These effects could explain in part the balance between Tregs and T effectors in corneal lesions . Prior studies had shown that TCDD administration in vivo causes thymocytes to undergo apoptosis [26] . We determined if apoptosis of Foxp3- CD4+ T cells could account for the reduced numbers of T effectors . We performed experiments with CD4+ T cells isolated from DLN or spleen on day 8 pi from HSV infected Foxp3-GFP mice . Cells were cultured ex vivo in the presence of TCDD for 5 hours and apoptosis of Foxp3- and Foxp3+ CD4+ T cells was measured using Annexin-V staining . The result showed a dose dependent increase in the apoptosis of Foxp3-CD4+ T cells , but no significant apoptosis of Foxp3+CD4+ T cells ( Figure 8A and C ) . Notably , there was no difference in the frequencies of Tregs with the addition of different concentrations of TCDD as compared to media ( Figure 8B–C ) . Taken together , these results indicate that AhR signaling by TCDD , can promote the apoptosis of Foxp3- CD4+ T cells in vitro , but did not cause the same effect in Foxp3+ Treg .
SK is a blinding immuno-pathological lesion induced by ocular infection with HSV [1]-[3] . Novel treatment procedures are needed to replace the current long term use of antivirals and corticosteroids which have unwanted side effects [27]–[29] . A key to controlling the severity of SK lesions is to suppress the activity of T cells that orchestrate lesions and enhance the representation of cellular and humoral events that inhibit effector cell function . In this report , we have evaluated the use of a novel approach to achieve lesion control in a murine model of SK . We demonstrate that modulation of AhR signaling with a single dose of a synthetic stable molecule ( TCDD ) causes cellular changes in the cornea after HSV infection that account for significantly reduced SK lesion severity . The outcome of therapy was reduced effector Th1 and Th17 cells that orchestrate lesions , a reduction of neutrophils that are mainly responsible for damage to the cornea , as well as an increase in the representation of Foxp3+ Treg . Accordingly , when the ratio of Treg per T effectors was compared to controls , a significant increase in the number of Treg per Th1 as well as Th17 cells in the TCDD treated mice was evident . Foxp3+CD4+ T cells are assumed to function by inhibiting the inflammatory effects of T effectors either directly , or by the generation of counter inflammatory molecules [30] . Since a single administration of TCDD provided effective treatment that lasted for as long as one month , this approach could represent an effective novel therapy for a lesion that is a common cause of human blindness . Aryl hydrocarbon receptors are found in animals in many levels of the evolutionary scale . They can recognize numerous low molecular weight synthetic chemicals as well as a list of endogenous ligands , some of which are photoproducts of tryptophan breakdown [24] , [31] . Several cell types express AhR that includes some , but not all cells , involved in innate and adaptive immunity [32] . Our own interest in AhR ligands stemmed from recent reports that synthetic AhR agonists had anti-inflammatory activity [9] , [15] . Moreover , the dioxin TCDD can provide long term activation of the AhR since it is resistant to metabolic degradation [25] . As a consequence , a single administration can result in long term effects on immune mediated diseases . A recent report using the animal model of multiple sclerosis , EAE , showed disease suppression when animals were pretreated with TCDD [9] . The diminished lesions in treated animals were correlated with expansion of the Foxp3+ CD4+ T cell population and the levels of some cytokines produced by effector cells were reduced . The expansion of the Treg population was explained in part by conversion of naïve T cells to become Treg as could be demonstrated in vitro . In our studies too , we observed that a single TCDD administration was an effective means of reducing HSK ocular lesions , but with our model the outcome appeared to be more the consequence of suppressed numbers of Th1 and Th17 T cells that orchestrate SK , than any notable effect on the expansion of Tregs . Accordingly , the cytokine producing cells in lesions were reduced several fold in treated animals , whereas Treg numbers remained approximately the same in treated and controls . We did confirm the Weiner group [9] observations that TCDD can cause some naïve T cells to convert and become Foxp3+ in vitro , but in our hands this was a modest effect . This notwithstanding , it could be that the relative increase in Treg in the SK lesions of treated animals was the explanation for the reduced lesions , the Treg acting by inhibiting the functions of effectors as well as producing anti-inflammatory cytokines such as IL-10 . Equally possible , however , was that the reduced lesions were the direct consequence of the fewer numbers and less functional effectors in the corneas of treated animals . Such effectors would be less able to recruit inflammatory cells such as neutrophils that are considered responsible for much of the tissue damage of SK [33] , [34] . The reduced numbers of effectors would likely arise either , or both , from an inhibitory effect of TCDD on effector cell proliferation and differentiation , or be explained by the drug causing apoptosis of effectors . The latter effect could readily be demonstrated by in vitro studies with TCR stimulated CD4+ T cells cultured with TCDD . In addition , Foxp3- CD4+ T cells from treated animals proliferated less in vivo than did cells from control animals . In some reports TCDD was shown to prematurely terminate the proliferation and decrease the survival of CD4+ T cell , although differential effects on T cell subsets were not investigated [35] . Nevertheless , since regulatory T cells may be more resistant to apoptosis than conventional T cells [32] , [36] frequencies of Tregs would be expected to increase when other T cell populations are depleted . The reduced number of effector cells present in drug treated animals in our studies were also functionally impaired in their ability to mediate inflammatory reactions . Accordingly , ex vivo stimulation of DLN cells from drug-treated mice produced lower levels of some proinflammatory cytokines as well as chemokines responsible for neutrophil recruitment than cells from control animals . During in vitro studies , AhR ligation was shown to affect the differentiation of T helper subsets , behaving differently under identical culture conditions depending on the ligand used . TCDD for example , was shown to trigger the conversion of Foxp3-CD4+ into Foxp3+CD4+ without the need for TGF-β addition [9] , [37] , to dampen Th17 differentiation [37] and to increase the frequency of IL-17 secreting cells induced by TGF-β plus IL-6 [38] . On the other hand , FICZ under identical culture conditions promoted Th17 differentiation , but not Treg differentiation [37] , [38] . Other ligands too , such as kynurenine ( the first tryptophan metabolite of the IDO pathway ) , was shown to optimally generate Tregs in the presence of TGF-β [39] . In our in vitro experiments not only did we find a conversion of naïve T cells into Tregs , but also provided support for the notion that the TCDD interfered with the primary induction of both Th1 and Th17 cells . Thus , in the presence of TCDD , TCR stimulated naïve CD4+ T cells cultured in conditions to cause their differentiation into either Th1 or Th17 cells , resulted in significant suppression . As it currently stands , our mechanistic experiments cannot establish which is the major explanation for the in vivo anti-inflammatory effects of TCDD against SK . Further investigations are needed and are underway . The use of TCDD represents a potentially valuable approach to control SK since a single injection provided an excellent level of lesion control for at least a month pi . So far our results can only be considered as quasi therapeutic since treatment was begun 5 days after infection , a time when most infectious virus has been eliminated but clinical lesions are yet to become evident [1]–[3] . Moreover , we elected to study only the dose shown to be effective in an autoimmune disease model [9] . Since in some studies the outcome of treatment has been shown to be dose dependent , [35] similar dose response studies are warranted in the SK system and these are planned . Nevertheless , our approach does stand in contrast to most other investigations where treatment was begun prior to disease induction , or before natural disease is expected to occur . With ocular HSV infection in mice , such an early treatment approach would not be recommended because when TCDD was given one day after infection up to half of the animals succumbed to lethal infection of the CNS . Others too have observed that TCDD administration in viral infections can result in increased mortality [40]–[42] . For example , with influenza A virus infection AhR activation by the administration of TCDD decreased the survival time to lethal infection and resulted in mortality with a non lethal dose of the virus [42] , [43] . The cause of lethality was unclear since TCDD treated animals cleared the virus from the lungs as well as a non treated mice [44] . More than likely animals succumbed to lung pathology associated with increased neutrophilia found in the lungs of TCDD treated mice [21] , [45] . Curiously however , in our model TCDD administration reduced the numbers of neutrophils in the infected corneas . Aesthetically , the use of TCDD for therapy , a molecule often castigated as an environmental pollutant , may have minimal appeal . However , the use of a natural ligand for AhR such as FICZ that can be metabolized by the body may not represent a good option . Several studies using FICZ have observed that inflammatory lesions can be exacerbated by such a treatment [9] , [23] . For example , in the studies on EAE by Weiner and colleagues [9] , FICZ treatment resulted in more severe lesions . A similar outcome was reported too by Stockinger and colleagues [23] . In our own studies , we observed no beneficial , or in fact harmful , effects when we treated HSV infected mice with FICZ . One reason AhR ligation with certain ligands can cause enhanced inflammatory lesions is that Th17 T cells , mainly responsible for mediating some inflammatory diseases , express high levels of AhR [22]17 , 18] . Consequently , ligation of AhR on Th17 cells can cause cell expansion and the production of cytokines that contribute to tissue damage [9] , [23] . In the SK model , Th17 cells appear to play only a minor role in SK pathogenesis [46] which may explain our failure to observe adverse effects of FICZ therapy . It could be however that using non-toxic ligands such as 2- ( 1′H-indole-3′-carbonyl ) -thiazole-4-carboxylic acid methyl ester which induces Foxp3+ T CD4+ T cells and suppresses EAE [47] could lead to a more acceptable therapeutic approach for SK . In conclusion , our results are consistent with the observation that modulation of AhR signaling through the use of TCDD plays a role in influencing the expression of SK lesions . The mechanisms involved to explain the outcome were multiple , and involve a change in the balance between effector and regulatory T cells . We anticipate that manipulating AhR signaling , preferable with non-toxic ligands , could represent a useful approach to control an important cause of human blindness .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Research Council . All animals were housed in Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) -approved animal facilities . The protocol was approved by the Institutional Animal Care and Use Committee of the University of Tennessee ( PHS Assurance number A3668-01 ) . HSV-1 eye infection was performed under anesthesia ( avertin ) , and all efforts were made to minimize animal suffering . Female 6 to 8 weeks old C57BL/6 mice were purchased from Harlan Sprague Dawley ( Indianapolis , IN ) . BALB/c DO11 . 10 RAG2 -/- mice were purchased from Taconic and kept in our pathogen free facility where food , water , bedding , and instruments were autoclaved . All manipulations were done in a laminar flow hood . All experiment procedures were in complete agreement with the Association for Research in Vision and Ophthalmology resolution on the use of animals in research . HSV-1 RE Tumpey and HSV-RE Hendricks were propagated and titrated on Vero cells ( American Type Culture Collecting no . CCL81 ) using standard protocols . The virus was stored in aliquots at −80°C until use . CD4-allophycocyanin ( RM4 . 5 ) , CD4-FITC ( RM4 . 5 ) , Foxp3-PE ( FJK-16s ) , anti-IFN-γ-FITC ( XMG1 . 2 ) , anti-IL17-PE ( TC11-18H10 ) , CD45-allophycocyanin ( 30-F11 ) , CD11b-PerCP ( M1/79 ) , Ly6G-PE ( 1A8 ) . Corneal infections of C57BL/6 mice were done under deep anesthesia induced by IP injection in tribromoethanol ( avertin ) as previously described [48] . Mice's corneas were scarified with a 27-gauge needle , and a 3 µl drop containing the specific viral dose was applied to the eye . Eyes were examined on different days pi ( dpi ) with a silt-lamp biomicroscope ( Kowa Company , Nagoya , Japan ) measuring the progression of SK lesion severity and angiogenesis of individual mice . The scoring system was as follows: 0 , normal cornea; +1 , mild corneal haze; +2 , moderate corneal opacity or scarring; +3 , severe corneal opacity but iris visible; +4 , opaque cornea and corneal ulcer; +5 , corneal rupture and necrotizing keratitis [49] . The severity of angiogenesis was recorded as described previously [50] . According to this system , a grade of 4 for a given quadrant of the circle represents a centripetal growth of 1 . 5 mm toward the corneal center . The score of the four quadrants of the eye were then summed to derive the neovessel index ( range 0–16 ) for each eye at a given time point . TCDD ( Sigma Aldrich ) diluent was evaporated with nitrogen and reconstituted with DMSO . Female 6 to 8 weeks old C57BL/C mice were ocularly infected under deep anesthesia with 1×104 PFU of HSV-1 RE Tumpey and divided randomly into groups . Animals in the treated groups were either treated with TCDD on day 1 pi or day 5 pi IP , being the dose administered of 1 µg/mice . Animals in the control groups were treated the same days ( either day 1 or day 5 pi ) with DMSO IP . Mice were observed for SK and angiogenesis progression from day 5 until day 15 or 28 as described elsewhere [49] . Most of the experiments were repeated at least three times . FICZ ( Biomol International , L . P . , Plymouth Meeting , PA ) was dissolved in DMSO . Female 6 to 8 weeks old C57BL/C mice were ocularly infected under deep anesthesia with 1×104 PFU of HSV-1 RE Tumpey and divided randomly into groups . Animals in the treated groups were either treated daily with FICZ from day 1 pi to day 11 pi ( IP ) , being the dose administered of 1 µg/mice . Animals in the control groups were treated the same days with DMSO IP . Mice were observed for HSK and angiogenesis progression from day 5 until day 15 as described elsewhere [49] . Eye swabs were taken from infected corneas using sterile swabs at the indicated time points . Infected corneas were extracted on day 6 pi and placed on ice sterile 2 . 0-mL straight-wall ground-glass tissue homogenizers ( Wheaton ) with media and homogenized . Homogenates were centrifuged ( 2 , 250 g at 4°C ) for 5 min , place on ice , and immediately plated . Titrations were performed by a standard plaque assay as described previously [51] . Titers were calculated as log10 pfu/ml per a standard protocol [52] . Eyes from control and TCDD treated mice were extirpated on day 15 pi and snap frozen in OCT compound ( Miles , Elkart , IN ) . Six micron thick sections were cut , air dried in a desiccation box . Staining was performed with hematoxylin and eosin ( Richard Allen Scientific , Kalamazoo , MI ) . RNA was extracted from cells and tissue using TRIzol LS reagent ( Invitrogen ) . Total cDNA was made with 500ng of RNA using oligo ( dT ) primer . Quantitative PCR ( Q-RTPCR ) was performed using SYBR Green PCR Master Mix ( Applied Biosystem , Foster City , CA ) with iQ5 real-time PCR detection system ( Bio Rad , Hercules , CA ) using 5 µl of cDNA for 40 cycles . The expression levels of different molecules were normalized to β-actin using Δ threshold cycle method calculation . Relative expression between mock infected samples and control or day 5 TCDD treated samples from day 15 pi were calculated using the 2-ΔΔCt formula: ΔΔCt = ΔCt , sample - ΔCt , reference . Here , ΔCt is the change in cycling threshold between the gene of interest and the ‘housekeeping’ gene β-actin , where ΔCt , sample was the Ct value for any day 5 TCDD treated or control samples from day 15 pi normalized to the β-actin gene and ΔCt , reference was the Ct value for the mock infected samples ( scratched and infected only with PBS ) also normalized to β-actin . Each of the samples was run in duplicates to determine sample reproducibility , and a mean Ct value for each duplicate measurement was calculated . The PCR primers used were the following: βactin F 5′-CCTTCTTGGGTATGGAATCCTG-3′ and R 5′-GGCATAGAGGTCTTTACGGATG-3′ , IL-6 F 5′-CGTGGAAATGAGAAAAGAGTTGTGC-3′ and R 5′- ATGCTTAGGCATAACGCACTAGGT-3′ , TNF-α F 5′-CAGCCTCTTCTCATTCCTGCTTGTG-3′ and R 5′- CTGGAAGACTCCTCCCAGGTATAT-3′ , IL-1β F 5′-GAAATGCCACCTTTTGACAG-3′ and R 5′- CAAGGCCACAGGTATTTTGT-3′ , IFN-γ F 5′-GGATGCATTCATGAGTATTGC-3′ and R 5′- GCTTCCTGAGGCTGGATTC-3′ , IL-17A F 5′-GCTCCAGAAGGCCCTCAG-3′ and R 5′- CTTTCCCTCCGCATTGACA-3′ , IL-10 F 5′- CCTTTGACAAGCGGACTCTC-3′ and R 5′- GCCAGCATAAAAACCCTTCA-3′ , CXCL-9 F 5′-CAAGCCCCAATTGCAACAAA-3′ and R 5′- TCC GGA TCT AGG CAG GTT TGA-3′ , CXCL-10 F 5′-TGC TGG GTC TGA AGT GGG ACT-3′ and R 5′- AAG CTT CCC TAT GGC CCT CA-3′ , CXCL-11 F 5′-GGTCACAGCCATAGCCCTG-3′ and R 5′- AGCCTTCATAGTAACAATC-3′ , CCL-20 F 5′-GCCTCTCGTACATACAGACGC-3′ and R 5′- CCAGTTCTGCTTTGGATCAGC-3′ . CD4+ T cells were purified from pooled DLN single cell suspension obtained from HSV-infected mice using a mouse CD4+ T cell isolation kit ( Miltenyi Biotec , Auburn , CA ) . The purity was achieved at the extent of 90% . Purified CD4+ T cells were analyzed by Flow cytometry and ELISA after stimulation for the expression of IFN-γ and IL-17 . DLN single cell suspensions from individual mice were collected at day 15 pi . Cells were stimulated in vitro with anti-CD3 ( 2 µg/ml ) and anti-CD28 ( 1 µg/ml ) for 48 h at 37°C . Additionally DLN single cell suspensions from mice were also collected at day 10 pi and CD4+ T cells were purified using magnetic columns . Cells were then stimulated in vitro with PMA ( 50 ng ) and ionomycin ( 500 ng ) for 4 h at 37°C . The concentrations of IFN-γ and IL-17 were measured by sandwich ELISA kits from eBioscience . Splenocytes isolated from DO11 . 10 RAG2 -/- mice were used as a precursor population for the induction of Foxp3+ in CD4+ T cells as described elsewhere [6] . Briefly , 2×106 splenocytes after RBC lysis and several washings were cultured in 1ml volume previously optimized doses of plate bound anti-CD3 Ab ( 0 . 123 µg/ml in 200 µl total volume ) , rIL-2 ( 25–100 U/ml ) and TGFβ ( 2 . 5–10 ng/ml ) for 5 days at 37°C in a 5% CO2 incubator . Different concentrations of TCDD were also added . After 5 days samples were characterized for Foxp3 intranuclear staining using an eBioscience kit and analyzed by flow cytometry . Naïve CD4+ T cells were stimulated for 4 to 5 days with plate bound antibody to CD3 ( 4 µg/ml ) and anti CD28 ( 2 µg/ml ) . For Th1 differentiation recombinant mouse IL-12 ( 10ng/ml ) and anti IL-4 ( 10 µg/ml ) were used . In the case of Th17 differentiation TGF-β ( 2 . 5ng/ml ) , IL-6 ( 30 ng/ml ) , anti IL-4 ( 10 µg/ml ) and anti IFN-γ ( 10 µg/ml ) were added . Concentrations of TCDD were added into cultures at the beginning of the experiment . After 5 days samples were analyzed by intracellular cytokine staining for the production of IFN-γ and IL-17 using a BD biosciences kit and then flow cytometry . The culture mediums used were IMDM ( Sigma-Aldrich ) for Th17 differentiation or RPMI 1640 ( Sigma-Aldrich ) for Th1 differentiation , both supplemented with 2×10−3 M L-glutamine , 100 U/ml penicillin , 100 µg/ml streptomycin , 5×10−5 M β-mercaptoethanol , and 5% FCS [53] . Foxp3+-GFP knock-in animals were kindly provided by Dr . M . Oukka of Seattle Children's Research Institute . Mice were infected and divided into two groups: non-treated and TCDD treated mice . 8 days after ocular HSV 1 infection mice were injected IP with BrdU ( 1mg/mouse ) and were terminated 12 hours later . 9 dpi , host Foxp3+CD4+ and Foxp3-CD4+ T cells that incorporated BrdU were analyzed by staining with anti BrdU antibody using an APC BrdU flow kit from BD Pharmingen as per the manufacturer's instructions . Samples were acquired with a FACSCalibur ( BD biosciences ) , and the data were analyzed using the FlowJo software . DLN cells and splenocytes isolated from HSV-infected Foxp3-GFP C57BL/6 mice at 8 days pi were incubated for 5h with various concentrations of TCDD in 96 well flat-bottom plate in 5% CO2 incubators . After incubation period was over , cells were stained for annexin V using a kit from BD biosciences . Additionally cells were costained for CD4 . Stained cells were analyzed immediately by flow cytometry . Most of the analyses for determining the level of significance were performed using unpaired two-tailed Student's t test . Values P≤0 . 001 ( *** ) , P≤0 . 01 ( ** ) , P≤0 . 05 ( * ) were considered significant . Results are expressed as means ±SEM . For some experiments , as mentioned in the figure legends , a one-way ANOVA test was applied . CD4 ( MGI:88335 ) , IFN-γ ( MGI:107656 ) , Foxp3 ( MGI:1891436 ) , IL-17 ( MGI:107364 ) , IL-1β ( MGI:96543 ) , TNF-α ( MGI:104798 ) , IL-6 ( MGI:96559 ) , CCL20 ( MGI:1329031 ) , CXCL9 ( MGI:1352449 ) , CXCL10 ( MGI:1352450 ) , CXCL11 ( MGI:1860203 ) , IL-10 ( MGI:96537 ) , β-actin ( MGI:87904 ) , CD45 ( MGI:97810 ) , CD11b ( MGI:96607 ) , Ly6G ( MGI:109440 ) , CD3 ( MGI:88332 ) , CD28 ( MGI:88327 ) , Annexin V ( MGI:106008 ) , IL-12 ( MGI:96540 ) , IL-4 ( MGI:96556 ) , TGF-β ( MGI:98725 ) , IL-6 ( MGI:96559 ) . | This report describes a novel approach to control a blinding immuno-inflammatory reaction in the eye caused by herpes simplex virus . We showed that a single administration of TCDD , a stable agonist of the aryl hydrocarbon receptor , significantly reduced the severity of herpes keratitis lesions . The outcome of the therapy was a change in the balance of effector cells responsible for orchestrating lesions , with regulatory cells able to inhibit the inflammatory effects of the effectors . Since a single administration of TCDD provided effective treatment that lasted for as long as one month , this approach could represent a valuable therapy for a lesion that is a common cause of human blindness . |
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GWAS of prostate cancer have been remarkably successful in revealing common genetic variants and novel biological pathways that are linked with its etiology . A more complete understanding of inherited susceptibility to prostate cancer in the general population will come from continuing such discovery efforts and from testing known risk alleles in diverse racial and ethnic groups . In this large study of prostate cancer in African American men ( 3 , 425 prostate cancer cases and 3 , 290 controls ) , we tested 49 risk variants located in 28 genomic regions identified through GWAS in men of European and Asian descent , and we replicated associations ( at p≤0 . 05 ) with roughly half of these markers . Through fine-mapping , we identified nearby markers in many regions that better define associations in African Americans . At 8q24 , we found 9 variants ( p≤6×10−4 ) that best capture risk of prostate cancer in African Americans , many of which are more common in men of African than European descent . The markers found to be associated with risk at each locus improved risk modeling in African Americans ( per allele OR = 1 . 17 ) over the alleles reported in the original GWAS ( OR = 1 . 08 ) . In summary , in this detailed analysis of the prostate cancer risk loci reported from GWAS , we have validated and improved upon markers of risk in some regions that better define the association with prostate cancer in African Americans . Our findings with variants at 8q24 also reinforce the importance of this region as a major risk locus for prostate cancer in men of African ancestry .
Genome-wide association studies ( GWAS ) have revealed more than 30 variants that contribute susceptibility to prostate cancer , with most of the discoveries having been made in populations of European ancestry [1]–[14] . However , as so far observed for most common diseases , variants identified through GWAS are of low risk both individually and in aggregate , and therefore provide only limited information about disease prediction [15] , [16] . Most risk variants for prostate cancer are located outside of annotated genes , with some positioned in gene poor regions and some regions harboring more than one independent signal [1] , [10] , [14] , [17] , [18] . Thus , for the vast majority of risk loci , the identity , frequency and risk associated with the underlying biologically relevant allele ( s ) are unknown . The risk variants revealed through GWAS have also been found to vary in frequency across racial/ethnic populations [19] . Even in the absence of functional data , the associated risk variants may highlight a genetic basis for differences in disease risk between populations , such as at 8q24 where genetic variation is suggested to contribute to population differences in risk of prostate cancer [10] . Testing of the risk variants and fine-mapping in diverse populations will help to identify and localize the subset of markers that best define risk of the functional allele ( s ) within known risk loci , as well as to determine their contribution to racial and ethnic differences in prostate cancer risk . In the present study , we tested common genetic variation at the prostate cancer risk loci identified in men of European and Asian descent in a large sample comprised of 3 , 425 African American prostate cancer cases and 3 , 290 controls , to identify markers of risk that are relevant to this population . More specifically , we conducted GWAS and imputation-based fine-mapping of each risk locus to both improve the current set of risk markers in African Americans as well as to identify new risk variants for prostate cancer . We then applied this information to model the genetic risk of prostate cancer in African American men .
The African American prostate cancer cases ( n = 3 , 621 ) and controls ( n = 3 , 502 ) in this study are part of a collaborative genome-wide scan of prostate cancer that includes 11 individual studies ( Table S1 , Methods ) . Samples were genotyped using the Illumina Infinium 1M-Duo bead array , and following quality control exclusions ( see Methods ) , the analysis of variants at the known risk loci was performed on 3 , 425 cases and 3 , 290 controls . The ages of cases and controls ranged from 23 to 95 , with cases and controls having similar ages ( mean 65 and 64 years , respectively ) . We tested 49 known prostate cancer risk variants located in 28 risk regions ( Table S2 , Table 1 , and Table 2 ) ; 43 SNPs were directly genotyped ( with call rates >95% ) , while 6 were imputed with high accuracy ( see Methods ) [1] , [3] , [4] , [6]–[14] , [17] , [18] , [20]–[23] . The minor allele frequencies ( MAF ) of all 49 variants were common ( ≥0 . 05 ) in African Americans , except for rs721048 at 2p15 ( MAF , 0 . 04 ) and rs12621278 at 2q21 ( MAF , 0 . 02; Table 1 , Figure 1 ) . On average , across all variants tested , the risk allele frequencies ( RAFs , i . e . alleles associated with an increased risk of prostate cancer in previous GWAS ) were 0 . 05 greater in African Americans than in Europeans . However , when removing the 12 risk variants at 8q24 ( Table 2 ) the average difference in RAF over the remaining risk loci was only 0 . 03 . We examined the association of local ancestry with prostate cancer risk at each of the 28 risk regions ( Table S3 ) . In addition to 8q24 , which we had previously found to be strongly associated with African ancestry [5] ( OR per European chromosome = 0 . 81 , p = 4 . 7×10−5 ) , we observed significant associations at 22q13 ( OR = 0 . 88 , p = 0 . 01 ) , 7p15 ( OR = 1 . 16 , p = 1 . 6×10−3 ) and 10q26 ( OR = 1 . 14 , p = 6 . 2×10−3 ) . To address the potential for confounding by genetic ancestry , we adjusted for both global and local ancestry in all analyses ( see Methods ) . In previous GWAS , the index signals outside of 8q24 had very modest odds ratios ( 1 . 05–1 . 30 per copy of the risk allele ) and our sample size provided ≥80% power to detect the reported effects for 24 of the 37 variants ( at p<0 . 05; Table S2 ) . We observed positive associations with 28 of the 37 variants ( odds ratios ( OR ) >1 ) in African Americans and 18 reached nominal statistical significance ( p≤0 . 05; Table 1 ) . Results were similar without adjustment for local ancestry in each region ( Table S4 ) . Of the 19 variants that were not replicated at p<0 . 05 , power was <80% for 9 of the variants . While power was limited to detect associations at some loci , the lack of replication at loci where power was acceptable ( >80% ) suggests that the particular risk variant revealed in GWAS in European and Asian populations may not be adequately correlated with the biologically relevant allele in African Americans . In an attempt to identify a better genetic marker of the biologically relevant allele in African Americans we conducted fine-mapping across all risk regions using genotyped SNPs on the 1 M array and imputed SNPs to Phase 2 HapMap ( Table S5 , see Methods ) . If a marker associated with risk in African Americans represents the same signal as that reported in the initial GWAS , then it should be correlated to some degree with the index signal in the initial GWAS population . Using HapMap data ( CEU or JPT+CHB depending upon the initial GWAS population ) we catalogued and tested all SNPs that were correlated ( r2≥0 . 2 ) with the index signal ( within 250 kb ) , applying a significance criteria αa , of 0 . 004 given the large number of correlated tests . This level of significance was based on the number tag SNPs in the HapMap YRI population that capture ( r2≥0 . 8 ) all SNPs that were correlated with the index signal in the HapMap CEU ( r2≥0 . 2; see Methods ) . We also looked for novel independent associations , focusing on the genotyped and imputed SNPs that were uncorrelated with the index signal in the initial GWAS populations . Here , we applied a Bonferroni correction for defining novel associations as significant in each region , with αb estimated as 0 . 05/the total number of tags needed to capture ( r2≥0 . 8 ) all common risk alleles across all risk region in the YRI population ( αb = 5 . 6×10−6 ) . This is similar to the genome-wide-type correction of 5×10−8 , which accounts for the number of tags needed to capture all common alleles in the genome . For each region , stepwise regression was used with SNPs kept in the final model based on αa or αb ( results for each model are provided in Table S6 ) . Among the SNPs correlated with the index signal in the GWAS population , a more significantly associated marker was identified at 12 regions . For 5 of these regions , the new marker showed only a slightly more significant association than the index signal ( <1 order of magnitude change in the p-value; Table 1 ) . However , for 7 regions ( 2p24 , 2p15 , 3q21 , 6q22 , 8q21 , 11q13 , and 19q13 ) , the new marker appeared to capture risk more strongly than the index signal in African Americans . The risk region at 3q21 is provided in Figure 2 as an example . Here the index signal was not significantly associated with risk in African Americans ( rs10934853 , OR = 1 . 03 , 95% CI , 0 . 95–1 . 03 , p = 0 . 43 ) , with the most significantly associated marker in African Americans located ∼200 kb centromeric from the index signal ( rs7641133 , OR = 1 . 16 , 95% CI 1 . 08–1 . 25 , p = 1 . 0×10−4 ) . These two markers are strongly correlated in Europeans ( HapMap CEU , r2 = 0 . 91 ) but not in Africans ( HapMap YRI , r2 = 0 . 11; Table 1 ) , which suggests that in African Americans rs7641133 is a better proxy of the biologically important allele and may better localize the true association . For some of these regions , the size of the LD blocks differ in populations of African ancestry compared with the GWAS population and thus , may assist in localizing the functional allele ( Figure S1 ) . Using a strict αb of 5 . 6×10−6 for discovery of novel risk variants we observed no evidence of a second independent signal at any risk region . For variants identified as significantly associated with risk ( Table 1 ) , odds ratios for homozygous carriers were generally greater than for heterozygous carriers , which provides support for their associations ( Table S7 ) . We examined 12 risk variants at 8q24 that had been reported previously to be associated with prostate cancer risk [1] , [7] , [10] , [13] , [14] , [20] with 7 being statistically significant and positively associated with risk ( p<0 . 05 ) . The risk SNP BD11934905 [10] is not on the Illumina 1 M array and was not genotyped in this study . In contrast with what has been reported in Europeans , the risk allele for rs12543663 was observed to be significantly inversely associated with risk in African Americans ( OR = 0 . 89 , p = 0 . 028; Table 2 ) . The RAFs for 8 of the 12 alleles are more common in African Americans than Europeans , with the average RAF being 0 . 46 in African Americans and 0 . 32 in Europeans . The largest difference in RAFs between populations are noted with variants rs13252298 , rs13254738 , rs6983561 , rs6983267 and rs7000448 , which have RAFs that are >0 . 20 greater in African Americans than in Europeans . When all 12 variants were included in a multivariate model , only 5 remained nominally associated with risk ( Table 2 ) . In African Americans , many of these index signals were weakly correlated ( Figure S2 ) and demonstrated stronger multi-allelic correlations ( Table 2 ) , which suggests that some variants may define similar haplotypes marking the same biologically relevant variants in this population . No significant association was observed with rs7008482 ( OR = 0 . 96 , p = 0 . 52 , computed using data included in the initial report [24] ) or markers of risk at 8q24 for cancers of the breast , bladder , ovary , or leukemia ( rs13281615: OR = 1 . 03 , p = 0 . 48; rs9642880: OR = 1 . 07 , p = 0 . 13; rs10088218: OR = 0 . 91 , p = 0 . 06; rs2456449: OR = 1 . 06 , p = 0 . 24 ) [25]–[28] . To identify markers at 8q24 that best capture risk in African Americans we performed a stepwise analysis of 1 , 549 genotyped and imputed SNPs spanning the established risk locus ( 127 . 8–129 . 0 Mb ) . This region contained 132 SNPs with nominal p-values<0 . 001 ( Figure 3 ) , and 9 common alleles with per allele ORs of 1 . 16–1 . 42 ( Table 2 ) defined the most parsimonious model . Similarly to the previously reported risk variants at 8q24 four of these markers are substantially more common in African Americans than Europeans ( average RAF difference = 0 . 07 ) . Eight of these markers show some degree of correlation with the known risk variants and thus are likely to be tagging the same functional allele , albeit for 4 SNPs the correlations are quite weak in the CEU and YRI populations ( r2<0 . 2; Table S8 ) suggesting that they may be marking independent risk variants . For example , SNP rs6987409 ( RAF = 0 . 15 ) , which is monomorphic in Europeans , remains significantly associated with risk conditional on the 12 known risk alleles at 8q24 ( OR = 1 . 31 , 95% CI , 1 . 16–1 . 47 , p = 7 . 1×10−6 ) , which suggests that this SNP may be marking a novel variant that is relevant in African Americans; rs6987409 was the most significant marker in the region ( Figure 3 ) . We next estimated the cumulative effect of all prostate cancer risk alleles , and compared a summary risk score comprised of unweighted counts of all GWAS reported risk alleles to a risk score that included variants we identified as being associated with risk in African Americans ( Table 3 ) . Using index signals from GWAS ( see Methods ) , the risk per allele was 1 . 08 ( 95% CI , 1 . 06–1 . 09; p = 6 . 0×10−26 ) and individuals in the top quartile of the risk allele distribution were at 2-fold greater risk of prostate cancer compared to those in the lowest quartile ( Table 3 ) . As expected , the risk score was improved when utilizing the markers that we identified at the known risk loci as being more relevant to African Americans ( OR = 1 . 17 95% CI , 1 . 15–1 . 19; p = 5 . 1×10−74 ) , with risk for those in the top quartile being 3 . 5-times those in the lowest quartile . When stratifying by first-degree family history of prostate cancer , risk was 4 . 7-fold greater for those with a family history and in the top quartile of the risk score distribution ( 3 . 5% of the population ) compared to those without a family history and in the first quartile ( Table 3 ) . The risk score was associated equally with risk for advanced ( n = 1 , 087 ) and non-advanced ( n = 1 , 968 ) prostate cancer ( case-only test: OR = 1 . 02 , 95% CI , 1 . 00–1 . 05 phet = 0 . 082 ) . Using this risk score , we estimate ( see Methods ) that in the aggregate , all risk alleles tested explain approximately 11% of risk in first-degree relatives of cases .
In this large study of prostate cancer risk in African American men we tested 49 variants that had been reported primarily in populations of European and Asian ancestry , and we were able to replicate associations ( at p≤0 . 05 ) with roughly half of these markers . We had adequate power ( >80% ) to detect relative risks of the magnitude reported previously for the majority of risk variants ( although we realize that power was overestimated as the effect estimates from the initial report may be inflated due to the winner's curse phenomenon [29] . ) Through fine-mapping , we identified markers in many regions that were more strongly associated with risk in African Americans than the index variant , and thus , are likely to be better proxies of the biologically relevant allele in this population . Our ability to detect associations in African Americans with either the index signal or correlated variants suggests that most loci contain a biologically relevant allele that is not unique to the initial GWAS population . These findings improve upon previous studies to replicate associations in African Americans [30] , efforts which included some of these same studies , but in substantially smaller sample sizes for most variants examined [19] , [31] . Within 12 regions , fine-mapping in African Americans revealed a more significantly associated marker ( with evidence over the index signal being clearly greater at 7 loci ) . For some of the regions , the signal in African Americans was located in a smaller region of LD than that observed in the GWAS population which should aid in localizing the functional variant ( s ) . Confirmation of these associations in the initial GWAS populations will be required before they can be declared as proxies of the underlying functional alleles; however in many cases , given their modest to strong correlation , based on HapMap data , with the index signal in the GWAS population , most markers are expected to be strongly associated with risk . At each locus , fine-mapping was based on the Illumina 1 M-Duo content supplemented with SNPs imputed from Phase 2 HapMap ( CEU/YRI ) , which is expected to provide good coverage of the vast majority of common alleles in the admixed African American population . Of the ∼1 . 5 million common SNPs ( MAF≥0 . 05 ) in the HapMap YRI population that we did not genotype , we were able to impute ∼1 . 4 million with Rsq≥0 . 8 . Our inability to detect associations at 10 regions ( p>0 . 05 for an index signal and p>0 . 004 for a proxy ) could be due to low power , the functional allele being rare or non-existent in African Americans and/or inadequate tagging in these specific regions . Because of limited LD , fine-mapping in African Americans is thought to be an effective approach for localizing functional risk alleles for common phenotypes as populations of African ancestry are expected to have , on average , fewer alleles that are correlated with a functional variant . Fine-mapping in multiple racial/ethnic populations should prove to be even more powerful for isolating these variants as only a subset SNPs that are correlated with the functional allele in different populations will be similar . Thus , conducting association testing across multiple populations should narrow the subset of potentially functional alleles in a region . A complete resource of genome-wide variation data from multiple populations provided by the 1000 Genomes Project will assist in further interrogating these risk loci and together with large-scale association testing in diverse samples , will guide researchers in defining the subset of alleles that are correlated with risk across populations and hence are the most logical candidates for functional characterization . A number of prostate cancer risk regions have been found to harbor more than one risk variant ( e . g . 8q24 , 17q12 and 11q13 ) [1] , [10] , [17] , [18] . Aside from 8q24 , the search for independent markers at known risk loci has been limited to populations of European ancestry . Using a relatively strict threshold for declaring significance ( average α<5 . 6×10−6 ) , we observed no evidence of association that is independent of the index signal . While suggestive associations were observed at many loci , testing of these variants in additional African American samples will be needed to confirm these associations , followed by testing in other populations to assess whether the associations may be limited to African Americans . The risk region at 8q24 is the strongest susceptibility locus for prostate cancer that has been identified to date , with a number of different risk variants having been reported in different populations [1] , [6] , [7] , [10] , [13] , [14] . We identified nine SNPs at 8q24 that best captured the genetic risk in African Americans , including SNP rs6987409 [1] which is not observed in Europeans ( or is present at an extremely low frequency ) . Like the reported index signals at 8q24 ( Table 2 ) , many of these markers are more common in African Americans than in Europeans ( average RAF difference = 0 . 07 ) . This is in contrast to the index signals in regions outside of 8q24 where the RAF average difference was only 0 . 03 . If the frequency of these 8q24 variants is a good correlate of the frequency of the underlying biologically relevant alleles then some of the variants in this region may to contribute to the excess risk of prostate cancer in African Americans , as suggested previously [10] . A precise estimate of its contribution will only come once the functional alleles have been found and we understand their associations in the context of other genetic and environmental factors ( or host factors such as age ) . The cumulative effects of GWAS-identified variants for common cancers are not yet clinically informative for risk prediction [15] , [16] . Until the functional alleles are identified and their effects are accurately estimated , modeling of the genetic risk will rely on markers that best capture risk at an established susceptibility locus for a given population . Many of the markers we identified at these risk loci in African Americans appear to provide substantial improvement over the GWAS-identified variants in defining those who are at greater risk of prostate cancer in this population . However , as estimated with the index signals in European populations [3] , these alleles likely account for only a small fraction of the familial risk of the disease ( ∼10% ) in African Americans . Validation of this risk model in African Americans and in other populations will be needed , as will incorporating novel risk variants identified through this GWAS in African American men .
The Institutional Review Board at the University of Southern California approved the study protocol . Nine studies were genotyped as part of the GWAS of prostate cancer in African American men . Below is a brief description of each study . Genotyping of 7 , 123 samples from these studies ( 3 , 621 cases and 3 , 502 controls ) was conducted using the Illumina Infinium 1 M-Duo bead array at the University of Southern California and the NCI Genotyping Core Facility ( PLCO study ) . Following genotyping samples were removed based on the following exclusion criteria: 1 ) unknown replicates across studies ( n = 24 , none within studies ) ; 2 ) call rates <95% ( n = 126 ) ; 3 ) samples with >10% mean heterozygosity on the X chromosome and/or <10% mean intensity on the Y chromosome - we inferred 3 samples to be XX and 6 to be XXY; 4 ) ancestry outliers ( n = 108 , discussed below ) , and; 5 ) samples that were related ( n = 141 , discussed below ) . To assess genotyping reproducibility we included 158 replicate samples; the average concordance rate was 99 . 99% ( ≥99 . 3% for all pairs ) . Starting with 1 , 153 , 397 SNPs , we removed SNPs with <95% call rate , MAFs<1% , or >1 QC mismatch based on sample replicates ( n = 105 , 411 ) . The analysis included 1 , 047 , 986 SNPs among 3 , 425 cases and 3 , 290 controls . | Prostate cancer is one of the most common cancers in men and is especially frequent in men of African origin , as incidence rates in African Americans in the United States are >1 . 5–fold greater than rates in European Americans . In order to gain a more complete understanding of the genetic basis of inherited susceptibility to prostate cancer in men of African origin , we examined the associations at risk loci identified in men of European and Asian descent in a large African American sample of 3 , 425 cases of prostate cancer and 3 , 290 male controls . In testing 49 known risk variants , we were able to demonstrate that at least half of these variants also contribute to risk in African American men . We were able to find additional risk variants in many of the previously reported regions that better captured the pattern of risk in African American men . In addition , we verified and improved upon the evidence we previously reported that there are multiple risk variants in a region of 8q24 that are important in men of African origin . |
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Numerous plant viruses that cause significant agricultural problems are persistently transmitted by insect vectors . We wanted to see if apoptosis was involved in viral infection process in the vector . We found that a plant reovirus ( rice gall dwarf virus , RGDV ) induced typical apoptotic response during viral replication in the leafhopper vector and cultured vector cells , as demonstrated by mitochondrial degeneration and membrane potential decrease . Fibrillar structures formed by nonstructural protein Pns11 of RGDV targeted the outer membrane of mitochondria , likely by interaction with an apoptosis-related mitochondrial protein in virus-infected leafhopper cells or nonvector insect cells . Such association of virus-induced fibrillar structures with mitochondria clearly led to mitochondrial degeneration and membrane potential decrease , suggesting that RGDV Pns11 was the inducer of apoptotic response in insect vectors . A caspase inhibitor treatment and knockdown of caspase gene expression using RNA interference each reduced apoptosis and viral accumulation , while the knockdown of gene expression for the inhibitor of apoptosis protein improved apoptosis and viral accumulation . Thus , RGDV exploited caspase-dependent apoptotic response to promote viral infection in insect vectors . For the first time , we directly confirmed that a nonstructural protein encoded by a persistent plant virus can induce the typical apoptotic response to benefit viral transmission by insect vectors .
In mammals , viral infection can induce or activate apoptosis , a process of programmed cell death , which generally is important in the regulation of viral pathogenesis [1] . Apoptosis is a normal process during development and aging to regulate cell populations in multicellular organisms [2–3] . Caspases , a family of cysteine proteases , are crucial proteases responsible for the execution of the apoptotic cascade , while the inhibitor of apoptosis protein ( IAP ) serves as a pivotal regulator of apoptosis [4] . Apoptosis is triggered either via an extrinsic death receptor or an intrinsic mitochondria-dependent pathway [5–6] . The initial event of mitochondria-dependent apoptosis is the loss of mitochondrial membrane potential , leading to the release of apoptosis-related factors associated with the mitochondrial membranes [7–10] . Later , the chromatin is cleaved into nucleosomal fragments , and apoptotic bodies are generated [11] . These fundamental stages are first elucidated for mammalian systems , due to the important function of apoptosis in development and diseases [2] . Although apoptosis is commonly involved in viral pathogenesis , some viruses appear to have evolved to exploit this mechanism to promote their survival and replication in different ways [12–14] . Thus , the role of apoptosis in host–virus interactions is diverse among different viruses . Many plant viruses that cause significant agricultural problems are transmitted via insect vectors such as thrips , aphids , leafhoppers and planthoppers in a persistent manner [15] . Growing evidence has shown that the persistent transmission of viruses causes only a limited adverse effect , rather than pathogenesis in their insect vectors [15–20] . We now know that a conserved small interfering RNA ( siRNA ) antiviral response is triggered by the replication of viruses in the insect vectors to modulate a metastable balance between viral accumulation and adverse effects , allowing for viral persistence and highly efficient spread in nature [15 , 21–24] . Generally , persistent infection by arthropod-borne viruses ( arboviruses ) can induce apoptosis in mosquito and Drosophila vectors , but it is usually restricted to a low level to avoid serious damage to the insects [14 , 25–27] . The apoptosis induced by arboviruses may serve as an innate antiviral mechanism to protect against or benefit viral transmission by insect vectors [12 , 14] . The cytopathologic changes caused by virus-induced apoptosis may also damage functionally relevant tissues and organs , decreasing insect fitness . Using the terminal deoxynucleotidyl transferase dUTP nick-end labeling ( TUNEL ) assay , Huang et al . demonstrated that apoptotic signs appeared in limited regions of the principal salivary glands in brown planthopper infected with rice ragged stunt virus , a plant reovirus [28] . However , the functional roles for apoptosis induced by persistent plant viruses in insect vectors are still poorly understood . This gap in knowledge is , in part , attributable to the lack of reliable tools such as insect vector cell cultures for real-time analysis of virus-induced apoptosis in insect vector cells . In the present study , we used continuous cell cultures derived from insect vectors to trace the apoptosis process induced by rice gall dwarf virus ( RGDV ) , a plant reovirus that was first described in 1979 in Thailand and caused a severe disease of rice in southern China and Southeast Asia [29] . RGDV is mainly transmitted with high efficiency in a persistent-propagative manner by the leafhopper vector Recilia dorsalis ( Hemiptera: Cicadellidae ) [17] . RGDV has icosahedral and double-shelled particles approximately 65 to 70 nm in diameter [24] . Its genome consists of 12 double-stranded RNA ( dsRNA ) segments , encoding six structural proteins and six nonstructural proteins [29] . The outer capsid shell is composed of the major outer capsid protein P8 and the minor outer capsid protein P2 [24] . Nonstructural protein Pns11 assembles into fibrillar or tubular structures to facilitate viral spread in insect vectors [17 , 30–31] . The leafhopper R . dorsalis and its cultured cells support the efficient propagation of RGDV to a high titer in a persistent and nonlethal infection that causes limited damage [17 , 19] . We have demonstrated that the siRNA antiviral pathway modulates the persistent infection of RGDV in R . dorsalis , thus preventing serious harm [24] . However , we still do not know how RGDV regulates physiological processes in R . dorsalis to permit effective viral propagation . Previously , we found that RGDV infection could directly remodel and utilize a variety of cellular structures and pathways for efficient propagation in its insect vectors [30–32] . For example , RGDV particles were often observed to associate with the bundles of fibrillar structures to facilitate viral infection [30–31] . Furthermore , RGDV particles are distributed close to the periphery of degenerated mitochondria during viral replication of vector cells , suggesting that mitochondria might support the energy demands of viral propagation [33] . The degeneration of virus-associated mitochondria surprised us , suggesting that RGDV infection may induce apoptotic response in insect vector cells and adversely affect the insects . Here , by applying a system that combines insect vector cell cultures , immunofluorescence and electron microscopy , we revealed that the fibrillar structures that are composed of nonstructural protein Pns11 of RGDV , targeted mitochondria and activated typical apoptotic response to promote viral infection and transmission by insect vectors .
RGDV exerts an adverse effect on its vector R . dorsalis , including reduced survival , emergence , fecundity and longevity of adults [19] . To explore how the virus causes these adverse effects , we first investigated whether RGDV infection caused apoptotic changes in continuous cultured cells of R . dorsalis , which were originally established from embryonic fragments dissected from eggs [17] . At 72 h post-inoculation ( hpi ) with RGDV at a multiplicity of infection ( MOI ) of 1 , bright field microscopy showed that the infected cultured insect cells had slight cytopathological changes , such as cell clumping and loss of confluent monolayers ( S1 Fig ) . Electron microscopy showed that RGDV-infected cells had apoptotic characteristics , including crescent-shaped nuclei and condensed and marginalized chromatin , compared with the round nuclei and finely dispersed chromatin in mock-infected cells ( Fig 1A–1C ) . At this time , virus-containing apoptotic bodies , the typical characteristic of the end stage of apoptosis were present ( Fig 1D ) . The mitochondria in RGDV-infected cells appeared to be degenerating , and cristae were diffuse and indistinct ( Fig 1E and 1F ) . Bundles of fibrillar structures , absent in virus-free cells , were in contact with the periphery of these degenerated mitochondria ( Fig 1F ) . Some RGDV particles were closely associated with the free ends of the virus-induced fibrillar structures and along their edges ( Fig 1F ) . Thus , RGDV infection caused cytopathological changes that included the hallmarks of apoptosis . To better understand the prevalence of RGDV-induced apoptotic response in cultured cells , we examined 400 cells in mock- or RGDV-infected treatment to count the number of apoptotic cells using electron microscopy and found that 34 . 5% of the cells were apoptotic , significantly higher than in the mock treatment ( Fig 1G ) . Therefore , RGDV specifically induced specific morphology changes of apoptosis in cultured cells of R . dorsalis . One of the key characteristics of apoptosis at an early stage is the disruption of the mitochondrial membrane potential [2 , 34] . The degeneration of mitochondria in RGDV-infected cells is probably caused by a change of the mitochondrial membrane potential , which initiates a mitochondria-dependent apoptotic cascade . We thus used the JC-1 assay , a flow cytometry-based method widely used to detect any changes in mitochondrial membrane potential . Continuous cultured cells of R . dorsalis were inoculated with RGDV at a MOI of 1 . At 48 hpi , RGDV infection caused the mitochondrial membrane potential to decrease in 42 . 7% of the cultured leafhopper cells , which was significantly higher than in the mock-infected treatment ( Fig 2A ) . Thus , RGDV infection induced early-stage apoptotic response . At 72 hpi , the infected cells were then examined for nucleosomal fragmentation , a hallmark event of later-stage apoptosis [11] . At this later time , a clear ladder of DNA fragments was detected in RGDV-infected cells , but not in the mock , which had a single intact chromosomal DNA band ( Fig 2B ) . Furthermore , the TUNEL assay , widely used to detect apoptotic bodies [35] , showed positive apoptotic signals in virus-infected regions , but none in the uninfected cells ( Fig 2C ) . We calculated that about 43% of infected cells were TUNEL-positive ( Fig 2D ) . These results strongly indicated that RGDV infection specifically induced the early and later events of apoptotic response in insect vector cells . Because RGDV infection of insect vector cells has been shown to induce the formation of various inclusions composed of nonstructural proteins for viral replication or spread [29–31 , 36] , we used immunoelectron microscopy to investigate which viral nonstructural proteins ( Pns4 , Pns7 , Pns9 , Pns10 , Pns11 or Pns12 ) were involved in the formation of virus-associated fibrillar structures along the degenerated mitochondria . Immunoelectron microscopy showed that Pns11-specific IgG specifically recognized the fibrillar structures surrounding the degenerated mitochondria ( Fig 3A ) . Confocal microscopy further confirmed that some Pns11-specific fibrillar structures colocalized with the mitochondria , which were stained by MitoTracker Red in virus-infected R . dorsalis cells ( Fig 3B ) . Thus , the fibrillar structures composed of Pns11 of RGDV apparently targeted the mitochondria and may induce mitochondrial degeneration during viral infection of insect vector cells . Previously , we showed that expression of RGDV Pns11 alone can induce the formation of fibrillar or tubule-like structures in cells of the nonhost Spodoptera frugiperda ( Sf9 ) [17] . To determine whether Pns11 of RGDV had an inherent ability to target and induce mitochondrial degeneration , Sf9 cells were inoculated with recombinant baculovirus that expressed Pns11 . Confocal microscopy demonstrated that some Pns11-specific fibrillar structures colocalized with the mitochondria stained by MitoTracker Red in the cytoplasm of Sf9 cells at 48 hpi ( Fig 3C ) . Immunoelectron microscopy further showed that the fibrillar structures-associated mitochondria were degenerating , with diffuse and indistinct cristae in the cytoplasm of Sf9 cells at 48 hpi ( Fig 3D ) . These results illustrated that RGDV Pns11 had an inherent ability to target and induce mitochondrial degeneration in the absence of viral infection . To characterize the cytopathological effect of Pns11 of RGDV in Sf9 cells , we stained cells with trypan blue to observe the cellular phenotypes and measure cell viability using a cell counter . Bright field microscopy showed that the viability of Sf9 cells had greatly decreased after Pns11 accumulation at 72 hpi , indicating the relative cytotoxicity of Pns11 ( S2A and S2B Fig ) . We then used rhodamine 123 , a specific fluorescent dye used to assess mitochondrial membrane potential [37] , to determine whether Pns11 alone induces the collapse of mitochondrial membrane potential . We found that Pns11 caused an 87 . 1% decrease in total rhodamine 123 fluorescence intensity of Sf9 cells at 72 hpi , compared with that of the mock ( Fig 3E ) , suggesting that Pns11 alone potentially reduced mitochondrial membrane potential . Because the disruption of the mitochondrial membrane potential can lead to cytochrome c release [7–10] , we then determined whether the cytochrome c could translocate from the mitochondrial to cytosol . The immunofluorescence assay demonstrated that at 72 hpi , the cytochrome c in Pns11-expression Sf9 cells was largely localized within the cytosol , but not together with the mitochondria ( S2C Fig ) . Thus , Pns11 alone potentially induced the release of apoptosis-related factors and the subsequent apoptotic cascade . A yeast two-hybrid ( Y2H ) assay was then used to screen a cDNA library of R . dorsalis to identify putative mitochondrial factors interacting with RGDV Pns11 . From this library screen , 116 colonies of 207 positive ones were randomly sequenced . Finally , 36 sequences were annotated using the BLASTX program in GenBank . Among these candidates , an apoptosis-related protein named voltage-dependent anion channel ( VDAC ) ( also called mitochondrial porin ) captured our attention . The VDAC , a class of porin channel located on the outer mitochondrial membrane , serves as a major diffusion pathway for ions and metabolites [38] . This protein plays a crucial role in apoptosis [39] . At the early stage of apoptosis , the VDAC increases the permeability of mitochondrial membrane to allow the release of apoptotic factors , such as cytochrome c and apoptosis-inducing factor , then initiates the apoptotic cascade [39] . Therefore , the VDAC of R . dorsalis was analyzed further . Based on the transcriptome data from R . dorsalis in our lab , the BLASTX searching method in the GenBank demonstrated the 90% similarity of putative full-length open reading frame ( ORF ) of VDAC ( 846 bp long ) with counterpart of Homalodisca vitripennis ( S3A Fig ) . Then this putative ORF of VDAC was amplified , and the gene sequence was deposited in GenBank with accession number MG241500 . The predicted protein product ( 282 amino acid residues ) possessed characteristic porin3 domains ( S3B Fig ) , which can form a β-barrel to span the mitochondrial outer membrane [40] , but did not show the significant predicted transmembrane domains analyzed with TMHMM . The phylogenic analysis revealed that the amino acid sequence of VDAC clustered with those of other insect species in the order Hemiptera ( S3A Fig ) . Both Gal4 transcriptional activator-based and membrane-based yeast two-hybrid ( MbY2H ) systems were applied and revealed the strong interaction between VDAC and Pns11 ( Fig 4A ) . We then used a glutathione S-transferase ( GST ) pull-down assay to confirm such interaction . The GST-tag and His-tag were fused to the N-terminal of Pns11 and VDAC , respectively , to express fusion proteins GST-Pns11 and His-VDAC . The result showed that the purified GST-Pns11 pulled down His-VDAC from cell lysates ( Fig 4B ) . By contrast , no such interaction was obtained with the purified GST ( Fig 4B ) . Thus , RGDV Pns11 specifically interacted with the VDAC , suggesting that the VDAC was likely the target protein mediating the binding of Pns11-specific fibrillar structures with the mitochondria . To confirm this possibility , we then performed RNA interference ( RNAi ) experiments to test the effect of the reduced expression of VDAC gene on the interaction between Pns11 and VDAC . Cultured cells of R . dorsalis were transfected with synthesized dsRNA targeting the gene of VDAC ( dsVDAC ) , then infected with purified RGDV ( MOI of 1 ) . RT-qPCR assay demonstrated that dsVDAC treatment caused approximately 70% or 20% reduction in the relative expression of VDAC or Pns11 at 48 hpi , respectively ( Fig 4C ) , suggesting that the formation of Pns11-specific fibrillar structures was independent of VDAC . Immunofluorescence and immunoelectron microscopy further demonstrated that at 48 hpi , dsVDAC treatment significantly inhibited the colocalization of Pns11-specific fibrillar structures with the mitochondria in cultured cells of R . dorsalis ( Fig 4D and 4E ) . Taken together , our results indicated that the target of Pns11-specific fibrillar structures with the mitochondria may depend on the specific interaction of RGDV Pns11 with an apoptosis-related mitochondrial outer membrane protein . For clarifying that the apoptotic pathway is induced by RGDV infection , IAP and two caspase orthologs , caspase-2-like ( CASP2L ) and caspase-8-like ( CASP8L ) , were first identified in transcriptome data from R . dorsalis . The full-length ORFs of CASP2L , CASP8L and IAP genes of R . dorsalis were amplified , and each gene sequence was deposited in GenBank ( accessions MG241499 , MG241497 and MG241498 , respectively ) . Phylogenic analysis showed that the amino acid sequences of CASP2L and CASP8L clustered with those of other insect species ( S4 Fig ) . To determine the expression profiles of apoptosis-related genes during viral infection , cultured cells of R . dorsalis were infected with purified RGDV ( MOI of 1 ) . At 48 hpi , an RT-qPCR assay showed that the expression of three apoptosis-related genes was increased significantly ( Fig 5A ) . We further determined that the treatment with the broad-spectrum caspase inhibitor Z-VAD-FMK also significantly inhibited relative gene expression of RGDV P8 at 48 hpi , but did not significantly affect viability of R . dorsalis cells ( Fig 5B ) . Thus , the typical apoptotic response induced by RGDV infection was caspase-dependent . To confirm this result , we also silenced gene expression for CASP2L , CASP8L or IAP by RNAi to inhibit or induce apoptotic responses , respectively . Continuous cultured cells of R . dorsalis were treated with dsRNAs targeting the CASP2L , CASP8L or IAP genes or the gene for green fluorescence protein ( GFP ) ( dsCASP2L , dsCASP8L , dsIAP or dsGFP ) . At 8 h post transfection , cells were inoculated with RGDV at a MOI of 0 . 1 . At this low MOI , the early viral infection rate was low ( about 20–30% ) , and the spread of viruses among R . dorsalis cells could be easily monitored . We also tested the efficiency of the knockdown of the targeted genes 48 h post-transfection with dsRNAs in continuous cultured cells ( S5A Fig ) . By 48 hpi , after examining at least 1000 cells , immunofluorescence microscopy indicated that treatment with dsCASP2L or dsCASP8L decreased the percentage of infected cells from an average of 65% to 20% , compared with the dsGFP-treated cells control , respectively ( Fig 5C and 5D ) . In contrast , treatment with dsIAP increased the percentage of infected cells from an average of 65% to 90% , compared with dsGFP-treated cells ( Fig 5C and 5D ) . As expected , the number of TUNEL-positive cells was positively correlated with viral infection ( Fig 5C–5E ) . Meanwhile , in dsCASP2L- , dsCASP8L- or dsGFP-treated uninfected cells , no specific TUNEL signals appeared ( Fig 5C ) . However , about 10% of cells in the dsIAP-treated uninfected cells were TUNEL-positive , significantly lower than in the dsIAP-treated infected cells ( Fig 5C ) . RT-qPCR assay demonstrated an approximately 70% reduction in the relative expression of CASP2L , CASP8L or IAP after treatments with dsCASP2L , dsCASP8L or dsIAP , respectively , at 48 hpi ( Fig 5F ) , indicating that transfection with the dsRNA specific for these genes indeed triggered RNAi in R . dorsalis cells . RT-qPCR assay showed that the treatment with dsIAP increased gene expression of P8 by more than 2-fold ( Fig 5F ) . By contrast , gene expression of P8 was reduced by the treatment with dsCASP2L or dsCASP8L by about 3- or 2-fold , respectively ( Fig 5F ) . Northern blot analysis showed that the treatment of dsCASP2L and dsCASP8L resulted in a marked reduction of the synthesis of viral mRNAs , but the treatment of dsIAP increased the synthesis of viral mRNAs at 72 hpi ( Fig 6A ) . Expectedly , the synthesis of viral genome dsRNAs and the accumulation of viral proteins were also decreased by treatment with dsCASP2L or dsCASP8L , but were increased by treatment with dsIAP ( Fig 6B and 6C ) . The pattern of genomic dsRNA segments separated from purified RGDV virons is considered as the reference [41] . Thus , the apoptotic response induced by RGDV infection was beneficial for viral infection . In addition , dsIAP treatment of virus-free cells induced a low level of apoptotic response , while dsIAP treatment under viral infection significantly induced distinct apoptotic response . Totally , our results confirmed that RGDV infection inherently activated an apoptotic response in its insect vector cells . To determine whether RGDV infection caused apoptotic response in intact insect vector , the intestines of nonviruliferous or viruliferous R . dorsalis adults were tested using the TUNEL assay . It is known that RGDV initially infects the filter chamber epithelium of the intestines by 2 days post-first access to diseased plants ( padp ) , then directly crosses the basal lamina into the visceral muscles at 4 days padp , from where it spreads throughout the entire intestines at 6 days padp [42] . By 10 days padp , RGDV is extensively present in the salivary glands [42] . Usually , our test confirms that about 70% of insects can transmit RGDV to healthy rice seedlings after a latent period of 10 days . At 6 days padp , TUNEL-positive signs could be detected in limited areas of virus-infected intestines in about 70% of viruliferous insects , while few TUNEL-positive cells were found in the nonviruliferous insects ( Fig 7A and 7B ) . However , we could not detect specific TUNEL-positive signs in the virus-infected salivary glands of viruliferous insects at 10 days padp . Thus , the RGDV-induced apoptotic response appears to be restricted to a low level to avoid serious damage to R . dorsalis , similar to results reported previously for arboviruses in mosquito vectors [14 , 25–27] . Electron microscopy showed that the epithelial cells of virus-free intestines had normal histology and ultrastructure , including intact and orderly microvilli , evenly distributed chromatin , and abundant mitochondria with tightly involuted cristae ( Fig 7C ) . In contrast , at 10 days padp , morphological changes in the intestinal epithelial cells of viruliferous R . dorsali were evident , including cytoplasmic reduction and vacuolization , damaged or decreased number of microvilli , and shrunken or crescent-shaped nuclei ( Fig 7D and 7E ) . Furthermore , the degenerated mitochondria with indiscernible cristae were surrounded by bundles of Pns11 fibrillar structures in virus-infected intestines , compared with the intact mitochondria in nonviruliferous control ( Fig 7F and 7G ) . This abnormal cytopathology of virus-infected intestinal epithelium further suggested that RGDV infection also induces typical mitochondrial-dependent apoptotic response in intact insect vectors . To further investigate the effects of apoptotic response on viral infection in insect vectors , from 6 to 14 days padp , we sampled 30 live viruliferous leafhoppers daily and then examined the gene expression profiles for apoptosis-related factors ( CASP2L and IAP ) and major outer capsid protein P8 of RGDV by RT-qPCR assay . During the latent period for RGDV in its insect vectors , before 10 days padp [42] , the transcript levels of apoptosis-related genes increased , and then decreased ( Fig 8A ) . Similarly , the transcript level of viral major outer capsid protein P8 gene also increased quickly to peak at 10 days padp , and then remained relatively stable ( Fig 8A ) . Although changes in the relative expression of apoptosis-related genes are not informative regarding the biology of apoptosis in an insect , our results suggest a positive association between viral infection and the expression of apoptosis-related genes in the insect vector . We then manipulated apoptotic response using RNAi to explore the role of apoptosis during viral infection in insect bodies . Viral titers in 30 live viruliferous leafhoppers after dsRNAs microinjection were examined daily . We tested the efficiency of the knockdown of the targeted genes after 2 days post microinjection of dsRNAs in intact insects ( S5B Fig ) . Time-course experiments showed that the profiles of mean number of RGDV P8 gene copies in all dsRNAs treatments were similar ( Fig 8B ) . From 6 to 14 days pdap , the mean number of RGDV P8 gene copies in the dsIAP , dsCASP2L and dsGFP treatment was 5 . 45 × 109 , 1 . 01 × 108 and 8 . 53 × 108 copies/μg insect RNA , respectively ( Fig 8B ) . These results demonstrated that blocking the apoptotic pathway inhibited viral infection , while promoting the apoptotic pathway facilitated viral infection . Thus , we concluded that RGDV induced and utilized apoptosis for viral infection in R . dorsalis vectors . We then calculated the mortality of 100 viruliferous or nonviruliferous insects daily after dsRNA microinjection . Our preliminary test indicated that the microinjection itself had little effect on the mortality rates of nonvirulifeorus R . dorsalis adults ( S6A Fig ) . For both nonviruliferous and viruliferous insects , the mortality of dsIAP-treated R . dorsalis was higher than those treated with dsGFP or dsCASP2L . For example , at 6 days post microinjection , approximately 43 . 0% of the dsIAP-treated viruliferous insects were dead , compared with mortality rates of about 33 . 0% for the dsGFP-treated and about 24 . 0% for dsCASP2L-treated viruliferous insects ( S6B Fig ) . However , at the same number of days post microinjection , the mortality rates for the dsCASP2L- , dsGFP- and dsIAP-treated nonviruliferous insects were approximately 11 . 3% , 16 . 3% and 26 . 0% , respectively ( S6B Fig ) , supporting the fact that IAP is necessary to maintain cell viability in insects [43] . Furthermore , our attempt to silence IAP resulted in a simultaneous increase in apoptotic response and death in viruliferous R . dorsalis , illustrating the possible positive association between virus-induced apoptotic response and insect mortality .
Many important plant viruses are persistently transmitted via insect vectors with limited harm to the insects . Here , we used the plant reovirus RGDV and leafhopper vector R . dorsalis to determine how the apoptotic response was activated during persistent viral transmission by insect vectors . We first demonstrated that RGDV infection induced typical apoptotic characteristics in cultured leafhopper cells , including degeneration of mitochondria , decrease in mitochondrial membrane potential , and appearance of condensed chromatin , chromosomal DNA fragments and virus-containing apoptotic bodies ( Figs 1 and 2 ) , verifying that RGDV triggered the typical apoptotic response in insect vector cells . The expression of caspases ( CASP2L and CASP8L ) and IAP genes were up-regulated during viral infection in cultured leafhopper cells ( Fig 5 ) . The knockdown of caspase gene expression using RNAi blocked apoptotic response and led to the significant inhibition of synthesis of viral mRNAs and genome RNAs , and the accumulation of viral proteins in insect vector cells ( Figs 5 and 6 ) . However , the knockdown of IAP gene expression using RNAi promoted apoptotic response , causing a significant increase of these viral replication processes in insect vector cells ( Figs 5 and 6 ) . Thus , RGDV exploit the apoptotic response in a caspase-dependent pathway to promote viral replication in insect vector cells . In insect bodies , RGDV infection also triggered a similar apoptotic response restricted to a low level , which appeared to benefit viral replication , but may have damaged functionally relevant tissues and organs , decreasing the fitness of the vectors ( Figs 7 and 8 ) . Thus , the typical apoptotic response can be induced and facilitated viral accumulation during viral replication and transmission by the insect vectors . As we observed for the RGDV–R . dorsalis combination , the similar apoptotic response is also restricted to a low level in mosquito vectors [27] . In general , the induction of apoptosis promoted viral infection but also harmed the insects . Thus , viruses have evolved some mechanisms to avoid stimulating extensive apoptotic responses in the bodies of insect vectors . During the latent period for RGDV in insect vectors , before 10 days padp [42] , the expression levels of apoptosis-related genes ( CASP2L , CASP8L and IAP ) increased , then decreased ( Fig 8A ) , indicating that the apoptotic response was activated during replication and then was suppressed . Such synchronous gene expression for CASP2L , CASP8 and IAP suggested that virus-induced apoptotic response was critically modulated during viral infection of insect vectors . For controlling the excessive viral accumulation and avoiding obvious pathology , IAP , the inhibitor of apoptosis was induced by viruses to restrict the apoptotic response to a low level . Other as-yet unknown anti-apoptotic mechanisms might also be activated to block or restrict apoptotic response . Furthermore , a conserved siRNA antiviral response was triggered by RGDV infection to control viral propagation , avoiding excessive viral accumulation past the pathogenic threshold in insect vectors [24] . Previously , we also found that cellular structures and pathways such as microtubules , intermediate filaments or autophagy were induced by RGDV infection , promoting viral infection but also causing some insect cytopathology [44 , 45] . It appeared that all these mechanisms were involved in modulating a metastable balance between viral accumulation and adverse effects , allowing the virus to be persistently transmitted by insect vectors . The multifunctional mitochondrion not only plays an essential role in host immune responses but also serves as an important control point in the regulation of apoptosis [7] . After apoptotic signaling , the mitochondrial membrane potential is lost , and apoptosis-related factors are released [8–10] . Numerous viral proteins , including Vpr protein of human immunodeficiency virus ( HIV ) , X protein of hepatitis B virus ( HBV ) , PB1-F2 of influenza A virus ( IAV ) , and NS4A of hepatitis C virus ( HCV ) , have been reported to directly target mitochondria to activate mitochondrial apoptosis in mammalian host cells [46–50] . VDAC , an outer membrane protein of mitochondria , is often activated during apoptosis and is targeted by viral proteins to initiate apoptosis [46 , 51–52] . In fact , VDAC is a porin channel that serves as a major diffusion pathway for ions and metabolites to control mitochondrial membrane permeabilization [39] . Our data revealed that the fibrillar structures composed of nonstructural protein Pns11 of RGDV could target the mitochondria , induce mitochondrial degeneration and decrease the mitochondrial membrane potential in the absence of viral replication ( Figs 3 and 4 ) , suggesting that RGDV Pns11 was responsible for initiating virus-induced apoptotic response . Such attachment of virus-induced fibrillar structures with mitochondria possibly was mediated by the specific interaction of RGDV Pns11 with the VDAC ( Figs 3 and 4 ) . How the association of VDAC with Pns11 of RGDV is involved in the induction of apoptotic response during viral replication in insect vectors is still unknown . Our study is the first to directly confirm that a nonstructural protein encoded by a persistent-propagative plant virus induce the apoptotic response in an insect vector to promote viral infection and transmission . Based on the results described , we propose that RGDV exploits the apoptotic mechanism for efficient infection in insect vector cells . However , there are still many unknowns in the apoptotic process . For example , how does RGDV trigger and exploit the apoptotic response for efficient infection and how does it evolve such a strategy to enable persistent infection ? In some cases , viruses may directly exploit apoptotic bodies for their dissemination and subsequent infection of a mammalian host or an insect vector [9 , 12 , 53 , 54] . In our study , whether packaging of the RGDV virions within apoptotic bodies protects them from insect immune mechanisms was not determined . In insect vectors , one possible consequence of apoptosis is that physical barriers within the insect are weakened . We thus deduced that the apoptotic response is exploited by RGDV to overcome multiple tissue and membrane barriers to enable efficient infection of its leafhopper vectors . In addition , we still do not know how virus-induced apoptotic response is restricted to a low level in insect vectors . New approaches based on the reverse genetics systems for plant reoviruses and on CRISPR/Cas9 technologies for the leafhopper vector , combined with continuous insect vector cell lines will provide new opportunities to unravel the molecular mechanisms for virus-induced apoptotic response in the RGDV–R . dorsalis system .
Nonviruliferous individuals of the leafhopper R . dorsalis were collected from Guangdong Province in southern China . The continuous cultured cell line derived from R . dorsalis was originally established from embryonic fragments dissected from insect eggs and maintained on growth medium as described previously [17] . The R . dorsalis cell line supported a uniform and synchronous viral infection , enabling the early viral replication process to be traced [42] . RGDV was purified from infected cultured insect vector cells as described previously , and resuspended in His-Mg buffer ( 0 . 1 M histidine , 0 . 01 M MgCl2 , pH 6 . 2 ) [17] . Synchronous infection of continuous cultured cells by RGDV was initiated as described by Wei et al . [55] . When the cultured monolayer of leafhopper cells on a coverslip ( 15 mm diameter ) reached 80% confluency , cells were inoculated with purified RGDV at a MOI of 0 . 1 or 1 for 2 h , washed twice , and covered with growth medium at 25°C . His-Mg buffer-treated cells served as controls . Rice samples infected with RGDV were initially collected from Guangdong Province . Rabbit polyclonal antisera against intact viral particles , major outer capsid protein P8 , and nonstructural proteins Pns11 and Pns12 were prepared as described previously [29 , 32 , 36] . IgGs were purified from specific polyclonal antisera , then conjugated to rhodamine or fluorescein isothiocyanate ( FITC ) , according to the manufacturer’s instructions ( Thermo Fisher ) . The antibody against cytochrome c was obtained from BD Biosciences . Sf9 cells infected with recombinant baculovirus vector containing Pns11 of RGDV have been previously described [17] . In brief , the coding region of the ORF for RGDV Pns11 was amplified by PCR . The purified product was cloned into Gateway vector pDEST8 ( Thermo Fisher ) to construct plasmid pDEST8-Pns11 . Then the recombinant baculovirus vector was introduced into E . coli DH10Bac ( Thermo Fisher ) to generate a recombinant bacmid . The isolated recombinant bacmid was used to transfect Sf9 cells in the presence of Cellfectin II ( Thermo Fisher ) according to the manufacturer’s instructions . After a high-titer baculoviral stock was generated , amplification of the viral stock was scaled up to an appropriate volume for cellular infection on coverslips or in flasks . At 48 hpi , Sf9 cells , growing on coverslips and infected with recombinant bacmids , were treated for immunoelectron microscopy . Sf9 cells inoculated with empty baculovirus vector served as negative controls . They were also fixed , permeabilized , and immunolabeled with Pns11-specific IgGs conjugated to FITC ( Pns11-FITC ) for immunofluorescence microscopy of Pns11 overexpression and mitochondria . Sf9 cells , growing in flasks and infected with recombinant bacmids , were harvested for cell viability tests using 0 . 4% trypan blue solution at 72 hpi . Cell images and counts were made in an automated cell counter ( Counter Star ) . Virus-infected cultured R . dorsalis cells growing in a monolayer on coverslips and intestines dissected from viruliferous R . dorsalis or Sf9 cells were fixed , dehydrated , and embedded , and ultrathin sections were cut as previously described [31] . For immunoelectron microscopy , sections were immunolabeled with the Pns11-specific IgGs as the primary antibody , followed by treatment with goat anti-rabbit IgG conjugated with 15-nm-diameter gold particles as the secondary antibody ( Abcam ) , as previously described [31] . The MitoProbe JC-1 Assay Kit for Flow Cytometry ( Thermo Fisher ) was used to measure the change in mitochondrial membrane potential in cultured cells of R . dorsalis at 48 hpi . Briefly , approximately 1 × 106 cells were collected and suspended in 1 mL PBS . JC-1 was added to cells at a final concentration of 2 μM , and after incubation at 37°C for 30 min , cells were washed once with warm PBS , then resuspended in PBS and immediately examined with a flow cytometer ( BD FACS Calibur ) . Data from three independent biological experiments were analyzed using Cellquest software and displayed as a dot plot of JC-1 green fluorescence ( x-axis ) against red fluorescence ( y-axis ) . Changes in mitochondrial membrane potential in Sf9 cells infected with the recombinant baculovirus expressing Pns11 were measured using rhodamine 123 fluorescence ( Thermo Fisher ) at 72 hpi . Sf9 cells inoculated with empty baculovirus vector served as a negative control . In brief , approximately 1 × 106 Sf9 cells were harvested and suspended in PBS , then incubated with rhodamine 123 at a concentration of 1 μM in PBS at 37°C for 1 h . Cells were washed with PBS , then resuspended in PBS and analyzed immediately using the flow cytometer . Data from three independent biological experiments were analyzed and displayed as a plot of fluorescence intensity of rhodamine ( x-axis ) against cell number ( y-axis ) . At 72 hpi , cultured cells of R . dorsalis were harvested , and DNA was extracted using the Cell Apoptosis DNA Ladder Detection Kit ( KeyGEN BioTECH ) , according to the manufacturer’s instructions . Chromosomal DNA fragments were separated using 2% agarose gel electrophoresis , and DNA ladders were visualized by ethidium bromide staining . R . dorsalis cells cultured in a monolayer on coverslips were fixed in 4% v/v paraformaldehyde and treated with 0 . 2% v/v Triton-X , as previously described [56] . Then the DeadEnd Fluorometric TUNEL System ( Promega ) was used for TUNEL staining . According to the manufacturer’s instructions , samples were treated with the equilibration buffer in the kit at room temperature for 10 min , then incubated with rTdT incubation buffer at 37°C for 60 min . Thereafter , the reaction was terminated by adding 2× sodium citrate in the kit , and then incubated with viral particle-specific IgG conjugated to rhodamine ( virus-rhodamine ) . Nick-end-labeling of nucleosome fragments with fluorescein-dUTP and viral infection were visualized using a confocal microscope . For observing TUNEL signals during viral infection of insect vectors , second instars of R . dorsalis were fed on diseased rice plants for 2 days and then transferred to healthy rice seedlings . At different days padp , the intestines or salivary glands were dissected and processed for TUNEL assay , as described above . Meanwhile , samples were immunolabeled with virus-specific IgGs conjugated to rhodamine ( virus-rhodamine ) and actin dye phalloidin-Alexa Fluor 647 carboxylic acid ( Invitrogen ) , then processed for immunofluorescence microscopy as described previously [17] . Three independent biological replicates were conducted and analyzed . Virus-infected cultured R . dorsalis cells or Pns11-expressing Sf9 cells growing on coverslips were incubated with MitoTracker Red CMXRos ( Thermo Fisher ) for 45 min using standard growth conditions . After the staining solution was carefully removed , cells were fixed in 4% v/v paraformaldehyde and permeabilized in 0 . 2% v/v Triton-X , immunolabeled with Pns11-FITC , then examined with immunofluorescence microscopy . To set up the immunofluorescence microscopy parameters , digital images ( 1024×1024 pixels ) were captured with either 488 nm excitation ( emission filters ) or 543 nm excitation . They were acquired with a 63 oil-immersion objective . Samples in the same group possessed the same parameters of immunofluorescence microscopy to unitize the background . A Matchmaker Gold Yeast-two-hybrid system ( Clontech , USA ) was used for Y2H screening . The cDNA library derived from R . dorsalis or the VDAC gene was constructed in the pGADT7 vector for prey plasmids . Full-length ORF of RGDV Pns11 was cloned in the pGBKT7 vector as a bait plasmid , which was then used to transform yeast strain AH109 to confirm the absence of self-activation and toxicity . Thereafter , the prey and bait were used to cotransform AH109 , and transformants were screened on the SD double-dropout ( DDO ) medium ( SD/-Leu/-Trp ) , SD triple-dropout ( TDO ) medium ( SD/-His/-Leu/-Trp ) and SD QDO medium ( SD/-Ade/-His/-Leu/-Trp ) . Positive clones were selected on QDO/X plates containing X-α-Gal ( 20 μg/mL ) to detect β-galactosidase activity . The interaction of pGBKT7-53 with pGADT7-T served as a positive control and that of pGBKT7-Lam with pGADT7-T served as a negative control . We then used the DUALmembrane starter kit ( Dualsystems Biotech ) to detect the interaction between membrane-associated VDAC and RGDV Pns11 according to the manufacturer’s instructions . Full-length ORF of RGDV Pns11 or VDAC was inserted into bait vector pBT3-STE or prey vector pPR3-N . Thereafter the bait and prey were used to transform the yeast strain NMY51 , and transformants were screened on the TDO and QDO medium . The clones were streaked on QDO/X plates containing X-Gal for color formation in a β-galactosidase assay . The pTSU2-APP/ pNubG-Fe65 interaction served as a positive control , and the pTSU2-APP/ pRR3N served as a negative control . A GST pull-down assay was performed as previously described [57] . The Pns11 gene of RGDV was cloned in the pGEX-3x vector to construct a plasmid expressing the GST fusion protein as a bait ( GST-Pns11 ) . The full-length ORF of the VDAC from R . dorsalis was cloned into the pHM4 vector to construct a plasmid expressing the His fusion protein as a prey ( His-VDAC ) . Recombinant proteins GST-Pns11 and GST were respectively expressed in the Escherichia coli stain BL21 . Lysates were then incubated with glutathione-Sepharose beads ( Amersham ) and subsequently , with the recombinant protein His-VDAC . Finally , eluates were analyzed using GST-tag and His-tag antibodies ( Sigma ) , respectively , in a Western blot assay . Cultured R . dorsalis cells in a monolayer with 80% confluency were inoculated with RGDV at a MOI of 1 . 0 for 2 h . His-Mg buffer-treated cells served as controls . At 48 hpi , the cells were collected at the same time . For viral acquisition by insects , about 500 nonviruliferous second instar nymphs of R . dorsalis were fed on RGDV-infected rice plants for 2 days , then transferred to healthy rice seedling . From 6 to 14 days padp , 30 leafhoppers were daily collected at the same time . Total RNA was extracted from cells or insects using TRIzol Reagent ( Thermo ) according to the manufacturer’s instructions . For synthesizing first-strand cDNA , total RNA was primed with oligo-dT primer and reverse transcribed with M-MLV Reverse Transcriptase ( Promega ) . The qPCR assays were performed in a Mastercycler Realplex4 real-time PCR system ( Eppendorf ) using GoTaq qPCR Master Mix kit ( Promega ) with efficient and specific primers ( S1 Table ) . For relative quantitation , the transcriptional level of the actin gene from the leafhopper was used as the control for each qPCR assay . Relative levels of genes were qualitatively analyzed using the 2−ΔΔCt method . For absolute quantification , the number of RGDV P8 gene copies and CASP2L and IAP gene copies were calculated as the log of the copy number/μg insect RNA based on a standard curve of the RGDV P8 gene , CASP2L gene and IAP gene , respectively . The T7 RNA polymerase promoter was added to the forward primer and reverse primer at the 5′ and 3′ terminal to amplify a region of about 500–900 bp in each gene ( S1 Table ) . PCR products were transcribed into dsRNAs in vitro using the T7 RiboMAX ( TM ) Express RNAi System , according to the manufacturer’s protocol ( Promega ) . Purified dsRNAs were examined using agarose gel electrophoresis to determine their integrity and quantified by spectroscopy . Three microliters of Cellfectin II Reagent ( Thermo ) and 4 μg dsGFP , dsVDAC , dsCASP2L , dsCASP8L or dsIAP were diluted individually in 25 μL LBM without antibiotics and fetal bovine serum , and mixed gently together at room temperature for 20 min . Then the dsRNA–lipid complex was incubated with the cultured cells of R . dorsalis in a monolayer ( at 80% confluency ) for 8 h . Thereafter , cells were inoculated with purified RGDV ( MOI of 0 . 1 ) for 2 h . Infected cells were processed for immunofluorescence or were harvested for RT-qPCR detection at 48 or 72 hpi . Alternatively , at 72 hpi , total proteins were extracted from infected cells and further analyzed by immunoblotting with antibodies against P8 and Pns12 , respectively . Insect actin was detected with actin-specific antibodies ( Sigma ) as a control . Furthermore , viral genome dsRNAs were isolated from cultured cells , as described previously [58] . In brief , viral genome dsRNAs from cell lysates were isolated at 72 hpi using the modified CF11 cellulose chromatography procedure . Cell lysates of each dsRNA treatment were mixed with 1× STE ( 0 . 1 M NaCl , 0 . 05 M Tris , 0 . 001 M EDTA , pH 6 . 8 ) , 10% SDS , and 2× STE-saturated phenol . Following centrifugation , the aqueous phase was recovered and added the ethanol to a final volume of 16 . 5% . Then CF11 cellulose ( Whatman ) was loaded and vortexed to mix thoroughly . The pellets after the centrifugation were washed with 1×STE in 16 . 5% ethanol . Finally , the dsRNAs were eluted from CF11 with 1×STE . Separation of genomic dsRNA segments were loaded on 1 . 0% agarose gel . The pattern of genomic dsRNA segments separated from purified RGDV virons was considered as the reference [41] . For the northern blots , at 72 hpi , the total RNAs from cultured cells were extracted with TRIzol Reagent ( Thermo Fisher ) . The DIG High Prime DNA labelling and Detection Starter KitⅠ ( Roche ) was used to examine the transcript level of RGDV P8 and Pns12 . In brief , about 500 bp DIG-labeled DNA probe of P8 or Pns12 about were generated after the incubation of denatured PCR products ( S1 Table ) and DIG-High Prime for 20 h at 37°C . About 5 μg total RNA of each dsRNA treatment were loaded and detected for the transcript level of P8 or Pns12 . The 5 . 8S rRNA stained with Methylene blue were served as a control to confirm loading of equal amounts of RNA in each lane . In addition , the relative abundance of CASP2L , CASP8L or IAP genes in virus-free cultured cells of R . dorsalis after different times of transfection with dsRNAs was quantified by RT-qPCR as described already . Three independent biological replicates were conducted and analyzed . Cultured R . dorsalis cells were treated for 4 h with 25 μM pancaspase inhibitor Z-VAD-FMK ( Promega ) dissolved in DMSO . Cells were treated with DMSO as the control . Cells were then inoculated with purified virus ( MOI of 0 . 1 ) , then assayed by RT-qPCR at 48 hpi , as described above . Three independent biological replicates were conducted and analyzed . Generally , first or second instar nymphs of R . dorsalis are the most efficient stage for acquiring RGDV from infected rice plants [19] . Furthermore , RGDV takes at least 2 days to infect the intestinal epithelium of R . dorsalis , so the synthesized dsRNAs are microinjected directly into the insect abdomen for efficient dsRNA delivery instead of oral feeding [59] . We first allowed 700 nonviruliferous second instar nymphs to feed on RGDV-infected rice plants for 2 days to acquire viruses , then microinjected them with synthesized dsGFP , dsCASP2L or dsIAP ( about 0 . 05 μg/insect ) using a Nanoject II Auto-Nanoliter Injector ( Spring ) . The treated insects were transferred to healthy rice seedlings until they were assayed . Viral titers in 30 live leafhoppers treated with dsRNAs were assayed daily by RT-qPCR for viral gene copies of the major outer capsid protein P8 . The equation of y = -3 . 349x +49 . 258 ( y = the logarithm of plasmid copy number to base 10 , x = Ct value , R2 = 0 . 9995 ) was used to calculate the viral genome copy as the log of the copy number per microgram of insect RNA [60] . For calculating mortality of insects , 100 insects of each dsRNA treatment were individually fed on a healthy rice seedling in one glass tube after microinjection . The dead insects were counted at the same time each day . The mortality rate was calculated as the number of dead dsRNAs-treated insects in the total number of dsRNAs-treated insects . In addition , the relative abundance of CASP2L and IAP genes in 10 nonviruliferous leafhoppers was also estimated by RT-qPCR assay . Three independent biological replicates were conducted and analyzed . All data for cultured cells and some data from insects , including percentage of TUNEL-positive intestines and relative expression of genes , were analyzed with a two-tailed t-test in GraphPad Prism 7 . The data for insect mortality were analyzed using SPSS , version 17 . 0 . Percentage data were arcsine square-root-transformed before analysis . Multiple comparisons of the means were performed using Tukey’s honestly significant difference ( HSD ) test and a one-way analysis of variance ( ANOVA ) . Data were back-transformed after analysis for presentation in the text and figures . | Of the approximately 700 known plant viruses , more than 75% are transmitted by insects . Numerous plant viruses can replicate inside the cells of the insects . Unlike in the plant hosts , the viruses do not seem to cause disease in the insect vectors that carry them . Here , we report that the replication of a plant reovirus , rice gall dwarf virus ( RGDV ) , activated the apoptotic response in limited areas of leafhopper vectors during viral replication . Interestingly , fibrillar structures constituted by nonstructural protein Pns11 , which is encoded by RGDV , targeted the mitochondria and induced apoptotic response in the absence of viral replication , possibly via the specific interaction of RGDV Pns11 with an apoptosis-related mitochondrial outer membrane-associated protein . Our findings further suggest that the activation of apoptotic response facilitates efficient viral infection , whereas inhibition of apoptotic response blocks viral infection in insect vectors . This work presents a novel discovery that a plant reovirus induces typical apoptotic response and thus promotes its transmission by insect vectors . |
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Adaptive mate choice by females is an important component of sexual selection in many species . The evolutionary consequences of male mate preferences , however , have received relatively little study , especially in the context of sexual conflict , where males often harm their mates . Here , we describe a new and counterintuitive cost of sexual selection in species with both male mate preference and sexual conflict via antagonistic male persistence: male mate choice for high-fecundity females leads to a diminished rate of adaptive evolution by reducing the advantage to females of expressing beneficial genetic variation . We then use a Drosophila melanogaster model system to experimentally test the key prediction of this theoretical cost: that antagonistic male persistence is directed toward , and harms , intrinsically higher-fitness females more than it does intrinsically lower-fitness females . This asymmetry in male persistence causes the tails of the population's fitness distribution to regress towards the mean , thereby reducing the efficacy of natural selection . We conclude that adaptive male mate choice can lead to an important , yet unappreciated , cost of sex and sexual selection .
We first develop a graphical model in which a single quantitative trait is a reliable , direct indicator ( rather than an indirect indicator , like a costly ornament ) of a female's “intrinsic” fecundity ( i . e . , fecundity in the absence of costly male persistence ) . For example , in a wide diversity of taxa , variation in female fecundity is strongly correlated with body size [2] , [4] because larger females have more resources to invest in fecundity . Henceforth , we arbitrarily assume that a female's body size is the phenotypic trait correlated with fecundity , but our logic applies to other indicator traits that directly influence her fecundity , such as parasite load [25] or abdomen size in many insects [7] . Males are expected to evolve a mating preference for larger females whenever this preference increases their own lifetime reproductive success [14] . Such an adaptive male mate preference will cause larger , intrinsically high-fecundity females , to receive more antagonistic male persistence , compared to smaller , intrinsically low-fecundity females . The fitness consequences of this relationship will depend upon how female resistance to male-induced harm scales with the indicator trait ( in this case , body size ) . Assuming that a female's resistance to the harmful male persistence does not rise sufficiently fast with increasing body size , the male preference should reduce the fecundity of large females and increase that of small females , thereby reducing the standing variance in fitness ( Figure 1 ) . As a result , the selective advantage of any beneficial genetic variation that makes females more competitive for limiting resources , and hence more fecund , will experience a smaller selective advantage than if harmful male persistence was randomly applied to females throughout the population . Such nonrandom male persistence will cause adaptive evolution in females to be slowed whenever female fecundity is: ( i ) heritable , ( ii ) genetically correlated with the indicator trait , and ( iii ) a major determinant of her lifetime fitness that does not strongly trade off with her other fitness components . A reduced rate of adaptive evolution by females can also be deduced from Fisher's fundamental theorem [26] , so long as the male-induced reduction in the phenotypic variation in female fecundity also leads to a reduction in the additive genetic variation among females . Furthermore , when there is a positive genetic correlation for fitness between females and males , adaptive male mate choice is expected to reduce the rate of adaptation in both sexes . Although a counteracting effect could occur if male preference for high-fecundity females increases the variance in male fitness , or if male preferences lead to positive assortative mating for fitness , here we focus on female fitness and the potential for male mate preferences to reduce its heritable variation . The conclusion that adaptive male choice leads to a reduced rate of adaptation by females can also be deduced by focusing on mutations at a single arbitrary locus . Let the mutation rate to new beneficial mutations be UBen and the selective advantage of the mutation , expressed as a selection coefficient and averaged across the sexes , be s . Assuming approximate additivity ( i . e . , little dominance ) , the probability of the mutation becoming fixed can be calculated using the diffusion approximation [27] as 2s ( Ne/N ) , where N is the population size and Ne is the effective population size . With recurrent mutation to new beneficial mutations , the rate of advance of adaptive evolution is approximated by: ( 1 ) ( 2 ) If we partition selection between the sexes and let the selective advantage of a mutation be s♂ in males and s♀ in females then , ( 3 ) ( 4 ) Next , we assume that the expression of the beneficial mutation also increases the attractiveness of females to males ( e . g . , as a result of increasing her body size ) , so that those females expressing the beneficial mutation receive an excess of antagonistic male persistence . We express this cost with an additional selection coefficient s♀biased-persist , which is applied only to females , ( 5 ) Comparison of Equations 4 and 5 demonstrates that the rate of adaptive evolution will always be slower whenever males bias their antagonistic persistence towards fitter females and cause s♀biased-persist to be negative; i . e . , when there is adaptive male mate choice and increased male persistence is harmful to females . Increased male persistence directed towards more fecund females that express the beneficial allele reduces the selective advantage of those females and thereby reduces the variance in fitness among females in the population . The primary prediction from our models is that , in species with antagonistic male persistence , adaptive male mate preference leads to a “cost of being an attractive female . ” This cost reduces the selective advantage of females expressing more beneficial genetic variation ( and hence are larger , on average ) and increases the fitness of females expressing less of this variation ( and hence are smaller , on average ) . Put more simply , adaptive male mate preference causes the tails of the population's distribution of female lifetime fecundity to regress towards the mean ( Figure 1 ) . This prediction is contingent on four assumptions that must be met in order for our model to operate: ( i ) lifetime fecundity and net fitness are strongly genetically correlated , ( ii ) body size and fecundity are positively correlated , both phenotypically and genetically , ( iii ) more antagonistic male persistence is directed towards females with higher intrinsic fecundity ( i . e . , potential fecundity in the absence of costly male persistence ) , and ( iv ) female resistance to male-induced harm does not rise sufficiently fast with increasing body size . We tested the major prediction of the model , and its underlying assumptions , using a laboratory population of the model species D . melanogaster . In this population , assumption ( i ) is well established [28] , [29] , so here we focus on testing whether our population meets assumptions ii–iv , before experimentally assessing whether male mate preference for high-fitness females causes the tails of the distribution of lifetime fecundity to regress towards the mean .
Joint measures of female body size and lifetime fecundity in our base population ( LHM ) of D . melanogaster indicated that these two phenotypic traits are strongly correlated . As predicted from past studies of many taxa [2] , [4] , [30] , fecundity was higher in large females compared to small females ( Figure 2 ) . This result was found both when females experienced minimal exposure to males ( mean ± standard error [SE]: large females , 27 . 3±1 . 59; small females , 18 . 42±1 . 07; t-test t = 4 . 64 , df = 98 , p<0 . 0001; p-values reported throughout the manuscript are two-tailed ) and when male exposure was continuous ( large females , 16 . 5±1 . 06; small females , 12 . 62±0 . 72; t-test t = 3 . 01 , df = 98 , p<0 . 003 ) . We tested for a genetic correlation between body size and fecundity in a separate study in which two populations each were artificially selected for either large or small body size . After 83 generations of divergent selection , lifetime fecundity was significantly higher in the lines selected for large body size compared to the lines selected for small body size ( mean ± SE: large females , 31 . 38±1 . 81; small females , 15 . 25±1 . 58; t-test t = 6 . 69 , df = 2 , p = 0 . 02 ) . Since body size was the only target of artificial selection , the large divergence in fecundity between treatments demonstrates a strong positive genetic correlation between body size and fecundity , a result consistent with other research [31] . To test this assumption , we first measured how the persistence ( courtship behaviour ) of individual males was allocated between two nonvirgin females ( differing in eye colour phenotype , brown or red , for ease of individual identification ) . We performed a two-way ANOVA on the amount of persistence behaviour directed towards each female , with the body size of that “target” female ( large or small ) , the body size of the competitor female present in the test tube ( large or small ) , the eye colour of the target female ( red or brown ) , and all possible interactions as predictor variables . This analysis was significant overall ( F7 , 232 = 7 . 50 , p<0 . 0001 ) , with significant effects of the target female body size ( F1 , 232 = 38 . 37 , p<0 . 0001 ) and the body size of the competitor female ( F1 , 232 = 13 . 53 , p = 0 . 0003 ) , but no effects of eye colour ( F1 , 232 = 0 . 41 , p = 0 . 52 ) , or any of the interactions ( all p>0 . 60 ) . When individual males were housed with two nonvirgin females differing in body size , males directed more persistence towards the larger female than towards the smaller female ( paired t-tests , p≤0 . 0002 , Figure 3 ) . When males were housed with two nonvirgin females of similar body size ( both small or both large ) , the levels of male persistence directed towards the red- and brown-eyed females were not significantly different ( paired t-tests , p≥0 . 50 , Figure 3 ) . Further evidence of a male mate preference for nonvirgin females of larger body size was obtained from mating assays conducted under conditions that more closely mimicked the normal culture environment of the LHM population ( 16 males combined with 16 females during the “adult competition” phase of the life cycle [28] , [32] ) . When presented with a choice of nonvirgin females differing in body size , males mated with large-bodied females at a greater rate than with small-bodied females ( generalized linear model [GLM] with binomial error terms; pconsensus = 1 . 17×10−5 , Replicate 1: χ21 , 18 = 5 . 90 , p = 0 . 015; Replicate 2: χ21 , 74 = 22 . 87 , p<0 . 0001; Figure 3 ) . These remating results are unlikely to have arisen from large females possessing a greater receptiveness to male courtship effort because males kept under “no-choice” mating conditions ( where either only large or only small nonvirgin females were present ) mated with small females more frequently than with large females ( GLM with binomial error terms; pconsensus<1×10−6 , Replicate 1: χ21 , 28 = 14 . 79 , p = 0 . 0001; Replicate 2: χ21 , 28 = 7 . 16 , p = 0 . 0075; Figure 4 ) . To test this assumption , we compared the reduction in lifetime fecundity of large and small females when they were either minimally or continuously exposed to males ( Figure 2 ) . Continuous male exposure harmed large females more than small females ( two-way ANOVA , interaction between body size and male exposure , F1 , 196 = 4 . 75 , p = 0 . 031 ) , indicating that larger females were not more resistant to the harmful male persistence that they received . Finally , we tested the model's key prediction by comparing the mean fecundities of large- and small-bodied females used in the choice and no-choice mating assays described earlier . In the no-choice assays , where all females were either of large or small body size , male preference for large female body size could not cause them to direct their antagonistic persistence away from smaller females and towards larger females . In contrast , in the choice assays , where females of different body sizes were simultaneously present , a redirection of antagonistic male persistence towards larger females was possible . We found that the difference in the mean fecundities of large- and small-bodied females was smaller when males could direct their antagonistic persistence towards large females ( Figure 5 , pconsensus = 0 . 012 , interaction tests for each replicate: F1 , 46 = 2 . 79 , p = 0 . 1 for the smaller , first replicate; F1 , 101 = 4 . 92 , p = 0 . 03 for the larger , second replicate ) .
The results of our male mate preference tests clearly demonstrate that males have mate preferences for larger nonvirgin females—a result consistent with earlier work on virgin females [33] . Rather than displaying an “undiscriminating eagerness” [34] to mate , when given a choice between females differing in body size , male D . melanogaster preferred to court and mate with large , high-fecundity females over small , low-fecundity females . Given the significant fecundity differences associated with female body size described above , this mate preference is likely to be adaptive from the male's perspective , as mating with larger , more fecund females is likely to yield greater direct , as well as indirect [35] , benefits . It is unlikely that this male mate preference is adaptive from the female's perspective , as several studies have established that chronic male persistence in the LHM population is very harmful to females , and it is not sufficiently compensated by indirect genetic benefits [36]–[38] . Our experiments demonstrate that larger females receive more harmful male persistence but do not reveal the specific mechanism by which this harm accrues . Further work will be needed to resolve the degree to which this increased harm is due to harassment during courtship [24] , damage associated with copulation [39] , and/or the activity of products transferred in the male's seminal fluid [23] , [40] . Having experimentally ascertained that the LHM population of D . melanogaster satisfied all the assumptions necessary in which to test the key prediction our model , we were able to meaningfully assess the fitness consequences of adaptive male mate preferences . When males had the ability to bias their antagonistic persistence towards large-bodied females , we saw a decrease in the mean fecundity of these preferred females , compared to those large females that were in an experimental environment where all females were of similar size , and biases of antagonistic male persistence were not possible . In contrast , small-bodied females were , on average , able to realize relatively higher fecundities when they were housed with larger females ( which , our study indicates , were attracting more harmful male persistence ) than they were when they were housed in an environment in which males had no other choice of mates . Although our study found that males directed more courtship towards large females and also mated them more frequently , both of which can be harmful in and of themselves [24] , the observed cost to large females might also have occurred because large females were mated , on average , to more harmful males [30] , [41] than were smaller females . Irrespective of the mechanism of this cost , together these assays revealed how male mate preferences will ultimately cause the tails of the distribution of fecundity to regress towards the mean . Since adult lifetime fecundity is strongly correlated with lifetime fitness in females of the LHM population [42] , this male-driven sexual selection is expected to reduce the rate of adaptive evolution of any trait that is positively correlated with female body size . It is common for deleterious mutations to reduce body size in D . melanogaster [43] , and it is reasonable to assume that many beneficial mutations will cause their carriers to be more competitive as larvae , allowing them to garner more resources during the larval competition phase of their life cycle and become larger , more fecund , adults . As a consequence , male mate preference for larger females is expected to commonly interfere with both progressive evolution and to increase the population's mutational load by interfering with purifying selection . For example , suppose that environmental change led to selection for alleles conferring higher desiccation tolerance . If more desiccation-tolerant females had a competitive advantage such that they grew to a larger size prior to reproduction ( e . g . , [44] ) , then a male preference for these females would reduce their relative fecundity and increase that of smaller , less desiccation-tolerant females . As a result , the population may be less responsive to environmental change , become an inferior competitor species , and be at a greater risk of extinction . Collectively , our results support our model's key prediction that male mate preference for high-fitness females reduces the selective advantage of larger , more fecund females and increases that of smaller , less fecund females . This finding , obtained in a laboratory population , is likely to apply to natural populations for two reasons . First , the study was done on a large , outbred population that has been maintained in a competitive laboratory environment , at continuous large size , for over 400 generations [28] , [32] . Over this period of time , the opportunity for adaptation to the laboratory environment should have been substantial , permitting the flies to be experimentally assayed under conditions to which they are highly adapted . Second , we measured natural variation in body size , rather than inducing extreme body size variation via nutritional deprivation and/or excessive larval crowding . This was accomplished using a sieve shaker device ( developed by ADS and WRR ) , which enabled us to quickly sort thousands of adult flies based on natural variation in their body size , and obtain the largest and smallest individuals to use in our experiments . Flies from these two body size groups differed markedly in fecundity , with the larger females producing over 30% more eggs than small females under both minimal and continuous male exposure conditions . Although our assays of male mate preference support a directional preference for large-bodied females , in one assay ( Figure 2 ) , males could only choose between females of large and small body size . Thus , there is the possibility that the true male preference function favours females of intermediate size . However , in our second assay ( Figure 3 ) , males were able to choose between large or small females versus random females ( average ) , and these data support the conclusion that male preference is monotonic for larger females . Our model of adaptive male mate choice in the context of harmful male persistence has important limitations . First , we have implicitly assumed that the increased male persistence ( directed toward larger , more intrinsically fecund females ) does not cause larger females to have lower than average fecundity . Second , male condition may be more variable in nature compared to the laboratory , and condition-specific patterns of male persistence could either enhance or reduce the bias of male persistence toward larger females . Third , we have ignored complicating factors such as size-assortative mating interactions , e . g . , smaller females receiving persistence predominantly from smaller or poor-condition males . Fourth , we have assumed that male mate choice is based on a female trait that directly influences her fecundity , such as body size . Theory predicts that this type of male mate preference will lead to a monotonic preference for larger females [45] , [46] . However , when the preferred female trait is a costly indicator of fecundity , such as an energetically expensive ornament , then males can evolve to prefer intermediate trait values in females [45] , [46] , and our model would not apply . Fifth , our model may not apply to species where females obtain direct net benefits from increased mating rates , such as those with nuptial feeding [47] . Lastly , we have assumed a static male preference and female indicator trait . In many contexts , these two traits can be expected to coevolve , and this dynamic is not included in our model . Nonetheless , our empirical work suggests that the requisite conditions for the model to operate , at least transiently , can feasibly be achieved . Our finding of harmful effects of adaptive male mate choice represents a previously unappreciated cost of sexual reproduction in species with antagonistic male persistence . Rather than simply showing that male-induced harm reduces overall female fecundity , we have shown that biases in the distribution of this harm among mates reduces the selection differential between females with intrinsically high and low fecundity . This reduced efficacy of natural selection will retard a population's rate of adaptive evolution and increase both its equilibrium mutational load and its stochastic accumulation of harmful mutations . The cost of adaptive male mate choice , however , only applies when males can reliably ascertain a female's fecundity using a trait that is heritable and correlated with heritable fitness variation . In Drosophila , female body size represents such a trait since it is influenced by both genotype [48] , [49] and a number of environmental factors ( including temperature , nutrition and larval crowding conditions [50] ) , and responds rapidly to directional selection . In species with little or no heritability for body size , however , an adaptive cost of male preference for high-fecundity females would not apply . Nonetheless , given the prevalence of male mate preferences [7] , this new cost that we describe may be a widespread evolutionary phenomenon . For this reason , it should be considered in the broader context of the ongoing debates over the interfering or reinforcing role that sexual selection plays in the process of adaptation , and whether sexual selection increases or decreases the risk of extinction of populations and species [51] .
For all male–female interaction assays , we used D . melanogaster adults obtained from the wild-type LHM population [28] , [29] or from a replicate population ( LHM-bwD ) in which a dominant brown-eyed marker ( bwD ) had been introgressed through repeated backcrossing into the LHM genetic background . The LHM population is maintained on a 14-d culture cycle with a 12-h L∶12-h D diurnal cycle at 25°C in humidity-controlled incubators . Briefly , each generation begins with eggs placed in 56 “juvenile competition” vials ( 150–200 eggs per vial; each vial containing 10 ml of cornmeal/molasses medium ) . After 11 . 25 d , emerging adults are lightly anesthetized with CO2 , mixed among vials , and transferred to “adult competition” vials ( 16 pairs of males and females per vial ) , which are seeded with 6 . 4 mg ( dry weight ) of live yeast . After 2 d of adult competition , the flies are transferred to “oviposition” vials , and then discarded after laying eggs for 18 h . The eggs laid in these oviposition vials are culled to a density of 150–200 eggs per vial and become the “juvenile competition” vials of the next generation . Because only eggs from the oviposition phase of the life cycle are used to propagate the next generation , and populations have been consistently maintained under these culture conditions for over 400 generations , the number of eggs laid during the 18-h oviposition phase represents a meaningful measure of lifetime fecundity in these populations . As such , experiments were designed to mimic these culture conditions as closely as possible . Detailed culturing protocols for these large populations ( adults n>1 , 800 per generation for LHM and n>1 , 300 per generation for LHM-bwD ) can be found elsewhere [28] , [29] ) . We altered the quality of potential female mates by collecting females of differing adult body size , a phenotypic trait that is frequently positively correlated with fecundity [2] , [30] , [52] . We collected flies from the ends of the normal distribution of body sizes that are produced under typical lab culture conditions . Flies were sorted by size with the use of a sieve shaker device ( Gilson Performer III , Gilson Company ) which mechanically separates anesthetized flies on the basis of their ability to pass through a series of 20 electroformed sieves , in which the diameter of the holes in each sieve was 5% larger than the diameter of the holes of the sieve below ( diameter of top sieve holes = 1 , 685 µm; diameter of bottom sieve holes = 800 µm ) . Flies were placed into the column ( under light CO2 anaesthesia ) , and were agitated at a rate of 3 , 600 vibrations min−1 for 2 min . By using this technique , it was possible to quickly sort hundreds of flies simultaneously on the basis of their body size . For all experiments , “small” flies were defined as those that were small enough to pass through the 1 , 095-µm diameter sieve , whereas “large” flies were those that were too large to pass through the 1 , 281-µm diameter sieve . To assess the phenotypic correlation between body size and fecundity , we collected adult flies from the LHM population as they eclosed as virgins on day 9 of their life cycle . Flies were separated by sex , and on the following day , females were sorted by size using the sieve sorter protocol described above . One hundred female flies each of large and small body size were then placed individually ( under light anaesthesia ) into small test tubes that had been seeded with 0 . 4 mg of yeast ( the amount of yeast per female experienced under normal culture conditions ) . Into each of these vials , three adult males were placed for a period of 2 h , during which time all virgin females were observed to have mated once . Males were then removed randomly from half of the vials to create 50 adult competition vials with minimal male exposure and 50 with continuous male exposure for each female body size category . Maintaining flies under these two conditions allows us to confirm that there is an intrinsic difference in fecundity between females of different sizes that is independent of the negative net fitness effects of continuous male presence . Matching the normal culturing protocol of the flies , vials were returned to the incubator for an additional 2 d , at which time flies were transferred to oviposition vials containing fresh medium ( with a scored surface to encourage oviposition ) for a period of 18 h before being discarded . The number of eggs laid in each vial was counted , and mean fecundities were compared using t-tests for females differing in size in each male-exposure treatment . The complete dataset was also used to test the assumption that female resistance to male-induced harm does not rise sufficiently fast with increasing body size , by examining whether or not female flies of one size were harmed more by continuous male exposure . In order to verify that there was a genetic correlation between body size and fecundity , we assessed the fecundity of females obtained from populations of D . melanogaster that are part of an ongoing experimental evolution project ( of ADS and WRR ) in which females had been artificially selected for either large or small body size using a size-sorting procedure similar to that described above . These populations are otherwise cultured in a manner similar to the LHM population from which they were all originally derived . At the time of the assay , the artificial selection had been operating for 83 generations in each of two replicate populations per treatment , and there had been considerable divergence in body size ( mean female diameter [µm] ± SE: large treatment , 1 , 218 . 5±37 . 67; small treatment , 786 . 7±43 . 5; t-test t = 7 . 51 , df = 2 , p<0 . 01 ) . For this assay , 72 virgin females were obtained at random from each of the four experimental populations . On day 11 of their life cycle , these females were placed in adult competition vials in groups of 16 , along with 16 males taken randomly from the LHM population , for a period of 2 h , during which time all females were observed to have mated once . Males were removed from the vials , and after 2 d in the incubator , females were transferred to individual oviposition vials containing fresh medium ( with a scored surface ) for a period of 18 h before being discarded . The number of eggs laid in each vial was counted and the mean fecundity of the two replicates of each treatment was compared using a t-test ( with population as the unit of replication ) . Since the selected trait in these experimental populations was body size , any consistent change in fecundity between the two treatments must be due to a genetic correlation between the two traits . In order to test whether males have mate preferences , a series of behavioural assays were conducted . Nonvirgin flies from both the LHM and LHM-bwD populations were collected on day 11 of their life cycle , and females were sorted by size to isolate large- and small-bodied individuals . Pairs of female flies differing in eye colour ( to aid individual identification ) were placed into small , adult competition vials ( test tubes ) in all possible combinations of body size . After a 1-h anaesthesia-recovery period , a single unanaesthetized adult male fly was added to each test tube , which were then placed on their sides in a well-lit room . Over the course of 11 sessions , spaced 40 min apart , the male in each test tube was observed . Male persistence behaviour was defined as being located within 5 mm of a female and oriented towards her [53]–[55] . Data on the frequency of the male persistence behaviour was collected for each type of female in each treatment . A total of 30 replicate test tubes per treatment were scored . In these assays , nonvirgin adult female LHM flies were collected on day 11 of their life cycle and sorted by size ( see above ) to isolate large and small individuals . Females were then placed into one of two types of adult competition vials ( a vial containing fresh medium seeded with 6 . 4 mg of live yeast ) . In the first , choice experiment , either eight large or eight small red-eyed LHM females were placed into an adult competition vial along with eight randomly collected LHM-bwD females and 16 LHM-bwD males . In the second , no-choice treatment , either 16 large or 16 small red-eyed LHM adult females were placed into an adult competition vial along with 16 LHM-bwD males . These vials were kept in the incubator ( on their sides ) for 24 h , at which time males were removed . The vials , containing females only , were then returned to the incubator for an additional 24 h . Remating rates were assayed by placing all females into individual oviposition vials ( test tubes ) containing fresh , scored medium for the purpose of measuring the paternity of her offspring . Eighteen hours later , the adult flies were discarded , and the test tubes containing eggs were incubated for 11 d . At this time , the presence and number of red-eyed and brown-eyed progeny in each brood were scored to ascertain whether the female had remated . The proportion of females in each adult competition vial that produced brown-eyed offspring ( indicating a remating event ) was recorded . To examine remating rates in relation to female body size and treatment , we constructed GLMs that used a logit link function and binomial error distribution , where the number of females that remated is the dependent variable and the total number of females assayed is the binomial denominator . We tested whether male mate preferences caused the tails of the distribution of female lifetime fecundity to regress towards the mean by performing a two-way ANOVA , with body size , remating treatment , and their interaction as predictor variables . A significant interaction term ( that was associated with a smaller difference between the mean fecundity of large and small females when male preference was possible ) would indicate that the tails of the fecundity distribution had regressed toward the mean . Each type of remating assay was repeated twice . The first , choice assay was comprised of ten adult competition vials ( the unit of replication ) for each body size treatment , whereas the second replicate was comprised of 38 adult competition vials in the large body size treatment and 37 in the small body size treatment . Both replicates of the no-choice assay were comprised of 15 adult competition vials for each body size treatment . | In many species , females are frequently subject to harassing courtship from males attempting to mate with them . These persistent male behaviors can result in females incurring substantial direct fitness costs . We set out to examine how these costs may influence adaptive potential in a species that also exhibits male mate choice , i . e . , a preference by males for females exhibiting certain traits . We found that harmful courtship behaviors were directed predominantly towards females of greater reproductive potential ( and away from females of lesser potential ) , resulting in a reduction in the variation of lifetime reproductive successes among females in the population . This change in distribution of realized fitnesses represents a previously unappreciated consequence of sexual conflict–adaptive male mate preference can slow the rate of accumulation of beneficial mutations and speed the rate of accumulation of harmful mutations , thereby creating a “sexual conflict adaptive load” within a species . |
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The concept of coding efficiency holds that sensory neurons are adapted , through both evolutionary and developmental processes , to the statistical characteristics of their natural stimulus . Encouraged by the successful invocation of this principle to predict how neurons encode natural auditory and visual stimuli , we attempted its application to olfactory neurons . The pheromone receptor neuron of the male moth Antheraea polyphemus , for which quantitative properties of both the natural stimulus and the reception processes are available , was selected . We predicted several characteristics that the pheromone plume should possess under the hypothesis that the receptors perform optimally , i . e . , transfer as much information on the stimulus per unit time as possible . Our results demonstrate that the statistical characteristics of the predicted stimulus , e . g . , the probability distribution function of the stimulus concentration , the spectral density function of the stimulation course , and the intermittency , are in good agreement with those measured experimentally in the field . These results should stimulate further quantitative studies on the evolutionary adaptation of olfactory nervous systems to odorant plumes and on the plume characteristics that are most informative for the ‘sniffer’ . Both aspects are relevant to the design of olfactory sensors for odour-tracking robots .
According to the ‘efficient-coding hypothesis’ [1] , the sensory neurons are adapted to the statistical properties of the signals to which they are exposed . Because not all signals are equally likely , sensory systems should best encode those signals that occur most frequently . This idea was first tested by Laughlin [2] in a pioneering study of first order interneurons in the insect compound eye , the large monopolar cells , which code for contrast fluctuations . He showed that the response function of these graded potential cells , measured by intracellular recording , approximates the cumulative probability distribution function of contrast levels measured in the natural fly's habitat with a photodiode . The efficient coding hypothesis has been much studied in the visual system [2]–[7]; reviewed in [8] and to a lesser extent in the auditory system [9] , [10] . However , it has been rarely discussed in the context of olfactory sensory neurons [11] , [12] . With a nonlinear stimulus-response function , the neuron encodes differently an equal change in stimulus intensity depending on the actual concentration ( Figure 1A ) . The key question is , how should a neuron weigh its input so as to transfer as much information as possible ? Information theory [13] , [14] provides the solution . In the simplest scenario ( with no other constraints on the response range ) , the inputs should be encoded so that all responses are used with the same frequency [2] . The optimal stimulus statistics is given by the stimulus probability distribution ( Figure 1B ) , which is obtained directly from the stimulus-response curve . This simple solution , however , does not hold in the case of olfaction because of the large differences in reaction time at different stimulus concentrations . This is a major difference with respect to Laughlin's approach , in which all response states were assumed to be equiprobable . In this paper , we paralleled Laughlin's approach [2] , adapting his method to suit the specificity of olfaction . We chose a well studied olfactory receptor neuron , the pheromone receptor neuron of male moths , to investigate its adaptation to the natural signal it processes , the sexual pheromone emitted by conspecific females . To our knowledge this neuron and its stimulus provide the only example in olfaction for which enough data are available on the odorant plume and the neuron transduction mechanisms to make a quantitative comparison possible between the predicted optimum signal and the natural signal . Flying male moths rely on the detection of pheromone molecules released by immobile conspecific females for mating . The atmospheric turbulence causes strong mixing of the air and creates a wide spectrum of spatio-temporal variations in the pheromonal signal ( Figure 2 ) . The largest eddies are hundreds of metres in size and may take minutes to pass a fixed point , while the smallest spatial variations are less than a millimetre in size and last for milliseconds only [15] , [16] . Due to inhomogeneous mixing , a very high concentration of pheromone can be found in a wide range of distances from the source , though their frequency decreases with distance [15] . Because of its complicated and inhomogeneous structure , the description of the plume must rely on statistical methods , notably the histogram of the fluctuations in pheromone concentration [15]–[19] . These fluctuations are essential for the insect to locate the source of the stimulus . Experiments in wind tunnels showed that moths would not fly upwind in a uniform cloud of pheromone [20]–[22] . Characteristics like the frequency and intensity of the intermittent stimulation play a key role in maintaining the proper direction of flight [23] . The goal of this paper is to present arguments specifying in which sense the perireception and reception processes occuring in pheromone olfactory receptor neurons ( ORNs ) can be considered as optimally adapted to their natural stimulus . Although , in the light of previous studies on similar sensory neurons , the ORN may be considered a priori as adapted to the pheromone plume , the exact nature of this adaptation and its proof are more challenging questions . Despite widespread agreement that environmental statistics must influence neural processing [24] , precise quantification of the link proved difficult to obtain [8] . So , the main aim of this paper was to identify the specific characteristics to which the pheromone ORN is adapted and to provide quantitative evidence for their adaptation . We proceeded in two steps . First , using the statistical theory of information , we predicted the characteristics of the optimal pheromonal signal that the ORN is best capable of encoding based on the properties of the initial steps of signal transduction . Second , we compared these theoretically-derived properties with statistical characteristics most often determined in experimental measurements , i . e . , the probability distribution function of the fluctuations in pheromone concentration , the spectral density function of the stimulation course and the intermittency of the odorant signal .
Pheromone components are detected by specialized ORNs located in the male antenna . We considered a specific ORN type of the moth Antheraea polyphemus detecting ( E , Z ) -6 , 11-hexadecadienyl acetate , the major component of the sexual pheromone in this species , for which a wealth of precise information is available ( reviewed in [25] ) . The pheromone molecules are adsorbed on the cuticle , diffuse inside the sensory hair to the neuron membrane and are thought to be enzymatically deactivated [25] then degraded . The initial cell response is triggered by the binding of the pheromone molecules to the receptor molecules borne by the dendritic membrane and the ensuing receptor activation . A cascade of events follows , amplifying this initial response and finally leading to the generation of a train of action potentials conveyed to the brain . The pheromone concentration at each instant determines the ORN response . However the extreme temporal variability of pheromone concentration in plumes prevents a full description of stimulus-response relationships by direct electrophysiological measurements . For this reason we based our study on a model of perireception and reception processes describing how any stimulus ( concentration of pheromone in the air ) is transformed into the receptor response ( concentration of activated receptors ) . This model , based on extensive biochemical , radiochemical and electrophysiological experiments , was developed by Kaissling and coworkers [25] , [26] . It involves the following system of chemical reactions: ( 1 ) ( 2 ) ( 3 ) The network includes ( 1 ) the translocation of the ligand from the air ( input pheromone signal Lair ) to the hair lumen ( L ) ; ( 2 ) the reversible binding of L to receptor R and the reversible change of the complex RL to an activated state R* ( output signal ) ; ( 3 ) the reversible binding of L to a deactivating enzyme N and its deactivation to product P which is no longer able to interact with the receptor . The concentrations of individual components in the network 1–3 are denoted by square brackets and the concentration values are functions of time . For simplicity we omit here the explicit dependence on the time variable t and adopt the following notation for the individual concentrations: Lair = [Lair] ( t ) , = [L] ( t ) , R = [R] ( t ) , RL = [RL] ( t ) , R* = [R*] ( t ) , N = [N] ( t ) , P = [P] ( t ) and NL = [NL] ( t ) . The evolution of the system 1–3 in time given the external signal Lair is fully described by five first order ordinary differential Equations 4–8 and two conservation Equations 9 and 10: ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) ( 10 ) Equations 9 and 10 follow from the fact that the total concentration of the receptor molecules , Rtot = R+RL+R* , as well as the total concentration of the deactivating enzyme , Ntot = N+NL , do not change over time . We assume that at t = 0 the concentrations L , RL , R* , NL and P are zero . The parameter values , derived from extensive experimental investigations , are given in Table 1 . The efficiency of information transfer in the system 1–3 depends critically on its stimulus-response relationship under single and repeated stimulus pulses . For transferring as much information as possible the response states must be optimally utilized . The actual amount of information transferred is limited by biological constraints . In the system studied , information transfer from Lair ( stimulus ) to R* ( response ) presents three main limitations . First , it is limited by the finite number of receptor molecules per neuron which places an upper bound on the range of responses . Whatever the pheromone concentration ( height of the step ) the concentration of activated receptors cannot exceed at any time [26] . Second , temporal details in the stimulus course shorter than a certain lower limit Δt cannot be analyzed by the system . The smallest period of stimulation of the model studied here is 0 . 4 s [26] , [27] , in agreement with experimental measurements [28] , [29] . With smaller periods , at higher frequencies , the amplitude of the oscillations of R* becomes too small to be effective . Therefore we set Δt = 0 . 4 s . Two successive pheromone pulses separated by a time shorter than Δt cannot be distinguished . Third , information transfer in time is also limited by the response duration , which depends on the deactivation rate of the activated receptors . The time course of R* in response to stimulations of different heights Lair and limited duration ( 0 . 4 s ) is shown in the inset of Figure 3A . The concentration of activated receptors rises at first , reaches RΔ* at the end of the stimulus pulse , i . e . , RΔ* = R* ( t = Δt ) , and finally decreases . We consider RΔ* as the “response” of the system and for the sake of simplicity in the following , we omit index Δ . The duration of the falling phase ( receptor deactivation ) gets progressively longer for higher pheromone concentrations . This deactivation takes typically much longer than the time resolution parameter Δt . The falling phase is often described by the half-fall time , τ ( R* ) , which is the time required for R* ( t ) to decrease from R* to R*/2 . The relationship between R* and τ ( R* ) is shown in Figure 3A . A unique value of R* corresponds to each value Lair , which defines the stimulus-response curve ( Figure 3B ) . The fact that the deactivation of activated receptors is relatively slow suggests that the reception system cannot encode a long sequence of pheromone pulses in arbitrarily quick succession . This observation plays an important role in the definition of the optimal stimulus course . In the simplest scenario ( with no other constraints on the response range and stimulus-independent additive noise ) , the inputs should be encoded so that all responses are used with the same frequency [2] , [30] . The optimal stimulus is thus described by its probability distribution function , which is obtained directly from the stimulus-response curve . Due to the large differences in reaction times at different stimulus concentrations , all response values R* from 0 to 0 . 24 µM cannot be considered as equally “usable” ( the long falling phases decrease the efficacy of the information transfer ) . Therefore , the longer the half-fall time of a given response R* ( i . e . the greater concentration R* is ) the less frequent it must be . The particular form of the optimal response cumulative probability distribution function ( CDF ) , FR ( R* ) , which was determined by maximizing the information transferred and minimizing the average half-fall time ( see Methods ) , is shown in Figure 3C . Then , based on the three factors mentioned ( stimulus-response curve , Figure 3B; time resolution Δt = 0 . 4 s; and optimal response probability distribution , Figure 3C ) , an optimum stimulus course in time can be predicted as explained in the Methods section . Examples of predicted temporal fluctuations in pheromone concentration are shown in Figure 4 at various time scales and compared to experimental observations . Even though the time resolution of the system studied here is only 0 . 4 s , it seems sufficient to capture the main bursts of pheromone ( see the 10 s sample in Figure 4A ) . The comparison can be made more precise by describing statistically the heights and occurences in time of the pulses . Concerning temporal aspects , the bursts of non-zero signal do not occur at periodic intervals but appear randomly . An important descriptor of the temporal structure is the intermittency [15] , [16] , which is the fraction of total time when the signal is present . The intermittency of the predicted optimal stimulus is 20% , which is in relatively good agreement with experimental data . It has been shown using various types of ion detectors [17] , [19] as well as electroantennogram responses [17] , [31] , that the natural signal is always present less than 50% of the total time , and usually smaller values are found . The average intermittency values reported are 10–20% [15] and 10–40% [16] , [17] , depending on the experimental conditions , such as the detector size or the global meandering of the plume ( see Discussion ) . Concerning pulse height , the overall character of the predicted stimulus course is that pulses of high concentration are much rarer than those of low concentration . This feature of the predicted stimulus can be best quantified by the CDF , P ( Lair ) , of the stimulus . The shape of the CDF is one of the most important properties for comparing theoretical predictions to experimental measurements because it describes the relative distribution of odorant concentrations throughout the plume . In fact , because measuring pheromone concentration in the field is not presently feasible [17] , pheromone molecules must be replaced by measurable tracers . Relative quantities are valid for both pheromones and tracers ( see Discussion ) . They are the only quantities known experimentally for pheromone plumes . So , although our model predicts them , we cannot compare values of Lair to actual measurements . Given the definition of the optimal stimulus , function P ( Lair ) can be directly computed ( see Methods ) . Figure 5 shows a comparison between experimentally measured ( A ) and predicted ( B ) concentration CDF . The optimal pheromone concentration CDF ( Figure 5B , solid line ) is not known in analytical form but it can be well approximated by an exponential CDF ( Figure 5C , dashed line ) . The differences between the predicted and true exponential shape can be considered as non-significant , namely , very high values of Lair are predicted to be less frequent than in the exponential model . The exponential CDF is in agreement with experimental CDF ( Figure 5A ) , [18] , [19] , [32] , [33] and holds well especially for observations closer to the source ( less than 100 m ) . Although the precise form of the CDF varies with distance from the plume centerline [19] and may be affected by the measurement technique , the shape is always highly skewed . Other predicted relative quantities ( peak-to-mean ratios , dimensionless concentrations Lair/〈Lair〉 ) were compared with their experimental counterparts . The results , summarized in Table 2 , show that the predicted statistical properties of the stimulus are not contradicted by the experimental observations . Spectral density functions of the concentration time course , which analyze the contribution of various frequencies to the overall stimulus course , characterize other properties of the plume which are independent on the nature of the odorant ( pheromone or ion source ) [19] , [33] . Furthermore , spectral density function represents a point of view different from the concentration probability distribution . Several spectral density functions , shown in Figure 6 , were calculated from the predicted optimal pheromone stimulation ( see Methods ) . The spectral shapes seem to be almost flat from 0 . 02 Hz to 0 . 2 Hz with a decreasing slope close to −2/3 above 0 . 2 Hz . The same slope −2/3 , which is theoretically predicted by the inertial subrange theory [19] , was reported in the spectral densities obtained from measurements close to the source ( less than 100 m ) , in the range 0 . 1 Hz ( or 0 . 5 Hz , depending on records ) to 1 Hz [19] , [33] , although the precise range may depend on the technique of measurement .
In theory and in practice , the quantitative description of odor plumes and their spatiotemporal distribution is less straightforward than that of visual or auditory scenes . Contrary to light and sound , for which the physical description is essentially complete , the turbulent phenomena which underlie the plume characteristics are still an incompletely mastered domain of physics [34] . In Laughlin's classical experiment in vision a single time-independent variable , the contrast level , was measured [2] and directly compared with experimental data . In olfaction , however , the odorant concentration ( an analogue to the contrast level ) is essentially time dependent which results in a complex optimal stimulus course ( Figure 4 ) . Complexity and time dependence make a meaningful direct comparison between predictions and experimental records , but also between different experimental records , impossible . Instead , the comparison must rely on global , statistical descriptors [15] , [17] , [19] , [33] . We identified 5 such descriptors of odor plumes , actually measured and usable in the present context ( see Table 2 ) , which summarize the present knowledge on odor plumes . Moreover , there are no easy-to-use instruments to measure odor plumes in the field , comparable to luxmeters and microphones . For example , the absolute pheromone concentration cannot be easily known in field experiments [17] . This explains why no experimental values were given for this descriptor in Table 2 . In practice , only ratios of concentrations are presented because they are independent of the dispersed molecules . The pheromone is often substituted by an ion or a passive tracer ( polypropylene for example ) whose concentration can be measured [15] , [17] , [19] . Because both pheromone and tracer compounds in the air are governed by the same physical laws , the relative ( dimensionless ) values are conserved , as confirmed by independent experiments with different sources [15]–[17] , [33] . More generally , this limitation explains why we compared only relative quantities ( i . e . shape of probability distributions , spectral density functions , peak-to-mean ratios , dimensionless concentrations Lair/〈Lair〉 and intermittency values ) . Other limitations of plume measurements are discussed below . The essentially multidimensional and stochastic nature of the odor stimulus has a profound influence on the analysis of olfactory transduction system in its natural context , as undertaken here . Indeed to investigate the problems at hand , the kinetic responses of the system to a very large number of stimuli , varying in intensity , duration and temporal sequence must be known in order to simulate the diversity of stimuli encountered in a natural plume . This task is difficult , if not impossible , to manage in a purely experimental approach . However , this difficulty can be overcome with an exact dynamic model of the system because its response to the diverse conditions mentioned can be computed , provided it includes all initial steps from molecules in the air to the early neural response . This is the case of the perireception and reception stages of the moth pheromonal ORN and the reason why it was chosen in the present study . This choice brings about two questions , one about the validity of the model , the other on its position within a larger context . The computational model employed has been thoroughly researched and improved over the last three decades [25] , [35]–[37] . It describes perireceptor and receptor events in the ORN cell type sensitive to the main pheromone component of the saturniid moth Antheraea polyphemus . At the time of writing it represents the most completely researched computational models of its kind , agreeing with extensive experimental data from various authors and a wide range of experimental techniques . This model is the best description presently available for early events in any ORN and it summarizes in a nutshell a wealth of dispersed knowledge . This model is based on ordinary differential equations 4–8 , following the law of mass action for chemical reactions , and is therefore purely deterministic . This approximation is acceptable when the concentrations of reactants are high enough above single-molecular levels , so that the stochastic fluctuations can be neglected . In this paper , the concentration of R* is always well above that corresponding to one activated receptor molecule per neuron ( approximately 10−6 . 2 µM ) because we do not investigate the effect of extremely small pheromone doses . Then , the response of the system can be considered as deterministic , in accordance with the efficient coding hypothesis [8] . The system studied here constitutes only a small part of the whole pheromonal system , although its role is absolutely essential and all other parts depend on it . First , in ORNs , post-receptor mechanisms modify the receptor signal , primarily by a large amplification factor and by sensory adaptation . Second , the ORN population includes cell types with different properties , e . g . the ORN type sensitive to the minor pheromone components can follow periodic pulses up to 10 Hz [29] , a performance not yet accounted for in present models [27] . Third , in the brain antennal lobe , convergence of a large number of ORNs on a few projection neurons ( PNs ) provides another amplification and supports the ability of some PNs to follow periodic signals at 10 Hz or greater [38] . Evolutionary adaptation of an integrated ORN response is difficult to study at the present time because no complete model of the ORN from receptors to the generation of the receptor potential and the ensuing spike train , is yet available , at least with the required degree of precision . The same argument holds a fortiori for higher order processes . Notwithstanding , the study of the early sensory events is not as restrictive as it may seem because any incoming odor signal must be first transduced in the population of membrane receptors . No information can be extracted by the post-receptor transduction system which has not been encoded by the receptors in the first place . For this reason it is essential to investigate the nature of the adaptation of the initial events ( pheromone interaction with receptors ) to the pheromone signal . Different response states of the pheromone reception system have different efficacies from the coding point of view: the “high” states , with large concentrations of activated receptors , take much more time to deactivate than the “low” states , so that for some time after its exposition to a large concentration of pheromone the system is “dazzled” . It means that in the optimal stimulus the low pheromone concentrations must be more frequent than the high ones . This is a difference with respect to the classical problem where the efficacy of all response states at transferring information is considered the same , as in the vision of contrasts for example . The problem to solve is to find the right balance between two conflicting demands: to use all response states ( including the high ones ) and to react rapidly ( the short transient responses must be as frequent as possible ) , i . e . to maximimize the information transferred per time unit . The solution to this optimization problem is provided by information theory as detailed in the Methods section . The optimal balance derives from Equation 19 which relates the average half-fall time and the maximum response entropy distribution . The key factor to consider in the optimization is the average half-fall time , which characterizes globally the “swiftness” of the system – smaller average half-fall time means faster stimulation rate . In other words , the average half-fall time characterizes the bias towards “low” response states . Simultaneously , the condition of maximum response entropy guarantees that the temporal dynamics of the system is as varied as possible and that during the course of stimulation every possible response state is used ( with appropriate frequency ) . By taking into account only the average half-fall time , and not the precise sequence of its individual values , we therefore do not neglect or limit the temporal dynamics of receptor molecules activation . It is important to note , that the average half-fall time is not a free parameter of the problem; it is not set a priori: its optimal value follows from the optimization procedure ( Equation 20 ) . The resulting optimal response CDF is highly biased towards low response states , as expected ( see Figure 3C ) . The main achievement of the present investigation was to predict the characteristics of the stimulus optimally processed by the receptor system based on its biochemical characteristics and an information theoretic approach . The predicted optimal plume was shown to be close to the actual plumes for a series of characteristics , namely intermittency , peak/mean ratio and peak/standard deviation ratio of pheromone pulses , probability distribution of dimensionless pheromone concentration and spectral density function of pheromone concentration ( Table 2 , Figures 4–6 ) . The correspondence between the predictions and measurements is very good for the last two characteristics ( probability distributions ) and fair for the first three ( numerical values ) . These differences in precision of the predictions may be interpreted by taking into account technical factors . Increasing the noise rejection threshold leads to a decrease of the measured intermittency [15] , [19] , while increasing the detector size or averaging the signal over longer time windows has the opposite effect [39] . So , for example , the small size of olfactory sensilla with respect to detectors may explain in part why in Figure 4B , the predicted intermittency seems lower than that in the corresponding experimental record sample , and also why the peak-to-mean ratio and peak-to-standard deviation ratio are relatively higher . The immobility of the measurement devices , in contrast with the active movements of the moths , is another significant factor . For example , long pauses ( of the order of minutes ) of zero signal are missing in the prediction but visible in the longest available field record ( 350 s , Figure 4C ) . They are caused simply by the plume being blown away from the immobile field detector . First , this loss of signal is clearly an extraneous effect , which cannot be included in our optimal signal predictions and therefore cannot be seen in our results . Second , the moth is not subjected to this extraneous effect , or at least not to the same extent , because , in case of signal loss , it actively seeks the pheromone plume , whereas the fixed detector must passively wait for its return . This difference of mobility may substantially affect the intermittency values , but does not affect the shape of probability distributions ( see Methods ) , hence the better quality of the fits in the latter case . In conclusion , the results obtained suggest that the perireceptor and receptor system investigated here is evolutionary adapted to the pheromone plumes . Even if one considers that the pheromone olfactory system must be a priori adapted to the average characteristics of the pheromone plumes , it does not logically follow that the system studied is itself necessarily well adapted . Indeed , it is conceivable that the global adaptation results mainly , not from perireception and reception processes but from other downhill intra- and intercellular processes involved in higher signal processing . The respective importance of the former and latter processes in global adaptation cannot be decided a priori . Therefore , the relatively close correspondence between predicted and observed plume characteristics presented here is not trivial . It suggests that the adaptation at the level of receptors is already substantial , and consequently that the global adaptation is not predominantly the result of post-receptor mechanisms involving amplification , sensory adaptation , convergence of different ORN types in the antennal lobes etc . The role of these mechanisms in the global adaptation of the animal remains to be established , as well as the relative importance of the various components of the olfactory system ( receptor population , ORN as a whole , population of pheromonal ORNs in the antenna , projection neurons in the antennal lobes , etc . ) . The response characteristics of these other subsystems , e . g . their various temporal resolutions , will have also to be interpreted , maybe in relation with changing plume characteristics with distance to the source and other factors yet to be identified .
As mentioned in the Results section , information transfer in the pheromone reception system is limited by the finite response range , ( ) , and by the deactivation rate of the activated receptors for each concentration value R* . This deactivation rate is described by the half-fall time τ ( R* ) . The optimal performance of the system is thus reached by a trade-off between two conflicting demands: to employ full response range ( maximum information ) vs . to employ only the “fastest” responses ( minimum average half-fall time ) . In other words we need to maximize the information transferred per average half-fall time . In the following we provide the mathematical framework that enabled us to find the probability distribution function over the response states R* that realizes this trade-off . The optimal stimulus course in time was calculated as follows . First , at time t0 = 0 a random value p0 is drawn randomly from a uniform probability distribution function over the range [0 , 1] . The concentration corresponding to probability p0 is obtained by solving the equation ( 23 ) where FR ( R* ) is the optimal CDF given by formula 22 ( Figure 3C ) . The predicted optimal concentration Lair , 0 for a pheromone pulse of duration Δt = 0 . 4 s which corresponds to is obtained by solving the equation ( 24 ) where R* ( Lair ) is the stimulus-response function ( Figure 3B ) . The value Lair , 0 is plotted at t0 ( Figure 4 ) . Second , the concentration Lair , 1 and time of appearance t1 of the next pulse are determined . Time t1 follows from the falling phase of activated receptors: optimality requires that no pheromone pulse appears before R* returns to its resting level . In practice it is considered that the resting level is reached when R* falls below 0 . 01 µM ( less than 5% of the coding range ) . The concentration Lair , 1 of the pulse at t1 is determined in the same way as for the pulse at t0 by drawing a new random number p1 from the uniform probability distribution function over [0 , 1] . The same process can be repeated as many times as needed to create an optimal pheromone pulse train of arbitrary length . It is common in the literature on the statistical analysis of plumes [15] , [18] , [19] to define two types of mean concentrations . The total mean concentration , 〈Lair〉 , describes the “true” mean concentration obtained from the whole record of concentration fluctuations in time , i . e . , including the parts where no signal was available . On the other hand , the conditional mean concentration , 〈Lair〉cond , describes the mean concentration inside the plume , i . e . , with zero concentrations excluded . The intermittency , γ , relates the two means as [19] ( 25 ) ( Analogously , the total variances and total standard deviations are calculated by taking into account also the parts where no signal is available [19] . ) By combining Equations 23 and 24 we may symbolically express the optimal CDF of the stimulus , P ( Lair ) , as ( 26 ) Though P ( Lair ) cannot be expressed in a closed form , it can be well approximated by the exponential CDF ( 27 ) where ξ = ( 5 . 24±0 . 01 ) ×10−4 µM is the estimated value of 〈Lair〉cond by least-squares fitting of Fexp ( Lair ) to P ( Lair ) . In order to compare concentration probability distribution functions from different measurements meaningfully , authors [19] plot the CDF for a dimensionless concentration Lair/〈Lair〉 . ( In the Figure 5A C/〈C〉 is used , since the data plotted were obtained using a propylene source , not pheromone ) , see Figure 5A . The scale of such plots is affected by intermittency due to the presence of the total mean in the ratio . Furthermore , information about intermittency is included explicitly in the plots by letting the probability P ( Lair = 0 ) of zero concentration be ( 28 ) Consequently the CDF P ( Lair ) must be renormalized [19] . Intermittency affects only the dimensionless scale , Lair/〈Lair〉 , and the value of P ( Lair = 0 ) but not the overall shape of CDF [19] . Therefore we can use formulas 25 and 28 to compare our predictions with experimentally measured data by correcting for different intermittency values . The optimal stimulus course is represented by pulses of different pheromone concentrations , Lair , occurring in time intervals 0 . 4 s long . In order to calculate the spectral density function of such stimulation course we sample the time axis with step Δt = 0 . 4 s . Thus we obtain a series of pheromone concentrations at these time points , {Lair , j} , j = 1…n , where n should be even . The discrete Fourier transform , φk , of {Lair , j} is defined for k = 1 , … , n values as [41] ( 29 ) where i is the complex unit . The zero-frequency term is thus at position k = 1 . The spectral density , S ( f ) , of the complete time course of the stimulus can be calculated for a total of n/2+1 values of frequency f ( given in Hz ) [42] ( 30 ) where m = 0 , 1 , 2 , … , n/2−1 , n/2 and f = m/ ( nΔt ) are the frequency values . The function Π ( f ) is the Fourier transform of a pulse of unit height , 0 . 4 s long and starting at t = 0 [41] , ( 31 ) where a = 2 . 5 and δ = −0 . 5 . The function Π ( f ) appears in formula 30 because the whole stimulus course ( such as shown in Figure 4 , bottom panels ) can be reconstructed by convolving the discrete series {Lair , j} with such a pulse of unit height in the time domain [41] . | Efficient coding is an overarching principle , well tested in visual and auditory neurobiology , which states that sensory neurons are adapted to the statistical characteristics of their natural stimulus - in brief , neurons best process those stimuli that occur most frequently . To assess its validity in olfaction , we examine the pheromone communication of moths , in which males locate their female mates by the pheromone they release . We determine the characteristics of the pheromone plume which are best detected by the male reception system . We show that they are in agreement with plume measurements in the field , so providing quantitative evidence that this system also obeys the efficient coding principle . Exploring the quantitative relationship between the properties of biological sensory systems and their natural environment should lead not only to a better understanding of neural functions and evolutionary processes , but also to improvements in the design of artificial sensory systems . |
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Bacteria use trans-translation and the alternative rescue factors ArfA ( P36675 ) and ArfB ( Q9A8Y3 ) to hydrolyze peptidyl-tRNA on ribosomes that stall near the 3' end of an mRNA during protein synthesis . The eukaryotic protein ICT1 ( Q14197 ) is homologous to ArfB . In vitro ribosome rescue assays of human ICT1 and Caulobacter crescentus ArfB showed that these proteins have the same activity and substrate specificity . Both ArfB and ICT1 hydrolyze peptidyl-tRNA on nonstop ribosomes or ribosomes stalled with ≤6 nucleotides extending past the A site , but are unable to hydrolyze peptidyl-tRNA when the mRNA extends ≥14 nucleotides past the A site . ICT1 provided sufficient ribosome rescue activity to support viability in C . crescentus cells that lacked both trans-translation and ArfB . Likewise , expression of ArfB protected human cells from death when ICT1 was silenced with siRNA . These data indicate that ArfB and ICT1 are functionally interchangeable , and demonstrate that ICT1 is a ribosome rescue factor . Because ICT1 is essential in human cells , these results suggest that ribosome rescue activity in mitochondria is required in humans .
The presence of a stop codon at the end of an open reading frame signals that the nascent protein is complete . Decoding of the stop codon by a release factor results in peptidyl-tRNA hydrolysis , releasing the completed protein and allowing the ribosome to be recycled [1] . Specific contacts between the release factors and bases in the stop codon are required for efficient catalysis of peptidyl-tRNA hydrolysis [2] . This stop codon recognition is necessary to prevent release factors from acting at sense codons and prematurely terminating translation . However , ribosomes can sometimes translate to the end of an mRNA without terminating at an in-frame stop codon . Translation cannot terminate normally at these “non-stop” complexes , because there is no stop codon in the decoding center to promote release factor activity . Ribosomes must be rescued from non-stop complexes so they can be recycled for productive protein synthesis [3 , 4] . In bacteria , non-stop complexes are rescued primarily by trans-translation . During trans-translation , a small protein , SmpB ( P0A832 ) , and a specialized RNA , tmRNA ( EG30100 ) , recognize a non-stop complex and release the ribosome at a stop codon within tmRNA . trans-Translation also targets the nascent polypeptide and mRNA from the non-stop complex for degradation [3 , 5] . Genes encoding tmRNA or SmpB have been identified in >99 . 9% of sequenced bacterial genomes [4] . trans-Translation is essential in some bacteria [6–8] , but other species can survive without ssrA ( encoding tmRNA ) and smpB [9–11] . Some species , such as C . crescentus , have a severe growth defect when ssrA is deleted [12] . In other species , such as Escherichia coli , there is a relatively mild phenotype [13] . Synthetic-lethal screens have identified two alternative rescue factors , ArfA and ArfB , that can rescue non-stop complexes in the absence of trans-translation [14–16] . ArfA , found in E . coli and closely related bacteria , allows the release factor RF-2 to hydrolyze peptidyl-tRNA on non-stop ribosomes [17–20] . ArfB , found in C . crescentus and species from many phyla , contains a catalytic domain similar to release factors but does not include domains required for stop codon recognition . ArfB catalyzes hydrolysis of peptidyl-tRNA on non-stop ribosomes [15 , 16 , 21] . The C-terminal tail of ArfB is important for its activity [22] , and structural studies suggest that it binds in the empty mRNA channel of non-stop ribosomes , similar to the C-terminal tail of SmpB [23] . arfA is essential in E . coli ΔssrA cells [14] , and arfB is essential in C . crescentus ΔssrA cells [21] , indicating that these species require at least one ribosome rescue mechanism . These observations have led to the suggestion that ribosome rescue activity may be essential for most or all bacteria [5] . Eukaryotes use Dom34/Pelota ( P33309/Q9BRX2 ) and Hbs1 ( P32769 ) to rescue ribosomes from non-stop mRNAs during translation in the cytoplasm [24 , 25] , but factors required for this system are not present in mitochondria . Mammals have an ArfB homolog , ICT1 , which is encoded in the nucleus and transported to mitochondria [26] . Knockdown experiments have demonstrated that ICT1 is essential in human cells [26 , 27] , but conflicting models have been proposed to explain why ICT1 is essential [28–30] . Like ArfB , ICT1 can hydrolyze peptidyl-tRNA on E . coli non-stop ribosomes in vitro [22] . Because ICT1 can also hydrolyze peptidyl-tRNA on E . coli ribosomes assembled on short mRNAs with a stop or sense codon in the A site , it has been proposed to act as a general release factor that can terminate translation at any codon [26] . ICT1 has also been proposed to act on ribosomes stalled in the middle of an mRNA based on its ability to promote protein synthesis in reactions stalled by omission of a cognate release factor or tRNA [31] . Two mRNAs encoded in human mitochondria terminate with an AGA or AGG codon , so if ICT1 can act as non-specific release factor it might terminate translation of these messages . However , the sequence similarity between ICT1 and ArfB suggests that these factors are likely to have the same activity . ICT1 and ArfB share the conserved GGQ motif found in release factor catalytic domains , as well as residues in the C-terminal tail that are required for ArfB activity on non-stop ribosomes ( Fig 1 ) [22 , 31] Using a direct assay for peptidyl-tRNA hydrolysis in vitro , we find that the substrate specificity of ICT1 is almost identical to that of ArfB . Both factors catalyze peptidyl-tRNA hydrolysis on ribosomes stalled with no mRNA in the A site , or with mRNA extending a short distance past the A site , but have little activity on ribosomes stalled in the middle of an intact mRNA . In addition , we find that ArfB and ICT1 are interchangeable in vivo , both in C . crescentus and in human cells . These data indicate that ICT1 is a ribosome rescue factor and cannot terminate translation in the middle of mRNA , and suggest that mitochondrial ribosome rescue activity is essential in humans .
To evaluate the substrate specificity of ICT1 , we used a gel-based assay to measure peptidyl-tRNA hydrolysis on E . coli ribosomes translating protein from mRNA . In these experiments , protein is produced using in vitro transcription-translation reactions and the components are separated on Bis-Tris gels that preserve the ester bond in peptidyl-tRNA . The fraction of protein that remains in the peptidyl-tRNA band indicates the extent of peptidyl-tRNA hydrolysis during the reaction [21] . To confirm that ICT1 can hydrolyze peptidyl-tRNA on non-stop ribosomes in a manner similar to ArfB , a coupled transcription-translation system lacking RF1 , RF2 , and RF3 was used to express a folA gene ( encoding DHFR ) that lacked a stop codon . Consistent with previous observations of peptidyl-tRNA hydrolysis on non-stop ribosomes [15 , 16 , 21] , addition of a release factor mixture containing RF-1 , RF-2 , and RF-3 to the reaction had little effect on the percentage of DHFR found in peptidyl-tRNA , but when ArfB was included 74 ± 1% of the DHFR was released ( Fig 2 ) . When ICT1 was included in the reaction , 78 ± 8% DHFR was released , indicating that ICT1 has a similar activity to ArfB on non-stop ribosomes . To determine if ICT1 and ArfB can also release ribosomes stalled with mRNA extending into or past the A site , the peptidyl-tRNA hydrolysis assay was repeated with longer folA templates . A stop codon was added to the end of the non-stop template , and 0 , 6 , 14 , or 33 nucleotides were added after the stop codon . Each template was designed to produce an mRNA with a stem-loop at the 3’ end to limit exonuclease activity during the reaction . Translation of these mRNAs will result in a ribosome stalled at the stop codon with peptidyl-tRNA in the P site . As expected , addition of release factors to reactions with any of these templates resulted in release of most of the peptidyl-tRNA ( Fig 2 ) . ICT1 and ArfB hydrolyzed peptidyl-tRNA as efficiently as release factors when the stop + 0 template was used . Substantial peptidyl-tRNA hydrolysis activity by ICT and ArfB was also observed with the stop + 6 template , but significantly less activity was observed when the template had a longer sequence past the stop codon ( p < 0 . 001 ) . Almost no hydrolysis was observed with ICT1 or ArfB on the stop + 33 template . These results indicate that ICT1 and ArfB can hydrolyze peptidyl-tRNA on ribosomes stalled near the 3’ end of an mRNA , and that a codon in the A site does not interfere with ribosome rescue . However , mRNAs that extend ≥ 14 bases past the A site substantially decrease activity of both ICT1 and ArfB .
Rescue of ribosomes from non-stop translation complexes is a critical function for most bacteria , and the results presented here indicate that rescue of mitochondrial ribosomes is also essential in some eukaryotes . C . crescentus cells can survive without tmRNA and SmpB because they have ArfB to rescue ribosomes in the absence of trans-translation [21] . We have previously shown that C . crescentus ArfB can hydrolyze peptidyl-tRNA from non-stop ribosomes in vitro [21] . The data described here show that ArfB is also active on ribosomes stalled with a full codon in the A site or with 6 nucleotides past the A site , but longer mRNA extensions strongly inhibit ArfB activity . This substrate specificity is similar to that observed for tmRNA-SmpB [34–36] , and indicates that ArfB is unlikely to interfere with translation elongation or with ribosomes paused during translation of full-length mRNAs . Instead , ArfB activity is consistent with a role in rescuing ribosomes that have translated to the 3’ end of an mRNA without terminating , ribosomes that have stalled after cleavage of the mRNA in the A site by a nuclease such as RelE [37] , and stalled ribosomes that have had the 3’ portion of the mRNA removed by exonuclease activity [36 , 38] . Several lines of evidence demonstrate that human ICT1 is a ribosome rescue factor like ArfB , and not a non-specific release factor that can act on ribosomes stalled in the middle of an mRNA . First , the specificity of ICT1 in vitro for non-stop ribosomes or ribosomes with short mRNA extensions past the A site is similar to that of ArfB . Second , ICT1 can functionally replace ArfB in C . crescentus . Expression of ICT1 suppresses the synthetic lethality of deleting ssrA and arfB , and over-expression of ICT1 increases the growth rate in ΔssrA cells to the same extent as over-expression of ArfB . Third , expression of ArfB in human cells suppresses the lethal effects of silencing ICT1 , indicating that ArfB can functionally replace ICT1 in human cells . Finally , ICT1 would have little opportunity to act as a non-specific release factor during translation of intact transcripts in the mitochondria because mammalian mitochondrial transcripts are polyadenylated with ~50 nucleotides [39–44] . Based on the substrate specificity of ICT1 in vitro , this poly ( A ) tail would block ICT1 activity unless the mRNA was truncated , so ICT1 substrates for in vivo are likely to be non-stop complexes . Because ICT1 is essential in human cells [26 , 27] , these results suggest that ribosome rescue in mitochondria is essential for human cell viability . The activity of ICT1 as a rescue factor and not a non-specific release factor would also explain why ICT1 does not interfere with translation elongation in mitochondria . ICT1 substrate specificity has important implications for translation termination in mitochondria . Two human mitochondrial genes end in an AGG ( ND6 ) or AGA ( COI ) codon . There are no cognate mitochondrial tRNAs for these codons and the mitochondrial release factor mtRF1a is unable terminate translation at AGA or AGG [45] , so it is unclear how translation is terminated for these genes . ICT1 has been proposed to function as the termination factor at these codons based on analysis of activity in vitro on ribosomes stalled with an mRNA extending up to 14 nucleotides past the A site [30 , 31] . Our data show that ICT1 activity is greatly reduced on ribosomes stalled with an mRNA extending 14 nucleotides past the A site , and ICT1 activity is completely absent when the mRNA extends 33 nucleotides past the A site . Because the COI AGA codon is 72 nucleotides from the end of the transcript and the ND6 AGG codon is 500–550 nucleotides from the end of the transcript [39 , 42] , ICT1 should have no activity at these codons in either their unmodified or polyadenylated form . In addition , ICT1 can support viability in C . crescentus , so it cannot have an intrinsic ability to terminate translation at AGA or AGG because these codons encode arginine in bacteria . Likewise , ArfB does not recognize AGA or AGG in C . crescentus , so the ability of ArfB to replace ICT1 in human mitochondria suggests that termination at AGA or AGG in the middle of a transcript is not an essential function for ICT1 . One possible mechanism for both ICT1 and ArfB to terminate translation at these codons is that stalling of the ribosomes leads to truncation of the mRNA 3’ of the ribosome , thereby producing a substrate for the rescue factors . A second possible mechanism for termination at AGA or AGG by ICT1 and ArfB would be that mitochondrial ribosomes might respond differently when they stall in the middle of an mRNA than do bacterial ribosomes . Mitochondrial ribosomes are descended from bacterial ribosomes , but are highly specialized for translating a small number of mRNAs encoded in the mitochondrial genome . The decoding center and peptidyl transfer center of mitochondrial ribosomes are very similar to bacterial ribosomes , but other architectural features are highly diverged [46] . For example , mammalian mitochondria have dramatically reduced rRNAs and lack 5S rRNA , but contain 36 proteins not found in bacteria and incorporate a tRNA as a structural component of the large subunit [47] . It is possible that this different architecture causes mitochondrial ribosomes to adopt a conformation that promotes rescue by ICT1 and ArfB when they are stalled in the middle of an mRNA . If ICT1 does not terminate translation at these codons , one of the other members of the mitochondrial RF family might perform this function . Mitochondria descend from a progenitor of α-proteobacteria [48 , 49] , the bacterial class that includes C . crescentus . Most α-proteobacterial species contain both trans-translation and ArfB , and some protist mitochondria encode ssrA and smpB [4] , suggesting that the primordial mitochondrion had both ribosome rescue systems . Why did mitochondria keep ArfB and discard trans-translation , whereas almost all bacteria have kept trans-translation whether they have an alternative rescue factor or not ? Mitochondria encode only 13 proteins , all of which are integral membrane proteins . Perhaps this limited proteome decreases the selective advantages of trans-translation , for example by enabling proteases to recognize incomplete versions of proteins without the tmRNA-encoded tag . Alternatively , the constraints of importing factors encoded in the nucleus might have favored ICT1 over tmRNA-SmpB , because ICT1 acts as a single protein . Interestingly , some plants encode an ArfB/ICT1 homolog with a chloroplast-targeting signal , ( Q84JF2 , e . g . ) suggesting that ribosome rescue activity may be found in other organelles , and that the ArfB-type ribosome rescue may be generally favored in eukaryotic organelles .
Strains are described in Table 1 . C . crescentus strains were grown at 30°C [50] in peptone-yeast extract ( PYE ) medium supplemented with tetracycline ( 2 μg/ml ) , streptomycin ( 50 μg/mL ) , spectinomycin ( 100 μg/mL ) , kanamycin ( 20 μg/mL ) , or xylose ( 0 . 3% ) where appropriate . Growth was monitored by measuring optical density at 600 nm . To construct pET28ICT1 for expression and purification of mature ICT1 , the coding sequence ( codon-optimized for E . coli ) ICT1 lacking the mitochondrial localization signal was purchased as a gBlock Gene Fragment ( IDT ) and inserted into pET28b by Gibson assembly [51] . Construction of parfB ( formerly pCC1214 ) has been described previously [26] . pICT1 was constructed by digesting pET28ICT1 with NdeI and BamHI and ligating the resulting fragment into parfB digested with the same enzymes . pMCSVICT1 was constructed by Gibson assembly of the human ICT1 sequence into the EcoRI and NotI sites of pMSCVneo . Alternative codons were selected for the region of ICT1 targeted by the siRNA to ensure that only endogenous ICT1 would be silenced . pMCSVarfB was similarly constructed by Gibson assembly of a gBlock Gene Fragment into pMSCVneo at the EcoRI and NotI sites . The arfB construct encodes the 62 residue N-terminal extension of human ICT1 to target it to mitochondria followed by the 142 residues of C . crescentus ArfB sequence codon-optimized for expression in human cells . The ArfB coding sequence was codon-optimized for expression in human cells . Purification of C . crescentus ArfB has been described previously [21] . ICT1 was purified using a similar protocol . Strain KCK477 was grown to OD600 ~ 0 . 8 and expression was induced by addition of isopropyl-ß-D-thiogalactopyranoside ( IPTG ) to 1 mM . Cells were grown for 3 h at 37°C , harvested by centrifugation at 4°C , and resuspended in 30 ml lysis buffer ( 6 M guanidine hydrochloride , 20 mM sodium phosphate , 400 mM NaCl ) [pH 7 . 8] . Cells were lysed by sonication and the lysates were cleared by centrifugation at 11 , 000 g for 30 min and applied to a column packed with 500 μl Ni-nitrilotriacetic acid ( NTA ) agarose ( Qiagen ) slurry equilibrated in DB buffer ( 8 M urea , 20 mM sodium phosphate 500 mM NaCl ) [pH 7 . 8] . The column was washed 3X by rocking with 10 bed volumes DB buffer , washed 3X by rocking with 20 bed volumes DW buffer ( 8 M urea , 20 mM sodium phosphate , 500 mM NaCl ) [pH 6 . 0] , and washed 3X with 20 bed volumes of DW buffer [pH 5 . 3] . Protein was eluted in 1 ml fractions in elution buffer ( 8 M urea , 20 mM sodium phosphate , 500 mM NaCl ) [pH 4 . 0] . Fractions containing ICT1 were dialyzed against ICT1 dialysis buffer ( 10 mM HEPES [pH 7 . 6] , 150 mM NaCl ) . The 6 histidine tag was cleaved with Thrombin CleanCleave Kit ( Sigma ) at 4°C for 3 h according to the manufacturer’s instructions . Residual 6x-His tagged protein was removed by incubation with Ni-NTA agarose . Peptidyl-tRNA hydrolysis by ArfB , ICT1 , and release factors was assayed using the PURExpressΔRF1 , 2 , 3 kit ( New England Biolabs ) . Template used for in vitro transcription and translation was generated by PCR using the primers listed in Table 1 . Each template was designed to produce an mRNA with a stem-loop at the 3’ end to limit exonuclease activity during the reaction . PURExpressΔRF1 , 2 , 3 kit components were mixed according to the manufacturer’s instructions and incubated with 200 nM ArfB , 200 nM ICT1 , or 100 nM RF1 , RF2 and RF3 for 1 h at 37°C . Anti-ssrA oligonucleotide was added to 5 μM to inhibit any trans-translation activity from tmRNA in the kit components . Samples were precipitated in 20 μl cold acetone , resuspended in loading buffer pH 6 . 5 ( 5 mM sodium bisulfite , 50 mM MOPS [morphonlinepropanesulfonic acid] , 50 mM Tris , 1 μM EDTA , 0 . 1% SDS , 5% glycerol , 0 . 01% xylene cyanol , and 0 . 01% bromophenol blue ) , heated to 65°C for 5 minutes , and resolved on Bis-Tris gels with MOPS running buffer ( 250 mM MOPS , 250 mM Tris , 5 mM EDTA and 0 . 5% SDS ) . ΦCR30 lysate was prepared from strain KCK428 as described previously [32] . The resulting lysate was used to transduce wild-type or ΔssrA cells harboring pICT1 , parfB , or empty vector . Overnights of each strain were grown in PYE supplemented with tetracycline and xylose to mid log phase . 25 μl ΦCR30 prepared from KCK428 was added and cultures were incubated at 30°C for 2 . 5 h with shaking . Cells were then plated on PYE with kanamycin and xylose to select for transductants . The resulting colonies were tested for spectinomycin and streptomycin resistance to determine the frequency with which arfB:aadA co-transduced . Pre-annealed non-targeting control siNT , or targeting siICT1[26] RNAs were purchased from Eurofins MWG Operon . To demonstrate efficient knockdown , 7 . 5 × 105 HEK293 cells were seeded in a 6-well plate and transfected once every 24 h with siNT and siICT1 as follows: a mixture containing 90 μl serum-free DMEM ( Corning ) , 3 . 4 μl siRNA ( 20 μM stock ) , and 4 μl TransIT-siQUEST ( Mirus Bio ) was incubated at room temperature for 30 min and added to the cells . After 48 h , cells were lysed by addition of buffer containing 150 mM sodium chloride , 50 mM Tris [pH 8 . 0] , 5 mM EDTA , 2 mM phenylmethylsulfonyl fluoride , 2 mM sodium orthovanadate , 10 mM sodium fluoride , and 1% Igepal TM CA-360 ( USB ) . The efficacy of ICT1 silencing was then determined by western blot using polyclonal mouse anti-human ICT1 ( Sigma ) . HEK293 cells ( ATCC ) were seeded in 6-well plates at 5 × 104 cells per well and allowed to adhere for 18 h . Cells were transfected by combining 1 μg pMSCV vector , pMSCVICT1 , or pMSCVarfB , 90 μl serum-free DMEM , and 3 . 8 μl TransIT-293 ( Mirus Bio ) and allowing the mixture to incubate for 30 min at room temperature . 8 h after transfecting with plasmid , cells were transfected with siNT or siICT1 according to the siRNA transfection protocol described in the previous section . Viable cell numbers were determined by 0 . 4% trypan blue staining of trypsinized cells 6 days after silencing . | Ribosomes can stall during protein synthesis on truncated or damaged mRNAs that lack a stop codon . In bacteria , these “non-stop” ribosomes are rescued by trans-translation or by an alternative rescue factor , ArfA or ArfB . Most eukaryotes do not have trans-translation , but mammals have a homolog of ArfB named ICT1 . ICT1 is targeted to mitochondria , and is essential in human cells . Here , we show that human ICT1 and ArfB from the bacterium Caulobacter crescentus have the same biochemical activity and specificity . We also demonstrate that ICT1 and ArfB are functionally interchangeable in both bacteria and human cells . Collectively , this work demonstrates a new essential function in human cells—rescue of mitochondrial non-stop translation complexes . |
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Paired Immunoglobulin-like Type 2 Receptor Alpha ( PILRA ) is a cell surface inhibitory receptor that recognizes specific O-glycosylated proteins and is expressed on various innate immune cell types including microglia . We show here that a common missense variant ( G78R , rs1859788 ) of PILRA is the likely causal allele for the confirmed Alzheimer’s disease risk locus at 7q21 ( rs1476679 ) . The G78R variant alters the interaction of residues essential for sialic acid engagement , resulting in >50% reduced binding for several PILRA ligands including a novel ligand , complement component 4A , and herpes simplex virus 1 ( HSV-1 ) glycoprotein B . PILRA is an entry receptor for HSV-1 via glycoprotein B , and macrophages derived from R78 homozygous donors showed significantly decreased levels of HSV-1 infection at several multiplicities of infection compared to homozygous G78 macrophages . We propose that PILRA G78R protects individuals from Alzheimer’s disease risk via reduced inhibitory signaling in microglia and reduced microglial infection during HSV-1 recurrence .
Alzheimer’s disease ( AD ) results from a complex interaction of environmental and genetic risk factors [1] . Proposed environmental risk factors include a history of head trauma [2–4] and infection [5–7] . In recent years , large-scale genome-wide association studies ( GWAS ) and family-based studies have made considerable progress in defining the genetic component of AD risk , and >30 AD risk loci have been identified [8 , 9 , 18–20 , 10–17] . A key role for microglial/monocyte biology in modulating risk of AD has emerged from analysis of the loci associated with AD risk . Rare variants of TREM2 , a microglial activating receptor that signals through DAP12 , greatly increase AD risk [11 , 14] . Beyond TREM2 , a number of the putative causal genes mapping to AD risk loci encode microglial/monocyte receptors ( complement receptor 1 , CD33 ) , myeloid lineage transcription factors ( SPI1 ) , and other proteins highly expressed in microglia ( including ABI3 , PLGC2 , INPP5D , and PICALM ) .
The index variant for the Alzheimer’s disease risk locus at 7q21 is rs1476679 ( meta P value = 5 . 6 x 10−10 , odds ratio = 0 . 91 ) [15] . In addition to reduced disease risk , the C allele of rs1476679 has been associated with age of onset [21] and lower odds of pathologic AD ( plaques and tangles ) in the ROSMAP study [22] . In the 1000 Genomes project CEU population ( phase 3 data ) , there were 6 variants in strong linkage disequilibrium ( r2>0 . 9 ) with rs1476679 ( S1 Table ) . None of the 6 variants were predicted to alter regulatory motifs that might influence gene expression ( Regulome DBscore ≤ 4 ) , but one variant ( rs1859788 ) encoded a missense allele ( G78R , ggg to agg transition ) in Paired Immunoglobulin-like Type 2 Receptor Alpha ( PILRA ) protein . Using a cohort of 1 , 357 samples of European ancestry whole genome-sequenced to 30X average read-depth ( Illumina ) , we confirmed the strong linkage between rs1476679 ( in ZCWPW1 intron ) and rs1859788 ( G78R PILRA variant ) ( S1 Table ) . We hypothesized that PILRA G78R was the functional variant that accounts for the observed protection from AD risk . As expected from the strong linkage disequilibrium ( LD ) between PILRA G78R and rs1476679 ( Fig 1A ) , conditional analysis demonstrated that the 2 variants were indistinguishable for AD risk in individuals of European ancestry . In a cohort of 8060 European ancestry samples ( a subset of samples described in 19 ) , individuals homozygous for R78 ( OR = 0 . 72 ) and heterozygous ( OR = 0 . 89 ) for R78 were protected from AD risk relative to G78 homozygotes . We note that the allele frequency of PILRA G78R varies considerably in world populations . Indeed PILRA R78 is the minor allele in populations of African ( 10% ) and European descent ( 38% ) but is the major allele ( 65% ) in East Asian populations [23] . The index variant in the 7q21 locus ( rs1476679 ) has been associated with expression levels of multiple genes in the region , including PILRB [24 , 25] . However , the strongest cis-eQTL in the region is a haplotype tagged by rs6955367 which has a low coefficient of determination to rs1476679 ( r2 = 0 . 085 , D’ = 0 . 982 ) in Europeans and is more strongly associated with expression in whole blood of multiple genes in the region ( PILRB , STAG3L5 , PMS2P1 , MEPCE ) compared to rs1476679 [26] . Since the PILRB eQTL P value for rs1476679 is not significant ( P = 0 . 31 ) after conditioning rs6955367 ( S2 Table ) in whole blood , we conclude that rs1476679 and rs1859788 are not significant causal eQTLs in the 7q21 region and the observed relationship of these SNPs with PILRB expression is due to the weakly correlated variant rs6955367 ( S1 Fig ) . Of interest , the G allele of rs6955367 ( increased expression of PILRB ) is linked to rs7803454 ( r2 = 0 . 83 ) , a variant associated with increased risk of age-related macular degeneration and suggests the presence of independent effects in the PILRA/PILRB region [27] . Paired activating/inhibitory receptors are common in the immune system , with the activating receptor typically having weaker affinity than the inhibitory receptor toward the ligands . PILRA and PILRB are type I transmembrane proteins with highly similar extracellular domains that bind certain O-glycosylated proteins [28–31] , but they differ in their intracellular signaling domains [32–34] . PILRA contains an immunoreceptor tyrosine-based inhibitory motif ( ITIM ) , while PILRB signals through interaction with DAP12 , which contains an immunoreceptor tyrosine-based activation motif ( ITAM ) . Analysis of PILRA knockout mice suggests that PILRA is a negative regulator of inflammation in myeloid cells [35–37] , with knockout macrophages showing increased production of cytokines ( IL6 , IL-1b , KC , MCP-1 ) in addition to increased infiltration of monocytes and neutrophil via altered integrin signaling . PILRA is known to bind both endogenous ( including COLEC12 , NPDC1 , CLEC4G , and PIANP ) and exogenous ligands ( HSV-1 glycoprotein B ( gB ) ) [30 , 31 , 36 , 38] . Because the G78R ( R78 ( AD protective ) ) variant resides close to the sialic acid-binding pocket of PILRA , we tested whether the glycine ( uncharged , short amino acid ) to arginine ( basic , long side chain amino acid ) substitution might interfere with PILRA ligand-binding activity . All non-human PILRA sequences , as well as all PILRB sequences , encode glycine at this position . We also generated amino acid point variants in and around the sialic acid-binding pocket of PILRA . A residue conserved among PILR proteins and related SIGLEC receptors , R126 in PILRA , is well known to be essential for sialic acid interaction [29 , 31 , 38] and so was not further studied here . Based on their location in the crystal structure , evolutionary conservation [31] , and involvement in binding HSV-1 gB [38] , amino acids R72 and F76 were predicted to be important for ligand binding and were substituted to alanine as positive controls for loss-of-function [31] . In addition , S80 , a residue outside of the sialic acid-binding pocket was substituted to glycine . The R72A , F76A , and S80G mutations have not been detected in human populations ( dbSNP v147 ) . To study receptor-ligand binding , 293T cells were transfected with G78 ( AD risk ) PILRA or variants , and then incubated with purified NPDC1-mIgG2a protein ( Fig 1B ) , followed by flow cytometry to detect PILRA and the NPDC1 fusion protein . Among known PILRA ligands , NPDC1 is expressed in the central nervous system and binds with high affinity to PILRA [31] . Expression of the PILRA variants on the transfected 293T cells was comparable to or greater than G78 ( AD risk ) PILRA ( S2 Fig ) . G78 ( AD risk ) PILRA binding to NPDC1 was considered 100% . Both R72A and F76A mutations severely impaired NPDC1 binding ( ~20% of G78 , p-value < 0 . 0001 ) . The R78 ( AD protective ) variant also showed significantly reduced ligand binding ( ~35% of G78 , p < 0 . 0005 ) , while the G80 mutant was the least affected ( ~60% of G78 , p < 0 . 0001 ) ( Fig 1C and S3A and S3B Fig ) . To further test the hypothesis that the AD protective PILRA R78 variant impacts ligand binding , NPDC1 or alternative PILRA ligands HSV-1 gB and PIANP were expressed on the cell surface of 293T cells , and the binding of purified PILRA protein variants was measured by flow cytometry . PILRA R78 showed reduced binding to the various ligands in these assays as compared to G78 ( Fig 1D to 1G and S4A to S4G Fig ) . These data confirmed that the R78 variant impairs ligand-binding activity of PILRA . A peptide motif for PILRA interaction has been established ( Fig 2A ) that includes an O-glycosylated threonine , an invariant proline at the +1 position , and additional prolines at the -1 or -2 and +3 or +4 positions [31 , 38] . Of note , PILRA is capable of binding murine CD99 and human NPCD1 ( both contain the consensus motif ) , but not human CD99 or murine NPCD1 ( both lack the consensus motif ) , suggesting divergence between human and mouse in the range of endogenous ligands bound by PILRA [31] . We sought to identify novel endogenous PILRA ligands by searching for human proteins with either the PTPXP , PTPXXP , PXTPXP or PXTPXXP motif . A total of 1540 human proteins carry at least 1 of these putative PILRA-binding motifs ( S3 Table ) . Narrowing the search , we considered proteins with the motif that have previously been shown to be O-glycosylated in human cerebral spinal fluid [39] , and measured the binding of these proteins to PILRA variants . By flow cytometry , complement component 4A ( C4A ) bound to G78 ( AD risk ) PILRA in a manner comparable to NPDC1 , while APLP1 and SORCS1 showed relatively little interaction with PILRA ( Fig 2B and S5A and S5B Fig ) . We further demonstrated that the PILRA R78 ( AD protective ) variant has reduced binding for C4A ( Fig 2C and S5C Fig ) . We did not test C4B , but its putative PILRA-binding motif is identical to that of C4A . To understand the conformational changes that might occur in the PILRA sialic acid-binding pocket during receptor-ligand interactions in the presence of G78 ( AD risk ) or R78 ( AD-protective ) variants , we evaluated available experimental crystal structures ( Fig 3A to 3C ) [38 , 40] . Structures of G78 ( AD risk ) PILRA reveal a monomeric extracellular domain with a single V-set Ig-like β-sandwich fold that binds O-glycan ligands ( Fig 3B and 3C ) [38] . By analogy to a molecular clamp , the sialic acid-binding site in PILRA undergoes a large structural rearrangement from an “open” to a “closed” conformation upon binding its peptide and sugar ligands simultaneously ( Fig 3A to 3C ) . The essential R126 side-chain engages the carboxyl group of sialic acid ( SA ) directly in a strong salt bridge ( Fig 3C ) . The CC’ loop which contains F76 and G78 undergoes a large conformational change where F76 translates ~15 Å to participate in key interactions with the peptide of the ligand and abut the Q140 side-chain of PILRA ( Fig 3B and 3C ) . In this ligand-bound “closed” conformation of PILRA , Q140 helps to position R126 precisely for its interaction with SA ( Fig 3C ) . Notably , in the structure of R78 ( AD protective ) PILRA crystallized in the absence of any ligand [40] , the long side-chain of R78 is observed to hydrogen bond with Q140 directly ( Fig 3A ) . This unique R78-Q140 interaction has three important consequences: 1 ) it sterically hinders F76 from obtaining a ligand-bound “closed” conformation , 2 ) it affects the ability of R126 to interact with the carboxyl group of SA by altering the R126-Q140 interactions observed in G78 ( AD risk ) PILRA and , 3 ) it likely alters CC’ loop dynamics , ( Fig 3B to 3C ) . Overall , the structure of the R78 ( AD protective ) variant shows that this single side-chain alteration appears to stabilize the “open” apo form of PILRA and likely alters the conformational sampling of the molecular clamp required to obtain its “closed” form to engage its ligands . We therefore propose that in G78 PILRA ( AD-risk associated ) , the engagement of SA by R126 and peptide by F76 is facilitated by G78 ( Fig 3C ) . However , in the AD-protective PILRA variant R78 , the R78 side-chain competes with the central R126-Q140 interaction and alters the positioning of F76 ( Fig 3A ) , which leads to an overall decrease in PILRA ligand binding . This structure-based hypothesis is consistent with the reduced functional cellular binding observed for the R78 variant ( Fig 1 ) . To further test this model , we generated two additional alanine mutants of PILRA at amino acids predicted to be essential ( Q140 ) or non-essential ( S141 ) for conformational changes associated with ligand interaction . 293T cells were transfected with G78 ( AD risk ) , R78 ( AD protective ) , Q140A and S141A variants of PILRA , and receptor-ligand interaction was measured after incubating cells with soluble NPDC1-mIgG2a . PILRA expression was comparable among variants , matching or exceeding G78 ( AD risk ) expression ( S2 Fig ) . R78 ( 44% of G78 , p = 0 . 02 ) and Q140A ( 22% of G78 , p = 0 . 0004 ) variants showed significantly decreased binding to NPDC1 , while S141A ( 117% of G78 , p = 0 . 5 ) had no significant effect ( Fig 3D and S6A and S6B Fig ) . These data are consistent with the experimental structural models that show the interaction of Q140 with R126 is important for productive sialic acid binding ( Fig 3A to 3C ) . Consistently , the Q140A mutation has a strong effect because the Q140-R126 interaction network is completely abolished . By contrast , the AD-protective R78 variant likely has an intermediate effect since it only modulates the Q140 interaction with R126 , which is expected to only alter the frequency or strength of relevant PILRA-ligand interactions . We next investigated the interaction of PILRA variant and ligands in vitro using surface plasmon resonance ( SPR ) . Human PILRA–Fc variants ( G78 , R78 , or Q140A ) were immobilized on a ProteOn GLC sensor chip and binding of NPDC1-mFc or a control mFc-tagged protein was measured . Qualitatively , NPDC1-Fc bound to the R78 ( AD-protective ) and Q140A ( essential for R126 conformation ) variants to a much lesser extent than to G78 ( AD risk ) PILRA , while control Fc-tagged protein showed no binding ( Fig 3E ) . To further probe the mechanistic basis of R78 ( AD protective ) function and phenotype , a more complete SPR characterization of NPDC1-His binding to PILRA variants was performed ( S6C Fig ) . The affinity of NPDC1 toward R78 ( AD-protective ) PILRA ( 76 . 5 nM ) was 4 . 5-fold weaker than the affinity toward G78 PILRA ( 16 . 8 nM ) . The on-rate constant kon for NPDC1-His binding to R78 ( AD protective ) ( 6 . 8×10+3 M-1s-1 ) was ~3-fold lower than binding to G78 ( AD risk ) PILRA ( 2 . 2×10+4 M-1s-1 ) , while the koff rate constants were comparable ( S6C Fig ) . These results are consistent with the idea that , once engaged , the affinity and disassociation rate of R78-ligand complexes are similar to G78 PILRA , but the frequency with which PILRA can productively engage with ligands is reduced in the R78 ( AD protective ) variant by R78 side chain interactions favoring the apo-state ( Fig 3 ) . Taken together , these data support a structural model in which R78 impairs PILRA-ligand interactions by altering the accessibility of a productive sialic acid-binding conformation in PILRA . Given that PILRA is a known entry receptor for HSV-1 [41] and the R78 ( AD protective ) variant showed reduced binding to HSV-1 gB ( Fig 1F ) , we next determined whether there were differences in HSV-1 infectivity based on PILRA genotype . We isolated and differentiated human monocyte-derived macrophages ( hMDMs ) from five pairs of healthy volunteers homozygous for either the G78 ( AD risk ) or R78 ( AD protective ) PILRA variants ( matched for age , gender and ethnicity ) . hMDMs were infected with HSV-1 at different multiplicities of infection ( MOI ) ( 0 . 01 , 0 . 1 , 1 and 10 ) , and infectivity was measured morphologically by light microscopy , by using an LDH cytotoxicity assay , by measuring intracellular viral DNA and in a viral plaque assay . No notable cytopathic effects were observed in the first 6 h of infection , however at 18 hours post infection , extensive cytopathy was detected in G78/G78 PILRA-expressing hMDMs , including loss of cell shape , increased cell volume , birefringence , and formation of both cell aggregates and multinucleated giant cells ( syncytia ) ( Fig 4A and S7 Fig ) . Cytopathic changes were less pronounced in R78/R78 ( Alzheimer’s protective ) homozygous hMDMs ( Fig 4A and S7 Fig ) . hMDMs from R78/R78 PILRA donors showed significantly less HSV-1-induced cytotoxicity at 18 hrs post infection in the LDH assay at 0 . 01 , 0 . 1 , or 1 MOI ( Fig 4B and S4 Table ) . The difference was no longer significant at 10 MOI or if the infection was allowed to proceed for 36 hrs , except at the lowest MOI of 0 . 01 ( Fig 4B , S8A Fig and S4 and S5 Tables ) . hMDMs from R78/R78 donors showed 5–10 fold decreased amounts of HSV-1 DNA at 6 hrs at all MOIs ( 0 . 01 , 0 . 1 , 1 and 10 ) , and at 18 hrs at lower MOIs ( 0 . 01 and 0 . 1 ) , compared to those from G78/G78 donors ( Fig 4C and S8C and S8D Fig ) . No significant differences in HSV-1 DNA were observed between the two genotypes at 18 hrs at higher doses ( 1 and 10 MOI ) ( Fig 4C and S8D Fig ) , or at 36 hrs for any dose of virus ( S8B and S8E Fig ) . Finally , we measured the amount of infectious HSV-1 virus by harvesting supernatants from HSV-1-infected hMDMs and measuring viral titer by plaque assays on Vero cells . Viral plaque forming units ( PFUs ) were significantly lower after 6 and 18 hrs of infection for all MOIs tested , and at 36 hrs for lower MOIs ( Fig 4D and 4E , and S9 Fig ) . Taken together , these data indicate that R78/R78 macrophages were less susceptible to HSV-1 infection than G78/G78 macrophages .
We show here that PILRA G78R is a likely causal variant conferring protection from AD risk at the 7q21 locus . G78R alters the access to SA-binding pocket in PILRA , where R78 PILRA shows reduced binding to several of its endogenous cellular ligands and with HSV-1 gB . Reduced interaction with one or more of PILRA’s endogenous ligands ( including PIANP and NPDC1 ) could impact microglial migration or activation [35–37] . In fact , microglia up-regulate the expression of the PIANP gene in the PS2APP , 5xFAD , and APP/PS1 mouse models of AD [42–44] . The identification of C4 as a novel PILRA-interacting protein is also intriguing , given the increased expression of C4 in mouse AD models [42] , the increase in amyloid deposition observed when complement activation is inhibited [45] , and the genetic association of complement receptor 1 with AD [46] . Finally , we note that both TREM2 and PILRB function as activating receptors and signal through DAP12 [32 , 34 , 47] . A reduction of PILRA inhibitory signals in R78 carriers could allow more microglial activation via PILRB/DAP12 signaling and reinforce the cellular mechanisms by which TREM2 is believed to protect from AD incidence [48] . The relevant ligands for PILRA/PILRB in vivo and the mechanism by which reducing PILRA-ligand interaction confers protection from Alzheimer’s disease remain to be elucidated . A role for infection in accelerating AD has been proposed , but remains controversial [49] . HSV-1 is a neurotropic virus that infects a large fraction of the adult population and has frequent reactivation events . HSV-1 has been implicated in AD pathogenesis by several lines of evidence , including the presence of HSV-1 viral DNA in human brain tissue [50 , 51] , increased HSV-1 seropositivity in AD cases [52–55] , the correlation of high avidity HSV-1 antibodies with protection from cognitive decline [55] , the binding of HSV-1 gB to APOE-containing lipoproteins [56] , HSV-1-induced amyloidogenic processing of amyloid precursor protein ( APP ) [57–59] , and preferential targeting of AD-affected regions in HSV-1 acute encephalitis [60] . In addition , HSV-1 gD receptors and gB receptor PILRA increase with age in multiple brain regions , including the hippocampus [61] . Additional AD risk loci have been proposed to play a role in the life cycle of HSV-1 [62] , including CR1 , which is capable of binding HSV-1 [63] . The reduced infectivity of HSV-1 in R78/R78 macrophages suggests that brain microglia from R78/G78 and R78/R78 individuals are less susceptible to HSV-1 infection and more competent for immune defense during HSV-1 recurrence . These data provide additional evidence for a key role of microglia in AD pathogenesis and provide a mechanism by which HSV-1 may contribute to AD risk . Inhibiting the interaction of PILRA with its ligands could therefore represent a novel therapeutic mechanism to prevent or slow AD progression .
Blood samples and genotypes from healthy human volunteers from the Genentech Genotype and Phenotype program ( gGAP ) were used in this project . Written consent was obtained from all participants in the gGAP program . The study was reviewed and approved by the Western Regional Institutional Board ( Study Number: 1096262 , IRB Tracking Number: 20080040 ) . The conditional analysis between rs1476679 and rs1859788 was performed using the Genome-wide Complex Trait Analysis ( GCTA ) program’s Conditional & joint ( COJO ) analysis option . This program takes summary statistics as input . We used the summary statistics for rs1859788 , rs1476679 from IGAP stage1 GWAS [15] . The COJO program also needs a reference population to calculate the LD and to perform the conditional analysis . For reference population analysis we used the raw genotype data from ADGC cohort . There were 22 , 255 individuals in this cohort that had the non-missing genotype for the rs1859788 . The ADGC dataset was also used for the minor allele frequency calculations that are provided in the text . The coding sequences ( CDS ) of full length PILRA ( AJ400841 ) , human herpesvirus 1 strain KOSc glycoprotein B ( HSV-1 gB ) ( EF157316 ) , and neural proliferation , differentiation and control 1 ( NPDC1 ) ( NM_015392 . 3 ) were cloned in the pRK neo expression vector . Several PILRA point mutations were generated , including R72A , F76A , G78R , S80G , Q140A and S141A . The PILRA variants were incorporated into a full-length G78 ( AD risk ) PILRA construct by site-directed mutagenesis as per the manufacturer’s recommendation ( Agilent Cat . No . 200523 ) and sequences were verified . A full length myc-DDK tagged PIANP construct was purchased from Origene ( Cat . No . RC207868 ) . Full length complement component 4A ( Rodgers blood group ) C4A ( NM_007293 . 2 ) , extra cellular domain ( ECD ) of amyloid beta precursor like protein 1 ( APLP1 ) ( NM_005166 ) ( 1–580 aa ) and ECD of sortilin-related VPS10 domain-containing receptor 1 ( SORCS1 ) ( NM_052918 ) ( 1–1102 aa ) were fused with C-terminal gD tag ( US6/gD , partial [Human alphaherpesvirus 1 ) ( AAP32019 . 1 ) and GPI anchor in pRK vector . The ECD of all PILRA variants ( 1–196 aa ) and NPDC1 ( 1–190 aa ) were PCR amplified and cloned with C-terminal murine IgG2a Fc tag in a pRK expression vector . ECDs of PILRA variants ( G78 ( AD risk ) , R72A , F76A , G78R , S80G , Q140A and S141A ) and NPDC1 fused to the Fc region of murine IgG2a were expressed in a CHO cell expression system , supernatants collected , protein A/G affinity-purified and verified by SDS-PAGE and mass spectroscopy . 293T cells were transfected with lipofectamine LTX reagent ( ThermoFisher ) with various full-length constructs of PILRA variants ( G78 ( AD risk ) , R72A , F76A , G78R , S80G , Q140A and S141A ) . After 48 hours , the transfected cells were harvested and incubated with soluble mIgG2a-tagged ligand , NPDC1-mFc at 50 μg/ml ( as described above ) for 30 minutes on ice . Cells were then washed and stained with 1 μg/ml chimeric anti-PILRA antibody ( mouse Fc region is substituted to human IgG1 backbone on anti-PILRA antibodies [31] ) on ice for 30 min followed by APC-conjugated mouse anti-human IgG ( BD Pharmingen Cat . No . 550931 ) and FITC anti-mouse IgG2a ( BD Pharmingen Cat . No . 553390 ) secondary antibodies according to manufacturer’s instruction . PILRA-transfected 293T cells were examined by flow cytometry for binding of NPDC1 by measuring the frequency of APC and FITC double-positive cells . Double positive cells were gated on the WT sample and than the gates were overlaid on subsequent samples to maintain the same cell population throughout the experiment . For each PILRA variant , the mean percentage of the number of cells binding to NPDC1-mFC relative to the wild type PILRA binding for each experiment was calculated . In the inverse experiment , 293T cells were transfected with lipofectamine LTX reagent [ThermoFisher] with known full-length PILRA ligand ( NPDC1 , HSV-1gB and PIANP ) and predicted ligand constructs ( SORCS1 , APLP1 and C4A ) ( described above ) . After 48 hours , the transfected cells were harvested and incubated with soluble mIgG2a-tagged variants of PILRA ( G78 ( AD risk ) , R72A , F76A , G78R , S80G ) ( described above ) 50 μg/ml for 30 min on ice . Cells were then washed and stained with FITC anti-mouse IgG2a ( BD Pharmingen Cat . No . 553390 ) secondary antibody according to manufacturer’s instruction . PILRA ligand-transfected 293T cells were examined by flow cytometry for binding to PILRA variants by measuring the frequency of FITC-positive cells . The percentage of mean fluorescence intensity ( MFI ) of PILRA-mFC binding on ligand-transfected cells relative to the wild type PILRA binding for each experiment was calculated . Binding of human NPDC1 . Fc to PILRa-Fc variants was measured by SPR using a ProteOn XPR36 ( Bio-Rad ) . PILRA-Fc WT and variants ( G78R and Q140A ) were immobilized on a ProteOn GLC sensor chip ( Bio-Rad ) by EDC/NHS amine coupling ( 2000–2400 RU’s ) and the chip surface was deactivated by ethanolamine after immobilization . NPDC1-Fc diluted in PBST or a control Fc-tagged protein was injected at a concentration of 100 nM over the immobilized PILRA proteins at room temperature[31] . Healthy human volunteers from the Genentech Genotype and Phenotype program ( gGAP ) were genotyped for rs1859788 ( PILRA G78R ) using custom design ABI SNP genotyping assay with the following primers; Forward primer seq: GCGGCCTTGTGCTGTAGAA , Reverse primer seq: GCTCCCGACGTGAGAATATCC , Reporter 1 sequence: VIC- ACTTCCACGGGCAGTC-NFQ , Reporter 2 sequence: FAM- ACTTCCACAGGCAGTC-NFQ . To control for a possible effect of the eQTL for PILRB , all volunteers selected were homozygous AA ( lower PILRB expression ) for rs6955367 ( http://biorxiv . org/content/early/2016/09/09/074450 ) . Genotype for rs6955367 was determined using an InfiniumOmni2 . 5Exome-8v1-2_A . bpm . Peripheral Blood Mononuclear Cells ( PBMC’s ) were obtained by Ficol gradient from five pairs of homozygous donors for rs1859788 ( one with each genotype AA/GG ) . The pairs of samples were matched for age [± 5 years] , gender and self-reported ethnicity . Monocytes were purified from PBMC’s by negative selection using the EasySep Human Monocyte Enrichment Kit without CD16 Depletion ( 19058 ) , as recommended by the manufacturer . Isolated monocytes were differentiated into macrophages in DMEM + 10%FBS + 1X glutaMax and 100 ng/ml MCSF media for 7–10 days . The gGAP program was reviewed and approved by the Western Regional Institutional Board . Macrophages differentiated from healthy human monocytes were incubated with 10 , 1 , 0 . 1 and 0 . 01 multiplicity of infection ( MOI ) of HSV-1 virus at 37°C for 1 hour with gentle swirling to allow virus adsorption . Cells were washed after 1 hr of adsorption and infection was continued for 6 , 18 and 36 hrs . Supernatant was harvested at 6 , 18 and 36 hrs of infection and cell debris were removed by centrifugation at 3000 rpm for 5 min at 4°C . DNA was isolated from infected cells using the QIAamp DNA mini-kit ( Qiagen Cat . No . 51304 ) . Additional cells were fixed with 4% paraformaldehyde after infection and stained with DAPI for microscopy . The CytoTox 96 Non-Radioactive Cytotoxicity Assay ( Promega Cat . No . E1780 ) was performed on supernatant harvested from HSV-1-infected human macrophages as per manufacturer’s recommendations to measure cell toxicity after HSV-1 infection . For each sample , the percent cytotoxicity was calculated as the ratio of LDH released in culture supernatant after infection to completely lysed cells ( maximum LDH release ) . HSV-1 DNA was quantitated using a custom design ABI TaqMan gene expression assay , with the following primers: Forward primer seq: 5'-GGCCTGGCTATCCGGAGA-3' , Reverse primer seq: 5'-GCGCAGAGACATCGCGA-3' , HSV-1 probe: 5'-FAM-CAGCACACGACTTGGCGTTCTGTGT-MGB-3' . GAPDH DNA was quantitated using ABI endogenous control ( Applied Biosystem Cat . No . 4352934E ) . Amplification reactions were carried out with 5 μl of extracted DNA from infected cells in a final volume of 25 μl with TaqMan Universal PCR Master Mix ( Applied Biosystems Cat . No . 4304437 ) as per manufacturer’s recommendations . HSV-1 DNA ( Ct values ) was normalized to cell GAPDH ( Ct values ) to account for cell number . Virus titers from HSV-1-infected cells were determined following a standard plaque assay protocol [64] . In brief , the plaque assay was performed using Vero cells ( African Green Monkey Cells ) seeded at 1x105 cells per well in 48-well plates . After overnight incubation at 37°C , the monolayer was ~90–100% confluent . Supernatants harvested from HSV-1-infected human macrophages were clarified from cells and debris by centrifugation at 3000 rpm for 5 minutes at 4°C . Virus-containing supernatants were then diluted from 10−1 to 10−8 in DMEM ( 1 ml total volume ) . Growth media was removed from Vero cells and 250 μl of supernatant dilution was transferred onto the cells , followed by incubation at 37°C for 2 hrs with gentle swirling every 30 min to allow virus adsorption , after which the virus-containing media was aspirated . The cells were then overlaid with 2% methylcellulose containing 2X DMEM and 5% FBS and incubated at 37°C . 48 hrs post-infection , plaques were enumerated from each dilution . Virus titers were calculated in pfu/ml . | Alzheimer’s disease ( AD ) is a devastating neurodegenerative disorder resulting from a complex interaction of environmental and genetic risk factors . Despite considerable progress in defining the genetic component of AD risk , understanding the biology of common variant associations is a challenge . We find that PILRA G78R ( rs1859788 ) is the likely AD risk variant from the 7q21 locus ( rs1476679 ) and PILRA G78R reduces PILRA endogenous and exogenous ligand binding . Our study highlights a new immune signaling axis in AD and suggests a role for exogenous ligands ( HSV-1 ) . Further , we have identified that reduced function of a negative regulator of microglia and neutrophils is protective from AD risk , providing a new candidate therapeutic target . |
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Schistosomiasis , a neglected tropical disease , owes its continued success to freshwater snails that support production of prolific numbers of human-infective cercariae . Encounters between schistosomes and snails do not always result in the snail becoming infected , in part because snails can mount immune responses that prevent schistosome development . Fibrinogen-related protein 3 ( FREP3 ) has been previously associated with snail defense against digenetic trematode infection . It is a member of a large family of immune molecules with a unique structure consisting of one or two immunoglobulin superfamily domains connected to a fibrinogen domain; to date fibrinogen containing proteins with this arrangement are found only in gastropod molluscs . Furthermore , specific gastropod FREPs have been shown to undergo somatic diversification . Here we demonstrate that siRNA mediated knockdown of FREP3 results in a phenotypic loss of resistance to Schistosoma mansoni infection in 15 of 70 ( 21 . 4% ) snails of the resistant BS-90 strain of Biomphalaria glabrata . In contrast , none of the 64 control BS-90 snails receiving a GFP siRNA construct and then exposed to S . mansoni became infected . Furthermore , resistance to S . mansoni was overcome in 22 of 48 snails ( 46% ) by pre-exposure to another digenetic trematode , Echinostoma paraensei . Loss of resistance in this case was shown by microarray analysis to be associated with strong down-regulation of FREP3 , and other candidate immune molecules . Although many factors are certainly involved in snail defense from trematode infection , this study identifies for the first time the involvement of a specific snail gene , FREP3 , in the phenotype of resistance to the medically important parasite , S . mansoni . The results have implications for revealing the underlying mechanisms involved in dictating the range of snail strains used by S . mansoni , and , more generally , for better understanding the phenomena of host specificity and host switching . It also highlights the role of a diversified invertebrate immune molecule in defense against a human pathogen . It suggests new lines of investigation for understanding how susceptibility of snails in areas endemic for S . mansoni could be manipulated and diminished .
Schistosomiasis is one of the world's most tenacious neglected tropical diseases , infecting an estimated 207 million people , mostly children [1] . The persistence of schistosome parasites stems in part from their use of freshwater snails for their larval development and transmission . Snails are often abundant and difficult to control , and it is in snails that the cercariae infective to humans are produced in prolific numbers . It takes only a single schistosome miracidium to establish a snail infection capable of producing hundreds of cercariae on a daily basis for months [2] . The amplification of schistosomes that occurs within snails creates a reoccurring problem for control efforts and is a significant obstacle for sustained prevention . It highlights the importance of understanding the dynamics of schistosome infections in snails and is the reasoning behind studies focused on characterizing the mechanistic basis for snail resistance to schistosome infection . If we could understand the underlying factors that enable snails to resist schistosome infection , then we could better understand the basis of compatibility in field snails . The level of compatibility exhibited will directly influence both transmission dynamics and control efforts . We could also potentially exploit resistance to favor development of more sustainable control strategies that go beyond today's largely one-dimensional control programs that depend primarily on treatment of infected people with praziquantel [3] . Not all snails are created equal: some are susceptible and some resistant to schistosome infection . Resistance is genetically controlled and affects immunological factors [4] , [5] that vary among snail species , strains or age categories . For example , the human parasite Schistosoma mansoni infects only certain species of Biomphalaria ( such as B . glabrata ) . Furthermore , only some strains of B . glabrata are compatible with this parasite . Many studies have focused on characterizing the transcriptional profiles of schistosome resistant strains compared to susceptible counterparts , and have identified a number of putative resistance-associated factors in the process [6] , [7] . Amongst these molecules are the fibrinogen-related proteins ( FREPs ) , members of a multi-gene family that undergo somatic diversification and point mutation events . FREP proteins couple together fibrinogen and immunoglobulin superfamily domains , to generate a protein that is unique as far as presently known to gastropod molluscs [8] . FREPs are capable of precipitating secretory/excretory products from digenetic trematode sporocysts [9] , and binding to diversified glycoproteins produced by parasites [10] . One individual FREP , FREP3 , has been singled out for further study because of its role in the snail defense response against the trematode Echinostoma paraensei [4] . FREP3 , like other FREPs , is a lectin-like molecule that recognizes a number of monosaccharides and is able to enhance the phagocytic uptake of targets , acting as an opsonin [11] . Knockdown of FREP3 in a normally resistant snail phenotype , and subsequent challenge of those snails with E . paraensei resulted in a significant proportion of the snails becoming infected with E . paraensei [11] . Trematode infection of a snail host is achieved , in part , by evading and suppressing the snail defense response . This provides a window for establishment of infection and then preventing the immune response from interfering with parasite development . These immune-evasion strategies can be observed in vitro [12] , and also by transcriptional analysis [7] , which suggests that many of the transcripts expressed by resistant snails during successful defense are suppressed in susceptible snails that become infected [11] . Immunosuppression is especially strong following exposure to E . paraensei , a parasite that can alter snail hemocyte morphology and interfere with hemocyte function [12] , and that can suppress the expression of important immune molecules almost immediately upon entry into the snail [7] . One of the factors we identified as being suppressed by E . paraensei during infection is FREP3 [11] . This observation prompted us to use , in one of the experiments described below , a protocol first employed by Lie and Heyneman [13] in which pre-exposure of schistosome-resistant snails to E . paraensei was used to abrogate resistance to subsequent schistosome infection . We hypothesized specifically that this treatment would interfere with FREP3 expression ( and likely with expression of other immune components as well ) , as compared to schistosome-resistant control snails not exposed to E . paraensei . In this study , we report on the results of two different manipulations undertaken with the intention of abrogating resistance to S . mansoni in the naturally resistant BS-90 strain of B . glabrata . We first examined the effects of knocking down FREP3 using RNAi on the subsequent ability of BS-90 snails to support S . mansoni development . Secondly , we also expressly repeated the classic experiment of Lie et al . ( 1977 ) [14] , using both BS-90 snails and accompanying microarray monitoring for the first time . We first exposed BS-90 snails to radiation-attenuated miracidia of E . paraensei , and then assessed their resistance level to S . mansoni as compared to snails not pre-exposed to E . paraensei . Radiation-attenuated E . paraensei parasites do not establish long-term , proliferative infections in snails , avoiding the potential complication that persistent larvae of this species would prevent the potential development of S . mansoni . It is known , however , that irradiated E . paraensei larvae , during their brief lifespan , exert a potent immunosuppressive effect just as do normal E . paraensei larvae [7] , [11] , [13] . Infection with S . mansoni of FREP3 knockdown snails and those first exposed to irradiated E . paraensei was also assessed by histological examination as well as by checking for shedding S . mansoni cercariae , which were tested for infectivity to mice . We compared the transcriptional profiles of BS-90 snails exposed only to irradiated E . paraensei to those exposed to irradiated E . paraensei and then challenged with S . mansoni . Our study seeks to demonstrate the involvement of a specific molecule in snail resistance to S . mansoni infection , and to provide a plausible natural mechanism by which trematode-mediated immunosuppression of the defense responses of a snail could facilitate infection by a parasite that it would normally successfully resist .
BS-90 and M-line strain Biomphalaria glabrata ( B . g . ) snails , and Schistosoma mansoni ( S . m . ) and Echinostoma paraensei ( E . p . ) were maintained as previously described [15] . Four independent 27 nucleotide oligos were designed to specific regions of FREP3 that displayed high conservation within the known diversified FREP3 transcripts . These oligos were combined and diluted in sterile snail saline at a final total concentration of 2 µg/µl , which was then injected into snails in a 5 µl volume . BS-90 snails were separated into two groups , the first to be injected individually with FREP3-specific siRNA oligos , and the second as a control , with GFP-specific oligos [16] , siRNA oligo design and injection techniques have been previously described [11] . Four hours later , all snails were exposed individually to 30 S . m . miracidia . Snails were collected for histology at 2 , 8 , 18 , 21 , and 28 dpe . Snails were examined for the presence of infection ( presence or absence of primary and secondary sporocysts ) as described above for signs of infection at 21 , 28 , 34 , 41 , 48 , and 54 dpe . Snails that shed cercariae were collected for histology and the rest were dissected to look for infections . Knockdown of FREP3 was confirmed by RT-PCR and western blot analysis both of which have been previously described [11] . Specific knockdown of FREP3 protein levels was confirmed by probing the same samples with a FREP4 specific antibody . For both Western blot analyses , 100 µg of cell free plasma was loaded into each well of an SDS acrylamide gel . FREP3 was detected using a FREP3 specific antibody , and the Western blot was developed using the Supersignal West Femto Chemiluminescent Substrate ( Pierce ) . FREP4 was detected using a FREP4-specific antibody and the Western Blot was developed using alkaline phosphatase . Injection of siRNA oligos and challenge of both FREP3 knockdown and GFP knockdown snails with S . mansoni resulted in similar mortality in both groups of snails . 36% of the FREP3 knockdown snails and 31% of the GFP knockdown snails died as a result of treatment . In addition to RT-PCR and Western blot confirmation of FREP3 knockdown using FREP3-specific siRNA oligos as previously described [11] , we confirmed the specificity of FREP3 knockdown using microarray analysis . BS-90 snails ( 4–8 mm ) were injected with either FREP3 or GFP-specific siRNA oligos , and 2 hours later exposed to 30 S . m . miracidia . At 2 and 4 dpe , ten snails from each group were collected , RNA was extracted and then used to generate template for the microarray as previously described [15] . Ten arrays were completed . Each array was probed with template from an individual FREP3 knock-down snail labeled with Cy5 and an individual GFP knock-down snail labeled with Cy3 . Hybridization , scanning , and analysis of the microarrays were previously described [7] , using a significance cutoff of +/−log 1 . 5 , and a false detection rate of 5% . Microarray results were submitted to GEO under the accession number GSE33525 . The microarray revealed that indeed FREP3 expression was reduced at the transcriptional level by 2 . 4 fold at 2 dpe and by 5 . 1 fold at 4 dpe . The only other significant results from that array revealed a slight reduction in FREP13 expression by 1 . 2 fold at 2 dpe and 2 fold at 4 dpe and a slight up-regulation of TGFR-1 at 1 . 8 fold at 2 dpe and 2 . 6 fold at 4 dpe ( Fig . S1 ) . 18 other transcripts including FREP2 and FREP6 displayed slight alterations in expression however these changes were not considered statistically significant; none of these other 16 transcripts were FREPs . To confirm viability of the cercariae produced from the FREP3 knock-down BS-90 snails that shed at 31 dpe we collected all cercariae produced ( ∼150 ) , and exposed one mouse , using standard procedures as previously described [17] . Seven weeks post-exposure , the mouse was injected with a heparin solution and perfused by cutting the hepatic portal vein and injecting a standard RPMI medium into the heart . S . mansoni adult worms were collected and the liver was homogenized to collect S . mansoni eggs . The presence of adult worms confirmed the cercariae isolated from BS-90 snails were viable and the miracidia hatched from the eggs were also viable , being able to infect M-line B . glabrata snails ( data not shown ) . Size ( 4–8 mm shell diameter ) matched snails were distributed into five groups: 1 ) . BS-90 exposed to 25–30 irradiated E . p . miracidia at day 0 and secondarily challenged 4 days later with 15 S . m . miracidia , 2 ) . BS-90 exposed to 25–30 irradiated E . p . only at day 0 , 3 ) . BS-90 unexposed control , 4 ) . BS-90 exposed to only 15 S . m . miracidia at day 4 , and 5 ) . M-line exposed to only 15 S . m . miracidia at day 4 to confirm S . m . infectivity . For groups 2 and 3 , RNA was collected at 1 , 2 , and 4 days post-exposure ( dpe ) to E . p . RNA was collected from groups 1 , 2 and 4 at 1 , 2 , 4 , and 8 dpe to S . m . and snails were collected for histology from groups 1 and 4 at 2 , 8 , 18 and 28 dpe to S . m . Snails from group 5 at 18 dpe to S . m . were also collected for histology . At days 18 , 28 , and 34 post-exposure to S . m . , all remaining snails were placed into large tissue culture wells with artificial spring water and examined for the presence of developing primary sporocysts in the head foot or mantle , or secondary sporocysts in the mantle or digestive gland/ovotestes . Snails that were shedding S . m . cercariae were collected for histology . All snails that did not shed cercariae , were individually placed in snail saline , dissected and examined with the aid of a dissecting microscope for any signs of infection ( sporocysts , germ balls , cercarial embryos ) which dissection of known infected snails indicates can be seen under the 40× magnification used . Irradiation of E . p . , and RNA extraction were previously described [15] . RNA was collected from whole snails at 2 and 4 dpe to 15 S . m . miracidia from groups 1 and 2 above , and was used to generate template for the microarray as previously described [15] . Each array was probed with RNA from an individual snail from the experimental group ( 1 from above ) , labeled with Cy5 and with RNA from a snail from control group 2 , labeled with Cy3 . There were twelve arrays in total , six from 2 dpe and six from 4 dpe , as a previous study revealed a great differentiation in transcription between these two time points [7] . Hybridization , scanning , and analysis of the microarrays were previously described [7] , using a significance cutoff of +/−log 1 . 5 , and a false detection rate of 5% . Microarray results were submitted to GEO under the accession number GSE28293 . Snails were collected and placed whole into tubes containing Railliet-Henry's fixative ( 930 ml H2O , 50 ml formalin , 20 ml acetic acid , and 6 g NaCl ) to both fix the tissue and dissolve the shell . Any remaining shell was removed before the tissue was transferred into 10% buffered formalin . All tissue processing , sectioning , mounting , and hematoxylin and eosin staining was performed by TriCore Reference Laboratories in Albuquerque , New Mexico . The images generated from these sections were taken using a Nikon D5000 SLR camera attached to a Zeiss Axioskop compound microscope with an MM-SLR adapter and T-mount by Martin Microscope Company .
Specific siRNA-mediated suppression of FREP3 expression in BS-90 snails was confirmed at both the transcriptional ( Fig . 1A ) and protein levels ( Fig . 1B ) using RT-PCR and Western blot respectively . To assess whether FREP3 participated in an anti-S . mansoni defense response a total of 70 S . mansoni-resistant BS-90 strain snails were injected with FREP3-specific siRNA oligos to assess the impact of FREP3 knockdown on the subsequent ability of S . mansoni to develop . Knockdown of FREP3 resulted in cercariae-producing S . mansoni infections in 15 ( 21 . 4% ) of these normally resistant snails ( Fig . 1C ) . In contrast , none of 64 control BS-90 snails receiving GFP specific siRNA oligos shed cercariae . As a check of the viability of the S . mansoni miracidia used in this experiment , over 85% of schistosome-susceptible M-line snails exposed to infection in both trials became infected , a level of infection typical for exposure of such snails ( Fig . 1C ) . Histological observations revealed that S . mansoni miracidia penetrated snails receiving either FREP3 or GFP siRNA oligos . The early stage mother sporocysts ( from 2 to 4 days post-infection ) we observed were not conspicuously encapsulated in either group of snails . In most of the FREP3 knockdown snails that shed cercariae , shedding was light and intermittent over a 1–2 week observation period , after which they were fixed for histology at 31 days post-exposure to S . mansoni . Histological examination of S . mansoni-challenged FREP3 knockdown BS-90 snails revealed a small number of large sporocysts in the head-foot of each of these snails ( Fig . 2 B , C ) . No disseminated daughter sporocysts were found in the digestive glands of these snails , however ( Fig , 2A ) . The head-foot sporocysts had clearly grown considerably in size beyond that of young mother sporocysts , and whether they represented mother , or ectopic daughter sporocysts could not be determined . They were not encapsulated by hemocytes , nor were hemocytes prominently found near them . Developing cercariae were not seen within them but the sporocysts were of a size that easily could have supported cercariae development . One of the infected FREP3 knock-down snails more persistently released cercariae over a 2 week observation period . Histological examination revealed this snail to have daughter sporocysts disseminated throughout the digestive gland . Hemocytes were conspicuous around them and encapsulation responses were noted ( Fig . 2D ) . Only one of eight control BS-90 snails injected with GFP-specific siRNA oligos and sectioned at 28 days post-exposure to S . mansoni was observed to contain S . mansoni sporocysts , but they had not grown and did not contain germ balls . BS-90 snails exposed to irradiated E . paraensei miracidia were challenged with S . mansoni miracidia 4 days later . After another 35 days , the snails were checked for shedding of viable S . mansoni cercariae , an indication that the infection was successful . Of 48 snails , 22 ( 46% ) shed S . mansoni cercariae , compared to 0% ( n = 35 ) of control BS-90 snails exposed to only S . mansoni ( Fig . 3 ) . To confirm the infectivity of the S . mansoni used , 22 M-line B . glabrata were challenged and 82% were successfully infected ( Fig . 3 ) . Histological comparison of S . mansoni cercariae-shedding BS-90 snails to normal resistant control BS-90 snails ( Fig . 4A ) showed they had disseminated S . mansoni sporocysts throughout the digestive gland ( Fig . 4B , C ) typical of normal infections . Snails exposed to irradiation-attenuated E . paraensei only did not develop disseminated E . paraensei infections , as expected . Degenerating E . paraensei sporocysts could be observed in the hearts of the sensitized snails , including those subsequently challenged with S . mansoni ( Fig . 4E ) . To confirm the viability of the E . paraensei cohort used , BS-90 snails were exposed to non-irradiated control miracidia from the same cohort that was irradiated and were successfully infected by 28 dpe , as expected ( not shown ) . BS-90 snails first exposed to irradiated E . paraensei miracidia were challenged four days later with S . mansoni miracidia . Microarray analysis was then undertaken on individual snails either 2 or 4 days post-exposure to S . mansoni . Schistosome-specific markers on the array were used to indicate whether each snail had been successfully infected with S . mansoni , or if it had resisted the challenge . At both 2 and 4 days post S . mansoni challenge , 50% of the snails assayed using the array were positive for S . mansoni infections ( 3 positive , and 3 negative for S . mansoni for each time point ) . Immunosuppression ( as indicated by the greater number of down-regulated than up-regulated features ) resulting from exposure to irradiated E . paraensei miracidia was noticeable for all 12 snails studied with the arrays ( Fig . 5A ) . However , snails negative for S . mansoni markers displayed increased expression of a variety of known and putative defense-related factors ( Fig . 5B ) . For some factors ( FREP3 , Dermatopontin , Heat shock protein 70 , Superoxide dismutase 1 Cu-ZnA , Serpin B4 , and Matrilin-1A ) increased expression in snails negative for S . mansoni was contrasted by a suppression of expression in snails positive for this parasite . Other factors ( FREP2 , Coagulation factor XI , Dual oxidase , Galectin 4 , Migration inhibition factor , Peroxiredoxin , and SOD Cu-Zn B ) increased in expression in snails not infected by S . mansoni , but remained unaltered as compared to control values in snails that were successfully infected . FREP4 expression differed from other putative resistance molecules in that it was increased compared to control levels in both snails positive or negative for S . mansoni ( Fig . 5B ) .
Schistosome parasites , including those that infect people , continue to thrive the world over , in no small measure owing their success to their productive use of snails as intermediate hosts . Particularly given that schistosome infection is harmful to the snail and results in its castration [18] , it is reasonable to expect that the snail would mount defense responses to prevent infection . Although schistosomes obviously frequently prevail and establish long-term , infections , it is likely that many schistosome-snail encounters in the field result in failed infections . Such failures go overlooked but may well have a significant impact on transmission . Furthermore , the efficacy of present-day chemotherapy-based control operations could potentially be enhanced if we could also exploit snail resistance responses to further limit the number of new snail infections that arise . After all , it is in snails where cercariae - the source of reinfections in people that so frustrate control efforts - are produced in such prodigious numbers . To fully understand the potential impact of snail defenses on schistosome transmission to people , we need to achieve a better understanding of the mechanistic basis of snail defenses to infection , and how these defenses are overcome by schistosomes . In the process , we will also learn a great deal about the general nature of invertebrate ( snail ) defense mechanisms and the intimate interplay between host and parasite . With respect to the immune responses of snails , our studies have lead us to focus on fibrinogen related proteins , or FREPs . One of the most noteworthy aspects of their biology is that two FREPs ( first shown for FREP3 , then FREP2 ) have been shown to undergo somatic diversification driven by gene conversion events and point mutations , creating a diversity of expressed sequences from a limited number of germ-line source sequences [10] , [11] , [19] . Recently , functional assessment of FREP3 demonstrated that it is capable of binding to carbohydrates and acts as an opsonin to enhance phagocytosis of targets by snail hemocytes . RNAi-mediated knockdown of FREP3 in snails resistant to the digenetic trematode Echinostoma paraensei resulted in an abrogation of resistance , resulting in one third of the snails developing established E . paraensei infections . Additionally , this study identified that FREP3 , while increased in expression in resistant snails challenged with S . mansoni or E . paraensei , was suppressed in snails that were successfully infected by either parasite [11] . FREP2 , another FREP that has the capacity for diversification , has been co-immuno-precipitated with S . mansoni polymorphic mucins , suggesting that this complex family of diversified parasite molecules may be the targets for FREPs [10] . Building on these earlier studies , here we demonstrate that FREP3 also plays a role in defense against S . mansoni infection . Knockdown of FREP3 resulted in 21% of the resistant BS-90 strain B . glabrata snails becoming successfully infected ( shedding cercariae ) with S . mansoni . In contrast , none of the 64 snails injected with GFP siRNAs shed cercariae . As previously hypothesized , FREP3 is likely working in combination with other defense mechanisms to manifest the resistant qualities of the BS-90 snails . However , this study clearly demonstrates that it is an important component of defense against S . mansoni . Examination of sectioned snails revealed that S . mansoni miracidia penetrated both control GFP and experimental FREP3 knockdown snails , but observations of sporocysts at 2 and 8-days post-infection did not yield obvious evidence in either group of snails of sporocysts under conspicuous attack by hemocytes including within multilayered hemocyte capsules . Rather , sporocysts were found with only loose aggregates of hemocytes in their vicinity . This is compatible with observations reported by Galvan et al . , 2000 [20] who noted that mother sporocysts of S . mansoni could remain viable in BS-90 snails for as long as 33 days . However , none of the S . mansoni-exposed BS-90 snails that they observed , nor any that we have observed over the years prior to this study , have ever shed cercariae . Our observations suggest that the inability of S . mansoni to thrive in BS-90 snails - at least in some cases - may be more dependent on inhibitory humoral factors than on overt hemocyte aggression and dismemberment . For example , humoral factors might serve to inhibit S . mansoni larval development or nutrition acquisition . In all but one of the BS-90 FREP3 knockdown snails from which cercariae were shed , cercariae production must have originated from a small number of sporocysts in the head-foot of the snail . This likely explains why cercariae were produced by them in small numbers and intermittently . The daughter sporocysts producing these cercariae were either within or adjacent to the mother sporocyst that produced them . As these snails were fixed for histology , it is not clear how long they might have persisted in shedding cercariae . We suggest FREP3 knockdown in these snails allowed sporocysts to persist and enlarge , but was insufficient to enable them to proliferate and establish disseminated infections in the digestive gland . Hemocytes were not prominent around the head-foot sporocysts suggesting they had acquired some ability to protect themselves from attack . In snails receiving GFP siRNAs , in only one snail examined could sporocysts be found . They were small and showed no evidence of germ ball development . Based on these results , one possibility is that FREP3 plays a role in suppressing development of S . mansoni sporocysts in BS-90 snails , and if its effects are temporarily reduced , sporocysts may be released from this inhibition sufficiently well to enable some sporocyst development and multiplication to occur . As the knock-down effects inevitably wane , then the sporocysts may be prevented from further development such that proliferative infections do not usually result . For the one FREP3 knockdown snail noted to have a disseminated infection , hemocytes accumulated in the digestive gland and in some cases were seen to be encapsulating daughter sporocysts . This is reminiscent of what was noted by Lie et al . [21] in some of the 10-R2 B . glabrata snails they observed in which resistance to S . mansoni had been broken down by pre-exposure of these snails to irradiated miracidia of E . paraensei . In about 30% of these snails , “self-cure” was eventually noted , characterized by hemocyte reactions to daughter sporocysts . Results of both experiments imply that snails inherently resistant to S . mansoni can reinvigorate an effective resistance response later in the course of infection , even though their ability to prevent establishment and development of infection had been earlier compromised by experimental manipulation . This suggests that the machinery for generating resistance is still intact . Furthermore , even though their collective biomass is large , daughter sporocysts may not be as effective as newly-penetrated ( and much smaller ) mother sporocysts in preventing effective responses . As noted in the previous paragraph , and initially documented in studies by Lie and co-workers ( 13 , 25 ) , both normal and irradiated sporocysts of E . paraensei have a potent ability to interfere with the resistance of B . glabrata to trematode infection . Their classic work has since stimulated a number of studies to reveal the underlying mechanisms of immunosuppression . Hemocytes collected from B . glabrata infected with E . paraensei exhibited reduced adhesive , spreading and phagocytic capacity compared to uninfected controls [22] , [23] . B . glabrata hemocytes exposed to live E . paraensei sporocysts in vitro actively move away from the parasite [12] , and eventually lose adherence to the substrate if exposed to parasite excretory/secretory ( ES ) products [24] . Furthermore , ( ES ) products of a related parasite , Echinostoma caproni , significantly impact the functional capacity and behavior of snail hemocytes , including a loss of adhesion , spreading and phagocytosis [25] . As these effects seem to be specific to suitable snail hosts , not extending to echinostome-resistant snail strains [25] or species [12] , [26] , the mechanism of their effects must be tailored to specific aspects of the defense system of compatible snails . To pursue the molecular basis of E . paraensei-induced immunosuppression , we have followed the transcriptional responses of exposed snails using microarrays . By as early as 12 hours post-exposure , E . paraensei provokes down-regulation of snail defense responses , including FREP3 expression [7] . Many of the targets of E . paraensei immunosuppression are putative or known resistance-associated factors such as FREPs 1 , 3 , 5 , 8 , 9 , and 10 [8] , migration inhibition factor [27] , dermatopontin [28] , alpha-2- macroglobulin receptor [29] , mannose receptor [30] , peroxiredoxin [31] , and galectins 4 and 7 [32] . Reduction in the presence of these factors theoretically would impact many aspects of defense function such as activation and phagocytosis by hemocytes ( FREPs , mannose receptor , alpha-2- macroglobulin receptor ) , intra- and extra-cellular killing ( peroxiredoxin ) , hemocyte adhesion and encapsulation ( dermatopontin , migration inhibition factor ) , and coagulation ( galectins ) . Based on these results , we sought to repeat the basic design of the experiment of Lie et al . [14] , to see if we could use pre-exposure of irradiated miracidia of E . paraensei to interfere with resistance of BS-90 snails to S . mansoni . Our experiment represents the first repeat of their classic experiment that has been accompanied by molecular ( microarray ) studies , and it is the first experiment to employ the naturally resistant BS-90 snails as hosts as opposed to other resistant B . glabrata snails of the 10-R2 or 13-16-R1 strains that were bred and selected for resistance [14] . We show that irradiated E . paraensei sporocysts suppress the BS-90 defense response sufficiently to allow 46% of the snails so treated to develop patent S . mansoni infections . When snails that permitted S . mansoni development were compared with those that did not using the B . glabrata microarray , we observed a number of immune-relevant transcripts that exhibited expression patterns indicating S . mansoni contributed to the suppression as well . FREP 2 and 3 , coagulation factor IX , dermatopontin , dual oxidase , galectin 4 , MIF , peroxiredoxin , superoxide dismutase Cu-Zn , and heat-shock protein 70 all exhibited increased expression in snails that successfully resisted infection compared to those that were infected by S . mansoni . Thus , we confirm previous hypotheses [7] suggesting that S . mansoni also utilizes a program targeted at suppressing the expression of important defense factors involved in killing larval parasites . This past work indicates that S . mansoni and E . paraensei differ in the timing and targets suppressed , E . paraensei beginning aggressive immunosuppression by 12 hours post challenge , S . mansoni beginning between 2 and 4 days post challenge [7] . Our results also suggest that irradiated echinostome larvae are more effective than our FREP3 knockdown protocol in protecting S mansoni sporocysts in resistant snails . This may be because irradiated echinostomes provide more persistent down-regulation of FREP3 , and also have effects on other immune factors as well . The irradiated echinostome experiment indicates that if S . mansoni sporocysts are sufficiently protected , they can reliably develop disseminated infections in resistant snails . This may be because irradiated echinostomes provide S . mansoni sporocysts a longer interval to acquire and express their own immunosuppressive effects . Our studies indicate that both echinostomes and schistosomes employ means of immunosuppression to colonize snails , and that this property can be manipulated to increase the breadth of strains of a single species , B . glabrata , that can be colonized . This work also bears on two important related general issues in parasitology , host specificity and host switching . Even though most digenetic trematodes are very host specific with respect to their choice of snail hosts , phylogenetic studies suggest that host-switching with respect to snails has been common in the history of trematodes like schistosomes [33] . The suppression we document offers one potential mechanism to resolve this apparent paradox: down-regulation of defense responses by one parasite may open the door for colonization of another parasite normally incompatible with that host . Field studies indicating the ability of one trematode to facilitate infection with another are consistent with this possibility [34] , [35] . Cercariae produced in this study , both from BS-90 B . glabrata infected by S . mansoni due to reduced FREP3 , or E . paraensei-mediated immunosuppression , were viable and able to infect mice . Thus , there is the potential for continuation of a trematode life cycle from a normally resistant snail host . It remains to be seen whether eggs produced from these mice have improved success at infecting BS-90 B . glabrata . We suggest that this study provides proof of principle that parasite-induced immunosuppression improves the chances that normally incompatible parasites can be successful in new , and hostile host environments . Furthermore , it provides a specific mechanism and molecules to target for future studies aimed at experimentally studying host specificity and host switching . Another potential application of this work relates to the role of FREP3 in resistance of wild B . glabrata to infection with S . mansoni . Although it is clear that other factors are involved in resistance , this line of work suggests efforts to up-regulate FREP3 expression in snails from natural populations could have the effect of diminishing S . mansoni infections . We now must focus our efforts on understanding whether snails from endemic areas mount FREP3 responses following exposure to natural schistosome infections . It also raises the question as to whether snails differ in their inherent FREP3 responsiveness , and if this trait can be manipulated or favored to diminish natural schistosome infections . | Schistosomiasis , a neglected tropical disease , owes its continued success to freshwater snails that support production of prolific numbers of human-infective cercariae . Encounters between schistosomes and snails do not always result in the snail becoming infected , in part because snails can mount immune responses that prevent schistosome development . Understanding the factors important for snail resistance to schistosome infection will facilitate new lines of investigation to 1 ) understand the underlying basis of compatibility between schistosomes and snails in endemic areas and how this affects transmission dynamics and control efforts; and 2 ) to reveal ways to manipulate natural snail populations to enhance their resistance to schistosome infections . Here , we present the first evidence that a snail immune molecule , fibrinogen related protein 3 ( FREP3 ) , is important for successful defense against schistosome infections in Biomphalaria snails . In addition , we demonstrate that FREP3 is a target suppressed by trematode parasites to facilitate their establishment within the snail . |
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Macrophage activation of NAD ( P ) H oxidase ( NOX2 ) and reactive oxygen species ( ROS ) is suggested to kill Trypanosoma cruzi that causes Chagas disease . However , the role of NOX2 in generation of protective immunity and whether these mechanisms are deregulated in the event of NOX2 deficiency are not known , and examined in this study . Our data showed that C57BL/6 p47phox−/− mice ( lack NOX2 activity ) , as compared to wild-type ( WT ) mice , succumbed within 30 days post-infection ( pi ) to low doses of T . cruzi and exhibited inability to control tissue parasites . P47phox−/− bone-marrow and splenic monocytes were not compromised in maturation , phagocytosis and parasite uptake capacity . The deficiency of NOX2 mediated ROS was compensated by higher level of inducible nitric oxide synthase ( iNOS ) expression , and nitric oxide and inflammatory cytokine ( TNF-α , IFN-γ , IL-1β ) release by p47phox−/− macrophages as compared to that noted in WT controls infected by T . cruzi . Splenic activation of Th1 CD4+T cells and tissue infiltration of immune cells in T . cruzi infected p47phox−/− mice were comparable to that noted in infected control mice . However , generation and activation of type 1 CD8+T cells was severely compromised in p47phox−/− mice . In comparison , WT mice exhibited a robust T . cruzi-specific CD8+T cell response with type 1 ( IFN-γ+TNF-α>IL-4+IL-10 ) , cytolytic effector ( CD8+CD107a+IFN-γ+ ) phenotype . We conclude that NOX2/ROS activity in macrophages signals the development of antigen-specific CD8+T cell response . In the event of NOX2 deficiency , a compromised CD8+T cell response is generated , leading to increased parasite burden , tissue pathogenesis and mortality in chagasic mice .
Chagas disease is caused by the protozoan Trypanosoma cruzi [1] , [2] . During acute phase of infection , parasites can be found in the circulating blood , and host may develop fever or swelling around the site of inoculation , and rarely , severe inflammation in heart muscle or brain . Several years after exposure to T . cruzi , ∼30% of the infected individuals develop clinical symptoms of chronic cardiomyopathy associated with progressive cardiomegaly , arrhythmia , thromboembolic events , and heart failure [3] , [4] . Both innate and acquired immune responses are required for control of T . cruzi and critical for host survival ( reviewed in [5] , [6] ) . Upon infection , macrophages serve as first responders by activation of phagocytic NADPH oxidase , referred as NOX2 . NADPH oxidase is a multi-subunit complex and utilizes NADPH as an electron donor to reduce O2 to superoxide ( O2·− ) , that is then dismutated into other oxidants ( e . g . H2O2 ) [7] . The plasma membrane-associated proteins gp91phox and p22phox compose the flavocytochrome-b558 complex that is the major component responsible for enzyme stability and activity . Phosphorylation of cytosolic factors ( p47phox , p67phox , and p40phox ) , and small Rho GTPases in response to exogenous or endogenous stimuli initiates their translocation to the cell membrane , and NADPH oxidase activation [7]–[9] . Activated phagocytes exert cytotoxic effects via NOX2-dependent reactive oxygen species ( ROS ) production that mediates pathogen killing by oxidative damage of DNA , proteins and lipids , and suggested to play an important role in control of T . cruzi [10]–[14] . Besides innate immune mechanisms , a body of literature demonstrates that adaptive immune responses are required for parasite control . CD4+T cells assist in the control of T . cruzi through secretion of Th1 cytokines , amplification of the phagocytic activity of macrophages , stimulation of B cell proliferation and antibody production , and enhancement of the CD8+T cells response ( reviewed in [6] , [15] . CD8+T cells recognize processed parasite antigens presented in association with MHC class I molecules on the surface of infected host cells and contribute to the control of T . cruzi , either by cytolysis of parasite-infected cells or by the secretion of cytokines that may induce trypanocidal activity ( reviewed in [6] , [16] ) . Current literature suggests that NADPH oxidase activity may modulate adaptive immune responses via ROS signaling of cytokine gene expression and regulation of the efficient antigen presentation for T cell activation and proliferation [17] , [18] , though the cell type involved in NADPH oxidase-mediated regulation of adaptive immunity are not fully detailed . In this study , we have assessed the host response to T . cruzi infection in the event of phagocytic NADPH oxidase deficiency . We first monitored the susceptibility of wild-type ( WT ) versus p47phox−/− mice to T . cruzi infection , and then proceeded with a step-wise approach to identify the immune mechanisms that may be altered and contributed to susceptibility of p47phox−/− mice to T . cruzi . Our data show that p47phox−/− macrophages were not compromised in phagocytic activity , and mounted enhanced levels of inducible nitric oxide synthase ( iNOS ) , nitric oxide ( NO ) , and cytokines in response to T . cruzi infection . In vivo activation of CD4+T cell subset and inflammatory cytokine response was also similar to or more pronounced in p47phox−/− mice when compared to that observed in WT controls in response to T . cruzi infection . However , in the event of NOX2 deficiency , generation and activation of CD8+T cell response was severely compromised leading to increased parasite burden , tissue pathogenesis and mortality . We discuss the involvement of distinct innate receptor signaling pathways governing the activation and proliferation of T cell subsets and the various mechanisms contributing to increased susceptibility of p47phox−/− mice to T . cruzi infection .
We used well-established experimental models [19] , [20] to assess the role of NAD ( P ) H oxidase ( NOX2 ) in immunity to T . cruzi infection . C57BL/6 ( WT and p47phox−/− ) mice were assessed at day 7 post-infection ( pi ) for the expression level of p47phox as an indicator of NOX2 activation in innate immune cells . The low level of baseline expression of p47phox was increased by 2-fold in splenic ( Fig . 1A ) and bone-marrow monocytes/macrophages of WT mice . The splenic and BM monocytes of p47phox−/− mice exhibited no expression of p47 before or after T . cruzi infection . These data confirmed that p47phox−/− mice lacked the ability to induce NOX2 activity in phagocytes in response to T . cruzi infection . Challenge infection with 10 , 000 T . cruzi per mouse proved to be lethal for p47phox−/− mice as all mice succumbed within 28 days pi ( Fig . 1B . a ) . When inoculum was reduced to 2000 parasites , 70% of p47phox−/− mice still succumbed by 30 days pi . In comparison , 100% of WT mice challenged with 2000 or 10000 parasites survived ( Fig . 1B . a ) . The increased mortality of p47phox−/− mice was associated with increased tissue parasites ( Fig . 1B . b , Fig . 1 . C–E ) . Histological analysis of skeletal muscle and heart tissue sections ( three sections/tissue >10-microscopic fields ( mf ) per slide , n = 8 mice/group ) was conducted to obtain a score of parasite foci in tissues ( Table 1 ) . An average of skeletal tissue parasite foci in WT and p47phox−/− mice infected with 2000 or 10000 parasites is presented in Fig . 1B . b . The p47phox−/− mice infected with 2000 or 10000 parasites exhibited an early increase in tissue parasitemia by day 7 pi that further increased in a linear manner at days 14 and 21 pi ( Fig . 1B . b ) . In comparison , WT mice exhibited a delayed , 2–5-fold lower level of parasite foci in skeletal muscle tissue during 7–21 days pi ( Fig . 1B . b ) . At 30 days pi , parasite foci in WT mice infected with 2000 or 10000 parasites were controlled , while p47phox−/− continued to exhibit an increase in tissue parasite foci ( Fig . 1B . b ) . A similar pattern of increase in parasite foci was observed in heart tissue of p47phox−/− mice during the 7–30 days pi ( Table 1 ) . We noted >5 parasite foci/mf in heart tissue sections of p47phox−/− mice infected with 2000 or 10000 parasites and harvested at day 30 pi ( Fig . 1C . b ) . In comparison , contained ( 0–2 pseudocysts/mf ) were noted in heart tissue of WT mice infected with 2000 or 10000 parasites ( Fig . 1C . a , Table 1 ) A semi-quantitative PCR showed the Tc18SrDNA signal was significantly higher in the myocardium of infected/p47phox−/− mice at day 30 pi than was observed in the myocardium of WT mice infected with the same dose or 5-fold higher dose of parasites ( Fig . 1D ) . Quantitative real-time PCR validated the findings of semi-quantitative PCR and showed 2–5-fold increase in myocardial ( Fig . 1E , ##p<0 . 01 ) , and skeletal muscle and circulatory parasite burden in infected/p47phox−/− mice as compared to that detected in infected/WT mice . These data suggested the p47phox−/− mice failed to control tissue parasites and succumbed to T . cruzi infection . One plausible explanation for increased susceptibility of p47phox−/− mice to T . cruzi could be that p47phox−/− macrophages were compromised in phagocytic activity , and , therefore , failed to control parasites' dissemination . To test this , we isolated BM and splenic monocytes from WT and p47phox−/− mice , in vitro differentiated to macrophages with interferon gamma ( IFN-γ ) , and incubated in presence of T . cruzi for 0 , 6 , 12 , 24 h . The data presented in Fig . S1 are from splenic monocytes and representative of the results from triplicate experiments with splenic and BM monocytes . Giemsa staining showed the monocytes of p47phox−/− mice had a similar or better capacity than the WT monocytes to differentiate to macrophages by 6 h pi ( Fig . S1 . b&g ) . Likewise , p47phox−/− phagocytes' capacity to uptake parasites ( i . e . phagocytic efficiency ) was not significantly compromised . Counting of >200 cells/slide showed that by 6 h , 25% and 15% of WT and p47phox−/− macrophages were infected ( average 6–15 parasites/cell ) , and at 12 h , >50% of WT and p47phox−/− macrophages were full of replicative , amastigote form of parasites ( Fig . S1 . c , d , h , i ) . At 24 h pi , some of the WT macrophages were ruptured releasing parasites while p47phox−/− macrophages continued to exhibit parasites contained within phagosome ( Fig . S1 . e&j , p<0 . 01 ) . To obtain a quantitative measure of parasite uptake , primary BM and splenic cells were incubated for 24 h with CFSE-labeled T . cruzi , and then labeled with fluorescence-conjugated antibodies to examine the frequency of CFSE+ macrophages ( APC-CD11b+ ) and neutrophils ( PE-Ly6B+ ) by flow cytometry . Representative flow cytometry data from BM-macrophages and BM-neutrophils incubated with CFSE-labeled parasites are presented in Fig . 2A , and percentage of CFSE+CD11b+ and CFSE+Ly6B+ macrophages and neutrophils , respectively , from BM and splenic cells are presented in Fig . 2B . We noted a higher extent of infection of BM cells derived from p47phox−/− mice as compared to that noted in BM cells from WT mice ( CD11b+ macrophages: 71% versus 36%; Ly6+ neutrophils: 23 . 5% versus 13 . 5%; p47phox−/− versus WT , respectively , Fig . 2A & Fig . 2B . a ) . The splenic cells from p47−/− and WT mice exhibited comparable rate of infection efficiency at 24 h post-incubation that were not statistically different ( CD11b+ macrophages: 10 . 65% versus 16 . 8%; Ly6+ neutrophils: 3 . 28% versus 9 . 45% , p47phox−/− versus WT , respectively , Fig . 2B . b ) . The hemacytometer counting of parasites in supernatants showed comparable number of parasites were released from p47phox−/− and WT macrophages at 24 h and 48 h pi ( Fig . 2C ) . Together , the data presented in Fig . S1 and Fig . 2 suggested that BM and splenic monocytes from WT and p47phox−/− mice were equally competent in differentiating to macrophages and parasite uptake , and NOX2 deficiency did not result in increased parasite release from infected macrophages . Macrophages control the invading pathogen through production of ROS , NO , and inflammatory cytokines . Isolated BM and splenic monocytes from WT and p47phox−/− mice were incubated for 0 , 6 , 12 and 24 h with T . cruzi ( ± recombinant IFN-γ ) . We measured ROS levels using H2DCFDA that is cell permeable and when oxidized by ROS , releases fluorescent DCF . A gradual increase in DCF fluorescence beginning at 6 h that reached the maximal level at 24 h pi was observed in WT macrophages . Macrophages from WT mice responded to T . cruzi infection ( ± rIFN-γ ) by 5-fold increase in DCF fluorescence ( Fig . 3A . a ) . The p47phox−/− splenic macrophages ( ±rIFN-γ ) exhibited 2 . 5-fold lesser DCF fluorescence in response to T . cruzi when compared to that noted in infected/WT cells ( Fig . 3A . a , ##p<0 . 01 ) . Likewise , the p47phox−/− BM macrophages ( ± rIFN-γ ) exhibited a 2-fold decline in ROS levels as compared to that noted in WT BM macrophages upon T . cruzi infection . The superoxide-dependent formation of formazan blue crystals was noted to be significantly increased in WT and slightly increased in p47phox−/− splenic macrophages incubated for 24 h with T . cruzi ( Fig . 3A . b ) . T . cruzi-induced increase in DCF fluorescence and nitroblue tetrazolium ( NBT ) reduction was quenched by >90% when cells were incubated in presence of 0 . 5 mM apocynin ( inhibits NADPH oxidase activity ) or 10 µM N-acetyl cysteine ( ROS scavenger ) , suggesting the observed increase in ROS is primarily due to NOX-dependent ROS from infected macrophages . The iNOS mRNA level , determined by qRT-PCR , was increased by 4-fold in p47phox−/− splenocytes as compared to that noted in WT controls , infected in vitro for 24 h ( Fig . 3B ) . The release of cytokines in supernatants of primary BM and splenic cells incubated with T . cruzi for 24 h was measured by an ELISA . The p47phox−/− BM and splenic cells responded to T . cruzi by >10-fold increase in IFN-γ and TNF-α release that was significantly higher than that observed in infected/WT cells ( Fig . 3C ) . Together the data presented in Fig . 3 suggested that p47phox−/− macrophages lacked the ability to mount a strong NOX2-dependent ROS; however , exhibited a higher extent of iNOS and proinflammatory cytokine expression in response to T . cruzi infection . The p47phox−/− monocytes/macrophages also responded to heat-inactivated T . cruzi with a strong proinflammatory cytokines production . To further gain an indication of the effects of phagocytes' NOX2 deficiency on host immunity to T . cruzi; we looked at the tissue infiltration of immune cells in T . cruzi infected WT and p47phox−/− mice by histological studies ( Fig . 4 , Fig . S2 , and Table 1 ) . The p47phox−/− mice injected with 2000 parasites exhibited infiltration of inflammatory infiltrate in skeletal muscle and heart tissue as early as day 7 post-infection ( pi , Fig . 4 . e , Table 1 ) . The inflammatory foci were observed in all tissue sections by 14 days pi ( score: 2 ) , and extensive inflammation with large inflammatory foci or diffused inflammation throughout the tissue section ( score: 2–4 ) was observed at 21–30 days pi in skeletal muscle ( Fig . 4 . f–h ) and heart tissue ( Fig . S1 . m–o ) of p47phox−/− mice . In WT mice , infection with 2000 parasites resulted in minimal inflammation of the skeletal muscle ( Fig . 4 . a&b ) and heart tissue ( Table 1 ) at 7–14 days pi; and inflammatory infiltrate was moderately increased ( score: 1–2 ) at 21–30 days pi ( Fig . 4 . c&d , Table 1 ) . These data suggested that p47phox−/− mice responded to T . cruzi infection ( 2000 parasites/mouse ) with an increase in tissue infiltration of inflammatory infiltrate that was higher than that observed in WT mice given the same dose of parasites . Infection with a 5-fold higher dose of parasites was required to elicit the extent of increase in inflammatory infiltrate in skeletal muscle ( score: 2–4 , Fig . S1 . d&e ) and heart tissue ( score: 1–2 , Fig . S1 . i&j ) of WT mice as was noted in skeletal muscle and heart tissue of p47phox−/− mice infected with 2 , 000 parasites . To examine the quality of inflammatory response in vivo , WT and p47phox−/− mice were harvested at day 7 , 14 , 21 , and 30 post-infection . BM and splenic cells from infected mice were either directly analyzed or in vitro stimulated in presence of T . cruzi trypomastigote lysate ( TcL ) and utilized for functional assessment . Shown in Fig . 5A are intracellular ROS levels in splenic cells of infected mice at day 30 pi , determined by dihydroethidium ( DHE ) fluorescence . DHE is cell permeable , and when oxidized to ethidium , accumulates in nuclei and fluoresces bright red . We noted a significant increase in ethidium fluorescence in splenocytes of infected/WT , but not of infected/p47phox−/− mice , at all time-points pi ( Fig . 5A . a&b ) . Likewise , BM cells isolated at day 7 , 14 , 21 , and 30 from infected/WT mice , but not from infected/p47phox−/− mice , exhibited a significant increase in DHE fluorescence . DHE fluorescence was quenched when cells were incubated in presence of 0 . 5 mM apocynin ( NOX2 inhibitor ) . Note that 4′-6-diamidino-2-phenylindole-dihydrochloride ( DAPI , binds nuclear DNA ) staining of the splenocytes ( Fig . 5A . c&d ) of infected/WT and infected/p47phox−/− mice was comparable . The intracellular nitric oxide levels in BM and splenic cells harvested at day 7 , 14 , 21 and 30 pi was first determined by DAF-FM-based fluorimetry . DAF-FM is cell permeable , and forms fluorescent benzotriazole upon reaction with nitric oxide . Shown in Fig . 5B . a are arbitrary units of DAF-FM fluorescence in splenic cells of infected mice harvested at day 30 pi . Our data showed a 4-fold increase in DAF-FM fluorescence in splenocytes of infected/p47phox−/− mice that was further increased upon in vitro stimulation with TcL . In comparison , splenic cells of infected/WT mice exhibited a significant increase in intracellular nitric oxide ( DAF-FM fluorescence ) only after secondary in vitro stimulation with TcL , and this response was ∼3-fold lesser than that observed with splenocytes of infected/p47phox−/− mice ( Fig . 5B . a ) . Because DAF-FM may exhibit non-specific signal by reacting with N compounds others than nitric oxide , we also performed a Griess reagent assay to evaluate the nitric oxide production rate , reflected by nitrite release . Splenocytes of infected/p47phox−/− mice , in vitro stimulated with TcL , exhibited a robust increase in nitrite release that was >5 . 8-fold higher than that noted with splenic cells from infected/WT mice ( Fig . 5B . b ) . Likewise , BM monocytes of infected/p47phox−/− mice responded to in vitro antigenic stimulus ( TcL ) by a robust 7-fold and 8-fold increase in DAF-FM fluorescence and nitrite release , respectively . The extent of TcL-stimulated nitrite release was 4-fold ( 32 . 8±4 . 7 versus 8 . 07±0 . 6 pg nitrite/ml ) higher in BM cells of infected/p47phox−/− mice than that noted in BM cells of infected/WT mice ( ##p<0 . 001 ) . In all experiments , incubation of splenic or BM monocytes from infected mice with 5 µmol/ml L-NAME ( inhibits iNOS activity/nitric oxide ) abolished the DAF-FM fluorescence and nitrite release . To examine the in vivo cytokine profile in response to infection , BM and splenic cells from WT and p47phox−/− mice were harvested at 7 , 14 , 21 , and 30 days pi , in vitro incubated with or without second antigenic stimulus for 48 h , and supernatants were submitted to an ELISA . Overall , splenocytes of WT and p47phox−/− mice ( ± T . cruzi lysate ) were activated early upon infection , as is evidenced by a significant increase in TNF-α , IFN-γ and IL-10 levels at day 7 pi ( Fig . 5C ) . No IL-4 release was observed . The splenocytes of infected/WT mice exhibited a predominance of TNF-α ( TNF-α>IL-10>IFN-γ ) release throughout the course of infection ( Fig . 5C . a , c , e ) . In comparison , splenocytes of infected/p47phox−/− mice ( ± TcL ) exhibited a mixed response with a predominance of IL-10 ( IL-10>TNF-α>IFN-γ ) at 7 , 14 , 21 and 30 days post-infection ( Fig . 5C . b , d , f ) . The BM cells of WT and p47phox−/− mice infected with T . cruzi ( ±TcL ) exhibited a similar pattern of cytokine response as was noted in splenocytes . Together , the data presented in Fig . 5 suggested that compromised ROS production capacity due to NOX2 deficiency was compensated by an increased iNOS and nitric oxide levels in p47phox−/− mice infected with T . cruzi . However , p47phox−/− mice exhibited a subdued proinflammatory cytokine response ( IL-10>TNF-α ) during T . cruzi infection . CD4+ and CD8+ T cells are important constituents of the adaptive immunity against T . cruzi . To gain an appreciation for the role of NOX2 in determining T cell functional profile , we evaluated the in vivo quality and potency of the cellular immune responses elicited in WT versus p47phox−/− mice . Splenocytes , harvested at 30 days post-infection , were incubated in presence and absence of TcL antigenic stimulus , and T cell proliferation determined by an MTT assay ( Fig . 6A ) . The CD4+ and CD8+ T cells were examined for proliferative capacity ( Ki67+ ) , intracellular cytokine profile ( IFN-γ , TNF-α ) and marker of lytic capacity ( CD107a ) by flow cytometry . The mean fluorescence intensity ( ±SD ) indicative of T cell profile ( Fig . 6B , n = 6/group ) were derived from representative quadrant images of flow cytometry results presented in Fig . 6C . Splenic lymphocytes of WT mice exhibited 3 . 6–4 . 2-fold increase in proliferation in response to T . cruzi infection ( ± in vitro stimulation with TcL , Fig . 6A ) . In comparison , p47phox−/− mice exhibited a ∼40% lower rate of splenic lymphocyte proliferation in response to T . cruzi infection , and no effect of in vitro stimulation with TcL was noted ( Fig . 6A , ##p<0 . 01 ) . When we performed the specific T cell population analysis , we found that in vivo population of CD4+T cells was comparable in naïve WT and p47phox−/− mice ( range: 12–14% , Fig . 6B . a ) , and exhibited no significant change in proliferation ( Ki67+ ) or IFN-γ+ phenotype in response to T . cruzi infection or subsequent incubation with TcL antigenic lysate . Instead , in vivo percentage of CD8+T cells in p47phox−/− naïve mice ( 4 . 5% ) was ∼2-fold lower than that noted in WT normal controls ( 7 . 9% ) ( Fig . 6B . a ) . In response to T . cruzi infection , CD8+T cells expanded by 3 . 4-fold in WT mice ( 27% of total ) while these cells expanded at a very low frequency in p47phox−/− mice ( Fig . 6B . a ) . Functional characterization of CD8+T cells showed that a majority of the CD8+T cells were proliferative ( Ki67+ ) in infected/WT mice ( Fig . 6B . b ) and a significant proportion of the CD8+Ki67+ cells ( up to 13% ) produced IFN-γ in an antigen-specific manner ( Fig . 6B . c ) . In p47phox−/− mice , CD8+T cells exhibited no significant proliferating phenotype in response to T . cruzi infection ( Fig . 6B . b ) . Further , up to 16% of the CD8+T cells exhibited IFN-γ+ phenotype in naïve p47phox−/− mice and these didn't increase in response to T . cruzi infection or second antigenic stimulation with TcL ( Fig . 6B . b ) . Transport of CD107a and CD107b to the plasma membrane of effector T cells is required for a ) the cytolytic activity mediated by perforin and granzymes and b ) the release of IFN-γ which exerts pleiotropic effects to suppress intracellular pathogens . Our data showed T . cruzi infection induced CD107a+CD8+T cells ( 2–4% ) in infected/p47phox−/− mice ( Fig . 6B . d ) , and a majority of these exhibited dual-positive ( CD107a+IFN-γ+ ) cytolytic phenotype , comparable to that noted in infected/WT mice . Together , the data presented in Fig . 6 suggested that splenic CD8+T cells in p47phox−/− mice were low in number and failed to expand in response to T . cruzi infection , resulting in a substantial decline in proliferating , IFN-γ-producing cytolytic CD8+T cell response . In comparison , an expansive CD8+T cells proliferation that were predominantly IFN-γ+ with cytolytic capacity , and , thus , had a potential to act as effector T cells was induced in WT mice infected by T . cruzi .
The present study shows that in the absence of NOX2 activity , a defective activation of CD8+T cell occurs , and contributes to the inability of mice to successfully control T . cruzi infection . Our data suggested that the NOX2 deficiency was compensated by enhanced levels of iNOS , nitric oxide , and inflammatory cytokines in macrophages; however , p47phox−/− mice were highly susceptible to T . cruzi because of the inability to activate a type 1 CD8+T cell response that is known to be essential for intracellular parasite control . Our study highlights how redox state of innate immune cells alters the adaptive immunity to intracellular pathogens , and understanding the molecular and cellular mechanisms affected by redox state of immune cells at basal level could be exploited in designing future vaccination strategies against T . cruzi infection and Chagas disease . Current literature demonstrates that macrophage-derived free radicals ( O2•− , nitric oxide ) generated by the NOX2 complex and iNOS participate in cytotoxic mechanisms against microorganisms ( reviewed in [21][22] ) . In the context of T . cruzi , it is suggested that nitric oxide plays a central role through its action on macrophage-derived peroxynitrite formation , a strong cytotoxic oxidant that is formed by the reaction of nitric oxide with O2•− [14] , [23] , [24] . Our in vitro studies showed that p47phox−/− monocytes were better than ( or equal to ) the WT controls in their ability to differentiate into macrophages and phagocytize parasites ( Fig . S1 ) . Though it appeared that a higher number of intracellular parasites were present in infected p47phox−/− macrophages at 24 h pi ( Fig . S1 & Fig . 2 ) ; however , the extent of parasite release from p47phox−/− cells at 24 and 48 h pi was comparable to that noted in WT controls ( Fig . 2C ) , thus suggesting that NOX2 deficiency did not result in increased parasite survival in infected macrophages . Our observations are supported by others demonstrating the enhanced replication of bacteria ( e . g . Coxiella burnetii ) in p47phox−/− macrophages , that was followed by a slightly delayed control of infection at a rate similar to the WT macrophages [25] . We propose that p47phox−/− macrophages , despite a lack of NOX2/ROS , were equipped to phagocytize and control the parasites through compensatory mechanisms . One , a low but detectable level of O2•− production in p47phox−/− macrophages ( Fig . 3A&B ) was sufficient to support the nitric oxide mediated cytotoxic peroxynitrite formation for parasite killing . Indeed , O2•− and nitric oxide can rapidly diffuse ( diffusion control rates: ∼1010 m−1 s−1 ) and react to form peroxynitrite that is significantly more potent cytotoxin against trypomastigotes than H2O2 only [13] , [14] . Secondly , a significant up regulation of the iNOS , nitric oxide and inflammatory cytokines ( IFN-γ/TNF-α ) in p47phox−/− macrophages in response to T . cruzi infection ( Fig . 3 ) could have controlled the infectious pathogen . Others have also demonstrated increased iNOS and nitric oxide levels in gp91phox−/− mice infected by T . cruzi [26] . We surmise that in the event of defects in mounting NOX2/ROS , macrophages are capable of using alternative , compensatory mechanisms for pathogen control . Further studies will be required to conclusively establish if the peroxynitrite formation rate is indeed enhanced and identify the signaling mechanisms that were up regulated resulting in enhanced iNOS and inflammatory cytokines' expression in p47phox−/− macrophages in response to T . cruzi infection . The production of cytokines ( IL-12 , TNF-α ) by innate immune cells ( macrophages , dendritic cells ( DCs ) shapes the adaptive immunity via activation of T cells . CD4+ and CD8+ T cells producing type 1 cytokines and CD8+T cell mediated cytolytic activity are required for control of T . cruzi infection ( reviewed in [6] , [16] , [27] . Our observation of increased release of IFN-γ/TNF-α by p47phox−/− macrophages in vitro infected with T . cruzi ( Fig . 3 ) suggest that NOX2/ROS might control the cytokinopathy via regulating the cytokine gene expression; however , NOX2 deficiency did not inhibit the phagocytes ability to provide inflammatory cytokine milieu for the recruitment and activation of T cells . Indeed flow cytometry analysis showed the CD4+T cells in p47phox−/− mice responded to T . cruzi infection and/or in vitro antigenic stimulus by activation and proliferation to a similar extent as was noted in WT mice ( Fig . 6 ) . Others have shown that IFN-γ/LPS-treated p47phox−/− mice secrete more IL-12 from DCs than similarly treated WT mice , and IFNγ/LPS matured p47phox−/− DCs biased more ovalbumin-specific CD4+T cells toward a Th1 phenotype than the WT controls in a ROS-dependent manner [28] . It is also suggested that CD4+ T cells from p47phox deficient mice exhibit augmented IFN-γ and diminished IL-4 production and an increased ratio of expression of T-bet ( Th1-specific transcription factor ) versus GATA-3 ( Th2-specfic transcription factor ) , consistent with a Th1 skewing of naïve T cells [29] . Selective inhibition of TCR-induced STAT5 phosphorylation was identified as a potential mechanism for skewed helper CD4+T cell differentiation in p47phox−/− mice [29] . We surmise that p47phox-dependent NOX2 deficiency enhanced the macrophage maturation and inflammatory cytokine response; and provided help for CD4+ T cell activation in the context to T . cruzi infection in p47phox−/− mice . Yet , early splenic response to T . cruzi infection ( 7 days pi ) in p47phox−/− mice was dominated by type 2 cytokines evidenced by a >2-fold decline in splenic TNF-α production and ∼2-fold increase in IL-10 release when compared to that noted in infected/WT controls , and likely responsible for susceptibility to T . cruzi infection ( Fig . 5 ) . The phenotypic and functional characterization of CD8+T cells in p47phox−/− mice provides clues to the cellular mechanisms contributing to increased susceptibility to T . cruzi infection . It was intriguing to find that splenocytes from p47phox−/− mice , as compared to WT controls , contained ∼40% lower number of naïve CD8+ T cells . Further , CD8+T cells in p47phox−/− mice exhibited no proliferation and activation evidenced by none-to-minimal increase in cell frequency overall or the frequency of IFNγ+ , CD107+ or IFNγ+CD107+ CD8+T cells in response to T . cruzi infection and secondary in vitro stimulation with antigenic lysate ( Fig . 6 ) . Others have shown that T∶ B cell ratio is lower in p47phox−/− mice as compared to the WT mice [30] and the CD8+T cells from p47phox−/− mice express higher levels of pro-apoptotic Bim and Puma proteins that promoted their removal by apoptosis [31] . Since FOXO3 dephosphorylation ( activation ) by protein phosphatase 2A ( PP2A ) is known to contribute to transcriptional control of various apoptosis factors including pro-apoptotic Bim , blocking the PP2A activity attenuated the FOXO3 activation and Bim transcription and prolonged the survival of CD8+T lymphocytes in p47phox−/− mice [31] . These studies suggest that p47phox deficiency adversely affects the development and survival of naive CD8+T cells . Additionally , treatment with apocynin that suppresses ROS production by NOX2 directly inhibited the production of proinflammatory cytokines ( e . g . TNF-α , IFN-γ , and IL-2 ) in anti-CD3/anti-CD28-stimulated CD8+T cells . It is proposed that apocynin effects were mediated via attenuation of anti-CD3/anti-CD28-induced NF-κB activation in CD8+T cells [32] . The compromised CD8+T cell activation was not likely due to inefficient antigen presentation as p47phox−/− dendritic cells are shown to be highly efficient in presentation of antigen to B cells in the context of antibody response to Streptococcus and Listeria infection [33] . Others have shown p47phox−/− DCs elicit enhanced ovalbumin-specific CD4+ T lymphocytes [28] . Further studies will be required to delineate the complex role of p47phox in antigen presentation by DCs , CD8+T lymphocytes survival and ROS-dependent mechanisms involved in NF-κB activation in cell-dependent manner . However , the literature discussed above and our findings allow us to surmise that compromised development of splenic CD8+T cells and their inability to respond to antigenic stimulus by generation of IFN-γ and cytolytic activity contributed to high tissue parasite burden in p47phox−/− mice . It is important to note that the components of NADPH oxidase have diverse effects in heart failure . For example , the survival rate of p47phox−/− mice 4-weeks after myocardial infarction ( MI ) was significantly higher than that of WT mice ( 72% versus 48% ) and the survival benefits were associated with a decline in LV dilatation and dysfunction , cardiomyocyte hypertrophy , apoptosis , and interstitial fibrosis in p47phox−/− mice [34] . Others have suggested the loss of p47phox enhanced the susceptibility to heart failure . Patel et al [35] showed that the expression of N-cadherin and β-catenin was up regulated in p47phox−/− mice subjected to biomechanical stress; however , actin filament cytoskeleton was disrupted because these mice lacked the ability to induce p47phox dependent cortactin-N-cadherin interaction required for adaptive cytoskeletal remodeling . In comparison , gp91phox−/− mice exhibited no increase in susceptibility to pressure overload and were equally capable of adaptive cytoskeletal modeling as was noted in controls . In the context of T . cruzi infection , Santiago et al [26] showed that gp91phox−/− mice develop increased circulatory collapse and succumbed to infection . Authors proposed that while a lack of superoxide from phagocytes was not detrimental in hosts' ability to control parasites , superoxide regulates nitric oxide concentrations , and enhanced nitric oxide levels in these mice resulted in a critical drop in blood pressure . These studies suggest that targeting NADPH oxidase system as a potential novel therapeutic target to prevent cardiac failure should be considered with caution . In summary , we present the first evidence that NOX2/ROS of macrophage origin shapes the T cell-mediated adaptive immunity , and its deficiency results in compromised CD8+T cell response to T . cruzi infection . Our data show that macrophages from p47phox−/− mice were not compromised in the phagocytic activity and showed an enhanced iNOS/nitric oxide and pro-inflammatory cytokine levels in response to T . cruzi infection . However , in the event of NOX2 deficiency , generation and activation of CD8+T cell response was compromised , leading to increased parasite burden , tissue pathogenesis and mortality . We propose that future studies focused on understanding how NOX2/ROS induced innate receptor signaling pathways govern the activation and proliferation of T cell subsets will have the potential to identify specific targets for modulating the adaptive immunity and prevent T . cruzi infection and persistence in Chagas disease .
All animal experiments were conducted following NIH guidelines for housing and care of laboratory animals and in accordance with The University of Texas Medical Branch at Galveston in accordance with protocols approved by the institution's Institutional Animal Care and Use Committee ( protocol number 08-05-029 ) . T . cruzi trypomastigotes ( SylvioX10/4 strain ) were maintained and propagated by continuous in vitro passage in C2C12 cells . C57BL/6 mice ( WT and p47phox−/− ) were purchased from Jackson Laboratory ( Sacramento , CA ) . Mice ( 8-weeks-old ) were infected with T . cruzi ( 2 , 000 or 10 , 000 trypomastigotes/mouse , intra-peritoneal ) . Survival from infection was monitored daily . Mice were sacrificed at day 7 , 14 , 21 , and 30 post-infection ( pi ) , and sera/plasma and tissue samples were stored at 4°C and −80°C , respectively . Bone marrow ( BM ) and splenic monocytes/macrophages , and heart and skeletal tissue were washed with ice-cold Tris-buffered saline ( TBS ) , and homogenized in lysis buffer ( tissue∶ buffer ratio , 1∶10 , w/v ) . Homogenates ( 30-µg protein ) were resolved on denaturing 10% acrylamide gels . Proteins were transferred to PVDF membranes , and probed with anti-p47phox primary antibody ( 1∶1000 , Santa Cruz , Dallas TX ) for 24 h at 4°C . Membranes were washed with TBS containing 0 . 1% Tween-20 and TBS , incubated with horseradish peroxidase ( HRP ) -conjugated secondary antibody , and signal was developed by using a chemiluminiscence detection system ( GE-Healthcare , Piscataway , NJ ) [36] . Skeletal muscle and heart tissues ( 50 mg ) were subjected to Proteinase K lysis , and total DNA purified by phenol/chloroform extraction and ethanol precipitation method . Total DNA ( 100 ng ) was used as a template in a PCR reaction for 28 cycles with oligonucleotides specific for T . cruzi 18S rDNA sequence ( Forward: 5′-TAGTCATATGCTTGTTTC-3′ , Reverse: 5′-GCAACAGCATTAATATACGC-3′ ) [19] . Quantitative estimate of parasite burden was obtained by real-time PCR on an iCycler thermal cycler with SYBR Green Supermix and Tc18S-sepecific oligonucleotides . Fold change was calculated as 2−ΔCt , where ΔCt represents the Ct ( infected sample ) - Ct ( control ) [20] . All data were normalized with host-specific GAPDH . Total RNA was isolated by using the RNeasy plus Kit ( Qiagen ) , and analyzed for quality and quantity on a SpectraMax UV microplate reader . After reverse transcription of 2 µg RNA with poly ( dT ) 18 , first-strand cDNA was used as a template in a real-time PCR on an iCycler Thermal Cycler with SYBR-Green Supermix ( Bio-Rad ) and specific oligonucleotides for iNOS ( 5′-GTTTCTGGCAGCAGCGGCTC-3′ and 5′-GCTCCfTCGCTCAAGTTCAGC-3′ ) and GAPDH ( 5′-TGG CAA AGT GGA GAT TGT TG-3′ and 5′-TTC AGC TCT GGG ATG ACC TT-3′ ) . The PCR Base Line Subtracted Curve Fit mode was applied for Threshold Cycle ( Ct ) and mRNA level measured by iCycler iQ Real-Time Detection Software ( Bio-Rad ) . The threshold cycle ( Ct ) values for target mRNA were normalized to GAPDH mRNA , and the relative expression level of iNOS was calculated with the formula n-fold change = 2−ΔCt , where ΔCt represents Ct ( iNOS ) – Ct ( GAPDH ) [37] . Tissue sections were fixed in 10% buffered formalin for 24 h , dehydrated in absolute ethanol , cleared in xylene , and embedded in paraffin . Five-micron tissue-sections were stained with hematoxylin and eosin , and evaluated by light microscopy using an Olympus BX-15 microscope equipped with a digital camera . In general , we analyzed each tissue-section for >10-microscopic fields ( 100× magnification ) , and examined three different tissue sections/mouse ( 4 mice/group ) to obtain a semi-quantitative score of parasitic pseudocysts ( foci ) . Myocarditis ( presence of inflammatory cells ) was scored as 0 ( absent ) , 1 ( focal or mild with ≤1 foci ) , 2 ( moderate with ≥2 inflammatory foci ) , 3 ( extensive with generalized coalescing of inflammatory foci or disseminated inflammation ) , and 4 ( diffused inflammation with severe tissue necrosis , interstitial edema , and loss of integrity ) [38] . Inflammatory infiltrates was characterized as diffused or focal depending upon how closely the inflammatory cells were associated [39] . Splenic and BM monocytes from WT and p47phox−/− mice were isolated as described [39] . Monocytes were distributed in 24-well plates ( 105/well ) incubated with T . cruzi trypomastigotes ( live or heat-inactivated; cell: parasite ratio , 1∶3 ) for 0 , 6 , 12 , and 24 h at 37°C , 5% CO2 . In some experiments , monocytes were incubated with 5-µg/ml IFN-γ for 4 h before exposure to T . cruzi . Cells were submitted to Giemsa staining ( Sigma-Aldrich , St . Louis , MO ) , and parasite uptake and intracellular replication monitored in 200 randomly selected cells by light microscopy . T . cruzi trypomastigotes were labeled with 5-µM carboxyfluorescein succinimidyl ester ( CFSE ) fluorescent dye for 10 min , washed , and then incubated with splenocytes or BM monocytes ( 106 cells/well; cell: parasite ratio , 1∶3 ) for 4 h . Cells were labeled with AP-anti-CD11b ( macrophage marker ) or R-PE-anti-Ly6G ( neutrophil marker ) antibodies ( 0 . 5-µg/100-µl , e-Biosciences , San Diego , CA ) , fixed with 2% paraformaldehyde , and re-suspended in 100-µl PBS/1% BSA , and analyzed by flow cytometry . Trypomastigotes release in supernatants of T . cruzi-infected macrophages after 24 h and 48 h incubation was counted under a light microscope by using a hemacytometer . Isolated primary monocytes from WT and p47phox−/− mice were in vitro exposed to T . cruzi for 0–24 h as above . Cells were incubated for 30 min with 5-µM CM-H2DCF-DA ( detects intracellular ROS , Ex498 nm/Em598 nm ) in Hank's Balanced Salt Solution ( HBSS ) , and signal was monitored on a SpectraMax M5 microplate reader ( Molecular Devices , Sunnyvale , CA ) . In some experiments , isolated primary monocytes from WT and p47phox−/− mice were in vitro exposed to T . cruzi for 24 h , and then incubated for 30 min with 0 . 1% nitroblue tetrazolium ( NBT ) . NBT is a yellow water-soluble nitro-substituted aromatic tetrazolium compound that reacts with cellular superoxide ions to form water insoluble blue formazan crystals . Cells were counter-stained with safranin , and the percentage of NBT+ cells monitored by monitoring >200 randomly selected cells by light microscopy . Mice ( WT and p47phox−/− ) were harvested at day 7–30 pi , and single cell suspension of splenic and BM cells were depleted of red blood cells by hypotonic lysis . Cells were cyto-spinned on glass slides ( 104 cells/slide ) , equilibrated in Kreb's buffer , and incubated with 5-µM dihydroethidium ( DHE , detects intracellular ROS , Ex518 nm/Em605 nm ) and images captured by fluorescence microscopy [40] . Cells stained with DAPI ( stains all nuclei , blue ) were used as controls . Splenocytes ( 106-cells/well/50 µl ) were also incubated with APC-conjugated anti-CD11b antibody ( e-Biosciences ) and DHE , and macrophage-specific ROS production monitored by flow cytometry . All assays for monitoring the DCF or DHE fluorescence , NBT-based formazan crystal formation were performed in the presence and absence of 0 . 5 mM apocynin ( NADPH oxidase inhibitor ) to confirm the source of ROS . Splenocytes of T . cruzi-infected mice ( 106-cells/well/100 µl ) were incubated in presence or absence of T . cruzi antigenic lysate ( TcL , 25-µg protein/well ) for 24 h . TcL was prepared by subjecting parasites ( 1×109/ml PBS ) to 5–6 freeze-thaw cycles followed by sonication on ice for 30-min . Cells were stained with 5-µM DAF-FM ( detects intracellular nitric oxide ) , and florescence ( Ex495 nm/Em515 nm ) was monitored on a SpectraMax M5 microplate reader [41] . Nitrite level in supernatants of splenocytes , in vitro stimulated in presence or absence of TcL , was measured by Griess reagent assay . Briefly , supernatants were reduced with 0 . 01 unit/100 ml of nitrate reductase , and incubated for 10 min with 100 ml of 1% sulfanilamide made in 5% phosphoric acid/0 . 1% N- ( 1-napthyl ) ethylenediamine dihydrochloride ( 1∶1 , v/v ) . Formation of diazonium salt was monitored at 545 nm ( standard curve: 2–50 mM sodium nitrite ) . DAF-FM fluorescence and Griess reagent assays were performed in presence and absence of 5 µmol/ml N ( G ) -nitro-L-arginine methyl ester ( L-NAME ) that is an inhibitor of nitric oxide synthase [42] . Isolated primary splenocytes or BM monocytes were in vitro incubated with T . cruzi ( live or heat-inactivated ) for 48 h . The release of cytokines ( IFN-γ , TNF-α , IL-4 , IL-10 ) in cell free supernatants was determined by using optEIA™ ELISA kits , according to the manufacturer's specifications ( BD Biosciences ( San Jose , CA ) . For estimating splenic production of cytokines , infected mice were harvested at day 7 , 14 , 21 and 30 pi , and single cell suspension of splenocytes ( 106-cells/well/100 µl ) incubated with media for 48 h ( ±TcL ) . Cytokine release was measured by an ELISA , as above . Single-cell splenocytes from WT and p47phox−/− mice harvested at day 30 pi were suspended in RPMI-5% FBS and distributed in 24-well plates ( 106 cells/well/200 µl ) . Cells were incubated in presence of Con A ( 5 µg/ml ) , or T cruzi trypomastigote lysate ( TcL , 25-µg/ml ) at 37°C , 5% CO2 for 48 h . The cell suspensions were utilized to measure the T cell proliferation by MTT assay [43] . To identify the T cell subsets in infected mice , splenocytes were incubated with or without ( TcL ) , and then labeled for 30 min on ice with PE-Cy7-anti-CD3 ( binds all T cells ) , FITC-anti-CD8 and PE-anti-CD4 antibodies ( 0 . 5–1 µg/100 µl , e-Biosciences ) . Following incubation , cells were fixed , washed and re-suspended in 100 µl PBS/2% BSA , and analyzed by flow cytometry [44] . To monitor the intracellular cytokine response , splenocytes were in vitro stimulated as above except that brefeldin A ( 10-µg/ml; Sigma ) was added in the final 6 h to prevent protein secretion . Cells were labeled with PE-anti-CD4 and FITC-anti-CD8 antibodies , fixed , suspended in 100-µl permeabilization buffer ( 0 . 1% saponin/1% FBS in PBS ) and then utilized for intracellular staining with e-Fluor-anti-IFN-γ , Cy5-anti-TNF-α and PerCP-PA-anti-Ki67 antibodies ( 0 . 5–2-µg/100-µl , e-Biosciences ) . Splenocytes were also incubated with Alexa-Fluor488-anti-CD107 antibody to determine the cytolytic activity of the activated/proliferating T cell subpopulations . Cells stained with isotype-matched IgGs were used as controls . Samples were visualized on a LSRII Fortessa Cell Analyzer by six-color flow cytometry , acquiring 30–50 , 000 events in a live lymphocyte gate , and further analysis performed using FlowJo software ( ver . 10 . 0 . 6 , Tree-Star , San Carlo , CA ) [44] . Data ( mean ± SD ) were derived from at least triplicate observations per sample ( n≥8 animals/group ) . Normally distributed data ( confirmed by Histogram and Q-Q plots ) were analyzed by student's t-test ( comparison of 2-groups ) and 1-way analysis of variance ( ANOVA ) with Tukey's post-hoc test ( comparison of multiple groups ) . The level of significance is presented by * ( normal-versus-infected ) and # ( WT-versus-p47phox−/− ) ( * , #p<0 . 05 , ** , ##p<0 . 01 ) . | Macrophage activation of NADPH oxidase NOX2 ) and reactive oxygen species ( ROS ) is suggested to mediate control of Trypanosoma cruzi infection that is the causative agent of Chagas disease . However , how NOX2/ROS deficiency affects parasite persistence and chronic disease is not known . In this study , we present the first evidence that NOX2 and ROS shape the T cell-mediated adaptive immunity , and its deficiency result in compromised splenic activation of type 1 cytotoxic CD8+ T cell response to T . cruzi infection . Subsequently , p47phox−/− mice that lack NOX2 activity were more unable to control parasite replication and dissemination and succumbed to susceptible to T . cruzi infection . Our study highlights how redox state of innate immune cells alters the adaptive immunity to intracellular pathogens; and suggests that understanding the molecular and cellular mechanisms affected by redox state of immune cells at basal level could be exploited in designing future therapeutic and vaccination strategies against T . cruzi infection and Chagas disease . |
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The immunization with genetically attenuated Leishmania cell lines has been associated to the induction of memory and effector T cell responses against Leishmania able to control subsequent challenges . A Leishmania infantum null mutant for the HSP70-II genes has been described , possessing a non-virulent phenotype . The L . infantum attenuated parasites ( LiΔHSP70-II ) were inoculated in BALB/c ( intravenously and subcutaneously ) and C57BL/6 ( subcutaneously ) mice . An asymptomatic infection was generated and parasites diminished progressively to become undetectable in most of the analyzed organs . However , inoculation resulted in the long-term induction of parasite specific IFN-γ responses able to control the disease caused by a challenge of L . major infective promastigotes . BALB/c susceptible mice showed very low lesion development and a drastic decrease in parasite burdens in the lymph nodes draining the site of infection and internal organs . C57BL/6 mice did not show clinical manifestation of disease , correlated to the rapid migration of Leishmania specific IFN-γ producing T cells to the site of infection . Inoculation of the LiΔHSP70-II attenuated line activates mammalian immune system for inducing moderate pro-inflammatory responses . These responses are able to confer long-term protection in mice against the infection of L . major virulent parasites .
Leishmaniases are a group of vector-borne diseases caused by the transmission of the protozoan parasite Leishmania in different mammalian hosts , during the blood meal of the invertebrate vectors ( phlebotomine sandflies ) . Depending on the species of the parasite and the immune response of the host , the disease outcome varies from asymptomatic infections to clinical forms of the disease . The cutaneous forms of the disease ( cutaneous leishmaniasis; CL ) are characterized by the generation of disfiguring skin ulcers . In the Old World , is caused , among others , by the infection of Leishmania major species and together with the other forms of leishmaniasis is included in the list of neglected tropical diseases , affecting various developing countries [1] . Many efforts have been made in terms of prevention of leishmaniasis in the last decades , since it is believed that a vaccine against leishmaniasis is feasible given that patients recovered from the disease become resistant to new infections . Mice infected with L . major have been widely used as experimental models for screening of vaccines . When BALB/c mice are experimentally challenged with L . major they suffer a progressive form of the disease , developing cutaneous lesions correlated to parasite multiplication at the site of infection as well as parasite dispersion to internal organs [2 , 3] . Parasite specific IL-4 driven production of antibodies as well as the development of Leishmania related IL-10 deactivating responses are correlated with susceptibility [4] . On the other hand , C57BL/6 mice experimentally infected with L . major promote T-cell dependent IFN-γ production that results in the activation of infected macrophages to produce nitric oxide and to destroy the intracellular parasites [4] . Control of parasite mediated inflammation by regulatory T cells results in parasite persistent infection and resistance to reinfection [5] . Despite the fact that memory T cells can persist after parasite control [6] , healed C57BL/6 mice lose their immunity to reinfection if they are manipulated to clear completely the parasites [7] . This has been taken as an evidence that persistence of live parasites is inevitably necessary for the maintenance of long-term protection [8] . In this context , the use of live attenuated parasites as vaccine candidates is a promising field of research . Live vaccines can induce adaptive immune responses relevant to protection by mimicking natural infection , without the adverse effects of leishmanization with virulent parasites . Heat Shock Protein 70 ( HSP70 ) plays a central role in both prokaryotic and eukaryotic cells because of its involvement in different aspects of protein metabolism ( folding , assembly , activation , subcellular location , and so on ) influencing many aspects of the cell biology , like cell growth and differentiation [9] . In Leishmania , the HSP70 also plays important roles in particular aspects affecting host-parasite interaction like virulence , drug resistance as well as in the induction of host immune responses ( reviewed in [10] ) . There are two types of genes encoding HSP70 in Leishmania infantum ( = L . chagasi ) , the causative agent of canine and human VL in the Mediterranean countries and in South America . Differences in their 3’ untranslated region ( UTR ) sequences have a great importance in the regulation of Lihsp70 gene expression [11] . Since mRNAs having the 3’UTR-II are preferentially translated at 37°C [12] , the expression of Lihsp70-II gene has been related to the response against the thermal stress caused by the parasite entry in the vertebrate host . Genetic elimination of the Lihsp70-II alleles resulted in a knock-out parasite line ( LiΔHSP70-II ) presenting a pleiotropic effect , influencing cell morphology , replication and , of special interest , virulence [13] . Hence , promastigotes ( the form found in the insect vector ) of the mutant line present some growth deficiencies in culture , and amastigotes show a limited capacity of multiplication inside macrophages , although Lihsp70-II gene deletion did not alter parasites uptake by these host cells . The inoculation of the mutant line did not produce any pathology in either hamster ( highly susceptible for L . infantum infection ) or in immune-deficient SCID mice , even though specific cellular responses were observed [13 , 14] . In addition , the mutant LiΔHSP70-II was able to induce a short-term protection against L . major infection in BALB/c mice [14] . In this work , we have extended the study of these protective capacities analyzing short- and long-time protection after intravenous ( i . v . ) or subcutaneous ( s . c . ) infection with the LiΔHSP70-II line in the BALB/c-L . major model of progressive leishmaniasis . The immune correlates of protection have been also analyzed . The studies regarding the prophylactic properties of the LiΔHSP70-II line administration have been extended to the L . major infection resistant C57BL/6 mouse model . The vaccine-mediated robust protection shown in this line has been associated to the rapid recruitment of pre-existing CD4+ and CD8+ IFN-γ producing T cells to the site of L . major challenge .
In a previous work , it was described that i . v . infection with the LiΔHSP70-II attenuated parasites ( vaccination ) resulted in short-term protection against L . major infection when challenged four weeks after vaccination [14] . This protection was correlated to the presence of the attenuated parasites in the liver and spleen of the protected animals [14] . Here , we firstly analyzed the evolution of the attenuated parasites in the internal organs of the i . v . vaccinated mice for a longer period of time . Parasites detected at week 4 after vaccination ( S1 Fig panel A ) [14] were undetectable in the spleen or liver at week 12 after vaccination ( Fig 1A ) . However , in the bone marrow ( BM ) , although at week 12 post vaccination parasite burden significantly decreased compared to the 4th week ( P < 0 . 05; unpaired T-test ) , we still detected parasites in 4 out of 8 mice ( Fig 1A ) . Vaccination induced a Leishmania-specific cellular response that was revealed after stimulation with soluble leishmanial antigen ( SLA ) of spleen cells . We detected SLA-specific secretion of IFN-γ and IL-10 to a lesser extent , both at the 4th ( S1 Fig panel B ) or at the 12th ( Fig 1B ) week post-vaccination . Alternatively , we administered s . c . the LiΔHSP70-II based vaccine to analyze its immunogenicity in a different vaccination setting . No parasites were observed in liver , spleen and BM of these animals at any time . Parasites were detected in the right popliteal which corresponds to the draining lymph node ( DLN ) close to the inoculation site ( Fig 1C ) . Although parasite numbers decreased over time ( P = 0 . 003 ) ( Fig 1C ) , all animals presented live parasites in the DLN at week 12 . The analysis of the parasite burden in the site of vaccination ( right footpad ) revealed the presence of live parasites in all the animals at week 4 after vaccination , that decreased significantly at week 12 ( P = 0 . 0013 ) . At this time , parasites were only detected in two out of eight vaccinated animals ( Fig 1C ) . Interestingly , although only a local infection occurred , the immune response detected in the spleen was similar in profile and magnitude to that found in the i . v . vaccinated animals , with a predominant SLA-specific production of IFN-γ , which was more prominent at 12 weeks after vaccination ( Fig 1D ) . The analysis of the parasite-specific production of cytokines by cells derived from the DLN ( right popliteal ) showed also an IFN-γ predominant response , higher in magnitude at the short-term ( Fig 1E ) , coinciding with the presence of high numbers of the attenuated parasites ( Fig 1C ) . In addition , at week 4 after vaccination , IL-10 and IL-4 were detected in SLA-stimulated cultures ( Fig 1E ) . Regarding the humoral response elicited by vaccination , i . v . inoculated mice showed a mixed IgG response at week 4 ( S1 Fig panel C and [14] ) and at week 12 after vaccination ( Fig 1F ) , with titers that decreased over time and were predominantly of the IgG1 isotype rather than IgG2a . Very low levels of anti-SLA IgG1 and IgG2a levels were detected in the sera of s . c . vaccinated animals , especially at long-term ( Fig 1F ) . Further , we explored the percentages of T cell populations in the spleen from control and vaccinated animals by flow cytometry . Two subsets of helper T cells ( CD3+CD4+ ) were characterized according to the presence of CD44 and CD62L molecules . All vaccinated groups showed an increase in the percentage of antigen-experienced CD4+ cells ( CD44high ) compared with the control ( Saline ) ( Fig 2A ) . Comparison between inoculation routes indicates that similar levels of CD4+ central memory T cells ( Tcm; CD44highCD62Lhigh ) were found in both groups . On the other hand , i . v . immunization elicited further expansion of CD4+ effector memory ( Tem ) or effector ( Teff ) T cells ( CD44highCD62Llow ) compared with s . c . route ( Fig 2A; S2 Fig panel A ) . Also , we determined that i . v . and s . c . vaccinated mice exhibited a higher frequency of both CD4+ and CD8+ IFN-γ producing splenic T cells compared to the unvaccinated group after in vitro stimulation with anti-CD3/anti-CD28 antibodies ( Fig 2B; S2 Fig panel C ) . This increment was also observed in the DLN ( right popliteal ) of the s . c . group ( Fig 2C; S2 Fig panel D ) . Altogether , these data allowed concluding that inoculation of the LiΔHSP70-II attenuated line , independently of the inoculation route , caused a persistent regressive infection that resulted in the induction of Tcm and Tem/Teff cell responses . Vaccinated animals showed a parasite dependent production of IFN-γ in which CD4+ and CD8+ T cells seem to be involved . To analyze the effect of the live vaccine on the development of a progressive leishmaniasis , BALB/c mice vaccinated with the LiΔHSP70-II line administered i . v . were challenged with L . major parasites ( 5 × 104 stationary phase promastigotes ) s . c . in the left footpad . As a control , mice inoculated with PBS at the time of the vaccination were also infected with L . major . Infective challenge was performed short- ( 4 weeks ) or long-term ( 12 weeks ) after vaccination . Fig 3A shows that i . v . vaccination also induced long-term protection , since very low footpad swelling was observed in the vaccinated groups , as it was reported for the short-term [14] and confirmed in this work ( S1 Fig panel D ) . The lack of lesions correlated to a decrease in L . major parasite burdens relative to saline controls in all analyzed organs ( Fig 3B ) . Regarding the presence of the parasite in visceral organs , a significant decrease was observed in both vaccinated groups with respect unvaccinated controls ( P < 0 . 05 ) . In the 4 weeks group , 50% ( 4/8 ) and 37 . 5% ( 3/8 ) of the mice had undetectable parasites in the spleen or liver , respectively ( S1 Fig panel E ) . In the 12 weeks group the percentage of negative mice reached values of 87 . 5% ( 7/8 ) in both organs ( Fig 3B ) . Data comparison among control and vaccinated mice groups revealed a higher decrease in parasite loads at the DLN for long-term infected mice ( 12 weeks; 2 . 4-log reduction; P < 0 . 001 ) ( Fig 3B ) than in the short-term group ( 4 weeks 1 . 2-log reduction; P < 0 . 01 ) ( S1 Fig panel E ) . A decrease in the evolution of footpad swelling was also observed in mice s . c . vaccinated compared to control mice ( saline group ) ( Fig 3C ) . Although no significant differences were found in the cutaneous lesions developed between mice of the 4 weeks and 12 weeks groups , short-term infected mice showed a progressive evolution of the footpad swelling evident from week 7 to week 8 . At that time , mice from all groups were euthanized because of the appearance of necrotic lesions in some mice of the control group . The clinical lesions evolution can be taken as an indication of a partial short-term protection driven by the s . c . inoculation of the LiΔHSP70-II line that was improved long-term . Determination of L . major parasite burdens in the spleen and liver ( Fig 3D ) also demonstrated a significant decrease compared to control animals in both s . c . vaccinated groups ( P < 0 . 05 and P < 0 . 001 , for 4 weeks and 12 weeks , respectively ) . In this case , most of the mice of the 4 weeks group were positive for L . major parasites in the spleen ( 75% , 6/8 ) or liver ( 87 . 5% , 7/8 ) , supporting the partial protection concluded from the clinical data . On the other hand , only two mice from the long-term protected groups were positive for live parasites in both internal organs ( Fig 3D ) . Regarding parasite loads in the left popliteal DLNs , a significant decrease ( P < 0 . 05 and P < 0 . 01 , for 4 weeks and 12 weeks respectively ) was obtained when vaccinated mice were compared to control mice . Similar decreased values with respect saline controls were found in the parasite numbers for both vaccinated groups in the liver and in the spleen ( 1-Log and 1 . 3-Log for 4 weeks and 12 weeks groups , respectively ) ( Fig 3D ) . On the other hand , we evaluated the presence of the LiΔHSP70-II attenuated parasites in different organs and tissues of the vaccinated mice after L . major challenge ( S3 Fig panel A ) . These analyses indicated that challenge with infective parasites did not reactivate the infection of the attenuated line . For i . v . vaccinated mice ( S3 Fig panel B ) no LiΔHSP70-II parasites were found in the internal organs , except parasites detected in the BM at short-term that were equivalent to those determined in the 12 wk vaccinated group ( Fig 1A ) . Interestingly , BM became negative for LiΔHSP70-II parasites at week 20 ( S3 Fig panel B ) . In addition , no attenuated parasites were found in the left popliteal LNs , in spite of the presence of L . major . Regarding the s . c . vaccinated mice , we only observed the persistent presence of the LiΔHSP70-II parasites in the LN draining the site of vaccination at week 20 ( S3 Fig panel C ) . Once we determined that vaccination with LiΔHSP70-II parasites induced protection for both , clinical manifestations and parasitemia , we analyzed the immune correlates of protection . For that , we next determined humoral and cellular responses specific for the parasite using SLA in ELISA assays and for cell stimulation in all vaccinated groups and their corresponding saline controls , 8 weeks after L . major challenge . Protection correlated with an IgG subclass redirection to Th1-related IgG2a subclass of SLA-specific antibodies in the vaccinated mice that were mainly of the IgG1 subclass in saline controls ( Fig 4A and 4B , for i . v . and s . c . , respectively ) . The magnitude of the IgG2a response was higher in long-term protected mice than in short-term groups ( P < 0 . 0018 and P < 0 . 023 for i . v . and s . c . , respectively ) . Cellular responses against SLA in the L . major infected mice were determined by stimulating spleen cells from mice receiving saline or the attenuated line ( 4 weeks and 12 weeks groups ) i . v . ( Fig 4C–4E ) or s . c . ( Fig 4F–4H ) . In agreement with the Th1-like profile of the humoral response , a SLA-dependent IFN-γ predominant response was found in all protected groups reaching higher P values with respect to saline control in short-term protected mice ( P = 0 . 0003 and P = 0 . 001 for i . v and s . c . groups , respectively ) than long-term groups ( P = 0 . 026 and P = 0 . 021 for i . v and s . c . groups , respectively ) . Interestingly , long-term protected mice showed a concomitant significant decrease in the IL-10 levels secreted after stimulation with parasite proteins when compared to saline controls ( P = 0 . 0006 and P = 0 . 0063 for i . v and s . c . groups , respectively ) ( Fig 4D and 4G ) . This decrease was absent in short-term protected mice . On the contrary , short-term protected group secreted higher amounts of IL-10 than control mice although only significant differences were observed in the i . v . vaccinated group ( P = 0 . 0361 ) ( Fig 4D ) . When IL-4 production was analyzed ( Fig 4E and 4H ) , a decrease in the levels of SLA-specific IL-4 in the culture supernatant was only found when saline controls were compared to long-term i . v . vaccinated mice ( P = 0 . 003 ) ( Fig 4E ) . The cytokine production specific for SLA was higher in short- than in long-term protected mice: P = 0 . 0003 , P = 0 . 0002 and P = 0 . 0062 for IFN-γ , IL-10 and IL-4 , respectively in the i . v . group ( Fig 4C , 4D and 4E ) and P = 0 . 022 and P = 0 . 0072 for IFN-γ and IL-10 , respectively in the s . c . group ( Fig 4F and 4G ) . Since s . c . inoculation of the attenuated parasites was able to long-term protect BALB/c mice against a L . major infective challenge , we decided to analyze the prophylactic properties of the s . c . administered vaccine in C57BL/6 mice . Inoculation of 1 × 107 LiΔHSP70-II promastigotes in the right footpad of mice produced a chronic infection in the DLN ( right popliteal ) as revealed by the analysis of parasite burdens at week 4 and week 12 post-vaccination , whereas parasites were found in the site of vaccination ( right footpad ) at week 4 after vaccination but disappeared at week 12 ( in 87 . 5% of the mice; 7/8 mice ) ( Fig 5A ) . Attenuated parasites were absent of the internal organs ( Fig 5A ) . The presence of a persistent number of LiΔHSP70-II parasites in the popliteal lymph node draining the site of the attenuated line inoculation was maintained after L . major challenge up to 25 weeks ( S4 Fig ) . Short-term and long-term vaccinated mice showed an IgG2c predominant antibody response against the parasite ( Fig 5B ) and their spleen cells secreted IFN-γ after in vitro stimulation with L . infantum SLA ( Fig 5C ) . Contrary to the long-term vaccinated group , short-term vaccinated mice secreted detectable levels of IL-10 in response to SLA ( Fig 5C ) . A SLA-dependent production of IFN-γ was detected in the popliteal LN culture supernatants from both vaccinated groups , higher in magnitude at week 4 after infection along with IL-10 production ( Fig 5D ) . Most importantly , neither short-term nor long-term vaccinated mice showed any inflammatory lesion when challenged with 1 × 103 L . major metacyclic promastigotes in the ear dermis ( Fig 6A ) . At week five after challenge , L . major burdens were similar in short- and long-term vaccinated mice , showing a 1 . 5-Log ( Fig 6B ) and 2-Log ( Fig 6C ) reduction in the ears and DLNs , respectively , when compared to the saline controls . No L . major parasites were found in visceral organs ( liver or spleen ) . Retromandibular LNs cells from mice of the saline group were able to secrete higher amounts of cytokines than vaccinated mice when analyzed at week 5 after L . major challenge ( Fig 6D ) . A significant increment in IFN-γ ( P = 0 . 023 relative to week 4 and P = 0 . 041 relative to week 12 ) and IL-10 ( P = 0 . 022 relative to week 4 and P = 0 . 012 relative to week 12 ) was observed in LN culture supernatants from cells obtained from saline controls when compared with both vaccinated samples after in vitro stimulation with SLA . As it was expected because of the presence of inflammatory lesions , control mice DLN cells secreted IFN-γ in the absence of SLA stimulation whereas this cytokine was absent in unstimulated cultures stablished from vaccinated mice ( Fig 6D ) . On the other hand , similar amounts of IFN-γ were observed among the three groups when the stimulation assay was performed in spleen cell cultures ( Fig 6E ) . These data , besides the presence of IgG2c anti-SLA antibodies in all groups ( Fig 6F ) allowed the conclusion that all mice groups have a systemic Th1 response against the parasite . In controls , the lymph node inflammatory response was related to the presence of high numbers of L . major parasites ( i . e . showing inflammatory lesions ) . In the vaccinated mice the limited infection in the DLNs was correlated to a lower IFN-γ local response . Thus , systemic response mounted by the asymptomatic infection of the attenuated line , resulted in a protective response against L . major challenge in the absence of pathological lesions . Next , we tested whether the Th1 response induced by the inoculation of the attenuated line is able to anticipate the response against L . major parasites in the site of infection , resulting in a non-pathological protection . For that purpose , C57BL/6 mice were inoculated with the attenuated line 4 weeks or 12 weeks before , and then challenged in the ears with 1 × 103 metacyclic forms of L . major . A progressive increment in the number of parasites found in the ear an in the DLNs was observed in the saline groups up to day 28 post-challenge ( Fig 7A–7D ) . On the contrary , the number of parasites were stabilized in vaccinated mice 28 days after challenge in the short-term protected mice ( Ear Fig 7A; DLNs Fig 7B ) , and from day 14 in the ear or day 21 in the DLNs in the long-term group ( Fig 7C and 7D , respectively ) . Parasite replication control was correlated to the early presence of circulating anti-SLA IgG2c antibodies in the sera from vaccinated mice after L . major challenge ( Fig 7E ) . A higher reactivity that was not statistically different was observed in mice of the long-term group when compared to the short-term vaccinated animals . In addition , short-term and especially long-term vaccinated mice were able to mount earlier cellular responses against the parasite than control mice , as demonstrated by the levels of IFN-γ secreted to the culture supernatants in the three groups , after in vitro stimulation with SLA of the cells obtained from L . major infected DLNs ( Fig 7F ) . Finally , we analyzed IFN-γ synthesis at the site of L . major challenge upon long-term vaccination ( Fig 8 and S5 Fig ) . For that purpose , mice inoculated with the attenuated parasites and their corresponding saline controls were challenged , 12 weeks after vaccination in the ear dermis with L . major ( 1 × 105 ) . As an additional control , a group of vaccinated mice was i . d . injected with PBS in the ears at the time of L . major challenge . Three days after inoculation , the presence of IFN-γ secreting cells in the ears and the DLNs was analyzed by flow cytometry . Both CD4+ ( Fig 8A ) and CD8+ ( Fig 8B ) IFN-γ secreting T cells were detected in mice vaccinated with the attenuated line shortly after L . major challenge . Such cells were absent from the site of infection of unchallenged vaccinated mice , or in non-vaccinated and infected animals .
The fact that patients recovered from CL disease are usually resistant to reinfection has been taken as an indication that a vaccine against this form of the disease is feasible . Historically , leishmanization ( inoculation of live virulent L . major parasites ) was employed to induce immunity against CL , and although it is currently in disuse , the practice is coming back in regions of high incidence because of its effectiveness [15–17] . The use of murine models of CL demonstrated that leishmanization protects C57BL/6 resistant mice from vector transmitted L . major infection contrary to vaccines based on parasite extracts [18] which failed to protect against natural infection . Also , some limitations were observed in the prophylactic properties of the most evolved recombinant molecules based vaccines showing different protection degree in distinct murine models of CL due to sand fly transmitted infection [19 , 20] . In addition , to maintain immunity , protein-based vaccines require boosting doses , since transient effector T cell responses preclude the induction of long-term immunity [21] . On the contrary , the balanced effector/memory T response induced by the infection with virulent parasites can be maintained by parasite persistence , resulting in long-term immunity [16 , 17] . A possible limitation of leishmanization has emerged after analyzing the influence of L . major challenge on the evolution of leishmaniasis caused by other species of Leishmania in murine models . Whereas cutaneous infection with L . major provided heterologous protection against VL due to L . infantum infection in C57BL/6 mice [22] the IL-4 mediated humoral response elicited against the parasite by leishmanization in BALB/c mice caused an aggravation of VL disease when ‘leishmanized’ mice were challenged with L . infantum [23] . In recent years , vaccination with genetically attenuated parasites is being contemplated as a promising alternative to leishmanization , avoiding the problems derived from using non-attenuated parasites [17 , 24] . As reviewed in [25] genetically modified L . major attenuated lines have shown some limitations when tested as vaccines against CL . Thus , protection against L . major challenge induced in resistant or susceptible mice by the inoculation of the L . major conditional auxotroph due to targeted deletion of the dihydrofolate reductase-thymidylate synthase gene ( Lmdhfr-ts-/- ) [26] was not reproduced in a primate model [27] . In addition , vaccines based on the L . major line lacking phophoglycans ( LmLpg2- ) presented differences in the induced protective immunity depending on the murine model assayed [28 , 29] . Also , infection of a L . major genetically modified arginase deficient line resulted in a chronic disease in which lesions did not disappear in the resistant mouse strain [30] . The most efficient genetically modified vaccine for CL was constructed in L . major by the inclusion of two suicide genes ( Lmtkcd+/+ ) that render the parasite susceptible to ganciclovir and 5-flurocytosine [31] . This vaccine has been tested to be effective in the BALB/c [31] or in the C57BL/6 models [32] , but treatment needs to be administered to recipients after vaccination complicating the vaccine schedule . As an alternative , in this work we propose the use of single dose of a live vaccine based on a L . infantum genetically attenuated line [13] to induce protection against CL . Regarding the evolution of the attenuated parasite burdens in the vertebrate host , inoculation of LiΔHSP70-II in BALB/c mice using the i . v . route led to a systemic infection with a pattern of parasite clearance with time post-infection in all internal organs . Importantly , challenge with infective L . major , did not produce the reactivation of the attenuated line . Similarly , i . v . inoculation of the latest and more promising attenuated vaccines based on L . donovani ( LdCen-/- ) deficient in centrin , a calcium binding cytoskeletal protein [33] or Ldp27-/-; lacking a protein forming part of the active cytochrome c oxidase complex [34] ) produced a transient systemic infection , resulting in the impossibility to detect vaccine parasites in internal organs at long-term [34 , 35] . The absence of detectable parasites in the spleen is characteristic of the infection with different attenuated viscerotropic lines and differs from the chronic infection of the spleen observed after challenge with infective parasites [36 , 37] , reinforcing the attenuated nature of the LiΔHSP70-II line . Further , a previous report indicated that the LiΔHSP70-II line tend to be undetectable when inoculated i . v . in immuno-deficient SCID mice [14] . These data were taken as a suggestion that parasite clearance did not strictly depends on the induction of T cell dependent responses . Something similar occurred with the LdCen-/- attenuated line [35] , but not with other versions of genetically modified parasites , as for example the SIR2-deficient L . infantum ( LiSIR+/- single knock-out for the sirTuin encoding gene ) [38] . Additionally , it was reported that LiΔHSP70-II intra-cardiac ( i . c . ) inoculation of hamsters , a highly susceptible VL model by L . infantum challenge [39] , generates an asymptomatic infection . The absence of clinical signs of disease was correlated to the impossibility to detect attenuated parasites in the internal organs up to 9 months after inoculation [14] . All these data may be taken as an indication of the high biosafety degree of the LiΔHSP70-II line . Interestingly , s . c . inoculation induced a localized infection without dissemination to internal organs , resulting in the persistence of the parasites in the DLN of the infection site in BALB/c and C57BL/6 mice . On the contrary , footpad parasite burdens decreased after infection and became undetectable at longer times . The presence of parasites in the DLN accompanied by a parasite clearance with time in the site of challenge has been also described for infective L . donovani [40] or L . infantum [41] , but in these models spleen macrophages resulted chronically infected . Since mice from the s . c . and i . v . vaccinated groups showed similar long-term protection against L . major challenge , it can be hypothesized that in the i . v . vaccinated mice some parasites may persist dispersed in different internal organs , but remain undetectable perhaps due to their low number . In addition , the presence of parasites in other cell types maintaining latent infections can be also a source of parasite persistence [42 , 43] . This is an important issue , since maintenance of the parasite in the vertebrate host would be assuring the maintenance of immunity in the absence of recall doses against Leishmania [8] or other parasitic infections [44–47] . In this regard , the persistence of parasites may be indispensable to produce a concomitant immunity maintaining the number of Teff cells [48] . Nevertheless , other cells implicated in protection , namely Tcm or tissue resident memory T ( Trm ) cells can persist after parasite clearance [6 , 49–51] . In this context , immunization with the LiΔHSP70-II line elicits both Tcm cells and Teff or Tem responses . One limitation of this work is the lack of studies performed to analyze the implication of Trm cells in the observed protection , an interesting question that should be addressed in future research . On the other hand , the implication of Teff cells in the robust protection associated with vaccination was demonstrated by the data obtained in the C57BL/6 mice model . It has been described that C57BL/6 mice healed from a first infection with L . major develop concomitant immunity to re-challenge consisting in the rapid migration of IFN-γ producing Teff cells to the site of reinfection [22 , 48 , 52 , 53] . Here , we observed that 3 days after L . major challenge , a group of IFN-γ producing CD4+ and CD8+ T cells were specifically detected in the ears and lymph node cells of vaccinated mice . These cells may correspond to pre-existing Teff cells , since Tcm cells may need more time to elicit a protective response [48] . Another limitation of our work is related with the fact that L . major was administered using a needle . However , the rapid Teff cell response demonstrated in the ear and retromandibular DLNs of C57BL/6 mice after L . major challenge may be considered as a good predictor for protection against natural challenge as occur in ‘leishmanized’ C57BL/6 mice [18] . The humoral and cellular response elicited by the BALB/c mice inoculated with the LiΔHSP70-II was quantitatively similar to that observed in BALB/c mice when infected i . v . or s . c . with the L . infantum infective parasites [36 , 41] or to that generated after intraperitoneal inoculation with another L . infantum based attenuated line vaccine ( LiSIR2+/- ) [38] . A mixed IgG1/IgG2a humoral response , higher in titer at short-term , was observed concomitant with the systemic secretion of parasite-specific IFN-γ and IL-10 by splenocytes and a local production of SLA-dependent IFN-γ , IL-10 and IL-4 cytokines in the LN draining the site of s . c . vaccination , especially at short-term . The higher IFN-γ/IL-10 ratio showed at long-term can be correlated with clearance of the attenuated parasite , since IL-10 production has been largely related to parasite persistence of viscerotropic species [54–57] . LiΔHSP70-II s . c . administration to C57BL/6 mice resulted in the induction of both local and systemic Th1-like response in short- and long-term vaccinated groups , characterized by the induction of SLA-dependent IFN-γ and the presence of IgG2c anti-Leishmania antibodies . The existence of susceptible and resistant models of L . major infection is an advantage when testing experimental vaccines . In the case of C57BL/6 mice , infection with a low number of metacyclic promastigotes in the dermis of the ear generates a clinically silent phase in which parasite replicates . In a second phase the IFN-γ mediated local inflammatory response reduces parasitic load leading to skin lesions similar to those of CL human patients [58] . In contrast , challenge with large numbers of parasites in the footpad of BALB/c mice generates a progressive infection associated with parasite-specific responses mediated by IL-10 and IL-4 [4 , 59 , 60] . Many vaccine candidates have been tested in both models with different results . In the resistant model , the protection has been linked to an anticipation of the inflammatory response , with the induction of CD4+ and CD8+ T cells producing IFN-γ which results in early control of the parasite and , therefore , in the appearance of lower grade lesions [61–64] . In the BALB/c model , numerous evidences suggest that the control of the infection not only depends on the induction of IFN-γ-mediated responses , but also on the control of IL-10 and IL-4 cytokines that are associated with pathology [29 , 62 , 65] . This is the case of the LmLpg2- line that was able to control the pathology in BALB/c mice alleviating disease associated responses , but did not reach the same degree of protection in the resistant model when inoculated in the absence of a cellular inducing adjuvant [28 , 29] . As occurred for some subunit [62] or live vaccines [31 , 32] , LiΔHSP70-II line inoculation was able to induce a robust protection in both murine models of CL . For the susceptible BALB/c mice , we first used the i . v . route , since it is the classical route of administration for viscerotropic specie based vaccines . Given that more acceptable routes of vaccination are desirable for human use the s . c . administration was also tested . Independently of the administration route and compared to unvaccinated mice , BALB/c mice inoculated with the LiΔHSP70-II line showed significant control of the leishmaniasis disease . This is an interesting property of our vaccine , since the Lm dhfr-ts-/- line conferred protection against L . major infective challenge in BALB/c mice when it is i . v . but not s . c . administered [26] . It was also reported that protection conferred against L . mexicana by intraperitoneal inoculation of an attenuated line of the same species ( lacking guanosine diphosphate-mannose pyrophosphorylase; LmΔGDP-MP ) was not attained when it was s . c . administered [66] . Our data demonstrated that i . v . or s . c . BALB/c vaccinated mouse groups showed a Th1-like response against parasite antigens after L . major challenge . Anti-SLA humoral responses changed from the IgG1 subclass ( found in the non-vaccinated controls ) towards a IgG2a response . Higher IgG2a titers were observed long-term compared to short-term , correlating to a better protection degree . The parasite dependent IFN-γ response was higher in vaccinated than in control animals . The production of this cytokine was detected in short-term groups , but accompanied by the secretion of the highest levels of IL-10 among all groups . On the other hand , long-term protected mice showed a moderate SLA dependent IFN-γ production accompanied by very low parasite dependent IL-10 responses , similar to the protection conferred by the Lmlpg2- parasites that was associated with control of parasite mediated IL-10 responses in this susceptible model [29] . For C57BL/6 mice , whereas the infection of non-vaccinated mice evolved as described [5 , 48 , 58] , vaccinated mice showed no lesions at all . During the first two weeks after L . major challenge parasites similarly grew in the ear and the DLN in both vaccinated and non-vaccinated mice . Afterwards , control group continued in the silent phase incrementing their parasite burdens , while immunized mice do not allow parasites to expand further and showed earlier production of IFN-γ in the DLN . The anticipation of the effector response implies that the production of low levels of IFN-γ is sufficient to control parasite burdens without producing tissue damage . Then , LiΔHSP70-II parasites achieve the gold standard of protection against CL in C57BL/6 mice reaching a degree of protection comparable to that described for the more protective subunit based vaccines [61–63] . Although genetically attenuated vaccines may be an alternative to leishmanization to control human CL , concerns regarding biosafety remain , as it is mandatory that the attenuated phenotype is maintained even in cases of severe immunosuppression . In this regard , it is very important to target parasite genes whose function cannot be regained by compensatory mutations that can lead to recover the virulent phenotype to genetically modified parasites [67] . Nevertheless , the results shown in this work together with promising results observed using attenuated parasites to control malaria [68–71] and other pathologies [72–76] support the idea that live attenuated vaccines might be the basis for the development of vaccines against human CL in the next future .
Female BALB/c mice and C57BL/6 ( 6–8 weeks old ) were purchased from Harlan ( Barcelona , Spain ) . All procedures were performed according to the Directive 2010/63/UE from the European Union and RD53/2103 from the Spanish Government . Procedures were approved by the Animal Care and Use Committee at the Centro de Biología Molecular Severo Ochoa ( CEEA-CBMSO 21/138 ) , the Bioethical Committee of the CSIC ( under reference 100/2014 ) . The final approval was authorized by the Government of the Autonomous Community of Madrid under the reference PROEX121/14 . The following parasites cell lines were employed: L . major clone V1 ( MHOM/IL/80/Friedlin ) ; L . infantum ( MCAN/ES/96/BCN150 ) and the attenuated line ( L . infantum MCAN/ES/96/BCN150 [Δhsp70-II::NEO/Δhsp70-II::HYG] ) [12] . The attenuated line was created as described in [12] . Briefly , both alleles of the single hsp70-II gene located at chromosome 28 of the L . infantum genome were replaced sequentially with the ORF of the NEO and the HYG selectable marker genes by homologous recombination using plasmids constructions containing the marker genes flanked by specific regions located upstream and downstream of the ORF for hsp70-II gene [12] . The LiΔHSP70-II line showed a mild growth-rate defect in the logarithmic growth phase , concomitant with a longer duration of the G2/M phase of the cell cycle . In addition , promastigotes of the mutant line reached lower cell density than wild type parasites in culture , suffering a rapid decrease after reaching the stationary growth phase [12 , 13] . Lack of functional HSP70-II gene did not affect the rate of macrophage in vitro infection but the infected macrophages showed reduced number of internal amastigotes when compared to the wild type line [13] . Parasite persistence was demonstrated in experimental infections performed in the BALB/c mice strain , since four weeks after challenge viable parasites were recovered from different organs [13 , 14] . The promastigote forms of the parasites were grown at 26°C in Schneider medium ( Gibco , NY , U . S . A . ) supplemented with 10% Fetal Calf Serum ( FCS ) ( Sigma , MO , U . S . A . ) , 100 U/ml of penicillin and 100 μg/ml of streptomycin . For the attenuated line , medium was supplemented with 20 μg/ml of G418 and 50 μg/ml of hygromycin . Parasites were kept in a virulent state by passage in BALB/c mice . For vaccination , two administration routes were employed . BALB/c mice were immunized by the administration of 1 × 107 LiΔHSP70-II promastigotes suspended in 100 μl of phosphate saline buffer ( PBS ) in the vein tail ( intravenously; i . v . ) . Subcutaneously ( s . c . ) immunization of BALB/c and C57BL/6 mice were performed with 1 × 107 LiΔHSP70-II promastigotes suspended in 30 μl of PBS in the right footpad . As control , mice were inoculated with PBS . In all experiments performed with BALB/c mice and in those shown in Fig 6 for C57BL/6 a single control group was employed for long- and short-term protection analyses . In these cases , mice were inoculated twice with PBS ( week 12 and 4 ) i . v . ( BALB/c ) or s . c . ( both mice strains ) . To obtain data shown in Fig 7 employing C57BL/6 mice , two different control saline groups were employed for long- or short-term analyses , receiving only one PBS dose coinciding with vaccination . For challenge , BALB/c mice were infected with 5 × 104 stationary-phase promastigotes of L . major suspended in 30 μl of PBS into the left footpads . Infection follow-up was performed by measuring footpad swelling with a metric digital caliper . Lesion size was expressed as thickness of the L . major infected left footpad minus thickness of the right footpad . C57BL/6 mice were challenged with 1 × 103 ( or 1 × 105 when indicated ) L . major metacyclic promastigotes isolated by negative selection with peanut agglutinin , suspended in 10 μl of PBS into the dermis of both ears ( intradermal; i . d . ) . Ear lesions diameter was measured with a metric caliper . The number of LiΔHSP70-II parasites was determined in the liver , spleen , BM ( after i . v . or s . c . administration ) and also in the DLNs and footpads after s . c . administration . In addition , L . major parasite burdens were determined in the DLNs ( popliteal for BALB/c mice and retromandibular for C57BL/6 mice ) , ears ( C57BL/6 mice ) or liver and spleen ( both strains ) . The number of parasites was determined by a limiting dilution assay as described in [77] . For cell preparation , the complete spleens , lymph nodes and footpads , or a piece of approximately 20 mg of liver were stored in Schneider medium containing 20% heat-inactivated , 100 U/ml of penicillin and 100 μg/ml of streptomycin at 4°C . Tissues were homogenized and filtered through 70 μm cell strainers ( Corning Gmbh , Kaiserslautern , Germany ) to obtain a cell suspension . BM samples were obtained by perfusion of the mouse femur marrow cavities with Schneider medium before filtration . For ear processing , the ventral and dorsal sheets were separated and incubated in Dulbecco's modified Eagle medium ( DMEM; Thermo Fisher Scientific , MA , U . S . A . ) containing Liberase CI enzyme blend ( 50 μg/ml; Roche Diagnostics , Basel , Switzerland ) . After 2 h of incubation at 37°C , the tissues were cut into small pieces , and homogenized and filtered using a cell strainer as indicated above . Each homogenized tissue sample was serially diluted ( 1/3 ) in a 96-well flat-bottomed microtiter plate containing the same medium employed for homogenization ( in triplicates ) . For LiΔHSP70-II parasite number determination , medium was also supplemented by 20 μg/ml G418 and 50 μg/ml hygromycin . The number of viable parasites was determined from the highest dilution at which promastigotes could be grown up to 10 days of incubation at 26°C and is indicated per whole organ ( spleen , lymph nodes and footpads ) , per g ( liver ) or as number of parasites in 107 cells for the BM samples . Sera were obtained from blood samples taken before and after leishmanization with the attenuated line or after infective challenge . The reactivity against parasite proteins was determined by ELISA , using SLA prepared from L . major or L . infantum promastigotes . Briefly , SLA was prepared by three freezing and thawing cycles of stationary promastigotes suspended in PBS followed by centrifugation for 15 min at 12 , 000 × g using a microcentrifuge . After determining protein concentration by the Bio-Rad Protein Assay Dye Reagent ( Bio-Rad laboratories , München , Germany ) supernatants were collected and stored at -70°C . Sera reactivity was calculated as the reciprocal end-point titer calculated as the inverse value of the highest serum dilution factor giving an absorbance > 0 . 15 . Briefly , MaxiSorp plates ( Nunc , Roskilde , Denmark ) were coated with 100 μl of SLA diluted in PBS ( 12 μg/ml for 12 h at 4°C ) . After four washes with 200 μl of PBS-Tween20 0 . 5% ( washing buffer ) , wells free binding sites were blocked with the same volume of the blocking solution ( PBS-Tween 20 0 . 5%–5% non-fat milk ) for 1 h at room temperature ( RT ) and incubated with serial dilutions ( 1/2 dilution factor in blocking solution ) of mouse sera for 2 h at RT . After four washes with 200 μl of washing buffer , wells were incubated for 1 h at RT with secondary antibodies . Anti-IgG , anti-IgG1 , anti-IgG2a or anti-IgG2c horseradish peroxidase-conjugated anti-mouse immunoglobulins were used as secondary antibodies at 1/2 , 000 dilution in blocking buffer ( Nordic BioSite Täby , Sweden ) . After four washes performed as above , the reaction was developed through incubation with orto-phenylenediamine for 10 min in the dark . Color development was stopped by the addition of 2 N H2SO4 . Optical densities were read at 490 nm in an ELISA microplate spectrophotometer ( Model 680 , Bio-Rad Laboratories ) . For cytokine analysis , primary cultures were stablished from spleens and LNs as described above , but using RPMI complete medium ( RPMI medium ( Sigma ) supplemented with 10% heat-inactivated FCS , 20 mM L-glutamine , 200 U/ml penicillin , 100 μg/ml streptomycin and 50 μg/ml gentamicin instead of Schneider medium . Cells ( 5 × 106 ) were cultured during 72 h at 37°C in 5% CO2 in the absence or in the presence of SLA at 12 μg/ml of final concentration . The levels of IFN-γ , IL-10 or IL-4 in culture supernatants were determined by sandwich ELISA using commercial kits ( Pharmingen , San Diego , CA , USA ) . For the analysis of effector T cells ( Teff ) or effector memory T cells ( Tem ) ( CD44+ CD62Llow subset ) and central memory T cells ( Tcm ) ( CD44+ CD62Lhigh subset ) , single cell suspensions from the spleen on the BALB/c mice were processed as above , and the single splenocytes were harvested , washed in PBS with 1% heat-inactivated FCS and incubated with Rat Anti-Mouse CD16/CD32 ( FcBlock , BD , Franklin Lakes , NJ , USA ) followed by the staining with the surface markers: AlexaFluor 647 Rat Anti-Mouse CD3 Molecular Complex ( 17A2 Clone , BD ) , APC/Fire 750 Anti-Mouse CD44 ( IM7 Clone , BioLegend , San Diego , CA , USA ) , BV421 anti-mouse CD62L ( MEL-14 Clone , BioLegend ) and BV570 anti-mouse CD4 ( RM4-5 Clone , BioLegend ) for 20 min at 4°C . After washing , cells were fixed and permeabilized with Cytofix/Cytoperm ( BD ) . Finally , cells were washed and analyzed . For identification of cell producing cytokines in BALB/c mice , single cell suspensions from the spleens or the popliteal lymph nodes of the BALB/c mice were processed as above . Subsequently , cells ( 1 x 106 ) were stimulated for 2 h at 37°C in RPMI complete medium with anti-mouse CD28 ( eBioscience , San Diego , CA , USA ) in flat-bottom 96-well plates previously coated with anti-mouse CD3e antibody ( eBioscience ) 24 h before . Afterwards , 10 μg/ml Brefeldin A was added to stimulated and non-stimulated cells and incubation continued for 4 h more . Then , cells were harvested , washed in PBS with 1% heat-inactivated FCS and incubated with Fc block followed by the staining with the surface markers FITC anti-mouse CD8a ( 53–6 . 7 Clone , BioLegend ) , and BV570 anti-mouse CD4 for 20 min at 4°C . After washing , cells were fixed and permeabilized with Cytofix/Cytoperm ( BD ) . Next , PE/Cy7 anti-mouse IFN-γ ( XMG1 . 2 Clone , BioLegend ) antibody was added for 30 min at 4°C . Finally , cells were washed and analyzed . For the analysis of the frequency of T cell producing IFN-γ in the ears and retromandibular lymph nodes of C57BL/6 mice , single cell suspensions were processed 3 days after L . major challenge and 1 × 106 cells were stimulated for 2 h at 37°C with anti-mouse CD3/CD28 ( eBioscience ) as described above . Afterwards , 10 μg/ml Brefeldin A was added and cells were incubated for 4 h more . Then , cells were washed and incubated with FcBlock followed by the staining with the surface markers FITC anti-mouse CD8a , AlexaFluor 647 Rat Anti-Mouse CD3 Molecular Complex and BV570 anti-mouse CD4 for 20 min at 4°C . After washing , cells were fixed and permeabilized with Cytofix/Cytoperm . Next , PE Rat Anti-Mouse IFN-γ ( XMG1 . 2 Clone , BD ) antibody was added for 30 min at 4°C . Finally , cells were washed and analyzed . All cells were analyzed using a FACS Canto II flow cytometer and FACSDiva Software ( BD ) and processed and plotted with FlowJo Software ( FlowJo LLC , Ashland , Oregon , USA ) . Statistical analysis was performed using the Graph-Pad Prism 5 program . Data were first analyzed by the D'Agostino & Pearson normality test when sample was n ≥ 8 . Parametric data were analyzed by a two-tailed Student t-test when comparing two samples or one-way ANOVA followed by the Tukey test when comparing more than two groups . Non-parametric data ( or data with n < 8 ) were analyzed by a Mann Whitney test or a Kruskal-Wallis test ( Dunn's post-test ) when comparing two or more groups , respectively . Differences were considered significant when * P < 0 . 05 . | Despite numerous efforts made , a vaccine against leishmaniasis for humans is not available . Attempts based on parasite fractions or selected antigens failed to confer long lasting protection . On the other side , leishmanization , which consists in the inoculation of live virulent parasites in hidden parts of the body , is effective against cutaneous leishmaniasis in humans but objectionable in terms of biosafety . Some efforts have been made to design live vaccines to make leishmanization safer . A promising strategy is the development of genetically attenuated parasites , able to confer immunity without undesirable side effects . Here , we have employed an attenuated L . infantum line ( LiΔHSP70-II ) as a vaccine against heterologous challenge with L . major in two experimental models . Infection with LiΔHSP70-II parasites does not cause pathology and induces long-term protection based on the induction of IFN-γ producing T cells that are recruited rapidly and specifically to the site of challenge with the virulent parasites . These results support the idea of using attenuated parasites for vaccination . |
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Gene assembly , which recovers gene segments from short reads , is an important step in functional analysis of next-generation sequencing data . Lacking quality reference genomes , de novo assembly is commonly used for RNA-Seq data of non-model organisms and metagenomic data . However , heterogeneous sequence coverage caused by heterogeneous expression or species abundance , similarity between isoforms or homologous genes , and large data size all pose challenges to de novo assembly . As a result , existing assembly tools tend to output fragmented contigs or chimeric contigs , or have high memory footprint . In this work , we introduce a targeted gene assembly program SAT-Assembler , which aims to recover gene families of particular interest to biologists . It addresses the above challenges by conducting family-specific homology search , homology-guided overlap graph construction , and careful graph traversal . It can be applied to both RNA-Seq and metagenomic data . Our experimental results on an Arabidopsis RNA-Seq data set and two metagenomic data sets show that SAT-Assembler has smaller memory usage , comparable or better gene coverage , and lower chimera rate for assembling a set of genes from one or multiple pathways compared with other assembly tools . Moreover , the family-specific design and rapid homology search allow SAT-Assembler to be naturally compatible with parallel computing platforms . The source code of SAT-Assembler is available at https://sourceforge . net/projects/sat-assembler/ . The data sets and experimental settings can be found in supplementary material .
As gene families of interest are used as input , our algorithm employs homology search against gene families in assembly graph construction . Using genomes or proteome of related species to boost and optimize genome assembly has been proposed or implemented in a group of assembly programs [23] , [24] , [27]–[35] . The contigs belonging to a single gene or a block of genome in the related species are ordered , oriented , and assembled . Most of these programs are designed to improve genome assembly . A few of these comparative or gene-boosted assembly programs are specifically designed for RNA-Seq or metagenomic assembly . For example , Surget-Groba et al . [30] carefully considered the highly heterogeneous sequence coverage of transcripts and employed multi-k and proteome of a related species to optimize transcriptome assembly . Dutilh et al . [29] used one closely related reference genome to increase assembly performance of microbial genomes in metagenomic data . Ye's group used homologous genes to stitch gene fragments for gene assembly in metagenomic data [24] . Our work is different from existing comparative assembly approaches in the following aspects . First , our tool does not require any related species as input . Most of the existing comparative approaches are limited by the availability of closely related reference genomes . Low similarity between the to-be-assembled genes or genomes and the related genes or genomes can lead to wrongly assembled contigs . Our tool uses well-characterized genes of particular interest or ubiquitously represented sequence families such as those from family databases of proteins , domains , or functional sites as input to guide assembly . Second , as we use sequence families rather than a single sequence as reference , profile-based alignment methods rather than pairwise sequence alignment or exact sequence mapping are applied to conduct homology search . Profile-based methods tend to be more sensitive for remote homology search . Third , to our best knowledge , SAT-Assembler is the first tool that uses consistency between sequence overlap and alignment overlap for edge creation in an overlap graph . Another type of RNA-Seq assembly tool , Cufflinks , assembles gene isoforms due to alternative transcription and splicing and improves transcriptome-based genome annotation [17] . Cufflinks and SAT-Assembler require different types of input and are targeted at different applications . Their major differences are summarized below . First , Cufflinks needs the reference genome as input while SAT-Assembler uses sequence families as input . The input families do not need to contain any genomic sequence or protein products from the reference genome . Instead , they may contain a large number of gene or protein sequences from other species with variable evolutionary distances . For example , the average sequence identity of protein ( domain ) families in Pfam ranges from 20% to over 90% . The conservation and evolutionary changes of the member sequences are summarized into a profile HMM , which can share high or low similarity with the genes in the reference genome . Second , Cufflinks and SAT-Assembler have different applications . Cufflinks is used to annotate transcripts and gene isoforms for species with known reference genomes . SAT-Assembler is applied to RNA-Seq data of non-model organisms or metagenomic data , which do not have reference genomes . Third , although both tools conduct sequence alignment between reads and the reference genomes or sequence families in the first step , the alignment algorithms are highly different . The read alignment in Cufflinks relies on read mapping tools such as TopHat [36] and Bowtie [37] , which allow only minor changes caused by , for example , sequencing errors . On the other hand , the profile HMM-based alignment in SAT-Assembler can handle a large number of evolutionary changes including substitutions , insertions , and deletions .
Our tool can be divided into two main stages . First , we align reads against profile hidden Markov models ( HMMs ) , which effectively represent the underlying gene families . This stage classifies the whole input data set into subsets of reads sequenced from different gene families . Second , SAT-Assembler constructs a family-specific overlap graph and assembles reads from the same family into contigs using a graph traversal algorithm . The graph construction is supervised by the alignment information from the first stage and aims to obtain maximum connectivity between reads while avoiding false connections . In particular , it can accurately capture small overlaps between reads from lowly sequenced regions and improves the assembly of lowly transcribed or encoded genes . The graph traversal is guided by multiple types of information to avoid generation of chimeric contigs . Finally , paired-end reads are used to scaffold contigs from the same genes into super contigs , which are sets of contigs that are from the same scaffolds . Fig . 1 is a schematic representation of the pipeline of SAT-Assembler . Our method conducts homology search on reads first . Depending on the algorithms and the target databases , homology search methods can be divided into two types . The first type compares the sequences against protein sequence databases using pairwise alignment tools such as BLAST [38] . The second type uses profile-based homology search to classify queries into characterized protein domain or family databases such as Pfam [39] , [40] , TIGRFAM [41] , FIGfams [42] , InterProScan [43] , etc . Applying profile-based homology search to NGS reads has several advantages . First , the number of gene families is significantly smaller than the number of sequences , rendering a much smaller search space . For example , there are only about 13 , 000 manually curated protein families in Pfam . Together these cover nearly 80% of the UniProt Knowledgebase and the coverage is increasing every year as enough information becomes available to form new families [40] . As the profile-based homology search tool HMMER is as fast as BLAST [44] , using profile-based search provides a shorter search time . Second , previous work [45] has shown that using family information can improve the sensitivity of remote protein homology search . For the transcriptomes of non-model organism and metagenomic data , sensitive remote homology search is especially important for identifying possible new homologs . Third , although short reads can pose challenges to both types of homology search [46] , [47] , empirical studies on thousands of families [47] showed that the performance of profile-based homology search improved quickly with increasing read size . For a read length of 85 bases , the sensitivity is close to 1 . 0 for moderately and highly conserved domains . Thus , for read lengths produced by modern NGS technologies , profile-based homology search methods are capable of classifying many reads into their native families with high specificity . SAT-Assembler aligns query reads against input families using HMMER with the default E-value threshold 10 . Reads that generate HMMER hits are classified into the corresponding family and fed into the next stage . In most cases , a read can only be classified into a single family . However , because some input families share similarities , some reads may be classified into multiple input families . In practice , we only classify a read into at most three families with the three smallest E-values . The first stage not only classifies query reads into their native families but provides important alignment information for de novo assembly . The alignment positions are used to guide overlap graph construction . A standard overlap graph is defined as , where each non-duplicate read is a node and an overlap larger than a given cutoff is indicated by a directed edge . Our overlap graph is different from a standard overlap graph [48]–[50] in the edge creation criteria and graph construction procedure . In a standard overlap graph , edge creation only depends on the sequence overlap , which is not ideal for genes of heterogeneous sequence coverage . We add edges by considering the relationship between two types of overlaps: alignment overlap and sequence overlap . As HMMER outputs alignments represented by amino acids , all overlaps are converted into the unit of bp for consistency . For simplicity of explanation , a read corresponds to vertex in G . For two reads and , an edge is created from the corresponding node to node if the following criteria are satisfied: i ) the alignment position of is smaller than ; ii ) the alignments of and overlap by at least , a user-defined threshold; iii ) the sequence overlap of the two reads is consistent with the overlap in their alignment positions . Suppose aligns to the model between and , and aligns to the model between and , where and are alignment starting positions in the model and and are alignment ending positions in the model . The alignment overlap is the number of bases converted from the number of amino acids in the overlapping region between and . For example , the overlapping region between and in Fig . 2 . ( C ) contains 22 amino acids , which are converted into 66 bases of alignment overlap . Criterion 3 is the key observation to connect reads that are sequenced from the same gene rather than from orthologous or paralogous genes because the latter can have very different sequence and alignment overlaps . An example is given in Fig . 2 , in which read and read are from two homologous genes . Their sequence overlap and alignment overlap are 25 and 66 respectively . Other assembly tools such as Trinity will create an edge between and when the k-mer size is 25 . However , because their alignment overlap and sequence overlap are inconsistent , SAT-Assembler does not connect them , avoiding a wrong connection between reads from homologous genes . The consistency-based edge creation also allows us to improve connectivity in regions with low sequence coverage . For relatively small overlaps , we still allow an edge if the alignment overlap and sequence overlap are similar to each other . The intuition is that the chance that reads with random overlaps can be aligned to the same model with similar alignment overlap is small . To quantify the consistency between the two types of overlaps , we introduce the relative overlap difference defined by , where is alignment overlap and is sequence overlap . Criterion 3 is satisfied only when , where is a predefined cutoff with a default value of 0 . 15 . We examined relative overlap difference in both real RNA-Seq and metagenomic data sets . For reads sequenced from the same gene and from different genes , the average relative overlap differences are 0 . 072 and 0 . 89 respectively . To avoid small random overlaps , we use 20 as the default value for , the alignment overlap threshold . Our overlap graph construction is different from standard overlap graph construction in that it does not need all-against-all sequence comparison . We first sort the reads by their alignment positions in a non-decreasing order . We only check the sequence overlap between two reads if their alignment overlap passes . Therefore , the alignment information increases the efficiency of graph construction ( running time analysis can be found in the section of Running Time Analysis ) . To incorporate substitution sequencing errors introduced by some NGS platforms , we allow a certain number of mismatches in the sequence overlap . That is , the overlap between two hits and is the longest suffix of that has a Hamming distance to a prefix of . In our current implementation , . The parameter can be adjusted to fit the error rate of the input data . Transitive edges correspond to edges whose two end nodes are connected by an alternative path ( usually with higher coverage ) . They add unnecessary edges without contributing to the connectivity of the graph and are removed before de novo assembly . Before removing them , SAT-Assembler keeps a record of all the pairs of nodes connected by transitive edges because a transitive edge indicates that a pair of nodes are from the same gene region . This information will be used to guide the graph traversal . If a node has only one outgoing edge that points to another node that has only one incoming edge these two nodes can be merged as one node . Tips are identified and removed using the topology-based pruning methods as in Velvet [11] . Although our edge creation method excludes most random sequence overlaps , some erroneous edges still exist . An edge is highly likely to be erroneous if it is inferior to another edge that shares a head node or tail node with it . An edge is inferior to another edge if the following two criteria are met: i ) the sequence overlap of is smaller than half of that of ; ii ) the Hamming distance of sequence overlap of is larger than that of . A random overlap is more likely to be much smaller and have more mismatches than a true overlap . Therefore , these two criteria will help us remove most erroneous edges . Once a family-specific graph is constructed and optimized , the goal is to conduct a graph traversal to output paths corresponding to genes . The traversal starts with nodes without incoming edges . The challenge arises when two or more genes contain a common or similar subsequence , leading to chimeric nodes such as and in Fig . 3 . Chimeric nodes add to the complexity of the graph traversal by leading to chimeric contigs . For example , the path contains nodes exclusively from both genes and thus is a chimeric path . SAT-Assembler employs three types of information to guide the graph traversal to recover correct gene paths: transitive edges , paired-end reads , and coverage . We describe the key steps of our graph traversal algorithm using Fig . 3 . The goal is to output two correct paths corresponding to the two genes . A paired-end read represents two reads appearing in the same genome with known order ( by our homology search ) and distance range ( insert size ) . Although transitive edges are removed at the stage of graph pruning they can act as a paired-end read with a small insert size . Therefore , both transitive edges and paired-end reads can be used to examine whether two nodes are from the same gene . Two nodes that are not connected by an edge are said to have supports or be supported if there are transitive edges or paired-end reads between them . For paired-end read supports , we further require that their distance in the path be consistent with the insert size . Different from previous traversal algorithms , we divide supports into two types , critical supports and non-critical supports . Critical supports can be used to resolve branching in graph traversal while non-critical supports are not able to distinguish different gene paths . For example , a graph traversal generates a path . The node has two outgoing edges and . If there is a support between and , such as the transitive edge in Fig . 3 , the traversal will be guided to visit in next step . This transitive edge provides a critical support for correct traversal . However , the support between and is not "critical" for guiding the graph traversal because any path that has visited needs to visit . In Fig . 3 , the support between and is a non-critical support while all the other supports are critical supports . When there is no support between two non-chimeric nodes , node coverage will be used to resolve the branches . The coverage of a node is the total size of reads normalized by the length of the assembled sequence of the node . For protein-coding genes , although the sequence coverage is usually not uniform along the genes its change is gradual rather than sharp . Thus , the coverage of two consecutive non-chimeric nodes in the same path should reflect this observation . Any sharp change indicates a wrong path . For example , in Fig . 3 , and have similar coverage , as do and . and , however , have significantly different coverage . Therefore , a path that has visited and should next visit instead of . We use a bounded depth-first search ( DFS ) algorithm to generate correct paths . While a typical DFS takes exponential time to generate all simple paths between two nodes , our graph traversal method makes use of critical supports to bound the search and only visits the correct paths , effectively reducing the time complexity of path generation . During search , we will proceed to those successors of the current node that provide critical supports . If none of the successive non-chimeric nodes has supports with any of the previously visited non-chimeric nodes , we will proceed to the one that has a similar coverage to the recently visited non-chimeric node given that their coverage is similar enough . Otherwise , it is highly likely that the current node is not from the same gene as any of its successors . Therefore , we will output the current path and start a new path from its successors . The traversal stops when there is no appropriate successive node available . All paths with critical supports above a given threshold will be output . Assembly tools may output multiple contigs from the same gene . There are two main reasons for the fragmentation: i ) some regions between contigs are not sequenced due to sequencing bias , PCR bias , low transcription level or abundance; ii ) reads from lowly conserved regions of the gene may not pass the homology search and thus are not used to construct the graph . The contigs are oriented and connected using their alignment positions against the underlying profile HMM and paired-end reads . The scaffolding results in super contigs . SAT-Assembler can distinguish not only different target genes but also gene isoforms caused by AS events . Here we classify the seven different types of alternative splicing events [51] into four different groups . Fig . 4 shows how overlap graphs are constructed for these four groups of AS events . Each of the group is represented by one AS event in [51] . All the other types fall into one of these groups . In Cases ( A ) , ( C ) , and ( D ) of Fig . 4 , different isoforms correspond to different paths of the overlap graphs . Therefore , isoforms generated by AS events from these groups can be distinguished by generating contigs from these paths . In Case ( B ) , there are two paths that begin with a root node and end with a sink node: and . The first path recovers the first isoform and the second path corresponds to a chimeric contig . However , reads in and are not from the same isoform . Therefore , there will be no paired end support between them . Our graph traversal algorithm will stop in node without proceeding to node . Therefore , SAT-Assembler can still correctly recover both isoforms in this case . In practice , different types of alternative splicing events can occur together , further compounding the assembly . In these cases , SAT-Assembler relies on multiple types of information such as paired end reads , transitive edges , and coverage to distinguish different isoforms , as described in the section of Guided Graph Traversal Using Multiple Types of Information . The performance of assembly tools on distinguishing gene isoforms can be found in the section of Performance of Recovering Gene Isoforms . Let the number of input reads be and the average read length be . The time complexity of the homology search stage is for one input family , where is length of the profile HMM and . Suppose reads have passed the homology search stage . Usually , . The time complexity of graph construction is , where is the average number of overlapping alignments longer than a given cutoff . During graph construction , we use alignment positions to guide the overlap computation , avoiding the all-against-all comparison needed in a standard overlap graph construction . The time complexity of graph traveral is , where is the number of nodes , is the number of edges , is the number of read pairs that have critical supports , and is the number of correct paths in the graph . The time complexity of the scaffoding stage is . Because of various optimization techniques and heuristics , the latest version of HMMER is as fast as BLAST [44] . Considering , the time complexity of scaffolding is much smaller than graph traversal . Therefore , the overall running time is determined by the graph traversal stage .
In this experiment , we applied SAT-Assembler to an RNA-Seq data set sequenced from a normalized cDNA library of Arabidopsis generated using paired-end Illumina sequencing [52] . There were a total of 9 , 559 , 784 paired-end reads of 76 bp . Pfam was used as our database of input families . Some Pfam families use sequences from Arabidopsis to train their profile HMMs . Therefore , we eliminated Arabidopsis sequences from these families and recomputed the profile HMMs for them . We compared the performance of SAT-Assembler with Velvet , Oases , Trinity , IDBA-Tran , and Trans-ABySS . Velvet is a widely used short read de novo assembly tool . Oases , Trinity , IDBA-Tran , and Trans-ABySS are assembly tools specially designed for transcriptomic data . To determine which genes are transcribed in this data set , we conducted read mapping ( using Bowtie [37] ) on all the coding sequences ( CDS ) of Arabidopsis thaliana of version TAIR10 [53] . 59 . 62% of the input reads were mapped to the CDS with at most 2 mismaches allowed . There is no commonly accepted criterion to define transcribed genes . In this work , we defined CDS with at least 10 mapped reads as transcribed CDS . Assembly results of different tools were compared on these transcribed CDS . There are 29 , 030 different transcribed gene isoforms corresponding to 21 , 452 genes . A total of 3 , 163 protein or domain families from Pfam that can be aligned to these CDS using HMMER with gathering thresholds ( GAs ) were used as input to SAT-Assembler . Among the mapped reads , 65 . 39% generated HMMER hits against these protein or domain families using HMMER's default E-value threshold 10 . The rest of the mapped reads failed to be aligned by HMMER due to the following main reasons: i ) some Arabidopsis genes are not covered by Pfam families; ii ) the average sequence identities of some Pfam families that Arabidopisis genes belong to are low , rendering marginal alignment scores , especially for short reads [47]; iii ) some Arabidopsis genes are too remotely related to the Pfam families . In this experiment , we conducted targeted gene assembly using a metagenomic data set sequenced from highly diverse bacterial and archaeal synthetic communities with 16 archaea members and 48 bacteria members [57] . We downloaded all reference genomes from NCBI ftp site ( ftp . ncbi . nih . gov/genomes/ ) . The metagenomic data set was downloaded from NCBI Sequence Read Archive ( SRA ) ( http://www . ncbi . nlm . nih . gov/sra ) using Accession No . SRA059004 . After we trimmed low-quality reads , there were 51 , 933 , 622 paired-end reads with an average read length of 100 bp . We were interested in assembling the family of butyrate kinase pathway genes , which play important roles in butyrate synthesis . We downloaded the profile HMM of the family from RDP's functional gene repository [58] . It was built from 77 seed butyrate kinase pathway genes . The seed genes are not in the genomes . We annotated the butyrate kinase gene regions in the genomes by aligning reference genomes against the gene family using HMMER with gathering thresholds ( GAs ) . We compared the performance of Velvet , IDBA-UD , MetaVelvet , and SAT-Assembler on assembling contigs from these regions . IDBA-UD and MetaVelvet are both de Bruijn graph based and specially designed for de novo assembly of metagenomic data . We used VelvetOptimiser to search for the best assembly result by trying k-mer sizes from 53 to 83 bp with “” as the optimization function . VelvetOptimiser reported 55 as the optimal k-mer size . For IDBA-UD , which accepts multiple k-mer values , we used the same range of k-mer sizes as Velvet in a single run . Meta-Velvet used the hash table generated by Velvet and its k-mer size was thus 55 as well . For SAT-Assembler , we used its default parameters . A total of 15 , 254 reads were classified into the butyrate kinase family by the homology search stage , which accounted for 0 . 15% of the query reads . Table 4 shows a performance comparison between these assembly tools . SAT-Assembler had the best gene coverage , chimera rate , and memory usage . Its contigs were usually shorter than IDBA-UD . A closer examination reveals the reason: a number of reads did not pass the profile HMM homology search and thus were not used as input to assembly . Gene coverage of assembly tools in this experiment was much lower than in the first experiment because of lower sequencing depth and higher data complexity . MetaVelvet had the best running time performance because it directly used the optimal k-mer size and hash table from VelvetOptimiser while Velvet and IDBA-UD both ran a range of k-mer sizes . The low memory usage of SAT-Assembler further showed the advantage of using homology search in targeted gene assembly for large-scale NGS data . Due to the high complexity of the metagenomic data set , SAT-Assembler constructed a much more complex overlap graph compared with the overlap graphs in the first experiment , leading to higher runtime overhead . Table 4 shows that none of the tested assembly tools is the best in all metrics . If users prefer high gene coverage and high accuracy , especially on hardware with limited resources , we recommend SAT-Assembler . If long contigs and high contig coverage are more important , IDBA-UD is the best choice . In this experiment , we compared the performance of SAT-Assembler with Velvet , IDBA-UD , and MetaVelvet on a human gut metagenomic data set . There were 47 , 117 , 906 paired-end and 5 , 528 , 102 unpaired reads of various lengths . The average length of the query reads was 95 . 72 bp and 75% of them were 100 bp . We were interested in assembling butyrate kinase pathway genes as in the second experiment . The profile HMM of the gene family was built from 77 seed genes from RDP's functional gene repository [58] . We also downloaded a set of 2 , 352 annotated genes of butyrate kinase family and eliminated the seed genes from them . By using read mapping , a total of 58 genes with at least 10 mapped reads were identified and were used to evaluate the performance of all assembly tools . We used VelvetOptimiser to search for the best assembly result by trying k-mer sizes from 51 to 81 bp with “” as the optimization function . VelvetOptimiser reported 51 as the optimal k-mer size . For IDBA-UD , which accepts multiple k-mer values , we used the same range of k-mer sizes as Velvet in a single run . Meta-Velvet used the hash table generated by Velvet and its k-mer size was thus 51 as well . For SAT-Assembler , we used its default parameters . A total of 16 , 136 reads were classified into the butyrate kinase family by the homology search stage , which accounted for 0 . 31% of the query reads . Table 5 shows a performance comparison between these assembly tools . In this experiment , the result of performance comparison was similar to the second experiment . SAT-Assembler still had the best gene coverage , chimera rate , and memory usage . IDBA-UD had the best contig length and contig coverage . Compared with the second experiment , the chimera rates of all assembly tools increased . Without knowing all reference genomes , the computed chimera rates might be an over-estimation for all tools because the assembled contigs may contain novel members of the family .
The experiments on RNA-Seq and metagenomic data sets show that our novel consistency-based edge creation strategy and guided graph traversal can effectively avoid chimeric contigs . Moreover , by reducing the original search space into a much smaller subset of reads from targeted genes , the memory usage was significantly decreased , making it a more economical tool for the assembly of targeted genes from a single or multiple pathways . We have also tried to use Velvet and Trinity on the reads that passed the homology search stage on the Arabidopsis RNA-Seq data set . The gene coverage and chimera rate of Velvet were 64 . 38% and 17 . 25% respectively . The gene coverage and chimera rate of Trinity were 78 . 72% and 22 . 90% respectively . Compared with the performance when using Velvet and Trinity directly on the input data set , their gene coverages were decreased . One reason is that the homology search stage does not have 100% sensitivity . The missed reads may lead to poorer performance of Velvet and Trinity . SAT-Assembler also provides an easier way for users to set the parameters . Our edge creation strategy is based on both the overlap threshold ( ) and the consistency between alignment overlap and sequence overlap ( ) . The consistency strategy poses a strong constraint on the overlap between two reads . The alignment overlap threshold is mainly used to avoid random overlaps , which are generally very small . The default overlap threshold 20 is chosen based on the length of the reads in our experimental data sets . This value is smaller than the k-mer value chosen by VelvetOptimiser for other assembly tools . This helps us generate better connectivity between reads from the same genes . At the same time , the consistency constraint guarantees the accuracy of the edge creation ( Table 1 ) . We have also tried different values from 15 to 30 and found that the edge creation performance is not sensitive to the choice of unless a very large value of is used . The values of and control the trade-off between sensitivity and of edge creation . Users can adjust them based on their specific need . Based on our observation , an overlap threshold that is 20% of the read length is recommended . The value of is independent of the read length and we suggest that users use the default value . There are still some challenges to address to further improve SAT-Assembler's performance . First , gene segments from some poorly conserved gene regions are fragmented because some reads from these regions fail to pass the homology search . We have aligned all the reads in the human gut metagenomic data set against protein/domain families in Pfam using HMMER and 38 . 65% of them have HMMER hits . There are three main reasons for the low coverage of Pfam domains in the metagenomic data set: i ) Pfam is a collection of protein/domain families . Therefore , reads sequenced from intergenic regions will not have hits . In addition , even reads sequenced from protein-coding regions may not be part of any domain . They will not have hits either . ii ) Some genes of the microbial species are very remotely homologous to the families in Pfam . iii ) Some reads in the metagenomic data set are very short , resulting in low sensitivity of HMMER [47] . This problem can be alleviated by increasing the sensitivity of the homology search . In the future , we will incorporate our proposed position-specific score threshold ( PSST ) [47] , [56] into SAT-Assembler to classify more reads into their native families . Second , although the edge creation strategy of SAT-Assembler captured more overlaps between reads from the same genes , some positive overlaps still failed to be captured . When the conservation between the input family and target genes is not good , the alignment overlap and sequence overlap may not always be consistent . Therefore , reads from poorly conserved regions of the genes may lose consistency between their alignment overlaps and sequence overlaps . In this case , connectivity between these reads will not be captured by SAT-Assembler's edge creation strategy , leading to segmented contigs in the final assemblies . Fig . 7 shows an example of missing edge connection due to poor conservation between gene isoforms and the input family . Both gene isoforms are from the same family X . The first gene isoform has a global alignment against Family X while the skipped exon 2 in the second gene isoform leads to two local alignments . According to our edge creation strategy , all the reads in the first gene isoform can be correctly connected . However , because the green read shares sequence overlap but no alignment overlap with neighboring reads , there will be no edge between the green read and its neighboring reads , leading to two disconnected contigs . In this case , the two contigs can usually be connected in our scaffolding stage using paired-end reads . As part of our future work , we will take into consideration the conservation between target genes and the family to improve our edge creation strategy . Moreover , insertion/deletion ( indel ) or substitution errors in the overlapping regions may also lead to false negative connections . Masella et al . [59] proposed a sophisticated method that can probabilistically correct these errors based on the overlap data from the paired-end reads . HMM-FRAME [60] can be used to accurately detect and correct indel errors using profile HMM-based homology search . We plan to incorporate these methods in our edge creation strategy to generate more positive connections . Third , the running time of the graph traversal stage is the bottleneck of SAT-Assembler , especially for complex metagenomic data . Therefore , we plan to add more bounding strategies into the graph traversal , such as a more stringent threshold for critical supports . Moreover , we will implement SAT-Assembler using C++ to reduce its running time . | Next-generation sequencing ( NGS ) provides an efficient and affordable way to sequence the genomes or transcriptomes of a large amount of organisms . With fast accumulation of the sequencing data from various NGS projects , the bottleneck is to efficiently mine useful knowledge from the data . As NGS platforms usually generate short and fragmented sequences ( reads ) , one key step to annotate NGS data is to assemble short reads into longer contigs , which are then used to recover functional elements such as protein-coding genes . Short read assembly remains one of the most difficult computational problems in genomics . In particular , the performance of existing assembly tools is not satisfactory on complicated NGS data sets . They cannot reliably separate genes of high similarity , recover under-represented genes , and incur high computational time and memory usage . Hence , we propose a targeted gene assembly tool , SAT-Assembler , to assemble genes of interest directly from NGS data with low memory usage and high accuracy . Our experimental results on a transcriptomic data set and two microbial community data sets showed that SAT-Assembler used less memory and recovered more target genes with better accuracy than existing tools . |
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Methodology to estimate malaria incidence rates from a commonly occurring form of interval-censored longitudinal parasitological data—specifically , 2-wave panel data—was first proposed 40 years ago based on the theory of continuous-time homogeneous Markov Chains . Assumptions of the methodology were suitable for settings with high malaria transmission in the absence of control measures , but are violated in areas experiencing fast decline or that have achieved very low transmission . No further developments that can accommodate such violations have been put forth since then . We extend previous work and propose a new methodology to estimate malaria incidence rates from 2-wave panel data , utilizing the class of 2-component mixtures of continuous-time Markov chains , representing two sub-populations with distinct behavior/attitude towards malaria prevention and treatment . Model identification , or even partial identification , requires context-specific a priori constraints on parameters . The method can be applied to scenarios of any transmission intensity . We provide an application utilizing data from Dar es Salaam , an area that experienced steady decline in malaria over almost five years after a larviciding intervention . We conducted sensitivity analysis to account for possible sampling variation in input data and model assumptions/parameters , and we considered differences in estimates due to submicroscopic infections . Results showed that , assuming defensible a priori constraints on model parameters , most of the uncertainty in the estimated incidence rates was due to sampling variation , not to partial identifiability of the mixture model for the case at hand . Differences between microscopy- and PCR-based rates depend on the transmission intensity . Leveraging on a method to estimate incidence rates from 2-wave panel data under any transmission intensity , and from the increasing availability of such data , there is an opportunity to foster further methodological developments , particularly focused on partial identifiability and the diversity of a priori parameter constraints associated with different human-ecosystem interfaces . As a consequence there can be more nuanced planning and evaluation of malaria control programs than heretofore .
Estimation of incidence , and in some cases recovery , rates for malaria infection is a central objective of ongoing community surveillance programs [1] . By incidence rate we mean the number of new infections acquired in a small interval of time per person at risk ( i . e . uninfected ) at the beginning of the interval . Analogously , a recovery rate is the number of terminations of infection in a small interval of time per person at risk ( i . e . infected ) at the beginning of the interval . It is important to observe that incidence rate , as defined above , is not the same as incidence , as commonly used in the contemporary malaria literature [e . g . , see reference 2] . In the latter case , incidence is defined as [Number of positive species-specific clinical cases observed during the duration of a survey]/[ ( Number of people observed over the survey duration ) * ( duration of survey ) ] . We quantitatively compare and contrast these notions in the Discussion section . Ideally one would like to have continuous histories of infection status on designated populations so that incidence rates could be directly ascertained over short intervals starting at any designated time . This would facilitate showing the impact of local-in-time weather events , seasonal variation , and the impact of intervention strategies on these rates at arbitrary times of interest to health service workers , government personnel setting malaria control policy , and research investigators . Continuous infection status histories are virtually never available at a community level . The most common longitudinal data collection plan is a time series of 2-wave panel data sets ( or observations of infection status on the same individuals taken at two time points separated by an interval of length Δ ) , with spacing between waves varying from a few weeks [3 , 4] to several months [5] . Thus , there are unobserved transitions between states ( uninfected and infected ) that create a challenge for estimation of incidence and recovery rates . To address this challenge , we require specification of a class of continuous-time 2-state stochastic process models of infection status dynamics that must be shown to be consistent with observed data , and within which estimation of incidence and recovery rates is feasible . The first attempt to carry out this program was by Bekessy et al . [6] using data from the Garki malaria surveys [3] in Kano State , Nigeria from 1970–1975 . They introduced the 2-state continuous time Markov chains as candidates to represent the unobserved infection dynamics . The empirical question associated with this choice was whether or not a transition matrix P ( Δ ) generated by a member of this class of models could represent a transition matrix P^ ( Δ ) arising from field data . Here Δ is the time interval between observations collected at a survey date T1 and a later survey date T2 . P ( Δ ) is a 2x2 transition matrix associated with a continuous time Markov chain with entries pi , j ( Δ ) = conditional probability that an individual has infection status j at the end of a time interval of length Δ given that his/her infection status at the beginning of the interval is i . Here , i and j can take on the values 1 = uninfected or 2 = infected . P^ ( Δ ) is a 2x2 stochastic matrix with entries ni , j/ ( ni , 1 + ni , 2 ) , where ni , j counts the number of individuals observed to be in state i at the beginning of interval Δ and in state j at the end of the interval . The entries are interpreted as conditional frequencies of observing a transition i → j between the consecutive survey dates . For the Garki surveys , Δ ≈ 10 weeks . The transition matrices P ( Δ ) have a representation in terms of incidence and recovery rates given by: P ( Δ ) =exp ( ΔQ ) Q= ( −q1q1q2−q2 ) with qi≥0 for i=1 , 2 ( 1 ) Here q1 is the incidence rate at any time t in the interval Δ , and q2 is the corresponding recovery rate . Bekessy et al . [6] showed that a 2x2 stochastic matrix , P* , has a representation of the form Eq ( 1 ) if and only if trace P* > 1 ( 2 ) Thus , the empirically determined stochastic matrices P^ ( Δ ) can be generated by observations taken at two points in time on a continuous time Markov chain provided trace P^ ( Δ ) >1 . Formal statistical tests of this hypothesis were put forth by Singer & Cohen [7] . Interestingly , Bekessy et al . [6] , Singer & Cohen [7] , and Molineaux & Gramiccia [3] found that all pairs of consecutive surveys during the baseline period of data collection in the Garki project satisfied eq ( 2 ) . However , there were pairs of consecutive surveys conducted during the intervention phase of the project where trace P^ ( Δ ) <1 . In these instances , Molineaux & Gramiccia [3] claimed that incidence and recovery rates were not estimable . While this assertion is correct for the class of model eq ( 1 ) , the unanswered question as of 1980 was: what alternative and substantively defensible models could generate transition matrices satisfying trace P^ ( Δ ) <1 and within which incidence and recovery rates could be estimated ? Surprisingly , to the best of our knowledge , this question has not been taken up in the past 40 years . Nevertheless , its importance stems from the fact that 2-wave panel data in a diversity of malaria surveys/surveillance projects have a majority of their transition matrices , P^ ( Δ ) , satisfying trace P^ ( Δ ) <1 . In addition , incidence and recovery rates are important quantities for evaluation of malaria intervention programs . The purposes of this paper are to: ( i ) present a class of models with associated 2x2 transition matrices , P* , within which those satisfying eq ( 1 ) are nested , some members of which satisfy trace P* < 1 , and for all of which incidence and recovery rates can be calculated; ( ii ) exhibit identifiability and/or partial identifiability criteria arising from specific malaria contexts that ensure uniqueness , or highly constrained non-uniqueness of incidence and recovery rates; and ( iii ) apply the models and methods in ( i ) and ( ii ) to a time series of 2-wave panel data sets from Dar es Salaam , Tanzania as an example of the applicability of the proposed method [5 , 8 , 9] . To facilitate dissemination and utilization of the methodology by the malaria community , we developed a code to calculate incidence rates from longitudinal data utilizing the R package v . 2 . 15 . 1 [10] , which we make available as Supporting Information in a documented version ( S1 Code ) and as a R file ( S2 Code ) . To allow replication of our results , we provide the Dar es Salaam data utilized in this paper ( Tables 1 and 2 ) . An important feature of our considerations is allowance for non-identifiability in situations where subject-matter-based constraints are too weak to ensure unique parameter identification from input information . Depending upon the particular setting , the extent of non-identifiability—or partial identifiability—may be sufficiently limited that the unidentified parameters are restricted to a narrow range of values , with accompanying incidence rates also varying over a small range . This is , indeed , the situation in Dar es Salaam , the site of our empirical data . As a consequence , we show how to explicitly incorporate variability due to a small degree of under-identification together with sampling variability to produce composite uncertainty intervals for incidence rates . Useful discussions of partial identifiability , including in the setting of mixture models , are given by Manski [11] , Gustafson [12] , and Henry et al . [13] .
Ethical approval to use the UMCP data was provided by the Harvard T . H . Chan School of Public Health Institutional Review Board ( Protocol # 20323–101 ) . We begin with the observation that every 2x2 stochastic matrix , P , with non-zero entries can be a transition matrix for at least one 2-component mixture of continuous-time Markov chains . Thus , P has a representation of the form P=SU+ ( I−S ) V ( 3 ) where S is a diagonal matrix with entries si , i = 1 , 2 in the unit interval , [0 , 1] . U and V are each stochastic matrices having representations of the form eq ( 1 ) . Hence they satisfy the condition eq ( 2 ) : trace U > 1 and trace V > 1 . It will be convenient to represent the matrices P , U , and V as points in the unit square; namely p = ( p1 , p2 ) , u = ( u1 , u2 ) , and v = ( v1 , v2 ) . The coordinates pi , ui , and vi , i = 1 , 2 are the diagonal entries in P , U , and V respectively . Depending on the empirical setting , we will also have occasion to consider data of the form p = ( p1 , 0 ) and p = ( 0 , p2 ) ; i . e . points on the boundary of the unit square . In the first of these conditions , P has a representation of the form eq ( 3 ) , but with u = ( 1 , 0 ) ( hence , trace U = 1 ) and trace V > 1 . For p = ( 0 , p2 ) , the representation eq ( 3 ) also holds but now with u = ( 0 , 1 ) and trace V > 1 . Data of the form p = ( p1 , 0 ) occur frequently in the Dar es Salaam data , as discussed later . In these boundary cases , U still has a representation of the form eq ( 1 ) but with q = ( 0 , −∞ ) when p = ( p1 , 0 ) , and q = ( −∞ , 0 ) when p = ( 0 , p2 ) . In the first case , the interpretation of q2 = ∞ is that there is an infinitely fast recovery rate . This would be associated with a population where everyone is on prophylaxis , or where effective anti-malarial drugs are administered immediately following a diagnosis of infection . In the second case , q1 = ∞ corresponds to an infinitely fast incidence rate . This would be a situation where there is instantaneous new infection of any exposed individual . For data corresponding to p in the interior of the unit square—i . e . P^ ( Δ ) with non-zero entries—we calculate the incidence and recovery rates for the mixture eq ( 3 ) via: r1=s1q1 ( U ) + ( 1−s1 ) q1 ( V ) ( incidence rate ) r2=s2q2 ( U ) + ( 1−s2 ) q2 ( V ) ( recovery rate ) ( 4 ) In terms of U and V , the rates qi ( U ) and qi ( V ) , i = 1 , 2 can be expressed as ( note that these formulas are entries in matrix logarithms of U=eQ ( U ) Δ and V=eQ ( V ) Δ – see e . g . [7] ) : qi ( U ) =log ( trace U−1 ) trace U−2 ( 1−ui ) Δqi ( V ) =log ( trace V−1 ) trace V−2 ( 1−vi ) Δ When p = ( p1 , 0 ) , we have r1= ( 1−s1 ) q1 ( V ) = ( 1−s1 ) log ( trace V−1 ) trace V−2 ( 1−v1 ) Δ =log ( trace V−1 ) trace V−2 ( 1−p1 ) Δ , since s1=p1−v11−v1 ( 5 ) The recovery rate is ∞ for every s2 > 0 . When s2 = 0 , we have r2=log ( trace V−1 ) trace V−2 ( 1−v2 ) Δ An analogous argument yields r1 and r2 when p = ( 0 , p2 ) . We first re-express Eq ( 3 ) via the system of equations p1=s1u1+ ( 1−s1 ) v1p2=s2u2+ ( 1−s2 ) v2 ( 6 ) where ( si , ui , vi ) ∈ [0 , 1]6 for i = 1 , 2 and u1+u2>1 , v1+v2>1 . Given p = ( p1 , p2 ) , eq ( 6 ) is an under-identified system . Additional subject-matter motivated constraints must be imposed to either identify ( s , u , v ) uniquely or restrict this vector to a small set in [0 , 1]6∩ ( ( u , v ) :u1+u2>1 , v1+v2>1 ) . In the context of malaria in Dar es Salaam , we impose the constraints: u1 ≤ 0 . 2 , u2 = 1 , 0 . 9<v1 < 1 , v2 < 0 . 5 , s1 < 0 . 5 , and v1 − v2 ‘large’ . A full rationale for the above restrictions will be given later when we present the Dar es Salaam case study . However , the central point here is that a system of constraints such as these is essential for parameter identification or partial identification . The conditions that v1 − v2 be ‘large’ and v1 < 1 require additional comment . First , it is a matter of judgment about what is a high probability of being observed uninfected at consecutive surveys of the V-process , while still not being a sure thing—i . e . v1 = 1 . In identifying parameters , we first select v1 ∈[0 . 9 , 1 ) and secondarily choose v2 as small as possible consistent with the other constraints . Two examples will serve to illustrate the issues . When p = ( p1 , p2 ) is in the interior of the unit square , we generate 1 , 000 tables by doing binomial sampling for row 1 with probability p1 and for row 2 with probability p2 with sample sizes n11 + n12 and n21 + n22 , respectively . If , for a particular table , the system of eq ( 6 ) has a unique solution ( s , u , v ) , subject to context-specific constraints , then we compute an incidence rate , r1 , for that table . If there is a zone of non-identifiability , as previously exemplified by the equation 0 . 95s1 − s1u1 − 0 . 25 = 0 in Example 1 , then we compute r1 for each of 100 values u1 ( which then determines s1 ) subject to the a priori constraints on s1 and u1 . This yields a set of incidence rates that reflect variation due to non-identification . We used the minimum , median , and maximum values of r1 from each such set of 100 values and viewed them as the summary rates for the particular table . Finally we take the summary rates , for tables where non-identifiability is an issue , and the unique rate for tables when the system eq ( 6 ) is identified , and rank this full set of rates . We designate the 2 . 5th percentile and the 97 . 5th percentile of the ranked list as the upper and lower bounds on a 95% variation interval for the incidence rate of the original table . This takes both sampling variability and variation due to non-identifiability into account . When p = ( p1 , 0 ) , we treat the 0 as a structural zero—in the case of Dar es Salaam—and only do binomial sampling on the first row to generate 1 , 000 tables having this same structure . We then describe the variation in r1 in the same manner as indicated above . In applications where p = ( p1 , 0 ) does not have a structural zero , we perturb the second coordinate to a small value—e . g . 10−5 or less—and do binomial sampling for the second row with this value . Then we proceed as in the above paragraph to calculate a confidence interval for r1 . Light microscopy has limitations as a technology for diagnosing Plasmodium falciparum infections , particularly in low transmission settings [14–16] . In a recent study of Okell et al . [16] , the supplementary information for the paper contains an especially interesting and useful table comparing prevalence estimates using microscopy and PCR on the same blood samples . The data come from a wide variety of settings , and exhibit considerable variation in prevalence rates as ascertained via microscopy . The prevalence ratio , p = [prevalence rate from microscopy]/[prevalence rate from PCR] provides a basis for adjusting empirical microscopy rates to what you would expect to find if PCR had actually been done on the same blood samples . This calculation will , of course , only yield adjusted prevalence rates . For our longitudinal data , it would have been desirable to have microscopy and PCR based estimates of p12 , p21 and p22 , from which we could directly recover p11 . However , the lack of identifiability of transition probabilities from prevalence rates can still be dealt with in particular settings , such as Dar es Salaam , by invoking an additional , and obviously context dependent , constraint . We exhibit the methodology on p = ( 0 . 7 , 0 . 2 ) –the transition rates in example 1–augmented by a table of counts with entries nij consistent with these values . Using an adjusted table of counts , nij* , and thereby an adjusted vector , p*= ( p1* , p2* ) , we calculate the incidence rate , r1* , that represents what we might have expected to find if PCR had been done on the blood samples in Dar es Salaam . We introduce the table of counts {nij , 1 ≤ i , j ≤ 2} with n11 = 100 , n12 = 43 , n21 = 90 , and n22 = 23 . For this table , p = ( 0 . 7 , 0 . 2 ) . It is one of a myriad of tables that could have been selected to illustrate our points about submicroscopic infection . However , it is comparable in size to many of the sub-ward tables in the Dar es Salaam data , and thus especially useful for illustrating an adjustment methodology . The prevalences at the initial and final rounds of data collection for the above table are: at initial survey = [90 + 23]/256 = 0 . 4414 , and at final survey = [23 + 43]/256 = 0 . 2578 . From Table S1 in Okell et al . [16] , we find the prevalence from microscopy that is closest to the prevalence at initial survey in Dar es Salaam given by 0 . 4414 . This is the prevalence of 0 . 481 based on data from Guinea Bissau . The corresponding prevalence ratio is p = 0 . 551 . Thus our estimate for a corresponding PCR-based prevalence rate at the initial survey is 0 . 4414/0 . 551 = 0 . 8011 . In contrast to the initial survey situation , there are four nearby microscopy-based prevalence rates to associate with the prevalence rate for the final survey given above by 0 . 2578 . These values , their associated prevalence ratios , and our estimate for the corresponding PCR-based prevalence rates are shown in Table 4 . Each PCR rate is equal to 0 . 2578/p . For our analysis , we use the average of these PCR rates , namely 0 . 5173 . To obtain an associated table of counts nij* , we first observe that n11*+n12*+n21*+n22*=256= total count from the microscopy-based table with entries nij . From PCR prevalence at initial survey = 0 . 8011 , we obtain n21*+n22*=205 . From PCR prevalence at final survey = 0 . 5173 , we obtain n12*+n22*=132 . Adding and subtracting n22* to the equation for total count , we can rewrite it as ( n11*−n22* ) +n12*+n22*+n21*+n22*=256 . Then we have that n11*−n22*=−81 . To be consistent with the microscopy-based vector , p = ( 0 . 7 , 0 . 2 ) , where obviously p1 > p2 , we choose n11* to ensure that p1*>p2* . Table 5 shows some choices for estimated PCR-based tables . Here we use as an example p1*=0 . 784 and p2*=0 . 590 ( third line of Table 5 ) for subsequent calculations . To obtain what is interpreted as a PCR-based incidence rate , we proceed as in the previously described microscopy-based rate calculations . First set v1*=0 . 95 . Then from p1*=s1*u1*+ ( 1−s1* ) v1* , we obtain 0 . 95s1*−s1*u1*−0 . 166=0 . Using v2*=[0 . 590− s2*]/ ( 1−s2* ) >1−v1*=0 . 05 , we set s2*=0 . 5 . Then v2*=0 . 18 , and v1*+v2*=1+ε=1 . 13 . Thus q1 ( V ) *=logεε−1 ( 1−v1* ) Δ=0 . 00173 , with ε = 0 . 13 and Δ = 40 . Along the curve of ( s1* , u1* ) values given by 0 . 95s1*−s1*u1*−0 . 166=0 , we obtain values of q1 ( U ) * and r1* as indicated in Table 5 .
The continuous-time Markov chains all have exponentially distributed waiting time distributions in each state , which implies that their hazard rates are constant . Two-wave panel data , assumed to be generated by some 2-state continuous-time stochastic process , is not sufficiently rich to provide a basis for testing this hypothesis . However , several basic facts about malaria in diverse ecosystems make this assumption untenable . For example , in the Garki study [3] , persons who survive repeated episodes of malaria in infancy and childhood have antibody titers that ensure an increasing hazard rate in the infected state—i . e . the longer an individual has detectable parasites , the more likely he/she is to clear parasites free of any intervention , and return to the uninfected state . For uninfected individuals at the end of a dry season , the hazard rate for onset of a new infection is also increasing , corresponding to the propensity for rain and , thereby , standing water . One of many possible parameterizations of these qualitative ideas is given by the waiting time distributions F ( i ) ( t ) , t > 0 , where i = 1 , 2 designate the states uninfected ( i = 1 ) and infected ( i = 2 ) , and having probability density functions fi ( t ) =βiαitαi−1e−βitΓ ( αi ) , αi , βi , t>0 for i=1 , 2 ( 7 ) and hazard rates hi ( t ) =fi ( t ) 1−F ( i ) ( t ) , i=1 , 2 . Here hi ( t ) is: increasing if αi > 1 , constant if αi = 1 , and decreasing if αi < 1 . The family of Gamma densities eq ( 7 ) are the basis for obtaining an expression for P ( Δ ) within the class of semi-Markov models by solving the backward integral equation system [30] . pij ( 0 , t ) =δij[1−Fi ( t ) ]+∑k=1r∫0tfi ( s ) mikpkj ( 0 , t−s ) ds ( 8 ) where δij = 1if i = j , δij = 0 if i ≠ j , and 1 ≤ i , j ≤ r with M = ‖mik‖ an r x r stochastic matrix having mii = 0 for 1 ≤ i , j ≤ r . Specification eq ( 8 ) holds for general r-state process and waiting time densities fi ( t ) , 1 ≤ i ≤ r . For our purposes , r = 2 , m12 = m21 = 1 , and we focus on the 2-parameter family eq ( 7 ) . Here we set t = Δ and identify pij ( 0 , Δ ) with the ( i , j ) entry in P ( Δ ) . The equation system eq ( 8 ) is amenable to the following interpretation: ( i ) when i ≠ j , pij ( 0 , t ) consists of the sum of products of three factors: the probability of a first departure from state i at time s , the probability of a transition from state i to state k at that instant , and the probability of transferring to state j by some combination of moves in the interval t−s The summation is over all intermediate states k and all time divisions s in ( 0 , t ) ; ( ii ) when i = j , in addition to the preceding term , there is the probability of not transferring out of state i during ( 0 , t ) . This is given by the first term . With empirical data , solving the system of equations p^i , j ( Δ ) =pij ( 0 , Δ ) , i=1 , 2 for parameter values as in the Gamma specification above , requires a priori context-dependent constraints on the parameters—to secure identification or partial identification—and numerical inversion calculations in eq ( 8 ) . Going back to the empirical analyses in the Garki baseline surveys [3] , the disconcerting issue that now arises is that the entire set of 2-wave panel data sets shown in Singer & Cohen [7] , with incidence and recovery rates computed within the class of continuous time Markov chain models , could just as well have been used to estimate incidence and recovery rates within the class of 2-state semi-Markov models with Gamma distributed waiting times . The same can , of course , be said for the rates computed in the prior section for Dar es Salaam now using , in some of the trace P < 1 cases , 2-component mixtures of semi-Markov models with analogous inequality constraints facilitating identification or partial identification of parameters . If we use a crude incidence rate given by r1 ( crude ) =p^12 ( Δ ) Δ , this essentially assumes that there is at most one unobserved transition in the inter-survey interval , Δ . For the Garki surveys where Δ = 10 weeks , we find , not surprisingly , that r1 ( crude ) <r1 ( Markov ) in the baseline surveys . In the lower endemicity setting of Dar es Salaam , the same inequality holds when trace P^>1 , but now Δ ≈ 40 weeks . If Δ had been approximately 10 weeks for Dar es Salaam , we would anticipate very little difference between r1 ( crude ) and r1 ( Markov ) . We also find that r1 ( crude ) < r1 ( Mixture ) when trace P^<1 in Dar es Salaam , but this is decidedly influenced by the long inter-survey intervals . As two examples , consider the ward Buguruni , for survey intervals R23 ( with Δ = 42 . 2 weeks ) and R56 ( with Δ = 49 . 2 weeks ) . For R23 , r1 ( crude ) = 0 . 0031 and r1 ( Mixture ) = 0 . 0081 . For R56 , r1 ( crude ) = 0 . 0007 and r1 ( Mixture ) = 0 . 0039 . Thus , during the last survey interval , R56 , when the larvicide intervention was operating effectively , we still have r1 ( crude ) and r1 ( Mixture ) differing by a factor of 5 . 6 . Taking formal account of the possibility of multiple unobserved transitions clearly makes a difference , and is preferable to the crude incidence rate , generally . For the 2-wave panel data in Dar es Salaam , we have: Inc=n22+n21+n12N*Δ ( 9 ) where N = n11+n12+n21+n22 . Using eq ( 9 ) we compare Inc with r1 ( Mixture ) for two wards , Buguruni and Kurasini , early in the larviciding program , R23 , and at the end of it , R56 ( Table 8 ) . The general lesson here is that Inc < r1 ( Mixture ) except for survey rounds where there are almost no infected cases . Indeed , for Kurasini at R56 we have a raw table of counts given by ( n11n12n21n22 ) = ( 63010 ) . Here there is no apparent transmission between survey rounds 5 and 6 . The infected individual at survey is , in accordance with the study protocol , treated with anti-malarial drugs . The general inequality Inc < r1 ( Mixture ) is basically a consequence of the fact that unobserved transitions are not taken into account in Inc . In moderate to high transmission areas , we anticipate that Inc will be substantially downward biased as a result of lack of formal consideration of unobserved transitions . Different methods have been proposed to generate incidence ( as defined in this paper , or the rate of occurrence of an event ) from prevalence data [31–34] . As more applications of the methodology here introduced are made , analysis that would produce both incidence and prevalence rates could provide a unique opportunity to evaluate the best approach to obtain incidence rate estimates from prevalence rates—currently , it is unclear what is the best strategy . Such an exercise could lead to clear recommendations that would have both a wide applicability in malaria endemic countries and a crucial importance for National Malaria Control Programs ( e . g . , planning and evaluation of the cost-effectiveness of interventions ) . In conclusion , this paper introduced new methodology for estimating incidence rates from 2-wave panel data with interval censoring , which is applicable in the many cases where the extant Markovian models are inapplicable . The methodology is suitable to settings with any malaria transmission level , given the availability of longitudinal data . In addition , we present a strategy for quantifying the uncertainty in estimation of incidence rates . It is hereby distributed with a well-documented programming code that allows the use of the method in R software . | Incidence rates measure the transitions between the states of noninfected to infected per unit of time and per person at risk . Usually calculated from longitudinal observations , they provide an indication of how rapidly a disease develops in a population over time . In the context of malaria , longitudinal data on infection status are obtained through consecutive survey rounds , separated by a certain time interval . Depending on the length of the interval , some changes of infection status may be missed , and thus only uncensored information would be available . Methodology to calculate incidence rates from this type of data was first proposed in 1976 , but its assumptions were not applicable to low transmission settings , particularly in the presence of control measures . No alternative methodology has been proposed in the past 40 years , limiting attempts to obtain estimates of incidence rates in the current scenario of declining malaria transmission worldwide . In this paper we address this gap and introduce new methodology to estimate malaria incidence rates from longitudinal data that can be applied to settings with any transmission level . We provide a complete example of the method , including sensitivity analysis , and an assessment of possible differences between data based on microscopy vs . PCR diagnostics . To facilitate replication and wide use of the method , we make available a programming code in R language and the example dataset . |
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Bifunctional dihydrofolate reductase–thymidylate synthase ( DHFR-TS ) is a chemically and genetically validated target in African trypanosomes , causative agents of sleeping sickness in humans and nagana in cattle . Here we report the kinetic properties and sensitivity of recombinant enzyme to a range of lipophilic and classical antifolate drugs . The purified recombinant enzyme , expressed as a fusion protein with elongation factor Ts ( Tsf ) in ThyA- Escherichia coli , retains DHFR activity , but lacks any TS activity . TS activity was found to be extremely unstable ( half-life of 28 s ) following desalting of clarified bacterial lysates to remove small molecules . Stability could be improved 700-fold by inclusion of dUMP , but not by other pyrimidine or purine ( deoxy ) -nucleosides or nucleotides . Inclusion of dUMP during purification proved insufficient to prevent inactivation during the purification procedure . Methotrexate and trimetrexate were the most potent inhibitors of DHFR ( Ki 0 . 1 and 0 . 6 nM , respectively ) and FdUMP and nolatrexed of TS ( Ki 14 and 39 nM , respectively ) . All inhibitors showed a marked drop-off in potency of 100- to 1 , 000-fold against trypanosomes grown in low folate medium lacking thymidine . The most potent inhibitors possessed a terminal glutamate moiety suggesting that transport or subsequent retention by polyglutamylation was important for biological activity . Supplementation of culture medium with folate markedly antagonised the potency of these folate-like inhibitors , as did thymidine in the case of the TS inhibitors raltitrexed and pemetrexed .
Human African trypanosomiasis ( HAT ) is an infectious disease caused by two distinct subspecies of the protozoan parasite Trypanosoma brucei ( T . b . gambiense and T . b . rhodesiense ) . Existing therapies for this otherwise fatal disease are limited due to toxicity , difficulty in administration , emerging drug resistance and cost . As such , new safe and affordable drugs are required for the continued treatment and control of HAT . Enzymes of essential metabolic pathways in T . brucei , such as N-myristoyltransferase [1] and trypanothione synthetase [2 , 3] , are of continuing interest as novel targets for the development of new treatments , while a number of other putative drug targets remain to be fully exploited . One example is the bifunctional folate and pyrimidine-metabolising enzyme dihydrofolate reductase-thymidylate synthase ( DHFR-TS ) . In T . brucei , this enzyme is expressed from a single gene as a homodimer comprising of an N-terminal DHFR domain fused via a linker peptide to a TS domain at the C-terminus . In contrast , DHFR and TS are expressed separately from independent genes in many other organisms , including humans . In trypanosomatids , DHFR catalyses reduction of dihydrofolate ( DHF ) by NADPH to form tetrahydrofolate ( THF ) which is then converted to N5 , N10-methylenetetrahydrofolate ( CH2THF ) , either via the glycine cleavage system or by serine hydroxymethyltransferase ( the latter is absent in T . brucei ) . CH2THF serves as carbon donor for the reductive methylation of deoxyuridine monophosphate ( dUMP ) to form thymidylate ( dTMP ) catalysed by TS [4] . dTMP is ultimately phosphorylated to thymidine triphosphate ( dTTP ) and used for DNA synthesis and DNA repair ( Fig 1 ) . T . brucei can also salvage extracellular thymidine by-passing de novo synthesis . Unlike apicomplexan parasites , trypanosomes lack the ability to synthesise folate and take up serum folate or 5-methyltetrahydrofolate via putative folate transporters . Concentration and retention of folate may involve polyglutamylation as in other organisms , although this has not been established for T . brucei . DHFR-TS is essential for cell survival and has been previously validated ( both genetically and chemically ) as a potential drug target in T . brucei [6] . Despite significant evolutionary separation between protozoa and mammals , T . brucei TS is highly similar to its human homologue with 60% identity and an active site that is identical at the amino acid level . T . brucei DHFR is less well-conserved with only 28% identity with the human enzyme . Indeed , the DHFR domain from several protozoan species has been successfully exploited as a drug target , most notably in the treatment of malaria by the DHF-competitive inhibitors pyrimethamine and cycloguanil [7] which , based on their structural similarity to natural folates , belong to the class of antimetabolites known collectively as the antifolates . These compounds deplete the cellular THF pool , which in turn inhibits dTMP and DNA synthesis resulting in what is known as ‘thymineless-death’ [8 , 9] . To date , antifolates have not been evaluated as chemotherapeutics in animal models of HAT . Newer antifolates such as nolatrexed [10] , pemetrexed [11] and raltitrexed [12] have been designed to directly inhibit TS and have proven useful as cancer chemotherapies; however , these compounds only possess low potency against trypanosomes in thymidine-rich medium [6] . In contrast to Leishmania DHFR-TS , the TS domain of TbDHFR-TS has long proven to be an elusive drug target due to an inability to express the enzyme in an active recombinant form [13] , which precluded a thorough characterisation of its activity and its sensitivity to inhibitors . Here we describe the first successful recombinant production of bifunctional T . brucei DHFR-TS ( TbDHFR-TS ) in the form of a fusion protein incorporating Escherichia coli elongation factor Ts ( Tsf ) [14] . We also biochemically characterise the two activities of TbDHFR-TS and describe their sensitivities to a variety of known inhibitors , along with corresponding in vivo potencies in wild type T . brucei . We show that the challenges faced in recombinant TbDHFR-TS production are the result of instability in the TS domain , rather than proteolysis , as was previously hypothesised [13] , and how a combination of TS-stabilising small molecules and macromolecules can overcome this limitation , suggesting that an as-of-yet unidentified TS-stabilising factor could be present in T . brucei and possibly other species as well . Through comparisons of in vitro and in vivo potencies of known DHFR and TS inhibitors , we also show that additional targets for these compounds remain to be identified in T . brucei .
T . brucei strain 427 was the original source for DNA used in recombinant enzyme production . All reagents were of the highest quality available from Sigma , unless otherwise specified . Recombinant protein expression employed a previously described TS-deficient ( thyA- ) E . coli strain [6] , derived from Invitrogen BL21 Star ( DE3 ) . Restriction enzymes and Pfu DNA polymerase were from Promega . Site-directed mutagenesis was performed using the QuikChange Site-Directed Mutagenesis Kit , Stratagene . DHFR and TS inhibitors were sourced as follows: methotrexate , 5-fluorouracil , 5-fluorodeoxyuridine monophosphate ( FdUMP ) , trimethoprim and pyrimethamine from Sigma Aldrich; nolatrexed , pemetrexed and raltitrexed from Sequoia Research Products; and trimetrexate from Tocris Bioscience . The solubility enhancing factor Tsf [14] was engineered into a modified pET15b expression vector containing a Tobacco Etch Virus ( TEV ) protease recognition sequence in place of a thrombin recognition sequence ( pET15b_TEV ) [15] . The Tsf open reading frame was amplified by PCR from the genomic DNA ( gDNA ) of E . coli strain K12 using specific oligonucleotides ( EcTsf_s and EcTsf_as , S1 Table ) and pfu polymerase . The stop codon in the Tsf gene was replaced with a threonine-encoding ACC codon and the PCR product ( 866 bp ) was cloned into the NcoI restriction site on the pET15b_TEV vector resulting in an expression cassette containing Tsf- ( His ) 6-TEV . The open reading frame of DHFR-TS was amplified by PCR from T . brucei gDNA using specific oligonucleotides ( TbDHFR-TS_s and TbDHFR-TS_as , S1 Table ) . To express TbDHFR-TS on its own or in frame with EcTsf , the PCR product ( 1597 bp ) was cloned into the BamHI restriction site in either pET15b_TEV or pET15b_Tsf-TEV to generate the pET15b_TEV-DHFR-TS and pET15b_Tsf-TEV-DHFR-TS expression constructs , respectively . To create a pET15b_Tsf-TEV-TS fusion construct without the DHFR domain , TS ( 884 bp ) was PCR-amplified using oligonucleotides TbTS_s and TbTS_as ( S1 Table ) from pET15b_Tsf-TEV-DHFR-TS and cloned into the BamHI restriction site on pET15b_Ts-TEV . To express DHFR without the TS domain , a stop codon ( TAA ) , immediately after the last amino acid ( Arg 239 ) of DHFR , was introduced into the above DHFR-TS expression constructs ( using oligonucleotides TbDHFR_mut_s and TbDHFR_mut_as , S1 Table ) by site-directed mutagenesis ( Stratagene ) , as per manufacturer’s instruction . The accuracy of all constructs was verified by DNA sequencing ( http://www . dnaseq . co . uk ) . Expression constructs carrying a TS domain from T . brucei , Leishmania major and human TS ( pET15b_Tsf-TEV-TbTS , pET15b_Tsf-TEV-TbDHFR-TS , pET15b_LmDHFR-TS and pET17b_hTS , respectively ) were expressed in a TS-deficient E . coli strain ( thyA- ) , while TbDHFR without TS ( pET15b_TEV-DHFR ) was expressed in the parental thyA+ strain . Transformants were selected on LB agar plates containing carbenicillin ( 50 μg ml-1 ) . Plates were initially incubated at 37°C for 18 h and those not displaying colonies were incubated at room temperature for a further 3–6 days until colonies appeared . Single colonies were used to set up starter cultures to inoculate 1 litre of auto-induction media [16] containing 50 μg ml-1 carbenicillin . Cultures were incubated at room temperature with shaking at 200 r . p . m for 72 h and aliquots of 50 ml harvested by centrifugation ( 2 , 000 g , 10 min , 4°C ) . Cell pellets were stored at -80°C before use . Pellets were resuspended in lysis buffer ( 100 mM HEPES , 100 mM NaCl , 1 mM EDTA , 1 mM DTT , pH 7 . 0 ) , lysed by sonication ( 3 × 30 s , 10 micron amplitude ) , clarified by centrifugation ( 20 , 000 g , 5 min , 4°C ) and supernatants were analysed for DHFR or TS activity . To purify the recombinant proteins , cultures ( 1 litre ) were harvested by centrifugation ( 2 , 800 g , 30 min , 4°C ) , resuspended in lysis buffer containing cOmplete Protease Inhibitor Cocktail ( Roche ) and lysed using a cell disruptor ( Constant Systems ) at 30 , 000 psi . Lysates were clarified by centrifugation ( 50 , 000 g , 30 min , 4°C ) and recombinant Tsf-TbDHFR-TS purified using methotrexate affinity chromatography , as previously described [13] . To cleave the Tsf tag , TEV protease was used in a 5:1 ( mass to mass ) ratio , estimated from DHFR specific activity , at 4°C for up to three days , either in assay buffer following purification or prior to purification in E . coli lysate treated with up to 40% glycerol . A methotrexate agarose column ( 5 ml ) was loaded by recirculation , monitoring DHFR activity until the column was saturated , and then washed exhaustively with buffers consisting of 50 mM HEPES , 1 M KCl , pH 7 , 10% glycerol , followed by 0 . 5 M KCl , until no further change in absorbance at 280 nM could be detected . Protein was eluted with one column volume of 50 mM HEPES , 0 . 5 M KCl , pH 8 , 10% glycerol with 5 mM DHF . Up to 1 mM dUMP was added to buffers and the column operating temperature reduced to 4°C in an effort to preserve recombinant TS activity . The relative molecular mass of the cleaved recombinant enzyme was determined by size exclusion chromatography on a Superdex 200 column using Bio-Rad gel filtration standards . All animal experiments were approved by the Ethical Review Committee at the University of Dundee and performed under the Animals ( Scientific Procedures ) Act 1986 ( UK Home Office Project Licence PPL 60/4039 ) in accordance with the European Communities Council Directive ( 86/609/EEC ) . T . brucei trypomastigotes were purified from blood of infected Wistar rats by anion exchange chromatography [17] . Parasites were resuspended ( 2 . 5 x 109 cells ml-1 ) in lysis buffer plus cOmplete Protease Inhibitor Cocktail ( see above ) and biologically inactivated by three rapid freeze-thaw cycles before lysis using a one-shot cell disruptor ( Constant Systems ) at 30 , 000 psi . Aliquots ( 500 μl ) were stored at -80°C and clarified by centrifugation ( 20 , 000 g , 20 min , 4°C ) before use . DHFR activity was determined spectrophotometrically at 340 nm [18] . DHFR ( 5 nM ) was pre-incubated with 100 μM NADPH ( Medford ) in assay buffer ( 50 mM HEPES , pH 7 . 4 , containing 100 mM KCl ) for 1 min at 25°C ( 1 ml final assay volume ) , before the addition of 100 μM DHF ( Sigma Aldrich ) . Initial rates were calculated from the combined molar extinction coefficient for NADPH oxidation and DHF reduction ( ε = 12 , 300 M-1 cm-1 ) . The Kmapp values for DHFR substrates were determined by varying the concentration of one substrate in the presence of a fixed saturating concentration of the other . IC50 values were determined using 8-point doubling dilutions of inhibitor under the above standard assay conditions . The initial characterisation of TS was also carried out using a spectrophotometric assay [19 , 20] . Owing to the pronounced instability of the recombinant protein , clarified ThyA- E . coli lysates were used for characterisation , where the concentration of TS was calculated based on DHFR activity . To determine the Kmapp for dUMP , TS ( 200 nM ) was pre-incubated in DHFR-assay buffer containing varying amounts of dUMP ( 1 . 56–100 μM ) in 1 ml assay volumes . Enzymatic reactions were initiated by the addition of CH2THF ( 200 μM , Shircks Laboratories ) and initial rates of CH2THF oxidation to DHF monitored by the increase in absorbance at 340 nm ( ε = 6 , 200 M-1 cm-1 ) . This method is not suitable for determination of the Kmapp for CH2THF and a radiometric method was used instead [21] . This method measures the release of tritiated water from 5-[3H]-dUMP ( American Radiochemicals , 14 . 3 Ci mmol-1 ) . Assays ( 40 μl final volume ) contained 200 nM TS and varying amounts of CH2THF ( 37 . 5 μM– 4 . 8 mM ) in DHFR-assay buffer . Reactions were initiated by adding 200 μM [3H]-dUMP ( 5 . 55 × 105 dpm nmol-1 ) and stopped after 10 min by the addition of 20 μl trichloroacetic acid . Residual 5-[3H]-dUMP was removed by the addition of 200 μl of 10% ( w/v ) activated charcoal ( Sigma ) . Aliquots ( 100 μl ) of the supernatants were added to 2 ml of scintillation fluid ( Pico-Fluor 40™ , Packard Bioscience ) and radioactivity determined using a Beckmann LS 6500 Scintillation Counter . To determine TS activity in clarified T . brucei lysates the incubation time was increased to 30 min and the assay volume was increased to ~200 μl . To assay overexpressed recombinant activity under comparable linear conditions , working stocks of bacterial lysates were prepared by diluting 20- to 40-fold with 200 μM dUMP , unless otherwise noted . For stability experiments all lysates were desalted using 0 . 5 ml Zeba Spin Desalting Columns ( 7K MWCO ) . Protein concentrations were determined using the BioRad protein assay based on the method of Bradford [22] . Inhibitors ( 8-point doubling dilutions ) were assayed using the radiometric method in the presence of 100 μM CH2THF , 200 μM dUMP and 200 nM Tsf-TbDHFR-TS . Results were analysed by non-linear regression using GraFit v 5 . 0 . 13 ( Erithacus Software ) . Kmapp values were determined using the Michaelis-Menten equation . IC50 values were determined using eq 1 and Ki values calculated using the Cheng-Prusoff eq 2 [23] . For tight binding inhibitors , where the Hill slope of the IC50 equation was > 1 , the modified Morrison eq 3 [24 , 25] was used to calculate Kiapp to compensate for the effective reduction in total free enzyme concentration . Equation 1 . IC50 y=100%1+ ( xIC50 ) s ( 1 ) ( where y is the % activity remaining , x the inhibitor concentration and s the slope factor ) Equation 2 . Cheng-Prusoff equation Kiapp=Ki ( 1+[S]Km ) ( 2 ) ( where Km is the Michaelis-Menten constant , S is the substrate concentration and Kiapp is the apparent Ki , expressed here as IC50 , see eq 1 ) Equation 3 . Modified Morrison equation for tight-binding inhibition viv0=1− ( [E]T+[I]T+Kiapp ) − ( [E]T+[I]T+Kiapp ) 2−4[E]T[I]T2[E]T ( 3 ) ( where [E]T is the total enzyme concentration and [I]T the total inhibitor concentration ) . Wild type ( WT ) T . brucei bloodstream-form ‘single marker’ S427 were cultured in HMI9T medium [26] supplemented with 2 . 5 μg ml-1 G418 to maintain expression of T7 RNA polymerase and the tetracycline repressor protein [27] . HMI9T medium ( standard media for T . brucei cell culture ) contains high concentrations of folate ( ~9 μM ) and thymidine ( ~160 μM ) principally from IMDM and 10% Serum Plus components [6] . A medium based on HMI9T , only lacking Serum Plus , folate and thymidine was prepared in-house , named trypanosome base media ( TBM; the residual folate is provided by the serum component ) . A comparison of these media is described in S2 Table . WT T . brucei cells grow normally in TBM and the rate of growth is similar to HMI9T in TBM with no supplementation , supplementation with 9 μM folate , 160 μM thymidine or supplementation with both folate and thymidine ( 7–8 h doubling time ) . EC50 of antifolates against T . brucei were determined in 96-well microtitre plates . Serial doubling dilutions of antifolate drugs ( 10–50 mM stocks prepared in DMSO ) were prepared in 100 μl of the appropriate medium and trypanosomes ( resuspended in the same medium ) added in 100 μl to give a final concentration of 2 . 5 × 103 cells ml-1 . All wells , including controls , contained a final volume of 0 . 5% DMSO . Cultures were incubated for 72 h at 37°C / 5% CO2 before cell density was determined using a resazurin-based assay [28] . EC50 values were calculated using GraFit v 5 . 0 . 13 ( Erithacus Software ) with a 3-parameter non-linear regression from triplicate readings . Antifolates were tested against parasites cultured in TBM; this allowed for the addition of thymidine ( 160 μM ) and folate ( 9 μM ) respectively .
Initial attempts to express recombinant His6-tagged TbDHFR-TS resulted in a low yield of soluble , enzymatically active protein , as previously reported ( Table 1 ) [13] . Although DHFR activity in clarified E . coli lysates was ~100-fold above background ( determined by spectrophotometric assay ) , the equivalent assay was insufficiently sensitive to detect any TS activity . The functionality of TS could only be confirmed by complementation studies using TS-deficient ( thyA- ) E . coli strain ( S1 Fig ) . ThyA- cells were unable to grow in the absence of thymidine supplementation and growth , albeit very slow , was restored in cells transformed with the pET15b_TbDHFR-TS plasmid with bacterial colonies appearing after 3–6 days incubation . In stark contrast , numerous colonies were formed within 18 h for the positive controls of thyA+ E . coli and thyA- cells complemented with L . major DHFR-TS ( S1 Fig ) . Re-plating of TbDHFR-TS-complemented cultures resulted in comparable numbers of colonies as seen with positive controls; however , slow growth persisted . These results , together with the undetectable activity of TS in lysates , suggest that the TS domain of TbDHFR-TS could be highly unstable . SDS-PAGE of E . coli lysates also revealed that the majority of TbDHFR-TS was present in the insoluble pellet of clarified lysates , indicating formation of inclusion bodies . To determine whether the DHFR or TS domain was responsible for the poor solubility , constructs separating the two domains were generated , based upon previously reported boundaries [13] . Expression of the domain-specific constructs resulted in a ~10-fold increased soluble DHFR expression ( Table 1 ) , whereas the independent TS domain failed to complement thyA- E . coli ( S1 Fig ) . Attempts to improve the solubility of TS-active TbDHFR-TS using common fusion partners , such as NusA and thioredoxin , were unsuccessful . As an alternative approach , E . coli elongation factor Ts ( Tsf ) was examined as a solubility-enhancer [14] . Tsf was engineered upstream in frame with the His6-TEV site within the pET15b expression vector , to generate a Tsf expression construct ( pET15b_Tsf-His-TEV ) . This was used as an expression cassette for the bifunctional DHFR-TS and the individual domains ( Tsf-TbDHFR-TS , Tsf-TbTS , Tsf-TbDHFR ) . Functional TS activity of Tsf-TbDHFR-TS was confirmed by its ability to complement thyA- E . coli cells , whereas Tsf-TbTS did not ( S1 Fig ) . These results indicate that the DHFR domain provides a structural contribution for TS to be active , which is consistent with previous findings for Trypanosoma cruzi DHFR-TS [29] and Plasmodium falciparum DHFR-TS [30] , suggesting TS is only functional when in complex with DHFR . DHFR activity in lysates of E . coli expressing Tsf-TbDHFR-TS was ~6-fold more active than those expressing His6-TbDHFR-TS ( Table 1 ) . In addition , TS activity which had proved elusive in the His6-protein could now be detected . Crucially , the addition of dUMP to assay buffer prior to addition of the enzyme appeared to enhance TS activity . The possible role of dUMP in the stabilisation of TS was therefore further investigated . TS activity in clarified bacterial lysates was stable for up to 72 h at 4°C , whereas less than 5% TS activity was retained if small molecules and metabolites ( <1 , 000 Da ) were removed using a desalting spin column . The addition of 200 μM dUMP immediately following desalting preserved the activity of TS , consistent with several other studies reporting substrate-mediated TS stabilisation by dUMP in other organisms [31–34] . In some cases dUMP is reported to have a synergistic action with CH2THF; however , in the case of TbDHFR-TS , there was no stabilisation observed with CH2THF , possibly due to the low affinity for this substrate ( see below ) . Other pyrimidine nucleotides , including the uracil-containing ribonucleotides and deoxyribonucleotides , and the thymidine-containing deoxyribonucleotides , were also unable to stabilise TS . Furthermore , common stabilisers such as 10% glycerol , 1% BSA and 1 mM EDTA were ineffective in preserving TS activity . To determine if the oxidation of the TS catalytic cysteine could be a reason for its inactivation , 2-mercaptoethanol ( 10 mM ) was tested . However , not only was the reducing agent ineffective , it was found to inhibit TS activity at higher concentrations ( EC50 ~100 mM ) , suggesting cysteine oxidation is probably not the cause of TS-inactivation . Thus , preservation of TS activity seemed to be specific for dUMP . To characterise the stabilisation of recombinant T . brucei TS by dUMP , clarified thyA- E . coli lysate containing Tsf-TbDHFR-TS was diluted 100-fold into assay buffer containing 100 μM CH2THF and pre-incubated for different times before initiation of the reaction by the addition of dUMP ( Fig 2A ) . In the absence of dUMP following desalting , TS activity decayed rapidly with only 1% residual activity remaining after 3 min . The data was fitted to a single exponential decay yielding a rate constant of 1 . 46 ± 0 . 11 min-1 , from which a half-life of inactivation ( t½ ) of 28 ± 2 s can be derived ( Fig 2A , inset ) . The half maximal concentration of dUMP required to stabilise TS activity in the absence of CH2THF was 12 . 6 ± 2 . 5 μM ( Fig 2B ) . Over longer incubation times the addition of 200 μM dUMP markedly increased the stability of the diluted enzyme by 700-fold ( t½ = 330 min ) , but failed to completely stabilise TS activity ( Fig 2A ) . The stabilising effect of dUMP on Tsf-TbDHFR-TS was compared with LmDHFR-TS and human TS expressed in thyA- E . coli as controls ( Fig 2A ) . Both trypanosomatid enzymes were found to be relatively unstable compared to the mono-functional human TS . Human TS showed little or no activity loss over a five hour period in either the presence or absence of dUMP . In contrast , LmDHFR-TS behaved more like TbDHFR-TS , but was ~20-fold more stable than TbDHFR-TS ( t½ = 9 . 7 min ) . TbDHFR-TS was ultimately only partially stabilised by dUMP , as evidenced by the loss of 50% total activity over a five hour period , whereas LmDHFR-TS was effectively stable in presence of dUMP . To establish if instability of TbDHFR-TS was also the case with the native enzyme , DHFR and TS activity in lysates of T . brucei and thyA- E . coli expressing Tsf-TbDHFR-TS were compared ( Table 2 ) . Tsf-TbDHFR-TS in desalted lysates is considerably less stable at higher temperature , hence , lysates were incubated at 37°C for 1 h prior to activity determination . Before incubation , the ratio of DHFR- to TS-activity for Tsf-TbDHFR-TS was calculated to be 53:1 . Both DHFR and TS activities in the recombinant enzyme decreased drastically following incubation . Once again , the addition of dUMP improved the stability of TS while DHFR activity was unaffected . The ratio of DHFR:TS activity ( 5:1 ) of the native enzyme before incubation was in good agreement with values previously reported for T . brucei gambiense and T . lewisi lysates [35] . DHFR activity of the native DHFR-TS remained unchanged after incubation , while the decrease in TS activity was comparably less drastic compared to the recombinant enzyme . The addition of dUMP to T . brucei lysates , however , did not protect against loss of TS activity . These results suggest that the T . brucei endogenous TbDHFR-TS does not suffer the same inactivation as the recombinant enzyme . In the event that a hitherto unknown TS activating factor might be present in T . brucei lysate , native and recombinant clarified lysates were combined in a 1:1 ratio; this however resulted in no appreciable improvement in stability . Having established the importance of dUMP in stabilising recombinant TbDHFR-TS , it was subsequently included in all buffers used for the purification of Tsf-TbDHFR-TS and its TEV-cleaved counterpart . Despite the presence of dUMP , TS activity was still completely lost during purification regardless of whether TEV cleavage occurred at the start or end of the procedure . Cleavage of Tsf-TbDHFR-TS prior to purification was only possible when glycerol was added as a stabiliser to prevent rapid protein precipitation . Cleaved TbDHFR-TS was then purified by methotrexate agarose affinity chromatography to near homogeneity in ~10% yield , along with some residual un-cleaved Tsf-TbDHFR-TS ( Fig 3A ) . The specific activity for the purified enzyme was 24 . 3 U mg-1 for DHFR , with no detectable TS activity . The cleaved protein behaved as a homodimer on size exclusion chromatography ( Fig 3B ) and sequence identity confirmed by mass spectrometry fingerprinting with >70% sequence coverage , including the C-terminal amino acid shown to be crucial for TS activity [36] . MALDI-TOF determination of the exact total mass was not possible due to difficulties associated with desorption , although low-resolution data also confirmed the presence of dimeric TbDHFR-TS . In comparison , affinity chromatography of the un-cleaved Tsf-tagged TbDHFR-TS resulted in a ~7-fold greater yield , suggesting Tsf significantly improved the stability of this enzyme . Following elution from the methotrexate column , some minor contaminating proteins were visible by SDS-PAGE; these were identified by mass spectrometry fingerprinting as Ef-Tu , the binding partner of the Tsf tag , and E . coli hsp90 . The latter could be removed by washing the column with ATP prior to elution with methotrexate . In contrast , the control protein ( LmDHFR-TS ) could be purified to homogeneity with retention of TS activity ( DHFR 21 . 2 U mg-1; TS 0 . 89 U mg-1 ) . Purified Tsf-TbDHFR-TS was subsequently used for the kinetic characterisation of DHFR domain and clarified crude lysates for the TS domain ( S2 Fig ) . Using the spectrophotometric assay , DHFR displayed a classical bell-shaped pH-optimum profile , with an optimal pH of ~5 . 5 ( S2A Fig ) . In contrast , TS had a pH optimum of 7 . 0 . The optimal ionic strength for both enzymes required 100 mM KCl , with DHFR displaying 2 . 5-fold activation and TS 4 . 5-fold activation ( S2B Fig ) . This is consistent with previously reported KCl-dependent activation of this enzyme , although our pH optimum profiles disagree [13] . TS could also be activated 4 . 5-fold with 10 mM MgCl2 ( 30 mM ionic strength ) and this effect was not additive with activation by KCl . Higher concentrations of MgCl2 were inhibitory to TS , consistent with a previous report [37] . Since the intracellular pH of T . brucei has been reported to be 7 . 4 [38] , a standardised assay buffer consisting of 50 mM HEPES , pH 7 . 4 and 100 mM KCl was used for all subsequent studies . Under these conditions , Tsf-TbDHFR-TS obeys simple Michaelis-Menten kinetics with all four substrates ( Fig 4 ) . The Kmapp of Tsf-TbDHFR-TS and Tsf-cleaved TbDHFR-TS for DHF were identical ( 4 . 1 ± 0 . 6 μM for Tsf-TbDHFR-TS and 4 . 2 ± 0 . 5 μM for the cleaved enzyme ) demonstrating that the tag did not interfere with the enzyme activity . The catalytic efficiency for reduction of DHF was 6 . 8 x 106 M-1 s-1 consistent with DHFR from other organisms ( Table 3 ) . Active site titration with methotrexate confirmed DHFR activity corresponding to one site per monomer . Folic acid and the structurally related pterins ( biopterin , dihydrobiopterin , sepiapterin and neopterin ) were inactive as substrates for T . brucei DHFR ( <17 , 000-fold and <2 , 300-fold compared to DHF as substrate for folate and pterins , respectively ) . The inability of TbDHFR-TS to reduce folate is in agreement with the L . major enzyme [39]; folate is presumed to be reduced to DHF by PTR1 ( Fig 1 ) . The Kmapp for dUMP ( 8 . 2 ± 0 . 6 μM ) is in good agreement with TS from other organisms and is consistent with the half maximal concentration of dUMP ( 12 . 6 ± 2 . 5 μM ) required for stability of TS . In contrast , the Kmapp values for CH2THF were considerably more variable between TS from various organisms . The affinity of the T . brucei enzyme was more similar to that from the more distantly related C . fasciculata [37 , 41] ( Table 3 ) . The catalytic efficiency ( 1 . 5 x 103 M-1 s-1 ) was considerably lower compared to those reported for recombinant T . cruzi [29] and L . major [44] enzymes . These discrepancies could be due to the inherent instability of the T . brucei enzyme or due to competing metabolism of CH2THF by other enzymes in the crude thyA- E . coli lysate Known inhibitors of DHFR and TS from other organisms were tested for their potencies against recombinant Tsf-TbDHFR-TS ( Table 4 ) . The greatest degree of TS inhibition was seen with 5-fluorodeoxyuridine monophosphate ( FdUMP ) , a dUMP-competitive TS-specific inhibitor which displayed tight-binding inhibition . The most potent DHFR inhibitors were the classic antifolate methotrexate and the trimethoxy-substituted trimetrexate . These compounds were found to be tight-binding inhibitors with picomolar Ki values , while other antifolates exhibited linear competitive inhibition with respect to the substrate , DHF . The diaminopyrimidine antifolates trimethoprim and pyrimethamine were found to specifically inhibit DHFR , as did the diaminoquinazoline trimetrexate . Trimethoprim , pyrimethamine and raltitrexed were found to behave as potent competitive inhibitors of DHFR with Ki values of 11 . 4 ± 1 . 2 , 17 . 6 ± 2 . 3 and 70 . 4 ± 7 . 2 nM , respectively , ( Fig 5 ) . These values are in good agreement with those determined using the IC50 method in Table 4 . Other antifolates possessed varying degrees of both DHFR and TS inhibition . Apart from the TS substrate analogue FdUMP , the only inhibitor that possessed greater inhibition of TS than DHFR as tested was nolatrexed , showing 10-fold TS selectivity . Chemical structures of the inhibitors are shown in Fig 6 . Antifolate drugs had highest potency against bloodstream forms of T . brucei when tested in a medium deficient in folate and thymidine , with the exception of the lipophilic drug nolatrexed where potency did not change between media types ( Table 5 ) . Indeed methotrexate , pemetrexed and raltitrexed possess nanomolar potency in a thymidine and folate deficient media . The addition of folate and thymidine reduced the potencies of the antifolates , except nolatrexed . For methotrexate , pyrimethamine and trimethoprim the addition of folate had a greater effect in reducing potency than the addition of thymidine . For raltitrexed and pemetrexed the addition of thymidine had a greater effect in reducing drug potency than the addition of folate . For the lipophilic inhibitor trimetrexate the addition of folate or the addition of thymidine had a comparable effect on reducing drug potency .
The TS activity of recombinant TbDHFR-TS is highly unstable ( t½ 28 s ) compared to other organisms , with the T . brucei enzyme proving to be the least stable TS yet reported . Addition of dUMP increases enzyme stability , as in other organisms , but proved insufficient to achieve purification of active enzyme . Other stabilising agents , including mercaptoethanol , did not prevent inactivation , unlike human TS that can be stored at 4°C for 3 months without loss of activity [50] . This remarkable instability could account for the inefficient complementation and slow growth of TS-deficient E . coli expressing TbDHFR-TS . However , the basis for instability is not known . A previous report suggested that sequential degradations at the C-terminus together with internal cleavage in the TS domain may be responsible [13] . Our purified recombinant protein showed no evidence of proteolytic cleavage by either SDS-PAGE or MALDI-TOF MS and the C-terminus of TS was identified by MS fingerprinting , including the final residue required for catalysis . Thus , proteolysis can be discounted , as can oxidation of the catalytic cysteine since thiols did not stabilise the protein . Other possible stabilising agents include parasite-specific interacting macromolecules ( e . g . mRNA or protein chaperones ) , or parasite-specific post-translational modifications . Further research is required to test these possibilities . A variety of DHFR and TS inhibitors were examined using bifunctional recombinant TbDHFR-TS , all of which , apart from FdUMP , can be categorised as antifolates . Overall , T . brucei DHFR appears to be more exploitable in terms of selective inhibition over the human homologue than TS . In the current study , the only molecules to possess picomolar Ki values against DHFR were trimetrexate and methotrexate , consistent with a previous report [51] . Maximal methotrexate potency in vivo was found to be dependent on the absence of thymidine from T . brucei growth media , thus confirming that thymineless death is part of its mode of action . However , the inability of thymidine to completely reverse methotrexate toxicity suggests that additional targets also exist beyond DHFR and TS . One likely candidate is pteridine reductase 1 ( Kiapp 11 . 1 nM ) [51] , another validated target in these parasites [52 , 53] . Compared to methotrexate , the pronounced potency of trimetrexate against DHFR is not reflected against whole cells likely due to the fact that trimetrexate is lipophilic and lacks a terminal glutamyl moiety for polyglutamylation and increased retention in the trypanosome ( Fig 6 ) . Trimethoprim and pyrimethamine are both competitive inhibitors with intermediate potency against T . brucei DHFR . Our Ki values are 30- to 60-fold higher than first reported [13] , but consistent with a subsequent report [48] . For these diaminopyrimidines , this translates to modest selectivity at best between parasite and host DHFR , compared to the > 100 , 000-fold selectivity of the antibacterial trimethoprim between human and E . coli DHFR [54] . A variety of novel diaminopyrimidines reported by Chowdhury et al . have been shown to be potent against T . brucei DHFR with nanomolar Ki values and selectivity up to 610-fold over the human enzyme [48] . However , toxicity and poor in vivo potency were limitations associated with these compounds . Both trimethoprim and pyrimethamine displayed a marked drop-off in potency from target to cell , when cultured in a medium deficient in thymidine and folate . Like trimetrexate these are lipophilic antifolates and do not contain a terminal glutamyl moiety . The addition of thymidine had little impact on cell potency of pyrimethamine or trimethoprim , implying that thymineless death is not their sole mode of action . This is consistent with a previous report which showed that the potency of pyrimethamine was not significantly affected by knocking out dhfr-ts [6] . With regards to antifolates possessing selectivity for TS over DHFR , compounds with a primary amine at position 4 correlated with stronger inhibition of DHFR , as was expected from previous reports , whereas a carbonyl substituent in this position is known to favour TS inhibition by means of additional hydrogen bonding provided by the oxygen atom [55] . TS-targeted antifolates also frequently include a terminal glutamyl moiety which is polyglutamylated in vivo by the enzyme folylpolyglutamyl synthetase ( FPGS ) [56] . This results in tighter binding to TS , with little effect on DHFR , and improved cellular retention . Although a candidate gene for FPGS is present in the T . brucei genome [57] , it has not yet been studied in this species; however , in the related trypanosome Leishmania , intracellular folates possess on average 3–5 glutamates [58] , thus this is likely also the case in T . brucei . Of the TS-targeted antifolates tested , the only compound which cannot exploit polyglutamylation was nolatrexed . Without the need for polyglutamylation , nolatrexed was found to be 10-fold TS selective; however , in vivo data showed moderate whole cell potencies . By comparison , the monoglutamate forms of raltitrexed and pemetrexed , generally thought of as TS-targeted antifolates in their polyglutamate forms , were found to be more potent against DHFR than TS , as has previously been observed with the monofunctional human enzymes [11 , 12] . Raltitrexed and pemetrexed are more potent against whole parasites than they are against DHFR or TS suggesting that polyglutamylation is likely to occur inside T . brucei . Off-target effects can be discounted since the trypanocidal action of these drugs is completely abrogated by thymidine and suggests that these inhibitors are likely to be TS-specific in vivo . Further work is required to substantiate this hypothesis . Based on our observations regarding the inhibition of DHFR and TS by known antifolates , and their potency in vivo with regards to thymidine bypass , it is clear that additional targets must exist in T . brucei . Given their structural similarity to folate metabolites , possible alternative targets would be the FPGS , the glycine cleavage system , methionine synthase ( Fig 1 ) or the bifunctional N5 , N10-methylenetetrahydrofolate dehydrogenase-N5 , N10-methenyltetrahydrofolate cyclohydrolase ( DHCH ) . If the inhibition of one or more of these proposed targets can be identified then they could potentially be explored as alternative targets for the disruption of folate metabolism , which could be of interest for future drug discovery should TbDHFR-TS prove difficult to exploit . | There are few validated and fully characterised targets suitable for drug discovery against African trypanosomes , causative agents of sleeping sickness in humans and nagana in cattle . Here we report the biochemical properties of the bifunctional enzyme , dihydrofolate reductase–thymidylate synthase ( DHFR-TS ) , and its susceptibility to a range of classical inhibitors normally used in the treatment of cancer , bacterial or protozoal infections . Some of these drugs are extremely potent against the isolated enzyme , but much less so against the intact trypanosome . We have found that modulating certain medium components can affect drug sensitivity , presumably by either competition for uptake and competition for the active site of DHFR-TS . In the case of one human TS inhibitor ( raltitrexed ) the inhibitor is more potent against the intact parasite . We propose that addition of extra glutamic acid residues not only improves retention in the cell , but also increases potency against TS , as it does in human cells . |
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Cytomegalovirus ( CMV ) infection causes birth defects and life-threatening complications in immunosuppressed patients . Lack of vaccine and need for more effective drugs have driven widespread ongoing therapeutic development efforts against human CMV ( HCMV ) , mostly using murine CMV ( MCMV ) as the model system for preclinical animal tests . The recent publication ( Yu et al . , 2017 , DOI: 10 . 1126/science . aam6892 ) of an atomic model for HCMV capsid with associated tegument protein pp150 has infused impetus for rational design of novel vaccines and drugs , but the absence of high-resolution structural data on MCMV remains a significant knowledge gap in such development efforts . Here , by cryoEM with sub-particle reconstruction method , we have obtained the first atomic structure of MCMV capsid with associated pp150 . Surprisingly , the capsid-binding patterns of pp150 differ between HCMV and MCMV despite their highly similar capsid structures . In MCMV , pp150 is absent on triplex Tc and exists as a “Λ”-shaped dimer on other triplexes , leading to only 260 groups of two pp150 subunits per capsid in contrast to 320 groups of three pp150 subunits each in a “Δ”-shaped fortifying configuration . Many more amino acids contribute to pp150-pp150 interactions in MCMV than in HCMV , making MCMV pp150 dimer inflexible thus incompatible to instigate triplex Tc-binding as observed in HCMV . While pp150 is essential in HCMV , our pp150-deletion mutant of MCMV remained viable though with attenuated infectivity and exhibiting defects in retaining viral genome . These results thus invalidate targeting pp150 , but lend support to targeting capsid proteins , when using MCMV as a model for HCMV pathogenesis and therapeutic studies .
Cytomegalovirus ( CMV ) is a member of the β-herpesvirus subfamily of the herpesvirus family and can establish lifelong subclinical ( latent ) infection among the majority of the human population . Active human CMV ( HCMV ) infection is the leading viral cause of birth defects ( in utero and in neonates ) and often the culprit of life-threatening complications in immunocompromised individuals , such as organ transplant recipients and AIDS patients . Currently , there is no licensed vaccine against HCMV infection and conventional anti-HCMV drugs have well-known adverse side effects and are compromised by resistance [1] . These factors call for novel approaches toward vaccine design and drug development against HCMV infections . Thanks to similarities in pathology and disease manifestation caused by CMV infections of humans and mice [e . g . , 2 , 3–5 , and review 6] , pathogenesis studies and therapeutic developments have often relied on murine CMV ( MCMV ) as a model to evaluate the efficacy of lead compounds [7–9] and vaccine candidates [10 , 11] . Recent high-resolution cryoEM structures of human herpesviruses [12–14] , particularly the demonstration of inhibitors designed based on the structure of small capsid protein ( SCP ) [12 , 13] , have opened the door to structure-guided design of new drugs and vaccines targeting HCMV capsid proteins and the β-herpesvirus-specific tegument protein pUL32 [or phosphoprotein pp150 , see review 15 , 16–18] . Rationales for targeting pUL32 are manifold: First , it is essential to HCMV propagation [19 , 20]; Second , pUL32 is unique to human β-herpesviruses; Third , pp150 has been shown to be the most immunogenic in clinical setting [21]; Fourth , from a general point of view , regulatory functions of protein phosphorylation have been targeted against cancers [22] and viral infections , including HCMV [23] . The cryoEM reconstruction of HCMV at 3 . 9 Å resolution [14] reveals that pUL32 forms a unique capsid-binding tegument layer , likely to secure encapsidation of its dsDNA genome of 235 kbp , which is the largest among all herpesviruses . Particularly , 320 groups of three pUL32nt subunits form a “Δ”-shaped fortifying structure on every triplex . pUL32 is an abundant and immunogenic protein that is essential for HCMV virion egress and maturation [19 , 24] . Yet careful examination of their genomes suggests there might be structural differences between HCMV and MCMV . For example , HCMV pUL32 sequence is about 40% longer than pM32 ( the homolog of pUL32 in MCMV ) [25] , suggesting that an examination of the structure of MCMV in detail may be fruitful in assessing the similarities and differences between HCMV and MCMV . Therefore , the functional and structural significance of pM32 remains to be established , in stark contrast to the large body of MCMV-based cell and animal studies concerning CMV infections . Here , by cryoEM and sub-particle reconstructions , we have obtained structures of the MCMV capsid and its associated pM32 ( pp150 ) at near-atomic resolutions and built their atomic models , the first for any MCMV proteins . Comparison of the virion structures of MCMV and HCMV reveals that the patterns of pp150 binding to capsid differ between HCMV and MCMV , despite highly similar structures of their capsid proteins , including SCP . The atomic details underlying pp150-pp150 and pp150-capsid protein interactions rationalize the different capsid-binding patterns of pp150 in MCMV and HCMV . Our mutagenesis studies further establish pp150’s varying levels of functional significance in MCMV and HCMV infections . These results thus establish the validity of using MCMV as HCMV model for pathogenesis and therapeutic studies when targeting capsid proteins , but raise concerns when targeting pp150 .
A technical challenge of determining the structure of herpesvirus particles is the enormous size of herpesvirus virions exceeding 200 nm in diameter , creating a focus gradient across the large sample thickness needed to fully embed the virion and the breakdown of the Central Projection Theorem due to a curved Ewald-sphere [14 , 26] . Initial cryoEM reconstruction from the 1 , 200 MCMV virion particle images ( e . g . , S1A–S1D Fig ) recorded on a CCD camera was limited to ~12 Å resolution ( S1E Fig ) . To alleviate the Ewald-sphere curvature effect [26 , 27] , we attempted to reduce the sample thickness by partially solubilizing the viral envelope through mild detergent treatment ( S2A Fig ) . From a total of 47 , 982 particles of MCMV virions from 2 , 200 300kV cryoEM micrographs recorded on photographic films ( e . g . , S2A Fig ) , we obtained an icosahedral reconstruction at ~5 Å resolution ( Fig 1A and S1 Movie ) . Local resolution assessment by Resmap ( S2B Fig ) [28] indicates that densities of the capsid shell and especially near the base of the capsomers have the best resolution ( from 4 . 0 Å to 4 . 5 Å ) and that densities at the outmost radii and inside the capsid shell ( i . e . , DNA genome ) have resolutions worse than 4 . 5 Å , likely due to a combination of structural heterogeneity/flexibility ( for DNA-related densities ) and the more severe Ewald-sphere curvature effect for densities at larger radii . Though at different resolutions , both the early reconstruction from CCD images of intact virions and the higher resolution reconstruction from detergent-treated virions show identical structural organizations of tegument and capsid proteins ( S1E Fig and Fig 1A ) . The ~5 Å 3D reconstruction shows a highly conserved T = 16 icosahedral virion capsid revealing the molecular boundaries among 12 pentons , 150 hexons , 320 triangular triplexes and 260 pM32 ( pp150 ) dimers ( Fig 1A and 1B ) , allowing identification of individual molecules ( Fig 1C and 1D ) . Imposing icosahedral symmetry during 3D reconstruction both weakens the density and lowers the resolution of regions with deviation from strict icosahedral symmetry . One of the twelve icosahedral vertices of the herpesvirus capsid does not contain a penton , but a DNA packaging/ejection portal complex [29–33]; thus , tegument densities interacting with pentons are weaker than those interacting only with hexons . The 3-fold symmetrized tegument densities associating with triplex Tf are also weakened and are only visible when displayed at a lower density threshold ( see S3 Fig for a sub-particle reconstruction of this region showing a pp150 dimer attached to triplex Tf ) . The structural components within an asymmetric unit encompass 1/5 of a penton capsomer , 2 . 5 hexon capsomers ( 1 P hexon , 1 C hexon , and 1/2 of an E hexon ) , 5 and 1/3 triplexes/pM32 dimers ( Ta , Tb , Tc , Td , Te , and 1/3 Tf ) ( Fig 1C ) [34] . To overcome the aforementioned problem of weakened densities and limited resolutions , we have used a sub-particle reconstruction strategy to obtain structures of the MCMV capsid and its associated pM32 at near atomic resolutions ( Fig 2B–2E , S3–S8 Figs , S2–S4 Movies ) and also built atomic models for tegument protein pM32 and all capsid proteins ( S9–S12 Figs ) . In particular , our sub-particle reconstruction of the region around the 3-fold axis shows that pM32 on triplex Tf also exists as a dimer ( S3 Fig ) , as with triplexes Ta , Tb , Td , and Te ( Fig 1A–1D ) . Notably , all previous structural studies failed to establish the directionality of Tf . Our result suggests the underlying triplex Tf lacks strict 3-fold symmetry , as the other triplexes , thus resolving a historic mystery about the chemical composition of Tf . The improved resolution of the sub-particle reconstructions allowed us to build de novo atomic models for the N-terminal one-third portion of pM32 ( pM32nt ) and the four capsid proteins [the major capsid protein ( MCP ) , the small capsid protein ( SCP ) , the triplex monomer protein ( Tri1 ) , the triplex dimer protein ( Tri2 ) ] . In total , we have built atomic models of 55 unique conformers , including 16 MCP , 16 SCP , 5 Tri1 , 5 Tri2A , 5 Tri2B and 8 pM32nt ( Fig 2H ) , amounting to over 27 , 000 amino acid residues . As detailed below , the atomic models of pM32 of MCMV and pUL32 of HCMV differ , though those for their capsid proteins are similar , with root-mean-square deviation ( RMSD ) distances between corresponding capsid protein models ranging from 1 . 95 to 3 . 36Å ( 2 . 47 Å for penton MCP , 2 . 09 Å for hexon MCP , 1 . 95 Å for SCP , 3 . 36 Å for Tri1 , 2 . 05 Å for Tri2A , and 2 . 10 Å for Tri2B ) . Like that of HCMV [14] , the structure of the 1 , 353 a . a . long ( 149 kDa ) MCP monomer of MCMV consists of seven domains ( Fig 3A ) : upper ( a . a . 477–1015 ) , channel ( a . a . 398–476 and 1303–1353 ) , buttress ( a . a . 1090–1302 ) , helix-hairpin ( a . a . 190–233 ) , dimerization ( a . a . 291–362 ) , N-lasso ( a . a . 1–59 ) , and a bacteriophage HK97-like ( “Johnson” ) -fold ( a . a . 60–189 , 234–290 , 363–397 , and 1016–1089 ) . These domains are located in the tower ( upper , channel , and buttress domains ) and the floor ( helix-hairpin , dimerization , N-lasso , and Johnson-fold domains ) regions of each capsomer subunit ( Fig 2H ) . Because the Johnson-fold domain of MCMV MCP corresponds to the entire molecule of the HK97 MCP ( gp5 ) [35] , segments corresponding to HK97 gp5’s four domains—axial ( A ) , extended loop ( E loop ) , peripheral ( P ) , and spine helix—are designated as A , E-loop , P , and spine helix sub-domains , respectively ( Fig 3B ) . MCP monomers in hexons and pentons have distinct conformations . For instance , the sequence segment corresponding to the helix-loop-helix motif in the buttress domain of hexon MCP ( Fig 3C ) folds into a single long helix in penton MCP ( Fig 3D ) . As in HCMV [14] , there are three notable types of network interactions in the MCP floor regions ( Fig 3E and 3F ) . Type I interactions are intra-capsomeric β-sheet augmentations which occur between two adjacent MCPs within a capsomer . As exemplified by C4 and C5 MCPs in Fig 3F ( left panel ) , two β-strands from the E-loop of Johnson-fold domain and one β-strand from the dimerization domain of C5 MCP are joined by two β-strands from the N-lasso domain of C4 MCP , resulting in a five-stranded β-sheet ( Fig 3F , left panel ) . Type II interactions are inter-capsomeric interactions that occur between two pairs of α-helices in the dimerization domains of MCPs across local 2-fold axes , as illustrated by E2 and C5 MCPs in Fig 3F ( middle panel ) . Like type II interactions , type III interactions are inter-capsomeric interactions , but are formed by three MCPs featuring the lassoing action of the N-lasso domain ( E1 ) , which extends out and lashes around an E-loop ( C5 ) and a N-lasso neck ( C4 ) of two type I interacting MCPs located across a local 2-fold axis ( Fig 3F , right panel ) . Additionally , a small helix bundle is formed from an α-helix from E1 N-lasso , two α-helices from the helix-hairpin domain of C5 MCP , and an α-helix from the buttress domain of C5 MCP , further securing the E1 N-lasso ( Fig 3F right panel ) . Our model of SCP encompasses residues 36–95 of the 98 a . a . long M48 . 2 gene product and consists of three 3 . 5-turn α-helices and two connecting loops , folded into a triangular spiral with the N-terminal helix ( H1 ) pointing outwards ( Fig 4A ) . An MCP monomer binds an SCP monomer to form a heterodimer ( Fig 4B ) , which constitutes one of the five and six subunits in penton and hexon capsomers , respectively ( Fig 4C ) . The H3 helix of SCP inserts into a deep groove in a region of MCP upper domain rich in α-helices and loops ( Fig 4D ) . Sequence-based surface analysis indicates that predominantly hydrophobic interactions contribute to MCP-SCP binding ( Fig 4E ) . Each triplex is a heterotrimer containing one unique conformer of Tri1 and two conformers of Tri2—Tri2A and Tri2B—that “embrace” each other to form a dimer ( Fig 5A–5D and 5F–5G ) . Tri2 monomer consists of three domains: clamp ( a . a . 1–88 ) , trunk ( a . a . 89–183 and 291–311 ) , and embracing arm ( a . a . 184–290 ) . While the clamp and trunk domains in Tri2A and Tri2B are nearly identical ( RMSD is only 1 . 12 Å ) and are superimposable by a ~120° rotation about the local 3-fold axis ( Fig 5F , right panel ) , their embracing arms differ by a ~45° bend with an RMSD of 3 . 69 Å ( Fig 5H ) . Clung to the side of the two embracing Tri2 subunits is the Tri1 monomer , which consists of three domains: N-anchor ( a . a . 1–45 ) , trunk ( a . a . 46–171 ) , and third-wheel ( a . a . 172–294 ) ( Figs 2H and 5D ) . The helix-loop-helix-loop motif of the N-anchor domain penetrates the capsid floor near its local 3-fold axis such that both its helices fill the valley between the P-subdomain β-sheet and the spine helix of a Johnson-fold domain of an MCP subunit ( Fig 5C and 5E , and S1 Movie ) . Thus , N-anchor anchors Tri1 and the entire triplex from inside the capsid beneath the MCP floor , simultaneously sealing the hole at the local 3-fold axis where three neighboring MCP P-subdomains assemble . Notably , such “internally anchored” interactions could be pressure-fortified [12] , i . e . , when the N-anchors are pressed against the MCP floor region from within the capsid by incoming DNA during genome packaging , the capsid floor would be better sealed and further strengthened , rather than weakened [12] . The reconstruction densities exhibit different patterns of tegument-capsomer association between MCMV and HCMV ( Fig 6A and 6B ) . Our MCMV reconstructions show 260 tegument densities ( Fig 6A ) , as opposed to the 320 tegument densities of HCMV ( Fig 6B ) [14 , 36] . These different patterns and reduced number of the tegument densities in MCMV are not due to detergent treatment as reconstruction of intact MCMV virions at 12Å resolution shows identical structures ( cf . S1F and S1G Fig ) . In addition , their detailed structures differ , existing as a group of two subunits in MCMV ( Fig 6A ) and a group of three in HCMV ( Fig 6B ) , with arrangements resembling the Greek letters “Λ” and “Δ” , respectively . Moreover , as shown in yellow in Fig 6A , pM32 does not bind to triplex Tc in the MCMV capsid . Neither are pM32 density connections observed between edge and facet capsomers . Specifically , pM32 dimers of the edge type join P hexons , E hexons , ( S13 Fig ) and pentons on the 30 edges of the icosahedral capsid to triplexes Ta , Tb , and Td ( Figs 1B and 6A ) , while pM32 dimers of the facet type bind three C hexons together in the center of each icosahedral facet to triplexes Te and Tf ( Figs 1B and 6A , and S13 Fig ) . In contrast , all triplexes of the HCMV nucleocapsid—including triplexes Tc—are occupied by three pUL32 subunits arranged as a dimer and a monomer of pUL32 . Atop each triplex , dimeric pUL32 branches out to interact intimately with their closest respective capsomer subunits , in an analogous fashion to the “Λ”-shaped tegument densities in MCMV ( Fig 6B ) . However , the third ( monomeric ) copy of pUL32 in HCMV bridges the top of the triplex with a third neighboring capsomer ( Fig 6B ) . This monomeric tegument density , in conjunction with the presence of pUL32 subunits above triplex Tc , facilitates connections between the edge and facet capsomers of HCMV ( Fig 6B ) [36] not observed analogously in MCMV . Our atomic models of MCMV pM32 contain approximately 1/3 of the full-length ( 718 a . a . ) protein at its N-terminal region ( pM32nt-a , consisting of a . a . 66–87 , 91–105 , 137–150 , and 173–295; pM32nt-b , consisting of a . a . 66–113 , 128–156 , and 173–295 ) . These regions correspond to all visible densities in the cryoEM maps , suggesting that the rest of the pM32 protein is flexible . Similar to pUL32nt of HCMV , pM32nt of MCMV is dominated by α-helices ( Fig 7A and S14 Fig ) and characterized by upper and lower helix bundles joined by a central long helix ( ~69 Å in length , a . a . 208–253 ) ( Figs 2H and 7A ) . We also identified the conserved region 1 ( CR1 ) and region 2 ( CR2 ) in MCMV pM32nt ( Fig 7A ) . However , only one cysteine , rather than four , was identified in pM32’s equivalent sequence of HCMV pUL32’s cys tetrad ( Fig 7A ) [25] . All bound pM32s are dimerized in MCMV and can be classified into two types , either pM32-a ( cyan ) or pM32-b ( orange-red ) , based on their relative locations on the capsid ( Fig 1C and 1D ) . The subunits of pM32nt dimers cluster on each triplex and lean against two neighboring MCPs ( S15A Fig ) . pM32nt-a and pM32nt-b are similar in structure ( the magnitude of RMSD is 1 . 20 Å ) ( Fig 7B ) and form a “Λ”-shape configuration . As shown in S15B and S15C Fig , the two conformers interact with capsid proteins via hydrophobic and/or hydrophilic interactions , bearing a marked resemblance to those in HCMV ( S15E and S15F Fig ) . Careful comparison of the atomic model of pM32 dimer and the corresponding pUL32 subunits in HCMV ( for example , pM32 and pUL32 dimer from triplex Te regions ) reveals that 17 residues in the pM32-pM32 interface are within 3 Å of each other , as opposed to only 4 residues at the pUL32-pUL32 interface ( Fig 7C ) . The existence of 13 additional residues at the molecular interface of the pM32 dimer indicates a stronger and more rigid pM32-pM32 association than between pUL32-pUL32 , which is congruent with the pairwise presence/absence of pM32 subunits on MCMV capsid . This also supports the observation that pM32 exists only as a dimer in our reconstructions in contrast to the existence of both monomeric and dimeric forms of pUL32 in HCMV [14] . Additionally , rigid-body fitting of the HCMV triplex-pUL32nt atomic model ( for example , Td region ) into MCMV triplex density ( in order to align triplex models from MCMV and HCMV ) reveals further comparative insights into pM32/pUL32 binding in MCMV and HCMV ( Fig 7D ) . First , as mentioned above , only two pM32 subunits bind with MCMV triplex as a dimer instead of three pUL32 subunits for each HCMV triplex . Second , the organization of pM32/pUL32 dimers in MCMV and HCMV with respect to triplex resemble each other , though the specific orientation of dimer subunits possesses some distinctions ( Fig 7D ) . In contrast to the minor rotational displacements exhibited between pM32-a and pUL32-a , pM32-b and pUL32-b show greater translational and rotational displacements ( Fig 7D ) . Third , Tri1 residues of MCMV and HCMV that interact with pM32 and pUL32 , respectively , are conserved ( S16 Fig ) and conceivably play a crucial role in pM32/pUL32 binding . In contrast to the binding of a pM32 dimer to each of triplexes Ta , Tb , Td , Te , and Tf , no pM32 is bound to triplex Tc ( Figs 6A and 8A , and S19 Fig ) . Rigid-body fitting of triplex Td decorated with pM32nt dimer into triplex Tc density ( Fig 8B ) reveals that distances between pM32’s upper domains to their adjacent SCPs are greater at triplex Tc than triplex Td ( 7 Å vs . 4 Å for pM32nt-a; 14 Å vs . 6 Å for pM32nt-b ) ( Fig 8C , left panels ) . The rigidity of pM32 dimer discussed above conceivably prevents pM32 from extending , or “spreading , ” to span such long distances , explaining the absence of pM32 above triplex Tc in MCMV . Next , we show that pM32 is important , though not essential , for MCMV replication in vitro by successfully generating an infectious MCMV mutant with deletion of the coding sequence of M32 , which we term ΔM32 . To generate ΔM32 , we adopted our previously-published protocols for generating gene-deletion mutants of HCMV to mutagenize a BAC clone of the wild-type MCMV ( Smith strain ) genome ( MCMVBAC ) by deleting the M32 open reading frame [37–39] . Furthermore , rescued viral mutant , R-M32 , was generated from ΔM32 , by restoring the M32 sequence , following the procedures as described previously [38 , 40] . Deletion of the M32 gene in ΔM32 and the restoration of the M32 sequence in R-M32 were confirmed by PCR and Southern blot analysis . To determine whether ΔM32 has any growth defects in vitro and whether pM32 is essential for MCMV replication in cultured cells , we measured the growth rates of mutant ΔM32 , rescued mutant R-M32 , and parental MCMVBAC viruses in NIH 3T3 cells . ΔM32 exhibited growth defective phenotype as the titers of ΔM32 were lower than those of MCMVBAC in a 6-day growth study ( Fig 9A ) . At day 4 , the titer of ΔM32 was about 100-fold lower than that of MCMVBAC ( Fig 9A ) . The observations that ΔM32 grew in NIH3T3 cells indicate that M32 is not required for MCMV replication in vitro . Thus , in contrast to its HCMV homolog pUL32 , which is essential for HCMV replication [38 , 40] , M32 is not essential—though important—for MCMV replication in vitro . Though the reduced titer of ΔM32 limited the isolation of large numbers of viral particles , we still managed to purify ΔM32 viral particles . Consistent with the 100-fold reduction in its growth rate ( Fig 9A ) , the concentration of ΔM32 viral particles produced in the cell culture is low compared to that of the wild-type virus based on our EM analysis ( Fig 9B and 9C , and S1A–S1D Fig ) . Specifically , deletion of the M32 gene appeared to reduce the formation of infectious , DNA-containing virions , as it was difficult to find DNA-containing particles . Instead , most observed particles were non-infectious enveloped particles ( NIEPs ) and dense bodies ( Fig 9B and 9C ) , suggesting that DNA-containing particles are less stable in ΔM32 . From 200 fully-enveloped cryoEM particles ( an example of which is denoted by the open black arrow in Fig 9B and 9C ) , we obtained a 3D icosahedral reconstruction of ΔM32 at ~25 Å resolution ( Fig 9D ) . This resolution is sufficient to resolve pentons , hexons , and triplexes on the capsid , and as shown in the enlarged facet of the icosahedral reconstruction ( Fig 9E ) , the capsid of ΔM32 possesses the same molecular architecture as that of other herpesviruses [41 , 42] . In contrast to the ΔM32 sample , wild-type MCMV sample prepared by the same procedure demonstrated a significantly higher viral particle concentration when examined by cryoEM ( S1A Fig ) , and many particles are DNA-containing virions ( solid black arrow in S1A Fig ) . From 1 , 200 wild-type MCMV virion particles ( e . g . , S1B Fig ) , we obtained a ~12 Å resolution reconstruction ( S1E Fig ) . We also obtained a reconstruction from enveloped particles without DNA ( NIEPs , an example is shown in S1C Fig ) , the capsid and tegument densities of which were identical to those of the virion reconstruction . Lastly , structural comparison between wild-type MCMV ( ~12 Å ) and the mutant ΔM32 ( ~25 Å ) confirmed the absence of pM32 atop triplexes ( Fig 9D and 9E ) .
In this study , we present the first cryoEM structures obtained from the MCMV virion and M32-deletion mutant , as well as functional data that together establish important differences concerning the structural and functional roles of pp150 in HCMV and MCMV . The attainment of an atomic model of the MCMV particle—consisting of 55 unique protein conformers of capsid and tegument proteins—is a remarkable endeavor , considering there were no atomic structures ever reported for any MCMV proteins prior to this study . Despite highly similar structures of their capsid proteins , MCMV and HCMV have distinctive capsid-tegument binding patterns: 260 “Λ”-shaped pM32nt dimers on all triplexes but Tc in each MCMV , as opposed to 320 “Δ”-shaped pUL32nt structures ( one dimer + one monomer of pUL32nt ) on all triplexes in each HCMV . Substantially more amino acids are involved in pM32-pM32 interactions in MCMV than in pUL32-pUL32 associations in HCMV , suggesting a more rigid pM32-pM32 dimer structure than pUL32-pUL32 dimer . Finally , M32-deletion mutant can be successfully generated , albeit with a growth rate reduced ~100-fold , whereas UL32 deletion in HCMV is lethal . The different extent of pp150-pp150 interaction and pp150 dimer rigidity in MCMV and HCMV may account for the distinctive capsid-binding patterns of pp150 in the respective viruses . The additional rigidity of pp150 dimer in MCMV may prevent the spreading of the dimer necessary to span the increased distances to adjacent SCPs surrounding the triplex Tc , presumably leading to the lack of pM32-binding in this region . The more extensive inter-pp150 interactions observed for MCMV CATC may also account for the observation that pp150 ( pM32 ) does not exist as a monomer in MCMV , while approximately 1/3 of pp150 ( pUL32 ) exists as a monomer in HCMV . Interestingly , HCMV pp150 is about 40% longer in sequence than its homologs in MCMV ( S1 Table ) , simian CMV , and human herpesviruses 6 and 7 [25] though a correlation has not been established between this length difference and the polymorphic difference in tegument-capsomer association due to the limited resolutions of the existing cryoEM structures of these β-herpesviruses [43 , 44] . On the basis of no viral growth in cultured cells electroporated with a bacterial artificial chromosome containing the UL32-deletion HCMV genome [38] , pUL32 was considered to be an essential tegument protein that can be targeted for therapeutic development against HCMV infection [14] . In contrast , its counterpart in MCMV , pM32 , is nonessential: M32-deletion mutant is viable in vitro , although defective in generating DNA-containing virions and its infectivity attenuated by 100-fold ( Fig 9A ) . Evidence shows that “SCP-deficient” HCMV viral particles have decreased viral yield ( 10 , 000-fold ) compared to that of wild-type virus [45] . As mentioned previously , SCP structures in MCMV and HCMV are highly conserved . Thus , it is worth considering SCP as a drug target while rationally designing novel drugs against MCMV infections . While our results establish the structural basis of using MCMV as a model for HCMV pathogenesis and therapeutic studies when targeting capsid proteins such as SCP , caution is warranted when targeting tegument protein pp150 due to its different structural organization and functional roles in MCMV and HCMV reported in this study .
Mouse NIH3T3 cells ( ATCC® CRL-1658™ ) were cultured in Dulbecco’s Modified Eagle Medium ( DMEM ) plus 10% fetal bovine serum ( FBS ) . Twenty flasks ( 175 cm2 each ) of cells were grown to 90% confluence and then infected with MCMV Smith strain at a multiplicity of infection ( MOI ) of 0 . 1 . At 6 days post infection , when half of the cells were lysed , the media was collected and centrifuged at 10 , 000g for 15 min to remove cell debris . The clarified supernatant was then collected and centrifuged at 80 , 000g for 1 hr to pellet MCMV virions . Pellets were resuspended in a total volume of 2 ml phosphate buffered saline ( PBS , pH 7 . 4 ) and loaded on a 15%-50% ( w/v ) sucrose density gradient and centrifuged at 100 , 000g for 1 hr . We usually observe three light-scattering bands—top , middle , and bottom–containing mainly noninfectious enveloped particles ( NIEPs ) , virions , and dense bodies , respectively . The middle band ( virions ) was collected and diluted in PBS to a total volume of 13 ml . Virion particles were pelleted again at 80 , 000g for 1 hr and resuspended in 30 μl PBS for cryoEM sample preparation . Purified intact MCMV virions were mixed with NP-40 detergent at 1% final concentration to partially solubilize the viral envelope . Immediately after , aliquots of 2 . 5 μl of this treated sample were applied to 200-mesh Quantifoil R2/1 grids , blotted with filter paper , and plunge-frozen in liquid ethane . CryoEM images were collected at liquid nitrogen temperature in an FEI Titan Krios cryo electron microscope operated at 300 kV with parallel illumination . Images were recorded on Kodak SO163 films with a dosage of ~25 e−/Å2 at 47 , 000× nominal magnification . A total of 2 , 200 films were recorded and digitized using Nikon Super CoolScan 9000 ED scanners at 6 . 35 μm per pixel ( corresponding to 1 . 351 Å per pixel at the sample level ) . Defocus values of all micrographs were determined with CTFFIND3 [46] to be in the range of -1 μm to -3 μm . Particles were picked with Ethan [47] and then manually screened with the boxer program in EMAN [48] to keep only well-separated and artifact-free particles . A total of 58 , 254 particle images were boxed out from the micrographs with EMAN . The original particle images were binned 8× , 4× , or 2× stepwise to speed up data processing . Icosahedral refinement and reconstruction were carried out with the common line-based IMIRS package [49 , 50] and GPU-implemented reconstruction program eLite3D [51] , respectively . The final capsid reconstruction was obtained by averaging 47 , 982 particles . Due to the large size of the MCMV capsid ( over 1 , 300 Å in diameter ) , resolution of our initial icosahedral reconstruction was limited to 5 Å , likely due to slight particle deformation and defocus gradient across the depth of the sample [12 , 26] . To obtain higher resolution structures for reliable atomic model building , we applied a localized reconstruction strategy [12 , 52–54] to reconstruct subareas surrounding the 2-fold , 3-fold , and 5-fold axes of the icosahedral MCMV capsid ( the defocus value for each sub-particle was recalculated based on the geometric location of the sub-particle on the capsid ) . The 47 , 982 high-quality particle images selected from IMIRS refinement were binned 4x and reprocessed using Relion [55] with icosahedral symmetry applied . Using the script downloaded from www . opic . ox . ac . uk/localrec [52] , positions of sub-particles in the original particle images ( i . e . , without binning ) were calculated with a radial distance of 576 . 8 Å from the center of the viral particle . A total of 575 , 784; 959 , 640; and 1 , 439 , 460 sub-particles in 400 x 400 pixels were then boxed out for the 5-fold , 3-fold , and 2-fold axis , respectively . Localized reconstruction of these sub-particles were iteratively refined in Relion , reaching a final estimated resolution of 3 . 8 , 3 . 6 , and 3 . 8 Å for the 5-fold axis , 3-fold axis , and 2-fold axis sub-particle maps , respectively , based on the 0 . 143 FSC criterion [56] . Each asymmetric unit of the T = 16 icosahedral reconstruction of the MCMV particle contains 55 unique copies of protein subunits: 16 MCPs , 16 SCPs , 15 triplex subunits ( excluding triplex Tf , the resolution of which at 6 . 8 Å is insufficient for atomic model building ) , and 8 pM32s . We utilized the SWISS-MODEL server [58] to generate homology models of penton MCP , hexon MCP , SCP , Tri1 , Tri2A , and Tri2B with the corresponding subunit conformers in the atomic model of HCMV [14] as templates . These initial models were docked into the sub-particle reconstructions ( sharpened with a B factor of -150 Å2 for the 5-fold axis map , -160 Å2 for the 3-fold axis map , and -160 Å2 for the 2-fold axis map ) and then adjusted manually in Coot [59] . All models were then iteratively improved by Phenix real space refinement [60] and manual readjustment in Coot . Eventually , all the atomic models built based on high-resolution sub-particle reconstructions were assembled together and refined against the icosahedral reconstruction with Phenix to correct for inter-molecular clashes . Unlike for the capsid proteins , the atomic model for pM32nt was built de novo . The resolution of densities corresponding to pM32nt was poorer than that of capsid proteins even in the improved sub-particle reconstructions . Thus , the map obtained by averaging triplexes Tb , Td , and Te regions as previously mentioned was also used to facilitate atomic model building for pM32nt . Secondary structures predicted by Phyre2 server [61] were used to guide backbone tracing using the Baton_build utility in Coot . Observable main chain residue bumps in the density informed Cα placement , and registration was accomplished using distinguishable side chain densities . Iterative model refinement was performed as for the capsid proteins described above . We used a previously reported bacterial artificial chromosome ( BAC ) -based clone of MCMV Smith strain ( MCMVBAC ) , maintained as a BAC-based plasmid in E . coli , to produce infectious progeny in mouse NIH3T3 cells [37–39] . As reported , this progeny retained wild-type growth characteristics in vitro , as previously shown [37–39] . Mutant ΔM32 , which contained a deletion of the entire coding sequence of M32 , was derived from MCMVBAC using a two-step mutagenesis protocol [38 , 40] . In the first step , we inserted at the M32 sequence with a cassette ( tet/str ) containing the tetracycline resistance gene tetA and rpsL gene conferring streptomycin susceptibility , following the mutagenesis procedures as described previously [38 , 40] . The bacteria harboring the mutant BAC constructs were electroporated with the PCR-amplified tet/str cassette . Successful insertion of the tet/str cassette was screened by selecting for bacterial colonies resistant to tetracycline . In the second step , the tet/str cassette was targeted for deletion . The resulting mutant , which only contained the deletion of M32 ORF sequence , was streptomycin-resistant and , therefore , was easily selected in the presence of the antibiotics [38 , 40] . Rescued mutant R-M32 was generated from ΔM32 , following the experimental procedures for construction of rescued viruses as described previously [38 , 40] . The M32 regions in the mutant and the rescued virus were analyzed by restriction digestion profile and sequencing analyses . Virus growth analyses were carried out by infecting NIH 3T3 cells ( n = 1x106 ) with viruses ( MOI = 0 . 5–1 ) [62 , 63] . The cells and medium were harvested at different time points postinfection . Viral stocks were prepared and used to infect NIH3T3 cells , followed by agar overlay . Viral titers were determined by counting the number of plaques 5–7 days after infection [62 , 63] . The values obtained were averages from three independent experiments . For cryoEM , we used a sucrose density gradient to purify viral particles from the supernatant of culture media of wild-type or ΔM32-infected NIH3T3 cells as previously described [36] . The sucrose gradient fraction with the most viral particles from the wild-type MCMV prep and the corresponding fraction from the ΔM32 prep were collected for cryoEM imaging . We suspended a 3 μl aliquot of each of these samples to a holey carbon-coated cryoEM grid , which was blotted and immediately plunge-frozen so that viral particles were suspended within vitreous ice across holes of the holey carbon [64] . Low-dose ( ~20 e−/Å2 ) cryoEM images were recorded on a Gatan 16-megapixel CCD camera in a Titan Krios cryo electron microscope operated at 300 kV at a magnification of 79 , 176× with Leginon [65] . Consistent with the 100-fold reduction in its growth rate , the concentration of ΔM32 viral particles produced in cell culture is low when compared to that of the wild-type virus . Because it was hard to find viral particle-containing sample regions for imaging , many imaging sessions were painstakingly carried out to obtain just 500 micrographs for ΔM32 . ( On average , each micrograph only contained one viral particle . ) We selected 200 fully-enveloped cryoEM particles ( see an example pointed by the open black arrow in Fig 9B and 9C ) to reconstruct a 3D structure of ΔM32 at ~25 Å resolution with the IMIRS package [49 , 50] . In contrast to the ΔM32 sample , wild-type MCMV preparation obtained from the same procedure had a significantly higher viral particle concentration . 3D reconstructions obtained from wild-type virions and NIEPs with IMIRS were thus at higher resolutions than ΔM32 due to the larger number of particles used .
CryoEM maps and atomic models are deposited in the Electron Microscopy Data Bank ( EMDB ) and the RCSB Protein Data Bank ( PDB ) , respectively . They include the cryoEM density maps of the MCMV capsid , sub-particle reconstructions at 2-fold , 3-fold , and 5-fold axes ( accession code EMD-9366 , EMD-9367 , EMD-9368 , and EMD-9369 , respectively ) and a single coordinate file containing 55 atomic models ( PDB accession code 6NHJ ) . | Cytomegalovirus ( CMV ) infection is a leading viral cause of birth defects and could be deadly to AIDS patients and organ transplant recipients . Absence of effective vaccines and potent drugs against human CMV ( HCMV ) infections has motivated animal-based studies , mostly based on the mouse model with murine CMV ( MCMV ) , both for understanding pathogenesis of CMV infections and for developing therapeutic strategies . Distinct from other medically important herpesviruses ( those responsible for cold sores , genital herpes , shingles and several human cancers ) , CMV contains an abundant phosphoprotein , pp150 , which is a structurally , immunogenically , and regulatorily important tegument protein and a potential drug target . Here , we used cryoEM with localized reconstruction method to obtain the first atomic structure of MCMV . The structure reveals that the organization patterns of the capsid-associated tegument protein pp150 are different in MCMV and HCMV , despite their highly similar capsid structures . We also show that deleting pp150 did not eliminate MCMV infection in contrast to pp150’s essential role in HCMV infections . Our results have significant implication to the current practice of using mouse infected with MCMV for HCMV therapeutic development . |
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Two theories address the origin of repeating patterns , such as hair follicles , limb digits , and intestinal villi , during development . The Turing reaction–diffusion system posits that interacting diffusible signals produced by static cells first define a prepattern that then induces cell rearrangements to produce an anatomical structure . The second theory , that of mesenchymal self-organisation , proposes that mobile cells can form periodic patterns of cell aggregates directly , without reference to any prepattern . Early hair follicle development is characterised by the rapid appearance of periodic arrangements of altered gene expression in the epidermis and prominent clustering of the adjacent dermal mesenchymal cells . We assess the contributions and interplay between reaction–diffusion and mesenchymal self-organisation processes in hair follicle patterning , identifying a network of fibroblast growth factor ( FGF ) , wingless-related integration site ( WNT ) , and bone morphogenetic protein ( BMP ) signalling interactions capable of spontaneously producing a periodic pattern . Using time-lapse imaging , we find that mesenchymal cell condensation at hair follicles is locally directed by an epidermal prepattern . However , imposing this prepattern’s condition of high FGF and low BMP activity across the entire skin reveals a latent dermal capacity to undergo spatially patterned self-organisation in the absence of epithelial direction . This mesenchymal self-organisation relies on restricted transforming growth factor ( TGF ) β signalling , which serves to drive chemotactic mesenchymal patterning when reaction–diffusion patterning is suppressed , but , in normal conditions , facilitates cell movement to locally prepatterned sources of FGF . This work illustrates a hierarchy of periodic patterning modes operating in organogenesis .
Diverse structures , such as the skeletal elements of the limb , rugae of the palate , cartilaginous rings of the trachea , intestinal villi , and feathers , scales , or hair follicles , develop in a periodically patterned manner . Although many specific models to explain the spontaneous emergence of such repeating patterns in embryonic tissues have been proposed [1] , these can be grouped into 2 general classes ( Fig 1A ) . The first class , based on the Turing reaction–diffusion system , relies on the operation of 2 opposing signalling processes: an activator , which is self-enhancing and has a limited spatial range , coupled with the production of an inhibitor with a greater spatial range . These activating and inhibiting processes were originally presented as a pair of chemical signals [2] , though more complex networks can produce similar patterns [3–7] . These activating and inhibitory processes induce a spatially patterned change in cell state , typically reflected in altered gene expression . This arrangement of cell states , termed a prepattern , is then used as a template to produce anatomical structures by locally influencing cell aggregation , growth , or survival ( Fig 1A ) . The second class of models focuses on the potential of mobile mesenchymal cells to generate periodic foci of high cell density directly through chemotaxis , adhesion , or mechanical deformation of their environment [8–13] . Such mesenchymal patterning does not require opposing intercellular signals , as the self-activation phenomenon arises from local cell clustering , while the inhibitory effect is achieved by widespread cell depletion as these are drawn away to nascent clusters ( Fig 1A ) . Thus , these 2 theories differ in the entity that is moving: in the Turing reaction–diffusion system , cell movement is limited and chemical signals diffuse , while in mesenchymal self-organisation systems , moving cells are themselves agents of pattern formation . Both types of model produce similar patterns in computational simulation [4 , 14–16] as they both ultimately rely on the principle of local self-activation and long-range inhibition [17] . Thus , experimental investigation is required to define the contribution made by each mechanism during organ development . In the mouse embryo , a periodic pattern of hair follicles first arises at late embryonic day 13 ( E13 ) [18 , 19] . At this stage , the skin is composed of an epidermal sheet overlying an extracellular matrix containing dispersed mesenchymal cells . Sites of hair follicle initiation first become identifiable as groups of cells expressing specific marker genes and by cellular reorganisation ( Fig 1B ) . The latter involves the closer packing of epidermal cells to form a placode [20] and the clustering of mesenchymal cells to form a dermal condensate directly underneath . Modulation of hair follicle size , shape , and spacing during skin growth and after experimental perturbation has provided the strongest evidence that a local self-activation and long-range inhibition system ultimately determines the hair follicle arrangement . Such a process can explain why new follicles are inserted between existing ones as the skin expands [21] and why the follicles will align along the edge of a cut made prior to pattern formation [4 , 22] . This mechanism also explains how the hair follicle pattern can transition from an array of spots to one of stripes in a labyrinthine pattern [4] . As both cell–cell signalling and mesenchymal cell aggregation are prominent features of early hair follicle formation , it is plausible that either process , or a combination of both , is responsible for defining the hair follicle array . Classical tissue recombination experiments indicated that the dermal mesenchyme is the compartment in which pattern generation occurs , this spatial information subsequently being conveyed to the epidermis through inductive signalling [23 , 24] . This sparked a search for the molecular identity of the “first dermal message” thought to induce the epidermal placode pattern according to a dermal prepattern . Several intercellular signalling pathways have since been found to be critical for early hair follicle development , though none have the exact characteristics of the hypothesised first dermal message . Nascent follicle primordia are foci of high wingless-related integration site ( WNT ) activity [18 , 25 , 26] and display elevated production of fibroblast growth factors ( FGFs ) [27] , bone morphogenetic proteins ( BMPs ) , and BMP inhibitors [22 , 28 , 29] . Consistent with a driving role for the dermis in hair follicle patterning , this process does not initiate in mice upon abolition of dermal WNT/β-catenin activity [30] . However , activity of WNT/β-catenin in the epidermis is also essential for hair follicle patterning , as no focal expression of any molecular markers or cellular rearrangements that accompany normal hair follicle formation occur when this is ablated [25] . Furthermore , forced activation of epidermal β-catenin is sufficient to drive placode identity across the entire epidermis [31 , 32] . Mutation of epithelial FGF receptor 2 leads to loss of cell rearrangements and placode markers [33] , while mutation of Fgf20 allows formation of epidermal placodes and expression of a nearly full suite of epidermal placode markers without any sign of an accompanying dermal condensate nor of patterned dermal gene expression [27] . However , administration of FGF7 to skin inhibits hair follicle formation [34] , suggesting both positive and negative roles for FGF signalling in this process . The BMP family act as inhibitors of hair follicle formation , based on their effects when applied to cultured skin [21 , 22 , 29] , an increased primary follicle density when epithelial BMP receptor is deleted [35] , and the suppression of follicle formation when the BMP inhibitor NOGGIN is ablated [28] . Once the spatial pattern has been defined , the cells selected to become a follicle activate expression of other genes to progress their development into construction of the mature organ . These later-acting genes are typically expressed in the ‘de novo’ mode , indicating their activation only after assumption of the new cell fate , as opposed to the ‘restrictive’ mode of expression , which is characteristic of genes involved in the patterning process itself [36] . An example is Shh , which is expressed only once hair follicle cell fate has been established [37] , and , agreeing with its role being only subsequent to definition of the follicle pattern , mutations in this gene do not impair hair follicle fate acquisition but rather arrest the follicles’ development due to lack of growth [38] . Simple signalling interactions have been proposed as the basis of hair follicle periodic patterning , though none represent a complete system capable of de novo pattern formation . WNT signalling coupled with induced expression of Dickkopf ( DKK ) family members was proposed to contribute to a reaction-diffusion system , though no WNT activator positive feedback loop was identified [39] . An activator/inhibitor relationship between ectodysplasin A receptor ( EDAR ) and the BMP pathway was suggested to contribute specifically to primary hair patterning [5 , 22] . This system , however , stabilises a labile prepattern of focal WNT/β-catenin activity [22 , 25 , 40 , 41] , rather than acting to create order in naïve tissue . Thus , these proposed models do not integrate all pathways implicated in patterning and do not report a set of interactions sufficient to act as a periodicity generator , raising questions as to whether a simple reaction–diffusion system underlies hair follicle patterning or whether this process may have inputs other than intercellular signalling that contribute to symmetry breaking . Here , we identify a set of interactions between the BMP , FGF , and WNT pathways capable of breaking symmetry to produce a prepattern that guides local dermal cell condensation . Strikingly , by modulating components of this system to mimic the microenvironment of the hair follicle primordium , we identify a transforming growth factor ( TGF ) β-driven patterning system in dermal mesenchyme that is independent of epidermal instruction . Whereas TGFβ signalling is capable of driving mesenchymal self-aggregation when reaction–diffusion signalling is suppressed , in normal development , this pathway potentiates cell accumulation at sites of focal FGF production , thereby assuring timely dermal condensate formation according to the epidermal template . Thus , both reaction–diffusion signalling and mesenchymal self-organisation potentials reside in the developing skin , but the former dominates and directs the latter , highlighting the hierarchical nature of patterning mechanisms .
To determine the functions and relationships between cell signalling pathways and whether their interactions are sufficient to constitute a complete pattern forming network , we focussed on the BMP , FGF , and WNT/β-catenin pathways [25–34] . We assessed the effects of modulating each pathway in E13 . 5 cultured skin on both epidermal placode , defined by expression of Dkk4 [41 , 42] , and using the TCF/Lef::H2B-green fluorescent protein ( GFP ) reporter line [43] to visualise dermal cell arrangement and condensation ( Fig 1C and S1C Fig ) . Treatment with BMP4 inhibited placode and dermal condensate formation , while the BMP receptor inhibitor LDN193189 increased placode and condensate size . We focused primarily on the FGF9/16/20 subfamily signalling as FGF family representatives due to their demonstrated involvement in hair follicle formation [27 , 44] . Treatment with recombinant FGF9 suppressed placode and condensate appearance ( Fig 1C ) , as did FGF7 , a member of a different subfamily ( S2 Fig ) . Blocking FGF signalling with SU5402 inhibited both Dkk4 expression and dermal condensate appearance , consistent with FGF signalling being both necessary for , and inhibitory to , hair follicle development ( Fig 1C ) . Treatment of skin explants with the GSK3β-inhibitor CHIR99021 to stimulate β-catenin activity led to the enlargement of hair follicle primordia , transitioning from discrete spots to a merged labyrinthine pattern . Inhibition of WNT/β–catenin signalling reduced placode density , accompanied by weaker expression of Dkk4 ( Fig 1C ) . In each condition , the effects on placode pattern were matched by corresponding changes in dermal condensate pattern . These pattern transitions are in agreement with WNT signalling being activatory , BMPs functioning as inhibitors , and FGFs playing a more complex role , being both required for and inhibitory to hair follicle initiation . Having defined conditions to stimulate and repress each of these 3 signalling pathways , we set out to delineate their transcriptional regulatory interactions and assess whether these could comprise a functional pattern forming network . Turnover of activator and inhibitor molecules is required to produce a reaction–diffusion pattern [2 , 4 , 17] , and simulations show that activator and inhibitor half-lives have a major influence on the timing of pattern emergence [4] . As mouse skin takes approximately 10 h to form a pattern from a naïve state [22] , in a simple reaction–diffusion network , this constrains the half-lives of the oscillating components of this system to being under 90 minutes [4] . We used this temporal constraint as a filter to identify transcripts from these 3 pathways that could play a driving role in pattern formation . We performed a transcriptome-wide screen of mRNA half-lives in E13 . 5 mouse dermis and epidermis , subsequently applying a data filtering criterion based on half-life ( t1/2 ≤ 90 minutes ) and focusing upon extracellular molecules ( ligands , receptors , and extracellular signal-attenuating molecules ) of the BMP , FGF , and WNT pathways ( S3 Fig , S2 and S3 Tables , S1A Appendix for detail ) . The focus on ligand , receptor , and extracellular antagonist molecules is motivated by their critical role in reaction–diffusion processes by conveying information between cells [2 , 17 , 45] . This yielded a candidate list of mRNA species that fit within the 3 signalling pathways of interest , have half-lives of less than 90 minutes , encode extracellular proteins , and are expressed in embryonic skin ( S1 Table ) . This set contains many genes already implicated in skin patterning , including Bmp2 and Bmp4 [21 , 22 , 29] , the BMP inhibitor Nog [28 , 46] , the WNT inhibitors Dkk1 and Dkk4 [26 , 39 , 41 , 47] , and Fgf7 and Fgf20 [27 , 34] . Transcripts encoding proteins in the EDAR and TGFβ pathways had longer half-lives , consistent with their acting later to stabilise emerging patterns [22] and promote morphogenesis [48] . To identify regulatory relationships between the BMP , FGF , and WNT pathways , we stimulated and repressed each pathway in unpatterned E13 . 5 skin explants for 6 h and assessed the resulting change in abundance of each candidate transcript ( Fig 1D ) . This approach identified a number of previously reported regulatory interactions , including upregulation of Fgf20 and Dkk4 by WNT activity [27 , 41 , 42] and stimulation of Bambi and Nog by BMP signalling [49 , 50] . After excluding candidates unresponsive to BMP , FGF , or WNT signal modulation , 15 genes were found to be regulated within this period ( Bmp2 , Bmp4 , Ctgf , Dkk1 , Dkk4 , Wnt9a , Ror2 , Fgf7 , Fgf10 , Fgf18 , Fgf20 , Rgma , Fzd10 , Bambi , and Nog ) . From these data , we derived a transcriptional network describing the interactions between the candidates ( Fig 1E ) . In this network , WNT/β–catenin signalling stimulates expression of patterning genes and hair follicle primordium markers , while BMP has opposing effects on most of these . FGF represses expression of all epithelial WNT/β–catenin activated targets ( Dkk4 , Bmp2 , and Fgf20 ) , which are indicators of placode fate , but does not alter expression of any dermally expressed genes in the network , even though we detect the classical FGF target Etv5 being upregulated in both tissue layers ( S4A Fig ) . The inhibition of hair follicle development following widespread application of FGF to skin cultures ( Fig 1C ) can thus be explained by its suppression of epithelial β-catenin activity , consistent with the increased epidermal β-catenin activity reported in Fgf20 null embryonic skin [27] and reduction of active β-catenin protein in FGF-stimulated epidermis ( S4B and S4C Fig ) . In this network , some features of classical Turing activator–inhibitor dynamics are present , including activator stimulation of inhibitor production , but we did not detect direct positive feedback for any pathway . Rather , each pathway displays prominent self-inhibition . Thus , in its number of components and its structure , this network does not conform to the classical topology of a reaction–diffusion system . Therefore , we took a mathematical approach ( S1B Appendix ) to assess whether the structure of this network is capable of producing a periodic pattern . We grouped the candidates into 5 species: BMP ( Bmp2 , Bmp4 , Rgma ) , WNT ( Wnt9a , Ror2 , Fzd10 ) , WNT inhibitor ( Dkk1 and Dkk4 ) , BMP inhibitor ( Bambi and Nog ) , and FGF ( Fgf20 ) ( see S1A Appendix ) . We then defined a matrix in which the interactions between these species were represented by either a + sign ( stimulation ) or a − sign ( inhibition ) ( S3F Fig ) . From the matrix of interactions , we performed linear stability analysis [2] , a method that assesses whether small perturbations in a system will decrease or increase over time . If they decay , patterning cannot occur , and if they grow , there is a prospect of pattern formation , as a result of diffusion in the case of the Turing mechanism . This analysis found that the conditions of Turing instability are met when all 5 species are considered to be diffusible ( Fig 1F ) , which is expected as each grouping contains at least 1 secreted factor . The structure of this BMP , FGF , and WNT network can , therefore , generate a stable periodic pattern ( see S1C Appendix for detail ) through rapid regulatory interactions . Intuitively , the main drivers in this system lie in WNT stimulating expression of a suite of hair follicle primordium-specific genes , while BMP broadly inhibits their expression and FGF acts as an inhibitor selectively in the epidermis . While direct positive feedback is not apparent in this system , the sequence through which WNT upregulates FGF , FGF downregulates BMP , and BMP downregulates WNT could have an overall positive effect that would allow WNT to undergo indirect self-upregulation . Having defined the reciprocal relationships between BMP , FGF , and WNT signalling , including their organisation into a network capable of periodic patterning , we investigated the origin of the dermal condensate and the influences of these pathways on its formation . We performed live cell imaging of TCF/Lef::H2B-GFP E13 . 5 dorsal skin explants using confocal microscopy ( Fig 2A , S1 and S2 Videos ) to track dermal cells during condensate formation . This revealed undirected movement of dermal cells up to the timepoint of condensate formation , when local directed cell movement produces the structure . Condensate-entering cells were observed to do so individually , with no sign of collective migration ( S1 Video ) . We found that the dermal cells which ultimately make up the condensate are those present in its immediate vicinity , with a simple relationship between initial cell location and probability of incorporation into the condensate ( Fig 2B ) . We performed an analysis of the direction of movement of individually tracked cells by dividing each cell track into 6-h windows and determining the Euclidean angle of movement with respect to the future condensate centre for each window , as well as the Euclidean and accumulated distance travelled over this period . To determine whether individual cell movement was directed , we compared the distribution of Euclidean angles and distances for 2 classes of cells: ( i ) those cells initially outside the condensate area which subsequently entered it ( ‘condensate’ ) and ( ii ) those cells which remained outside the condensate area throughout ( ‘intercondensate’ ) ( Fig 2C ) . Intercondensate Euclidean angles ( n = 893 ) in these windows did not deviate from a uniform distribution ( Kolmogorov–Smirnov test D = 0 . 05 , p > 0 . 05 ) while condensate-bound track angles ( n = 45 ) were consistently lower ( Kolmogorov–Smirnov test D = 0 . 44 , p < 0 . 001 ) . The median Euclidean distance travelled in these 6-h windows was also greater for condensate-bound cells ( Mann–Whitney U test p < 0 . 0001 ) than intercondensate cells ( Fig 2C ) . Condensate-entering cells therefore exhibit directed movement towards sites of follicle initiation . To investigate the timing of the distinct behaviours of cells entering condensates , we quantified cell behaviour by comparing the three 6-h windows prior to follicle entry ( or the end of the track for intercondensate cells ) . We compared the Euclidean angle and level of persistence ( calculated as the Euclidean distance/accumulated distance ) of movement by the 2 cell classes ( intercondensate and condensate ) at 0–6 , 6–12 , and 12–18 h prior to follicle entry ( Fig 2D ) . The median Euclidean angle relative to the nearest prospective condensate centre differed significantly between intercondensate and condensate-bound cells in the 0–6 h window ( Kruskal–Wallis test p < 0 . 0001 , Mann–Whitney U test with Bonferroni’s correction p < 0 . 01 ) . Consistent with this behaviour , the median persistence for condensate-bound cells also differed from intercondensate cells in the 0–6 h window ( Kruskal–Wallis test p < 0 . 01 , Mann–Whitney U test with Bonferroni’s correction p < 0 . 01 ) . These results show that the directed cell movement in the condensate-entering cell population occurs locally and only in the hours immediately prior to condensate appearance . Prior to this time , the behaviour of the entire mesenchymal cell population is the same , indicating that condensate formation involves selection of cells from an equivalent population based simply on their location . Using the TCF/Lef::H2B-GFP line , we set out to determine the relative order of placode specification and cell condensation . We fixed cultured dorsal skin cultures at intermediate stages of development and compared the timing of appearance of Dkk4 expression with that of cell condensates in individual samples of skin . We found that patterning is first detectable in the epidermis as spatially organised focal Dkk4 expression with a lack of corresponding dermal cell clustering ( Fig 2E ) . As patterning continues , dermal condensates become apparent , and the foci of Dkk4 expression resolve . However , at this stage , not all epidermal placodes have corresponding dermal condensates , and there are regions of skin where the epidermal pattern is present in the absence of dermal organisation . As the process completes , each Dkk4-positive placode is underlain by a dermal condensate ( Fig 2E ) . These results show that an epidermal gene expression prepattern precedes the formation of dermal condensates . Based on these findings , we hypothesised that local signalling from the epithelial placode might coordinate the condensation of the underlying dermal cells . We considered FGF a good candidate for the local attractant signal as this induces dermal condensations during feather development [51 , 52] and because knockout mice lacking Fgf20 , a gene selectively expressed in epidermal placodes , do not form dermal condensates [27] . To investigate whether a local FGF source could attract dermal cells , we cultured E12 . 75 TCF/Lef::H2B-GFP dorsal skin explants with beads soaked in recombinant FGF9 or control protein bovine serum albumin ( BSA ) beads . FGF9 beads induce cell accumulation in their vicinity , unlike BSA beads ( Fig 2F and S3 Video ) . The area surrounding the FGF9 bead-induced condensate contains fewer GFP+ve nuclei , indicative of cell depletion from this area , and exhibits lack of hair follicle primordia ( Fig 2F ) . Together , these observations show that local sources of FGF stimulate dermal condensate formation , demonstrating a positive role for FGF in hair follicle formation in the mesenchyme , in contrast to its effect on the epidermis . Having established that FGF attracts mesenchymal cells to form a condensate and knowing that reception of WNT signals by mesenchymal cells is also required for this process [30 , 53] , we set out to define the effects of BMP signalling on dermal cell organisation . TCF/Lef::H2B-GFP skin explants treated with the BMP inhibitor LDN193189 have enlarged dermal condensates and modestly increased placode size ( Fig 3A and 3C ) . We found that inhibition of BMP signalling significantly reduced the density of nuclei in the area between condensates ( Fig 3A and 3D ) , indicating that increased dermal condensate size results from greater recruitment of dermal cells to produce enlarged follicle primordia . Thus , active BMP signalling normally limits cell recruitment to incipient condensates , and when BMP is impaired condensates continue to expand through cell recruitment , depleting cells from the intercondensate region . To further define the influence of BMP on dermal cell arrangement , we stimulated this pathway in skin cultures containing existing dermal condensates . BMP treatment led to erosion of condensates , with a profound reduction in their size ( Fig 3E and 3F ) . This demonstrates that BMP stimulation destabilises and depletes cells from mesenchymal aggregates when present in excess . Although dermal condensates are sites of high BMP4 production , BMP activity within the hair follicle rudiments is normally restrained through expression of the inhibitors NOGGIN and CTGF in the condensate and placode , respectively [22 , 28 , 29] . This , together with selective Fgf20 expression in the placode [27] , produces a niche microenvironment of low BMP and high FGF activity at sites of follicle formation . These results indicate roles for FGF and BMP signalling in influencing mesenchymal cell aggregation . We tested whether these influences could be modulated to trigger mesenchymal condensation by mimicking the hair follicle primordium microenvironment—that is , high-FGF and low-BMP signalling ( hereafter , FGFHiBMPLo ) —across the entire skin . Strikingly , imposing these conditions using FGF9 together with LDN193189 caused dermal condensates to arise in a periodically spaced manner without corresponding expression of the epidermal placode marker Dkk4 ( Fig 4A ) . Histological sections from these samples showed the presence of large dermal condensates at the dermal–epidermal junction but an absence of epidermal placodes ( Fig 4A ) . Neural cell adhesion molecule ( NCAM ) staining reveals the continued dermal identity of cells in the condensates thus induced , together with the absence of the distinct placodal NCAM expression that indicates an epidermal contribution to patterning ( Fig 4B ) . As observed for Dkk4 , other markers of epidermal placodes ( Shh , Bmp2 and Edar ) [18 , 54] are not expressed in FGF9- and LDN193189-treated skins , while dermal condensate markers Bmp4 and Sox2 [54 , 55] do display patterned expression , demonstrating the dermal condensate identity of the mesenchymal aggregates ( Fig 4C ) . In FGFHiBMPLo conditions , epidermal TCF/Lef::H2B-GFP reporter signal is not detectable , demonstrating that FGF suppression of epidermal β-catenin signalling is not alleviated by simultaneous inhibition of BMP signalling . The dermal condensates that form under FGFHiBMPLo conditions have increased overall condensate size and markedly low cell density between the broad condensates ( Fig 4D ) . The ability to pattern mesenchymal condensates in an FGFHiBMPLo environment is not restricted to a specific developmental stage ( S5 Fig ) , and these structures , once induced to form , are autonomously stable upon restoration of normal FGF and BMP function ( S6 Fig ) . To determine the relative rates of dermal condensate patterning , we cultured skin explants under normal or FGFHiBMPLo conditions and imaged the samples at different time points . Pattern formation under normal conditions is more rapid than in FGFHiBMPLo conditions , with the control pattern appearing and stabilising quickly , while the FGFHiBMPLo pattern is slower to appear ( S7 Fig ) . Time-lapse imaging of the formation of mesenchymal condensates under FGFHiBMPLo conditions reveals the gradual emergence of these focal aggregates across the entire skin ( Fig 5A , S4 and S5 Videos ) . To investigate the cell behaviours underlying mesenchymal self-organisation , we performed live cell imaging of TCF/Lef::H2B-GFP E13 . 5 dorsal skin explants using confocal microscopy ( Fig 5B–5D ) . We used the 6-h window analysis as before to determine the Euclidean angle of movement with respect to the future condensate centre for each window , as well as the Euclidean and accumulated distance travelled over this period . In FGFHiBMPLo conditions , the intercondensate Euclidean angles ( n = 612 ) deviate from a uniform distribution ( Kolmogorov–Smirnov test D = 0 . 09 , p < 0 . 01 ) , while condensate-bound track angles ( n = 91 ) were consistently altered from both a uniform distribution ( Kolmogorov–Smirnov test D = 0 . 32 , p < 0 . 001 ) and the distribution of intercondensate angles ( Kolmogorov–Smirnov test D = 0 . 25 , p < 0 . 001 ) . As observed under normal ( control ) conditions ( Fig 2C ) , the median Euclidean distance travelled in these 6-h windows was greater for condensate-bound cells ( Mann–Whitney U test p < 0 . 001 ) than intercondensate cells ( Fig 5B ) . The behaviour of these cells entering condensates is remarkably similar to that observed in conditions of normal patterning ( Fig 2C and 2D ) , with cells showing directed movement to condensates ( Fig 5B–5D ) . The median Euclidean angle of movement differed significantly between intercondensate and condensate-bound cells in the 0–6-h window ( Kruskal–Wallis test p < 0 . 001 , Mann–Whitney U test with Bonferroni’s correction test p < 0 . 001 ) ( Fig 5C ) . Consistent with this behaviour , the median persistence for condensate-bound cells also differed from intercondensate cells in the 0–6-h window ( Kruskal–Wallis test p < 0 . 001 , Mann–Whitney U test with Bonferroni’s correction p < 0 . 001 ) ( Fig 5D ) , further demonstrating directed movement of condensate-entering cells . To further investigate cell behaviour between normal ( control ) and FGFHiBMPLo conditions , we compared summary statistics for individual cell tracks ( Fig 5E ) . As expected , the median accumulated velocity of condensate-entering cells in normal and FGFHiBMPLo conditions ( Fig 5E ) was increased when compared to intercondensate cells under control conditions ( Kruskal–Wallis test p < 0 . 0001 , Mann–Whitney U test with Bonferroni’s correction p < 0 . 001 and p < 0 . 0001 , respectively ) . However , surprisingly , the median accumulated velocity of intercondensate cells in the FGFHiBMPLo conditions was also significantly higher than intercondensate cells in control conditions ( p < 0 . 0001 ) ( Fig 5E ) . This was not reflected by a change in the median Euclidean velocity nor the persistence of intercondensate cells under FGFHiBMPLo conditions ( Kruskal–Wallis test p < 0 . 0001 , Mann–Whitney U test with Bonferroni’s correction p > 0 . 05 for both cases ) , suggesting that the increase in cell movement was due to a chemokinetic effect of FGFHiBMPLo conditions ( Fig 5E ) . Taken together , these results show that slower pattern emergence under FGFHiBMPLo conditions is not a result of sluggish cell movement but a reflection of the dynamics of the pattern-forming process itself . To gain an overall view of cell displacement across the field during the course of cellular condensation , we used particle image velocimetry ( see S1D Appendix ) to analyse the time-lapse videos ( S1 and S5 Videos ) . This approach delineates the average paths of cells ( Fig 5F , see S1D Appendix ) revealing that a major distinction between unperturbed and mesenchyme-only patterning is the large zone of attraction which extends to collect cells in the latter condition . As the underlying individual cell behaviour driving condensate formation is the same in control and FGFHiBMPLo conditions , we asked whether these are fundamentally distinct patterning mechanisms or whether they might be differently regulated outputs of a single underlying mechanism . Reaction–diffusion- and cell aggregation-based patterning mechanisms behave differently at tissue boundaries [4 , 22 , 56 , 57] . Simulations of reaction–diffusion systems display an edge affinity in the arrangement of their cell clusters [4] . In contrast , simulations of cell aggregation-based systems display cell clusters that form at a distance from the cut edge ( Fig 5G , see S1E Appendix ) . We manipulated the tissue boundaries under both control and FGFHiBMPLo conditions by introducing a cut edge into the skin explants and found , consistent with the mesenchyme-only patterning arising from a different mechanism , edge effects to be different for normal versus mesenchyme-only patterning . The normal pattern aligns close to the cut edge of the tissue , as previously reported [4 , 22] , while the latter respects the shape of the boundary but maintains a large distance from the edge to the nearest pattern foci ( Fig 5H ) . Intuitively , these behaviours can be thought of as recognising an advantage for cells adjacent to an edge in a reaction–diffusion system , as they lack competition on 1 side and are able to dilute the inhibitor that they produce into the culture medium . Conversely , in a patterning system based on cell aggregation , the cells close to the edge are disadvantaged , as they have reduced numbers of neighbours with which to nucleate clustering . As the mesenchymal patterning mechanism is fundamentally distinct from the normal condition , we sought a mechanistic basis for this process . We treated skin cultures with modulators of pathways previously implicated in dermal cell condensation to identify signalling pathways with selective effects on mesenchyme-only patterning . We found that mesenchyme , in both control and FGFHiBMPLo conditions , patterned robustly , despite the presence of inhibitors or activators of the CXCL/CXCR , Notch , or platelet-derived growth factor ( PDGF ) pathways ( S8 Fig ) . Alteration of TGFβ signalling , however , profoundly suppressed mesenchyme-only patterning while having relatively modest effects on normal patterning . The TGFβ receptor type I and II inhibitor LY2109761 ( Fig 6A ) slowed the assembly of normal dermal condensates , while placode patterns became expanded and threaded through the epidermis ( Fig 6B and S9 Fig ) , matching the delayed hair follicle phenotype reported for the Tgfb2 mutant mouse [48] . Augmenting signalling by administration of recombinant TGFβ2 permitted a normal array of placodes and condensates to arise , with condensates appearing more prominent than in control conditions . However , either suppression or widespread stimulation of TGFβ2 signalling abolished the ability of mesenchyme to pattern autonomously in FGFHiBMPLo conditions ( Fig 6B ) . Thus , active TGFβ signalling is required for mesenchyme-only patterning but must be restricted for this process to yield a spatially organised array of condensates . Tgfb2 is broadly expressed by mesenchymal cells [54 , 58] during primary hair follicle formation and in cell clusters at sites of dermal condensation ( Fig 6C ) . This expression is consistent with widespread SMAD2 phosphorylation in early dermal fibroblasts , becoming focussed in incipient dermal condensates ( Fig 6D ) [59] . To test whether TGFβ2 can attract mouse dermal mesenchymal cells , as previously reported for chicken mesenchyme [60] , we applied TGFβ2-coated beads to skin and detected a strong accumulation of cells around the bead ( Fig 6E ) . Thus , TGFβ2 represents a widely expressed attractant serving to draw mesenchymal cells together . Having defined restricted TGFβ as a required self-attractive factor in mesenchyme-only patterning , we sought its role in an interplay between normal patterning and mesenchymal self-organisation . In addition to its function as a direct mesenchymal chemoattractant ( Fig 6E ) [61] , TGFβ signalling also influences cell migration and cell–matrix interactions through modulation of gene expression to create an environment conducive to cell migration [62 , 63] . We set out to identify in developing skin whether TGFβ regulates the expression of genes known to be associated with cell migration , adhesion , and matrix composition in other tissues [64–68] . We compared responses to TGFβ in both E13 . 5 and E13 . 75 skin with those elicited by similar FGF and BMP treatments , 2 signalling pathways we previously found to promote or antagonise dermal cell aggregation , respectively ( Figs 2F and 3 ) . We identified TGFβ regulation of genes encoding the cell migration modulators transforming growth factor beta induced ( Tgfbi ) and thrombospondin family members ( Thbs2 and Thbs4 ) , as well as the matrix components Fibronectin ( Fn1 ) , Syndecan-1 ( Sdc1 ) , and Tenascin-C ( Tnc ) ( Fig 7A and 7B ) , suggesting that TGFβ alteration of cell–matrix interaction may contribute to its aggregative effect . The TGFβ gene regulatory effects are broadly distinct from FGF-elicited responses , despite both signals sufficing to stimulate dermal cell aggregation at their local sources , while BMP suppresses Fn1 and Sdc1 expression and potentially downmodulates TGFβ signalling through induction of inhibitory Smad7 and suppression of the TGFβ type III receptor ( Tgfbr3 ) , a known enhancer of TGFβ2 signalling [69] . These gene expression changes related to cell–matrix interactions and movement suggested that TGFβ may play a general role in promoting cell migration . To assess whether TGFβ could promote cell attraction to placodes , as indicated by the prominent condensates it induces during patterning ( Fig 6B ) , we tested the effect of generalised TGFβ availability on directed movement to FGF sources , where loaded beads replicate the local source of FGF produced in a placode . We found that the extent of mesenchymal cell attraction to FGF-coated beads was strongly increased by the presence of TGFβ2 in the culture medium . Conversely , cell attraction to FGF beads was greatly diminished by TGFβ signal suppression ( Fig 7C and 7E ) . We detected no reciprocal effect of ubiquitously available FGF on cell recruitment to local TGFβ2 ( Fig 7D and 7F ) . Thus , TGFβ generates an environment conducive to efficient cell recruitment at FGF sources , explaining the phenomenon of delayed dermal condensate formation when this signal is suppressed .
Embryonic pattern formation proceeds rapidly; periodic arrangements of limb digits arise within approximately 16 h [70] and hair follicles in approximately 10 h [22] . By focussing on short-lived mRNAs , we have identified a network of interactions between the BMP , FGF , and WNT pathways capable of breaking symmetry to produce a periodic pattern . In this system , WNT signalling acts to stimulate expression of genes denoting placode fate ( Dkk4 , Bmp2 , Fgf20 ) , while BMP and FGF signalling inhibit their expression . We note that the half-lives of the proteins encoded by components of the network would also need to be relatively short to achieve rapid pattern formation . The many mRNAs with long half-lives that become preferentially expressed in the placodes , including Ctnnb1 , Wnt10a , Wnt10b , and Edar , may act to stabilise the rapidly emerging pattern and promote hair follicle growth , as previously shown for EDAR function [22 , 25 , 40] . Following definition of the epidermal placode arrangement , dermal cells are locally recruited to assemble a condensate . Previously , a mesenchymal cell-sorting mechanism for hair follicle patterning was proposed based on genetic correlations between hair follicle number and size [71] , conceptually similar to the well-studied and diverse colour patterns on fish skin arising from sorting of distinct mesenchymal lineages [15] . Though there is abundant heterogeneity of dermal mesenchymal cells that could be exploited to achieve such a mechanism [72] , our quantitative analyses do not support the operation of such a system and instead show that the cells ultimately forming the condensate are those located in its immediate proximity ( Fig 2 ) . The local movement of the dermal mesenchymal cells is thus similar to the short-range movements that act to construct the epithelial placode [20] . In addition to their contribution to pattern formation through rapid gene regulatory interactions , the BMP and FGF pathways also influence mesenchymal cell behaviour . FGF , normally produced locally at the placode , directs mesenchymal clustering , while BMP suppresses aggregation . By mimicking the hair follicle microenvironment of high-FGF and low-BMP signalling , we identify a latent potential of dermal mesenchyme to self-organise , yielding a stable periodic pattern of cell aggregates in the absence of signalling directives from the epidermis . This demonstrates that dermal condensates do not require spatially restricted signals to form . Rather , the widespread BMP activity in the skin prior to hair follicle patterning , together with the slow rate of dermal self-organisation , subordinates the mesenchyme’s patterning potential to reaction–diffusion patterning in the epidermis . Our identification of a mesenchyme-only patterning condition is , in structural terms , reciprocal to that arising from Fgf20 deficiency , which results in epidermal placode formation without accompanying mesenchymal condensates [27] . Thus , epidermal and dermal patterning are separable self-organising systems , coupled through epidermal FGF-guided mesenchymal cell attraction with TGFβ potentiation of this accumulation . Mechanistically , however , these epidermis-only and mesenchyme-only patterns arise through very different mechanisms . Epidermis-only patterning in the Fgf20 mutant is achieved through focally restricted signals , agreeing with the majority of patterning signals being produced in this tissue layer ( see Fig 1D and S1 Table ) , while it is motile cells that determine the dermis-only pattern . The expansion of placode identity upon suppression of TGFβ signalling ( Fig 6B ) , similar to that of the unresolved prepattern ( Fig 2E ) , may be a secondary result of delayed condensate formation together with its accompanying inhibitors ( BMP4 and DKK1 ) failing to define the placode edges . Our findings of dermal condensate formation in the absence of an epidermal prepattern are consistent with modes of tissue patterning in which moving mesenchymal cells themselves are agents of pattern formation . These models do not require interacting diffusible signals and such mechanisms integrate tissue morphogenesis and patterning as part of a single process , rather than occurring in sequence , as in the Turing system [4 , 8 , 9 , 73] . An aggregative potential of dermal papilla cells , the product of the embryonic dermal condensate , has been noted [74] and may not be unique to the skin , as spontaneous condensation of mesenchymal stem cells from a range of organs has been identified , given suitable culture substrates in vitro [75] . Beyond the formation of simple condensates , spatially organised aggregates of embryonic limb bud mesenchymal cells arise spontaneously in culture [76] , with a suggested role for TGFβ2 driving this process for mesenchymal cells cultured in isolation from epithelium [77] . These observations may suggest a broad role for TGFβ in driving mesenchymal morphogenesis , whether spatial organisation is guided by a prepattern or occurs de novo through cell movement . Together , these results support a model for hair follicle positioning and construction in which a reaction–diffusion system , operating largely in the epidermis [25 , 27 , 39] , limits the locations at which mesenchymal organisation can occur by providing both local FGF direction and relief from BMP activity ( Fig 8 ) . Dermal cells respond to these sources of FGF , with widespread TGFβ2 generating an environment promoting entry into condensates according to the FGF gradients . As it forms , the condensate produces BMP4 , thereby restricting further condensate and placode expansion . Ultimately , the signals operating in reaction-diffusion patterning also serve to modulate mesenchymal behaviour to direct , inhibit , or stimulate mesenchymal condensation , such that , under FGFHiBMPLo conditions , the components of the normal patterning network are recast to achieve mesenchyme-only patterning . Thus , we conclude that the dermal mesenchyme does possess pattern-generating ability but that this is preceded by a rapid , primarily epidermal , pre-patterning system that acts to specify the restricted locations at which mesenchymal organisation is permitted . The rationale for the existence of 2 distinct routes to achieve periodic patterning during hair follicle formation may lie in a shifting balance between these 2 potentials in the waves of hair follicles that form at different developmental stages . Thus , it may be that the pattern of secondary or tertiary hair follicles is defined by mesenchymal cell behaviour to a greater extent than the primary follicles focussed upon here . Alternatively , the existence of 2 periodicity generators may be a remnant of the evolutionary history of skin development , as it has been suggested that turnover of patterning mechanisms can occur during the course of evolution [78 , 79] and that direct cell-driven patterning systems tend to become captured by signal-based pre-patterning systems [80] . Our work demonstrates experimentally such a superimposition of different patterning mechanisms , with 1 potential for self-organisation being suppressed and subjected to instruction by another .
All animal work was conducted under approval of the Animal Welfare and Ethical Review Body ( AWERB ) at The Roslin Institute , University of Edinburgh , and by the United Kingdom Home Office in accordance with the Animals ( Scientific Procedures ) Act 1986 . Euthanasia was carried out according to Schedule 1 of the Animals ( Scientific Procedures ) Act 1986 . Mice were on the FVB/N background , apart from those employed in time-lapse and static imaging of cell rearrangement , which were offspring of a cross between male hemizygous TCF/Lef::H2B-GFP mice [43] on a mixed C3H/C57 genetic background and female FVB/N mice . Noon on the day of discovery of the vaginal plug was designated day 0 . 5 of development . Skins were examined for pre-existing signs of hair morphogenesis and not used if these were detected for experiments designated to start prior to follicle morphogenesis . Embryos were harvested in high-glucose Dulbecco’s modified Eagle’s medium ( DMEM ) ( Sigma ) supplemented with 1% penicillin/streptomycin ( Gibco , Life Technologies ) and kept on ice for short periods . For the qRT-PCR experiments used in the network derivation , dorsal skin explants were halved along the midline to generate treated and control halves from each embryo . Skin halves were placed onto a cellulose filter ( Millipore , pore size 0 . 45 μm ) . The tissue was then submerged in DMEM supplemented with 2% FBS ( complete DMEM ) in a centre-well dish ( Falcon ) and incubated at 37°C and 5% CO2 for 6 h . For the TGFβ2 , FGF9 , and BMP4 experiments , whole dissected skins were cultured in complete DMEM containing the required recombinant protein for either 8 h or 24 h . For longer-term culture , whole skin explants were dissected and mounted on filters as described above , submerged in complete DMEM , and supported by a metal grid in a centre-well dish . E13 . 5 explants were cultured for 27 h unless stated otherwise . For experiments extending beyond 27 h , medium was replaced every 24 h . Epidermal–dermal separations for RNA-seq and qRT-PCR experiments were performed by incubating skin samples with 2 mg/mL Dispase II ( Gibco ) at 37°C for 10 minutes . For imaging , epidermal–dermal separations were performed by incubating skin in 10 mM EDTA/PBS for 25 minutes at 37°C followed by 20 minutes in PBS at RT prior to separation with fine forceps . For counterstaining , samples were fixed in 4% PFA , washed several times in PBS/0 . 1% Tween 20 ( PBST ) , treated with 20 μg/ml proteinase K for 3 minutes , washed in PBT , incubated with 100 μg/ml RNase for 20 minutes , washed again with PBT and then stained with 1/2000 diluted propidium iodide ( PI ) solution ( Life Technologies ) for 5 minutes . Following staining , samples were washed in PBT and mounted in Prolong Gold ( Life Technologies ) . Recombinant FGF7 ( mouse ) , FGF9 ( mouse and human ) , BMP4 ( mouse ) , and TGFβ2 ( human ) were from R&D Systems . LDN193189 ( Stemgent ) , CHIR99021 ( Axon biochem ) , IWR-1 ( Tocris ) , DAPT ( Bio-Techne ) , LY2109761 ( Cambridge Biosciences ) , Pertussis toxin ( Tocris ) , Imatinib Mesylate ( Sigma Aldrich ) , and SU5402 ( Sigma Aldrich ) were reconstituted according to the manufacturer’s recommendations . We did not detect any significant effect from treatment with recombinant FGF20 protein obtained from 2 commercial suppliers on either placode pattern formation , skin development , or on expression of the direct FGF pathway target gene Etv5 . Total RNA was isolated from skin explants using Tri Reagent ( Sigma ) and treated with RQ1 DNAse ( Promega ) to remove contaminating genomic DNA . cDNA was synthesised from total RNA using random primers and Superscript III reverse transcriptase ( Roche ) in a 20-μl reaction . Reactions were diluted 20-fold and had 3 μl used as a template for each qRT-PCR . Each reaction was performed in a 20-μl volume using Universal SYBR Green Master Mix ( Roche ) containing Rox reference dye . Reactions were performed in triplicate , with at least 3 biological replicates used to determine each data point . Relative expression levels were determined from cDNA dilution standard curves and normalised to Tbp ( or Capzb for half-life determination ) . Oligonucleotide sequences used are given in S1 Supporting Methods . See S1 Supporting Methods for Actinomycin D dose determination and treatment , sample quality control and processing , RNA-sequencing , analysis , and qRT-PCR validation . Skin explants were fixed overnight in 4% PFA at 4°C . Samples were dehydrated into 100% methanol , bleached in 6% H2O2 , then rehydrated and treated with 20 μg/mL proteinase K . After postfixing in 4% PFA containing 0 . 2% glutaraldehyde for 20 minutes , skin explants were hybridised with probe at 60°C overnight in 50% formamide , 5 X saline sodium citrate ( SSC ) , 1% SDS , 50 μg/mL heparin , and 50 μg/mL yeast RNA in diethyl pyrocarbonate ( DEPC ) -treated H2O . Samples were washed to remove unbound probe and signal detected using an alkaline phosphatase sheep antidigoxigenin antibody ( Roche , 1:1 , 000 dilution ) and 5-bromo-4-chloro-3'-indolylphosphate/nitro-blue-tetrazolium ( BCIP/NBT ) colour reaction ( Sigma ) . For immunohistochemistry , samples were fixed in 4% PFA , embedded in 0 . 12 M sodium phosphate buffer/7 . 5% gelatin/15% sucrose , and cryosectioned . Sections were rehydrated in PBS at 37°C for 30 minutes , briefly washed with tris-buffered saline containing 0 . 01% Tween 20 ( TBST ) , then incubated in blocking buffer ( TBST containing 5% heat-treated sheep serum and 1% BSA ) for 1 h at RT before overnight incubation at 4°C with primary rabbit antibodies ( 1:200 mouse antiactive beta catenin ( Millipore #05–665 ) or rabbit antiphosphorylated SMAD2 ( pSMAD2 ) ( Cell Signalling Technologies #3108 ) diluted in blocking buffer . Samples were washed in TBST , then incubated with fluorescent secondary antibodies ( 1:500 Life Technologies ) in blocking buffer for 1 h at RT . Samples were washed with TBST , counterstained with DAPI ( Sigma ) and mounted in Prolong Gold ( Life Technologies ) . Sections were imaged using a Zeiss LSM710 confocal microscope ( Carl Zeiss ) . For immunoblotting , protein was extracted from skin samples using radioimmunoprecipitation assay ( RIPA ) lysis buffer ( Santa Cruz Technologies ) and a handheld homogeniser . Protein samples were diluted in NuPage LDS sample buffer ( Life Technologies ) , separated by gel electrophoresis on 4%–12% Bis-Tris NuPage precast gels under denaturing conditions and transferred to nitrocellulose membrane ( Amersham ) . Membranes were blocked in 5% milk/TBST for 1 h at RT , followed by overnight incubation at 4°C in 5% BSA/TBST containing primary antibody ( 1:1 , 000 mouse anti-active β-catenin [Millipore #05–665] , 1:3000 rabbit anti-β-catenin [BD biosciences #610153] , 1:1 , 000 rabbit anti-phospho SMAD 2/3 [Cell Signalling Technologies #8828] , 1:1 , 000 rabbit anti-SMAD2 [Cell Signalling Technologies #5339] , or 1:3 , 300 mouse anti-γ tubulin [Sigma Aldrich #T6557] ) . Following primary antibody incubation , membranes were washed in TBST and incubated for 1 h at RT in 5% milk/TBST containing horseradish peroxidase ( HRP ) -conjugated species-specific secondary antibodies ( Dako ) . Membranes were washed several times with TBST before detection with the Novex ECL chemiluminescent substrate reagent kit ( Life Technologies ) and developed on ECL Film ( Amersham ) . Affi-Gel Blue Gel beads ( Bio-Rad ) were washed twice in PBS and incubated in either 100 μg/ml recombinant human FGF9 , 100 μg/ml recombinant human TGFβ2 , or 100 μg/ml BSA diluted in PBS for at least 2 h at RT or overnight at 4°C . Beads were placed onto a nitrocellulose Millipore filter ( pore size 0 . 45 μm ) , and dissected E12 . 5 TCF/Lef::H2B-GFP dorsal skin explants were manoeuvred and placed on top of the beads , dermis side down . Skins were then imaged , as described in S1 Supporting Methods , over a period of 48–72 h . E13 . 5 TCF/Lef::H2B-GFP dorsal skin explants were dissected and imaged as described previously using a custom imaging chamber [81] with the following modifications . Instead of using a central imaging clip , the entire base of the chamber was filled with 1% ( weight/volume ) agarose in PBS . Dissected embryonic skin was mounted dermis-side down onto a black nitrocellulose filter membrane ( Millipore ) with a 45 μm pore size . The membrane was subsequently placed onto the agarose block , and a lummox membrane ( Greiner ) was clamped across it with an o-ring such that the skin was sandwiched between the 2 membranes . The imaging chamber was filled with DMEM without phenol red containing 4 , 500 mg/L glucose , 2% FBS , 1% penicillin/streptomycin , and 0 . 584 g/l L-glutamine . Images were captured with a 20X objective using a Nikon A1R inverted confocal microscope in a heated chamber supplied with 5% CO2 in air . Bead and FGF9/LDN193189 combination experiments were performed using a Zeiss Live Cell Observer and are described in S1 Supporting Methods . All image analysis tasks were performed using custom written macros for the Fiji [82] distribution of ImageJ , an open-source image analysis package based on NIH Image [83] . The source code is available through GitHub ( https://github . com/richiemort79/cell_patterning ) . In order to track cell behaviour in an unbiased manner , maximum intensity Z-projections of time-lapse sequences were drift-corrected and cropped to include the area of the final condensate ( of varying size ) with approximately 100 μm of space surrounding each . A window of at least 1 , 500 minutes that incorporated condensate formation was considered . At least 50 cells per condensate from at least 4 independent skins were then selected at random prior to condensate formation and tracked manually until they either entered a condensate or the video ended . A cell was deemed to acquire a ‘condensate identity’ if , at the end of the tracking period , it was incorporated within this structure; for analysis purposes , tracking was halted on follicle entry . Cells that did not enter the condensate were termed ‘intercondensate . ’ Visual analysis of these cells up to a later time point was also performed to ensure that these cells did not become part of the condensate at a later stage . | Repeating anatomical units , forming a pattern , are present in different parts of the body . This is particularly obvious in the skin , where the many hair follicles become regularly arranged and fixed in place in the embryo as a pattern of evenly spaced spots . The basis for the formation of such repeating patterns has attracted a number of theoretical explanations . One influential theory is that cells first issue chemical signals to one another to produce a map of the pattern , which they then follow to assemble a structure , like a hair follicle . Alternative theories instead suggest that cells cluster together directly , without reference to a pre-existing map , to drive pattern formation . In this study , we find that a set of chemical signals known to be important for early hair follicle formation form a network capable of producing a patterned template to which cells then respond , moving according to its instructions . This supports the signal-based theory for pattern formation . Strikingly , though , by imposing the conditions of the incipient hair follicle evenly across the entire skin , we reveal that skin cells can also form patterns directly by coalescing , without requiring instructions from a pre-existing pattern . However , this ability of cells to pattern directly is normally subservient to their following of a quickly forming pattern template defined by chemical signals . This work highlights that different ways of making biological patterns can coexist in the same embryonic organ but that one is normally subordinated to the other . |
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An important task performed by a neuron is the selection of relevant inputs from among thousands of synapses impinging on the dendritic tree . Synaptic plasticity enables this by strenghtening a subset of synapses that are , presumably , functionally relevant to the neuron . A different selection mechanism exploits the resonance of the dendritic membranes to preferentially filter synaptic inputs based on their temporal rates . A widely held view is that a neuron has one resonant frequency and thus can pass through one rate . Here we demonstrate through mathematical analyses and numerical simulations that dendritic resonance is inevitably a spatially distributed property; and therefore the resonance frequency varies along the dendrites , and thus endows neurons with a powerful spatiotemporal selection mechanism that is sensitive both to the dendritic location and the temporal structure of the incoming synaptic inputs .
Neurons are constantly bombarded by thousands of synaptic inputs , so it is essential that neurons are able to listen selectively to subsets of these inputs . Throughout the sensory pathways , topographic maps ensure that neurons are able to sample a limited range of the stimulus space [1] . But the use of space is only one means by which input selectivity is achieved in the central nervous system . Another effective means is to respond selectively to particular temporal input patterns . A range of mechanisms can facilitate temporal selectivity ranging from pre-synaptic short-term plasticity [2]–[6] , learning strategies of specific temporal patterns [7]–[10] , to post-synaptic membrane resonances which enhance responses to specific input rates [11]–[13] . The focus of this study is the latter mechanism of resonance , membrane resonance , which has been traditionally considered a scalar property of a neuron: one neuron has one preferred resonance frequency [11] , [14] . This view , however , is inconsistent with the increasing awareness of the complexity of dendritic ramifications , the non-uniform spatial distribution of their ionic channels and highly localized non-linearities . Such elaborate biophysics can endow single neurons with multiple resonances occuring at a wide range of frequencies and bandwidths , and thus enable neurons to act as multi-dimensional input classifiers . Here , we explore this idea using both analytic methods and numerical simulations of neurons with both simplified and realistic dendritic structures . We show how spatial profiles of resonance frequencies emerge naturally in dendrites , facilitating selective filtering of synaptic inputs based on their location and temporal signature . Our findings thus counter the widely-held assumption that input selection is based on a single prefered frequency band regardless the location of the synaptic input .
Resonance in neuronal membranes has been described by many experimentalists and theoreticians [11] , [12] , [15]–[18]; it requires an interplay between at least two conductances with different dynamics . Figure 1A illustrates how an interaction between a membrane's passive electrical properties ( resistance and capacitance ) and one voltage-dependent current ( low voltage-activated potassium current , ) can give rise to a resonant membrane impedance ( ) comprised of two admittances: . The interplay between these admittances produces the impedance resonance in much the same way as the restorative and regenerative conductances interact to form a resonance . The first admittance , , is an effective leak ( red curve in Figure 1B ) that is mostly associated with the classic membrane passive RC-circuit ( time-constant ; see METHODS ) , and which acts as a shunt at high frequencies as schematically illustrated by the large red arrow below the plots . The second admittance ( blue curve in Figure 1B ) is due to the channels whose limited activation rate ( time-constant ) leaves them increasingly closed at frequencies higher than , as depicted by the small blue arrow at right . The sum of these two admittances often results in a minimum at a mid frequency range producing a peak impedance at a resonance frequency ( Figure 1A ) . This minimum occurs when the increase in counter-balances the drop of . Since the increase of takes place for frequencies higher than , the resonance frequency is always higher than . This is demonstrated in figure 1C where is color-coded for different values of and while is displayed as black line contours . Clearly , the resonance frequency and its sharpness ( Q ) depend on , , and , and through them on any biophysical parameters affecting the resting state of the membrane . As such , is affected by the reversal potential , membrane leak conductance , and maximal potassium conductance ( see METHODS , Figure 1C and Supplementary Figure S1 ) . As shown in Figure 1C , the resonance frequency increases monotonically both with increasing potassium channel density and with its steady state level ( set by ) . The sharpness of tuning Q depends on how much can decrease before the increase in takes place and on how close in frequency these two changes occur . Hence , the dependence of Q upon the biophysical parameters is complex . For instance , Supplementary figure S1 A2 illustrates how changes in the leak conductance produces nonmonotonic changes in Q . To conclude , even in an isopotential patch of membrane with a linearized model of channel dynamics , the resonance frequency can vary substantially ( 300% or 120<<350 Hz ) depending on a range of parameter values typically found at different locations of a dendrite ( Figure 1C and Supplementary Figure S1 ) . A key objective of our study is to explore the influence of “space” ( namely dendritic location ) on the resonance properties . To do so , we distinguish between local input impedance , , and the transfer impedance , that is the total transfer function between the input at location x and a recording electrode at the soma , as illustrated in Figure 1D . It has been shown [19] that if the membrane impedance is bandpass , then so are the transfer impedance and the cable space constant , a measure of the electrical compactness of the dendrite . Computing the transfer impedance using just a uniform membrane model already reveals a strong spatial profile of resonance frequencies as illustrated in Figure 1D ( see METHODS ) . This dependence arises mostly from an inherent mismatch between the resonance of the input impedance ( ) and that of the space constant ( ) as shown in figure 1E . By definition the space constant and the input impedance are related ( see Methods ) and the mismatch , which is influenced by , , and , is non-zero for a large set of parameters ( i . e . ; see Supplementary Figure S1 B1 ) . This implies that in most cases , a spatial profile of resonance frequencies emerges along the semi-infinite cable: When the injection and recording site are close to one another , the resonance frequency of the transfer impedance is mostly that of the input impedance . With increasing distance between both sites , the resonance frequency of the transfer impedance becomes more influenced by the resonance frequency of the frequency-dependent space constant . Figure 1F illustrates this effect and demonstrates that with plausible parameters the resonance frequency of the transfer impedance can change by as much as 11% over just the first 500 µm ( of a semi-infinite cable model ) . Thus , the mere spatial extent of a dendrite already results in a spatially distributed profile of resonant frequencies . A dendrite , however , is structurally far more elaborate than the simplified morphology and uniform membrane of the cable presented so far . Dendritic membranes , for example , often exhibit non-uniform distributions of ionic channels , as well as branching and tapering geometries . To understand such different cases , one can assume as a first approximation that a dendrite is constituted of small uniform cable segments ( piecewise constant approximation ) . The boundary conditions at each end of the uniform segment affect the spatial profile of resonance frequency of the transfer impedance . Therefore , we consider the effects of boundary conditions using a linearized cable model ( with parameters similar to Figure 1D , E , F ) . Figure 2B and C illustrates the spatial profile of resonant frequencies under two geometric configurations: the branching of daughter dendrites at the apical end ( Figure 2B ) and the attachment of a soma at the basal end ( Figure 2C ) . In both cases , the boundary conditon at the tip of the segment is given by a “lumped” impedance ( e . g . representing the impedance of the daugther dendrites lumped together ) . Moreover , this “lumped” impedance can be set to have different resonance frequencies by varying , , . In Figure 2A the “lumped” impedances are presented color coded by their resonant frequency from blue ( = 150 Hz ) to red ( = 420 Hz ) . The spatial profile produced by each resonant “lumped” impedance is compared to a control condition where the boundary impedance is that of an uniform semi-infinite cable ( shown in black in Figure 2A ) . Compared to the uniform semi-infinite boundary condition , the impedance at the recording location can shift considerably depending on the specific boundary condition and segment dimensions . For example , changes in the resonance frequency of the transfer impedance can be observed throughout the entire length of the segment in the case of a short segment ( 75 µm ) while in the case of a long segment ( 300 µm ) these changes are mainly located close to the modified tip . Interestingly , while boundary conditions modify strongly the profile of resonance frequency , the spatial profile of sharpness is not much affected ( see Supplementary Figure S2 ) . We then investigated the extent to which a spatially nonuniform conductance distribution contributes to the range of resonance frequencies expressed by a neuron . Simulations exploring the distribution of two conductances ( and ) were performed in four types of abstract morphologies: a cable , soma-and-dendrite , bipolar and y-dendrite model ( Figure 3 ) . The left panels of Figure 3 A–D provide a schematic of the optimized conductance distribution along the dendrite . Right panels provide the spatial profile of the resonance frequency ( red ) and sharpness ( blue ) . Optimizing the membrane properties to obtain a large range of resonant frequencies combined with moderate sharpness resulted in specific effects of the non-uniform distribution in each morphology . For the cable , a gradient of conductances with a constant but high produced the largest range of resonance frequencies as shown in Figure 3A . The spatial gradient of along the cable produces an increasing reversal potential toward its distal tip as well as an increasing total leak ( from 0 . 32 mS/cm2 to 1 mS/cm2 ) . Both effects tend to raise the input resonance frequency ( Supplementary figure S1 A1 ) . Moreover , because of the gradient of , each segment of this non-uniform cable will be connected at its proximal tip to a segment of lower characteristic frequency and at its distal tip , a segment of higher input resonance frequency . This configuration is similar to the configuration of a linear resonant cable producing the largest frequency range along its length ( Figure 2 B , C ) and the spatial profile of resonance frequency ranges from 292 to 325 Hz . Finally , the density of is constant and high ( 15 mS/cm2 ) and ensures a sharp tuning of input resonance ( Supplementary Figure S1 , A2 ) . Therefore , the optimization results extend the analytical insights obtained by linearization of the ionic channel dynamics . A similar gradient is observed in the case of a soma-and-dendrite morphology as depicted in Figure 3B . The density of is decreasing from 1 mS/cm2 to 0 . 83 mS/cm . The range of transfer resonant frequencies observed is both caused by the conductance-density gradient the discontinuous boundary condition introduced by the soma ( as analyzed in Figure 2 ) . Overall , the increased complexity of the ball-and-stick morphology increased both the range of frequencies expressed ( 256 to 315 Hz ) and the overall sharpness of tuning ( <Q> = 0 . 92 ) compared to the case of the finite cable shown in figure 3A . The density of is decreasing from 1 mS/cm2 to 0 . 83 mS/cm2 . The range of transfer resonant frequencies observed is both caused by the conductance-density gradient the discontinuous boundary condition introduced by the soma ( as analyzed in Figure 2 ) . Overall , the increased complexity of the ball-and-stick morphology increased both the range of frequencies expressed ( 256 to 315 Hz ) and the overall sharpness of tuning ( <Q> = 0 . 92 ) compared to the case of the finite cable shown in Figure 3A . The optimized conductance profile for the bipolar neuron morphology lead to an even larger range of resonant frequency and Q-factors ( Figure 3C ) . In the bipolar case , the range of transfer resonance frequencies differs in both dendrites mosty due to the different distributions of the leak conductance . In one branch , a low density of both and caused relatively low resonance frequencies of the transfer impedance along the branch while a high density in both conductances caused relatively high resonance frequencies in the other branch . As a result , the range of resonance exhibited in the whole neuron was large ( between 268 and 338 Hz ) and maintained good sharpness ( <Q> = 0 . 99 ) . Thus , thismorphological construct exploited both non-uniform densities and changes in boundary conditions between the soma and each of its two branches . Similarly , the optimized Y-branch produced a large range of resonance frequencies from its low resonance frequency in the parent branch to the high resonance frequency in the daugther branches ( Figure 3D ) . Thus , dendritic constructs such as branching , tapering and non-uniform channel distributions enrich the spatial distribution of resonant frequencies caused by space alone . For a more realistic experimentally reconstructed morphology ( downloaded from NeuroMorpho . org , see Methods ) , the non-uniform distribution of conductances , the complex branching and tapering of dendrites can lead to an even richer spatial distribution of resonance frequency as shown in Figure 4A . We optimized the density of and for each branch of this model . Each branch was allowed to have a linear gradient of these two channels and the optimization criteria was to find the model with largest range of resonance frequencies ( in the complete neuron ) while maintaing a reasonable sharpness ( <Q>>0 . 8 , see METHODS ) . Figure 4A illustrates the model neuron resulting from that first stage of optimization . At each location on the dendritic tree , the resonant frequency of is color-code ranging from 207 Hz ( blue ) to 247 Hz ( red ) . In this model based on a real morphology , the combination of dendritic geometry and non-uniform ion-channel distribution endow any morphologically realistic model neuron with a rich spatial profiles of resonance . Such spatially distributed and sharply tuned resonance frequencies can effectively act as spatiotemporal filters for a neuron's inputs , which leads us to consider in more detail the functional significance of these resonances . With distinct dendritic locations expressing a preference for certain frequencies , one can envision the dendrite as powerful spatio-temporal filter of synaptic inputs: viewed from the vantage point of the soma , each point on the dendritic tree has a preferred input modulation rate that it amplifies while attenuating all others input rates . This is demonstrated by the simulations in Figure 4B where the temporal and the spatial selectivity are illustrated separately ( see Methods ) . Temporal selectivity can be demonstrated when one set of synapses ( at fixed locations ) can cause a differential/preferential response at the soma of the neuron when stimulated with different temporal activation patterns , as illustrated in the scenario of Figure 4B1 . Here , the spatial distribution of the green synapses was chosen on the dendritic tree of Figure 4A so as the combined transfer function optimally responds to a 208 Hz modulated spike train while ignoring a 228 Hz input . This simulation demonstrates the dendritic temporal filtering abilities achieved with a combined spatial profile of transfer resonances . Note that in arriving at this result , we did not need to optimize the synapse properties , which are assumed to simply enhance signal transduction to ensure that the frequencies arising on the post-synaptic membrane are near the resonance frequencies shown in panel Figure 4A . Spatial selectivity is illustrated by two sets of synapses at distinct dendritic locations responding differentially to the same signal as shown in Figure 4B2 . The red synapses are located at dendritic locations corresponding to a resonance frequency of 228±4 Hz and the blue synapses at 208±4 Hz . When both groups were stimulated separately by Poisson processes modulated at 228 Hz ( see Methods ) , the input at the blue synapses generated only a few spikes at the soma ( blue trace ) . By contrast , the same input signal at the red synapses , elicited many more spikes ( red trace ) . The same signal therefore induced different somatic responses when conveyed to the neuron through distinct sets of synapses with different resonance properties to the soma . To conclude , a neuron can perform elaborate spatiotemporal filtering of its inputs utilizing the distribution of its dendritic resonances , a capability that is substantially more elaborate than is widely assumed possible of a neuron expressing only one prefered resonant frequency [12] , [13] , [20] .
In summary , building upon the work of Koch and colleagues [19] , [21] , we have shown that a model of a simple neuronal membrane with typical biophysical properties and ionic channels can readily exhibit a resonant transfer impedance . When viewed from a distance down the cable , the resonance can take a wider range of frequencies and bandwidths . This range expands greatly when considering nonuniform cable models with complex boundary conditions and changing ionic channel densities and types . Finally , the full power and versatility of this dendritic resonance idea comes into focus in a more realistic multi-compartmental model which allowed us to demonstrate its potential functional significance as it enables a neuron to serve as a spatiotemporal filter . Given the ubiquity and diversity of dendritic resonances , why has their functional significance been thus far neglected ? The answer probably lies in the commonly-held view that resonance mainly plays a role in synchrony ( and participation therein ) at lower frequencies ( e . g . , α , β , and θ-bands at <10 Hz ) . At those frequencies it is hard to distinguish experimental variability from a real range of resonance frequencies ( a range of 50% around 4 Hz is 2–6 Hz ) . At the much higher frequencies considered here ( and in only one previous report [14] ) , a 50% range translates to 225–375 Hz . Resonances in those ranges correspond to high gamma . Interestingly , in the lower auditory system , where neurons are known to express fast-activated potassium channels , these higher modulation frequencies can be transmitted by neuron to encode modulation of the sound energy . Temporal modulations at these frequencies convey periodicity cues critical in the perception of pitch [22] . Also , in more central neurons these rates can readily occur in the high-conductance state during which neurons are constantly bombarded with seemingly irregular firing rates [23] . As long as there is a temporal modulation ( envelope ) rate , dendritic transfer resonance can still filter relevant signals . It should be pointed that neurons with a rich variety of dendritic transfer resonance may rather be the rule than the exception . Indeed , as we have highlighted here both nonuniform channel conductance and boundary conditions enhance the usual range of transfer resonance expressed by a cable . There have been many studies demonstrating that channels are non-uniformly distributed on the dendrite [24]–[25] . Given that a diverse range of resonances is ubiquitous and inevitable in dendrites , we can speculate on further implications of our findings . A first important observation is the difference between resonant frequencies of the input versus transfer impedance: the input impedance dominates locally while the tranfer impedance is global insofar it spans the complete dendritic membrane along which an input signal travels to the soma . Plasticity can , in principle , differentially exploit local and global effects . At the local level , a signal that temporally matches the resonant frequency in the input impedance may trigger a large local voltage-depolarization giving rise to a calcium transient that , in turn , triggers plasticity mechanisms [26] . At the global level , a different ( but not mutually exclusive ) hypothesis is based on pre and post-synaptic spike times [27] . In this scenario , the combined synaptic input to a neuron triggers a post-synaptic spike , which then back-propagates into the dendritic tree and activates plasticity mechanisms . Since the strength of somatic depolarizaion depends on the global resonant frequency of the transfer impedance , the most likely inputs to induce spiking ( and hence plasticity ) are those with modulation rates that match this global resonance . A slight variation on the latter hypotheses is the case in which a “teacher” signal impinges onto the soma and triggers spikes . In that situation , the neuron can associate the modulation of the “teacher” signal to a specific the set of synapses that have an equal transfer resonance to the soma . Indeed , such a neuron would be responsive only when the preferred modulation rate at the synapses matches that of the teacher signal . Inputs from synapses with transfer resonance modulated at any other rate would not be carried out to the soma and would not interact constructively with the “teacher” signal . This situation is particularly interesting in the auditory system where low frequency cell could provide “teacher” signals to modulation detector neurons with dendritic branches spread across tonotopy ( such as octopus cells [28]–[30] or inferior colliculus stellate cells [31] ) . Since the output modulation rate of low frequency cells is determined by their location , while that of high frequency cell is not , cross-frequency modulation detectors could arise by such a learning of specific input location . This idea provides a neural basis to solve the central problem of linking the rate modulation of low and high frequency places in auditory pitch perception [32] . Thus , resonant frequencies in dendrites not only enable the neurons to perform elaborate spatio-temporal filtering , it can also have pivotal consequences for plasticity , and different plasticity mechanism could be activated by local or global post-synaptic potentials dependent on the temporal signature of the pre-synaptic signal .
The resonance introduced by can be described in the Fourier domain [16] , [19] , [34] after linearizing the current balanced equation around the resting membrane potential . A small variation in the potassium current is composed of three terms: an ohmic part ( i . e . the steady-state potassium conductance ) and two other terms describing the increase and decrease in subsequent changes in activation and inactivation of the channels . The membrane impedance is given by , where is the effective conductance of the membrane composed of the leak and the steady-state potassium conductance and is the effective membrane time constant . The conductance represents the extra conductance associated with opening additional activation gates following a variation of voltage around rest . Correspondingly , represents the decrease in conductance associated with the closing of some inactivation gates . The frequency dependence of and allows a further simplification . Since ms [33] while ms , any voltage changes at frequencies above 12 . 5 Hz have little effect on the inactivation and thus we can neglect effect of the inactivation . Therefore , we use the following expression for the membrane impedance in Figure 1A: . For the spatially extended models ( Figure 1D , E and 2 ) , the current-balanced equation for each compartment is similar to that of the membrane with the addition of terms describing the current between compartments which is proportional to the axial resistance . The space constant for a dendrite describes the distance between an injection and recording site for which the DC component has decayed of a factor . More generally , the membrane impedance determines the frequency dependent space constant , of the dendrite ( where denotes the real part of a complex number ) . The transfer impedance between any two points separated by a distance can be computed by solving the generalized cable equation given in the Fourier domain by with its appropriate boundary conditions , where . For the semi-infinite cable described in Figure 1 , its magnitude reads and this was used to compute the spatial profile of the resonant frequency and spatial profile of Q-factor , denoted ( see below ) . The space constant of the semi-inifinite cable is thus related to input impedance by where with . This relationship demonstrates why an inherent mismatch exists between the resonance frequency of the space constant is different than that of the the input impedance . When more specific boundary conditions are used ( Figure 2 ) , the transfer impedance does not easily relate to the concept of space constant . Different approaches [21] , [35] , [36] can be used to compute and from the boundary conditions . We have used the expression of rule I and III of Koch and Poggio [21] . Numerical simulations to determine the influence of complex dendritic morphologies on resonance were performed using the NEURON+Python [37] , [38] software . In order to explore the wide range of parameters that leads to significant spatio-temporal input filtering , we performed evolutionary optimizations [39] , [40] of abstract ( cable , bipolar , multipolar , “Y” dendrites ) model neurons ( Figure 3 ) as well as morphological detailed model neurons ( see Figure 4 ) . Optimization by evolutionary algorithms involved two critical steps: parametrization of the model neurons so they can be systematically optimized and , the quantitative assessment of the models to guide the optimization . The parameters used for the optimization are summarized in Table S1 . These parameters are based on neurons from the early auditory pathway [31] , [41]–[43] . Note that in each of these models the segment diameters as well as the conductance densities may follow a linear gradient between an initial and ending value . The diameter is additionally constrained not to increase . The length of the dendritic branches in the abstract models is adjusted so that the total length of the path between soma and termination point is 200 micron . The quantitative assessement of the models we are established by two means . First , the spatial profile of resonance frequency allows us to compare quantitatively the range of frequencies obtained on a fixed morphology . For the linear cable , this is obtained by numerically computing . For the compartmental model with nonlinear channel dynamics , an “impedance amplitude profile”- current ( ZAP-current [44] ) is injected at a specific location in the dendritic segment and the frequency at which the membrane potential is maximal ( ) is taken as the resonant frequency ( i . e . ) . The second assemement is based on the sharpness of tuning , also called the Q-factor . Rather than defining the Q-factor by , as done in various study [12] , [19] , [45] , we use a definition focusing on the bandpass properties offered by dendritic resonance , that is: how quickly the resonant response drops around the resonant frequency . The Q-factor is thus defined by where denotes the bandwidth of the resonance and are such that . The spatial profile of the Q-factor , is determined by computing Q at each point along the dendrite . We can then decide to optimize for range of resonance frequencies obtained , the overall Q factor or both Simultaneously ( as in Figure 3 ) . To demonstrate the spatio-temporal filtering in a spiking model with a realistic morphology , a neuron model with an archetypical multipolar morphology [46] ( “P2-DEV139” originally published in [44] available at the NeuroMorpho . org archive [47] ) is simulated and optimized . We optimize this model neuron in two steps . First , the membrane properties ( Table S1 ) are modified iteratively to obtain a large range of resonance frequencies ( resulting in a 207 to 247 Hz range – see Figure 3A ) and with reasonable sharpness in the dendrites ( 0 . 79<Q<0 . 89 ) . Second , while using these optimal membrane parameters , we optimize synaptic parameters and input parameters for two tasks: temporal or spatial filtering . Both tasks exemplify the single property of the optimized neuron , namely to perform spatio-temporal input classification . For both tasks , the synaptic input parameters optimization is performed as follows . Inputs spike trains onto 25 synapses are obtained from independent non-homogeneous Poisson processes ( NHPP ) with sinusoidal firing rate where and are both optimization parameters . A DC current is added to the soma segment representing the global background activity . To demonstrate the temporal selectivity , we fix the modulation frequency to a target frequency ( Hz ) or a null frequency ( Hz ) . The synapses' location and strength is optimized for a discrimination task: output spike rate is maximized for and minimized for , that is , the location and strength is kept identical for the two different inputs ( figure 3B1 , green dots ) . Because the synaptic locations are the same in both cases , the neuron can only use temporal information of the input to filter the target from the null signal . To demonstrate the spatial selectivity , we fix the input frequency at Hz and optimize synapses' location and strength for two different sets of synapses: the “target set” which should maximize the output firing rate and the “null set” which is optimized for a different frequency . Because the input signal is identical in both cases , the neuron can only use the location of the synapse to filter one signal but not the other ( Figure 3 , B2 ) | Neurons are constantly bombarded by thousands of inputs . Synaptic plasticity is generally accepted as a mechanism to select certain inputs by strengthening their synapses while reducing the effects of others by weakening them . Another biophysical mechanism to select inputs is through membrane resonance that enhances neuronal response to inputs arriving at a specific temporal rate while reducing others . In the classical view , a neuron has one such resonance frequency at which inputs can be preferentially filtered . By dissecting the biophysical mechanism underlying neuronal resonance we find that neurons in fact express a wide range of resonance frequencies spatially distributed along their dendrites . We further show that such dendritic resonance can endow a neuron with a true spatio-temporal filtering property of its inputs: neurons can preferentially filter inputs based on their dendritic location and/or temporal signature . We speculate that this new insight has pivotal consequences for learning and plasticity . |
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Telomere length ( TL ) predicts health and survival across taxa . Variation in TL between individuals is thought to be largely of genetic origin , but telomere inheritance is unusual , because zygotes already express a TL phenotype , the TL of the parental gametes . Offspring TL changes with paternal age in many species including humans , presumably through age-related TL changes in sperm , suggesting an epigenetic inheritance mechanism . However , present evidence is based on cross-sectional analyses , and age at reproduction is confounded with between-father variation in TL . Furthermore , the quantitative importance of epigenetic TL inheritance is unknown . Using longitudinal data of free-living jackdaws Corvus monedula , we show that erythrocyte TL of subsequent offspring decreases with parental age within individual fathers , but not mothers . By cross-fostering eggs , we confirmed the paternal age effect to be independent of paternal age dependent care . Epigenetic inheritance accounted for a minimum of 34% of the variance in offspring TL that was explained by paternal TL . This is a minimum estimate , because it ignores the epigenetic component in paternal TL variation and sperm TL heterogeneity within ejaculates . Our results indicate an important epigenetic component in the heritability of TL with potential consequences for offspring fitness prospects .
Telomeres are evolutionarily conserved DNA sequence repeats , which form the ends of chromosomes together with associated proteins and contribute to genome stability [1] . Telomeres shorten due to incomplete replication during cell division , which can be accelerated by DNA and protein damaging factors and attenuated or counter-acted by maintenance processes , mainly based on telomerase activity , a telomere-elongating ribonucleoprotein [2] . On the organismal level , telomere length ( TL ) generally declines with age and short TL relates to ageing-associated disorders and reduced survival in humans [3 , 4] and other organisms [5 , 6] . Given this relationship of telomeres with health and lifespan it is of importance to understand how variation in TL among individuals arises , which is already present early in life [7–9] . TL has a genetic basis , but heritability estimates for TL are highly variable [10] . Compared with other traits , inheritance of TL is also unusual in that the TL phenotype is directly expressed in the zygote without any effect of its own genome . This is because the zygote’s set of chromosomes carries the telomeres of the two parental gametes . Subsequently , during development of the embryo , different telomere maintenance and restoration mechanisms , under the control of multiple genes , potentially regulate TL , but this process is poorly understood [11] . In the course of early development , such mechanisms can potentially compensate fully for gamete derived differences in TL ( as suggested by e . g . [12 , 13] ) , in which case the effect of gamete TL is transient ( Fig 1A ) . Alternatively , differences in gamete TL are carried over to later life ( as suggested by e . g . [14]; Fig 1B ) . The latter case would imply the inheritance of parental TL , which is independent of DNA sequence variation ( in vertebrates ( TTAGGG ) n [15] ) , but a change in telomere sequence length ( n ) . We interpret this as a form of epigenetic inheritance component on TL [16 , 17] . Note that this epigenetic inheritance mechanism differs from better known epigenetic mechanisms such as DNA methylation in that it does not affect the phenotype ( TL ) by modulating gene expression , but instead through direct inheritance of the phenotype itself and therefore has also been referred to as “epigenetic-like” [17] . Strongest evidence for an epigenetic mechanism of TL inheritance comes from studies that show a relationship between parental ( usually paternal ) age and offspring TL [18–23] with a particularly interesting example showing a cumulative effect over generations in humans [24] . In humans , where offspring TL increases with paternal age , this trend parallels a qualitatively similar change in sperm TL with age , which is generally assumed to underlie the TL increase in offspring [25] . However , studies of parental age effects in other species show mixed results and trends differ in direction between and within taxa [20 , 26] . More importantly , some critical uncertainties remain unresolved in any species . Firstly , studies to date are all cross-sectional [18–23] , thus , comparing offspring of different parents that reproduced at different ages . Such cross-sectional trends may differ from age related changes within individual parents if , for example , individuals with long TL are more likely to reproduce at older ages , which is not unlikely given the positive correlation between human TL and reproductive lifespan [27 , 28] . Secondly , parental age effects on offspring TL may arise from effects of parental age on pre- and postnatal conditions prior to sampling . Because telomere attrition is highest early in life e . g . [29 , 30] , these effects can be substantial , as illustrated by parental age effects on TL dynamics during the nestling phase in European shags Phalacrocorax aristotelis [31] and Alpine swifts Apus melba [21] . Lastly , due to their cross-sectional character , studies to date could not test whether changes in TL within parents over their lifetimes are predictive of changes in TL of the offspring in relation to parental age at conception . These points need to be resolved to establish whether the correlations between parental age and offspring TL can be attributed to epigenetic inheritance of TL , and before we can begin to understand why parental age effects on offspring TL appear to differ between and within taxa [20 , 26] . To investigate whether offspring TL changes with parental age at conception over the lifetime of individual parents we used our long-term , individual-based dataset of free-living jackdaws Corvus monedula . Telomere length was measured in nucleated erythrocytes using terminal telomere restriction fragment analysis [32] from multiple chicks of the same parents that hatched up to 9 years apart . As telomere attrition is highest early in life , we took blood samples for telomere analysis shortly after hatching , when the oldest chick in a brood was 4 days old . To test if TL was influenced by age-dependent parental care prior to sampling , we cross-fostered clutches between nests immediately after laying and tested whether foster parent age affected offspring TL . To investigate if the rate of telomere attrition within parents predicts the change in TL of the offspring they produce over consecutive years , we measured TL of the parents repeatedly over their lifetimes . For the first time , we here show that offspring TL declines as individual fathers age and that the change in TL over time in fathers is reflected in the TL of their offspring , which explains a substantial part of the telomere resemblance between fathers and offspring and can be interpreted as an epigenetic component in the inheritance of TL . Mother offspring resemblance on the other hand was independent of maternal age and within mother variation in TL was not associated with variation in the TL of her offspring .
To be able to separately evaluate between- and within-individual patterns of parental age , we used within-subject centering [33] . Instead of using age in our models , we used the mean age per individual over multiple years as one variable , and delta age , the deviation from that mean as a second variable . Thus , the coefficient of mean age estimates the parental age effect compared between individual parents , while the coefficient of delta age estimates the age effect on offspring TL within parents . As fathers aged , they produced offspring with 56±20 bp shorter TL for each additional year ( variable ‘delta age father’ in Table 2A , Fig 2A ) , showing that offspring TL declined with paternal age at conception within individual males . This effect was not apparent when comparing offspring of different fathers reproducing at different ages ( cross-sectional component of the statistical model , variable ‘mean age father’ in Table 2A ) . In contrast , there was no effect of maternal age on offspring TL ( Table 2B ) , neither when compared cross-sectionally , between offspring of different mothers over age ( mean age mother , Table 2B ) , nor within mothers as they age ( delta age mother ) . The negative , non-significant effect of maternal age on offspring telomere length we observed ( Table 2B ) we attribute to the age of their mates , because pair bonds in jackdaws are maintained over many years ( pers . obs . ) and hence maternal and paternal age are correlated . This interpretation is confirmed by the finding that the observed maternal age estimate ( delta age mother ) is close to what would be expected based on the estimate found in fathers and the observed correlation of r = 0 . 75 ( n = 298 ) between maternal and paternal age ( i . e . 0 . 75 * 56 bp = 42 bp , which is very close to the estimate ± s . e . for delta mother age , which was 38±23 bp; Table 2; see also [23] ) . Thus , we conclude a maternal age effect on offspring TL other than through the age of the females’ mates to be unlikely . The decline in offspring TL with fathers’ age was lower than the rate of TL attrition in the fathers themselves ( -56±20 versus -87±15 bp/year , respectively ) . Individual variation in telomere attrition slopes was negligible both between individual fathers ( additional variance explained by random slopes 1% ) and in their offspring produced across the fathers’ lifetimes as well ( variance explained by random slopes 0 . 3% ) . The paternal age effect on offspring TL could potentially be caused by age-dependent paternal care ( e . g . age-related feeding of the incubating partner , or the chicks prior to sampling ) , if this affects telomere dynamics between conception and the sampling age of 4 days . We tested this hypothesis by exchanging clutches between pairs shortly after clutch completion . Our analysis is based on telomere data of 61 chicks that hatched from 31 cross-fostered clutches . In a first test , we added the age of foster father or mother to the model in Table 2A , and neither parental age significantly affected offspring TL ( age foster father: 5 . 6±28 . 5 , p = 0 . 85; age foster mother: -9 . 7±17 . 4 , p = 0 . 58 ) . To avoid basing a conclusion solely on a negative statistical result , in a second analysis we compared the estimate of the age of the caring father ( i . e . the genetic father if not cross-fostered ) on offspring TL with the estimate of the age difference between genetic and foster father ( which is 0 in case of no cross-fostering or matching ages between genetic and foster father ) on offspring TL . Both estimates were negative and very similar ( Table 3 , Fig 2B ) . Because the age of the caring father and the age difference between the caring father and the genetic father add up to the age of the genetic father for the cross-fostered offspring , the similarity of the estimates implies that there was no effect of age-related care between conception and sampling on offspring TL . While the estimate of the age difference did not quite reach statistical significance in a two-tailed test ( p<0 . 09 ) , we consider the similarity of the estimates ( 10% difference ) the more salient result . Thus , the older the father , the shorter the TL of his offspring , independent of the age of the male that cares for the eggs and offspring up to sampling . These results show that the paternal age effect on offspring TL is explained by the age of the genetic father and that the influence of the age of the foster fathers on offspring TL at age 4 days is negligible . The paternal age effect on offspring TL raises the question whether changes in paternal TL with age predict the change in early life TL of the offspring produced over the fathers’ lifetimes . We tested this by replacing the two age terms in the model in Table 2 by TL at conception ( i . e . mean and delta TL ) of the father in the year the offspring hatched . Fathers’ mean TL as well as delta TL were strongly and positively correlated with offspring TL ( Table 4A , Fig 3 ) . The effect of father’s mean TL on offspring TL can be attributed to additive genetic inheritance , possibly augmented by effects of a shared environment [10] . The effect of fathers’ delta TL on offspring TL cannot be attributed to a genetic effect , because delta TL refers to variation of TL within fathers over their lifetime . We therefore consider an epigenetic effect the most likely explanation for the effect of fathers’ delta TL on offspring TL . The variance in offspring TL explained by mean and delta TL of the father was 1 . 87 and 0 . 96 respectively . This indicates that 34% ( 0 . 96 / 2 . 83 ) of the variance in offspring TL that was explained by paternal TL can be attributed to the paternal-age related epigenetic effect . In agreement with our finding that maternal age did not affect offspring TL , when we performed the same analysis for mother TL , we found that maternal TL shortening ( delta TL mother ) was not related to the TL of her subsequent offspring , with a slope of the variable delta maternal age that was more than 90% lower than the comparable slope in males ( Table 4B ) . However , mean maternal TL , reflecting a similarity between maternal and offspring TL per se ( independent of a maternal TL change over time ) , based on a combination of additive genetic and age-independent maternal effects , was highly significant ( Table 4B ) .
Resemblance of TL between parents and offspring is potentially due to a dual inheritance mechanism , with on the one hand a ‘classic’ additive genetic effect and on the other hand an epigenetic effect of variation in TL in the gametes that at least in part carries through to later life ( Fig 1 ) . Suggestive evidence for an epigenetic contribution to the inheritance of TL comes from studies showing a paternal age effect on offspring TL , but available results are based on cross-sectional analyses [18–23] . Using a unique longitudinal dataset on free-living birds , and a high precision TL measurement technique ( CV within individuals <3% ) , we show for the first time that offspring TL changes with age within individual fathers ( i . e . longitudinally ) . We used a cross-foster experiment to test whether the paternal age effect may be due to paternal age-dependent parental care prior to offspring sampling . This showed that the paternal age effect is already present at laying . Mother age was not significantly associated with offspring TL , and the non-significant estimate of the maternal age effect matched almost exactly the expected estimate based on the observed paternal age effect in combination with the correlation between the ages of pair members . Thus , we conclude that offspring TL declined with parental age within individual fathers , but not mothers . The parental sex dependent age effect on offspring TL is in agreement with most other studies [18–23 , 25 , 34] , and is usually attributed to the different replicative history of the gametes of the two sexes . Male gametes are newly formed throughout reproductive life , while a female’s complete stock of gametes is formed before birth [35 , 36] . Hence TL of female gametes is less prone to changes with female age compared to TL of male gametes [37–39 , but 40] . This is not to say that there is no epigenetic inheritance of TL through the female line , but only that its contribution to offspring TL does not depend on mothers’ age . While we consider epigenetic inheritance of TL via a carry-over effect from paternal gamete TL the most parsimonious explanation of our findings , we acknowledge that we cannot yet fully exclude other mechanisms . There is some scope for females to modulate the contents of their eggs , which may affect TL dynamics [41] . Thus , it remains a possibility that females adjusted the content of their eggs in response to the age of their partner in a way that causes the paternal age effect on the TL of their offspring . However , if there were such an effect , one would perhaps also expect it to be expressed in egg volume ( which varies considerably in jackdaws ) , but there was no evidence that females adjusted the volume of their eggs to the age of their partner ( p = 0 . 35 , n = 683 clutches , model including female identity and year as random effects ) . Another mechanism we cannot rule out is paternal age dependent expression of genes that control telomere dynamics of offspring . However , genetic influences on telomere dynamics are modest compared to environmental influences or heritability of TL itself [42] , making it unlikely that this hypothetical mechanism explains a substantial part of the paternal age effect . We tentatively estimated the relative contributions of additive genetic and epigenetic effects to the resemblance between males and their offspring using a statistical model in which we separated between- and within-individual variation in parental TL as predictors of offspring TL . In this model , the within-male component ( ‘delta TL father’ , Table 4A ) shows the strong epigenetic effect over the years within males on their offspring , while the between male component ( ‘mean TL father’ ) shows the putative additive genetic effect on offspring TL . When comparing the relative contributions of the two inheritance mechanisms , it appeared that 34% of the variance explained by paternal TL can be attributed to the epigenetic effect . Telomere loss within mothers ( ‘delta TL mother’ ) was unrelated to the TL of offspring produced over years ( Table 4B ) . Estimates of the between-male effect ( 0 . 26±0 . 08 , ‘mean TL father’ , Table 4A ) and the between-female effect ( 0 . 46±0 . 09 , ‘mean TL mother’ , Table 4B ) together equate to a narrow sense heritability of jackdaw TL of 0 . 72 , which is similar to results observed in humans [43] and within the range observed in other vertebrates [10] and is in line with other studies on birds estimating higher similarity between mothers and offspring [44 , 45] . We stress however that we measured telomere length in parental blood and not in sperm and that the estimates for the additive genetic and the epigenetic effects are tentative . Firstly , with respect to the additive genetic effect , it is of importance that shared environment effects are not controlled for in the present analysis . We note however that a more extensive analysis using multigenerational pedigree information and controlling for shared environmental effects [46] yielded a very similar estimate of the narrow sense heritability of TL in our study population ( Bauch et al . in prep ) . Secondly , the variance in TL between males is not only of genetic origin , given that in addition there appears to be an epigenetic contribution to the between-male variance . Thus , the effect of ‘mean TL father’ ( Table 4A ) will to an unknown extent contribute to the epigenetic effect , as well as heterogeneity of sperm TL in ejaculates . Hence the epigenetic contribution to the resemblance between father and offspring TL will be more than the 34% we estimated based on parent-offspring regression over a single generation . Narrow sense heritability of human TL has been estimated using monozygotic and dizygotic twins [e . g . 47] , assuming that a weaker resemblance between dizygotic twins compared to monozygotic twins can be attributed to the difference in genetic relatedness . However , as monozygotic twins develop from a single zygote , and hence from a single sperm cell and oocyte , the difference in resemblance within a monozygotic versus a dizygotic twin pair may in part be due to an epigenetic effect of having developed from the same or different gametes [14] . This process would lead to an overestimation of the narrow sense heritability compared to techniques that do not depend on twins . The direction of the paternal age effect in jackdaws ( decreasing ) is opposite to the direction of the paternal age effect in humans and chimpanzees ( increasing ) [20] . Assuming that paternal age effects in humans and chimpanzees [20] on the one hand and several bird species ( including our study species ) [20–23] and lab mice [34] on the other hand all reflect paternal age effects on sperm TL , this raises the question why these age effects on sperm TL are in opposite directions . Seasonality of reproduction may well play a role , with species that produce sperm for a small part of the year having less need to maintain sperm TL than species with year-round sperm production [20] . The lengthening of TL in human sperm with age has been interpreted as the result of an overshoot in telomere maintenance [25] that can be viewed as a safety margin in the maintenance process . Such a safety margin can be expected to be larger when the rate of sperm production and hence telomere attrition is higher . This may explain why chimpanzees , with a higher sperm production rate than humans , due to their promiscuous mating system , show a steeper paternal age effect on offspring TL compared to humans [48] . Information on the sign of the association between paternal age and offspring TL in strongly seasonal mammal species and / or continuously reproducing bird species would allow a test of this hypothesis . The epigenetic inheritance of TL potentially has more general implications . Parental age at conception has previously been shown to have negative effects on offspring fitness prospects in diverse taxa , a phenomenon known as the Lansing effect [22 , 49–52] . The underlying mechanisms are likely to be diverse , but in taxa where the paternal age effect on offspring TL is negative , given that TL predicts survival in wild vertebrates [6] , and TL early in life correlates strongly with TL in adulthood in jackdaws [7] , offspring born to older fathers may have a shorter life expectancy due to their epigenetically inherited shorter TL . A further implication is that there may be cumulative changes in TL over multiple generations [24] . This could lead to population level changes in TL when the age structure of the population changes , as has for example been observed in birds in response to urbanisation [53] . A population level change in TL may in itself have further demographic consequences [54] , providing a positive or negative feedback , depending on whether increasing paternal age has a positive or negative effect on offspring TL .
Data were collected under license of the animal experimentation committee of the University of Groningen ( Dierexperimenten Commissie , DEC , license numbers: 4071 , 5871 , 6832A ) . License was awarded in accordance with the Dutch national law on animal experimentation ( “Wet op de dierproeven” ) and research was carried out following the guidelines of the Association for the Study of Animal Behaviour ( ASAB ) [55] . Life-history data and blood samples originate from an individual-based long-term project on free-living jackdaws Corvus monedula breeding in nest boxes south of Groningen , the Netherlands ( 53 . 14° N , 6 . 64° E ) . Jackdaws produce one brood per year with mostly 4 or 5 chicks . They are philopatric breeders and socially monogamous with close to zero extra-pair paternity as shown in different populations [56 , 57] . Females incubate the eggs , while males feed their female partners . Chick provisioning is shared by the sexes . Each year , during the breeding season around the hatching date nest boxes were checked daily for chicks . Freshly hatched chicks were marked by clipping the tips of the toenails in specific combinations and therefore the exact ages of offspring were known . Between 2005 and 2016 , 715 jackdaw chicks were blood sampled when the oldest chick ( s ) was ( were ) 4 days ( note that chicks hatch asynchronously ) . These chicks originated from 298 nests , of 197 different fathers , whereof 66 were blood sampled repeatedly over years ( max . difference of age between offspring 8 years ) and 194 different mothers , whereof 62 were blood sampled repeatedly over years ( max . difference of age between chicks 9 years; see Table 1 for more information ) . 61 chicks ( that contributed telomere data ) hatched from 31 cross-fostered nests , i . e . eggs were exchanged between nest boxes ( selected for equal clutch sizes and laying dates ( or up to one day difference ) , but otherwise randomly ) soon after clutch completion . 54 ( 89% ) of those chicks were fostered by a father of different age . Jackdaws in this project are marked with a unique colour ring combination and a metal ring . Parents were identified by ( camera ) observation during incubation and also later during chick rearing when caught for blood sampling ( by puncturing the vena brachialis ) . Unringed adults were caught , ringed and assigned a minimum age of 2 years , as this is the modal recruitment age of breeders that fledged in our study colony . All jackdaws were of known sex ( molecular sexing [58] ) . Blood samples were first stored in 2% EDTA buffer at 4–7°C and within 3 weeks snap frozen in a 40% glycerol buffer for permanent storage at -80°C . Terminally located telomere lengths were measured in DNA from erythrocytes performing telomere restriction fragment analysis under non-denaturing conditions [29] . In brief , we removed the glycerol buffer , washed the cells and isolated DNA from 5 μl of erythrocytes using CHEF Genomic DNA Plug kit ( Bio-Rad , Hercules , CA , USA ) . Cells in the agarose plugs were digested overnight with Proteinase K at 50°C . Half of a plug per sample was restricted simultaneously with HindIII ( 60 U ) , HinfI ( 30 U ) and MspI ( 60 U ) for ~18 h in NEB2 buffer ( New England Biolabs Inc . , Beverly , MA , USA ) . The restricted DNA was then separated by pulsed-field gel electrophoresis in a 0 . 8% agarose gel ( Pulsed Field Certified Agarose , Bio-Rad ) at 14°C for 24h , 3V/cm , initial switch time 0 . 5 s , final switch time 7 . 0 s . For size calibration , we added 32P-labelled size ladders ( 1kb DNA ladder , New England Biolabs Inc . , Ipswich , MA , USA; DNA Molecular Weight Marker XV , Roche Diagnostics , Basel , Switzerland ) . Gels were dried ( gel dryer , Bio-Rad , model 538 ) at room temperature and hybridized overnight at 37°C with a 32P-endlabelled oligonucleotide ( 5’-CCCTAA-3’ ) 4 that binds to the single-strand overhang of telomeres of non-denatured DNA . Subsequently , unbound oligonucleotides were removed by washing the gel for 30 min at 37°C with 0 . 25x saline-sodium citrate buffer . The radioactive signal of the sample specific TL distribution was detected by a phosphor screen ( MS , Perkin-Elmer Inc . , Waltham , MA , USA ) , exposed overnight , and visualized using a phosphor imager ( Cyclone Storage Phosphor System , Perkin-Elmer Inc . ) . We calculated average TL using ImageJ ( v . 1 . 38x ) as described by Salomons et al . [29] . In short , for each sample the limit at the side of the short telomeres of the distribution was lane-specifically set at the point of the lowest signal ( i . e . background intensity ) . The limit on the side of the long telomeres of the distribution was set lane-specifically where the signal dropped below Y , where Y is the sum of the background intensity plus 10% of the difference between peak intensity and background intensity . We used the individual mean of the TL distribution for further analyses . Samples were run on 92 gels . Repeated samples of adults were run on the same gel , chicks were spread over different gels . The coefficient of variation of one control sample of a 30-day old jackdaw chick run on 26 gels was 6% and of one control sample of a goose , with a TL distribution within a similar range , run on 31 other gels was 7% . The within-individual coefficient of variation for samples run on the same gel was <3% [7] and the within-individual repeatability of TL was estimated to be 97% [59] . The relationships between parental age or parental TL and early-life TL of offspring were investigated in a linear mixed effects model framework using a restricted maximum-likelihood method ( testing specific predictions ) . To be able to separately evaluate between- and within-individual patterns of parental age or parental TL , we used within-subject centering [33] . Thus , instead of father age , mother age or father TL , mother TL per se we introduced the mean value per individual over ( if available ) multiple years and delta age or delta TL , the deviation from that mean , respectively . To account for ( genetic and potential other ) similarities in TL between offspring of the same father or mother , we included father ID or mother ID as random effect in the model . As the dataset contains also siblings raised in the same nest , we additionally added a random effect of nest ID as a nested term in father ID or mother ID to the models investigating paternal or maternal age effects on TL , respectively . The age of chicks at sampling differed slightly ( 2–4 days ) and as TL shortens with age [7] , we included their age ( in days ) as a covariate . Offspring sex was never significant and was therefore excluded from the final models . We added gel ID as random effect . Analyses were performed separately for fathers and mothers as their ages are correlated . The cross-foster experiment was designed to test for potential effects of parental age on early-life telomere attrition between egg laying and sampling ( age 2–4 days ) . First , we modified the linear mixed effect model with offspring TL as dependent variable testing for paternal age effects ( see above ) by adding the age of the foster father or mother as covariate . Second , in a linear mixed model with offspring TL as dependent variable , we included both the age of the father caring for the clutch after cross-fostering and the age difference between the genetic father and foster father as covariates ( age genetic father-age foster father ) . When the paternal age effect is independent of age-dependent effects between conception and sampling , we predict the coefficients of the caring father’s age and the age difference between genetic father and foster father to be indistinguishable . This is so because the age of the male caring for the clutch , and the age difference between the genetic and the caring father add up to the age of the genetic father . In contrast , when the paternal age effect is entirely due to age-dependent paternal effects after laying , the coefficient will be the same , but opposite in sign . In case of a mixture of the two effects , the coefficient will be intermediate . In this analysis we used all offspring , i . e . also those that were not cross-fostered , and further included genetic father ID , nest ID , gel ID and year of telomere analysis as random effects , and offspring age at sampling as covariate . Statistics were performed using packages lme4 [60] , lmerTest [61] , MuMIn [62] in R ( version 3 . 3 . 3 ) [63] . In the results mean ± standard error is given unless stated otherwise . | Telomeres are DNA-protein structures at chromosome ends and a short telomere length predicts reduced survival in humans , birds and other organisms . Variation in telomere length between individuals is thought to be largely of genetic origin , but telomere inheritance may be unusual because not only genes regulating telomere length are inherited , but a fertilised cell already has a telomere length ( from the parental gametes ) . Using long-term individual-based data of jackdaw families ( a small corvid species ) , we found that as fathers aged , they produced chicks with shorter telomeres . This suggests that telomere length inheritance has an epigenetic component . To investigate to what extent telomere length in the fertilised cell affects telomere length after birth , we compared telomere length over years within fathers with the telomere length of their consecutive offspring . This epigenetic component explained a substantial part ( ≥ one third ) of the telomere length inheritance; whereas there was no such effect of maternal telomere length . The sex difference fits the idea that lifelong sperm formation leads to change in telomere length of the sperm cells , whereas female gametes are all formed before birth and their telomere length does not change over time . |
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Myxobacteria are social bacteria that upon starvation form multicellular fruiting bodies whose shape in different species can range from simple mounds to elaborate tree-like structures . The formation of fruiting bodies is a result of collective cell movement on a solid surface . In the course of development , groups of flexible rod-shaped cells form streams and move in circular or spiral patterns to form aggregation centers that can become sites of fruiting body formation . The mechanisms of such cell movement patterns are not well understood . It has been suggested that myxobacterial development depends on short-range contact-mediated interactions between individual cells , i . e . cell aggregation does not require long-range signaling in the population . In this study , by means of a computational mass-spring model , we investigate what types of short-range interactions between cells can result in the formation of streams and circular aggregates during myxobacterial development . We consider short-range head-to-tail guiding between individual cells , whereby movement direction of the head of one cell is affected by the nearby presence of the tail of another cell . We demonstrate that stable streams and circular aggregates can arise only when the trailing cell , in addition to being steered by the tail of the leading cell , is able to speed up to catch up with it . It is suggested that necessary head-to-tail interactions between cells can arise from physical adhesion , response to a diffusible substance or slime extruded by cells , or pulling by motility engine pili . Finally , we consider a case of long-range guiding between cells and show that circular aggregates are able to form without cells increasing speed . These findings present a possibility to discriminate between short-range and long-range guiding mechanisms in myxobacteria by experimentally measuring distribution of cell speeds in circular aggregates .
Myxobacteria are social bacteria that upon starvation form multicellular fruiting bodies whose shape in different species can range from simple mounds to elaborate tree-like structures consisting of 105 − 106 cells [1 , 2] . The development of fruiting bodies is a result of collective movement of flexible rod-shaped cells in close contact with one another on a solid surface . After the movement of cells within the fruiting body has stopped , cells differentiate into dessication-resistant spores . Since collective cell motility during morphogenesis is also common in higher organisms [3] , myxobacteria serve as a relatively simple model organism to study multicellular movement , organization and development . In the course of development of myxobacteria , groups of cells move in circular or spiral patterns to form aggregation centers that can become sites of fruiting body formation [4 , 5] . Such cell aggregates are dynamic , i . e . they can disperse , split , merge with other aggregates , or stabilize and form a fruiting body [6] . Nascent cell aggregates grow as new cells enter in multicellular streams , where cells are aligned and move in concert [4 , 5 , 7] . Remarkably , circular and spiral patterns of cell movement are conspicuous during different stages of myxobacterial morphogenesis and can be observed on different spatial scales from several cells to large streams [8–16] . Several adjacent streams can move circularly within the fruiting body in opposite directions [17] . Spores in the fruiting body of Myxococcus xanthus , the most studied myxobacterium , have been shown to be organized in spiral patterns , presumably as a result of such movements [5] . The mechanisms of formation of streams and circular or spiral aggregates are not well understood . Circular aggregates can form by a stream of cells trapping itself [8] . Cells have been observed to travel long distances in streams and enter distant aggregates rather that the ones nearby , suggesting that aggregation is not caused by a long-range diffusible signal emitted from aggregation centers [18] . Further , it has been shown that myxobacteria development is regulated by the C-signal that is passed from cell to cell through end-to-end contact [19] . These findings resulted in a hypothesis that myxobacteria aggregation and development depends on short-range contact-mediated communication between cells , i . e . cell aggregation does not require long-range signaling in the population . Recent studies on aggregate merging and dispersal dynamics further argues against the presence of long-range diffusible molecules to signal the aggregation process [6] . Vegetative cells in swarms reverse their direction of gliding by switching leading and trailing poles approximately once every 10 min [20 , 21] . In the course of development , due to C-signaling , reversal frequency of cells is reduced and gliding speed is increased . Therefore , cell movement becomes essentially unidirectional at the final stages of development [17 , 22 , 23] . Søgaard-Andersen and Kaiser [24] proposed that streams form when reversal frequency of cells is decreased due to C-signaling as they come into end-to-end contact . As a result , collective cell movement in roughly the same direction becomes favored . However , this model does not explain what keeps cells in the chain . Moving cell masses and streams can turn and swirl [8] , but cells appear to follow one another over long distances and not escape the stream due to random fluctuations in cell orientation [25] or contacts with surrounding cells . A guiding mechanism seems to be present for streams to be stable , i . e . for cells to continue following one another and move as one unit . One possible mechanism of such stability could be a long-range guiding system other than a signal diffusing from aggregation centers . For example , at low cell population densities , cells are often observed to follow slime trails laid down by other cells [26] . This could establish a long-range order required to guide cells into aggregation centers . However , whether and how slime trails could persist in a high-density population , which is the usual state of myxobacteria communities , let alone in three-dimensions , is not clear . Alternatively , cells could employ a short-range guiding mechanism whereby guiding forces act only when cells are in contact or very close to one another . Possible hypothetical mechanisms for short-range guiding could include following slime immediately extruded by another cell , response to a diffusible signal from another cell , physical adhesion between cells or attachment with type IV pili [27] . A number of modeling studies investigated myxobacteria motility ( e . g . , see [28–30] ) . However , few of them examined mechanisms of circular motility patterns . Lattice cell simulations showed that streams and ring-shaped aggregates , where cells move in circular tracks , could form as a result of local , short-range contact mediated interactions between cells , whereby rod shaped cells would preferentially turn towards maximizing end-to-end contacts [31–33] . However , modeling approach used there is not mechanically accurate , as cells are perfectly rigid ( i . e . cannot bend ) , can overlap in space and move only in limited number of directions . In this study , by means of a more mechanically accurate two-dimensional ( 2D ) computational mass-spring model developed earlier [34] , we investigate how different types of short-range guiding interactions between the leading pole of one bacterium and the trailing pole of another bacterium could affect the formation of patterns in myxobacteria population . In addition , we consider a case of long-range guiding between cells analogous to slime-trail following and compare the resulting patterns with the ones of short-range guided populations .
To model guiding interactions between cells , we use a 2D mass-spring model previously described in [34] with changes to collision response algorithm presented in S1 Text . In brief , a rod-shaped cell is modeled as an array of particles connected by linear and angular springs . Linear springs maintain the distance between particles , and thus the length of a bacterium , whereas angular springs ( characterized by angular spring constant ka ) resist bending of a cell . Cells glide on a substratum powered by engine forces and change their direction of movement as a result of collisions with other cells . Here , only the distributed engine is considered ( i . e . engine with forces distributed along the whole length of a cell ) , given recent evidence strongly supporting its existence [35–37] . In addition to the features described in the basic model , here we introduce and study three kinds of short-range guiding forces ( Fig 1 ) . First , adhesion between the leading pole ( “head” ) of one cell and the trailing pole ( “tail” ) of another cell is considered . Thereby , adhesion forces between a pair of line segments that connects particles in bacteria are introduced only when the head of one bacterium and the tail of another ( or the same ) bacterium are involved . If both interacting cells have polarity ke = 1 , the head of bacterium j is the point P1 = 0 on segment Q1j , and the tail of bacterium l is the point P2 = 1 on segment Q ( N − 1 ) l ( see [34] for notation ) . Thus , when the smallest distance between the two segments d is W < d < dg , where dg is maximum guiding ( in this case , adhesion ) distance and W is cell width , adhesion forces to respective head and tail particles of interacting bacteria are introduced ( Fig 1 , forces marked F H g and F T g ) . The adhesion forces are described by the same 4 equations that govern collision response [34] , with kc replaced by k g = F max g / d g , where F max g is the maximum magnitude of the guiding force ( exerted when two segments are separated by distance dg ) . Essentially , adhesion in the model is collision response working in reverse , i . e . attracting cells when d becomes larger than W . As a result of these forces , the head of the trailing cell will tend to turn towards the tail of the leading cell when the distance between them is small enough , due to the normal component of adhesion force on the head particle ( F H g , n in Fig 1 ) . In addition , the component of adhesion force along the tangent of trailing bacterium body ( F H g , t in Fig 1 ) will result in increased speed of the trailing bacterium ( i . e . the leading cell will pull the trailing cell forward ) . As adhesion forces work in action-reaction pairs , the tail of the leading cell will also turn towards the head of the trailing cell and the speed of the leading cell will tend to decrease ( due to normal and tangent component of adhesion force respectively , F T g , n and F T g , t in Fig 1 ) . Overall , when d < W ( i . e . when cells overlap ) , only collision forces would act to separate the cells , and there will be no adhesion forces . Collision and adhesion forces would be zero when the head of the trailing cell touches the tail of the leading cell ( i . e . when d = W ) . As distance between head and tail particles d increases beyond W , the magnitude of adhesion forces increases linearly until distance dg , where the magnitude of adhesion forces gets its maximal value F max g . Beyond dg , adhesion forces would be zero and thus cells will have no guiding interactions . A second type of short-range guiding force represents active following , whereby the force described for adhesion is added only to the head of the trailing cell , but not to the tail of the leading cell ( i . e . only F H g in Fig 1 is added ) . It models the effect of the trailing cell responding to the presence of the tail of the leading cell by actively moving in its direction , but having no effect on the movement of the leading cell . A third type of short-range guiding force is passive following ( steering ) , whereby the force to the head particle of the trailing cell is added only in the direction normal to bacteria body n ^ ( F H g , n in Fig 1 ) . By this , only the steering effect on the head of the trailing cell is modeled , i . e . turning the tip of the cell left or right with respect to the normal trajectory of the cell , but having no effect on cell speed . In addition , we also consider a case of long-range guiding that is analogous to slime trail following by myxobacteria cells . To model slime trails , a square grid with elements of side Δx is defined on the substratum . Each grid element can contain a unit vector s indicating slime trail direction at that location , or a zero vector , if no slime trail is present [38] . When the head particle of a bacterium glides over a grid element containing a slime trail s , the guiding force on the head particle is introduced to reorient the leading tip of that cell along the slime trail . Since a cell can glide in both directions along the slime trail , the guiding force is defined to turn the cell by an acute angle [26] . Thus , if the orientation of the leading tip o is defined as the tangent to bacterial body at the leading particle ( i . e . t ^ 1 when engine direction ke = 1 and t ^ N when ke = −1 ) and F max s is the maximal magnitude of guiding force , a guiding force sgn ( o · s ) F max s s is found and its component in the direction of n ^ is added to the leading particle . The applied force is similar to passive following described above , because the force only orients the tip of the cell along the slime trail , without affecting cell speed along tangent t ^ . After each integration step , the deposition of slime by the rear of the bacterium is modeled by assigning a tangent t ^ at the rear particle to slime trail s at a grid element below . The deposition of slime overrides the previous value of slime trail direction at that grid location . Slime trails at each grid location persist until overridden by other cells . The parameters used in the simulations are the same as in [34] , with the addition of extra parameters describing guiding forces . The value F max g was chosen to be 200 pN , unless stated otherwise , and dg = 0 . 25 μm ( i . e . half of bacterium width W ) . Since guiding forces not only steer the head of the cell , but can also speed up the cell , the value of F max g was chosen in such a way that the speed-up due to the guiding force would be roughly within experimentally observed speed increase of myxobacteria cells during development , 1 . 5–2 . 5 times [23] . F max g = 200 pN results in 3-fold maximum increase of speed ( engine force of 100 pN and maximum guiding force of 200 pN results in maximal 3vb speed ) . For long-range guiding simulations , F max s was also set to 200 pN and Δx = 0 . 25 μm ( half of bacterium width W ) . Since bending stiffness of myxobacteria cells have not been experimentally determined , but only theoretically estimated for M . xanthus [34 , 39] , a wide range of angular spring stiffness values ka were studied in the simulations: 1 × 10−18 , 1 × 10−17 , 1 × 10−16 and 1 × 10−15 N·m . They correspond to cell bending stiffness ( B ) values of 7 × 10−25 J·m ( referred to in the text as “very flexible” ) , 6 × 10−24 J·m ( “flexible” ) , 6 × 10−23 J·m ( “rigid” ) and 6 × 10−22 J·m ( “very rigid” ) , respectively . Further , both low density 5 × 106 cells/cm2 and high-density 4 × 107 cells/cm2 populations were studied . For a low density population simulation , the collision stiffness between cells was set as in [34] , kc = 0 . 01 N·m−1 . For high density populations , the collision stiffness had to be reduced to kc = 0 . 002 N·m−1 , because high collision stiffness blocks the movement of cells in a crowded environment . To analyze cell movement , cell speeds and strain energies due to collisions between cells were shown for every line segment in the bacterium . Speed of a line segment was defined as an average speed of two particles at the ends of the segment . To find strain energies , for every two segments that overlap due to collision ( i . e . the when the smallest distance between segments d < W ) , potential energy of the collision response spring ( 1/2 ) kc ( d − W ) 2 was calculated and one half of the value was added to both segments involved .
Firstly , the effect of different types of short-range guiding forces on cell movement patterns of a low-density population of non-reversing cells was studied . All cells were initially placed on a planar substratum with random positions and orientations ( Fig 2A ) , and cell movement was simulated for 6 hours . A population of flexible non-guided cells at 6 hours formed clusters ( Fig 2B and S1 Video ) , whereas the presence of steering forces between cells ( passive following ) resulted in occasional chains of cells between clusters and small unstable circular structures that quickly dissipated ( Fig 2C and S2 Video ) . However , cells with active following and head-to-tail adhesion formed stable rotating circular aggregates ( Fig 2D and S3 Video , and Fig 2E and S4 Video , respectively ) . During the process of aggregate formation , streams of cells were first formed from randomly distributed cells . A stream could collide with other streams , turn , move in circular trajectories , close in upon itself and trap the leading cells . The rest of stream cells then swirled around the trapped cells . The seed of rotation could also be formed by several cells swirling around a fixed point . Later , additional cells or entire streams could join in to increase the size of the aggregate . Within the aggregate , cells were arranged spirally and new streams joined in by following the freely exposed tail of a cell at the aggregate edge . The decrease of guiding force F max g from 200 pN to 100 pN ( referred to as weak guiding ) resulted in a more dynamic population that was less likely to form stable rotating aggregates . In such a population circular aggregates were smaller , could dissipate , and streams could leave one aggregate and join another ( S5 Video ) . Interestingly , when a cell at the edge of the aggregate left , it often had a chain of trailing cells behind it . Stable circular aggregates also form in a population of rigid non-reversing cells with long-range guiding ( Fig 4A and S16 Video ) . However , in contrast to short-range guided aggregates , most of the cells traveled with equilibrium speed , including the ones at aggregate edges , as guiding forces affected only cell movement direction but not cell speed . Further , aggregates appear to be less tightly packed , i . e . they contain more voids than short-range guided aggregates . Stress accumulation patterns inside aggregates , however , are similar in both cases ( Fig 4B and Fig 2F ) . Interestingly , flexible cells also exhibited marked circular movement and produced small short-lived circular aggregates , but they were unstable ( Fig 4C and S17 Video ) . The instability could be explained by easier bending of flexible cells under stress inside nascent aggregates . As cells can travel in both directions on a slime trail , bent flexible cells can switch their movement direction to the opposite , squeeze in or be pushed through voids in the aggregate and thus disturb the circular arrangement of slime trails .
The mechanisms of myxobacteria aggregation during fruiting body formation are not well understood . Non-linear patterns of movement of myxobacteria cell masses and streams imply the existence of some sort of guiding mechanisms that keep the cells moving as one unit and direct them into aggregation centers [8] . It is not known whether these guiding mechanisms are long-range or short-range . In this study , by means of a computational mechanical mass-spring model , we demonstrate that short-range guiding between the head and the tail of two myxobacteria cells in close contact are sufficient to produce stable streams and circular or spiral aggregates in model myxobacteria populations . Many features of cell movement that are present in our short-range guiding simulations are also observed in experimental videos . Multicellular cell masses ( streams ) in the simulations can travel in straight lines , or , when colliding with other streams or clusters of cells , can wave and swirl ( S5 Video , [8 , 11 , 14] ) . A circular aggregate in the simulations often forms when a stream turns , closes in upon itself and traps the leading cells , a situation also observed in experimental videos ( S3 Video; [8] ) . Simulated circular aggregates exhibit rotational movements ( S3 Video ) . Similarly , circular and spiral movement is often observed in developing myxobacteria [9 , 12] . In fact , fruiting bodies often develop in places where such spiral aggregates initially form [5] . Additional streams of cells join existing aggregates to increase their size ( S3 Video , [23] ) . In the simulations , a circular aggregate sometimes forms from a rotation seed of several flexible cells ( S3 Video and S6 Video ) . Similar small rotating cell clusters have been observed experimentally [10 , 15 , 40 , 41] . Furthermore , a smaller magnitude of guiding forces results in a more dynamic aggregate behavior: simulated streams can travel from one aggregation center to another , aggregates can dissipate , split or join with other aggregates ( S5 Video ) . Our results show that stability of large aggregates increases with increasing cell rigidity , whereas more flexible cells tend to form separate rotation seeds inside aggregates due to easier bending and thus induce splitting of a large aggregate ( S9 Video and S10 Video ) . It has been experimentally observed that the size of initial aggregates of different myxobacteria species differs [42] . Our results suggest that it might be the result of different bending stiffness of cells of different species [43] . Finally , we also observed the formation of hollow aggregates and adjacent streams swirling inside aggregates in opposite directions ( S10 Video , [4 , 17] ) . The formation of ring-shaped aggregates , where cells move in circular tracks , both clockwise and counter-clockwise , was also observed in lattice cell modeling studies [31–33] . In these studies cells preferentially turn towards maximizing end-to-end contacts with other cells and therefore the interactions between cells are effectively similar to guiding forces in our model . Interestingly , short-range guided circular aggregates in our model rotate as rigid bodies , i . e . cells within the aggregate do not slide laterally past one another . As a consequence , the further from the rotation axis cells are located , the faster they travel . Furthermore , cells at the edge of a simulated aggregate and cells in the incoming streams move faster than the equilibrium cell speed ( S3 Video ) . Therefore , in order for stable rotating aggregates to form , short-range guiding must act in such a way that the trailing cell is both turned towards the tail of the leading cell and sped up to catch up with it . Otherwise , because guiding interactions are short-range ( half the cell width ) , the faster moving leading cell will escape the trailing cell and the short-range guiding interaction will be lost . It has been shown that during myxobacterial development average speed of cells does increase due to interaction with other cells [23 , 44] , but unfortunately it was not reported whether cell speed depended on cell location within circular aggregates or streams . Long-range guided populations were also able to form circular aggregates in the simulations , but in contrast to short-range guided populations , cell speed increase was not required , as most cells inside aggregates traveled with equilibrium speed , including the ones at aggregate edges ( S16 Video ) . This finding could be used to experimentally discriminate between short-range and long-range guiding mechanisms present in myxobacteria . Observation of currently available experimental videos does not allow to tell conclusively whether circular aggregates rotate fully or partially as rigid bodies and whether cell speed increases with increasing distance from the rotation axis [9 , 12] . Experimentally tagging part of the cells in the population with fluorescent markers could be used to obtain such data . Another experimentally testable prediction of the model is that cell speed at the short-range guided aggregate edge is independent of the aggregate size . This means that smaller aggregates rotate with larger angular speeds , and increase of the aggregate size due to incoming streams will result in decrease of the angular speed of aggregate rotation . To our knowledge , there is no experimental evidence about the existence of short-range guiding interactions between a head and a tail of two myxobacteria cells . The model proposed in this study does not imply any particular short-range guiding mechanism for myxobacteria aggregation , as long as the interaction would both steer the trailing cell and adjust its speed . One possibility could be mechanical adhesion force between a head and a tail of two myxobacteria , or physical link between cells by type IV pili . A gliding M . xanthus cell extends type IV pili that originate at the leading pole , attach to neighboring cells and pull to produce motility force [27 , 45] . Groups of myxobacteria cells are usually well aligned [46] , therefore it is likely that the extended pilus will attach to the rear of the leading cell . Alternatively , the leading pole of a trailing cell could respond to a diffusible substance or slime secreted from the rear of the leading cell . For a short-range interaction , such a substance should diffuse slow enough to form gradients on the spatial scale of cell width and should break down quickly not to interfere with signaling between other cells at the same location at a later time . For example , lipids could satisfy slow diffusion requirement [6] . In such a scenario , a trailing cell would turn and adjust its speed based on the concentration of the diffusing substance . It has been shown that bacteria are not too small for spatial sensing of chemical gradients [47] . Furthermore , it has been observed that in low-density populations myxobacteria cells tend to follow slime trails produced by other myxobacteria [26] . It is not clear , however , whether slime trails could persist for a long time in high-density population , a usual state of myxobacterial communities . At high cell densities , a particular spot on a substratum is continuously overrun by other cells and existing slime trails thus would be overridden . Therefore , it is possible that slime trails in high-density populations are short-lived and extend no longer than the distance between adjacent cells . For short-range guiding , the trailing cell should follow only the new slime immediately secreted by the leading cell , but not the old slime . If slime-contained signaling molecule were broken down quickly after slime extrusion from the cell rear , its concentration would show slime age , and therefore , the distance to the leading cell that produced it . If , further , a trailing cell responded to older slime by increasing speed , the situation would be akin to active following considered in our simulations , as cell speed would be dependent on the distance between interacting cells . Furthermore , type IV pili can also attach to slime left behind by other cells [45] . If the pili attached only to immediately extruded slime and the force of pulling were proportional to the length of extended pilus , it would also present active following . It has also been shown that myxobacteria development depends on contact mediated C-signaling [19] . C-signal is relayed by the end-to-end contact between cells [48] , and one of its effects is to decrease cell reversal frequency [23] . C-signal mutants are unable to aggregate , or the aggregates that form quickly dissipate [49] . These results are consistent with C-signal acting as a part of guiding mechanism suggested by our model . In our simulations , weak guiding forces resulted in the formation of very dynamic aggregates that could easily disperse ( S5 Video ) . Although a real fruiting body develops in three dimensions , at the initial stages of aggregation cells appear to move in independent monolayers that are stacked on top of one another [5 , 49] . This observation suggests the presence of forces that keep cells confined to two-dimensional sheets and do not allow them to escape crowded environment by moving upward . It also justifies a 2D model in this study and explains how cell trapping is possible when streams close in upon themselves . Further , our simulations show that mechanical stress accumulates inside circular aggregates because cells are trapped and squeezed . It has been experimentally observed that when a second layer forms on top of the original monolayer of M . xanthus cells , cells leave the base layer at one point [49] . Our results suggest that this phenomenon may occur when mechanical stress reaches a critical value at some point inside the aggregate and cells at that point are propelled upwards . Consistent with this idea is the observation that fruiting bodies develop at the places of traffic jams [50] or where spiral aggregates initially form [5] . During vegetative swarm phase of myxobacterial life cycle , cells reverse their direction of gliding by switching the leading and trailing pole approximately once every 10 min [20 , 21] . In the course of development , reversal frequency of cells decreases and cell movement become essentially unidirectional at the final stages of development [17 , 22 , 23] . Reversing cells , in contrast to non-reversing cells , are unable to form circular aggregates in our simulations . This result is in a good agreement with experimental observations that circular aggregates do not form during vegetative stage of myxobacterial life cycle [46] , but only during fruiting body development . Furthermore , our study suggests an extension of the conceptual model whereby cell streams form when cell reversal frequency is reduced due to contact-mediated C-signaling as two cells come into end-to-end contact [24] . It was proposed that collective cell movement in roughly the same direction would be favored as a result . However , this model does not address the question of what keeps cells in the chain . Streams can turn and swirl [8] , but cells appear to follow one another over long distances and not escape due to contacts with surrounding cells or random fluctuations in cell orientation [25] . Our results show that cells with only suppressed reversals would not be able to form streams from initially randomly distributed reversing cells . However , the presence of guiding interactions in addition to reversal suppression allows for the formation of stable streams and circular aggregates ( S15 Video ) . Interestingly , in our high-density population simulations , initially aligned but randomly oriented rigid cells could mechanically sort into adjacent streams of cells moving in the same direction ( S12 Video ) , but it is not clear whether this effect occured due to a relatively small simulation domain . Furthermore , bending stiffness of myxobacteria cells has not been determined experimentally , but evidence suggests that for M . xanthus it is closer to the “flexible” value used in our simulations [51] . Guiding forces allow cells to form stable streams and circular aggregates independently of bending stiffness value and initial cell configuration .
Supporting videos are encoded in H . 264 format . Please note that not all media players can handle this format by default . Installation of a proper codec is needed in such cases . For the best viewing experience , we recommend VLC Media Player , freely available for a number of different platforms ( http://www . videolan . org/vlc/ ) . | Myxobacteria are social bacteria that upon starvation form multicellular fruiting bodies whose shape in different species can range from simple mounds to elaborate tree-like structures . The formation of fruiting bodies is a result of collective cell movement on a solid surface . Since collective cell motility during biological morphogenesis is also common in higher organisms , myxobacteria serve as a relatively simple model organism to study multicellular movement , organization and development . In the course of myxobacterial development , groups of flexible rod-shaped cells form streams and move in circular or spiral patterns to form aggregation centers that can become sites of fruiting body formation . The mechanisms of such cell movement patterns are not well understood . In this study , by means of a computational mass-spring model , we demonstrate that the formation of streams and circular aggregates during myxobacterial development can be explained by short-range head-to-tail guiding between individual cells , whereby movement direction of the head of one cell is affected by the nearby presence of the tail of another cell . We suggest that such interactions between cells can result from physical adhesion , response to a diffusible substance or slime extruded by cells , or the action of cell motility engine . |
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Cytokinins and gibberellins ( GAs ) play antagonistic roles in regulating reproductive meristem activity . Cytokinins have positive effects on meristem activity and maintenance . During inflorescence meristem development , cytokinin biosynthesis is activated via a KNOX-mediated pathway . Increased cytokinin activity leads to higher grain number , whereas GAs negatively affect meristem activity . The GA biosynthesis genes GA20oxs are negatively regulated by KNOX proteins . KNOX proteins function as modulators , balancing cytokinin and GA activity in the meristem . However , little is known about the crosstalk among cytokinin and GA regulators together with KNOX proteins and how KNOX-mediated dynamic balancing of hormonal activity functions . Through map-based cloning of QTLs , we cloned a GA biosynthesis gene , Grain Number per Panicle1 ( GNP1 ) , which encodes rice GA20ox1 . The grain number and yield of NIL-GNP1TQ were significantly higher than those of isogenic control ( Lemont ) . Sequence variations in its promoter region increased the levels of GNP1 transcripts , which were enriched in the apical regions of inflorescence meristems in NIL-GNP1TQ . We propose that cytokinin activity increased due to a KNOX-mediated transcriptional feedback loop resulting from the higher GNP1 transcript levels , in turn leading to increased expression of the GA catabolism genes GA2oxs and reduced GA1 and GA3 accumulation . This rebalancing process increased cytokinin activity , thereby increasing grain number and grain yield in rice . These findings uncover important , novel roles of GAs in rice florescence meristem development and provide new insights into the crosstalk between cytokinin and GA underlying development process .
Rice panicle architecture , a valuable composite agronomic trait that includes grain number per panicle ( GNP ) , panicle length and so on , is strongly associated with rice grain yield . GNP is one of the most important agronomic characteristics of ideal plant architecture [1] . To improve rice grain yields to meet the needs of the rapidly growing population , numerous studies have focused on identifying and cloning genes/QTLs contributing to rice panicle architecture development . Many genes and pathways have recently been identified , including transcriptional and plant hormone regulators that contribute to the reproductive meristem activity maintenance processes . Cytokinins play a fundamental role in regulating reproductive meristem activity by promoting cell division [2] . Grain number 1a ( Gn1a ) , a cytokinin metabolism-related gene , encodes a cytokinin oxidase/dehydrogenase ( OsCKX2 ) that catalyzes the degradation of active cytokinins in reproductive meristems . Thus , a null allele of Gn1a leads to improved rice grain yield through increased active cytokinin levels and reproductive meristem activity [3] . Another gene , LONELY GUY ( LOG ) encodes a cytokinin nucleoside 5’-monophosphate phosphoribohydrolase . LOG transcripts are specifically enriched in the apical regions of vegetative and reproductive meristems . LOG functions in the activation of cytokinin , catalyzing the conversion of inactive cytokinins to biologically active forms . Reduced active cytokinin levels in the meristem due to malfunctioning of cytokinin activation is likely responsible for the defective meristem activity in the log mutant [4] . In additions , the zinc finger transcription factor DROUGHT AND SALT TOLERANCE ( DST ) directly induces the expression of OsCKX2 in the inflorescence meristems . The mutant allele DSTreg1 reduces OsCKX2 expression , thus increasing cytokinin levels in the inflorescence meristem , and therefore , the number of panicle branches and grains [5 , 6] . Gibberellins ( GAs ) are crucial for plant growth and developmental processes , such as seed germination [7] , grain setting [8] and so on . However , unlike cytokinins , GAs are primarily associated with high yield rice breeding due to their roles in plant height promotion . Most mutants or RNAi transgenic lines of GA biosynthesis genes , including CPS , KS , KAO [9] , KO [10] , GA20oxs [11–13] and GA3oxs [14] , show dwarfism phenotypes , which results in improved lodging resistance , a valuable trait for rice breeding under high inputs [15] . At the same time , transgenic-activated expression of GA catabolism genes , GA2oxs , also leads to dwarfism [16 , 17] . However , GA signals are also active in inflorescence meristems . OsGA20ox2 , OsGA3ox2 , Gα and SLR1 are highly expressed in inflorescence meristems and leaf primordia [18] . In maize , the expression domains of GA2ox1 and KN1 ( a maize KNOX gene ) overlap , mainly at the base of the shoot apical meristem . The KNOX gene KN1 directly induces GA2ox1 expression in reproductive meristems [19] . In tobacco and Arabidopsis , GA20ox expression could be directly excluded from the corpus of the shoot apical meristem [20 , 21] . These findings suggest that GAs are detrimental to meristem activity . Although the importance of GAs in meristem establishment and maintenance has been recognized , the GA biosynthesis and regulatory networks underlying this process are largely unknown , and it also remains to be determined whether certain GA biosynthesis and regulatory genes can be useful for increasing grain number and yield in rice . KNOX proteins are a class of homeodomain transcription factors that function in meristem establishment and maintenance . OSH1 ( a rice KNOX gene ) can directly activate the expression of other KNOX paralogs ( OSH15 , for example ) and itself . The positive autoregulation of KNOX genes and activation by cytokinin are both essential for meristem maintenance [22] . In rice and Arabidopsis , KNOX proteins can activate cytokinin biosynthesis in the meristems through the induction of genes encoding adenosine phosphate isopentenyltransferase ( IPT ) . IPTs are important enzymes that convert ATP , ADP and AMP to the iP riboside 5’-triphosphate ( iPRTP ) , iP riboside 5’-diphosphate ( iPRDP ) and iP riboside 5’- moophosphate ( iPRMP ) forms [23 , 24] . As KNOX proteins reduce GA activity , they play an indispensable role in maintaining shoot apical meristem activity , probably by balancing cytokinin and GA activity in the meristems , increasing cytokinin levels and reducing GA levels [25 , 26] . Here , we report the identification and characterization of a QTL , Grain Number per Panicle1 ( GNP1 ) , which encodes rice GA biosynthetic protein OsGA20ox1 . We propose that the upregulation of GNP1 in the inflorescence meristems may increase cytokinin activity via a KNOX-mediated feedback regulation loop and increase GA catabolism activity through inducing the expression of GA2oxs . This process would result in increased cytokinin activity , rebalancing cytokinin and GA activity and increasing grain number and grain yield . These results provide insights into the mechanism underlying KNOX-mediated cytokinin and GA crosstalk during rice inflorescence meristem development , and they suggest that GNP1 is a suitable target gene for high yield rice breeding .
To identify QTLs , we constructed two sets of reciprocal introgression lines ( ILs ) derived from a japonica rice variety Lemont ( LT ) and an indica variety Teqing ( TQ ) , TQ-ILs and LT-ILs . In these two ILs , multiple QTLs for Grain Number per Panicle ( GNP ) were identified in Beijing and Sanya , respectively ( S1 Table ) . Among these , QTLs affecting GNP in the RM227–RM85 region on chromosome 3 were detected in both TQ- and LT-ILs , suggesting that this QTL is stable for the grain number trait in rice . This QTL was designated Grain Number per Panicle1 ( GNP1 ) . From 201 LT-ILs , an IL named GG306 ( BC3F4 ) , containing chromosome segment RM227–RM85 from TQ and 92 . 6% of the genetic background of LT , was selected ( Fig 1A ) and backcrossed twice to LT . Self-pollination of BC5F1 plants heterozygous for this fragment resulted in heterozygous near-isogenic lines ( NILs ) with almost all of the genetic background of LT except for the introgressed segment ( Fig 1A ) . The BC5F2 was successively self-pollinated several times to obtain segregating NIL-F2 ( BC5F3 , BC5F4 and BC5F5 ) populations for fine mapping of GNP1 and construction of NILs , NIL-GNP1LT and NIL-GNP1TQ ( Fig 1B ) . An analysis of a BC5F3 population of 163 individuals derived by self-pollination of the BC5F2 heterozygotes at the region RM227–RM85 showed that the trait segregated as a single locus with a Mendelian ratio , which was confirmed by data from BC5F4 families ( S1 Fig and S2 Table ) . Through map-based cloning of GNP1 , we narrowed the GNP1 locus down to a 33 . 7 kb region between SL65 and SL54 ( Fig 1C and S2 Fig ) . This region contains four predicted genes ( LOC_Os03g63970 , LOC_Os03g63980 , LOC_Os03g63990 and LOC_Os03g63999 , http://rice . plantbiology . msu . edu/cgi-bin/gbrowse ) . To further investigate the effects of the GNP1 locus on grain number and other traits , we analyzed near-isogenic lines , NIL-GNP1LT and NIL-GNP1TQ , in the LT genetic background , which only differed in the ~66 . 1 kb region containing GNP1 derived from LT and TQ ( Fig 1A ) . We observed a significant increase in the total grain number per panicle ( GNP; +56% ) , filled grain number per panicle ( FGN; +28% ) and secondary branch number ( SBN ) in NIL-GNP1TQ ( Fig 1D , Fig 1E and S3F Fig , the same pattern in SBN between LT and TQ ( S3G Fig ) ) , but only a small increase in plant height ( +8%; Fig 1B and S3A Fig ) , a slight decrease in grain length ( -4%; S3B Fig ) , grain width ( -5%; S3C Fig ) and 1 , 000-grain weight ( -12%; S3D Fig ) and no effect on panicle length ( S3E Fig ) and primary branch number ( S3F Fig the same pattern in PBN between LT and TQ ( S3G Fig ) ) compared with the NIL-GNP1LT isogenic control in plants grown in Shanghai . These results indicate that the GNP1TQ locus in NIL-GNP1TQ has pleiotropic effects on rice development , primarily on inflorescence development , especially secondary branch number and grain number . To determine whether GNP1TQ affects grain yield , we evaluated the grain yields of NIL-GNP1TQ and the isogenic control ( Lemont ) , together with other related traits . In different fields , the grain number was still substantially higher in NIL-GNP1TQ than in the control , leading to a significant increase in grain yield ( 5 . 7–9 . 6% ) despite the slightly reduced grain weight ( Table 1 and S3 Table ) . These results suggest that the GNP1TQ locus can potentially be used in high yield rice breeding . According to the mapping results , LOC_Os03g63980 and LOC_Os03g63990 are predicted to encode transposon and retrotransposon proteins , LOC_Os03g63999 encodes a small peptide with unknown function and LOC_Os03g63970 encodes GA 20-oxidase 1 , which is thought to catalyze the conversion of GA12 to GA20 within a multi-step process . Therefore , LOC_Os03g63970 is the most likely candidate for the GNP1 locus . We sequenced the promoter ( 2 kb before ATG ) and LOC_Os03g63970 in both TQ and LT . The two parents exhibited base differences at 21 positions in the promoter region , including 17 single-base substitutions , as well as two single-base and two multi-base insertions and deletions . The coding region contains two single-base substitutions , one of which leads to an amino acid substitution ( S4 Fig ) . These results suggest that the sequence differences in the promoter and coding region of this gene might lead to changes in gene expression levels and protein function and may help increase grain number in NIL-GNP1TQ . To validate this hypothesis , we obtained the LOC_Os03g63970 T-DNA gain-of-function mutant gnp1-D from the Rice T-DNA Insertion Sequence Database . TAIR-PCR screening showed that the T-DNA was inserted at position -514 to -492 of the LOC_Os03g63970 promoter relative to the start codon ATG ( Fig 2A ) , which constitutively induces the expression of LOC_Os03g63970 throughout the plant . We analyzed traits of the homozygous gnp1-D mutant and control via PCR with specific primers designed based on the insertion sequence ( Fig 2A and Fig 2B ) , finding a significant increase in plant height ( Fig 2C and Fig 2D ) with increasing LOC_Os03g63970 expression in flag leaves ( Fig 2E ) . Interestingly , a substantial increase in GNP ( +51 . 5% ) and FGN ( +71 . 6% ) were also observed ( Fig 2F and Fig 2G ) . These results suggest that LOC_Os03g63970 is the gene for GNP1 and that the increased GNP1 expression in this mutant might influence GA biosynthesis during rice panicle meristem development . We then constructed a binary vector harboring the GNP1TQ coding sequence ( CDS ) driven by a CaMV 35S promoter , which we used to transform japonica rice ( O . sativa L . ) variety Zhonghua 11 ( ZH11 ) , whose GNP1 CDS matches that of LT . GNP1 was expressed at levels several hundred- to over a thousand-fold that of CK ( transgenic negative control ) in flag leaves ( Fig 3A ) . Compared with CK , the GNP of line p35S::GNP1TQ-3 increased by 36 . 3% , accompanied with hugely increased height ( S5A and S5B Fig ) and greatly increased sterility , while lines p35S::GNP1TQ-1 and p35S::GNP1TQ-2 had significantly increased GNP ( FGN ) by 27 . 8% ( 35 . 5% ) and 26 . 5% ( 33 . 4% ) ( Fig 3B and Fig 3C ) , and slightly increased height ( S5A and S5B Fig ) . These results indicate that the expression disturbances associated with the promoter activity variations at the GNP1 locus are responsible for the phenotypic variation in GNP and plant height with a dose-dependent manner and a very high expression level of GNP1 may have a negative effect on seed setting rate . Then , in order to find out whether decreased expression of GNP1 could show some negative effect on grain number phenotype , we transformed ZH11 with the mimic artificial microRNA oligo sequence designed for GNP1 silencing driven by the CaMV 35S promoter . Interestingly , the grain number of six transgenic-positive independent lines increased ( S6A Fig ) , which was negatively correlated with GNP1 expression ( S6B Fig ) . These lines also had reduced plant height ( S6C and S6D Fig ) . These results indicate that the reduced expression of GNP1 might contribute to attenuated GA biosynthesis activity , leading to reduced GA levels and partially reducing the negative effects of GAs on maintaining inflorescence meristem activity [26] , which might be responsible for the higher grain number in these mimic artificial miRNA transgenic lines . To further confirm the function of GNP1LT CDS , we transformed NIL-GNP1LT with GNP1LT CDS driven by the GNP1 promoter from Lemont ( pGNP1LT ) . Similar to gnp1-D gain-of-function mutant and GNP1TQ overexpression lines , as the expression level of GNP1 increased ( up to nearly ten-fold compared to the control; Fig 3D ) , we observed an increase in GNP and FGN ( Fig 3E ) , as well as plant height ( S5C Fig ) . These results indicate that both GNP1LT and GNP1TQ could affect panicle development . These results indicate that the accumulation of GNP1LT or GNP1TQ transcripts ( or both ) in the plant has a positive effect on grain number and plant height . To determine whether the differences between the GNP1LT and GNP1TQ promoter regions ( S4 Fig ) influence GNP1 expression , and account for the differences in grain number , we analyzed the expression patterns of GNP1 between NIL-GNP1LT and NIL-GNP1TQ in different tissues during panicle initiation to the booting stage . GNP1 was mainly expressed in developing panicles and nodes ( S7 Fig ) , which is consistent with effects of this gene on grain number and plant height . In addition , compared to NIL-GNP1LT , GNP1 transcripts were much more abundant in NIL-GNP1TQ tissues ( S7 Fig ) . Meanwhile , GNP1 expression in seedling leaf sheaths was negatively correlated with the dose of GA3 used for treatment ( Fig 4A and Fig 4C ) and positively correlated with that of the GA biosynthesis inhibitor uniconazole-P ( Fig 4B and Fig 4D ) , suggesting that GNP1 expression is controlled by biologically active GA levels . The GNP1LT allele was much more sensitive to uniconazole-P treatment and endogenous GA signal feedback regulation ( Fig 4B and Fig 4D ) , probably due to the sequence variations among promoters . We also investigated GNP1 expression in the shoot apical meristems and inflorescence meristems . Similar to OSH1 , a key factor in rice meristem maintenance and regulation , GNP1 was also expressed in the apical regions of these meristems ( S8 Fig ) . OSH1 expression signal in NIL-GNP1TQ meristems is still strong and specific ( S8 Fig ) , These results suggest that during NIL-GNP1TQ inflorescence meristem development , the sequence variations of the promoter might lead to a failure to maintain low GNP1 expression level , resulting in induced GNP1 expression in the panicle meristems of NIL-GNP1TQ . The above findings demonstrate that the variations in promoters leading to changes in GNP1 expression in the panicle meristems are the main contributor to the differences in grain number between NIL-GNP1TQ and NIL-GNP1LT . Moreover , the total GNP was positively correlated with the expression level of GNP1 . In vitro , GNP1 ( GA20ox1 ) directly catalyzes the biosynthesis of GA53 , GA44 , GA19 and GA20 in the early-13-hydroxylation pathway with various catalyzing efficiency for each steps [27] . GA20 is then used for GA1 and GA3 biosynthesis via catalyzing by GA3oxs ( Fig 5A ) [28] . We therefore measured the contents of five endogenous GA biosynthesis intermediates , finding that GA20 and GA12 accumulated preferentially in the panicle meristems of NIL-GNP1TQ , whereas GA44 levels were much lower and there were no changes in GA19 levels relative to NIL-GNP1LT ( Fig 5B and Fig 5C ) , indicating that GA20 biosynthesis was accelerated . GNP1 mRNA levels were much higher in NIL-GNP1TQ , suggesting that the catalytic activity of GNP1 markedly increased as well , leading to higher accumulation of the GA biosynthesis intermediate GA20 . The increased accumulation of GA12 suggests that GA biosynthesis activities including GA12 biosynthesis and previous steps might have been activated in this line . However , in the panicle meristems of NIL-GNP1TQ , bioactive GA1 and GA3 were not detected although they were detected in NIL-GNP1LT ( Fig 5D ) , indicating that GA1 and GA3 levels in the NIL-GNP1TQ panicle meristems were too low to quantify . Consistent with this result , the GA signal transduction-related genes RGL3 and SLR1 were induced in this line ( S9 Fig ) . RGL3 and SLR1 are DELLA proteins and negative regulators of GA signaling , whose degradation by GAs in collaboration with GID1 ( gibberellin receptor ) [29 , 30] and F-box protein is a key event in GA signaling activation [31–33] . Indeed , bioactive GA1 and GA3 levels were reduced in NIL-GNP1TQ panicle meristems . By contrast , most GA biosynthesis-related genes were upregulated , including OsKAO , OsKO , OsKS , OsCPS and OsGA3ox2 ( S9 Fig ) , leading to increased GA12 levels ( Fig 5B ) , likely due to feedback activation by reduced bioactive GA ( GA1 and GA3 ) levels . At the same time , most bioactive GA catabolism genes , i . e . , OsGA2oxs ( Fig 5E ) , were induced . As GA2oxs directly catalyze progressive catabolic processes that convert active GAs into inactive forms ( Fig 5A ) , the increased catabolic activities in NIL-GNP1TQ panicle meristems regulate GA levels much more effectively , regardless of the activated GA biosynthesis process described above . Based on these findings , during NIL-GNP1TQ panicle meristem development , GA ( GA1 and GA3 ) levels happened to be reduced , although the catabolic activities of GNP1 were enhanced . Cytokinins significantly affect reproductive meristem activity [2] . The abnormal GA metabolism in NIL-GNP1TQ observed in the current study might be caused by KNOX-mediated responses . To investigate this possibility , we analyzed the expression of five rice KNOX genes , including OSH1 , OSH6 , OSH15 , OSH43 and OSH71 . The expression of these genes significantly increased in the panicle meristems of NIL-GNP1TQ ( Fig 6A ) . OsIPTs , which are directly regulated by KNOX proteins , were also upregulated in NIL-GNP1TQ , as was the cytokinin activating gene LOG ( Fig 6B ) , perhaps leading to cytokinin accumulation . We also examined endogenous cytokinins levels in NIL-GNP1TQ , finding that the levels of several cytokinins and cytokinin biosynthesis intermediates increased in this line ( Fig 6C to 6F ) , leading to increased expression of cytokinin signal response factors ( Fig 6G ) . These results indicate that cytokinin activity was substantially enhanced in NIL-GNP1TQ panicle meristems , resulting in increased grain number compared to NIL-GNP1LT .
A previous in vitro study showed that recombinant OsGA20ox1 could catalyze the conversion of GA12 and GA53 to GA9 and GA20 , but it acts more effectively on GA53 [27] . The present study shows that GNP1 encodes a rice OsGA20ox1 protein . OsGA20ox1 activity is induced via increased expression of GNP1 , which increases GA20 levels in vivo . Moreover , GNP1 transcript levels in seedling leaf sheaths were positively correlated with the treatment dose of uniconazole-P and negatively correlated with that of GA3 ( Fig 4C and 4D ) , suggesting that GNP1 expression is controlled by biologically active GA levels . Moreover , NIL-GNP1LT was much more susceptible to endogenous GA signal feedback regulation than NIL-GNP1TQ , likely due to the sequence variations among promoters leading to altered expression of GNP1 . GNP1 transcripts were mainly detected in newly initiated panicles and in apical regions of meristems overlapping with OSH1 ( a rice KNOX gene ) expression ( S8 Fig ) . This specific expression pattern implies that GNP1 also plays a fundamental role in regulating panicle meristem activity that is similar to that of cytokinin biosynthesis and signaling genes . The increased grain number of NIL-GNP1TQ due to enhanced expression of GNP1 supports this notion . Cytokinins positively regulate reproductive meristem activity [2] , GAs are detrimental to meristem activity [20 , 21] and KNOX proteins play an irreplaceable role in balancing cytokinin and GA activity in the meristem [25] . We observed increased cytokinin activity in the panicle meristems of NIL-GNP1TQ , including KNOX-mediated induction of OsIPTs and increased levels of cytokinins and cytokinin biosynthesis intermediates , together with enhanced cytokinin responses . In additions , these plants failed to accumulate bioactive GA1 and GA3 and exhibited significantly increased KNOX transcript levels . Taken together , these results demonstrate that increased GNP1 activity positively induces the expression of KNOX genes via a feedback loop ( Fig 7 , red arrow ) . This promotion of KNOX gene expression leads to increased cytokinin activity through directly inducing OsIPT expression , as well as upregulation of GA2oxs , which negatively regulate GA biosynthesis , thereby reducing GA1 and GA3 levels . The activation of GA biosynthesis might be due to feedback regulation compensating for the defects in GA1 and GA3 accumulation , leading to increased accumulation of GA12 . The tendency for activated GA biosynthesis may be much less effective than that for GA catabolism . This feedback mechanism rebalances cytokinin and GA activity , resulting in increased cytokinin levels and contributing to the higher GNP1 expression level of NIL-GNP1TQ . On the other hand , decreased expression of GNP1 could lead to lower GA1 and GA3 level in those positive GNP1 mimic artificial miRNA transgenic lines , which might eliminate the suppression effect of higher GA1 and GA3 level on meristem activities , and increase grain number in turn ( Fig 7 ) . We propose that during inflorescence meristem development and maintenance processes , increased expression of GNP1 in those NILs leads to promoted cytokinin activities and gives increased grain number and yield , while decreased expression of GNP1 in those mimic artificial miRNA transgenic lines most probably contributes to alleviation of the detrimental effect of gibberellins to meristem activity , according to those previous reports , which in turn also gives increased grain number . Numerous efforts aimed at increasing food production to sustain the growing population have focused on elucidating the mechanisms underlying the development of several important agronomic traits in rice , such as panicle architecture . In this study , we cloned a rice GA20ox1 gene , GNP1 , whose expression strongly increases rice grain number . Increasing GNP1 expression may be useful for high yield rice breeding , as these GNP1 higher-expressed NILs exhibited increased grain number and grain yield , although they were also slightly taller than the controls . When we overexpressed GNP1 in ZH11 , similar results were obtained , thus representing a new strategy for high yield rice breeding .
Two sets of reciprocal introgression lines ( ILs ) derived from a japonica rice ( O . sativa L . ) variety Lemont and an indica variety Teqing were used as materials for QTL mapping [34] . ZH11 and Lemont were used for the transgenic experiments . The gnp1-D T-DNA mutant line PFG_2D-41474 . R was identified from the Rice Functional Genomic Express Database ( RiceGE , http://signal . salk . edu/cgi-bin/RiceGE ) and obtained from the Rice T-DNA Insertion Sequence Database ( RISD DB , http://cbi . khu . ac . kr/RISD_DB . html ) [35] . Oligo sequences used for genotyping the progeny of gnp1-D T-DNA insertional line are shown in S4 Table . For map-based cloning of GNP1 , we performed genotyping of 5 , 500 BC5F3 individuals from five BC5F2 plants that were heterozygous only at the region RM227–RM85 , harboring five markers . We identified 16 informative recombinants of four genotypes within this region . Using multiple comparisons of the homozygous recombinant BC5F4 lines for GNP with the non-recombinant controls , we localized GNP1 to a 309 . 5kb region between SL13 and RM85 . Further fine mapping using 9 , 500 BC5F4 plants with six new markers between SL13 and RM85 identified six informative recombinants and four genotypic classes in the target region . We localized GNP1 to a high-resolution linkage map by progeny testing of BC5F5 homozygous recombinant plants and narrowed the GNP1 locus down to a 33 . 7 kb region between SL65 and SL54 . Primers used for fine mapping are shown in S5 Table . GA3 and uniconazole-P treatment were carried out as previously described [36] with minor modifications . For GA3 treatment , manually dehulled seeds were sterilized with 75% ethanol for 1 min , washed three times with distilled water , sterilized with 2 . 5% sodium hypochlorite for 35 min , washed five times with sterile distilled water and incubated on 1/2 MS medium at 4°C for 3 days in the dark . The germinated seeds were transferred to plastic containers containing 1% ( w/v ) agar with various concentrations of GA3 ( 63492-1G , Sigma-Aldrich ) . For uniconazole-P treatment , the seeds were incubated in distilled water with various concentrations of uniconazole-P ( 19701-25MG , Sigma-Aldrich ) at 4°C for 24 h , followed by 26°C for an additional 24 h . The seeds were washed three times with distilled water and incubated for an additional 24 h in distilled water at 26°C . The germinated seeds were grown in 1% ( w/v ) agar in plastic containers . Seedlings were grown for 7 days under fluorescent light with a 12 h light/12 h dark photoperiod at 26°C . The second leaf sheath lengths of 48 seedlings per treatment were measured and analyzed . For qRT-PCR analysis , second leaf sheaths were also used , with six pooled replicates for each treatment . To produce the overexpression constructs , the full-length coding sequence of GNP1 was amplified from NIL-GNP1TQ and cloned into plant binary vector pCAMBIA1300 under the control of single CaMV 35S promoter . The artificial microRNA oligo sequences used for GNP1 silencing were designed as previously described [37] ( http://wmd3 . weigelworld . org/cgi-bin/webapp . cgi ? page=Home;project=stdwmd ) and amplified using primer set G-11491 and G-11494 . The oligo sequences were inserted into the XbaI and KpnI sites of pCAMBIA1300 containing one CaMV 35S promoter . Oligo sequences for three different target sites were independently used for construction and transformation . The overexpression and silencing plasmids were introduced into Agrobacterium tumefaciens strain EHA105 and transferred into the japonica variety ZH11 . To produce the construct for the complementary test , 2 . 2 kb promoter sequence with full-length coding sequences of GNP1 were amplified from NIL-GNP1LT . The sequences were then cloned into pCAMBIA1300 , introduced into Agrobacterium tumefaciens strain EHA105 and used for transformation of NIL-GNP1LT . All constructs were confirmed by sequencing . The primer sets are shown in S6 Table , and plant transformation processes were carried out as previously described [38] . Total RNA was extracted from various plant tissues using TRIZOL Reagent ( Invitrogen ) . Approximately 500 ng of total RNA was transcribed into first-strand cDNA using ReverTra Ace qPCR RT Master Mix with gDNA Remover ( TOYOBO ) . Real-time PCR data were obtained using an ABI 7300 Real Time PCR System with Fast Start Universal SYBR Green Master Mix with ROX ( Roche ) and analyzed using the ΔΔCt method . The cycling parameters were 10 min at 95°C , followed by 40 cycles of amplification ( 95°C for 10 s and 60°C for 1 min ) . The ubiquitin and actin genes were used for normalization . The standard amplification slope for real-time PCR primer OsGA2ox1f/OsGA2ox1r was -3 . 498971 , which was used to calculate amplification efficiency . All analyses were repeated at least three times . Primer sets are shown in S7 Table . NIL-GNP1TQ and NIL-GNP1LT plants were grown in open fields for approximately 5 weeks . Freshly initiated panicles approximately 1 cm long were harvested , and ~1 g samples were used for measurements , with three independent biological repeats per sample . Quantification of endogenous GAs [39] and cytokinins [40] was performed as previously described . NIL-GNP1TQ plants were grown in open fields for approximately 3 weeks . Samples ~0 . 5 cm in length including the meristem region were harvested and fixed in 4% ( w/v ) paraformaldehyde with 0 . 1% Tween-20 , 0 . 1% Triton-x-100 and 1% ( v/v ) 25% glutaraldehyde solution in 0 . 1 M sodium phosphate buffer ( pH 7 . 4 ) overnight at 4°C . The samples were then dehydrated with a graded ethanol series followed by a dimethylbenzene series . The samples were then embedded in Paraplast Plus ( Sigma , P3683 ) , cut into 10 μm sections and mounted on pre-coated poly-prep slides ( Sigma , P0425 ) . Digoxigenin-labeled RNA probes were prepared following the instructions of the DIG RNA labeling kit ( SP6/T7 ) ( Roche , 11175025910 ) . Hybridization and signal detection were performed as previously described [41] . The primer sets are shown in S8 Table . Yield and related traits for NIL-GNP1TQ and the isogenic control ( Lemont ) were evaluated at five locations: Beijing ( 40 . 2°N , 116 . 2°E ) ; Nanning ( 22 . 1°N , 107 . 5°E ) , Guangxi province; Jingzhou ( 30 . 3°N , 112 . 2°E ) , Hubei province; Pingxiang ( 27 . 6°N , 113 . 9°E ) , Jianxi province and Sanya ( 18 . 3°N , 109 . 3°E ) , Hainan province , China . NIL-GNP1TQ and Lemont plants were grown in a randomized plot design with three replications per line . The area of each plot was 13 . 2 m2 , with a single plant transplanted per hill at 25 d after sowing and a spacing of 17 cm between hills and 25 cm between rows . As a basal dressing , 50 kg ha-1 each of N , P and K was applied the day before transplanting , and 30 kg ha-1 of N was applied twice as topdressing at 1 and 5 weeks after transplanting . At the heading stage , heading date ( HD ) and plant height ( PH ) were recorded when 30% of plants contained panicles in each line . At maturity , whole plots were harvested for yield measurements based on a 14% moisture content after air drying . Eight plants were sampled and dried in an oven at 70°C for 5 d for trait investigation , including panicle number per plant ( PNP ) , panicle length ( PL ) , filled grains per panicle ( FGP ) , grain number per panicle ( GNP ) , thousand grain weight ( TGW ) , grain length ( GL ) and grain width ( GW ) . QTLs affecting GNP were identified using IciMapping 3 . 0 [42] , combined with genotypic data for 157 SSRs and three morphological markers ( Ph , gl-1 and C ) for the ILs [34] . The permutation method was used to obtain empirical thresholds for claiming QTLs based on 1 , 000 runs in which the trait values were randomly shuffled [43] . | Grain number per panicle , a valuable agronomic trait for rice yield improvement , is profoundly affected by reproductive meristem activity . This activity , in turn , is controlled by transcriptional and plant hormone regulators , especially KNOX proteins and cytokinins . However , little is known about the roles of GAs in these processes in rice and how the regulatory network functions due to the complexity of crosstalk between plant hormone regulators . In this study , we identify a novel GA biosynthesis gene in rice and demonstrate its role in improving grain number and grain yield . We also propose that the KNOX-mediated cytokinin-GA activity rebalancing mechanisms regulate inflorescence meristem development and maintenance processes , providing a possible tool for high-yield rice breeding . |
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Chromatin structure and gene expression are regulated by posttranslational modifications ( PTMs ) on the N-terminal tails of histones . Mono- , di- , or trimethylation of lysine residues by histone lysine methyltransferases ( HKMTases ) can have activating or repressive functions depending on the position and context of the modified lysine . In Arabidopsis , trimethylation of lysine 9 on histone H3 ( H3K9me3 ) is mainly associated with euchromatin and transcribed genes , although low levels of this mark are also detected at transposons and repeat sequences . Besides the evolutionarily conserved SET domain which is responsible for enzyme activity , most HKMTases also contain additional domains which enable them to respond to other PTMs or cellular signals . Here we show that the N-terminal WIYLD domain of the Arabidopsis SUVR4 HKMTase binds ubiquitin and that the SUVR4 product specificity shifts from di- to trimethylation in the presence of free ubiquitin , enabling conversion of H3K9me1 to H3K9me3 in vitro . Chromatin immunoprecipitation and immunocytological analysis showed that SUVR4 in vivo specifically converts H3K9me1 to H3K9me3 at transposons and pseudogenes and has a locus-specific repressive effect on the expression of such elements . Bisulfite sequencing indicates that this repression involves both DNA methylation–dependent and –independent mechanisms . Transcribed genes with high endogenous levels of H3K4me3 , H3K9me3 , and H2Bub1 , but low H3K9me1 , are generally unaffected by SUVR4 activity . Our results imply that SUVR4 is involved in the epigenetic defense mechanism by trimethylating H3K9 to suppress potentially harmful transposon activity .
In eukaryotes , gene expression and chromatin structure is specified by the combinatorial pattern of posttranslational modifications ( PTMs ) on the histone tails , which include phosphorylation , acetylation , methylation , SUMOylation and ubiquitination [1] , [2] . These PTMs are interdependent , thus providing regulatory cross-talk , and established at the histone tails in a coordinated manner by different classes of highly specific chromatin modifying enzymes . The combination of PTMs constitutes the so-called histone code , and their downstream effect on chromatin organization and gene expression is mediated by nonhistone effector proteins that contain domains that bind or “read” this code in order to specify epigenetic function . Such domains show specificity for particular modified residues ( e . g . acetylation or methylation of lysine ) in the context of its surrounding amino acid sequence , and for the state of the modification ( e . g . H3K9me1 vs H3K9me3 ) [1] , [3] . For example , domains belonging to the Royal Superfamily , including the chromodomain , Tudor domain and MBT domain and members of the PHD finger family , bind methylated lysine residues on the histone tails [4] . More specifically , the PHD finger of the ORC1 protein in Arabidopsis binds H3K4me3 , but not H3K4me1 or H3K4me2 at target genes , and this mediates H4K20 trimethylation and activates transcription [5] . Lysine ubiquitination of histones and other target proteins is a three step process involving Ub ( ubiquitin ) -activating ( E1 ) , Ub-conjugating ( E2 ) and Ub-ligating ( E3 ) enzymes , eventually leading to monoubiquitination , multi-monoubiquitination or polyubiquitination [6] , [7] . Ubiquitin binding domains ( UBDs ) represent a new class of motifs that enable proteins to bind non-covalently to the PTM ubiquitin . More than twenty families have been identified to date , and they differ in structure and the type of ubiquitin modification they recognize [6] , [7] . Poly-Ub chains linked via the K48 residue of ubiquitin are largely recognized by UBDs of receptors that target proteins for proteosomal degradation , while monoubiquitin is recognized by UBDs of proteins involved in processes like DNA repair , regulation of protein activity , chromatin remodeling and transcription [6]–[8] . The cross-talk between H2B monoubiquitination ( H2Bub1 ) and histone methylation has been extensively studied and is highly conserved from yeast to human . These studies show that monoubiquitination of H2B recruits proteins that direct histone H3K4 di- and trimethylation but not monomethylation by activation of the Set1 histone lysine methyltransferase ( HKMTase ) of the COMPASS complex ( reviewed in [9] , [10] ) . In Arabidopsis , H2B monoubiquitination at K143 coincides with active transcription [11]–[13] . Deubiquitinating enzymes ( DUBs ) oppose the function of E3 ligases by deubiquitinating Ub-conjugated proteins . Increased H2Bub1 caused by a mutation in the DUB SUP32/UBP26 , leads to reduced H3K9me2 and increased H3K4me3 at transposons that correlate with increased transcription [11] . A key function for DUBs is to generate a pool of free ubiquitin monomers from ubiquitin precursors synthesized from Ub-encoding genes , and from polyubiquitin chains and ubiquitin conjugates [14] . Free monomeric ubiquitin is required under stress conditions , and organisms defective in ubiquitin precursor proteins or DUBs are more sensitive to stress . In yeast , heat stress stimulates the production and activation of the Doa4 deubiquitinase which increases the supply of free monomeric ubiquitin by cleaving polyubiquitin [15] . HKMTases contain SET domains with specificities for different lysine residues on the histone tails , and may be involved in either gene activation or gene repression depending on which lysine residue is methylated [16] . In general , methylation of H3K9 , H3K27 and H4K20 has been associated with heterochromatin and gene repression , while H3K4 , H3K36 and H3K79 methylation has been related to euchromatin and gene activation [1] . The downstream effect of histone methylation also depends on the number of methyl groups at each lysine residue . Histones mono- , di- , or trimethylated at lysines are differently distributed within eu- and heterochromatin , each potentially indexing a specific biological outcome [17] , [18] . For example , in Arabidopsis , H3K36 trimethylation , but not H3K36 monomethylation , shows a strong positive correlation with transcription of MADS box genes involved in flowering-time and flower development [19] , [20] . Although lysine methylation to a large extent is conserved between eukaryotes , the distribution and biological outcome of the methylation may be different . H3K9me1 , H3K9me2 and H3K27me2 are for instance predominantly found in the chromocenters of Arabidopsis but not in mouse chromocenters ( reviewed in [21] , [22] ) . Conversely , H3K9me3 and H4K20me3 that localize to heterochromatin in mouse are mainly associated with euchromatin in Arabidopsis . Additionally , recent results suggest that in contrast to other eukaryotes , H3K9me3 methylation correlates with gene transcription and might have a slight activating function in Arabidopsis [23] , [24] . H3K9 methylation is carried out by proteins of the SU ( VAR ) 3-9 subgroup which consists of 14 proteins in Arabidopsis; the SU ( VAR ) 3-9 HOMOLOGs SUVH1-SUVH9 , and the more distantly related SU ( VAR ) 3-9 RELATED proteins SUVR1-5 [25] . In addition to the SET domain the SUVH proteins contain the YDG/SRA domain that has been shown to bind methylated DNA and might direct SUVH mediated H3K9me2 to heterochromatin or stimulate its activity [26] . Thus in Arabidopsis , the SUVH proteins link the epigenetic gene-silencing marks H3K9me2 and DNA-methylation and work as transcriptional repressors of transposons or inverted repeat sequences , for instance by directing CHG methylation via the CMT3 DNA methyltransferase ( reviewed in [27] ) . In contrast to the SUVH proteins , the SUVR1 , SUVR2 and SUVR4 proteins do not contain an YDG/SRA domain , but an N-terminal WIYLD domain of unknown function [28] , suggesting another mode of action for these proteins . SUVR proteins associate with the nucleolus or euchromatin , and we have earlier shown that SUVR4 can dimethylate H3K9 when this position is monomethylated [28] . In the present study we show that the WIYLD domain of SUVR4 specifically binds ubiquitin , demonstrating a close connection between ubiquitin binding and histone H3K9 methylation . We have furthermore revealed that ubiquitin stimulates the enzyme activity of SUVR4 and converts SUVR4 from a strict dimethylase to a di/trimethylase in vitro . Chromatin Immunoprecipitation ( ChIP ) analysis of Arabidopsis lines with reduced or enhanced expression of SUVR4 , demonstrate that SUVR4 localizes to both euchromatin and heterochromatin in vivo , but only converts H3K9me1 to H3K9me3 at transposons and pseudogenes . SUVR4 dependent H3K9 trimethylation correlates with locus specific transcriptional repression of transposable elements intercalated within euchromatin of the Arabidopsis genome .
To address the function of the SUVR4 WIYLD domain , a construct encompassing only this domain ( Figure 1A ) was used in a yeast two-hybrid screen to identify interacting proteins . One positive clone identified in this screen , contained the full-length coding sequence ( CDS ) of UBIQUITIN EXTENSION PROTEIN 1 ( UBQ1 , AT3G52590 ) ( Figure 1B ) . The UBQ1 protein consists of an N-terminal ubiquitin moiety and the C-terminal ribosomal protein L40 [29] . These moieties were subcloned and tested separately for their interaction with SUVR4-WIYLD . Clones containing the ubiquitin moiety , but not clones containing the L40 moiety , supported growth on selective media when transformed into yeast cells and mated with cells containing SUVR4-WIYLD , suggesting that SUVR4 specifically interacts with ubiquitin ( Figure 1B ) . This was confirmed in an in vitro pull-down experiment , where SUVR4-WIYLD pulled down full-length UBQ1 and ubiquitin but not L40 ( Figure 1C ) . To address whether the WIYLD domain binds ubiquitin in its unconjugated form and to identify residues directly involved in the interaction between WIYLD and ubiquitin , an NMR analysis was performed . The [1H , 15N]-HSQC spectrum of 15N-isotopically labeled SUVR4-WIYLD is well-dispersed demonstrating that the protein domain is folded ( Figure 1D ) . Upon titration of ubiquitin , chemical shift perturbations were observed for a number of residues including the six consecutive amino acids Y69TALVD74 of helix 3 ( Figure 1D ) , indicating that they are involved in binding . Alignment of SUVR4-WIYLD with WIYLD domains in other proteins have earlier shown that many of these residues are highly conserved ( Figure 1A and [28] ) . SUVR4 binds and efficiently methylates calf thymus histone H3 as well as H3K9me1 peptides in vitro , but shows only weak activity against recombinant histones , arguing that SUVR4 cross-talks to premodified histones [28] . Since the WIYLD domain binds ubiquitin , and SUVR4 binds and methylates histones , we tested whether the WIYLD domain binds H2B monoubiquitinated on lysine 143 ( H2Bub1 ) , which is the only ubiquitination on core histones reported so far in Arabidopsis [11] , [30] . In these experiments the WIYLD domain indeed was able to pull down H2Bub1 , however , when R37 and D74 were mutated , the interaction was strongly reduced ( Figure 1E ) . This supports the chemical shift perturbations shown by the NMR analysis , arguing that these residues are directly involved in ubiquitin binding . Interestingly , the invariant W61 residue that showed no shift in the NMR analysis , only weakly affected the WIYLD-ubiquitin interaction when mutated , confirming that this position is not crucial for ubiquitin binding . As the WIYLD domain was able to bind ubiquitin ( Figure 1D ) , we asked whether ubiquitin could stimulate SUVR4 enzyme activity , as previously shown for the deubiquitinase USP5 [31] . To this end , we compared the activity of a SUVR4 protein without the WIYLD domain to a full-length SUVR4 protein , both in fusion with the Maltose Binding Protein ( MBP-SACSET and MBP-SUVR4 , Figure 1A ) , with and without the addition of ubiquitin . In both cases the full-length protein showed higher enzymatic activity than the truncated SACSET fragment ( Figure 2A , B ) , suggesting that the WIYLD domain has a positive effect on the catalytic activity of SUVR4 although the domain itself does not contain HMTase activity ( Figure S1C ) . The difference in activity was more pronounced when ubiquitin was added to the reaction . With ubiquitin the full-length protein was stimulated 2-3 fold whereas the SACSET construct was only weakly affected , suggesting that most of the ubiquitin response is mediated through the WIYLD domain ( Figure 2A , B ) . Addition of free ubiquitin only stimulates enzymatic activity of the SUVR4 protein on histone H3 but does not affect its specificity as no other core histones becomes methylated ( Figure 2C ) . Using H3K9me1 and H3K9me2 peptides we tested whether the increased SUVR4 enzyme activity after the addition of ubiquitin also affected the product specificity . As expected from previous results [28] , H3K9me1 peptides were the preferred substrate as unmethylated peptides were only weakly methylated ( Figure S1A ) , and no activity against H3K9me2 peptides was observed in the absence of ubiquitin . Methylation of H3K9me1 modified peptides was increased 2 . 5–3 fold when ubiquitin was added to the reaction ( Figure 2D ) . Unexpectedly we also observed methylation of the H3K9me2 peptide in the presence of ubiquitin , suggesting that ubiquitin converted the SUVR4 protein to a histone H3K9 trimethylase ( Figure 2D , Figure S1B ) . The activity on H3K9me2 peptides was however several folds lower than when H3K9me1 peptides were used . No activity was observed on H3K9me3 peptides either with or without ubiquitin , excluding the possibility that any other lysine of histone H3 1-21 was methylated by SUVR4 , underscoring the specificity against H3K9 ( Figure 2D ) . The products from the enzyme reactions using peptide substrates were analyzed by peptide mass fingerprinting . After 3 hours incubation , the reactions containing SUVR4 only converted 40 . 9% of the H3K9me1 peptide to H3K9me2 , while 0% was converted to H3K9me3 ( Figure 2E , upper middle panel ) . In the reactions containing ubiquitin , 90 . 2% of the H3K9me1 peptide was converted to H3K9me2 while 3 . 5% was converted to H3K9me3 ( Figure 2E , upper right panel ) . When H3K9me2 peptides were used as substrate , we did not see any conversion to H3K9me3 above background level in the absence of ubiquitin ( 3% background H3K9me3 , versus 3 . 5% when SUVR4 was added to the reaction ) ( Figure 2E , lower middle panel ) , however when ubiquitin was present together with SUVR4 , a 16 . 4% conversion from H3K9me2 to H3K9me3 was found ( Figure 2E , lower right panel ) . This suggests that ubiquitin stimulates the catalytic activity of SUVR4 and alters the product specificity in that it converts SUVR4 from a strict dimethylase to a di/trimethylase . As SUVR4 converts H3K9me1 to H3K9me2/3 in vitro , we asked how these modifications were affected by SUVR4 in vivo . Since no SUVR4 T-DNA knock-out insertion lines were available , knock-down RNAi lines for SUVR4 were established . We also generated GFP overexpression ( OE ) lines where SUVR4-GFP expression was driven by the strong constitutive 35S promoter , giving a uniform SUVR4-distribution in the nucleus in addition to accumulation in the nucleolus or in foci of unknown function ( Figure S2 ) . A weaker glucocorticoid-inducible construct has earlier been reported to give an almost exclusive nucleolar localization of SUVR4 [28] . We did not observe any phenotypes under the tested growing conditions for neither the SUVR4-GFP line , nor the SUVR4 RNAi line . H3K9me1-3 display different nuclear distributions , with high H3K9me1/2 in chromocenters and pericentric heterochromatin , whereas H3K9me3 is distributed more uniformly in the nucleoplasm with highest concentration in euchromatin and at expressed genes [32] . Immunocytological analysis on seedling leaves using specific antibodies against H3K9me showed a strong reduction in H3K9me1 and a corresponding increase in H3K9me3 in nuclei with high SUVR4-GFP expression ( Figure 3A ) . Nuclei from lines with a low SUVR4-GFP expression did not show this effect on H3K9me1 and H3K9me3 methylation , suggesting that the global changes in H3K9me1 and H3K9me3 correlated with SUVR4-GFP expression ( Figure 3A ) . To analyze this effect at individual genes , ChIP experiments were performed with the same antibodies as used for immunocytological analysis and an antibody specific for GFP , respectively . Different classes of transposon sequences were selected for ChIP analysis , as these sequences are likely targets of SUVR4 because of their high H3K9me1 level ( Figure 3B and Table 1 ) . These experiments confirmed that SUVR4 is associated with transposons and genes both in eu- and heterochromatin , but a significantly higher amount of SUVR4-GFP is found at euchromatic genes like TUB8 and ACTIN2 ( Figure S3 ) . However , only transposon and pseudogenes like AtSN1 , AtGP1 , AtMU1 , AtCOPIA4 and MULE At2g15810 were affected by overexpression of SUVR4 , resulting in a drastic increase in H3K9me3 and reduction of H3K9me1 ( Figure 3B ) . We did not see any effect of SUVR4 OE for highly expressed genes like TUB8 or ACTIN2 , or for the moderately expressed transposon At4g13120 , all with an already low level of H3K9me1 . Although having a dramatic effect on H3K9me3 at transposons , SUVR4 OE did not affect the distribution of the euchromatic mark H2Bub1 at any of the tested sequences ( Figure S4A ) . As the 35S driven SUVR4-GFP construct could lead to unspecific downstream effects due to ectopic and elevated SUVR4 expression , we complemented the OE data with ChIP analysis of two of the transposons in knock-down SUVR4 RNAi plants . The RNAi lines showed a 90% reduction of the SUVR4 expression level compared to wild type ( Figure S5 A ) . In contrast to the OE line , there was an increase of H3K9me1 on AtSN1 and MULE At2g15810 ( Figure 3C ) . Furthermore , there was a corresponding reduction of H3K9me3 , suggesting that SUVR4 directs H3K9me3 methylation on transposons . The weak reduction of H3K9me3 could reflect the residual SUVR4 expression in the RNAi line and possibly redundancy with other H3K9me3 methyltransferases at these sequences . Together , these data suggest that although SUVR4 is localized in both eu- and heterochromatin , it is active only on target sequences with a high level of H3K9me1 , where its activity increases H3K9me3 at the expense of the H3K9me1 level . Recent studies suggest that in Arabidopsis H3K9me3 associates with euchromatin and transcriptional activation of genes [23] , [24] , [32] . In contrast , H3K9me1 is a mark mainly associated with repetitive sequences in chromocenters and pericentric heterochromatin in Arabidopsis [21] . The specific activity of SUVR4 on transposon chromatin although associated with both transposons and euchromatic genes ( Figure 3 , S3 ) , made us speculate that the lack of SUVR4 activity on euchromatic genes was due to cross-talk to PTMs characteristic for euchromatin . We thus tested histone tail peptides that were mono- or trimethylated at H3K4 but devoid of H3K9me in an in vitro HKMTase assay ( Figure 4 ) . SUVR4 activity was not affected by monomethyl H3K4 , whereas trimethyl H3K4 reduced SUVR4 activity significantly ( Figure 4 A , B ) , arguing that chromatin associated with genes like TUB8 and ACTIN2 , with a high level of this mark , might not be good substrate for SUVR4 activity . To evaluate the effect of SUVR4 mediated H3K9me3 methylation on transposon transcription we investigated the expression of three of the ChIP-analyzed transposons , MULE At2g15810 , AtIS112A ( At4g04293 ) and AtCOPIA4 , which all had a high level of H3K9me1 and were expressed in wild type plants ( Figure 3B , C , Figure S5 B and Table 1 ) . In the OE line , all the studied transposons showed significant reduction in expression compared to wild type ( 60% , 80% and 35% , respectively , Figure 5A ) , suggesting that SUVR4 acts as a repressor of these transposable elements . As a control , we used the At4g13120 transposable element of intermediate expression with a very low H3K9me1 level which is not a target of SUVR4 methylation ( Figure 3 , Figure 5A and Table 1 ) . This transposon was also unaffected in its transcription level in SUVR4-GFP overexpression lines . In the RNAi line we did not see a corresponding release of repression for the AtCOPIA4 and AtIS112A elements , however , the MULE At2g15810 element was induced 2 . 5 to 3- fold in the RNAi line compared to wild type ( Figure 5A ) . Interestingly , the gene Cyp40 which is known to be regulated by MULE [33] showed the same expression response to SUVR4 as MULE At2g15810 , although weaker ( Figure 5A ) . The AtSN1 repeat interspersed within euchromatin , and the heterochromatin localized AtMU1 that are silent in wild type plants ( Table 1 and Figure S5 B ) , were examined in both the RNAi and OE line but we did not detect any signal above the –RT control reaction , arguing that these transposons were not reactivated in any of the lines ( data not shown ) . H3K9me2 directed by SUVH proteins regulates non-CG methylation in Arabidopsis [34] . To determine if there was a similar correlation between DNA methylation and the H3K9me3 methylation directed by SUVR4 , bisulfite sequencing was performed on two of the transposons that are targets of SUVR4 histone lysine methylation . We did not detect an effect of SUVR4 activity on DNA methylation of the MULE At2g15810 transposon for CG , CHG or CHH in neither SUVR4 OE nor SUVR4 RNAi lines ( Figure 5B ) . This suggests that the repressive effect of H3K9me3 added by SUVR4 is not mediated by DNA methylation . In contrast , the AtSN1 transposon showed an increase in CHH methylation ( Figure 5C ) in the OE line . The CG and CHG methylation levels were unaffected . There was , however , no corresponding reduction of CHH methylation in the RNAi-line . The ubiquitin binding properties of the SUVR4 WIYLD domain and the ubiquitin-enhanced H3K9me3 activity of SUVR4 in vitro led us to look for links between ubiquitin and H3K9 trimethylation in vivo . Interestingly , deubiquitination of H2BUb1 by the nuclear UBP26/SUP32 ubiquitin protease , is required for repression of transposons [11] , which also are targets of SUVR4 . Therefore we investigated the H3K9me levels in the ubp26-1/sup32 mutant ( Figure S6 ) . No effect was seen on highly expressed genes like TUB8 and ACTIN2 ( Figure 6 ) , and consistent with earlier findings [11] , our ChIP analysis showed a reduction of H3K9me2 on transposons and repeat sequences ( Figure 6A ) . Similarly , H3K9me3 was also reduced on transposons in the mutant compared to the wild type ( Figure 6B ) . Although mutation in the UBP26/SUP32 gene has been reported to lead to a global accumulation of H2Bub1 [35] , the H2Bub1 level on transposons was only weakly affected by the mutation ( Figure 6C ) , and the level of free ubiquitin monomers in the nuclei of ubp26-1/sup32 was similar to the level in the wild type ( Figure 6D ) . We next tested the effect of global reduction of H2Bub1 on H3K9me3 level on transposon chromatin using the hub2-2 mutant . This mutant is defect in the HISTONE MONOUBIQUITINATION2 E3 ligase , which acts non-redundantly with HUB1 to monoubiquitinate histone H2B [13] . The hub2-2 mutant showed an almost complete lack of H2Bub1 at the TUB8 gene , while the effect was absent or negligible on the AtGP1 transposon . As reported for H3K9me2 [13] , [36] , the H3K9me3 level was not affected either on TUB8 or on transposon chromatin ( Figure S7 ) .
Our experiments have identified the WIYLD domain of the SUVR4 HKMTase as a new ubiquitin interacting domain , demonstrating a direct link between ubiquitin binding and H3K9 methylation . Ubiquitin is extensively distributed in the eukaryotic proteome , and exists as free ubiquitin monomers , ubiquitin extension proteins , polyubiquitin , or ubiquitin conjugates [14] . The interactions with free ubiquitin , the ubiquitin moiety of the ubiquitin extension protein UBQ1 and the ubiquitin conjugate H2Bub1 ( Figure 1 ) , indicate that the SUVR4 WIYLD domain can target ubiquitin either in its free or conjugated form . The interaction between the WIYLD domain of SUVR4 and ubiquitin is further supported by the WIYLD-dependent positive effect of ubiquitin on enzymatic activity ( Figure 2 ) . Free ubiquitin stimulated the HKMTase activity of the full-length SUVR4 protein without compromising the substrate specificity because no histones other than H3 were methylated ( Figure 2C ) . However , the addition of free ubiquitin ( Ub ) converted the protein from a strict H3K9me2 to a H3K9me2/me3 methyltransferase ( Figure 2D , 2E ) , suggesting that ubiquitin either in its free form or conjugated to other proteins like H2B can act as a signal for H3K9 trimethylation . We only observed 3% conversion of H3K9me1 to H3K9me3 after a 3 hour reaction time in our in vitro HKMTase assay while most of the H3K9me1 was converted to H3K9me2 ( Figure 2E ) . In contrast , a massive shift from H3K9me1 to H3K9me3 was seen in vivo when over-expressing SUVR4 ( Figure 3A , 3B ) . Together this implies the need for another component in addition to ubiquitin for SUVR4 to efficiently convert H3K9me1 to H3K9me3 in vitro , as shown for the murine ESET HKMTase [37] . In recombinant form in vitro ESET only catalyzes mono- and dimethylation of H3K9 , but in complex with the transcriptional repressor mAM the enzyme generates H3K9me3 . Interestingly , the truncated SUVR4 SACSET protein showed a lower HKMTase activity compared to the full-length SUVR4 protein on core histones ( Figure 2A ) , arguing that the N-terminal WIYLD domain is essential for normal activity of the C-terminal SET domain . Furthermore , the activity of the SUVR4 SACSET was only weakly enhanced by ubiquitin ( Figure 2A , 2B ) , demonstrating that ubiquitin in its free form stimulates SUVR4 activity mainly through the WIYLD domain . Several enzymes that are involved in Ub pathways have shown to be regulated by ubiquitin . Recently , the activity of the mammalian deubiquitination enzyme ataxin-3 was shown to be enhanced by ubiquitination [38] , and binding of free ubiquitin to the N-terminal ZnF-UBP domain of the deubiquitinase USP5 led to a conformational change that stimulated enzyme activity [31] . In Arabidopsis H3K9me3 methylation broadly marks 40% of all genes within euchromatin [39] . In addition a low but detectable level of H3K9me3 methylation is found in regions with silenced transposons and pseudogenes [24] ( Figure 3 and Figure 6 ) . Our ChIP results suggest that although associated with both eu- and heterochromatin , SUVR4 has no HKMTase activity on euchromatic genes , but specifically targets transposons and repeat sequences where it converts H3K9me1 to H3K9me3 ( Figure 3B , 3C ) . This is perfectly in line with our in vitro HKMTase results , which show that SUVR4 preferably uses H3K9me1 as substrate ( Figure 2D ) . Together the in vivo and in vitro data indicate that SUVR4 only methylates transposons with a high H3K9me1 level although the protein might also associate with regions with a low level of this modification ( Figure S3 ) . SUVR4 methylates unmethylated H3 poorly , and the level of H3K9me1 decreases in the OE line ( Figure S1A and Figure 3B ) . This suggests that SUVR4 does not itself monomethylate H3K9 in vivo . Both SUVH4 and SUVH6 are efficient monomethyl transferases in vitro [40] , which together with SUVH5 control the deposition of the majority of H3K9me1 at transposons and repeat sequences [41] . As SUVR4 targets the same type of sequences , it is likely that SUVR4 uses the monomethylated histone substrates created by the SUVH proteins to trimethylate H3K9 . In mammalian cells , the SUV39H1 HKMTase depends on a monomethylase as it preferably converts H3K9me1 of H3 . 1 , but not H3K9me2 of H3 . 3 , to H3K9me3 . [42] . Similarly , SUVR4 is stimulated by H3K9me1 , but is only active on H3K9me2 if ubiquitin is added to the in vitro reaction . The SUVH2 HKMTase has a strong impact on centromeric and pericentromeric heterochromatinization and gene silencing and reduces the level of H3K9me3 when overexpressed [32] . In contrast , overexpression of SUVR4 leads to increased H3K9me3 levels , and no changes in heterochromatinization could be observed ( Figure 3A ) . Pericentromeric regions contain high levels of H3K9me1 and H3K9me2 in plants , but also H3S10 phoshporylation during mitosis and meiosis II [22] . The cell cycle dependent H3S10ph modification generated by Aurora kinase 1 inhibits SUVR4 activity in vitro [43] . This and the uninterrupted regions of high levels of H3K9me2 associated with the many transposons and pseudogenes located in pericentromeric and centromeric heterochromatin [44] , may contribute to repress SUVR4 activity in these regions in dividing cells . Alternatively , SUVR4 might be able to methylate histones in pericentric heterochromatin before H3S10ph is added as Aurora kinase 1 is active on methylated histones . Although pericentric heterochromatin most likely is not the preferred target of SUVR4 activity because of the high level of uninterrupted H3K9me2 [44] , SUVR4 could potentially methylate transposons in these regions under certain conditions when ubiquitin levels are high , as demonstrated by the ability of SUVR4 to methylate H3K9me2 peptides when ubiquitin is added ( Figure 2D , 2E , Figure S1B , and Figure 7B ) . Mutation in the SUP32/UBP26 deubiquitinating enzyme that removes the ubiquitin conjugate from H2Bub1 has been reported to lead to reduction in H3K9me2 [11] . Using ChIP analysis we found low levels of H2Bub1 at all tested transposons , which were only weakly altered in the ubp26 mutant line ( Figure 6C ) . A reduction of both H3K9me2 and H3K9me3 was , however , observed on the same sequences targeted by SUVR4 ( Figure 3B , 3C and Figure 6A , 6B ) . We therefore suggest that SUVR4 and UBP26 act in the same pathway leading to repression of transposon activity , and speculate that the reduction of H3K9me3 in ubp26-1 mutant background can be due to reduced SUVR4 activity . Thus UBP26 can repress transposon transcription by lowering the H2Bub1 level at these sequences to maintain repressive H3 methylation as suggested by Sridhar et al . [11] , and/or by maintaining a high local level of free ubiquitin which stimulates SUVR4-mediated H3K9me3 ( Figure 7 ) . Possibly UBP26/SUP32 can also cleave the ubiquitin extension protein UBQ1 initially found in our yeast two-hybrid screen to obtain free ubiquitin , as it has been shown to also be active on the human homologue CEP52 [11] which has 92% sequence identity with UBQ1 . We did not however observe any reduction of free ubiquitin in the nuclear extracts of ubp26-1 mutants ( Figure 6D ) that might have affected SUVR4 activity , and there was no effect on H3K9me3 or H2Bub1 at transposon sequences in the hub2-2 line ( Figure S7 ) . Thus , HUB2 seems not to be involved in regulation of H2Bub1 or H3K9me2/3 or to be the counterpart of UBP26 on transposon chromatin . The minor reduction of H2Bub1 at transposons and the ability of UBP26/SUP32 to deubiquitinate the CEP52 in vitro , opens the possibility that UBP26 regulates SUVR4-dependent H3K9me2/3 by additional mechanisms , for instance transient changes in the levels or subnuclear distribution of free ubiquitin . Highly transcribed euchromatic genes like ACTIN2 and TUB8 were unaffected by SUVR4 , and the in vitro assay implies that SUVR4 activity is inhibited by H3K4me3 which is abundant in euchromatin ( Figure 4 ) . Furthermore , the in vivo data shows that the targets for SUVR4 activity have low levels of H3K4me3 , H3K9me3 and H2Bub1 ( Figure 3 , Figure 6 , and Figure S4 ) . Intercalary heterochromatic sequences located within euchromatin are associated with intermediate amounts of opposing histone marks like H3K4me2 and H3K9me2 [33] , [44] , but have comparable levels of H3K9me1 as heterochromatin ( Figure 3B , 3C ) . As depicted in the model in Figure 7 , this suggests that SUVR4 cross-talks to other PTMs and preferably targets transposons outside pericentric and centromeric heterochromatin , with low H3S10ph , H3K9me2 , H3K4me3 and H2Bub1 and high H3K9me1 in order to trimethylate H3K9 . For transposon sequences with a low or intermediate expression level in wild type plants , increase in H3K9me3 levels mediated by SUVR4 is associated with repression of transcription ( Figure 3 , Figure 5 , and Figure 7 ) . In the RNAi line only the MULE At2g15810 transposon , localized in euchromatin outside the typical pericentric heterochromatin or centromeric regions [33] , showed relief of repression ( Figure 5A ) , suggesting it to be a normal target of SUVR4 activity . However , AtIS112A , another transposon intercalated in euchromatin with an intermediate expression level , was only affected in the OE line . The heterochromatin localized AtMU1 and the euchromatin localized AtSN1 , both silent in wild type plants , were also targets for SUVR4 methylation but showed no reactivation in the RNAi line . This suggests that SUVR4-directed H3K9me3 regulates transposon activity in a locus specific manner , where SUVR4 activity alone is sufficient for repression of MULE At2g15810 , while it works redundantly with an unknown HKMTase at other elements like AtIS112A , AtMU1 and AtSN1 . A similar regulation can be seen for the SUVH2 and SUVH9 SET domain proteins that act redundantly at some loci but independently at others [45] . Thus different transposons are regulated by different combinations of epigenetic marks ( Table 1 ) . Genes in euchromatin have a much higher level of H3K9me3 than transposons , and in these regions this modification seems to correlate with activation of transcription and the deposition of other activating marks [23] , [24] . This argues for a combinatorial readout where the context of other PTMs with which H3K9me3 appears decides the biological outcome ( Figure 7 ) . In contrast to genes , transposon and repeat sequences contain a high level of H3K9me1 and low levels of H3K4me3 and H2Bub1 ( Figure 3B , 3C , and Figure S4 ) and in this context H3K9me3 may lead to repression of transcription . H3K9me1 on transposon chromatin seems to be a prerequisite and the preferred substrate for SUVR4 activity , as the control transposon At4g13120 , with very low H3K9me1 , was not methylated or affected at the transcriptional level ( Figure 5A ) . Several studies have reported the accumulation of H3K9me1 in heterochromatin ( reviewed in [22] ) but little is known about the function of this mark . Our data supports a model where H3K9me1 is associated with both pericentric and centromeric heterochromatin and transposons intercalated in euchromatin , but does not act as a repressive signal , but rather a template for other methyltransferases . This is supported by the observation that increased H3K9me1 level correlated with increased transcription in the SUVR4 RNAi line and inversely correlated with increased H3K9me3 and repression of transcription in the SUVR4-GFPOE line ( Figure 3A–3C and Figure 5A ) . The level of DNA methylation of the MULE At2g15810 transposon did not correlate with SUVR4 expression . At the AtSN1 transposon , however , increased H3K9me3 mediated by SUVR4 overexpression coincided with an increase of CHH while no effect was seen for CG methylation ( Figure 5B , 5C ) . Pericentric H3K9me2 shows a strong correlation with CHG methylation but a weaker correlation with CG and CHH methylation [44] , while transposons located outside pericentric or centromeric heterochromatin have shorter patches of H3K9me2 at lower levels . Together with the repressive effect of H3K9me2 on SUVR4 activity this argues that the main DNA methylation regulated by SUVR4 is CHH . The DRM2 methylase is the main regulator of asymmetric CHH methylation , while CHROMOMETHYLASE3 ( CMT3 ) is the main regulator of CHG methylation in Arabidopsis , but at some loci they work together [46] , [47] . At dispersed repeats within euchromatin like AtSN1 , DRM1 , DRM2 and CMT3 act redundantly to maintain CHH and CHG methylation [48] . At such loci we suggest that the H3K9me3 methylation by SUVR4 might mark the underlying transposon sequence for CHH methylation by DRM2/CMT3 ( Figure 7B ) . Interestingly , many transposon sequences contain both H3K27me3 and H3K9me3 , a combination that CMT3 has been shown to bind in vitro ( Table 1 , [24] , [30] , [49] , [50] ) . The redundant regulation of AtSN1 by CMT3 and DRM1 might thus explain the lack of reactivation and DNA methylation upon reduction of SUVR4 H3K9me3 methylation in the SUVR4 RNAi line . Although a target of SUVR4-directed H3K9me3 and repression , the MULE transposon was not affected at the DNA methylation level ( Figure 5B ) . In contrast to AtSN1 , this transposon has been shown earlier to be activated only in mom1 mutants , and not in mutants with reduced non-CG methylation and kyp/suvh4 mutants ( Table 1 ) . MOM1 is a transcriptional repressor that regulates transcriptional gene silencing of loci outside centromeric and pericentromeric heterochromatin , with only small effects on epigenetic marks [33] , [51] , [52] . This suggests that non-CG methylation is not involved in silencing of MULE . The similar relief of silencing without any effect on DNA methylation between SUVR4 RNAi and mom1 makes it tempting to speculate that SUVR4 recruits MOM1 to its targets in order to repress transcription at this locus ( Figure 7B ) . The intermediately expressed AtIS112A is repressed in SUVR4 OE lines but did not show any relief of expression in the RNAi line . As for AtSN1 , this transposon is regulated by non-CG methylation , but also by MOM1 . This argues that SUVR4 mediated repression might act via DNA methylation-independent mechanisms such as for MULE At2g15810 , but also by DNA methylation-dependent mechanisms as seen for AtSN1 , or possibly both as seen for AtIS112A . DUBs are important to maintain ubiquitin homeostasis by recycling ubiquitin from free ubiquitin chains , ubiquitin conjugates and ubiquitin fusion proteins [14] , [15] . UBP26 regulates H3K9me2 and H3K9me3 methylation as well as non-CG methylation at the same sequences as SUVR4 [11] . We hypothesize that UBP26 acts in concert with SUVR4 to trimethylate transposons with a high level of H3K9me1 and low level of H3K4me3 and H2Bub1 ( Figure 7 ) . The H3K9me3 methylation thus directs locus-specific methylation-dependent or -independent repression of transposon activity .
Arabidopsis plants , ecotype Columbia ( Col ) , were grown under long day greenhouse conditions at 18°C . Transgenic Arabidopsis plants were generated by the floral dip method [53] using the Agrobacterium tumefaciens strain C58 pCV2260 . Transgenic plants containing the pEG104 [54] or pART27 [55] vectors were selected on MS-2 medium ( 1x Murashige and Skoog salts , 0 . 05% 2-N-morpholino/ethanesulfonic acid , 2% sucrose , 0 . 8% agar ) containing 10 µg/ml basta or 50 µg/ml kanamycin , respectively . For ChIP , RT-PCR and cytology experiments , Col wild type plants and non-segregating lines containing the respective T-DNA constructs were grown on MS-2 without antibiotic selection . The ubp26-mutant [11] and the hub2-2 [13] mutant lines have been described earlier . RNA was isolated from approx . 100 mg of 14 day old seedlings using the Spectrum Plant Total RNA Kit with on-column DNase treatment ( Sigma ) . cDNA synthesis and Real time RT-PCR experiments were performed as described previously [20] using gene specific primers ( Table S1 ) , except that 4 µg of total RNA was used to synthesize first strand cDNA with Superscript III Reverse Transcriptase and random primers ( Invitrogen ) . SUVR4-Full ( At3g04380 ) , SUVR4-SACSET , SUVR4-WIYLD , UBQ1 , ubiquitin moiety of UBQ1 and L40 moiety of UBQ1 were PCR amplified from cDNA using gene specific attB gateway primers ( Table S1 ) and Pfu DNA polymerase ( Fermentas ) . The attB PCR products were recombined into the pDONR/Zeo vector using the Gateway BP Clonase II Enzyme Mix ( Invitrogen ) according to the manufacturer's instructions . The resulting pDONR/Zeo entry clones were recombined into destination vectors using the Gateway LR Clonase Enzyme Mix ( Invitrogen ) . All constructs were verified by sequencing . The knock-down SUVR4 RNAi construct was made by cloning a unique fragment from the SUVR4 5′end as an inverted repeat on each side of an intron into the binary vector pART27 . Cloning procedures are described in detail ( Text S1 ) . Two-hybrid interactions were screened by mating the yeast strain Y187 carrying the pGBKT7-SUVR4-WIYLD bait construct with the strain AH109 carrying a cDNA library ( Matchmaker library construction and screening kit , Clontech ) at 30°C ON . The cDNA library was created from Columbia wt 14 day old seedlings and recombined into the pGADT7-Rec vector to create an AD-fusion library . Selective media for the nutritional reporter genes ADE2 , HIS3 and MEL1 ( QDO ) containing 20 mg l-1 X-alpha-Gal , was used to identify positive two-hybrid interactions according to the suppliers suggestions . To confirm interaction with SUVR4-WIYLD , the pGADT7-UBQ1 , pGADT7-ubiquitin and pGADT7-L40 were mated separately with the pGBKT7-SUVR4-WIYLD or the empty pGBKT7 vector ( BD control ) . Diploid colonies were selected on SD –L/-T , and then streaked out on SD –L/-T/-H +3 AT medium selective for protein-protein interactions . pHMGWA-SUVR4-Full and pHMGWA-SUVR4-SACSET constructs were transformed into E . coli BL21-Star DE3 and grown at 150 rpm , 37°C in LB-medium with 1% Glucose and 100 µg/ml ampicillin . At an OD600 0 . 6–0 . 8 , the cells were induced with 1 mM IPTG overnight at 20°C . The cells were lysed with Express and then resuspended in pre-cooled lysis Buffer: 20 mM Tris–HCl , pH 7 . 5 , 400 mM NaCl , 100 mM KCl , 1 mM EDTA , 1 mM DTT , 0 . 05% Triton X-100 and Protease inhibitor . After centrifugation ( 15 , 000 rpm ) , the supernatant containing recombinant protein was filtered through 0 . 45 µm filters and prepared for affinity chromatography . Recombinant proteins SUVR4-Full and SUVR4-SACSET were purified by Ni-NTA affinity chromatography using HisTrap FF 5 ml ( GE Healthcare ) column in the ÄKTA purifier . Binding buffer or Buffer A and Elution Buffer or Buffer B in the purification step were as follows , Buffer A: 20mM Tris–HCl , pH 7 . 5 , 500mM NaCl , 1 mM EDTA , 1 mM DTT , 20 mM Imidazole and Buffer B: 20 mM Tris–HCl , pH 7 . 5 , 500 mM NaCl , 1 mM EDTA , 1 mM DTT , 500 mM Imidazole . HKMTase assays were essentially performed as described in [28] . Twenty µg of MBP-SUVR4 protein was incubated in reaction buffer ( 50 mM Tris pH 8 . 5 , 20 mM KCl , 20 mM MgCl2 , 10 mM β-mercaptoethanol and 250 mM sucrose ) with 7 . 5 µl µCi 14C S-adenosyl methionine ( SAM ) ( Amersham/Perkin Elmer ) or 100 µM unlabelled SAM ( New England Biolabs ) as methyl donor . Twenty µg of core histones from calf thymus ( Roche ) , or 5 µg histone H3 peptides were used as substrate . Reactions were incubated at 30°C for 3 hours , and each experiment was repeated at least 4 times . Core histones from calf thymus ( Roche ) , unmodified histone H3 peptide ( #12-403 , Millipore ) , monomethyl-histone H3 ( Lys9 ) peptide ( #12-569 , Millipore ) , dimethyl-Histone H3 ( Lys9 ) Peptide ( #12-430 , Millipore ) , Trimethyl-Histone H3 ( Lys9 ) Peptide ( #12-568 , Millipore ) , Trimethyl-Histone H3 ( Lys4 ) Peptide ( #12-564 , Millipore ) , monomethyl histone H3 ( Lys 4 ) peptide ( gift from Thomas Jenuwein ) and ubiquitin ( U6253 , Sigma ) were used in the assays . Recombinant proteins were expressed in BL21 cells , lysed in 1 X PBS with 0 . 1 mg/ml lysozyme , 0 . 2–1% Triton X-100 and protease inhibitor cocktail ( Roche ) , and immobilized on glutathione sepharose beads ( Amersham ) . 3 µg of GST-S4WIYLD was incubated with MBP protein lysates at 4°C for 2 . 5 hours or 10 µg of GST-SUVR4-WIYLD with 20 µg of precleared core histones ( Roche ) at 4°C for 3 hours , following a series of washes . Pull-down reactions were run on SDS-PAGE gels , blotted onto a PVDF membrane ( Machery Nagel ) and probed with either anti-MBP ( 1∶10000 , New England Biolabs , #E8030S ) or anti-H2Bub1 ( 1∶1000 , MediMabs , MM-0029 ) . Detection of primary antibody was performed with peroxidase-conjugated secondary antibody; goat anti-rabbit HRP for pulldown of MBP-proteins ( 1∶10000 , Thermo Scientific , PA1-74361 ) and anti-mouse HRP for pull-down of core histones ( 1∶10000 , Abcam , ab6728 ) using the ECL kit ( GE HealthCare , RPN2135 ) . Reverse phase ( C18 ) nano online liquid chromatographic MS/MS analyses of proteolytic peptides from HKMTase reactions using unlabelled SAM were performed using a HPLC system as described [56] . Uniformly 15N- or 15N , 13C-labeled SUVR4-WIYLD ( residues 1-89 ) was expressed as a GST-fusion ( pGEX4T3 ) in minimal media containing 15NH4Cl and 13C-glucose as the sole nitrogen and carbon sources , respectively , after induction at 18°C for 18 hours . Protein was purified by glutathione sepharose affinity and size-exclusion chromatography and thrombin digestion to remove the affinity tag . NMR samples contained 0 . 5 mM protein in PBS at pH 7 . 4 , 5 mM d10-DTT and 10% D2O . All spectra were acquired at 25°C on a 500MHz or 600MHz Bruker spectrometer . For each experiment 2-3 g of fifteen day old seedlings was crosslinked in 1% formaldehyde under vacuum until the tissue was translucent . Chromatin immunoprecipitation was done as described in [57] . The antibodies used for immunoprecipitation were anti-H2Bub1 ( #MM-0029 , Medimabs ) , anti-H3K9me1 ( #07-450 , Millipore ) , anti-H3K9me2 ( #07-212 , Millipore ) anti-H3K9me3 ( #07-442 , Millipore ) , anti-H3K4me3 ( #07-473 , Millipore ) and anti-GFP ( #ab290-50 , Abcam ) . Immunoprecipitated chromatin was eluted in a total of 250 µl elution buffer ( 1% SDS , 0 . 1 M NaHCO3 ) and after reversion of crosslinking , DNA was extracted using the Qiaquick PCR purification kit ( Qiagen ) and eluted in 100 µl elution buffer . 5 µl of a 4 X dilution was used as a template for real-time PCR in a Lightcycler ( Roche ) . Typically a program of: 1 cycle 95°C 10 min , 45 cycles of 95°C 20 s , 52° 30 s and 72°C 30 s was used to amplify target sequences with gene specific primers ( Table S1 ) . PCR was performed on ChIP DNA isolated from two independent experiments , each quantified two separate times . Nuclear protein extracts were isolated from a chromatin preparation as described [57] . The protein lysate obtained after sonication was separated on a 10-20% SDS-PAGE ( Invitrogen , catalog no . EC6625BOX ) and transferred to a PVDF membrane ( Machery Nagel ) . Nuclear protein levels were determined using the following antibodies; anti-ubiquitin ( 1∶4000 , Millipore , 07-375 ) , anti-H2Bub1 ( 1∶1000 , MediMabs , MM-0029 ) and anti-PBA1 ( 1∶1000 , abcam , ab98999 ) . Leaves from 14 day old seedlings were chopped in 4% formaldehyde on slides , covered with coverslips and flash frozen in liquid N2 . The coverslips were removed from the slides when the material was still frozen , and then the slides were washed three times 5 minutes in 1 X PBS . The material was then blocked for 30 min at 37°C in blocking solution ( 1% BSA in PBS ) , and incubated with primary antibody ( anti H3K9me1 , 1∶200; antiH3K9me3 , 1∶100 ) diluted in blocking solution for one hour at 37°C . After a series of washes in PBS , the slides were incubated with goat-anti rabbit Alexa 555 ( Invitrogen ) secondary antibody ( 1∶200 ) . Before microscopy the slides were washed in PBS and counterstained in DAPI and inspected with a Zeiss Axiovision2 microscope equipped with epifluorescence attachment . All images were captured using the same exposure times and at 100X magnification . 2 µg of genomic DNA , prepared from leaf material using the Invisorb Spin Plant Kit ( INVITEK Berlin ) , was restricted with ApaI and PstI and used in the bisulfite reaction with the EpiTect Bisulfite Kit ( Quiagene Hilden ) . Bisulfite treated DNA was used as template in a PCR with specific primers . The PCR-Fragments are ligated into pGEMT-vector ( Promega ) and transformed in DH5alpha cells . Plasmid DNA from several colonies was sequenced with the ABI Prism 310 . | The characteristics of the diverse cell types in multicellular organisms result from differential gene expression that is dependent on the level of DNA packaging . Genes that are essential for the function of the cell are expressed; while unessential genes , and DNA elements ( transposons or “jumping genes” ) that can move from one position to another within a genome and potentially cause deleterious mutations , are repressed . The mechanisms evolved in eukaryotes to avoid unwanted gene expression and transposon movement include DNA methylation and specific combinations of post translational modifications ( PTMs ) of the histones that package DNA . Here we show that the SUVR4 enzyme binds the signaling protein ubiquitin and that ubiquitin enables the enzyme to trimethylate lysine 9 ( H3K9me3 ) of histone H3 . In contrast to other reports demonstrating an activating role on expressed genes , we show that H3K9me3 has a locus-specific repressive effect on the expression of transposons . The specificity is maintained by the communication with other PTMs on transposons and euchromatic genes , which has a stimulating or repressing effect on enzyme activity , respectively . Our results demonstrate how repression of transcription can be restricted to specific targets and demonstrate that this repression involves a context-dependent read-out of different PTMs . |
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The high-risk Human Papillomavirus ( HPV ) E6 oncoproteins are characterised by the presence of a class I PDZ-binding motif ( PBM ) on their extreme carboxy termini . The PBM is present on the E6 proteins derived from all cancer-causing HPV types , but can also be found on some related non-cancer-causing E6 proteins . We have therefore been interested in investigating the potential functional differences between these different E6 PBMs . Using an unbiased proteomic approach in keratinocytes , we have directly compared the interaction profiles of these different PBMs . This has allowed us to identify the potential PDZ target fingerprints of the E6 PBMs from 7 different cancer-causing HPV types , from 3 HPV types with weak cancer association , and from one benign HPV type that possesses an ancestral PBM . We demonstrate a striking increase in the number of potential PDZ targets bound by each E6 PBM as cancer-causing potential increases , and show that the HPV-16 and HPV-18 PBMs have the most flexibility in their PDZ target selection . Furthermore , the specific interaction with hScrib correlates directly with increased oncogenic potential . In contrast , hDlg is bound equally well by all the HPV E6 PBMs analysed , indicating that this is an evolutionarily conserved interaction , and was most likely one of the original E6 PBM target proteins that was important for the occupation of a potential new niche . Finally , we present evidence that the cell junction components ZO-2 and β-2 syntrophin are novel PDZ domain–containing targets of a subset of high-risk HPV types .
High-risk human alpha-papillomaviruses are the causative agents of cervical cancer , other anogenital cancers , and an increasing proportion of head-and-neck cancers [1 , 2] . They express two major oncoproteins , E6 and E7 , which are essential for maintenance of the transformed phenotype , and blocking their expression results in cessation of tumour growth , with senescence or apoptosis of the tumour cells and derived cell lines [3] . This reflects their interaction with vital cellular pathways , with E7 stimulating cell-cycle progression , at least in part through its targeting of the pRB pocket proteins [4 , 5] , while E6 acts to inhibit apoptosis through its targeting of p53 [6] and Bak [7 , 8] . Despite the critical importance of targeting p53 for E6's oncogenic activity , it is clear that other functions of E6 also play essential roles during tumour formation . One such activity is the ability to bind proteins containing PDZ domains . All the high-risk HPV types contain a Class I PDZ ( PDS95/Dlg/ZO ) binding motif ( PBM ) at the extreme carboxy terminus of E6 , which is absent , or aberrant in the majority of low-risk types ( the Papillomavirus Episteme , PaVE , http://pave . niaid . nih . gov/ ) . The HPV E6 PBM plays a role in multiple E6 functions . It contributes directly towards increasing proliferation in the infected epithelium , and its loss results in a decrease of viral genome amplification and ultimately in loss of viral episomes [9 , 10 , 11 , 12 , 13] . In addition , it plays an important role in the ability of E6 to induce characteristics of cell transformation in various tissue culture models , and plays a role in the ability of E6 to cooperate with E7 in the induction of tumours in transgenic mice [14 , 15 , 9 , 16] . Many PDZ domain-containing proteins have multiple protein interaction domains , allowing them to act as nodes controlling various cellular processes whose disruption can contribute to malignant transformation [17] . The PBM gives E6 the potential to interact with a discrete set of cellular proteins–those containing PDZ domains—and approximately 12 PDZ-containing proteins have been proposed as bona fide E6 binding partners [3 , 18 , 19] although the isolated PDZ domains of many more proteins appear to be capable of binding to the E6 PBM [20 , 21 , 22] . Previous work indicated that there are likely to be differences in the spectrum of PDZ-containing binding partners of E6 , depending on differences in the protein sequence upstream of the canonical PBM , as well as differences in the non-conserved residues within it [20 , 23 , 24] . Despite a large body of data reporting multiple potential PDZ-binding partners of HPV-16 and HPV-18 E6 , there have been no comparisons with other HPV types , and in particular with those from different categories of known cancer risk . For example , HPV-16 and HPV-18 can be considered to be Group 1 cancer-causing , while HPV-66 is very rarely associated with cancer and is considered to be Group 2B , and HPV-40 is never found in cancers and is classed as Group 3 . Indeed HPV-40 E6 has recently been described as having an ancestral prototype class I PBM [25] . Therefore we have been interested in investigating whether the subtle differences in the different E6 PBMs are also reflected in differences in substrate selection . In addition , we also wanted to determine whether any of these substrate fingerprints , potentially unique for each HPV E6 type , might shed light on the key players required for oncogenic potential and , conversely , represent evolutionarily essential interactions that originally evolved to facilitate new niche-colonising characteristics . To investigate this we have performed an unbiased proteomic analysis in keratinocytes of the PDZ target specificities of a number of high-risk HPV E6 proteins . The results show that hDlg is a major PDZ-containing target of all the PBM-containing HPV E6 proteins analysed , regardless of their oncogenic potential . In contrast , differences in the selection of other PDZ-containing targets , both in their identities and in the numbers of different proteins bound , correlate closely with the degree of cancer-association reported for the various HPV types .
Previous work had shown that up to 9 amino acid residues upstream of the C-terminus could affect the PDZ protein selectivity of the E6 PBM [20 , 23 , 26] . We therefore synthesised peptides corresponding to the ten C-terminal amino acid residues of E6 proteins from HPV-16 , -18 , -31 , -33 , -35 , -51 , and -56 , from high-risk group 1 viruses , all of which are defined as carcinogenic in humans [1] . We also synthesised similar peptides from HPV-26 , -66 , and -70 E6s , from high-risk group 2B viruses that are defined as being possibly carcinogenic in humans [1] . We also included the C-terminal peptide from HPV-40 E6 , which has no cancer-risk and has recently been shown to possess an ancestral PBM [25] . These HPV types were selected to represent a wide variety of the E6 C-termini sequences , and these , as well as that of the scrambled Control peptide , are shown in Fig 1 . These peptides , conjugated to magnetic streptavidin beads , were incubated with extracts of HaCaT cells , as described in Methods , and the bound cellular PDZ proteins were identified by mass spectroscopy . The results discussed below are the pooled results obtained from at least two independent pull-downs for each peptide . No PDZ domain-containing proteins were pulled down by the scrambled peptide in any of the assays . The histograms in Fig 2 show a summary of the mean numbers of peptides from the different PDZ domain-containing proteins pulled down by each E6 C-terminus . As can be seen , a total of 19 different PDZ domain-containing proteins were identified as being potential interacting partners of the different HPV E6 peptides analysed , a number that compares favourably with those reported in other analyses [21 , 22] . However , it is also clear that no single E6 peptide interacts with all of the PDZ proteins detected , suggesting that each HPV E6 type has its own spectrum of PDZ domain-containing targets , although the HPV-18 E6 PBM appears to have the broadest substrate specificity . It was possible that differences in E6 peptide solubility , in the concentration of the peptides bound to the beads , or in bead loss during the purification might influence the numbers of PDZ domain-containing proteins precipitated by each E6 C-terminal peptide . However , when these data were normalised to the numbers of DLG1 peptides precipitated , no material differences were seen in the binding profiles ( S1 Fig ) . Perhaps more importantly , examination of the numbers of PDZ-containing proteins bound by each peptide ( Fig 3 ) showed that the mean number of proteins bound by all the Group 1 peptides was significantly higher than the number bound by the Group 2B ( P = 0 . 0029 ) and the Group 3 peptides ( P = 0 . 0015 ) . This trend is confirmed in Fig 4 , where the histograms of mean peptide number , corrected for protein molecular weight , are grouped by HPV phylogeny , in order of cancer association ( highest on the left ) . This analysis shows first that when the molecular weight of the PDZ protein is also taken into consideration this does not unduly affect the overall comparative interaction profiles of the different E6 proteins . Perhaps more importantly , these results also show that , within each phylogenetic group , the more strongly an HPV type is associated with cancer , the broader the range of PDZ proteins bound by its PBM . Similarly , the numbers of peptides bound increases correspondingly; for example , in the case of the α6 phylogenetic group , the Group 1 HPV-56 PBM interacts with a broader spectrum of PDZ domain-containing proteins than the Group 2B HPV-66 PBM , and binds them to a higher degree . To confirm that this trend is more generally applicable , and not specific only to HaCat cells , we then performed pulldowns using extracts of normal immortal keratinocytes ( NIKS ) and peptides corresponding to the C-termini of HPV-16 , HPV-18 , HPV-66 and HPV-40 ( being representative of Groups 1 , 2 and 3 , respectively ) . The results obtained from mass spectroscopy analysis of two separate experiments are shown in Fig 5 , where it can be seen that HPV-16 and HPV-18 E6 C-terminal peptides again interact with a broader spectrum of PDZ-containing substrates than C-terminal peptides from HPV-66 , which in turn binds more than the HPV-40 PBM . It is also clear that all the E6 PBM peptides bind to hDlg1 , but that only the peptides corresponding to Group1 E6 PBMs interact with Scrib or ZO-2 . The higher the percentage of cancers , the higher the likelihood of binding both proteins , while the HPV-40 E6 binds neither . It has long been clear that the HPV-16 and HPV-18 E6 proteins interact with hDlg1 in a PBM-dependent manner [14 , 27 , 28] , and it was suggested that such interactions might be important for transforming potential . However recent studies [25] have shown that the E6 proteins of the benign α8 HPV group ( HPV types 7 , 40 , 43 and 91 ) have a primordial type 1 PBM ( -ETxC ) , and we were therefore interested in comparing the profile of PDZ proteins bound by the HPV-40 E6 PBM with those obtained from the high-risk virus types . As was seen in Fig 2 , hDlg1 was the most conserved of the PDZ domain-containing proteins bound by the different HPV E6 C-terminal peptides . In fact , the HPV-40 E6 PBM appeared to have one of the highest affinities for hDlg1: as can be seen from Fig 6A and 6B , it bound hDlg1 to a level similar to that seen with HPV-16 E6 . It is also interesting to note that the hDlg1-associated proteins , MPP7 , Lin7c and CASK , are also present in all of these interaction assays , indicating that the different E6 peptides can bind the whole hDlg1 complex . To confirm that the full-length E6 proteins indeed interact with hDlg1 , we expressed the E6 proteins of HPV-16 and HPV-18 ( Group 1 ) , HPV-66 ( Group 2B ) and HPV-40 ( Group 3 ) as GST fusion proteins and performed GST pulldown assays with in vitro translated radiolabelled hDlg1 . It is clear from the representative autoradiograph and the quantitation that HPV-66 and HPV-40 E6s bind Dlg1 equally as well as the E6 proteins of HPV-16 and HPV-18 ( Fig 6C ) . The HPV-40 E6 PBM ( ETLC ) is quite distinct from the canonical type 1 PBM ( xT/SxV/L ) , but it is still , nonetheless , defined as a potential class I motif [29] . However , in order to determine whether it does indeed recognise hDlg1 in a manner similar to HPV-18 E6 , we performed pulldown assays using in vitro translated radiolabelled HPV-40 E6 protein and GST fusions with the hDlg1 full-length protein , with the N-terminus of DLG1 ( containing no PDZ domains ) , and with the N-terminus plus the first two PDZ domains of DLG1 , including HPV-18 E6 as control . From Fig 6D it can clearly be seen that the HPV-40 E6 protein binds to GST-hDlg1 through its PDZ domains , in a manner similar to the HPV-18 E6 protein , confirming a previous report [25] that the primordial PBM of HPV-40 E6 , although unconventional , is indeed functional . These results suggest that hDlg1 is an evolutionarily highly conserved target of these different HPV E6 oncoproteins . It also suggests that the ability to interact with DLG1 is , alone , independent of the ability of the viruses to cause cancer , and most likely represents a very early evolutionary adaptation during the development of the HPV E6 PBM . hDlg1 and hScrib are components of the Scribble polarity complex , which , amongst other functions , defines the adherens junctions ( AJs ) between cells . Paradoxically , hScrib is also a target of HPV-16 and HPV-18 E6 [30] , although previous studies have indicated subtle differences between the different HPV types in their preferences for hDlg1 or hScrib [31] . The numbers of hScrib peptides pulled down by the various HPV E6 C-terminal peptides is shown in Fig 7A , where it is clear that there is much greater variability in the degree of hScrib interaction compared with that of hDlg1 . It is also notable that the E6 PBMs from the Group 1 HPV types have a statistically significant ( P = 0 . 0083 ) increased preference for hScrib when compared with the E6 PBMs from the Group 2B HPV types ( Fig 7B ) . Interestingly , the HPV-40 E6 PBM consistently failed to interact with hScrib in any of our assays ( Figs 2 and 7A ) , and the full-length HPV-40 and HPV-66 E6 proteins , expressed as GST fusion proteins , also failed to bind to hScrib protein ( Fig 7C ) . These results indicate that the ability of the high-risk HPV E6 oncoproteins to interact with hScrib correlates closely with their oncogenic potential . The results from the proteomic analysis identified two potential novel interacting partners of certain high-risk HPV E6 proteins , the tight junction proteins , ZO-1/ZO-2 , and the cell polarity regulator β-2 syntrophin ( SNTB2 ) . The detection of ZO-2 was particularly intriguing since recent studies have reported that it is stabilised by HPV in transgenic mice expressing the HPV-16 E6 oncoprotein [32] . To confirm whether ZO-1/ZO-2 and SNTB2 are indeed novel interacting partners of certain high-risk HPV E6 proteins , we performed a series of pulldown assays using a range of GST-E6 fusion proteins incubated with an extract of HA-ZO-1 or HA-ZO-2-transfected 293 cells . After extensive washing the bound proteins were visualised by Western Blot probed with anti-HA antibody . The results in Fig 8 demonstrate a clear association between HA-ZO-2 and the E6 proteins from HPV types 16 , 18 , 31 , 51 and 70 , but not those from HPV types 66 or 40 , which is consistent with the results from the mass spectroscopy analyses . No binding was seen with the GST-HPV18 E6 T156E mutant , which is null for PDZ binding , demonstrating that the interaction is , indeed , PBM-dependent . In contrast , ZO-1 does not bind significantly to any of the GST-E6 proteins tested , suggesting that the presence of ZO-1 in the pulldown screens was most likely as a result of its association with ZO-2 . Likewise , in vitro translated , radiolabelled SNTB2 ( Fig 9 ) is efficiently bound by multiple high-risk HPV E6 oncoproteins in a PBM-dependent manner , with only HPV-40 E6 failing to bind , and again use of the GST-HPV18 E6 T156E mutant demonstrates that this interaction is PBM-dependent . Taken together these results indicate that ZO-2 and SNTB2 are novel PDZ domain-containing targets of the high-risk HPV E6 oncoproteins . Although association with each protein in the mass spectroscopy analyses varied substantially between the C-terminal peptides of different HPV types , with HPV-70 for example being a particularly strong binder of ZO-2 , there did not appear to be a significant correlation with the oncogenic potential of the different HPV types . However , it is noticeable that the majority of Group 1 E6 PBMs bind ZO-2 , while other Group 2B E6 PBMs do not bind to ZO-2 , and the ancestral HPV-40 E6 fails to recognise either ZO-2 or SNTB2 in these interaction assays . Having shown that the high-risk HPV E6 proteins can interact with ZO-2 and SNTB2 , we were interested to know what might be the effect of this association . To investigate this , we treated HPV-18-containing HeLa cells with siRNA to Luciferase , HPV-18 E6/E7 , ZO-2 or SNTB2 . After 48h the levels of ZO-2 and SNTB2 in the cell extracts were analysed by Western Blot . Fig 10 shows a representative Western Blot , together with histograms showing the combined results of at least 3 assays . Clearly , the ablation of E6/E7 expression results in a significant reduction in ZO-2 levels , which is consistent with previous reports of E6 stabilising ZO-2 expression [32] , whereas the levels of SNTB2 do not appear to be significantly affected , suggesting that the interaction with E6 has little effect upon the levels of SNTB2 protein . Having confirmed that HPV E6 expression results in ZO-2 stabilisation we next asked what the biological consequence of this might be . To investigate this question we performed wound-healing assays in HeLa cells . The cells were transfected with siRNA as before and at 48h post-transfection the confluent cell sheet was scratched . The scratches were photographed immediately and then 24h later , and the area of scratch remaining was calculated . Fig 11 shows a representative assay plus a histogram of the collated results of at least three assays . It is clear that ablation of E6/E7 almost completely prevents wound healing , in comparison with si-Luciferase . Clearly knock-down of ZO-2 also has a similar inhibitory effect upon wound-healing , while ablation of SNTB2 has little or no effect . These results indicate that loss of ZO-2 phenocopies loss of E6/E7 in these wound healing assays , and suggests that ZO-2 is a functionally relevant target of HPV-18 E6 . Since we had shown that the differences in protein sequences upstream of and within the PBM can influence target selection , we were interested in examining HPV-16 and HPV-33 , which have the only E6 PBMs ending with Leucine , rather than Valine residues; and in HPV-16 this is known to increase the preference for binding hScrib [31] . The PDZ binding profiles of HPV-16 and HPV-33 are quite distinct , especially with respect to ZO-2 , and comparison of their PBM sequences shows that HPV-16 has a penultimate Glutamine residue , while HPV-33 has Alanine at that position ( Fig 1 ) . To determine how this difference might affect the binding profiles we repeated the pulldowns using the HPV-16 and HPV-33 C-terminal peptides , together with the penultimate-swap peptides 16Q150A and 33A148Q . The histograms in Fig 12 show relatively minor changes in the binding profiles with respect to many of the target proteins , however ZO-2 binding is a major exception . The Q to A mutation in the 16E6 peptide gives a ~25% reduction in ZO-2 binding; conversely the A to Q mutation in the 33E6 peptides results in a large increase in ZO-2 binding . This is shown graphically in Fig 12 , where it can be seen that the amino acid swap has coordinate effects upon the target binding; thus DLG1 , hScrib , ZO2 , Lin7c and SNTB2 interactions are inversely affected by the change A to Q or Q to A . This underlines the importance of individual non-canonical residues of the PBM in target binding specificity .
In this study we have performed a systematic analysis , in two different keratinocyte cell-lines , of the substrate specificities of the E6 PBMs derived from multiple high-risk HPV types and confirmed at least 19 different PDZ domain-containing proteins as potential interacting partners of the HPV E6 oncoproteins . Previous studies had investigated the PDZ binding profiles of the HPV-16 or HPV-18 E6 PBMs [21 , 22] , but this is the first comparative analysis of the substrate recognition profiles of a range of cancer-associated and non-cancer-associated HPV types . This study demonstrates some major differences in the potential PDZ interaction profiles of different HPV E6s , highlighting intriguing aspects of the profiles that are conserved across multiple E6 proteins , whereas certain other E6-interaction profiles are associated with particular oncogenic HPV types . Whilst our study and previous analyses indicate a large number of potential PDZ interacting targets of the HPV E6 PBMs , the actual number of PDZ substrates bound by any specific HPV E6 PBM is generally much lower . This number varies depending on the specific HPV type , with , for example , the PBMs of highly oncogenic HPV-16 and HPV-18 E6 being capable of recognising the most substrates at any one time , whilst in contrast , the perfect consensus PBM of HPV-66 , interacts with many fewer PDZ substrates . In fact , a very clear trend was observed throughout this analysis , where the E6 PBMs from the most commonly cancer-associated HPV types bound significantly ( P = 0 . 0029 ) more PDZ substrates than the HPV E6 PBMs from types less frequently , or never , associated with human tumours . This suggests that an important feature of the E6 PBM is not just its capacity to interact with a particular cellular PDZ domain- containing protein , but also the functional diversity of the PBM , where there is a higher degree of functional flexibility in the cancer-causing HPV E6 oncoproteins . Throughout this study we did not detect any major differences between the Group 2B and the Group 3 viruses , although HPV-70 had a slightly broader interaction profile and does appear to have a marked ability to recognise ZO-2 . This is intriguing as recent studies have reported a low incidence of HPV-70 in single-infection cervical cancers [33] , and further studies are required to investigate this further . Analysis of the specific substrates bound by the different HPV E6 PBMs emphasises the overall commonality in interactions with hDlg , with all the types tested showing a very robust association . This result was particularly surprising for the non-oncogenic HPV-40 E6 of the α8 genus , which has recently been shown to possess an ancestral PBM [25] . Despite this E6 protein having a very different PBM from the other types analysed , it still bound hDlg and pulled down its associated CASK/MPP7/Lin7c complex in a similar manner to the canonical E6 PBMs of HPV-16 and HPV-18 . This suggests that the capacity for binding to hDlg , whilst probably being necessary to confer an oncogenic phenotype upon an E6 protein , is not , alone , sufficient to do so . Intriguingly , HPV-40 E6 demonstrated minimal levels of association with other PDZ domain-containing substrates , suggesting that this ancestral PBM has much less functional flexibility than the more well-known high risk HPV E6 PBMs . The recent study of van Doorslaer et al [25] suggests that the development of a PBM allowed primitive HPVs to colonise a new niche , but that the ability to bind PDZ domains was a two-edged sword for the virus , since survival in the new niche required the development of additional means of interfering with the cellular environment , with the consequent risk of oncogenic transformation of the host cell . This suggests that acquisition of an ability to interact with hDlg could have been instrumental in facilitating the occupation of a new niche upon the appearance of a PBM on E6 , and furthermore , that this has retained functional relevance for the virus life cycle throughout the evolution of these HPV types . In contrast to their interactions with hDlg , the different HPV E6 PBMs displayed striking differences in their capacities to interact with hScrib . In agreement with previous studies , hScrib was bound strongly by HPV-16 and HPV-18 E6 , and was pulled down by PBMs of all the cancer-causing category I HPV types 31 , 33 , 35 , 51 and 56 . Of these , HPV-56 interactions with hScrib are weak , but nonetheless consistent . It should be noted however that its association with cervical cancer is also very rare . In support of this , E6 PBMs from types less frequently associated with cancer bound hScrib either not at all or much more weakly ( P = 0 . 0083 ) , suggesting that interaction with hScrib is a good predictor of oncogenic potential . This correlates well with data showing that knockdown of hScrib in keratinocytes can decrease their cell-cell junction formation and increase their invasive potential [34] . In addition , recent studies have highlighted a potential critical role for hScrib in a number of models of tumour development through Ras/ERK/MAPK signalling [35 , 36 , 37] . Clearly , mislocalisation of Scrib in a murine model of breast cancer has potent oncogenic activity [38] that appears to be related to the control of mTOR signalling [39] , and this also occurs in HPV-positive cells [40] . Two novel PDZ domain-containing targets of E6 were identified in the course of this analysis , the ZO-1/ZO-2 complex and β-2 syntrophin ( SNTB2 ) . A recent study had indicated that the tight junction component ZO-2 was overexpressed in transgenic mice expressing HPV-16 E6 [32] , and we show here that ablation of HPV-18 E6 expression in HeLa cells also results in lower levels of ZO-2 protein . It thus appears that ZO-2 stabilisation is a common feature of HPV16 and HPV-18 E6s , and since we show here that ZO-2 is bound by all of the Group 1 HPV E6 proteins analysed , it seems likely that these interactions may also result in higher levels of ZO-2 within the cell . In addition , our finding that ZO-2 ablation will inhibit wound healing , even in the presence of the E6 and E7 oncoproteins , suggests that at least some of the migration-promoting potential of E6 may be mediated through ZO-2 . SNTB2 is a component of Dystrophin complexes , found at the inner surface of plasma membranes , in which it is thought to regulate membrane stability and to provide a scaffold for the assembly of multiprotein signalling complexes . Previous reports had also suggested that the HPV-16 and HPV-18 PBMs could recognise SNTB2 [21 , 22] . In our analysis SNTB2 was bound by all the E6 types tested , with the exception of HPV-35 E6 . There were residual levels of association with the ancestral PBM of HPV-40 E6 , with varying degrees of interaction with the E6 PBMs of other HPV types . In agreement with previous studies [23 , 24] , very minor alterations to the residues within the PBM confirm the critical contribution of non-canonical residues to PDZ substrate selection . This was exemplified perfectly with HPV-33 and HPV-16 E6 , which have very similar interaction profiles and very similar PBMs , except that HPV-16 E6 bound ZO-2 much more efficiently than HPV-33 . By swapping A/Q residues within the E6 PBMs we effectively swapped their respective capacities to recognise ZO-2 . Taken together this study presents an intriguing picture of how variations in the HPV E6 PBM influence PDZ substrate selection . We present compelling evidence of the acquisition of enhanced functional flexibility in the cancer-causing HPV E6 oncoproteins , and identify hDlg as an evolutionarily conserved target of all the HPV E6 PBMs analysed . In contrast , acquisition of an ability to interact with hScrib correlates closely with increased cancer-causing potential .
The sequences of the biotinylated peptides that were synthesised in-house are shown in Fig 1 . The spontaneously immortalised keratinocyte HaCaT cell line [41] was kindly provided by Dr Massimo Tommasino and was grown in Dulbecco's modified Eagle's Medium ( DMEM ) supplemented with 10% foetal calf serum , penicillin/streptomycin ( 100U/ml ) and glutamine ( 300μg/ml ) . HEK293 cells ( ATCC ) were grown on 10cm dishes in the same medium and were transfected by the calcium phosphate precipitation method [42] . HeLa cells ( ATCC ) were grown on 6-well plates in the same medium and were transfected with siRNAs using the Invitrogen RNAimax reagent . The Normal Immortal Keratinocytes ( NIKS ) cells [43 , 44] were kindly provided by John Doorbar and were grown in F medium ( 3:1[v/v] F12:DMEM media , supplemented with 5% foetal calf serum , 0 . 4ug/ml hydrocortisone , 5ug/ml insulin , 8 . 4 ng/ml cholera toxin , 10ng/ml EGF , 24ng/ml adenine , 100U/ml penicillin/streptomycin . Soluble proteins were extracted from 80% confluent HaCaT or NIKS cells by incubation for 10min on ice in lysis buffer ( 50mM HEPES pH7 . 4 , 150mM NaCl , 1mM MgCl2 , 1% Triton-x-100 , protease inhibitor cocktail I [Calbiochem] ) . The cells were removed from the plate by scraping and the debris removed by centrifugation at 14000rpm in a benchtop centrifuge for 2min at 4°C . 500μg of each peptide , in lysis buffer , was bound to streptavidin-conjugated magnetic sepharose beads ( Streptavidin-MagSepharose , GE Healthcare ) by incubation at 4°C on a rotating wheel for 1h , then washed three times with lysis buffer . The cell extract was pre-cleared by incubation with empty streptavidin conjugated magnetic beads at 4°C on a rotating wheel for 1h . After removal of the pre-clearing beads , the extract was incubated at 4°C on a rotating wheel for a further 2h with each of the biotinylated peptides bound to streptavidin-conjugated magnetic sepharose beads . The beads were washed three times with lysis buffer without protease inhibitors , transferred to fresh eppendorf tubes and washed twice more with lysis buffer without either protease inhibitors or Triton-x-100 . 5% of the beads were taken for western blot analysis and the remainder were subjected to trypsin-digest and the products analysed by mass spectroscopy , as described previously [45] . The cloning and use of the GST Dlg1 fusion proteins have been described previously [28] . The GST-E6 proteins from HPV types 16 , 18 , 31 , and 51 , plus HPV-18 T156E mutant have been described previously [23] , The HPV-40 , HPV-66 and HPV-70 E6s were synthesised by the GeneArt Gene Synthesis protocol ( Invitrogen ) and cloned into the BamHI/EcoRI sites of the pGEX2T and pCA plasmids for GST fusion protein expression and in vitro/in vivo expression , respectively . The Dlg1 , hScrib and SNTB2 proteins , plus the HPV-18 and HPV-40 E6 proteins , were expressed in vitro using the Promega TnT kit and the ZO-1 and ZO-2 proteins were expressed by transfecting the plasmids into HEK293 cells . The pCDNA plasmids expressing Dlg1 and hScrib have been described previously [28 , 31] . The pGWI:HA-ZO-1 and ZO-2 plasmids were the kind gift of Ron Javier , and the SNTB2 expressing plasmid was the gift of Marvin Adams . The following siRNAs were purchased from Dharmacon: si-Luciferase , si-HPV18 E6/E7 , si-ZO-2 , si-SNTB2 , and were transfected using the RNAimax reagent ( Invitrogen ) according to the manufacturer's instructions . HeLa cells were transfected with siRNA , as indicated , and after 48h the confluent cells were scratched with a sterile Artline p2 pipette tip ( Thermo Scientific ) . The cells were washed twice with PBS to remove any loose cells and immediately photographed using a Nikon COOLPIX995 camera . After 24h incubation the scratches were photographed again and the cell-free area quantified using the Image J and Prism programmes . The cells were then harvested in lysis buffer and the ZO-2 and SNTB2 levels analysed by western blot . Alpha actinin was used as a control for total cellular protein levels . The following antibodies were used: Mouse anti-DLG1 ( Santa Cruz ) ; goat anti-hScrib ( Santa Cruz ) ; rabbit anti-ZO-2 ( Cell Signaling ) ; mouse anti-SNTB2 ( Pierce ) ; mouse anti-HA ( GE Healthcare ) ; goat anti-CASK ( Santa Cruz ) . Appropriate HRP-coupled secondary antibodies were purchased from DAKO . | The cancer-causing Human Papillomavirus ( HPV ) E6 oncoproteins have a unique carboxy terminal PDZ binding motif ( PBM ) , which interacts with a number of different cellular PDZ domain-containing substrates . The PBM has important functions in both the viral life cycle and in HPV-induced malignancy . In this study we have used a proteomic approach to compare the ability of multiple HPV E6 oncoproteins to interact with different cellular PDZ proteins . We show a striking increase in the number of PDZ proteins recognised as the oncogenic potential of the individual E6 increases . Furthermore , we define combinations of PDZ proteins that are predictors of oncogenic potential , whilst others represent evolutionarily conserved targets bound across the evolutionary spectrum of low and high-risk PBM-containing E6 proteins . Taken together , these studies shed light on the functional conservation in the E6 PBM across multiple HPV types and support the hypothesis that ancestral PBM evolution originally conferred association with a restricted number of PDZ targets , which has , over time , evolved , to provide increased levels of flexibility and hence increase the number of cellular PDZ partners that can be bound by the cancer-causing E6 oncoproteins . |
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The major histocompatibility complex ( MHC ) region is strongly associated with multiple sclerosis ( MS ) susceptibility . HLA-DRB1*15:01 has the strongest effect , and several other alleles have been reported at different levels of validation . Using SNP data from genome-wide studies , we imputed and tested classical alleles and amino acid polymorphisms in 8 classical human leukocyte antigen ( HLA ) genes in 5 , 091 cases and 9 , 595 controls . We identified 11 statistically independent effects overall: 6 HLA-DRB1 and one DPB1 alleles in class II , one HLA-A and two B alleles in class I , and one signal in a region spanning from MICB to LST1 . This genomic segment does not contain any HLA class I or II genes and provides robust evidence for the involvement of a non-HLA risk allele within the MHC . Interestingly , this region contains the TNF gene , the cognate ligand of the well-validated TNFRSF1A MS susceptibility gene . The classical HLA effects can be explained to some extent by polymorphic amino acid positions in the peptide-binding grooves . This study dissects the independent effects in the MHC , a critical region for MS susceptibility that harbors multiple risk alleles .
Across the entire human genome , the major histocompatibility complex ( MHC ) on chromosome 6 makes the single largest contribution to multiple sclerosis ( MS ) susceptibility . The classical HLA-DRB1*15:01 allele has been documented as the strongest association to MS risk , and its role has been studied and replicated extensively [1] . Numerous other HLA alleles have been suggested to be associated with MS susceptibility , but the complex structure of the MHC has made it challenging to unequivocally pinpoint variants that play a causal role in MS [1] , [2] . For example , it has been suggested that DQB1*06:02 , an MHC class II allele in strong linkage disequilibrium ( LD ) with DRB1*15:01 , either has no independent effect [3] or acts in an extended haplotype with DRB1*15:01 , the DRB1*15:01—DQB1*06:02 haplotype , or the DRB1*15:01—DQA1*0102—DQB1*06:02 haplotype [4] , [5] . The ambiguity and the lack of replication for many of the MHC associations can be attributed to the extended LD structure of the MHC [6] , the limited number of HLA loci analyzed , and the relatively small sample size of previous studies . Thanks to a large sample size and a novel procedure to impute classical HLA alleles from SNP data , a recent study described independent MHC effects for DRB1*15:01 , *03:01 and *13:03 as well as HLA-A*02:01 and rs9277535 [7] . In the present study we sought to test not only the role of classical HLA alleles but also of potentially functional variation within the HLA genes . To this end , we imputed classical alleles as well as their corresponding amino acid sequences in 8 HLA genes in a large population of 5 , 091 MS cases and 9 , 595 healthy controls , with genome-wide data ( GWAS ) , following a recently described imputation protocol [8] . Both the samples and the imputation method used were independent of recent efforts exploring MHC associations to MS susceptibility [7] .
The most statistically significant variant in the univariate analysis ( see Material and Methods for details ) was HLA-DRB1*15:01 ( odds ratio [OR] = 2 . 92 , p = 1 . 4×10−234 , Figure 1A ) . Looking at each category of variants ( SNPs , two-digit HLA alleles , four-digit HLA alleles and amino acid positions ) , the amino acid position with the smallest p-value was position −5 in the leader peptide of DQβ1 ( p = 7 . 6×10−231 ) , and the most statistically significant SNP was at position 32 , 742 , 280 ( OR for the A allele = 2 . 96 , p = 5 . 1×10−229 ) . An equivalent effect was observed for HLA-DQB1*06:02 ( OR = 2 . 96 , p = 5 . 4×10−229 ) . We first tested whether the DRB1*15:01 effect could be explained by DQB1 . Adjusting for DQB1 variants , we observed that DRB1*15:01 always had a residual effect ( p<10−6 ) . Conversely , adjusting for DRB1*15:01 , the effect of DQB1 variants were accounted for ( p>0 . 8 ) , suggesting that DRB1*15:01 had a non-equivalent and more statistically significant effect than the DQB1 variants . Furthermore , the extended DRB1*15:01—DQB1*06:02 haplotype ( p = 7 . 5×10−231 ) did not improve upon the association of DRB1*15:01 alone . Similarly , the classical DQA1*01:02 allele—that was suggested to contribute to the effect of the haplotype—was strongly associated ( p = 4 . 8×10−178 ) , but its effect could be entirely explained by DRB1*15:01 . These observations strengthen the hypothesis that the primary MHC effect in MS is mediated by DRB1*15:01 and not by variants in the DQB1 or DQA1 loci . The DRB1 locus ( all four-digit alleles in one model ) had a p-value of 4 . 0×10−263 in the initial analysis ( Figure 1B ) . After adjusting for DRB1*15:01 , the residual DRB1 locus effect ( due to all remaining DRB1 four-digit alleles ) was still statistically significant ( p = 3 . 1×10−37 ) , indicating the presence of multiple independent DRB1 effects . Applying a forward stepwise strategy ( see Materials and Methods for details ) , we established statistical independence for 5 additional DRB1 alleles: *03:01 , *13:03 , *04:04 , *04:01 , and *14:01 ( Table S1 ) . After controlling for the effects of all 6 significant DRB1 alleles ( including *15:01 ) , there was no evidence for a residual signal ( p = 1 . 5×10−05 ) . We also applied several other variant selection approaches to test the robustness of these findings; all approaches identified the same six alleles ( Table S1 ) . Having analyzed the effects at HLA-DRB1 , we tested all other variation across the MHC while correcting for the six statistically independent DRB1 alleles , namely DRB1*15:01 , DRB1*03:01 , DRB1*13:03 , DRB1*04:04 , DRB1*04:01 , and DRB1*14:01 . The most statistically significant variant was SNP rs2844821 near HLA-A ( OR for G allele = 0 . 70 , p = 3 . 2×10−29 , Figure 1C ) . Due to LD , this SNP effect is statistically equivalent to the effect of HLA-A*02:01 ( OR = 0 . 70 , p = 7 . 4×10−29 ) and amino acid Val at position 95 in the peptide-binding groove of the HLA-A protein ( OR = 0 . 70 , p = 9 . 6×10−29 , Figure 2 ) . Controlling for this effect , there were no other HLA-A associations . Controlling for the 6 DRB1 alleles and the HLA-A effect , the next most statistically significant variant was rs9277489 ( OR for C = 1 . 31 , p = 2 . 6×10−18 ) . This SNP is in the intronic region of HLA-DPB1 gene and in perfect LD ( r2 = 1 , based on HapMap Phase II ) with rs9277535 that was previously associated with MS susceptibility [7] , [9] . The most statistically significant HLA allele was DPB1*03:01 ( p = 3 . 6×10−15 ) , but the effect of rs9277489 cannot be explained by DPB1*03:01 alone ( p = 1 . 7×10−06 for rs9277489 in the presence of DPB1*03:01 ) . The most statistically significant amino acid mapped to position 65 of HLA-DPβ1 ( OR for Leu vs . Ile = 1 . 37 , p = 3 . 7×10−18 ) , which explained the effect of rs9277489 ( p = 0 . 003 for rs9277489 in the presence of Leu65 in HLA-DPβ1 ) . This amino acid is also located in the peptide-binding groove of HLA-DPβ1 ( Figure 2 ) . After controlling for rs9277489 , there was no residual effect at the DPB1 locus ( p>1 . 0×10−5 ) . Adjusting also for the DPB1 effect , we identified rs2516489 as the next most statistically significant variant ( OR for T = 1 . 31 , p = 6 . 7×10−13 , Figure 1G , Figure S2B ) . This SNP tags a region of extended LD containing several non-classical MHC class I , class III and cytokine genes , i . e . MICB , DDX39B ( BAT1 ) , NFKBIL1 , TNF , LTA , LTB , and LST1 ( Figure 3 ) . We note that this region had no substantial effect in the univariate analysis ( Figure 1A , Figure S2A ) , but it became genome-wide significant once the DRB1*15:01 effect was accounted for ( Table S2 ) . There was no evidence of interaction either with DRB1*1501 ( Table S2 ) or any other of the identified effects . To explore this phenomenon further , we stratified the samples according to the presence of DRB1*15:01 into carriers ( heterozygous and homozygous ) and non-carriers . Univariate analysis in these two strata revealed a consistent but modest effect ( OR ∼1 . 2 ) for the associated SNP in both DRB1*15:01 carriers and non-carriers ( Table S2 , Figure S3 ) . This phenomenon can likely be explained by Simpson's paradox , where two subgroups share the same association but the overall population shows no association ( or even a reversed one ) [10] . This analysis therefore returns , for the first time , robust evidence supporting the role of non-HLA genes within the MHC . To explore any functional consequences of the SNPs in the MICB-LST1 region we tested these SNPs for cis-eQTL ( expression quantitative trait loci ) effects in peripheral blood mononuclear cells ( PBMCs ) of 213 MS subjects [11] as well as CD4+ T cells and CD14+ monocytes of 211 healthy controls ( Table S3 ) . None of the associated SNPs had a strong cis-eQTL effect ( p>1×10−5 ) : the strongest effect in this region is the relation of rs2516489 to LST1 expression ( p = 1 . 91×10−5 ) in the CD4+ T cells of healthy individuals . The next strongest effect also involved rs2516489 but was seen in relation to HCG18 ( p = 3 . 19×10−5 ) in the PBMCs of MS subjects . None of the SNPs had a statistically significant cis-eQTL effect on any of the class I or II classical HLA genes ( Table S3 ) . Leveraging the publicly available Encyclopedia of DNA Elements ( ENCODE ) [12] and NIH Epigenomics Roadmap [13] for immune cells and cell lines it is evident that the region has an abundance of functional elements ( Figure 3 ) . Of specific interest is the non-coding naturally occurring read-through transcription between the neighboring ATP6V1G2 ( ATPase , H+ transporting , lysosomal 13 kDa , V1 subunit G2 ) and DDX39B ( DEAD box polypeptide 39B ) genes . Two SNPs , rs2523512 and rs2251824 , tag this element that has a strong signal in the DNase hypersensitivity assay in all immune cell types , suggesting that it is an active cis-regulatory region . The histone markers for promoters , enhancers and active elongation also support these data , while this region is identified as an active transcription start site using chromatin states [14] . Other candidates are the TNF and LTB genes . Rs2516489 , the SNP with the best ( but not statistically significant ) cis-eQTL effects , lies within a region of heterochromatin , with no indication of regulatory potential in the available data . Adjusting for 6 classical DRB1 alleles , HLA-A*02:01 , rs9277489 ( HLA-DPB1 effect ) and rs2516489 , we observed another novel signal emerging from the HLA-B locus ( p = 7 . 9×10−11 ) . The most statistically significant variants were HLA-B*37 , HLA-B*37:01 , amino acid Ser at position 99 in HLA-B ( Figure 2 ) and a SNP in position 31 , 431 , 006 ( hg18 ) ( Figure 1I , J ) . All of these variants had statistically equivalent effects ( OR = 1 . 75 , p = 2 . 2×10−08 ) . Accounting for the effect of HLA-B*37:01 , no other variant in HLA-B exceeded our a priori defined threshold , although the residual effect at the HLA-B locus due to all remaining classical HLA-B alleles was still statistically significant in our analysis ( p = 6 . 5×10−06 , Figure 1L ) . This residual association could be accounted for by HLA-B*38:01 ( OR = 0 . 55; p = 4 . 1×10−05 ) . After adding HLA-B*38:01 to the model , there was no longer evidence for a residual effect of classical HLA-B alleles ( p>0 . 002 ) or elsewhere across the MHC . No amino acid position in HLA-B could explain the HLA-B*38:01 effect . Next , we set out to assess whether a specific set of amino acids within the HLA-DR molecule could explain the collective effect of the six classical DRB1 alleles identified above . To this end , we tested each polymorphic amino acid position using an omnibus test ( a regression model with all but one amino acids carried by a given position ) , adding all amino acids ( but one ) of the most statistically significant position to the model in a forward stepwise fashion . The most significant amino acid position in DRβ1 mapped to position 71 ( p = 1 . 2×10−227 , Figure S4A ) , which carries 4 possible alleles: Ala , Arg , Glu , and Lys . Controlling for the alleles at position 71 ( df = 3 ) , there was still a strong residual signal for DRB1*15:01 ( p = 5 . 8×10−13 ) , indicating that amino acid position 71 alone does not explain the DRB1*15:01 effect . Adjusting for the alleles at position 71 , position 74 was the next most statistically significant ( p = 1 . 2×10−16 , Figure S4B ) . This position harbors five possible alleles: Arg , Leu , Glu , Ala and Gln . Controlling for positions 71 and 74 , position 57 ( with four alleles: Asp , Ser , Val or Ala ) was the next most statistically significant association ( p = 4 . 9×10−11 , Figure S4C ) . Controlling for positions 71 , 74 and 57 , we found position 86 as the most statistically significant association ( OR = 1 . 35 for Val vs . Gly , p = 1 . 0×10−06 , Figure S4D ) . After controlling for these four positions , no other amino acid position exceeded our significance threshold ( Figure S4E ) , although HLA-DRB1*15:01 still showed a residual association signal ( p = 10−05 ) . The model with the four DRβ1 amino acid positions could explain the data better than a model with only DRB1*15:01 ( p = 2 . 6×10−26 in favor of the DRβ1 amino acid positions ) , but it was slightly worse than the model with the six DRB1 alleles ( p = 0 . 001 in favor of the 6 DRB1 alleles ) . All four amino acid positions reside in the peptide-binding groove of the HLA-DR molecule ( Figure 2; Table S4 lists the correspondence between the amino acids at these positions and the six associated classical DRB1 alleles ) . Integrating all of the results , HLA-DRB1*15:01 accounted for 10% of the phenotypic variance in the data , whereas all 6 independent HLA-DRB1 alleles explained 11 . 6% . A model with all identified statistically independent effects ( HLA-DRB1*15:01 , HLA-DRB1*03:01 , HLA-DRB1*13:03 , HLA-DRB1*04:04 , HLA-DRB1*04:01 , HLA-DRB1*14:01 , HLA-A*02:01 , rs9277489/Leu65 in HLA-DPβ1 , rs2516489 , HLA-B*37:01 , and HLA-B*38:01 ) accounted for 14 . 2% of the total variance in MS susceptibility .
We have imputed classical alleles of HLA genes , their corresponding amino acids and SNPs across the MHC , and tested all variants for association in a large case-control collection . Our analysis corroborates the effects of DRB1 alleles other than the well-known DRB1*15:01 association . Classical alleles DRB1*03:01 , *13:03 , *04:04 , *04:01 , and *14:01 display robust , independent associations in our data . The DQB1 and DQA1 genes have been suggested to form extended haplotypes with DRB1 alleles , mostly *15:01 [4] . In our hands , the effect of DQB1*06:02 does not explain the effect of DRB1*15:01 . Furthermore , the DRB1*15:01—DQB1*06:02 haplotype does not appear to explain the data as well as the effect of DRB1*15:01 alone . Based on these results , DRB1*15:01 and the remaining DRB1 alleles are better candidates than DQB1 variants for a causal role in MS susceptibility , a hypothesis that agrees with the MHC analysis of MS subjects with African origin [3] . We note that this interpretation counters evidence in favor of DQB1 from certain murine models that capture elements of human inflammatory demyelination by triggering experimental autoimmune encephalomyelitis induced with myelin-associated oligodendrocytic basic protein [15] or proteolipid protein [16] . A number of studies have highlighted the importance of class I HLA alleles in MS susceptibility , with HLA-A*02:01 being the most prominent allele [17]–[20] . Here , we replicated the HLA-A*02:01 association and attributed it to an amino acid polymorphism at position 95 in the peptide-binding groove of the HLA-A molecule . We also replicated the recently proposed DPB1*03:01 association , and identified a more statistically significant effect at amino acid position 65 in the peptide binding groove of HLA-DPβ1 [7] , [9] . Although our study has overlapping samples with the first study to identify an independent HLA-DPB1 effect [9] , these account for only 24% of the present sample set . The evidence of an HLA-DPB1 effect is strengthened by the fact that the second study reporting such a signal [7] has no overlapping samples with our study . Furthermore , we confirmed the presence of statistically independent HLA-B effects [21] , [22] . Our analysis fine-mapped these to B*37:01 and B*38:01 . Of these , B*37:01 can be explained by amino acid Ser99 of the HLA-B protein , which is also in this molecule's peptide-binding groove . The HLA-C locus demonstrated no convincing evidence for a statistically independent effect , suggesting that previous results may have tagged untested HLA-A or HLA-B effects across the class I region [23] . Although some of the above associations could be explained by specific amino acid polymorphisms in the corresponding HLA proteins , the picture at HLA-DRB1 however appears to be more complex as there was no single model based on amino acids that could explain the entire locus effect ( including the specific effect due to DRB1*15:01 ) . At this stage , our conservative interpretation of these results is that the implicated amino acids allow new hypotheses to be formulated for future functional studies . An interesting finding in our analysis was the association of the region spanning from MICB to LST1 , which contains several important class I , class III and cytokine-related genes . Although the identified SNPs were not significant in the initial ( univariate ) analysis , we established that these reached significance after adjusting for the strong DRB1*15:01 effect . One small study previously examined MICB along with DRB1*15 and had found evidence for an independent association [24] . Another study reported that variation in TNF can modify the effect of DRB1*15:01 [25] . We did not obtain evidence for statistical interaction between this locus and the other MHC variants , indicating that the MHC susceptibility variants we have catalogued likely act independently and additively in terms of MS susceptibility . Overall , we offer robust evidence for the role of a specific MS susceptibility haplotype in this region of the MHC . This region harbors evidence for association with several other diseases , e . g . Crohn's disease and ulcerative colitis [26] , rheumatoid arthritis [27] , Sjogren's syndrome [28] , and hepatitis C virus-associated dilated cardiomyopathy [29] . However , the identity of the causal gene ( s ) within this associated region remains unclear at this time , but it is intriguing that three of the genes ( TNF , LTA and LTB ) are ligands for one of the validated MS susceptibility genes , TNFRSF1A [30] . We did not observe any evidence of statistical interaction ( p>0 . 5 ) with this non-MHC locus in our data . Our preliminary analysis using cis-eQTL data in healthy individuals and MS subjects as well as the publicly available genomic data from the ENCODE and NIH Epigenomics Roadmap did not identify a single variant/gene as the likely causal one . From this information it seems that several genes have functional potential , but more detailed functional studies will be needed to unravel the causal variants and genes . Leveraging genome-wide genotype data , the collection of analyses presented here provides a well-powered investigation of thousands of genotyped and imputed SNPs , classical alleles of 8 class I and II HLA genes and amino acid sequence variation of these HLA proteins . The combination of the large sample size with additional variation types allowed us to present an enhanced dissection of the critical role of the MHC in MS susceptibility . Our results highlight a possible role for certain residues in the peptide-binding groove of HLA molecules associated with peptide antigen recognition . In HLA-DRβ1 we identified a set of four amino acids in positions 71 , 74 , 57 and 86 that capture most ( but not all ) of the DRB1 association . Of these , Val86 has been associated previously with MS [31]–[33] , and this residue appears to be important for the presentation of peptides from a putative target antigen in MS , myelin basic protein [34] , and for the stability of the DRαβ dimer [35] . Another study suggested an association at position 60 [36] and another one at position 13 [37] , although these were not replicated in the present study . Interestingly , the HLA-DRβ1 amino acids in positions 71 and 74 were recently also associated with susceptibility to rheumatoid arthritis [38] . Overall , consistent with the known biology of MS , it appears that disease-associated variants in HLA-DRB1 primarily influence the structural characteristics of the peptide-binding groove and presumably lead to alterations of the T cell repertoire that enhance the likelihood of an inflammatory demyelinating process . However , the MHC also harbors at least one other risk allele that does not directly affect an antigen-presenting molecule: the robust evidence supporting a risk haplotype in the vicinity of MICB will have a different mechanism , one that is likely to affect the function of one or perhaps several cytokines . This study displays an effective strategy for in-depth characterization of this complex region of the human genome . Increasing study sample sizes and more complete reference panels are likely to continue to provide a more detailed perspective on the architecture of genetic susceptibility in this region . The identified amino acid residues may help prioritize the identification of binding peptides and investigations of other potential roles that these susceptibility alleles might have in the biology of MS susceptibility aside from antigen presentation .
We used data from 8 genome-wide association studies ( GWAS ) of European ancestry ( Table 1 ) : ( a ) three GWAS of the GeneMSA [30] , [39] with samples from the Netherlands ( GeneMSA DU ) , Switzerland ( GeneMSA SW ) , and the United States ( GeneMSA US ) ; ( b ) an early GWAS from the IMSGC [30] , [39] , [40] with samples from the United States ( ISMGC US ) and the United Kingdom ( IMSGC UK ) , that was collapsed in one stratum removing the UK cases; ( c ) a GWAS with cases from the Brigham and Women's Hospital and controls from the MIGEN study ( BWH ) [30] , [39]; ( d ) the Australia and New Zealand Multiple Sclerosis Genetics Consortium ( ANZgene ) [41]; ( e ) an unpublished GWAS set from Erasmus Medical Center in Rotterdam , the Netherlands; and ( f ) an unpublished GWAS collection from the Kaiser Permanente MS Research Program ( Kaiser Permanente ) . All the above GWAS data sets were filtered with the same quality control criteria as part of an ongoing meta-analysis of Multiple Sclerosis GWAS . In each of these data sets we performed principal components analysis ( PCA ) to identify population outliers and to calculate covariates to control for population stratification between cases and controls . From each GWAS we extracted SNPs within the extended MHC region ( chr6:29 , 299 , 390 to 33 , 883 , 424; hg18 ) to impute classical alleles for class I HLA genes ( HLA-A , HLA-B , and HLA-C ) and class II HLA genes ( HLA-DPA1 , HLA-DPB1 , HLA-DQA1 , HLA-DQB1 , and HLA-DRB1 ) , their corresponding amino acid sequences and SNPs not captured in the genotypic platforms used . The imputation was performed with the software BEAGLE [42] using a collection of 2 , 767 individuals of the Type 1 Diabetes Genetics Consortium ( T1DGC ) with 4-digit classical allele genotyping for the above HLA genes as the reference panel . This method and reference panel have been used for fine-mapping MHC associations in HIV control [8] and seropositive rheumatoid arthritis [38] . Cases and controls from each GWAS dataset were imputed together . All variants in the reference panel were coded as biallelic markers ( presence vs . absence ) , allowing us to use BEAGLE for the imputation . Post-imputation we excluded variants with minor allele frequency less than 1% from the analysis . Table S5 lists the imputation quality for the identified variants . We analyzed each variant using a logistic regression model , assuming alleles have an additive effect on the log-odds scale . We also assumed the genetic effects were fixed across all eight GWAS . In each model we included the top 5 principal components to control for within-GWAS population stratification and 7 dummy variables to account for between-GWAS specific effects . Throughout the text we refer to such a model as univariate ( Mu ) , even if several covariates were included in the model , reflecting the fact that only one MHC-specific variant is included in the model . This is the representation of the univariate model: ( 1 ) Mu , Univariate logistic regression model where y is the log ( odds ) for the case-control status , β0 is the logistic regression intercept and βi , j is the log-additive effect for the allele j of the variant i with p alleles . In this paper , the term variant is used for any type of SNP ( biallelic , triallelic , etc ) , two-digit HLA allele , four-digit HLA allele and amino acid position . In any case we included p-1 alleles , with the one excluded being the reference allele . Where possible we tried to select the most frequent variant in the controls as the reference allele . The five included principal components are represented in the model as l and the last block in the model represents the dummy variables included for the n studies ( n-1 parameters added in the model ) . To calculate an omnibus p-value for the variant , regardless of the number of alleles included in the univariate model , we used using a log-likelihood ratio test ( 2 ) comparing the likelihood L0 of the null model ( 3 ) against the likelihood L1 of the fitted model: ( 2 ) Log-likelihood ratio test where D is the log-likelihood test value , also known as deviance . D follows an approximate chi-square distribution with k degrees of freedom , where k is the difference of the regressed parameters between the two models . Representation of the null model: ( 3 ) M0 , Null logistic regression model Besides testing variants for association , i . e . SNPs , HLA alleles and amino acids , we also fitted models that estimated the overall effect of the each of the eight HLA genes . We did so , by fitting all respective four-digit alleles of a given HLA gene in the same model . The respective p-values reflect the overall significance of the gene . In order to identify the statistically independent effects , we first tested all variants under a univariate logistic regression model and ranked them based on the p-value of the log-likelihood test . Next , in a forward stepwise fashion , we included in the logistic regression model the most statistically significant variant as a covariate , analyzed all remaining variants and ranked them based on the new p-value of the respective log-likelihood test . The models that included at least one variant as covariate are referred to as conditional throughout the text . In each iteration the null model used in the log-likelihood test was the original null model ( 3 ) with the variants that were used as covariates . We repeated the same steps until no variant or no HLA gene reached the level of statistical significance , which we a priori set to be 10−5 . This statistical significance threshold accounts of 5 , 000 independent tests using Bonferroni correction . Although most of the variants analyzed are correlated , we chose this threshold to account also for the multiple stepwise fitted models . If no variant reached the level of significance but an HLA gene did , we kept adding variants in the overall model until the HLA gene p-value was larger than 10−5 . To compare the effects of two ( or more variants ) , e . g . A and B , we fitted the following models: MA model with variant A , MB model with variant B , and MAB model with both variants A and B . All three model included the same other covariates . Then we used the log-likelihood test to compare MAB vs . MB and MAB vs . MA . These two comparisons represent the effects of variants A and B , respectively , in the presence of the other variant , i . e . B and A . For these comparisons we used the nominal ( α = 0 . 05 ) level of statistical significance . After adjusting for the most statistically significant variant , DRB1*15:01 , the residual effect of the DRB1 locus , i . e . the effect of all alleles besides *15:01 , was still the most statistically significant of any of the remaining variants . This led us to the hypothesis that several other DRB1 alleles could explain the overall DRB1 locus effect , already conditioning on DRB1*15:01 . To identify such effects inside the DRB1 locus , we applied the above forward stepwise logistic regression approach to the four-digit DRB1 alleles . , To test the robustness of the results from the forward stepwise regression , we also applied four other statistical methods for variant selection: i ) lasso , [43] ii ) elastic net , [44] iii ) least angle regression , [45] and iv ) forward Stagewise regression . [46] For the lasso and elastic net we selected the largest value of lambda ( l1 ) after 10-fold cross-validation , such that error was within 1 standard error of the minimum mean cross-validated error . In the respective results section , we illustrate that all methods reached the same conclusion independently . It has been proposed that extended DRB1*15:01–DQB1*06:02 haplotypes confer the risk for MS rather than individual HLA alleles . To test this hypothesis , we used the post-imputation phased data to estimate the DRB1*15:01–DQB1*06:02 diplotypes . Then we fitted a logistic regression that estimated the effect of the diplotype under a per-allelic model . Since this approach used phased data , rather than post-imputation probabilities , the imputation uncertainty is not properly accounted for . Thus , we expect the respective p-values to be slightly inflated . To investigate the functional potential of the MICB-LST1 region we queried: We used Nagelkerke's pseudo-R [47] to estimate the variance explained ( 4 ) Nagelkerke's pseudo-R2 where L0 and L1 are the likelihoods of the null model and fitted model respectively , and N is the number of individuals . We used PLINK for the initial analysis of the data and to estimate minor allele frequencies and imputation quality metrics , i . e . INFO score . [48] We fitted all models in R using the glm function and package lars and glmnet . This investigation has been approved by the Institutional Review Board of Partners Healthcare; the reference number is 2002p000434 . | Multiple sclerosis ( MS ) is an inflammatory and neurodegenerative disease with a heritable component . Although it has been known for a long time that the strongest MS risk factor maps to the major histocompatibility complex ( MHC ) on chromosome 6 , there are still many unresolved questions as to the identity and the nature of the risk variants within the MHC . Because the MHC has a complex structure , systematic investigation across this region has been challenging . In this study , we used state-of-the-art imputation methods coupled to statistical regression to query variants in the human leukocyte antigen ( HLA ) class I and II genes for a role in MS risk . Starting from available SNP genotype data , we replicated the strongest risk factor , the HLA-DRB1*15:01 allele , and were able to identify 11 independent effects in total . Functional studies are now needed to understand their mechanism in MS etiology . |
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During the development of the visual system , high levels of energy are expended propelling axons from the retina to the brain . However , the role of intermediates of carbohydrate metabolism in the development of the visual system has been overlooked . Here , we report that the carbohydrate metabolites succinate and α-ketoglutarate ( α-KG ) and their respective receptor—GPR91 and GPR99—are involved in modulating retinal ganglion cell ( RGC ) projections toward the thalamus during visual system development . Using ex vivo and in vivo approaches , combined with pharmacological and genetic analyses , we revealed that GPR91 and GPR99 are expressed on axons of developing RGCs and have complementary roles during RGC axon growth in an extracellular signal–regulated kinases 1 and 2 ( ERK1/2 ) -dependent manner . However , they have no effects on axon guidance . These findings suggest an important role for these receptors during the establishment of the visual system and provide a foundational link between carbohydrate metabolism and axon growth .
GPR91 and GPR99 are G-protein-coupled receptors ( GPCRs ) activated by Krebs cycle intermediates , part of the larger class of carbohydrate metabolites—an observation that renewed interest in a biochemical pathway discovered decades ago [1 , 2] . GPR91 , through its activation by succinate outside the tricarboxylic acid ( TCA ) cycle , has a wide range of functions in diverse diseases , such as hypertension and diabetes . Its study allowed greater understanding of the molecular links between the TCA cycle and metabolic diseases [2 , 3] . The development of the visual system requires high levels of energy to propel mitochondrial-enriched axons properly through the nervous system , as retinal ganglion cells ( RGCs ) are essential for transmitting information from the retina to the brain . The growth and survival of neurons depend on mitochondria as they perform aerobic ATP synthesis and play a significant role in apoptotic and necrotic cell death [4] . Thus , failures of mitochondrial function appear to be involved in degenerative diseases of the nervous system [5] . One of the most mitochondria-enriched regions of the axon is the active growth cone ( GC ) at the tip of the axon [6] . The GC contains multiple receptors that interact with guidance molecules , allowing the front end of a developing axon to navigate through the complex landscape of the early nervous system toward its appropriate targets [7] . However , the role of intermediates from carbohydrate metabolism during the development of the visual system has not been well characterized . In the past decade , increasing evidence has highlighted GPCRs as mediators of both repulsive and attractive axon guidance , as their ligands may serve as guidance cues for axon pathfinding; however , GPCRs involved in axon growth still remain to be found [8–11] . In a groundbreaking study in 2004 , GPR91 ( succinate receptor 1 [Sucnr1] ) and GPR99 ( 2-oxoglutarate receptor 1 [Oxgr1] ) were both identified as receptors of the Krebs cycle intermediates succinate and α-ketoglutarate ( α-KG ) , respectively [2] . GPR91 and the closely related GPR99 are expressed in multiple tissues , such as the kidney [2 , 12] and cardiac muscle [13–15] . Previous reports have shown that succinate and GPR91 regulate normal retinal vascularization , proliferative ischemic retinopathy [16] , and cortical revascularization post-ischemia [17] . Moreover , through the activation of GPR91 , succinate has been shown to have an effect on motility , migration , and growth , as it directly promotes chemotaxis and potentiates activation initiated by Toll-like receptor agonists in dendritic cells [18 , 19] . However , to date , scarce literature exists on GPR99 functions . Human neuronal mapping and vascular innervation are closely related , as similar molecules and signaling mechanisms are shared between axon guidance , neuronal migration , and blood vessel guidance and growth . For example , the Slit/Robo pathway plays a critical role in both angiogenesis and the guidance of neuronal migration of the olfactory system [20 , 21] . Moreover , semaphorins and their receptors play a pivotal role as axon guidance cues [22 , 23] while also acting as a vasorepulsive force that misdirects new retinal vessels toward the vitreous in a murine model of oxygen-induced retinopathy [24] . Therefore , we investigated the growth-promoting actions and guidance effects of the carbohydrate metabolites succinate and α-KG , through their respective receptor GPR91 and GPR99 , during the establishment of the retino-thalamic pathway in an embryonic mouse model . Elucidating carbohydrate metabolite functions during visual development may provide crucial insights regarding their potential roles in the plasticity and regeneration of the nervous system and allow the development of further pharmacological tools , expanding and improving central and peripheral nervous system repair strategies .
We utilized murine retinas obtained from embryos ( embryonic day 14/15 [E14/15] ) to characterize the presence of GPR91 and GPR99 and their possible involvement during retinal projection navigation . At E14/15 , GPR91 and GPR99 proteins were mainly present in the ganglion cell layer but were also detected in the ganglion cell fiber and neuroblast layers ( Fig 1A–1F ) . The retinas from adult and E14/15 knockout ( KO ) mice ( gpr91KO or gpr99KO ) showed no expression of GPR91 or GPR99 , confirming the antibodies’ specificity ( S1A–S1H Fig ) . In E14/15 wild-type ( WT ) murine retinal explants , GPR91 and GPR99 were present in neurites , GCs , and filopodia , in dendrites and axons ( Fig 1G–1R and S1I–S1L Fig ) . Retinal explants obtained from gpr91KO and gpr99KO E14/15 embryos did not express GPR91 or GPR99 , respectively ( S1M–S1P Fig ) , which also confirms the specificity of the antibodies used in this study . Moreover , we observed the presence of GPR91 and GPR99 at the RGC layer of P1 Syrian golden hamsters ( S1Q–S1R Fig ) . As previous studies have shown that GPCRs are involved in axon guidance , we evaluated the roles of GPR91 and GPR99 on GC actions using retinal explants isolated from E14/15 mouse embryos after 2 days in vitro ( DIVs ) in culture . Explants treated for 60 min with the specific agonists succinate ( 100 μM ) or α-KG ( 200 μM ) showed a significant increase in the GC surface area and the number of filopodia , compared to controls ( Fig 2A–2C and S2A–S2D Fig ) . As expected , the effect of succinate on GC size and filopodia number was completely abolished in gpr91KO but not in gpr99KO mouse retinal explants , demonstrating a specific action of succinate on GPR91 ( Fig 2A–2C and S2A–S2D Fig ) . Similarly , α-KG effects were maintained in gpr91KO and decreased in gpr99KO . The effects of both agonists were abolished in the retinal explants from double-KO ( gpr91KO/gpr99KO ) mice ( Fig 2A–2C and S2A–S2D Fig ) . Moreover , following 60-min succinate ( 100 μM ) or α-KG ( 200 μM ) treatment , similar effects were observed on GC surface area and filopodia number of cortical neurons ( 2 DIVs ) ; these effects were also abolished in neurons lacking the expression of GPR91 and/or GPR99 ( S2E–S2G Fig ) . To further evaluate the effects of GPR91 and GPR99 ligand treatment on axon growth , retinal explants from WT mouse embryos were treated for 15 h with succinate ( 100 μM ) or α-KG ( 200 μM ) . Both agonists induced an increase in total neurite growth ( Fig 2D and 2E ) . Moreover , stimulation of gpr91KO murine retinal explants with α-KG and the stimulation of gpr99KO murine retinal explants with succinate also induced neurite growth ( Fig 2D and 2E ) . Again , the effects of succinate were essentially abolished in gpr91KO murine retinal explants , whereas the increased outgrowth produced by α-KG was markedly reduced in gpr99KO murine retinal explants . In double-KO murine retinal explants , the effect produced by either succinate or α-KG was abolished ( Fig 2D and 2E ) . To investigate whether the effects of intermediates of carbohydrate metabolism on GC morphology and neurite outgrowth could also affect cell viability , we treated murine embryonic retinal explants or cortical neurons with succinate or α-KG and then used a LIVE/DEAD assay to evaluate cell death . Following a 15-h treatment with succinate ( 100 μM ) or α-KG ( 200 μM ) , retinal explants or cortical neurons showed no differences in cell viability compared to control explants ( S3 Fig ) . However , we observed a high induction of cell death in the positive control condition of staurosporine-treated explants or neurons ( S3 Fig ) . Taken together , these results indicate that the Krebs cycle intermediates succinate and α-KG , via GPR91 and GPR99 , increase axon growth in retinal explants and modulate GC morphology in retinal explants and primary neurons . GPR91 is coupled to at least two signaling pathways , Gi/Go and Gq11 , whereas the activation of GPR99 by α-KG triggers a Gq-mediated pathway [2] . Moreover , previous reports have demonstrated that succinate activates the mitogen-activated protein kinase ( MAPK ) signaling pathways via GPR91 [2 , 12 , 13 , 18 , 19 , 25] . Since MAPKs mediate axon outgrowth , migration , and guidance [26] , we determined whether the effects observed with succinate/GPR91 and α-KG/GPR99 were mediated via the ERK1/2 pathway . ERK1/2 phosphorylation was significantly increased , both in vitro in neurons and ex vivo in retinal explants , following succinate and α-KG stimulation , while these effects were abrogated by CI-1040 , a selective ERK1/2 inhibitor ( Fig 3A , 3B and 3H ) . CI-1040 treatment also abolished succinate- and α-KG-induced increases in GC surface area and filopodia number ( Fig 3C–3E ) ; no significant differences were observed between the untreated control and a control pretreated with CI-1040 . Inhibition of ERK1/2 interfered with succinate- and α-KG-induced projection length ( Fig 3F and 3G ) , whereas CI-1040 treatment alone had no significant effect on the total projection length , as observed in control conditions . Moreover , CI-1040 treatment did not affect the viability of embryonic retinal explants and cortical neurons , as no significant neuronal cell death was observed compared to controls with the LIVE/DEAD assay ( S3 Fig ) . These data implicate the ERK1/2 pathway in the GPR91- and GPR99-induced modulation of GC morphology and axon outgrowth via their respective TCA cycle metabolite ligand . To determine the contribution of GPR91 and GPR99 to the development of retinal projections in vivo , E14/15 murine embryos received an intraocular injection of DiI ( DiIC18[3] [1 , 1’-dioctadecyl-3 , 3 , 3’ , 3’-tetramethylindocarbocyanine perchlorate] ) , a lipophilic tracer . After 7 d of tracer diffusion , surgery was performed to visualize the optic nerve , chiasm , and tract . The photomicrographs obtained revealed that genetic deletion of either GPR91 or GPR99 had no detrimental effects on RGC axon guidance , as axon steering at the optic chiasm , after a single genetic deletion of gpr91 or gpr99 , was similar to the WT group ( S4A and S4B Fig ) . Moreover , succinate and α-KG treatment also failed to modulate axon steering in time-lapse microscopy experiments performed on GCs from E14/15 WT murine retinal explants at 1 DIV ( S5A–S5E Fig ) . Microgradient application of succinate or α-KG did not induce any significant directional GC turning compared to the vehicle control ( S5A–S5E Fig ) . Interestingly , short-term exposure to succinate induced an increase in the growth of retinal axons , while α-KG exposure had no significant effects ( S5E Fig ) . However , in double-KO mice , few retinal axon fibers projected to the ipsilateral side of the brain although , some extended into the contralateral optic nerve . The concomitant absence of GPR91 and GPR99 appeared to induce some abnormal projections in the ipsilateral and contralateral sides of the optic chiasm , suggesting a potential compensatory role played by each receptor in the absence of the other ( S4A and S4B Fig ) . Moreover , to assess the involvement of the citric acid cycle intermediate receptors in retino-geniculate development , we examined the projections to the dorsal lateral geniculate nucleus ( dLGN ) of adult mice . Contralateral and ipsilateral projections in the dLGN from all genetically modified mouse strains occupied the same area as those of WT mice ( S4C Fig ) . These data indicate a similar overlap between contralateral and ipsilateral RGC projections in the dLGN for all mouse genotypes ( S4D Fig ) . Taken together , these observations demonstrate that GPR91 and GPR99 do not appear to be implicated in guidance and target selection during the development of the retinogeniculate pathway in vivo . To investigate the in vivo effects of intermediates of carbohydrate metabolism during the development of the visual system , the mouse model presents limitations . Because the mouse visual system is completed at birth [27] , we further utilized a different rodent model . The Syrian golden hamster has a shorter gestation period ( 15 d versus 18 . 5 d ) , and pups are born with a relatively premature visual system [27] . As the axons of RGCs reach their thalamic and midbrain targets at P3 in the hamster , this model allows examination of the induction of axon growth by different agonists [10 , 28] . Taking advantage of this observation , hamsters were injected intravitreally 24 h after birth ( P1 ) with a mixed solution of cholera toxin subunit B ( CTb ) with either 0 . 9% saline solution , 100 mM succinate , or 200 mM α-KG , and immunohistological analyses were performed at P5 . Intraocular injections of CTb produced intense labeling of thalamic and midbrain targets such as the dLGN and superior colliculus , making the evaluation of the collateral growth of RGC axons difficult . Thus , we evaluated the RGC branch growth at the dorsal terminal nucleus ( DTN ) , one of the nuclei composing the accessory visual pathway and involved in mediating visuomotor reflexes underlying the generation of optokinetic nystagmus [29] . Compared with the control group , unilateral intraocular injections of succinate or α-KG induced significant increases in RGC collateral axon projection length and branch number in the DTN ( Fig 4A–4C ) . We next proceeded to investigate the impact of genetic deletions of gpr91 and gpr99 on axon growth during development in vivo . Within 24 h of birth , pups from all 4 murine genotypes received a unilateral intraocular injection of CTb to label their retinal projections . At P5 , immunohistological experiments revealed the effects of GPR91 and GPR99 on RGC axon development . Investigating RGC branch growth at the DTN , we showed a significant decrease in the collateral projection lengths of the KO animals compared to the control group ( Fig 4D and 4E ) . In addition , axon collateral density was significantly decreased in gpr91KO , gpr99KO , and , to a greater extent , in double-KO mice , compared to WT controls ( Fig 4F ) . These findings demonstrate—for the first time , to our knowledge—the essential role of GPR91 and GPR99 in the growth of RGC projections .
Most functional studies of GPR91 and GPR99 , receptors of intermediates of carbohydrate metabolism , have been performed outside the central nervous system , primarily in the kidney and heart [2 , 14 , 15] . In the present study , we showed that GPR91 and GPR99 are expressed on axonal and dendritic projections , GCs and filopodia of murine embryonic retinal explants , and on retinal projections and cell body of RGCs during the development of the retinothalamic pathway . We demonstrated that succinate and α-KG increase ERK1/2 phosphorylation , corroborating a large number of studies on signaling pathways triggered by GPR91 [2 , 12 , 18 , 25] . Moreover , stimulation of both GPR91 and GPR99 resulted in the modulation of GC morphology and an increase in RGC axon growth in an ERK1/2-dependent manner . The increased GC size , number of filopodia , and growth of RGC axons following stimulation of GPR91 and GPR99 by succinate and α-KG , respectively , is the first report , to our knowledge , implicating these ligands and receptors in axon growth . Interestingly , the deletion of GPR91 completely blocked the effects of succinate but also partially abolished the effects observed with α-KG . Nevertheless , in double-KO animals , the effects of both succinate and α-KG were abrogated . These results tend to demonstrate that succinate’s effects on RGC axon growth were mediated only through GPR91 , while α-KG could , through an as-yet-unknown mechanism , activate both GPR91 and GPR99 . A possible mechanism could be the conversion of α-KG into succinate , since α-KG is a precursor of succinate in the Krebs cycle . Moreover , our findings showed that GPR91 and GPR99 , while having no effect on axon guidance , have complementary roles in RGC axon growth during development . These data are consistent with previous observations in which succinate , via GPR91 , has shown highly proliferative and stimulating vascular effects in different tissues [16 , 17] , to promote chemotaxis [19 , 30] and to potentiate the activation and aggregation of platelets [18 , 31] . Axon guidance and angiogenesis share several fundamental challenges during the formation of their extensive networks . Tip cells—specialized endothelial cells at the end of each vessel sprout—are motile and dynamically extend long filopodia protrusions reminiscent of axonal GCs [32] . In light of the spatiotemporal link between axon growth and angiogenesis , as well as the morphological similarities between endothelial tip cells and axonal GCs , the observed increase in the morphology of GC and neurite growth could be explained by a similar mechanism in the presence of succinate . As the only type of neuron that sends axons out of the retina , RGCs ensure the visual and cognitive processing of information from the outside world to the brain . A combination of intrinsic and extrinsic signals also plays an important role in driving the axons through the visual pathway via responsive GCs , which detect and effectively translate a multitude of external chemotactic cues . In the mouse , the axon decussation occurs at the level of the optic chiasm at around E14–16 [33] . We observed that in WT , gpr91KO , or gpr99KO mice , the optic chiasm appeared relatively normal , as the majority of the axons at the midline crossed to project contralaterally . Our results suggest that in the mouse visual system , the absence of either GPR91 or GPR99 is insufficient to affect decussation . Moreover , neither GPR91 nor GPR99 activity at the GC modulated axon turning in an ex vivo experiment of retinal explants , since GCs are not attracted nor repelled in the presence of a succinate or α-KG microgradient , whereas succinate induced significant axon extension . Based on these results , succinate plays an essential role in axon growth by increasing axon motility , but succinate and α-KG do not affect GC and axon guidance . However , the visual projections of double-KO mice showed some mild abnormalities in axon guidance that could be explained by a compensatory effect between the two receptors , which would allow a rescue of this mild phenotype in gpr91KO or gpr99KO mice . Nevertheless , further experiments are needed to study this subtle defect in a more quantitative fashion in order to draw significant conclusions . In addition , our data show that deletion of either GPR91 or GPR99 in vivo did not affect target selection of retinal projections . Indeed , during perinatal development , RGC axons connect with multiple targets in the dLGN , sharing common terminal space , while RGC axons occupy distinct eye-dependent nonoverlapping regions of the dLGN in the adult rodent . Eye-specific segregation only occurs during postnatal development [34] . Accordingly , a similar relative eye-specific segregation of retinal projections was observed in the adults of all 4 mouse genotypes . Thus , our in vivo results support previous ex vivo findings that GPR91 and GPR99 do not modulate RGC axon guidance and target selection during the establishment of the visual pathway . However , we demonstrated that TCA cycle intermediates induce axon growth in vivo during the development of the visual system , as intraocular injection of succinate and α-KG induced significant increases in RGC collateral axon projection length and branch number in the DTN . Moreover , accordingly , genetic interference with GPR91 or GPR99 activity profoundly affects retinal projection growth in the DTN . We showed a significant difference between WT , gpr91KO , and gpr99KO mice in axon projection length and branching at the DTN . Furthermore , the relative lack of growth of retinal projections in double-KO mice demonstrates the fundamental role played by GPR91 and GPR99 during RGC axon growth . Nonetheless , these in vivo experiments do not conclude that the receptors involved in the growth-promoting actions of intermediates of carbohydrate metabolism are only those expressed at the GCs but could also be , to some extent , those expressed throughout the projections or on the cell body of RGCs as well . The levels of intermediates of carbohydrate metabolism adapt depending on tissue needs and the conditions in the surrounding regions . Investigating RGC projections and GC actions in the developing visual system faces technical limitations regarding intermediates of carbohydrate metabolism dosing . The amount of tissue needed ( and its isolation ) from mouse embryos or hamster newborn pups does not allow detection of metabolites due to the technique sensitivity and the rapid turnover of the metabolites . Nevertheless , based on previous published data and our own findings , we sought to avoid nonspecific responses by determining the lowest responsive doses for succinate and α-KG in our system , even if the physiological levels could not be measured [2 , 3 , 16–18] . In summary , this study demonstrates—for the first time , to our knowledge—a role for the intermediates of carbohydrate metabolism succinate and α-KG and their respective receptor GPR91 and GPR99 in axon growth during development in vivo . These receptors mediate axon growth in an ERK1/2-dependent manner , although succinate and α-KG have no effect on axon guidance . Moreover , these findings suggest a potential link between mitochondria and axon growth in development , outside the strict production of energy . This study not only demonstrates a new role for TCA cycle intermediates in the visual system development but also provides a foundation for the investigation of metabolite receptors in the visual , central , and peripheral nervous system development . This novel concept also provides new avenues for the elaboration of effective therapies aimed at the development and regeneration of the nervous system .
All experimental procedures were approved by the Animal Care Committee of Sainte-Justine’s Hospital Research Center or the relevant University of Montreal animal care committee’s regulations and were conducted in accordance with the Association for Research in Vision and Ophthalmology statement regarding the use of animals in ophthalmic and vision research and the guidelines established by the Canadian Council on Animal Care . The C57BL/6 WT control mice were purchased from Jackson Laboratory . Syrian golden hamsters ( Charles River Laboratories , Saint-Constant , Canada ) were used in this study . Sucnr1KO mice , generated by Deltagen through partial replacement of exon 2 ( 5’-GGCTACCTCTTCTGCAT-3’ ) with a lacZ-neomycin cassette , were generously provided by Dr . José M . Carbadillo at Norvartis Institutes for Biomedical Research , Vienna , Austria [19] . As described by Rubic and colleagues in 2008 , correctly targeted 129/OlaHsd embryonic stem cells were used for the generation of chimeric mice , which were crossed with C57BL/6 ( called “WT” here ) . F1 mice with germline transmission of the mutated gene were further backcrossed with WT mice for 10 generations ( in specific pathogen-free conditions at the Novartis Institutes for Biomedical Research , Vienna ) before being intercrossed to produce homozygous gpr91KO mice . gpr91KO mice were healthy and bred normally when maintained in specific pathogen-free conditions . All experiments in the production of the gpr91KO mice were conducted in accordance with Austrian Law on Animal Experimentation and the Novartis Animal Welfare Policy . All procedures were approved by the local government and the animal care and user committee of the Novartis Institutes for Biomedical Research , Vienna . Heterozygous ( GPR99+/− ) mice with mixed genetic background ( C57BL/6J− Tyrc-Brd x 129 Sv/EvBrd ) were developed and generously provided by Lexicon Pharmaceuticals Incorporated ( The Woodlands , TX ) . The full-length gpr99 gene was removed by homologous recombination as the PCR-generated selection cassette was introduced in a murine genomic clone by yeast recombination , followed by the electroporation of the linearized targeting vector in 129 Sv/EvBrd embryonic stem cells . In selected clones , gpr99 deletion was confirmed by Southern hybridization followed by their injection into C57BL/6J-Tyrc-Brd blastocysts . To generate F1 heterozygous offspring , the resulting chimeras were backcrossed to C57BL/6J-Tyrc-Brd . Heterozygous mice were intercrossed to generate WT control ( gpr99WT ) , homozygous-null ( gpr99KO ) , and heterozygous littermates , consistent with Mendelian ratios . The resulting homozygous-null gpr99KO mice were backcrossed onto the C57BL/6 background with C57BL/6 obtained from Jackson Laboratory ( Connecticut , USA ) for 10 generations in CHU Sainte-Justine’s Research Center animal facility before using them in experiments . The gpr99KO mice were viable , healthy , and bred normally when maintained in specific pathogen-free conditions . gpr99KO / gpr91KO mice ( double KO ) were generated by crossing gpr99KO and gpr91KO mice to produce gpr99+/− / gpr91+/− ( double-heterozygous ) parents . The double-heterozygous parents were then crossed together until we obtained double-KO gpr99KO / gpr91KO ( 1:16 pups according to Punnett Square ) male and female mice that were then crossed together to obtain a stable double-KO mouse lineage . The double-KO mice were viable , healthy , and bred normally when maintained in specific pathogen-free conditions . Mice were genotyped by PCR reactions of tail genomic DNA using specific primers for either the WT or mutant allele . For GPR91 mice , the primer pair WT-F: 5′-GTTCATTTTTGGACTGCTTGGG-3′ and WT-R: 5′-AATGGCAAATTCCTTCTTTTGTAGA-3′ generated a GPR91-specific fragment only present in the WT allele , while the primer pair KO-F: 5′- GGCACATATCGGTTGCTTATACAGA-3′ and KO-R: 5′- GGGTGGGATTAGATAAATGCCTGCTCT-3′ amplified a fragment specific to the selection cassette of the gpr91KO mutant allele . For GPR99 mice , a GPR99-specific fragment present in the WT but absent in the mutant allele was generated using the specific primer pair UTT069-21 ( 5′-GAGCCATGATTGAGCCACTG-3′ ) and UTT069-25 ( 5′-CACCACTGGCATAGTAATGG-3′ ) . Another primer pair amplified a fragment specific to the selection cassette of the gpr99KO mutant allele: UTT069-3 ( 5′-CAGAGCCATGCCTACGAG-3′ ) and GT ( 5′-CCCTAGGAATGCTCGTCAAGA-3′ ) . For double-KO mice , all pairs of primers were used ( 4 reactions ) to determine whether both genetic modifications were present . BSA , ciliary neurotrophic factor , DNase , forskolin , Hoechst 33258 , insulin , laminin , poly-D-lysine , progesterone , selenium , putrescine , succinate , α-KG , trypsin , and triiodothyronine were purchased from Sigma Aldrich ( Oakville , ON , Canada ) . B27 , N2 , Dulbecco’s phosphate-buffered saline , FBS , glutamine , Neurobasal medium , penicillin-streptomycin , Minimum Essential Medium Eagle Spinner Modification ( S-MEM ) , and sodium pyruvate were purchased from Life Technologies ( Burlington , ON , Canada ) . The standard donkey and goat sera were from Jackson ImmunoResearch ( West Grove , PA , USA ) . ERK1/2 inhibitor ( CI-1040 ) was obtained from Selleck Chemicals ( Houston , TX , USA ) . LNAC was acquired from EMD ( La Jolla , CA , USA ) . The CTb was from List Biological Laboratories ( Campbell , CA , USA ) . Triton X-100 was purchased from US Biological Life Sciences ( Salem , MA , USA ) . DiI stain was obtained from Molecular probes ( Eugene , OR , USA ) . Adult mice and P1 hamsters were euthanized by an overdose of isoflurane . Transcardiac perfusion was conducted with phosphate-buffered 0 . 9% saline ( PBS; 0 . 1 M , pH 7 . 4 ) , followed by 4% formaldehyde in PBS , until the head was fixed . The nasal part of the eyes of murine embryos and adult mice was marked with a suture and removed . Two small holes were made in the cornea before a first postfixation step in formaldehyde for a period of 30 min . The cornea and lens were removed , and the eyecups were postfixed for 30 min in formaldehyde . The eyecups were then washed in PBS , cryoprotected in 30% sucrose overnight , embedded in NEG 50 tissue Embedding Media ( Thermo Fisher Scientific Burlington , ON , Canada ) , flash-frozen , and kept at −80 °C . Sections ( 14-μm thick ) were cut with a cryostat ( Leica Microsystems , Concord , ON , Canada ) and placed on gelatin/chromium-coated slides . Retinal sections were washed in 0 . 1 M PBS , postfixed for 5 min in a 70% solution of ethanol , rinsed in 0 . 03% Triton X-100 in PBS , and blocked in 10% normal donkey serum and 0 . 5% Triton X-100 in PBS for 1 h . The sections were then incubated overnight with antibodies against GPR91 or GPR99 . The antibody Brn-3a was also used as a specific marker for RGCs . After incubation with the primary antibodies , the sections were washed in PBS , blocked for 30 min , and incubated for 1 h with the secondary antibodies Alexa Fluor 647 donkey anti-rabbit and Alexa Fluor 488 donkey anti-mouse . After washing , the sections were mounted using a homemade PVA-Dabco medium . The specifications of all the antibodies used in this study are detailed in S1 Table . Images of the central retina ( within 200 μm of the optic nerve head ) were taken using a laser scanning confocal microscope ( TCS SP2 , Leica Microsystems ) with a 40X ( NA: 1 . 25 ) oil immersion objective and 488 and 633 nm lasers . Image stacks ( 1 , 024 × 1 , 024 pixels × 0 . 5 μm per stack ) were captured with a frame average of 3 using the LCS software ( version 2 . 6 . 1; Leica Microsystems ) . The stacks were taken sequentially and in distant wavelengths to ensure no “bleed through” between channels and were collapsed into projection images . All images in which labeling intensities were compared were obtained under identical conditions of gain intensity . Because gray-scale photographs provide better contrast and more detail , individual channels are presented in gray scale , and the merged images are presented in color . The retinas were isolated from E14/15 mouse embryos , dissected into small segments in ice-cold Dulbecco’s phosphate-buffered saline , and plated on 12-mm glass coverslips previously coated with poly-D-Lysine ( 20 μg/ml ) and laminin ( 5 μg/ml ) in 24-well plates . The explants were cultured in Neurobasal supplemented with 100 U/ml penicillin , 100 μg/ml streptomycin , 5 μg/ml LNAC , 1% B27 , 40 ng/ml selenium , 16 μg/ml putrescine , 0 . 04 ng/ml triiodo-thyronine , 100 μg/ml transferrin , 60 ng/ml progesterone , 100 μg/ml BSA , 1 mM sodium pyruvate , 2 mM glutamine , 10 ng/ml ciliary neurotrophic factor , 5 μg/ml insulin , and 10 μM forskolin at 37 °C and 5% CO2 . At 0 DIV , 1 h following plating , the explants were treated for 15 h for projection analysis or for 1 h at 1 DIV for GC analysis . The photomicrographs were taken using an Olympus IX71 microscope ( Olympus , Markham , ON , Canada ) and analyzed with Image-Pro Plus 5 . 1 software ( Media Cybernetics , Bethesda , MD , USA ) . The total length of axon bundles was quantified and expressed as the mean ± SEM . Statistical significance of differences between means was evaluated by analysis of variance ( ANOVA ) with Bonferroni’s post-hoc test ( Systat Software Inc , Chicago , IL , USA ) . Primary cortical neurons were used in this study because of the large number of neurons that can easily be cultured and harvested for biochemical assays , which is hardly possible with RGCs . C57BL/6 WT , gpr91KO , gpr99KO , and double-KO pregnant mice were used . Brains from E14/15 embryos were dissected , and the superior layer of each cortex was isolated and transferred in 2 ml S-MEM containing 2 . 5% trypsin and 2 mg/ml DNase and incubated at 37 °C for 15 min . The pellet was transferred into 10 ml S-MEM with 10% FBS and stored at 4 °C . After centrifugation , the pellet was again transferred in 2 ml S-MEM supplemented with 10% FBS and triturated 3 to 4 times . The supernatant was transferred in 10 ml Neurobasal medium . Dissociated neurons were counted and plated at 50 , 000 cells per well on 12 mm glass coverslips previously coated with poly-D-lysine ( 20 μg/ml ) for immunocytochemistry or at 250 , 000 cells per 35 mm petri dish for western blot . Neurons were cultured for 2 d in Neurobasal medium supplemented with 1% B-27 , 100 U/ml penicillin , 100 μg/ml streptomycin , 0 . 25% N2 , and 0 . 5 mM glutamine . They were then treated with either a GPR91 agonist ( 100 μM succinate ) , GPR99 agonist ( 200 μM αKG ) , or ERK1/2 inhibitor ( 20 μM CI-1040 ) for 1 h to study GC morphology or 2 , 5 , and 15 min for ERK1/2 quantification using western blot analysis . LIVE/DEAD cell viability assay: Cell viability was assessed with the LIVE/DEAD assay using an ethidium homodimer/calcein acetoxy methyl ester ( L-3224 , Molecular Probes , Eugene , OR , USA ) combination of vital dyes , as previously described [35 , 36] . Staurosporine ( 5 μM ) , an inducer of apoptotic cell death , was used as a positive control [37] . After treatment , retinal explants and primary cortical neuron cultures were washed with PBS ( pH 7 . 4 ) , fixed in 4% formaldehyde ( pH 7 . 4 ) , and blocked with 2% normal goat serum ( NGS ) and 2% BSA in PBS containing 0 . 1% Tween 20 ( pH 7 . 4 ) for 30 min at room temperature . The samples were then incubated overnight at 4 °C in a blocking solution containing anti-GAP-43 , anti-GPR91 , anti-GPR99 , anti-MAP2 , or anti-NFM . The following day , the samples were washed and labeled with Alexa Fluor 488 and 555 secondary antibodies and Hoechst 33258 ( 1:10 , 000 ) , and the coverslips were mounted with a homemade PVA-Dabco medium [38] . Primary cortical neurons were cultured for 2 DIVs at a density of approximately 250 , 000 cells/dish in 35 mm poly-D-lysine-coated petri dishes . Following treatment , neurons were washed once with ice-cold PBS ( pH 7 . 4 ) and then lysed with Laemmli sample buffer . Thirty micrograms of protein/sample of the homogenate were resolved with 12% SDS-polyacrylamide gel electrophoresis , transferred onto a nitrocellulose membrane , blocked with 5% BSA , and incubated overnight with antibodies directed against ERK1/2 , p-ERK1/2 , and β-actin , the latter serving as a loading control . The blots were exposed to the appropriate HRP-coupled secondary antibodies ( Jackson Immunoresearch Laboratories , West Grove , PA , USA ) . Detection was performed using homemade enhanced chemiluminescence western blotting detection reagent ( final concentrations: 2 . 5 mM luminol , 0 . 4 mM p-coumaric acid , 0 . 1 M Tris-HCl [pH 8 . 5] , 0 . 018% H2O2 ) . Embryonic retinal explants were cultured on a coverglass in a borosilicate chamber ( Lab-Tek; Rochester , NY , USA ) for 2 DIVs and placed in an incubator mounted on an inverted microscope ( Olympus IX71 ) . They were maintained at 37 °C and 5% CO2 with a live cell chamber ( Neve Bioscience , Camp Hill , PA , USA ) throughout the whole experiment . A microgradient was created using a Picoplus micro-injector ( Harvard Apparatus , St-Laurent , QC , Canada ) . Glass micropipettes with a tip of 2–3 μm diameter were positioned at 45° and at 100 μm away from the GC of interest , as described previously [8 , 10 , 11] . Syrian golden hamsters ( Charles River ) were used for investigating the in vivo implication of succinate/GPR91 and α-KG/GPR99 in RGC projection growth during postnatal development . At P1 , 24 h after birth , anesthetized hamsters received a unilateral injection of 2 μl solution of CTb with either 0 . 9% saline solution , succinate ( 100 mM ) , or α-KG ( 200 mM ) . Briefly , under an operating microscope , a small incision was made in the eyelids to access the right eye . The injections were administered using a glass micropipette attached to a 10 μl Hamilton syringe . The micropipette was carefully inserted into the vitreous at an angle to avoid damage to the lens . Following the injection , the eyelids were closed with surgical glue ( Vetbond; 3M ) . At P5 , 4 d after the injection , hamsters were anesthetized and perfused transcardially with 0 . 1 M PBS , pH 7 . 4 , followed by 4% PFA in PBS . The brains were removed , postfixed overnight at 4 °C and cryoprotected with sucrose . Then , brains were frozen and kept at −80 °C until processing by immunohistochemistry according to a protocol previously described by Argaw and colleagues in 2011 [8] . Briefly , 40 μm—thick coronal sections of tissue were incubated in 90% methanol and 0 . 3% H2O2 in 0 . 1 m PBS , pH 7 . 4 , for 20 min . They were then rinsed and incubated in 0 . 1 M glycine/PBS for 30 min , followed by an overnight incubation ( 4 °C ) in PBS containing 4% NDS , 2 . 5% BSA , and 1% Triton X-100 . The sections were subsequently rinsed and immersed for 48 h at room temperature in a solution containing goat anti-CTb diluted 1:4 , 000 in PBS with 2% NDS , 2 . 5% BSA , and 2% Triton X-100 . Afterward , the sections were rinsed and incubated in 2% NDS and 2 . 5% BSA/PBS for 10 min . This was followed by a 1 h incubation in donkey anti-goat biotinylated secondary antibody diluted 1:200 in PBS with 2% NDS , 2 . 5% BSA , and 1% Triton X-100 . Tissues were rinsed , incubated in 2% NDS and 2 . 5% BSA in PBS for 10 min , and subsequently processed with an avidin-biotin-peroxidase complex ABC Kit ( diluted 1:100 in PBS ) for 1 h in the dark at room temperature . The sections were then rinsed and preincubated in 3 , 3′-diaminobenzidine tetrahydrochloride ( DAB ) in PBS for 5 min . The peroxidase reaction product was visualized by adding 0 . 004% H2O2 to the DAB solution for 2–4 min . Sections were finally washed 5 times ( 1 min each ) with PBS , mounted on gelatin-chromium alum-subbed slides , air-dried , dehydrated in ethanol , cleared in xylenes , and mounted on coverslips with Depex ( EMS ) . After 14–15 d of gestation , pregnant mice ( WT , gpr91KO , gpr99KO , and double KO ) were euthanized , and the embryos were removed . The lambdoid sutures of the embryos were incised , and the occipital bones were removed to expose the brain to the fixative ( 4% formaldehyde ) , where they were placed for 1 wk at 4 °C until tracing with DiI . For complete optic nerve labeling , 1 eye of each embryo was enucleated and crystals of DiI implanted unilaterally into the optic disk . Embryos were incubated at 37 °C in 4% formaldehyde for 7 d . Tissue clearing was performed according to Hama and colleagues ( 2011 ) [39] . Briefly , embryos were incubated for 2 d in Scale A2 solution ( 4 M urea , 10% glycerol , 0 . 1% Triton X-100 , in water ) followed by 2 d in Scale B4 solution ( 8 M urea , 0 . 1% Triton X-100 , in water ) and then to a fresh Scale A2 solution for 1 wk to complete the clearing [39] . The brains were then carefully removed with their optic nerves , and the proximal visual system was imaged with a fluorescence microscope to allow the observation of subtle guidance defects at the optic chiasm . For eye-specific segregation studies in the dLGN , C57BL/6 WT , gpr91KO , gpr99KO , and double-KO adult mice received an intraocular injection of CTb conjugated to Alexa Fluor 555 into the left eye and CTb coupled to Alexa Fluor 488 into the right eye ( 2 μl; 0 . 5% in sterile saline ) . Four days after the injection , the animals were anesthetized and perfused transcardially with 0 . 1 M PBS ( pH 7 . 4 ) followed by 4% formaldehyde . The brains were removed , postfixed overnight at 4 °C , cryoprotected , frozen , and kept at −80 °C . Retinal projections marked with the CTb were visualized on brain sections washed 5 times ( 1 min each ) with PBS , mounted on gelatin-chromium alum-subbed slides , air-dried , and mounted on coverslips with DEPEX ( EMS , Hatfield , PA , USA ) . The photomicrographs of the optic chiasm were taken with an IX71 microscope ( Olympus , Richmond Hill , ON , Canada ) , an Evolution VF camera ( Media Cybernetics , Warrendale , PA , USA ) and Image-Pro Plus 5 . 1 image analysis software . Universal gains and exposures were established for each labeling . Raw images of the dLGN were imported to MATLAB ( Natick , MA , USA ) , and an area of interest comprising the dLGN was cropped , excluding the ventral lateral geniculate nucleus and the intergeniculate leaflet . Then , the degree of left and right eye projection overlap was quantified using an established multithreshold method of analysis [40–42] . This approach allows for a better analysis of overlapping regions independent of the threshold . For these experiments , an observer “blind” to the experimental conditions to avoid any bias performed the quantification . Values are expressed as the means ± SEM . The significance of differences between means was evaluated by Student t test analysis ( Systat ) . To assess axon growth in vivo , photomicrographs of the DTN of mice and P5 hamsters were taken with a microscope ( Leica Microsystems , Concord , ON , Canada ) coupled to an Evolution VF camera ( Media Cybernetics ) . The images were quantified using Image-Pro Plus 5 . 1 software . The growth of axon branches was quantified on consecutive photomicrographs of coronal slices of brain tissue comprising the DTN . On each photomicrograph , the distance between the lateral border of the DTN and the tips of the longest axon branches was measured . To take into account brain size differences , axon branch lengths were normalized with the interthalamic distance ( distance between the right and left lateral borders of the thalamus; see S6A Fig for a schematic representation of such quantification ) . Axon collateral number was quantified on consecutive photomicrographs comprising the DTN using an adaptation of the Sholl technique [43] , as described by Duff and colleagues in 2013 [11] and illustrated in S6B Fig . Values are expressed as the means ± SEM . The significance of differences between means was evaluated by ANOVA with Bonferroni’s post-hoc test ( Systat ) . | Development of the visual system requires high levels of energy and tight regulation of multiple factors integrated by axon projections during navigation to their appropriate targets . While intermediates of carbohydrate metabolism have key roles in many biological processes , much less is known about their effects on receptors in the developing nervous system . We hypothesized that activation of two G-protein-coupled receptors ( GPCRs ) by metabolic intermediates could promote growth during retinal ganglion cell ( RGC ) axon extension and guidance from the retina to the brain . We first demonstrated that receptors for two intermediates of carbohydrate metabolism—succinate and α-ketoglutarate ( α-KG ) —are expressed on developing RGCs and their projections . We revealed that these receptors have a complementary role in regulating axon growth in an extracellular signal–regulated kinases 1 and 2 ( ERK1/2 ) -dependent manner , although with no effect on axon guidance . The absence of either receptor caused a strong decline in axonal projections from the retina to the thalamus , while the combined absence of both receptors had an additive effect . Taken together , our findings indicate , for the first time , an important role for intermediates of carbohydrate metabolism and their receptors in stimulating axon growth during the establishment of the visual system and suggest a wider involvement in the nervous system development . |
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Elastase-mediated cleavage of cyclin E generates low molecular weight cyclin E ( LMW-E ) isoforms exhibiting enhanced CDK2–associated kinase activity and resistance to inhibition by CDK inhibitors p21 and p27 . Approximately 27% of breast cancers express high LMW-E protein levels , which significantly correlates with poor survival . The objective of this study was to identify the signaling pathway ( s ) deregulated by LMW-E expression in breast cancer patients and to identify pharmaceutical agents to effectively target this pathway . Ectopic LMW-E expression in nontumorigenic human mammary epithelial cells ( hMECs ) was sufficient to generate xenografts with greater tumorigenic potential than full-length cyclin E , and the tumorigenicity was augmented by in vivo passaging . However , cyclin E mutants unable to interact with CDK2 protected hMECs from tumor development . When hMECs were cultured on Matrigel , LMW-E mediated aberrant acinar morphogenesis , including enlargement of acinar structures and formation of multi-acinar complexes , as denoted by reduced BIM and elevated Ki67 expression . Similarly , inducible expression of LMW-E in transgenic mice generated hyper-proliferative terminal end buds resulting in enhanced mammary tumor development . Reverse-phase protein array assay of 276 breast tumor patient samples and cells cultured on monolayer and in three-dimensional Matrigel demonstrated that , in terms of protein expression profile , hMECs cultured in Matrigel more closely resembled patient tissues than did cells cultured on monolayer . Additionally , the b-Raf-ERK1/2-mTOR pathway was activated in LMW-E–expressing patient samples , and activation of this pathway was associated with poor disease-specific survival . Combination treatment using roscovitine ( CDK inhibitor ) plus either rapamycin ( mTOR inhibitor ) or sorafenib ( a pan kinase inhibitor targeting b-Raf ) effectively prevented aberrant acinar formation in LMW-E–expressing cells by inducing G1/S cell cycle arrest . LMW-E requires CDK2–associated kinase activity to induce mammary tumor formation by disrupting acinar development . The b-Raf-ERK1/2-mTOR signaling pathway is aberrantly activated in breast cancer and can be suppressed by combination treatment with roscovitine plus either rapamycin or sorafenib .
Cyclin E has been extensively implicated in breast cancer [1]–[7] . The function of cyclin E is modulated via association of cyclin E with CDK2 , which promotes progression of cells into S phase [8]–[10] . In addition to demonstrating genomic and transcriptional amplification of the cyclin E gene in breast cancer cells [11] , our laboratory initially reported that cyclin E is cleaved by elastase into low molecular weight ( LMW ) isoforms in breast cancers [12] , [13] . Cleavage of cyclin E occurs at two N-terminal sites of full-length cyclin E ( EL ) , giving rise to trunk 1 [LMW-E ( T1 ) ] and trunk 2 [LMW-E ( T2 ) ] isoforms . Compared to EL , the LMW-E isoforms have higher CDK2-associated kinase activity , are more resistant to inhibition by CDK inhibitors p21 and p27 , and induce higher proliferation rates when introduced into cells [14] , [15] . Furthermore , examination of breast cancer patient samples revealed that approximately 27% of patients express high LMW-E protein levels as assessed by Western blot analysis , and high LMW-E expression significantly correlates with poor survival [16] . Although the connection between LMW-E and breast cancer outcome is clear , understanding of how LMW-E influences mammary tumor formation is lacking . In the mammary gland , the acinus is composed of a bilayer of luminal epithelial cells and basal myoepithelial cells; the lumen of each acinus is hollow and contains milk secretions during lactation [17] , [18] . Human mammary epithelial cells ( hMECs ) cultured on a reconstituted basement membrane undergo cellular proliferation and differentiation to form highly organized and polarized acinar structures [19] , [20] . Although this system serves as an excellent model for studying breast cancer development in vitro , a direct comparison of the proteomic profiles of hMECs in culture and the proteomic profiles of patient tissues has not been reported . Most studies aimed at elucidating the action of specific proteins in breast tumorigenesis or identifying inhibitors of proteins that warrant testing in clinical trials have been conducted using the traditional two-dimensional ( 2D ) culture . However , 2D culture do not reflect the important contribution of the tissue microenvironment both in mediation of normal breast tissue viability and in generation of the apoptotic-resistant phenotype of breast tumors . Culturing of cells in three-dimensional ( 3D ) matrices offers several advantages over 2D culture . Culturing cells in 3D matrices allows cells to organize into structures that mimic their in vivo architecture , and 3D culture is particularly useful for investigating gene functions and signaling pathways in a physiologically relevant context . In 3D culture , normal and nonmalignant hMECs can be distinguished from premalignant cells: whereas normal cells become quiescent by day 10 and organize into replicas of human breast acini with correct tissue polarity and proportions [19] , [20] , malignant cells continue to grow , pile up , and form large , disorganized , tumor-like colonies [21] . Additionally , 3D culture is superior to 2D culture for identifying the driving oncogenic pathways in tumor cells and the critical inhibitors that warrant testing in therapeutic trials [22]–[24] . Here , we used 3D culture to elucidate the mechanisms by which LMW-E leads to progression of breast cancer , as manifested by deregulated mammary acinar morphogenesis , increased tumorigenic potential , and altered activation of targetable signal transduction pathways identified from patient samples . Specifically , we provide evidence suggesting that the LMW-E/CDK2 complex induces breast tumor initiation and progression by disrupting the architecture of the mammary gland . Through proteomic analysis of both LMW-E-overexpressing hMECs and tumor tissue from breast cancer patients , we identify the b-Raf-ERK1/2-mTOR pathway to be critical in the tumorigenic properties of LMW-E . Consequently , we show that the disruption of the mammary gland architecture mediated by LMW-E/CDK2 can be effectively prevented by combination treatment with roscovitine ( inhibitor of CDKs ) plus either rapamycin ( inhibitor of mTOR ) or sorafenib ( a pan kinase inhibitor that has activity against b-Raf ) . Early steps in breast tumorigenesis are characterized by enhanced proliferation of epithelial cells and deregulated acinar formation , including enlargement of acinar structures and filling of the luminal space [21] . In this study , we report that the phenotypes mediated by LMW-E during acinar development closely resemble those of human mammary epithelial cells in the early steps of breast cancer development . Additionally , inducible LMW-E expression in transgenic mice generates hyper-proliferative terminal end buds ( TEBs ) resulting in enhanced mammary tumor development and metastasis . Finally , through proteomic analysis , we provide evidence that breast cancer patient samples and cells cultured in 3D matrices display a high degree of concordance , thus further supporting the usefulness of this in vitro culture system .
The presence of LMW-E in breast cancer patient samples as well as cell lines but not in normal tissues suggests that the LMW-E isoforms contribute to the development of breast cancer [13] , [16] , [25] . Therefore , we examined whether ectopic expression of LMW-E in a nontumorigenic cell line could render it tumorigenic . 76NE6 cells ( hMECs ) stably expressing vector , EL , or LMW-E ( “76NE6-vector” , “76NE6-EL” , and “76NE6-LMW-E” , respectively ) were injected subcutaneously into nude mice , and xenograft development was monitored . Only 7% ( 1/15 ) of the mice injected with 76NE6-EL cells developed tumors as compared with 74% ( 23/31 ) of the mice injected with the 76NE6-LMW-E cells ( p<0 . 0001 ) ( Table 1 ) . To investigate if LMW-E expression in hMECs is sufficient to maintain tumor growth and to determine whether cells from tumors generated by LMW-E-expressing hMECs can form new tumors , LMW-E-expressing tumor cells ( TDCs: tumor-derived cells ) were subjected to serial in vivo passaging in mice . More specifically , the 76NE6-LMW-E tumors were removed for in vitro expansion , and two T1G2 clones were injected into mice to generate the T1G3 clones ( Figure 1A ) . This process was repeated to generate three total generations of in vivo passaged clones ( T1G2 , T1G3 , and T1G4 ) . Interestingly , re-injection of the isolated cells from the tumors resulted in 100% tumor formation , suggesting that these cells became more tumorigenic during the process of in vivo passaging ( Table 1 ) . Western blot analysis indicated that the majority of the TDCs had higher LMW-E expression than the 76NE6-LMW-E cells ( Figure 1B ) . Furthermore , quantification of the cyclin E protein levels by densitometry indicated that in vivo passaging resulted in sequential reduction in the level of EL ( Figure 1C ) and an increase in the level of LMW-E protein with each generation of passaging ( Figure 1D ) . The protein level of elafin ( an endogenous inhibitor of the serine protease responsible for cleaving cyclin E into LMW-E isoforms [26] ) also diminished with increasing passaging in vivo , suggesting that cyclin E was subjected to elevated proteolytic processing in the mouse microenvironment ( Figure 1E ) . Additionally , immunohistochemical analysis of the xenograft tumors from the mice revealed strong cyclin E expression throughout the tumors and a number of the cells with enlarged nuclei and multinucleated morphology ( Figure 1F ) . These findings suggested not only that LMW-E is tumorigenic , but also that continued expression of LMW-E provides the cells a growth advantage to promote their sustained survival in mice . To examine the role of CDK2 in LMW-E-mediated tumorigenesis , we generated another model system , as previously described [27] in which the expression of FLAG-tagged vector , EL , and LMW-E in 76NE6 cells could be induced by varying doxycycline concentrations ( Figure 2A ) . In vitro kinase assay using histone H1 and GST-Rb as substrates confirmed that inducible EL , and LMW-E , had functional cyclin E-associated kinase activity ( Figure 2A ) . We injected the 76NE6 cells with inducible protein expression subcutaneously into nude mice and induced the expression of vector , EL , and LMW-E with doxycycline 24 hours later . The tumor incidence rates were significantly higher in mice treated with 500 µg/ml doxycycline than in mice not treated with doxycycline by Fisher exact test ( p<0 . 0001 ) ( Table 2 ) . In addition , LMW-E induction with 500 µg/ml doxycycline led to tumor formation in more than 90% of the injections , whereas EL induction with 500 µg/ml doxycycline led to tumor formation in only 17% of mice ( Table 2 ) . The tumor incidence rate mediated by LMW-E in this xenograft model is consistent with the transgenic model of LMW-E overexpression previously reported [28] . Since cyclin E is the regulatory subunit of the cyclin E/CDK2 complex and is enzymatically inactive when unbound , we speculated that the oncogenicity of LMW-E requires interaction with CDK2 . Consequently , we generated a point mutation at R130A in the cyclin E gene that prevents cyclin E from interacting with CDK2 , thereby suppressing the cyclin E/CDK2 kinase activity [29] . The CDK2-associated kinase activity of these inducible mutants was compromised as indicated by lack of histone H1 and GST-Rb phosphorylation ( Figure 2A ) . To determine whether cyclin E-mediated tumorigenesis is dependent on the kinase activity associated with CDK2 , we injected 76NE6 cells with inducible expression of ELR130A and LMW-ER130A into nude mice . The resulting tumor incidence was 17% or less for cells expressing ELR130A and LMW-ER130A ( Table 2 ) indicating that CDK2-associated kinase activity is necessary for LMW-E-mediated tumorigenicity . These results demonstrated that cells expressing LMW-E have a higher frequency of tumor formation than cells expressing EL , and this oncogenicity is critically dependent on the CDK2-associated kinase activity . This observation is consistent with our recently published results in which we reported that LMW-E overexpression does not induce mammary tumor development in CDK2−/− transgenic mice [28] . We next asked if deregulation of acinar development is responsible for LMW-E-mediated oncogenicity . Examination of acinar development of hMECs cultured on a reconstituted basement membrane revealed that while induced EL expression led to generation of large acini with the typical spherical structure , induced LMW-E expression led to generation of large acini with irregular shapes ( Figure 2B ) . Quantification of the size of the acini revealed that deregulation of acinar morphogenesis by LMW-E was dose dependent , with higher cyclin E expression generating larger acini ( Figure 2C ) . In contrast , induction of ELR130A and LMW-ER130A did not increase acinar size ( Figure 2B and 2C ) . Furthermore , only wild-type LMW-E expression generated multi-acinar complexes ( complexes with multiple acini forming aggregate structures ) , a phenotype not observed with EL , ELR130A and LMW-ER130A expression ( Figure 2D ) . Overall , our data suggested that LMW-E depends on CDK2-associated kinase activity to induce mammary tumorigenesis and aberrant acinar morphogenesis . The 3D cell culture system can be used to distinguish nonmalignant from malignant cells on the basis of the phenotypes observed [19] . 76NE6 cells and MCF-10A cells ( immortalized hMECs ) formed polarized acinar structures when cultured on Matrigel as indicated by α6-integrin staining on the basal surface and GM-130 staining on the apical surface ( Figure 3A ) . In contrast , breast cancer cell lines such as Hs 578T and MDA-MB-231 , which express endogenous LMW-E ( Figure 3B ) did not form coherent acini and demonstrated disordered polarity as indicated by unorganized α6-integrin and GM-130 staining ( Figure 3A ) . Using 76NE6 cells with stable vector , EL , and LMW-E expression , we found that , similar to what we observed in cells with inducible protein expression ( Figure 2B ) , overexpression of EL led to generation of large but still spherical acini , while overexpression of LMW-E led to generation of large , irregularly shaped structures and multi-acinar complexes ( Figure 3C ) . Aberrant acinar development was also observed in the TDCs , in which the acini were approximately 28% larger than the structures formed by the 76NE6 cells with vector expression ( p<0 . 05 ) ( Figure 3D ) . During normal acinar morphogenesis , cells are highly proliferative and then undergo apoptosis of the lumen with subsequent proliferative arrest and induction of differentiation by day 15 in culture [30] . As expected , the 76NE6 cells arrested proliferation by downregulating cyclin E in 3D culture ( Figure 3B ) . However , cyclin E protein levels in the 76NE6-LMW-E cells and in the TDCs were upregulated during acinar morphogenesis compared to the cyclin E protein levels in the 76NE6-V and 76NE6-EL cells ( Figure 3E ) . Moreover , the cyclin E-associated kinase activity of the LMW-E-expressing cells was also elevated , suggesting that cells in these acinar structures were still actively proliferating , passing through the G1/S-phase checkpoint and thus leading to formation of enlarged acini . We also observed that the levels of cyclin E protein as well as mRNA transcript were much higher in the 76NE6-LMW-E cells compared to the 76NE6-EL cells ( Figure 3E and Figure S1A ) , which is a phenomenon that was also observed in the transgenic mouse model with overexpression of LMW-E ( Figure S1B ) . To test if overexpression of LMW-E in the transgenic mice upregulates the endogenous mouse cyclin E gene , we analyzed mouse cyclin E mRNA expression levels in the tumor and the contralateral mammary gland of 3 different LMW-E-overexpressing transgenic mice ( Figure S1B ) . Quantitative RT-PCR analysis showed a 3-fold increase in the abundance of endogenous cyclin E mRNA in the tumors when compared to the contralateral mammary glands . These results are consistent with a model in which , during tumor progression , LMW-E expression activates a positive feedback loop leading to increase expression of endogenous cyclin E . BIM , a member of the Bcl-2 pro-apoptotic family , has been shown to be responsible for cell death during late acinar morphogenesis to generate a hollow lumen in the acinus [31] . We found that BIM protein levels were downregulated in the LMW-E-expressing acini , suggesting that these cells bypass morphogenetic cues that cause growth arrest and apoptosis of the luminal cells ( Figure 3E ) . To determine whether LMW-E expression was sufficient to prevent growth arrest of cells in mature acini , we fixed acini at 15 days and stained them for Ki67 . While Ki67 expression was not detectable in the 76NE6-V acini , LMW-E-expressing acini displayed high Ki67 staining , particularly in cells that were in contact with the basement membrane ( Figure 3F and 3G ) . Furthermore , we determined a strong positive correlation between the acinar diameter and the percentage of Ki67-positive acini , indicating that the formation of large acini may be due to increased proliferation ( Figure 3H ) . Collectively , these findings provided evidence that expression of LMW-E is sufficient to induce generation of large and misshapen acini that exhibit enhanced cell proliferation and decreased apoptosis . These phenotypes resemble those observed in ductal carcinoma in situ and also those caused by ErbB2 activation [21] and may explain the high tumorigenic potential of LMW-E over EL . Having shown that LMW-E expression renders hMECs tumorigenic and leads to altered acinar morphogenesis , we set out to determine whether there was a direct cause-and-effect relationship between induction of LMW-E expression and altered mammary ductal structures in a transgenic mouse model . We developed transgenic mice with doxycycline-inducible LMW-E expression and examined these mice for altered TEB formation in the mammary gland and tumorigenesis in response to induction of LMW-E expression ( Figure 4A ) . Following 4 days of doxycycline treatment , MTB/TLMW mice demonstrated a 685-fold increase in luciferase activity above background for line 4372 and about 39-fold above background for line 4382 and LMW-E protein expression was detected by Western blot analysis; however , in MTB/TLMW mice not treated with doxycycline and in doxycycline-treated MTB or TLMW mice , no increase in luciferase activity or LMW-E protein expression was observed ( Figure 4B ) . Morphological examination of carmine-stained whole mounts revealed striking hyperplastic abnormalities in mammary ductal trees of both MTB/TLMW lines of mice with induced expression of LMW-E ( Figure 4C , lower panels ) . The mammary glands of these mice displayed abnormal development , including the formation of solid cellular masses along the primary ducts that resembled abortive side buds and misshapen TEBs . In contrast , mammary tissues from MTB/TLMW mice without induced expression of LMW-E were histologically indistinguishable from tissues from wild type and MTB mice and had normal club-shaped TEBs ( Figure 4C , upper panels ) . In addition , the mammary epithelium of both MTB/TLMW lines with induced LMW-E expression showed 2-folds higher in BrdU incorporation as compared to the mammary epithelium of MTB/TLMW mice without induced LMW-E expression ( p = 0 . 03 ) indicating that LMW-E overexpression , as shown by immunohistochemistry ( Figure 4D; 34 . 9%±2 . 7% cyclin E-positive cells for line 4372 and 25 . 1%±1 . 6% cyclin E-positive cells for line 4382 ) , induces high proliferation in the mammary epithelium . These data obtained from the transgenic mice suggested that inducible LMW-E expression in the mouse mammary epithelium results in hyper-proliferation and aberrant acinar morphogenesis similar to what was observed with the hMECs expressing LMW-E cultured on Matrigel in the xenograft model system . We have shown previously that approximately 25% of transgenic mice with LMW-E expression developed metastasis as compared to 8 . 3% of tumors with EL overexpression [32] . Cellular invasion is one of the critical events leading to successful metastasis and requires migration of the tumor cells through the basement membrane to invade the surrounding tissues [33] . The Boyden chamber invasion assay was performed to investigate whether LMW-E expression in hMECs enhances cellular invasiveness . The cells were seeded on a microporous transwell insert on top of a thin layer of Matrigel with fibronectin on the other side of the membrane to act as a chemo-attractant . After 24 hours , the cells that have invaded to the bottom side of the membrane were stained with crystal violet for visualization . Figure 4E shows that while the vector control cells were unable to invade through the Matrigel basement membrane , cells with cyclin E expression were highly invasive . More specifically , quantification of the invaded cells demonstrate that while all cells with cyclin E expression invaded through the basement membrane significantly more than vector control cells , cells with LMW-E expression invaded significantly more than cells with EL expression ( p<0 . 05 ) ( Figure 4F ) . Collectively , we provide evidence suggesting that overexpression of LMW-E enhances the invasiveness of hMECs . While it is widely accepted that the 3D culture system serves as a more physiologically relevant model for the investigation of cell behavior compared to 2D plastic surface [18] , [20] , no direct comparison between cells cultured on this 3D model and human samples has been performed . Therefore , we next aim to compare the protein expression patterns between cells grown on 2D culture , 3D culture and human breast tumor tissues . The reverse-phase protein array ( RPPA ) assay was used to compare the expression levels of 73 different proteins involved in major signaling transduction pathways and cellular processes between xenografted tumor-derived cells ( TDCs ) grown on 2D monolayer and in 3D Matrigel cultures and 276 human breast cancer samples previously described [16] . The RPPA method is a proteomic protein expression analysis that has been shown to be highly reproducible in analyzing the expression patterns of proteins involved in cell signaling [34]–[36] . For these analyses , serially diluted lysates prepared from cell lines cultured on 2D and 3D as well as from 276 tumor specimen were arrayed on nitrocellulose-coated slides as described previously [16] . Each slide was then probed with a validated primary antibody plus a biotin-conjugated secondary antibody . Table S1 lists the antibody targets used for this study , which were selected as being relevant to breast cancer through a literature review . Hierarchical clustering was then performed using Euclidean distance and Ward's minimum variance for agglomeration ( Figure 5A ) . The resulting heat map demonstrated that the cells from 2D and 3D cultures had strikingly different protein expression patterns and that the protein expression pattern of the cells from 3D cultures more closely resembled that of patient tissues than did the protein expression pattern of cells grown on monolayer ( Figure 5A ) . Most of the proteins that show a distinct expression pattern between 2D and 3D cultures play key roles in cell proliferation , specifically , the G1 to S transition ( Table S2 ) . These results were expected since it has been established that the 3D culture system is a more physiologically relevant model than cell culture on a 2D plastic surface for the investigation of cellular behavior [18] , [20] . Furthermore , in an unsupervised analysis of the patient RPPA data , we observed separate clustering between the low and high LMW-E-expressing breast tumors but not between low and high full-length cyclin E ( Figure S2A and S2B ) . We next identified the proteins whose expression was significantly associated with LMW-E levels as well as patient survival in the tumor database ( Figure 5B ) . Our analysis revealed that the b-Raf-ERK1/2-mTOR pathway is activated in the breast cancer patient samples as well as in the tumor cells cultured on Matrigel with high LMW-E expression ( Figure 5B and 5C and Table S3 ) . Furthermore , a direct comparison between the levels of all the proteins analyzed in Figure 5C by Western blot and those obtained from the RPPA analysis showed high concordance ( Pearson coefficient = 0 . 723 , p<0 . 001 ) and also validated the activation of this signaling axis in vitro ( Figure 5C and 5D ) . Additionally , breast cancer patient tumors with high LMW-E expression also expressed high levels of b-Raf , pMEK1/2 ( S217 ) , ERK2 , mTOR , and eIF4E and a low level of pAkt ( T308 ) ( Table 3 and Figure S3 ) . Collectively , these data suggested that in terms of proteomic expression patterns , breast cancer cells grown in 3D culture more closely resemble human tumors than do breast cancer cells grown in 2D culture thereby underscoring the usefulness of this in vitro model system . Having established the importance of the CDK2-associated kinase activity in aberrant acinar morphogenesis in 3D culture and given that the b-Raf-ERK1/2-mTOR signaling axis was deregulated in tumor cells and patient samples with high LMW-E expression , we hypothesized that combination treatment with roscovitine ( a CDK inhibitor ) plus either rapamycin ( an mTOR inhibitor ) or sorafenib ( a pan kinase inhibitor that has activity against b-Raf ) can prevent the induced-aberrant acinar morphology . Combination treatments of cells cultured in Matrigel using these agents resulted in a larger reduction of the levels of pS6 ( S235/236 ) , pERK1/2 ( T202/Y204 ) , and pRb ( S807/811 ) than no treatment or treatment with single agents ( Figure 6A ) . Moreover , the combination treatments upregulated the expression of the CDK inhibitors p21 and p27 , consistent with a cell cycle arrest at the G1-S phase . Examination of the acinar formation as a result of the combination drug treatments revealed that the TDCs displayed a significant reduction in acinar size and Ki67 levels compared to the untreated cells and cells treated with single agents ( Figure 6B and 6D , Figure S4 , Figure S5A and S5B , and Tables S4 and S5 ) . In contrast , the 76NE6-V and 76NE6-EL cells displayed no change in these phenotypes in response to the drug treatments , suggesting that the absence of LMW-E expression may protect these cells from the toxic effects of the drugs . Thus , roscovitine in combination with either rapamycin or sorafenib can prevent the development of the aberrant acinar phenotypes caused by LMW-E expression , confirming a role for LMW-E/CDK2 kinase activity in causing formation of large , multilobular acini and demonstrating a potential therapeutic approach to treat cancer patients with high LMW-E expression . In a large retrospective clinical study , we previously found that breast cancer patients whose tumors had high levels of LMW-E expression , as determined by Western blot analysis , have significantly worse DSS than patients whose tumors had low LMW-E expression [16] . In the study reported herein , we used tissue samples from 276 of these patients for RPPA analysis to investigate large-scale protein expression pattern . The 276 patients were divided into 4 groups based on both LMW-E and EL expression and subjected to Kaplan-Meier analysis ( Figure 7A ) . The four groups consisted of ( i ) 22 patients with low LMW-E/high EL , ( ii ) 92 patients with low LMW-E/low EL , ( iii ) 33 patients with high LMW-E/high EL , and ( iv ) 129 patients with high LMW-E/low EL . Similar to our previous observation , we found that patients with high LMW-E protein levels had significantly worse DSS than patients with low LMW-E expression ( p<0 . 0001 ) ( Figure 7A ) . More specifically , only patients whose tumors overexpress LMW-E ( groups iii and iv ) regardless of whether or not they also overexpress EL , have a poor prognosis ( Figure 7A ) . Additionally , those patients whose tumors overexpress EL , in the absence of any LMW-E ( group i ) have the best prognosis . This new analysis clearly indicated that LMW-E overexpression , but not EL , is responsible for poor patient outcome . Next , we performed bivariate analysis of cyclin E level along with key nodes in the b-Raf-ERK1/2-mTOR pathway , which revealed that among breast cancer patients with high LMW-E expression , those with high FAK levels had significantly worse DSS than those with low FAK levels ( p = 0 . 0042 ) ( Figure 7B and Figure S6 ) . In contrast , among patients with high LMW-E expression , low BIM or low total Akt levels were associated with worse survival . Additionally , the overall DSS of patients with high LMW-E combined with these proteins in the b-Raf-ERK1/2-mTOR pathway was dramatically worse than in the patients with high EL expression ( Figure 7B and Figure S6 ) . To determine whether these individual proteins collaborate to reduce patient survival , we performed multivariate analysis by analyzing patients with high LMW-E expression and combining 2 additional proteins . We found that patients with high LMW-E , high FAK , and low BIM , Akt , or pAkt ( T308 ) experienced significantly worse DSS than the opposite groups ( p<0 . 05 ) ( Figure 7C ) . In addition , patients with high LMW-E , low BIM , and low Akt or pAkt ( T308 ) experienced significantly worse DSS ( p<0 . 05 ) . Interestingly , we were not able to find statistical significance between EL expression in the same multivariate analysis with these proteins ( Figure S7 ) . Essentially , our statistical analysis suggests that it is likely that LMW-E , FAK , BIM , Akt , and pAkt ( T308 ) function in the same pathway to adversely affect patient survival with breast cancer .
There is mounting evidence suggesting that the LMW-E isoforms play a unique role in mammary tumorigenesis . Our current understanding of cell cycle deregulation by LMW-E consists of enhanced S-phase entry [13] , aberrant centrosomal amplification [37] , and genomic instability [38] . In this report , we utilized three model systems ( xenograft transplantation , 3D acinar morphogenesis , and an inducible transgenic mouse model ) that recapitulate the human mammary gland to examine the tumor-initiating potential of LMW-E . We first demonstrated that LMW-E has greater oncogenic potential than EL , as indicated by tumor-initiating activity in nude mice with subcutaneous xenografts . Moreover , LMW-E expression is selected with increasing in vivo passaging suggesting that LMW-E provides a growth advantage in tumors . Indeed , selective pressure exerted from the in vivo microenvironment has previously been shown to favor further genetic and epigenetic alterations that eventually progress to highly advanced tumor stages [39] . Additionally , the inducible transgenic mouse model system provided evidence for a direct role of LMW-E in mediating alteration in the TEBs in the mammary gland , which is required for tumor generation in these mice . Furthermore , this model system underscores the important role of the microenvironment in the development of morphological characteristics and growth patterns . We observed an interesting phenomenon in which tumor cells with LMW-E expression and transgenic mice with inducible LMW-E expression demonstrated an elevation in the level of EL expression . We speculate that high LMW-E protein levels may lead to hyperactive G1-S transition causing a positive feedback loop acquired during tumor progression that activates the transcription of the endogenous cyclin E mRNA through activation of E2F . Increased E2F activity has been shown to stabilize cyclin E by reducing conjugation with ubiquitin [40] . Additionally , cyclin E transcription has been reported to be positively regulated by the E2F transcription factor , and in fact , the cyclin E promoter does contain several E2F binding sites [41] . Indeed , this observation warrants further investigation into the transcriptional regulation of cyclin E expression and the possible positive feedback loop that is critical for mammary tumorigenesis . The acinar morphogenesis assay has been widely used to model the early stages of mammary oncogenesis [19] , [21] . Our data suggest that LMW-E may exert its tumorigenic potential via disruption of the acinar morphogenetic process resulting in larger and misshapen acini due to failure of proliferation arrest and apoptotic induction [30] . High Ki67 expression in the cells on the outer layer of the acini suggests continued proliferation that likely leads to disruption of the spherical integrity of the structures . These aberrant morphological phenotypes mediated by LMW-E are similar to the characteristics described for ductal carcinoma in situ and may explain the role of LMW-E in mammary oncogenesis . The fact that LMW-E requires CDK2 kinase activity to drive multiacinar complexes and promote tumor-initiating activity of hMECs in mice suggests that LMW-E itself has no intrinsic oncogenic activity . This observation corroborates with our recent publication demonstrating that CDK2 is necessary for LMW-E-mediated mammary tumor formation in transgenic mice [28] . Therefore , treatment of tumors with high LMW-E protein levels can be achieved by inhibiting CDK2 kinase activity . Roscovitine is a promising agent for targeting multiple types of tumors , including breast cancer , sarcoma , non-small cell lung cancer , multiple myeloma , and lymphoma [42]–[46] . In fact , treatment of the mice with LMW-E-induced tumor using two different CDK inhibitors , meriolin and roscovitine , significantly delayed mammary tumor formation by approximately 6 weeks [28] . In this study , we also demonstrated that combination treatment using roscovitine together with rapamycin or sorafenib of LMW-E-expressing acini efficiently prevents the aberrant morphogenetic phenotypes without toxic effects on hMECs lacking LMW-E expression . These observations implicate an effective therapeutic strategy of inhibiting the CDK2-associated kinase activity and perhaps combining it with rapapmycin or sorafenib to treat breast cancer patients with high LMW-E expression . The results from the proteomic analysis demonstrated a marked contrast in the protein expression profiles of cells grown on monolayer and cells grown in 3D culture and illustrated a high similarity between cells in 3D culture and human tumor tissues , thus establishing a bridge between the 3D culture system and human tissues and further supporting the use of this culture system for biological study [47] . In fact , gene expression signatures of mammary cells extracted from this 3D culture system can be reliably used to predict patient outcome in which the signature of growth-arrested and well-organized hMECs predicts favorable clinical outcome [48] , [49] . Data from this study also allowed for the delineation of a signaling pathway that is deregulated in breast cancer patients who express high LMW-E levels . We demonstrated that tumors and cell lines with high LMW-E expression have upregulated b-Raf-ERK1/2-mTOR signaling , which has been reported to result in enhanced cell survival and reduced apoptosis [50]–[52] . Future pre-clinical studies will be aimed at examining if human breast tumors with high LMW-E expression are selectively sensitive to combination therapy with roscovitine , and sorafenib or rapamycin as compared with those without high LMW-E . These studies will help establish the clinical relevance of LMW-E expression as a marker for the targeted therapies identified in this report . In summary , LMW-E/CDK2 deregulates mammary acinar development , leading to enlarged and misshapen structures . Failure of LMW-E-expressing acini to arrest proliferation and undergo luminal apoptosis suggests upregulation of signaling involving cell survival and growth by hyperactive LMW-E/CDK2 complexes . Our data suggest that the combination of roscovitine with either rapamycin or sorafenib should be evaluated as a therapeutic strategy to treat breast cancer patients with high LMW-E expression .
All immortalized cell lines were cultured in DFCI-1 medium as described previously [53] . FLAG-tagged cyclin E gene constructs EL and LMW-E ( T1 ) were cloned into the pcDNA 4 . 0 vector ( Clontech , Mountain View , CA ) and transfected into 76NE6 hMECs . Transfected cells were selected with 80 µg/mL zeocin ( Invitrogen ) , and stable transfectants were maintained in culture with 10 µg/mL zeocin . To generate 76NE6 cells with tetracycline-inducible cyclin E expression we used a similar strategy to the one we used for generation of the inducible cyclin E expression in MCF-7 cells [27] . Specifically , the cyclin E gene constructs were cloned into the pRetro-CMV/TO vector and transfected into 293T cells to produce retroviruses carrying the cyclin E constructs , and the pBMN-BSR-TetR vector to produce retroviruses carrying the Tet repressor . The 76NE6 cells were infected first with the retroviruses carrying the Tet repressor ( TetR ) gene fused with blasticidin-S resistance gene and then with the retroviruses carrying the cyclin E constructs . 76NE6 cells inducibly expressing TetR-vector , EL , LMW-E ( T1 ) , ELR130A , and LMW-E ( T1R130A ) , were maintained in DFCI-1 medium with 20 µg/ml blasticidin-S and 1 µg/ml puromycin ( InvivoGen , San Diego , CA ) . Other cell lines used in this study ( i . e . Hs578T and MDA-MB-231 ) were obtained from American Type Tissue Collection and cultured as described previously [54] . Nude mice were purchased from Charles River Laboratories ( Wilmington , MA ) and maintained in the Department of Veterinary Medicine at The University of Texas MD Anderson Cancer Center . The mice were injected subcutaneously in the mammary fat pad with 1×106 cells suspended in 100 µL of a 1∶1 Matrigel∶media mix ( Matrigel from BD Biosciences , San Diego , CA ) . Doxycycline was added to drinking water containing 1% sucrose , and water was replaced twice weekly . Mice were sacrificed under an Animal Care and Use Committee ( ACUF ) -approved protocol when tumors reached approximately 12 mm in diameter or 10 weeks after injection , whichever came first . The tumors were harvested for histopathological analysis or for expansion of tumor cells in tissue culture for reinjection into mice for in vivo passaging . Tumors submitted for histopathology were fixed in 10% neutral buffered formalin , paraffin embedded , and serially sectioned at 5 µm thickness . RNA was isolated from mammary glands and tumors by using the RNAeasy kit ( Qiagen ) . Reverse transcription ( RT ) was performed using the First Strand cDNA Synthesis Kit using 1 µg of mRNA per reaction ( Roche ) . Real-time PCR was performed on the reverse-transcribed samples using SYBR Green PCR Master Mix ( Applied Biosystems ) . RT reactions in which no reverse transcriptase had been added served as a monitor for the efficiency of the DNase I digestion . All reactions were carried out in triplicate . The fold difference in mouse cyclin E transcripts was calculated by the ΔΔCT method using GAPDH as the internal control . For every reaction , we observed a single peak on the dissociation curve plot . Primer sequences were as follows: cyclin E-F , 5′-CAGAGCAGCGAGCAGGAGA-3′; cyclin E-R , 5′ CAGCTGCTTCCACACCACTG-3′; GAPDH-F , 5′-TGTACCGTCTAGCATATCTCCGAC-3′; GAPDH-R , 5′-ATGATGTGCTCTAGCTCTGGGTG-3′ . Transgenic mice with conditional expression of LMW-E were generated using the tetracycline regulatory system by cloning the coding sequence of LMW-E downstream of the minimal Tet operator in TMILA plasmid ( a gift from L . A . Chodosh , University of Pennsylvania , Philadelphia , PA ) . Additionally , an IRES-firefly luciferase expression cassette was cloned downstream of LMW-E to serve as a surrogate reporter for transgene expression . Two founder mice ( line 4372 and line 4382 ) harboring this TetO-LMW-E transgene ( referred to as TLMW ) were mated to a transgenic mouse harboring the MMTV-rtTA-pA transgene ( referred to as MTB ) to yield bi-transgenic MTB/TLMW mice [55] . Bi-transgenic mice carrying both of these transgenes express the rtTA transactivator in the mammary epithelium but do not express LMW-E unless doxycycline is added . TLMW founder line was generated by pronuclear injection and crossed with MTB mice . Whole-mount and bromodeoxyuridine ( BrdU ) staining were done as previously described [32] . Paraffin-embedded tumor sections were stained with hematoxylin and eosin and for cyclin E using a polyclonal anti-cyclin E antibody ( Santa Cruz Biotechnology , Santa Cruz , CA ) . The immunostaining was done as previously described [32] using a Vectastain ABC kit ( per manufacturer's web site ) and a BCIP/NBT chromogen detection system ( Vector Laboratories , Burlingame , CA ) . Briefly , the sections were incubated in 1% H2O2 to block endogenous peroxidase activity and then incubated for 20 min in 10 mmol/L sodium citrate buffer ( pH 6 . 0 ) at 90°C to retrieve nuclear antigens . Both primary and secondary antibody incubations were performed for 1 hr in blocking buffer ( 5% bovine serum albumin and 0 . 5% Tween-20 in 1× phosphate-buffered saline [PBS] ) at room temperature . Nuclei were counterstained with hematoxylin . For each sample , 100 µl of 1 mg/ml Matrigel in serum free-cold MEM media was aliquoted into the upper chamber of 24-well transwell plate ( Corning , Corning , NY ) and incubated at 37°C for at least 4–5 hours for adequate gelling . The cells were washed and suspended in serum free medium at a 1×106 cells/ml concentration . One hundred µl of cell suspension was transferred onto the upper chamber containing the Matrigel layer . The lower chamber of the transwell was filled with 600 µl of complete media containing 10 µg/ml fibronectin as an adhesive substrate . After 24 hours , the cells were fixed with 4% formaldehyde for 15 minutes , rinsed with PBS , and stained with 0 . 2% crystal violet for 10 minutes . The crystal violet was rinsed with excess ddH2O and the top chamber containing the Matrigel was thoroughly cleansed with Q-tips and the invaded cells were photographed with a light microscope . For quantification , the cells on the top and bottom of the chamber are collected using trypsin and counted using the culture counter . Each sample was counted 3 times and each experiment was repeated independently 3 times . 3D culture on basement membrane was performed as described previously [21] . Assay medium ( DFCI-1 medium with 2% growth factor-reduced Matrigel ) with or without drugs was replaced every 4 days , and cells were cultured for 15 days . Indirect immunofluorescence analysis of 3D cultures was performed as previously described with minor modifications [56] . Cells cultured in 8-well chamber slides were fixed with 2% paraformaldehyde at room temperature for 20 min , permeabilized with 1% Triton X-100 in PBS for 20 min , washed thrice with PBS/glycine buffer ( 130 mM NaCl , 7 mM Na2HPO4 , 3 . 5 mM NaH2PO4 , and 100 mM glycine ) , and blocked with IF buffer ( 130 mM NaCl , 7 mM Na2HPO4 , 3 . 5 mM NaH2PO4 , 7 . 7 mM NaN3 , 0 . 1% bovine serum albumin , 0 . 2% Triton X-100 , and 0 . 05% Tween-20 ) plus 10% goat serum for 1 hr at room temperature . Primary antibodies ( laminin V and α6-integrin [Chemicon , Billerica , MA] , GM-130 [BD Biosciences] , E-cadherin [BD Biosciences] , and Ki67 [Abcam , Cambridge , MA] ) were incubated in IF buffer at 1∶200 dilution overnight at 4°C . The cells were incubated with Alexa fluor-conjugated rabbit ( 488 ) , mouse ( 594 ) , or rat ( 680 ) secondary antibodies ( Molecular Probes , Carlsbad , CA ) , counterstained with 4 , 6-diamidino-2-phenylindole ( DAPI ) ( Sigma , St . Louis , MO ) for 15 min at room temperature , and mounted with antifade solution ( Molecular Probes ) . Confocal microscopy was performed at room temperature using an Olympus FV300 laser scanning confocal microscope ( Olympus America , Inc . , Center Valley , PA ) at 40× magnification , and images were processed using Adobe Photoshop ( Version 11 . 0 . 2 ) . For quantification , the diameter of each acinus was measured , and unpaired Student t test was used for statistical analysis . Cell lysates were prepared and subjected to Western blot analysis as described previously [12] . To obtain lysates from acini , acini were washed once with cold PBS , scraped , collected , and washed twice with cold PBS . Cell recovery solution ( BD Biosciences ) was added to the Matrigel/acini mixture at 1∶1 volume , and cells were incubated on ice for 1 hr , washed with PBS , and lysed as described previously [13] The protein blots were incubated with primary antibodies ( cyclin E , vinculin [Santa Cruz] , β-actin [Chemicon] , BIM [StressGen Biotechnologies , Victoria , British Columbia] , FAK , b-Raf , ERK1/2 , pERK1/2 ( T202/Y204 ) , pMEK1/2 ( S217/221 ) , pS6 ( S235/236 ) , mTOR , eIF4E , Akt , pAkt ( T308 ) , and pRb ( S807/811 ) [Cell Signaling Technology , Danvers , MA] ) at 4°C with gentle shaking overnight . Kinase assay with histone H1 and GST-Rb as cyclin E substrates was performed as described previously [13] . The RPPA approach was performed as previously described [34] . Cellular proteins that were prepared as described for western blotting were denatured by boiling in 1% SDS ( with beta-mercaptoethanol ) and diluted in five 2-fold serial dilutions in dilution buffer ( lysis buffer containing 1% SDS ) . Serial diluted lysates were arrayed on nitrocellulose-coated slides ( Grace Biolab ) using Aushon 2470 Arrayer ( Aushon BioSystems ) . Each sample was robotically printed in 5 fold serial dilutions on multiple slides including positive and negative controls prepared from mixed cell lysates or dilution buffer , respectively , as well as multiple cell lines incubated with and without growth factors to provide dynamic range . Each slide was probed with a validated primary antibody plus a biotin-conjugated secondary antibody . Antibody targets were selected as being relevant to breast cancer through a literature review . Antibodies where then obtained and validated against each potential target . Only antibodies with a Pearson correlation coefficient between RPPA and western blotting of greater than 0 . 7 were used in reverse phase protein array study . Antibodies with a single or dominant band on western blotting were further assessed by direct comparison to RPPA using cell lines with differential protein expression or modulated with ligands/inhibitors or siRNA for phospho- or structural proteins , respectively . The signal obtained was amplified using a Dako Cytomation-catalyzed system ( Dako ) and visualized by DAB colorimetric reaction . The slides were scanned , analyzed , and quantified using a customerized-software Microvigene ( VigeneTech Inc . ) to generate spot intensity . Each dilution curve was fitted with a logistic model ( “Supercurve Fitting” developed by the Department of Bioinformatics and Computational Biology in MD Anderson Cancer Center , “http://bioinformatics . mdanderson . org/OOMPA” ) . This fits a single curve using all the samples ( i . e . , dilution series ) on a slide with the signal intensity as the response variable and the dilution steps are independent variable . The fitted curve is plotted with the signal intensities – both observed and fitted - on the y-axis and the log2-concentration of proteins on the x-axis for diagnostic purposes . The protein concentrations of each set of slides were then normalized by median polish , which was corrected across samples by the linear expression values using the median expression levels of all antibody experiments to calculate a loading correction factor for each sample . The clinical data from 267 breast cancer patients used in this study were reported previously [16] . Each patient had received a diagnosis of breast cancer between 1990 and 1995 at 1 of 12 hospitals in the Chicago area . The study was approved by the institutional review board of the Wadsworth Center ( Albany , NY ) . In the study reported herein , we used tissue samples from a portion of these for RPPA analysis to investigate large-scale protein expression pattern . Each cell culture experiment was performed at least three times . Continuous outcomes were summarized with means and standard deviations . Comparisons among groups were analyzed by two-sided t test and Wilcoxon rank-sum test . These analyses were performed using SPSS software , version 12 . 0 . The differences in tumor incidence between groups ( for Table 1 and Table 2 ) were compared using Fisher's exact test . The analyses were performed using SAS ( version 9 . 2 ) . For analysis of the RPPA data , the SuperCurve quantified concentration of the proteins were natural log transformed . The differences in protein expression levels between high- and low LMW-E or EL expressed patient groups were assessed using Wilcoxon Rank Sum test . For the hierarchical clustering of the 71 proteins common between the 2D culture , 3D culture and patient data sets , the data for each protein in each data set were standardized ( subtraction of mean then divided by standard deviation ) separately , and then the standardized data were combined . The combined data set contains 93 cell line samples from 2D and 3D each , with three technical replicates for each of the 31 unique samples , and 276 breast tumor patient samples . Disease-specific survival ( DSS ) was calculated from the date of surgical resection of the primary tumor to the date of death or last follow-up . Data for patients who died from causes other than breast cancer were censored at the time of death . DSS curves were computed by the Kaplan-Meier method [57] . Bivariate analyses of DSS in patients with high LMW-E expression according to levels of FAK , BIM , total Akt , and pAkt ( T308 ) were performed with the use of a two-sided log-rank test [58] . Kaplan-Meier survival curves were calculated for the different cyclin E groups , and the log-rank test was used to compare the disease-free survival among groups . Stata statistical software ( SE 9 , StataCorp LP , College Station , TX ) was used for these statistical analyses . All P values were 2 tailed , and p<0 . 05 was considered significant . | Effective cancer treatment should include targeting not only drivers of tumorigenicity but also the downstream signaling pathways that these drivers activate . Special attention has to be given to the model systems that identify these targets and interrogating if these targets are poor prognostic indicators in patients . Using cell lines cultured on plastic and extracellular matrix ( Matrigel ) and comparing their proteomic profiles to breast cancer tumor samples , we demonstrated that overexpression of LMW-E is concomitant with activation of the b-Raf-ERK1/2-mTOR pathway . Using mouse models , we show that induction of LMW-E is sufficient to induce mammary tumor development in vivo . Next , cells established from the tumors were treated with combination therapy targeting the LMW-E/CDK2 complex and the b-Raf-ERK1/2-mTOR pathway . Results revealed that this combination therapy effectively inhibited the altered proliferation of these cells . Most significantly , we showed that breast cancer patients whose tumors overexpress both LMW-E and different components of the b-Raf-ERK1/2-mTOR pathway have the worst prognosis . In summary , through the use of multiple in vitro and in vivo model systems and translating the findings to clinical specimens , we have identified a novel targeted therapy in breast cancer patients whose tumors overexpress LMW-E . |
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Functional characterization of causal variants present on risk haplotypes identified through genome-wide association studies ( GWAS ) is a primary objective of human genetics . In this report , we evaluate the function of a pair of tandem polymorphic dinucleotides , 42 kb downstream of the promoter of TNFAIP3 , ( rs148314165 , rs200820567 , collectively referred to as TT>A ) recently nominated as causal variants responsible for genetic association of systemic lupus erythematosus ( SLE ) with tumor necrosis factor alpha inducible protein 3 ( TNFAIP3 ) . TNFAIP3 encodes the ubiquitin-editing enzyme , A20 , a key negative regulator of NF-κB signaling . A20 expression is reduced in subjects carrying the TT>A risk alleles; however , the underlying functional mechanism by which this occurs is unclear . We used a combination of electrophoretic mobility shift assays ( EMSA ) , mass spectrometry ( MS ) , reporter assays , chromatin immunoprecipitation-PCR ( ChIP-PCR ) and chromosome conformation capture ( 3C ) EBV transformed lymphoblastoid cell lines ( LCL ) from individuals carrying risk and non-risk TNFAIP3 haplotypes to characterize the effect of TT>A on A20 expression . Our results demonstrate that the TT>A variants reside in an enhancer element that binds NF-κB and SATB1 enabling physical interaction of the enhancer with the TNFAIP3 promoter through long-range DNA looping . Impaired binding of NF-κB to the TT>A risk alleles or knockdown of SATB1 expression by shRNA , inhibits the looping interaction resulting in reduced A20 expression . Together , these data reveal a novel mechanism of TNFAIP3 transcriptional regulation and establish the functional basis by which the TT>A risk variants attenuate A20 expression through inefficient delivery of NF-κB to the TNFAIP3 promoter . These results provide critical functional evidence supporting a direct causal role for TT>A in the genetic predisposition to SLE .
TNFAIP3 encodes A20 , an ubiquitin-editing enzyme with a key role in negatively regulating NF-κB pathway activity downstream of activating cell surface receptors [1]–[4] . Murine models have been illustrative in demonstrating the importance of A20 in limiting immune responses . For example , mice globally deficient for A20 experience widespread organ inflammation and perinatal death [2] . Mice with A20 deficiency localized to B lymphocytes demonstrate enhanced responses to toll-like receptor , B cell receptor and CD40 receptor stimulation , elevated numbers of plasma and germinal center B cells and immune complex deposition in the kidneys [5]–[7] . Mice with A20 deficient dendritic cells excrete high levels of proinflammatory cytokines and spontaneously activate lymphoid and myeloid cells resulting in lymphadenopathy and splenomegaly [8] . In humans , at least 8 GWAS in 5 autoimmune diseases have reported genome wide significant associations with variants in the vicinity of TNFAIP3 and others have reported suggestive association [9]–[18] . Lymphoid malignancies such as diffuse large B-cell lymphoma , marginal zone lymphoma , follicular lymphoma , MALT lymphoma and Hodgkin lymphoma , often carry deletions or inactivating point mutations in TNFAIP3 suggesting a role for TNFAIP3 as a tumor suppressor [19]–[23] . These observations , in both animal models and human subjects , highlight the need to clarify how SLE associated genetic variants in the TNFAIP3 locus may influence the maintenance of immune homeostasis toward the development of autoimmunity . SLE is a severe autoimmune disease characterized by immune complex mediated inflammation of target organs ( kidney , brain , skin ) , high titer autoantibody production and dysregulated interferon pathway activity . There is no curative therapy for SLE . Patients are most often treated with broad-spectrum immunosuppressive agents , the side effects of which contribute to the already considerable morbidity of the disease . Ongoing efforts to better understand the genetic , immunologic and environmental factors that contribute to SLE holds promise for future advances in the prognosis , diagnosis and therapy . To that end , genetic studies have convincingly identified over 30 loci associated with SLE [24] , [25] . However , for most loci , the variants responsible for association ( causal variants ) still await identification . Of the three known independent genetic effects reported in the TNFAIP3 locus , the most consistently replicated is a ∼100 kb risk haplotype that spans the TNFAIP3 gene body [9] , [15] , [17] , [26] . This risk haplotype has been observed in SLE subjects of both European and Asian ancestry but has not been convincingly detected in SLE subjects of African origin [27] . Genetic studies in other autoimmune diseases including systemic sclerosis , Sjogren's syndrome and rheumatoid arthritis indicate that they likely share this risk haplotype with SLE [28]–[31] . A coding variant , rs2230926 , which results in a phenylalanine to cysteine substitution at position 127 in exon 3 of TNFAIP3 , has been used as a marker of the TNFAIP3 risk haplotype in genetic studies . Even though the risk allele ( G ) of rs2230926 is associated with decreased potency for inhibiting NF-κB signaling compared to the nonrisk ( T ) allele using in-vitro transfection assays [26] , the evidence for this polymorphism as a causal variant is not convincing . The primary evidence supporting this conclusion comes from the observation that the G allele of rs2230926 has a minor allele frequency of 30–40% in African American SLE and yet no significant association with SLE is observed in this population [27] . Therefore , while this variant may alter A20 function , is not likely a causal variant . We recently proposed a pair of tandem polymorphic dinucleotides ( rs148314165 , rs200820567 ) located in the genomic DNA 30 kb telomeric of TNFAIP3 to be the most likely candidate variants responsible for association with SLE based on transpopulation differences in LD between in associated ( European and Asian ) and non-associated ( African American ) populations and bioinformatic annotation demonstrating that these variants are located in an evolutionarily conserved region of regulatory significance [27] . The TT>A risk alleles are carried on a risk haplotype that is associated with hypomorphic expression of TNFAIP3 transcripts and A20 protein [27] . The mechanism by which the TT>A risk variants might influence the hypomorphic expression of A20 is unknown and serves as key evidence for assigning causality . In this study , we demonstrate that the TT>A variants are located in a functional enhancer element that binds NF-κB and SATB1 and the risk alleles of TT>A directly lead to reduced expression of A20 by their inability to effectively bind and deliver NF-κB to the TNFAIP3 promoter through long-range DNA looping .
The variants rs148314165 ( -T ) and rs200820567 ( T>A ) , referred to as TT>A , are located in a conserved region of genomic DNA that exhibits open chromatin , epigenetic marks of active enhancers and interaction with several transcription factors including NF-κB ( Figure S1 ) . Since the TNFAIP3 gene product , A20 , functions to restrict NF-κB signaling , we focused on characterizing the binding of NF-κB subunits to the region . We used the UniProbe database [32] to evaluate the region defined by the ENCODE NF-κB binding signal ( chr6:138 , 229 , 889–138 , 230 , 230 , hg19 ) for the presence of NF-κB binding motifs . Three NF-κB sites were identified , with the first site incorporating the TT>A variant ( Figure S2A ) . Our previous work [27] used an EMSA probe that included both the TT>A site and the second NF-κB site , so we redesigned the probes to include only the TT>A site in order to isolate the contribution to the EMSA signal to this site . EMSA demonstrated stimulus enhanced binding of a nuclear protein complex to the 40 bp non-risk ( TT ) probe using nuclear extracts from EBV transformed B cells ( Figure 1A ) . Complex formation was reduced when the risk allele ( -A ) was introduced into the probe sequence , suggesting that the risk allele alters the binding affinity of this complex ( Figure 1A ) . Super shift experiments demonstrated NF-κB subunits NFKB1 ( p50 ) , cREL , RELA ( p65 ) in the nuclear protein complex ( Figure 1A ) . Similar results were also observed using nuclear extracts from the monocytoid cell line , THP1 ( Figure S3 ) . The specificity of our EMSA probes was confirmed by competition with unlabeled probe ( Figure S4 ) . To validate the EMSA results using an orthogonal approach , we performed chromatin-immunoprecipitation ( ChIP ) followed by quantitative PCR using EBV cell lines carrying all three genotype combinations ( TT/TT , TT/-A , -A/-A ) at the TT>A polymorphic site . We observed significantly lower enrichment as a percentage of input DNA from cell lines carrying the risk allele ( TT/-A or -A/-A ) for all three NF-κB proteins ( p<0 . 05 ) ( Figure 1B ) . Control experiments using antibodies to acetyl-histone H3 ( positive control ) or rabbit isotype control IgG ( negative control ) demonstrated no specific differences in enrichment as expected ( Figure S5 ) . Together with the EMSA data , these data confirm that NF-κB subunits bind to this regulatory element following cell stimulation and that this binding is impaired by the presence of the risk ( -A ) variant . Given the substantial distance of the TT>A polymorphism from the TNFAIP3 promoter , the stimulus dependent recruitment of NF-κB subunits to the site and ENCODE histone marks , we hypothesized that this element may function as an enhancer . To test this hypothesis , we cloned the non-risk ( TT ) or risk ( -A ) variants and approximately 168 bases of flanking sequence ( chr6:138 , 229 , 810–138 , 230 , 149; hg19 ) that included the two NF-κB sites downstream of TT>A ( Figure S2A ) into a minimal TK promoter construct . Plasmids were transfected into HEK293T or THP1 cells followed by stimulation with PMA/ionomycin ( PI ) ( HEK293T and THP1 ) or LPS ( THP1 only ) . Compared to the minimal TK promoter alone , we observed a significant increase in luciferase activity following stimulation with PI or LPS for both non-risk ( TT ) and risk ( -A ) plasmids suggesting that this regulatory element functions in a manner consistent with an enhancer ( Figure 2 ) . However , the risk ( -A ) construct produced significantly lower levels of luciferase activity compared with the non-risk ( TT ) construct ( Figure 2 ) . Similar differences were also observed using constructs lacking the two downstream NF-κB sites ( Figure S6 ) . These results demonstrate that the regulatory element containing the TT>A variant functions as an enhancer and the presence of the risk ( -A ) allele , which binds NF-κB with reduced affinity , impairs enhancer function . To identify other proteins that interact with the TT>A enhancer we affinity purified proteins bound to biotinylated probes used in our EMSA experiments followed by gel purification and mass spectroscopy ( MS ) analyses . We identified a band that migrated between 80 and 100 kD pulled down by both the risk ( -A ) and non-risk ( TT ) probes that was not observed using a control probe with a scrambled sequence ( Figure S7A ) . MS results from three separate experiments identified this protein as special AT-rich binding protein 1 ( SATB1 ) ( Figure S7B ) . Inspection of the probe sequences revealed a SATB1 binding motif ( AATAA ) adjacent to the NF-κB ( Figure S2B ) . We confirmed the presence of SATB1 by western blotting using eluted protein from affinity purification ( Figure S7C ) and EMSA supershift ( Figure S3 ) . No differences in affinity for the risk versus non-risk probes were observed suggesting that the TT>A polymorphism may not directly influence the binding affinity of SATB1 . A primary function of SATB1 is to facilitate long-range gene transcription through chromatin remodeling and DNA looping [33] , [34] . To determine if long-range DNA looping occurs between the TT>A enhancer and the TNFAIP3 promoter , we performed chromatin conformation capture ( 3C ) using a series of PCR primers ( Figure 3A ) distributed across key regulatory elements in the genomic sequence upstream of TNFAIP3 and in the TNFAIP3 promoter and gene body ( Figure 3A ) . We detected interaction from three regions of TNFAIP3 ( Figure 3B ) . The largest and most reproducible relative crosslinking frequency ( RCF ) was located in the TNFAIP3 promoter in a region enriched for transcription factor binding sites . Importantly , the peak RCF detected by primer 8 , is near a region of the promoter previously reported to bind NF-κB and stimulate transcription of TNFAIP3 [35] . The second highest RCF ( primer 16 ) was located in the second intron , again in a region enriched in transcription factor binding sites but was approximately 10 fold weaker than the promoter signal . The third and weakest RCF ( primer 24 ) was located in the 3′ untranslated region and was half the magnitude of the second signal . To verify these results we tested other cell lines derived from a variety of lineages . The RCF in the promoter of TNFAIP3 was reproducibly observed in all cell types evaluated ( Figure S8 ) . Stimulating THP1 cells with LPS for 2 hours produced a significant increase in the RCF detected between the TT>A enhancer and the TNFAIP3 promoter and was accompanied by a concomitant increase in A20 protein and phospho-IκBα expression ( Figure 3C ) . These results reveal a novel mechanism of transcriptional regulation whereby the TT>A polymorphic enhancer delivers an NF-κB payload to the TNFAIP3 promoter leading to increased expression of A20 . We next tested whether the RCF between the TT>A enhancer and TNFAIP3 promoter was dependent on expression of SATB1 ( Figure 4 ) . SATB1 expression was inhibited using a SATB1 specific shRNA construct transfected into HEK293T cells followed by 3C ( Figure 4B ) . Results from these experiments demonstrated a significant reduction in the RCF between the TT>A region and the TNFAIP3 promoter ( Figure 4A ) with inhibition of SATB1 expression , accompanied by a reduction in A20 protein expression ( Figure 4B ) . These data suggest that the TT>A polymorphism modulates TNFAIP3 transcription through a SATB1 mediated long range looping mechanism and that interfering with looping leads reduced TNFAIP3 transcription and A20 protein expression . Having established that the TT>A enhancer interacts with the TNFAIP3 promoter and that inhibition of looping results in reduced A20 expression , we wanted to determine if the autoimmunity associated risk allele ( -A ) influenced the interaction frequency . Evaluation of crosslinking frequencies in resting EBV transformed B cell lines demonstrated a significantly higher RCF in homozygous ( TT/TT ) non-risk cells compared to homozygous risk cells ( -A/-A ) ( Figure 5A ) . This was accompanied by reduced expression of A20 ( Figure 5B ) and increased basal NF-κB pathway activity as measured by IκBα phosphorylation in homozygous risk cell lines ( Figure 5C ) . To validate these results and to reduce potential bias in the detection of the RCF due to the multi-step 3C protocol , we developed a sequencing-based read-counting allele specific 3C assay that tallies the number of ligation products occurring from each allele in heterozygote ( TT/-A ) cell lines . Using this method , we again detected significantly fewer looping interactions produced from the risk allele ( -A ) compared with the non-risk ( TT ) allele thus confirming our results in homozygous cell lines ( Figure 5D ) . These results suggest that reduced binding of NF-κB to the TT>A risk allele results in less interaction with the TNFAIP3 promoter and lower expression of A20 protein .
In this report , we describe the functional characterization of the TT>A variants that are associated with human SLE in the region of TNFAIP3 on chromosome 6q23 for which previous genetic and bioinformatics analyses suggest they are likely to be causal variants . Identification and functional characterization of causal variants responsible for disease predisposition is a fundamental goal of human genetics . Even though GWAS have identified thousands of variants reproducibly associated with hundreds of complex genetic diseases [36] only a small fraction of these variants are presumed to be causal . This is due to the presence of linkage disequilibrium ( LD ) in the human genome , which streamlines GWAS discovery but renders causal variants statistically indistinguishable from noncausal variants on the same haplotype . Isolating causal from noncausal variants is a formidable task and most often involves a combination of genetic ( finemapping , resequencing , imputation ) and bioinformatic ( variant annotation , modeling building ) approaches . Typically , the end result of these studies is a prioritized list of variants that must be systematically evaluated for allelic differences in biological function . Our results provide a functional explanation for the genetic association between SLE and the minor alleles rs148314165 and rs200820567 and compelling evidence that these variants are causal variants for this risk haplotype . The TNFAIP3 locus exhibits complex genetic architecture with multiple variants demonstrating significant genetic associations across multiple autoimmune phenotypes . Our study focuses specifically on the ∼100 kb risk haplotype that spans the TNFAIP3 gene body first identified in SLE . These variants likely also explain the association signals detected in other autoimmune diseases testing variants in strong LD with rs148314165 and rs200820567 in subjects of European or Asian background including the coding variant rs2230296 . The TT>A risk haplotype is , however , distinct from a TNFAIP3 risk effect first reported in psoriasis and marked by SNP rs610604 [37] located in the sixth intron of TNFAIP3 . The correlation in European and Asian populations between rs610604 and rs7749323 , a perfect proxy for TT>A , is low ( r2<0 . 1 ) , indicating that psoriasis is likely associated with different causal variants . Our results also do not explain both risk and protective associations located ∼200 kb upstream of TNFAIP3 reported most robustly in rheumatoid arthritis [18] , [38] and celiac disease [39] but also in SLE and inflammatory bowel disease . It is possible that variants associated with these diseases will impact other uncharacterized enhancers in a manner similar to that described here for the TT>A enhancer . Alternatively , a long non-coding RNA encoded on the negative strand adjacent to TNFAIP3 ( AK124173 ) shares the same promoter region and may influence TNFAIP3 expression or translation through as yet to be defined mechanisms . Despite the uncertainties , further genetic and functional characterization in appropriate disease subjects will be required to clarify the causal variants responsible for these associations and the mechanisms of TNFAIP3 function that they govern . In summary , these results reveal a novel mechanism of TNFAIP3 transcriptional regulation whereby the TT>A enhancer element delivers NF-κB to the TNFAIP3 promoter through long-range DNA looping thus stimulating A20 protein expression . The SLE associate TT>A risk alleles , through their inability to effectively deliver NF-κB to the TNFAIP3 promoter , impair A20 expression leading to enhanced NF-κB pathway activity and predisposition to autoimmune disease . Clarifying the functional basis by which DNA sequence variants such as these perturb cellular pathways toward the disease state will be crucial in translating GWAS discoveries into knowledge that can improve human health .
Written informed consent was obtained from all study participants . The overall study was approved by the IRB of the Oklahoma Medical Research Foundation ( OMRF ) . THP-1 , U937 , Jurkat and Daudi were purchased from ATCC . EBV-transformed B cell lines were obtained from the Lupus Family Registry and Repository ( OMRF ) with IRB approval . EBV cell lines were selected using genotype data corresponding to the TT>A variant proxy marker rs7749323 . Cell lines were maintained in RPMI 1640 medium supplemented with 10% FBS , penicillin , streptomycin , L-glutamine and 55 µM β-mercaptoethanol . Lipopolysaccharide ( LPS ) , Phorbol myristate acetate and Ionomycin ( P/I ) were purchased from Sigma-Aldrich . The following antibodies were used in this study: Anti-phospho-IκBα , anti-SATB1 , anti-β-actin and anti-GAPDH ( Cell signaling Inc . , Danvers , MA ) , Anti-A20 antibody ( Ebioscience Inc . , San Diego , CA ) , anti-p50 , anti-p65 , and anti-cRel antibodies ( GeneTex Inc . , Atlanta , GA ) . 40 base pair ( non-risk ) or 39 bp ( risk ) DNA probes were synthesized and end-labeled with ( γ-32P ) adenosine triphosphate ( MP Biomedicals Int . ) using T4 polynucleotide kinase ( Invitrogen , Grand Island , NY ) . Nuclear protein extracts were prepared from cells stimulated with LPS ( 1 ug/mL ) or P/I ( 50 ng/ml , 500 ng/ml ) for 2 hours and incubated for 25 min at 37°C with labeled probes in binding buffer ( 1 ug poly dI-dC , 20 mM HEPES , 10% Glycerol , 100 mM KCl , and 0 . 2 mM EDTA , pH 7 . 9 ) . DNA-protein complexes were resolved on non-denaturing acrylamide gels . Supershift assays were performed by adding 80–100 ug of anti-p50 , p65 , c-Rel antibodies or Rabbit IgG isotype control antibody ( Alpha Diagnostic Int . Inc . ) to the mixture followed by incubation at room temperature for 15 min prior to adding labeled probe . ChIP assays were performed using the Magna ChIP A kit ( Millipore , Billerica , CA ) according to the manufacturer's recommendations . In brief , 1×107 EBV transformed B cells were treated with P/I ( 50 ug/ml , 500 ng/ml ) in 10 ml growth medium for 2 hours and were cross-linked with 1% formaldehyde . Nuclei were isolated and sonicated in 500 ul of lysis buffer with a Covaris S1 sonicator ( Woburn , MA ) . Fifty microliters of chromatin-protein complexes were immunoprecipitated overnight at 4°C by mild agitation with antibodies specific for p50 , p65 , cRel , acetyl-histone H3 ( positive control ) ( Millipore , Billerica , CA ) , or normal rabbit IgG ( negative control ) ( Millipore , Billerica , CA ) . DNA was eluted from the immunoprecipitated chromatin complexes , reverse-crosslinked , purified by Agencourt AMPure XP beads ( Beckman Coulter , Brea , CA ) and subjected to real-time PCR analysis using RT2 SYBR Green ( Qiagen , Germantown , MD ) and primers neighboring TT>A polymorphic region ( Table S1 ) . We cloned 340 bp ( non-risk ) or 339 bp ( risk ) of DNA sequence surrounding the TT>A polymorphism into a minimal promoter luciferase plasmid , pGLuc-mini-TK ( New England BioLab , Ipswich , MA ) . Each plasmid was transiently co-transfected using FuGene HD ( VWR , Radnor , PA ) with a pGL3-promoter control plasmid for calculation of transfection efficiency and normalization ( gift from Dr . Carol Webb , OMRF ) . Luciferase assays were performed in HEK293T and THP1 cells . Twenty fours hours post transfection , cells were treated with 1 ug/ml LPS for 24 hours or 50 ng/ml PMA/500 ng/ml ionomycin for 48 hours . To assay enhancer activity , Gaussia luciferase was analyzed from the cell culture media using BioLux GLuc assay kit ( New England BioLab ) . To measure transfection efficiency , cells were lysed and firefly luciferase activity was measured using the Luciferase Assay System ( Promega , Madison , WI ) . We screened for other proteins that bind to the EMSA probes by biotinylating the oligonucleotides used for EMSA and a scrambled oligonucleotide that served as a negative control ( Table S1 ) . Streptavidin magnetic beads ( 200 ug; Dynalbeads M-280 Streptavidin; Invitrogen ) were subjected to two rounds of blocking with 1% BSA in PBS for 15 min and washing with PBS containing 1M NaCl and TE buffer . Biotinlyated oligonucleotides were linked to half the amount of streptavidin beads by incubating for 30 min at room temperature in TE buffer followed by washing with TE buffer . To pre-clear the nuclear extracts of material that could bind non-specifically to the biotinylated oligonucleotides , we incubated the other half of the BSA-blocked beads with 100 ug of nuclear extract in binding buffer ( 250 mM NaCl , 50 mM Tris Cl , 50% glycerol , 2 . 5 mM DTT , 2 . 5 mM EDTA , pH 7 . 6 ) containing 15 ng/ul poly dI:dC ( Sigma-Aldrich ) , 0 . 5 ug/ml BSA , and 0 . 1% NP40 for 30 min on ice . We then incubated the pre-cleared nuclear extracts with the oligonucleotide-linked Streptavidin beads for 30 min in 37°C water bath with gentle shaking every 5 min , and subsequently washed the products with binding buffer containing 0 . 1% NP40 three times . The proteins were eluted in 50 ul of 0 . 2% SDS sample buffer by boiling for 5 min and were then resolved on a Nu-PAGE 4%–12% Bis-Tris gel followed by silver nitrate staining . Mass spectrometry analysis was performed using a ThermoScientific LTQ-XL mass spectrometer coupled to an Eksigent splitless nanoflow HPLC system . Bands of interest were excised from the silver nitrate stained Bis-tris gel and destained with Farmer's reducer ( 50 mM sodium thiosulfate , 15 mM potassium ferricyanide ) . The proteins were reduced with dithiothreitol , alkylated with iodoacetamide , and digested with trypsin . Samples were injected onto a 10 cm×75 mm inner diameter capillary column packed with Phenomenex Jupiter C18 reverse phase resin . The peptides were eluted into the mass spectrometer at a flow rate of 175 nL/min . The mass spectrometer was operated in a data-dependent mode acquiring one mass spectrum and four CID spectra per cycle . Data were analyzed by searching all spectra that were acquired against the human RefSeq databases using the program Mascot ( Matrix Science Inc . Boston , MA ) . Minimum identification criteria require two peptides with ion scores greater than 50 that are then verified by manual inspection . Western blots were performed to verify the identities of proteins . We performed the 3C-qPCR assays as described [40] with minor modifications . All cell lines were cultured and harvested in log phase growth . We incubated 1×107 cells in 10 ml of RPMI-1640 culture medium with 1% buffered formaldehyde at room temperature for 10 min . Crosslinking was stopped by adding 1 . 425 ml of ice cold 1 M glycine . Cells were lysed in 5 ml lysis buffer ( 10 mM Tris-HCl , pH 7 . 5; 10 mM NaCl; 5 mM MgCl2; 0 . 1 mM EGTA; Protease and Phosphatase Inhibitor Cocktail Tablets from Roche Applied Science ) for 10 min at 4°C . The nuclei were suspended in 500 µl 1 . 2× restriction buffer [1× Buffer 4; 1× bovine serum albumin ( BSA ) , New England BioLabs ( NEB ) Inc . , Ipswich , MA] containing 0 . 3% SDS and incubated at 37°C for 1 h with shaking at 900 rpm . The SDS was then sequestered by adding Triton X-100 to 2% and incubating at 37°C for another hour with shaking . One hundred units of the restriction enzyme NlaIII ( NEB ) were added for a 24 h digestion . The reaction was stopped by adding SDS to 1 . 6% and incubating at 65°C for 30 min . The digested chromatin was diluted in 6 . 125 ml of 1 . 15× ligation buffer ( NEB ) . Residual SDS was sequestered by adding Triton X-100 to 2% and incubating at 37°C for 1 h with shaking . The reaction was then cooled to 16°C and 2000 U of T4 DNA ligase ( NEB ) were added . After ligation overnight , the chromatin mixture was incubated with 100 mg/ml proteinase K at 65°C overnight to reverse crosslinks . RNA was removed by RNase A ( 0 . 5 mg/ml ) treatment for 60 min at 37°C . The 3C sample was purified by phenol-chloroform extraction and then amplified by PCR using specific primers listed in Table S1 . An enhancer constant primer was designed according to the negative strand of DNA 20 bp downstream of the TT>A polymorphism . A TaqMan probe was designed based on the positive strand DNA sequence located 10 bp downstream of the first NlaIII enzymatic digestion site and 10 bp upstream of the TT>A polymorphism , hybridizing to the opposite strand as compared to the enhancer constant PCR primer . Multiple primers were designed as close as possible to the NlaIII digestion sites in TNFAIP3 gene region . The primer/probe configurations guarantees that the probe only signals upon extension of the primer across the ligated junction . TaqMan quantitative real-time PCR was performed with TaqMan Universal PCR Master Mix according to the manufacturer's protocol using the following cycling conditions: 50°C for 2 min; 95°C for 10 min; and 45 cycles of 15 s at 95°C and 60 s at 60°C . PCR products were purified using a QIAGEN quick gel purification kit and the sequence of each chimeric DNA was determined by Sanger sequencing . To normalize primer efficiency , control PCR templates were generated by digestion and random ligation of bacterial artificial chromosomes containing TNFAIP3 gene and the TT>A enhancer ( clone RP11-76M10 , Empire Genomics , Inc , New York , USA ) [41] . A total of 5 µg of BAC clone was digested with NlaIII and then ligated with T4 ligase . The paired primers/probe designed for 3C-qPCR assay were tested on the random ligation product that contains all possible chimeric DNA ligation products in equal molar concentrations . The SATB1 shRNA and non-silencing shRNA constructs were purchased from SABiosciences , Valencia , CA . HEK293T cells were transiently transfected with SATB1 shRNA construct or control plasmids using the calcium phosphate method . The extent of shRNA-mediated inhibition of SATB1 and its effect on SATB1 expression were evaluated by western blot analysis with anti-SATB1 antibody . Protein expression of A20 was determined using Western blot with anti-A20 antibody . 3C was performed on EBV-transformed B cell lines heterozygous for the TT>A variant as previously described . Chimeric DNA generated by ligation was amplified by PCR and subject to gel purification with a DNA purification kit ( QIAGEN Inc . , Valencia , CA ) . Sequencing libraries were constructed using the Truseq DNA LT Sample Prep Kit v2 as per the manufacturer's protocol ( Illumina , San Diego , CA ) . Sequencing of indexed library pools was performed on an Illumina MiSeq instrument with 100 bp , paired-end reads . Reads were mapped to the human reference genome ( hg19 ) using the Burrows Wheeler Aligner ( BWA ) ( Li and Durbin , 2009 ) . The read-count from each allele of the 3C DNA was normalized to the read-count from each allele of genomic DNA ( Table S2 ) . A paired t-test was used to compare the difference in crosslinking frequencies occurring from two alleles within each individual . | A key objective of human genetics is the identification and characterization of variants responsible for association with complex diseases . A pair of single nucleotide polymorphisms ( rs148314165 , rs200820567 ) 42 kb downstream from the promoter of TNFAIP3 , have been proposed as the variants responsible for association with systemic lupus erythematosus based on comprehensive genetic and bioinformatic analyses . TNFAIP3 encodes for the ubiquitin-editing enzyme , A20 , which plays a central role in maintaining immune system homeostasis through restriction of NF-κB signaling . Cells that carry this risk haplotype express low levels of TNFAIP3 compared to cells carrying the nonrisk haplotype . How the risk alleles of rs148314165 and rs200820567 might influence low TNFAIP3 expression is unknown . In this paper , we demonstrate that these variants reside in an enhancer element that binds NF-κB and SATB1 enabling the interaction of the enhancer with the TNFAIP3 promoter through long-range DNA looping . Impaired binding of NF-κB directly to the risk alleles or shRNA-mediated knockdown of SATB1 inhibits interaction of the enhancer with the TNFAIP3 promoter resulting in reduced A20 expression . These results clarify the functional mechanism by which rs148314165 and rs200820567 attenuate A20 expression and support a causal role for these variants in the predisposition to autoimmune disease . |
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Defining endpoints for trachoma programs can be a challenge as clinical signs of infection may persist in the absence of detectable bacteria . Antibody-based tests may provide an alternative testing strategy for surveillance during terminal phases of the program . Antibody-based assays , in particular ELISAs , have been shown to be useful to document C . trachomatis genital infections , but have not been explored extensively for ocular C . trachomatis infections . An antibody-based multiplex assay was used to test two C . trachomatis antigens , pgp3 and CT694 , for detection of trachoma antibodies in bloodspots from Tanzanian children ( n = 160 ) collected after multiple rounds of mass azithromycin treatment . Using samples from C . trachomatis-positive ( by PCR ) children from Tanzania ( n = 11 ) and control sera from a non-endemic group of U . S . children ( n = 122 ) , IgG responses to both pgp3 and CT694 were determined to be 91% sensitive and 98% specific . Antibody responses of Tanzanian children were analyzed with regard to clinical trachoma , PCR positivity , and age . In general , children with more intense ocular pathology ( TF/TI = 2 or most severe ) had a higher median antibody response to pgp3 ( p = 0 . 0041 ) and CT694 ( p = 0 . 0282 ) than those with normal exams ( TF/TI = 0 ) . However , 44% of children with no ocular pathology tested positive for antibody , suggesting prior infection . The median titer of antibody responses for children less than three years of age was significantly lower than those of older children . ( p<0 . 0001 for both antigens ) . The antibody-based multiplex assay is a sensitive and specific additional tool for evaluating trachoma transmission . The assay can also be expanded to include antigens representing different diseases , allowing for a robust assay for monitoring across NTD programs .
Trachoma , an ocular disease resulting from infection by the bacterium Chlamydia trachomatis , causes an estimated 3 . 8 million cases of blindness and 5 . 3 million cases of low vision [1] in Africa and Southeast Asia . Trachoma is associated with an estimated $5 . 3 billion ( 2003 US dollar calculation ) in productivity loss based on impaired vision [2] . Currently , multiple organizations from governments and the private sector are scaling up efforts to eliminate blinding trachoma by 2020 through the World Health Organization's ( WHO ) Global Elimination of Trachoma by the year 2020 ( GET 2020 ) program . Elimination efforts are based on the components of the SAFE strategy , including Surgery to prevent blindness in those with trichiasis , the use of Antibiotics to treat active infection , the advocacy of Facial hygiene to prevent spread of infection , and Environmental change through sanitation improvements to disrupt transmission . As the cornerstone of the SAFE strategy , Pfizer has donated more than 225 million doses of Zithromax through the International Trachoma Initiative for distribution ( www . iti . org ) . As neglected tropical disease ( NTD ) programs reduce infection prevalence , defining program endpoints is a programmatic priority and challenge . This is particularly true for trachoma where clinical pathology may be observed in the absence of active infection [3] or , in some low-prevalence areas , may be caused by inflammation associated with non-Chlamydial bacteria [4] . Monitoring active infection through PCR is an option , but also costly at $10 to $15 per test . The current WHO endpoint for antibiotic use for trachoma is a rate of follicular trachoma less than 5% in children under age 10 years , but clinical exams can be difficult to standardize [5]–[7] . In principle , antibody responses can be used to monitor NTD exposures . Antibody-based tools are being investigated to define their potential contribution to programmatic decision making and surveillance for NTDs including lymphatic filariasis and schistosomiasis [8]–[10] . Antibody assays have been described for chlamydia infections [11] , but the utility of these assays for evaluating public health programs has not been extensively investigated . In this study , we used the multiplex bead assay to screen bloodspots from children from four trachoma mesoendemic villages participating in the Partnership for the Rapid Elimination of Trachoma ( PRET ) Kongwa study [12] , [13] . Information to correlate community prevalence ( village by village ) and individual clinical and laboratory findings ( compared with PCR and TF/TI score ) was gathered and compared to antibody response to C . trachomatis antigens measured by multiplex .
Studies were conducted in the Kongwa district ( Dodoma region ) of Tanzania as part of ongoing clinical trials to evaluate the impact of alternative models of community-wide treatment with azithromycin [12] , [13] . As part of routine post-MDA study evaluations , clinical exams , using an expansion of the WHO simplified grading scheme [3] , [14] were performed on 100 children several months to 9 years old who were randomly selected from each village , and in four villages all children were examined . Trachoma was graded as zero if the ocular signs did not meet WHO criteria for TF ( follicular trachoma ) or TI ( Trachoma Intense ) . Grade one TF or TI met the WHO criteria; grade two for TF was if there were 10 or more follicles size >0 . 5 mm in the tarsal conjunctiva , and TI grade two was present if all the deep tarsal vessels were obscured by inflammation . Eye swabs were collected for PCR analyses of C . trachomatis from all children , with careful attention to avoid field contamination . All PCR swabs were shipped to the International Chlamydia laboratory at Johns Hopkins for analyses of infection using Amplicor . Details of laboratory processing are described elsewhere [12] . According to the manufacturer's directions , the Amplicor test was positive if the signal was >0 . 8 and negative if the signal was <0 . 2 and equivocal if in-between . All equivocal tests were re-tested , and only counted positive if at least one test was positive . Four villages were selected for this study because of the schedule for ocular exams post treatment . Of the four villages selected , all received three rounds of treatment . Three villages were 12 months post-treatment ( villages 0401 , 1602 , and 1001 ) and one was 6 months post-treatment ( village 1501 ) . One child per family was selected for collection of bloodspots . Bloodspots were collected following finger prick onto filter papers with six circular extensions designed to absorb 10 µl of whole blood ( TropBio Pty Ltd , Townsville , Queensland , Australia ) . Parents or guardians provided written informed consent for children participating in the study . The study was approved by The Institutional Review Board of the Johns Hopkins University School of Medicine ( Baltimore , MD ) and the Tanzanian National Institute for Medical Research . Control samples were included in the analysis . De-linked serum samples from a population of 122 children under the age of 6 years from the United States were collected as part of an IRB approved blood lead study and used as a negative control and to establish a cutoff value for positivity in the multiplex assay . Sera from 86 children under 5 years of age , previously collected from multiple villages outside of Leogane , Haiti as part of IRB approved studies of lymphatic filariasis and other infectious diseases , were also used as a trachoma-negative population as no trachoma has been reported in the country since the 1970's . Candidate antigens were selected based on their recognition by sera from Chlamydia-positive patients in published studies [15] . pCT03 ( pgp3 ) is encoded as ORF5 of the eight total ORFs on the highly-conserved cryptic plasmid which is rarely found in Chlamydia pneumonia isolates [16] . CT694 is a secreted protein and has been found to be involved in pathogenesis . CT694 manipulates host proteins by acting as a T3S-dependent substrate , but its exact function is not known [17] . CT694 and pgp3 were expressed in the pGEX6p vector system ( Amersham Biosciences , Piscataway , NJ ) in XL1 Blue . Bacterial cultures were grown to an optical density of 0 . 7–0 . 8 in 2× yeast extract and tryptone media with 0 . 1 mg/ml ampicillin at 37°C . Cultures were induced with 0 . 2 mM IPTG and incubated three hours at 30°C . Cells were harvested by centrifugation and pellets stored until use at −20°C . All pellets were thawed on ice , suspended in cold PBS with 1 mg/ml chicken egg lysozyme , 1 mM phenylmethylsulfonyl ( PMSF ) , 5 mM EDTA , 0 . 1 µM Pepstatin A , 0 . 1 mM N-ethylmaleimide , and 0 . 1 µM E-64 . Pellets were sonicated , and then lysis completed using either 1% Tween-20 ( pgp3 ) or 1% Triton-X in PBS ( CT694 ) , and finally an additional 0 . 5 mM PMSF added to all samples . Lysed bacterial cultures were then centrifuged for 20 minutes at 15 , 000×g to collect the soluble fraction . The soluble fractions were filtered with a 0 . 45 micron polypropylene filter ( Whatman , Florham Park , NJ ) and purified on a glutathione Sepharose 4B affinity column according to the manufacturer's protocol ( GE Healthcare , Piscataway , NJ ) using PBS buffer . The protein containing glutathione elution fractions were dialyzed overnight ( Spectra/Por; 3 , 500-Da cutoff; Spectrum Laboratories , Rancho Dominguez , CA ) against 500 volumes of PBS twice at 4°C . After dialysis , antigen concentrations were quantified by BCA microassay ( Pierce , Rockford , IL ) and tested for immunologic reactivity by ELISA using previously described methods [18] . Antigens were coupled to 5 . 6 µm polystyrene beads ( SeroMap Beads; Luminex Corporation , Austin , TX ) as previously described [8] . Briefly , carboxyl groups on the beads were chemically modified to ester groups by 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide ( Calbiochem ) in the presence of N-hydroxysulfosuccinimide ( Pierce ) . Primary amine groups on the antigens then reacted with ester groups on the beads to create amide covalent bonds . Recombinant proteins ( 120 micrograms each ) were coupled with to 1 . 25×107 beads . Pgp3 and CT694 were coupled in PBS at pH 7 . 2 . After coupling , beads were quantified by hemocytometer and stored at 4°C with protease inhibitors . For each milliliter of bead suspension , 200 µg of Pefabloc ( Roche Diagnostics , Indianapolis , IL ) , 200 µg EDTA , and 1 µg each of leupeptin and pepstatin A were added . One bloodspot extension for each child , corresponding to 10 µl of whole blood , was eluted overnight at 4°C with 500 µl of PBS containing 0 . 5% BSA , 0 . 05% Tween 20 , 0 . 02% sodium azide , 0 . 5% polyvinyl alcohol ( PVA ) , and 0 . 8% polyvinylpyrrolidone ( PVP1 ) , designated as PBN1 . This elution was equivalent to a whole blood volume dilution of 1∶50 or a serum dilution of approximately 1∶100 . Eluates were diluted ( 100 µl ) to a final volume of 400 µl in PBN1 with 0 . 5% w/v of E . coli crude extract to block nonspecific binding [8] . Sera were diluted 1∶400 in 400 µl PBN1 with 0 . 5% w/v of E . coli crude extract and incubated one hour at 37°C . After incubation , dilutions were stored overnight at 4°C . Sera dilutions were centrifuged at maximum speed to clarify the extract before use . Bloodspot eluates were screened in duplicate with the antigen-coupled beads in a multiplex bead assay [8] . Filter-bottom plates ( 96-well ) ( Millipore , Bedford , MA ) were pre-wet with 0 . 5% BSA , 0 . 05% Tween 20 , 0 . 02% sodium azide in PBS ( PBN2 ) . Antigen-coupled beads ( 2500 each ) were added to each well and washed twice with PBN2 . Control sera and bloodspot eluates ( 1∶400 ) were added in duplicate at 50 µl per well to the beads . The plates were vigorously shaken for 30 seconds , covered , and shaken at room temperature for 1 . 5 hours . After incubation , wells were washed three times with 100 µl of 0 . 05% Tween 20 in PBS ( PBST ) with a vacuum device ( Millipore ) . Total IgG was detected with 50 ng of biotinylated mouse anti-human total IgG ( clone H2; Southern Biotech , Birmingham , AL ) and 40 ng of biotinylated mouse anti-human IgG4 ( clone HP6025; Invitrogen , South San Francisco , CA ) per well in 50 µl PBN2 . After incubation , wells were washed as above . R-phycoerythrin-labeled streptavidin ( Invitrogen , South San Francisco , CA ) was added at a concentration of 250 ng per well and incubated for 30 minutes at room temperature . Wells were washed as previously after incubation . Wells were additionally incubated in 50 µl of PBN2 to remove any loosely bound antibodies for 30 minutes with shaking . After the final incubation in PBN2 , wells were vacuum-evacuated and washed once with PBST . Beads were suspended in 125 µl PBS , shaken , and immediately read on a BioPlex 200 instrument ( Bio-Rad , Hercules , CA ) equipped with Bio-Plex Manager 6 . 0 software ( Bio-Rad ) . Antigens were examined for cross reactivity through two methods: antibody elution from beads and the addition of recombinant protein to block antibodies present in sera . For antibody elution , 150 , 000 beads of each antigen were suspended in 500 µl PBN1 . Sera ( 2 . 5 µl ) highly responsive to both antigens by ELISA was added to each suspension . The bead mixture was incubated for one hour at room temperature with shaking . Beads were then transferred to a filter-bottom plate ( two wells each ) and washed six times with 100 µl PBST . Beads were then suspended in 600 µl per well with Gentle Ag/Ab Elution buffer pH 6 . 6 ( Thermo Scientific , Rockford , IL ) . Bead-containing wells were pooled into a microfuge tube and spun for five minutes at 16 , 000×g to pellet the beads . Supernatants were then transferred to Centricon-30 centrifugal filter device ( Millipore Corporation , Bedford , MA ) and 800 µl ( 0 . 1 M Tris HCl pH 8 . 0 , 0 . 3 M NaCl ) ( THS ) added to each sample . Samples were spun at 6000 rpm until concentrated to 50 µl . An additional 2 ml of THS and 4 ml of PBS were added to each and spun to concentrate to 50 µl final volume . Equal volumes of PBN1 were added to each and stored overnight at 4°C . Alternatively , 10 µg of each protein was added to 1∶400 dilutions of sera and incubated for one hour at room temperature . After incubation , serum preparations were stored overnight at 4°C . Sera prepared by both methods were run in the multiplex assay as described above . Statistical analysis was conducted using GraphPad Prism 5 . 03 ( GraphPad Software Inc . , La Jolla , CA ) . Specificity and sensitivity were calculated for both antigens by receiver operator characteristic analysis . Comparisons of clinical trachoma , PCR results for infection , and age groups were calculated with confidence intervals ( CI ) of 95% using Mann-Whitney tests to generate p-values . Villages were compared by Kruskal-Wallis tests with 95% CI .
CT694 and pgp3 antigens were expressed into the bacterial soluble fraction and purified on an affinity column . Receiver operator curves ( ROC ) were generated for each antigen using finger prick sera samples from 122 children from the United States and blood spots from 11 infection PCR positive children from Tanzania . For pgp3 , a MFI-BG value of 1024 was established as the low-limit value for positivity , with an indeterminate range of 1024 to 5998 . For CT694 , a MFI-BG value of 232 was established as the low-limit value for positivity , with an indeterminate range of 232 to 1982 . Both antigens detected 10 out of 11 PCR positive children ( sensitivity 91% ) . The PCR positive Tanzanian sample that was not detected in the antibody assay was negative for both pgp3 and CT694 . Because PCR data were not available , specificity was determined by using multiplex values of the presumed negative US control children . For pgp3 , three presumed negative children were positive ( 98% specificity ) and two children were positive for CT694 ( 98% specificity ) . For the US children , only one child was positive for both antigens . Two children were pgp3 positive only and one was positive for CT694 only . Serum samples from 86 Haitian children were also included , representing another trachoma non-endemic country . IgG antibody responses are plotted by country in Figure 1 . The median antibody response of Tanzanian children was higher than children from Haiti and the US for both ( A ) pgp3 and ( B ) CT694 . Clinical evaluation results , PCR results from eye swabs , and IgG antibody response to pgp3 and CT694 are shown by village in Table 1 . Village 0401 had the highest proportion of children with trachoma and village 1501 had the highest infection by PCR criteria . Village 1001 demonstrated the lowest levels of clinical and antibody positivity . Antibody responses to pgp3 and CT694 were higher than either trachoma or PCR prevalence across all villages . IgG antibody responses for both recombinant antigens are plotted by village for individual persons in Figure 2 . The highest median antibody response was seen in village 0401 for both antigens . The lowest median antibody response was found in village 1001 for both antigens . IgG antibody responses are plotted by trachoma and PCR status for individual persons in Figure 3 . Overall , antibody responses to both antigens were associated with ocular pathology and infection status . The median antibody response for children with normal ocular findings ( a TF/TI score of zero ) is lower than those with either observed ocular disease ( pgp3 p = 0 . 0041 and CT694 p = 0 . 0282 ) or PCR positivity ( pgp3 p = 0 . 0008 and CT694 p = 0 . 0024 ) . All but one PCR-positive individual had a positive antibody response to both pgp3 and CT694 . Of note , a large proportion of children with no evidence of ocular pathology had elevated pgp3 and CT694 responses ( 44% above cutoff for both antigens ) . In Figure 4A and 4B , pgp3 and CT694 positive antibody responses are shown by age groups . Responses to both antigens increased with age , with the highest proportion of positive children in the 6–9 year old age group . For pgp3 , children under three responded significantly less than the other two age groups ( p = 0 . 0062 for 3 to <6 and p<0 . 0001 for ≥6 ) . For CT694 children under three also responded significantly less than the other age groups ( p = 0 . 0291 for 3 to <6 years and p = 0 . 0001 for ≥6 years ) . Interestingly , unlike the other age groups , most of the antibody-positive children under age 3 years had infection ( indicated in red ) or trachoma ( indicated in green ) , while those with neither were more likely to be antibody negative . This suggests that fewer children in this age group had been previously exposed to infection , as might be expected after three rounds of MDA . The older children , who were in the villages prior to the initiation of MDA , were more likely to be antibody positive in the absence of infection or trachoma , likely indicating past exposure . Antigens were tested for cross-reactivity using sera and bloodspots highly responsive to both antigens ( n = 6 ) . The addition of recombinant protein reduced detection by multiplex to its corresponding beads by 99 . 9% ( p = 0 . 0022 for both antigens ) and by less than 1% for the other antigen ( data not shown ) . This was true for both sera and bloodspot preparations . One of the high-responding sera was additionally screened by the antibody-elution method and demonstrated similar results . Antibodies eluted from pgp3 beads specifically recognized pgp3 but not CT694-conjugated beads . In the reverse experiment , CT694-specific antibodies recognized CT694 but not pgp3 ( data not shown ) .
Sensitive surveillance tools are important for the determination of exposure levels in low-prevalence settings by program managers who are faced with the decision as to whether or not to stop MDA , often in the absence of PCR data . The development of serological tools to detect antibody responses subsequent to trachoma exposure and infection might provide an alternative to clinical exams and PCR analysis for surveillance as an indicator of interruption of transmission . In principle , children born following MDA should experience fewer infections and this should be reflected by lower antibody responses . The absence of an antibody response to a trachoma antigen might indicate an interruption of transmission in formerly endemic areas . Although trachoma programs have not investigated the potential use of antibody tests for program surveillance , the use of serological markers has been shown to be useful in detecting exposure to C . trachomatis in the context of genital infections [19] , [20] . In this study , we used serological markers to screen for antibody responses in relation to ocular infections and clinical disease . Two antigens , pgp3 and CT694 , were selected for the multiplex assay after an extensive literature search . Pgp3 is the only plasmid-encoded ORF ( pORF ) secreted into the host cell cytoplasm during infection [15] . Pgp3's function remains unknown but it appears to play a role in pathogenesis . In addition , previous authors have suggested its potential use as a diagnostic marker for genital chlamydial infections [16] , [21] . CT694 is expressed during infection as a T3SS effector [17] and has also shown to be recognized by host antibodies [19] . We screened bloodspot eluates from Tanzanian children with two chlamydial antigens to measure antibody responses to trachoma antigens after MDA and compared results to clinical exam and PCR analysis data . We have demonstrated that these antibody responses are related to both disease and infection status , suggesting that responses to these chlamydial antigens should be further explored for utility for trachoma surveillance after MDA . In our assay , we were able to detect and correlate antibody response with clinical and PCR status both at the community and individual level . At the community level , communities with higher trachoma prevalence also had higher pgp3 and CT694 antibody responses , as seen in Table 1 . Village 0401 had the highest levels of pgp3 and CT694 antibody along with the highest numbers of positive clinical exams . Village 1001 showed lowest prevalence of positivity in all three tests . At individual level , we were able to correlate both clinical signs of trachoma and PCR positivity for Chlamydia with increased levels of pgp3 and CT694 antibody response . Of children with normal clinical exams , there were many with positive antibody responses . These likely represent children with previous infection [11] . Additional support for this conclusion comes from our analysis of age-specific responses . Antibody-positive children with normal ocular exams were typically older than 3 years of age and , thus , potentially infected prior to the beginning of MDA . In contrast , most antibody-positive children ( 89% of pgp3 positives and 88% of CT694 positives ) younger than three years of age were either PCR-positive or had ocular pathology , and antibody prevalence was significantly lower than among older children . Based on these results , we suggest that antibody-based tools may be valuable for post-MDA surveillance of trachoma . Lack of antibody in young children may be indicative of interruption of transmission and protection by the SAFE strategy , as shown for responses to pgp3 and CT694 in children less than three years of age . Antibody responses may be especially useful as evaluation tools as they represent a cumulative measure of infection , unlike PCR positivity which may be more transient . Six children ( 4% ) who showed signs of trachoma through clinical examination exhibited no pgp3 or CT694 antibody . This result may have occurred due to imprecision of clinical diagnosis , in which follicle formation was due not to trachoma but to other causes , such as allergic conjunctivitis . Alternatively , there may be some genetic differences in antibody response at the individual level . Finally , as Ghaem-Maghami et al suggest , there may be some suppression of antibody response in severe cases of inflammation [11]; if this is so , it was rare in our series . Additional studies are needed to define the kinetics of antibody response following infection and how antibody responses shift following repeated infections . The US samples are unidentified , and , without knowing the child's country of origin and medical and travel history , we do not know their true disease status . There is a possibility that chlamydial infection was acquired at birth [22] , [23] . The responses of Haitian children were not as easy to interpret . Eight of 86 children were positive for at least one antigen , and three of these children were positive for both . Even though Haiti is considered non-endemic for trachoma , it is difficult to determine from this sample group if antibody reactivity was due to chlamydial infection acquired at birth ( not trachoma ) or evidence of acquired chlamydia after birth . It may be that these responses reflect cross reactivity to other antigens . From our own analysis , we have demonstrated that the antibody responses to pgp3 and CT694 do not cross-react . The multiplex bead assay may be a valuable antibody-based tool for trachoma surveillance . Confirmatory data can be generated by using multiple antigens within the same sample . In theory , antigens might also be selected to distinguish between current infections and exposure or to differentiate infections caused by various serovars . Along with trachoma-specific antigens , antigens for monitoring and evaluation from other NTD public health programs may be included in the multiplex assay . Community profiling can be accomplished through a panel of antigens representing a multitude of diseases , such as helminths , viruses , and waterborne and vaccine-preventable diseases , which can be screened from a single bloodspot or microliter of serum , thus greatly increasing cost-effectiveness of surveillance activities . Using a panel of antigens also facilitates measurement of the impact of MDA on multiple diseases , which might not otherwise be tracked through separate monitoring programs . There are several limitations to consider in the context of this study . First , all villages have been treated by MDA prior to sample collection , so the baseline antibody responses are unknown for each antigen . Also , because samples were collected at only one time point , our understanding of how each child's antibody and disease status changed over time is limited . These points make it difficult to describe the kinetics and longevity of antibody responses with the antigens tested and , more specifically , whether antibody responses reflect current or previous infection . A longitudinal study , including a baseline collection is an important next step to confirm the value of the antibody tools described here . Because antigens were chosen based on literature specific to genital chlamydial infections [15] , we cannot be sure that these antigen choices are the most appropriate in terms of ocular infection . For example , cases of anti-chlamydial responses but no ocular pathology may be due to a non-ocular strain of Chlamydia . The use of serovar-specific antigens , such as the major outer membrane protein peptides , might distinguish between genital and ocular infections and better characterize the nature of the infecting bacteria . In summary , antibody-based assays , in particular the multiplex assay , could be valuable tools to evaluate the impact of MDA programs and for post-MDA surveillance for trachoma . | Trachoma is an ocular disease caused by repeated infections with the bacteria Chlamydia trachomatis that is observed mostly in children and women . Scarring after repeated infections causes eyelashes to turn under the lid , possibly leading to corneal opacity and blindness . Efforts have increased by multiple organizations to meet the World Health Organization's goal of eliminating trachoma by 2020 . As mass drug administration is carried out , it can be difficult to assess whether transmission has lessened enough to stop treatment without resurgence of disease . In this low prevalence setting , sensitive and specific surveillance tools are important . Currently , clinical diagnosis is carried out by examination of the inside of the eyelid for follicles and inflammation , and infection is best assessed using PCR analysis with commercial kits . These test results do not always align , indicating a need for additional tools for surveillance . Using the multiplex assay platform , we compared antibody responses to two chlamydial antigens with eye exams and PCR results of 160 Tanzanian children participating in a mass treatment program . Antibody responses were shown to be a good indicator of infection and disease status and antibody tests may be useful as surveillance tools . |
You are an expert at summarizing long articles. Proceed to summarize the following text:
Serological antibody levels are a sensitive marker of pathogen exposure , and advances in multiplex assays have created enormous potential for large-scale , integrated infectious disease surveillance . Most methods to analyze antibody measurements reduce quantitative antibody levels to seropositive and seronegative groups , but this can be difficult for many pathogens and may provide lower resolution information than quantitative levels . Analysis methods have predominantly maintained a single disease focus , yet integrated surveillance platforms would benefit from methodologies that work across diverse pathogens included in multiplex assays . We developed an approach to measure changes in transmission from quantitative antibody levels that can be applied to diverse pathogens of global importance . We compared age-dependent immunoglobulin G curves in repeated cross-sectional surveys between populations with differences in transmission for multiple pathogens , including: lymphatic filariasis ( Wuchereria bancrofti ) measured before and after mass drug administration on Mauke , Cook Islands , malaria ( Plasmodium falciparum ) before and after a combined insecticide and mass drug administration intervention in the Garki project , Nigeria , and enteric protozoans ( Cryptosporidium parvum , Giardia intestinalis , Entamoeba histolytica ) , bacteria ( enterotoxigenic Escherichia coli , Salmonella spp . ) , and viruses ( norovirus groups I and II ) in children living in Haiti and the USA . Age-dependent antibody curves fit with ensemble machine learning followed a characteristic shape across pathogens that aligned with predictions from basic mechanisms of humoral immunity . Differences in pathogen transmission led to shifts in fitted antibody curves that were remarkably consistent across pathogens , assays , and populations . Mean antibody levels correlated strongly with traditional measures of transmission intensity , such as the entomological inoculation rate for P . falciparum ( Spearman’s rho = 0 . 75 ) . In both high- and low transmission settings , mean antibody curves revealed changes in population mean antibody levels that were masked by seroprevalence measures because changes took place above or below the seropositivity cutoff . Age-dependent antibody curves and summary means provided a robust and sensitive measure of changes in transmission , with greatest sensitivity among young children . The method generalizes to pathogens that can be measured in high-throughput , multiplex serological assays , and scales to surveillance activities that require high spatiotemporal resolution . Our results suggest quantitative antibody levels will be particularly useful to measure differences in exposure for pathogens that elicit a transient antibody response or for monitoring populations with very high- or very low transmission , when seroprevalence is less informative . The approach represents a new opportunity to conduct integrated serological surveillance for neglected tropical diseases , malaria , and other infectious diseases with well-defined antigen targets .
There is large overlap in the distribution of global disease burdens attributable to neglected tropical diseases ( NTDs ) , malaria , enteric infections and under-vaccination . Despite nearly a decade of advocacy for integrated monitoring and control [1] , prevailing surveillance efforts maintain a single-disease focus , and the high cost of fielding surveys to collect specimens means that programs conduct surveillance infrequently or not at all . High throughput , multiplex antibody assays enable the simultaneous measurement of quantitative antibody responses to dozens of pathogens from a single blood spot [2] . When coupled with existing surveillance platforms , multiplex antibody assays could enable the global community to more quickly identify public health gaps , including: recrudescence of NTD or malaria transmission in elimination settings , stubborn areas of high transmission , emerging infectious diseases , and under-vaccination . Of particular interest are methods to analyze measurements collected in cross-sectional surveys because most large-scale global surveillance efforts use this design ( e . g . , immunization coverage surveys , malaria indicator surveys , transmission assessment surveys for NTD elimination programs , demographic and health surveys ) . A unique attribute of antibody measurements is that they provide an immunological record of an individual’s exposure or vaccination history , and thus integrate information over time [3] . Yet , the information contained in circulating antibodies varies greatly by pathogen and antibody measured , and it is this complexity that presents challenges to the use of antibody measurements for integrated surveillance . Most previous studies have reduced quantitative antibody measurements to seropositive and seronegative groups by choosing a cut point , and then have used models to estimate seroconversion rates from age-dependent seroprevalence as a measure of pathogen transmission [3 , 4] . The choice of seropositivity cut point can be ambiguous for many pathogens , as examples in this article will illustrate , and can vary widely in lower transmission settings depending on the reference population or statistical method used [5] . A second challenge in lower transmission settings is that seropositive individuals are extremely rare , and so accurate estimates of seroprevalence require large samples [6] . Conversely , in high transmission settings , seroprevalence can fail to capture the immune response from repeated infections where antibody levels increase following each exposure and wane over time [7 , 8] . Thus , analytical methods that use the quantitative response directly avoid the difficulty of defining cut points , accommodate complex , dynamic changes in antibody levels that can present difficulties to seroconversion models [4] , and may provide higher resolution information in very low- or very high transmission settings . To our knowledge there has not been a broad-based assessment for whether quantitative antibody measurements present an opportunity for integrated surveillance across diverse pathogens . Two recent contributions in the malaria literature proposed mathematical models to measure changes in transmission from quantitative antibody responses [8 , 9] . Both models require strong parametric assumptions such as constant rates of antibody acquisition and loss over different ages , or constant transmission over time , which may be difficult to justify for many pathogens of interest in an integrated surveillance platform . Our objective was to develop a general and parsimonious method to measure changes in infectious disease transmission from quantitative antibodies . We approached the problem from a different perspective than mathematical modeling , and instead focused on recent advances in machine learning and statistical estimation theory to measure differences in transmission within or between populations . We also aimed to assess whether the method could generalize across diverse pathogens that can be measured in multiplex assays , such as neglected tropical diseases , malaria , and enteric pathogens . A widely observed phenomenon across infectious diseases is that changes in pathogen transmission result in a “peak shift” of infection intensity by age: as transmission intensity declines in a population , the age-specific prevalence and intensity of infection tends to rise more slowly at younger ages and peak at lower overall levels [10] . We sought to extend this observation to measure changes in transmission using quantitative antibody levels rather than measures of patent infection-an approach suggested by mathematical models of parasite immunity [10 , 11] with empirical support in a comparison of populations with varying helminth transmission intensity [12] . We focused on a general mechanism of acquired immunity elicited by most infectious pathogens . Children are born with maternal immunoglobulin G ( IgG ) antibodies that wane over the first 3–6 months of life , and from ages 4–6 weeks begin to produce their own IgG antibodies in response to antigen exposure [13] . The aggregation of individual IgG responses generates a curve of population average IgG levels that rises in the first years of life until it plateaus at adult levels [14] . Transferred maternal immunity-a function of maternal immunologic memory-likely influences the magnitude of the population-average IgG curve’s intercept near birth [13] . Antigen exposure is needed to maintain antibodies in blood , either by stimulating the proliferation of memory B-cells to replenish short-lived plasma cells or by stimulating the production of non-germinal center short-lived plasma cells [14] . Antigen exposure induces rapid proliferation and differentiation of short-lived B-cells , with somatic hypermutation leading to increased affinity following each exposure . As transmission declines , population-average serum IgG levels should rise more slowly as the age of first infection increases and repeated exposures become infrequent . For pathogens that elicit antibody responses that wane over time , the number of long-lived antibody secreting cells should decline without recent antigen exposure [14] , which in turn should be reflected in a lower plateau of the age-dependent antibody curve . We therefore hypothesized that reduced pathogen transmission would cause pathogen-specific IgG antibody curves to increase more slowly with age and plateau at lower levels , and that quantifying changes in the curves would provide a robust and sensitive measure of changes in transmission within or between populations .
To test this hypothesis , we examined age-dependent antibody responses ( “age-antibody curves” ) to diverse pathogens in populations with likely differences in transmission intensity . We fit age-antibody curves with a data adaptive , ensemble machine learning algorithm that can include additional covariates to control for potential confounding [15] . The curves represent a predicted mean antibody level by age ( a ) for each exposure group ( x ) , which we denote E ( Ya , x ) in the statistical methods . We used the age-adjusted mean antibody response within each group ( x ) as a summary measure of transmission , denoted E ( Yx ) , and estimated differences between group means . For example , below we describe an analysis of age-antibody curves using antibody response to the Wuchereria bancrofti Wb123 antigen in a population before ( X = 0 ) and after ( X = 1 ) mass drug administration ( MDA ) . We estimated a separate curve in the population before E ( Ya , 0 ) and after E ( Ya , 1 ) MDA , and tested for differences between the curves by comparing summary mean Wb123 response between the two measurements , E ( Y1 ) —E ( Y0 ) , averaged over age and potentially other confounding covariates ( statistical methods include details ) . The age-adjusted mean antibody response equals the area under the age-antibody curve ( S1 Text ) . The approach thus integrates the steepness of the curve’s initial rise at young ages as well as its sustained magnitude at older ages , with lower transmission measured by reductions in group means . Comparing group means intuitively represents an average difference between groups across all points in the curves . If particular age ranges are of interest , such as young children , then the mean can be estimated over restricted regions of the age-antibody curve . Mauke , Cook Islands was endemic for W . bancrofti in decades past , and in 1987 there was an island-wide MDA of all individuals ≥5 years old with diethylcarbamazine . The present analysis included serum samples from two cross-sectional measurements of the permanent resident population; the first in 1975 ( N = 362 , approximately 58% of the population ) and the second in 1992 , 5 years after the island-wide MDA ( N = 553 , approximately 88% percent of the population ) [16] . Both studies preserved serum samples by freezing them in liquid nitrogen within hours of collection and storing them at -80°C . Serum samples were tested for IgG antibody levels to the Wb123 antigen using a Luciferase Immunoprecipitation System ( LIPS ) assay , as previously described in detail [17] . Data presented are in luminometer units from averaged duplicate samples . We re-analyzed data from the original assessment of the effect of the MDA campaign on Wb123 antibody levels [16] using the statistical methods described below . We estimated separate age-antibody curves in 1975 and 1992 . To make statistical comparisons between the curves , we estimated means for each survey year and differences between surveys , stratified by 5 year age group for ages ≤20 years old . For a subsample of 114 individuals who were measured in both 1975 and 1992 , we compared Wb123 antibody levels in subgroups defined by whether they had circulating antigen to adult W . bancrofti—an indication of active infection-at one or both time points . We plotted individual changes in Wb123 antibody levels to visualize antibody acquisition and loss in different subgroups . The Garki Project , led by the World Health Organization and the Government of Nigeria , included a comprehensive malaria intervention study that took place in 22 villages in the rural Garki District , Nigeria ( 1970–1976 ) [18] . We obtained publicly available study datasets for this analysis ( http://garkiproject . nd . edu ) . The intervention included a combination of insecticide spraying and mass drug administration of surfanene-pyrimethamine in 1972–1973 , along with targeted distribution of chloroquine to children <10 and self-reporting fever cases in the 1974–75 post-intervention period . The study documented large reductions in the proportion of individuals testing positive for Plasmodium falciparum infection by microscopy as a result of the intervention . In a subset of two control villages and six intervention villages , the study collected multiple serological measures that have been described in detail [18] . Briefly , the study collected serum from all members present in a village in eight rounds that alternated between wet and dry seasons . We limited the analysis to 4 , 774 specimens collected from individuals <20 years old because that age range captured nearly all of the change in the age-antibody curve ( median serum samples per round in each village: 74 , range: 19–158 ) . Serological survey rounds 1–2 took place in the wet and dry season before the intervention started , rounds 3–5 took place during the active intervention period at 20 , 50 , and 70 weeks after intervention initiation , and rounds 6–8 took place at 20 , 40 , and 90 weeks after the conclusion of intervention activities . The sixth measurement was collected in the intervention villages only . From each participant , finger prick blood samples were collected in two 0 . 4-ml heparinized Caraway tubes for immunological testing . Individuals contributed between 1 and 8 samples over the course of the study ( median = 3 ) . We focused on P . falciparum antibody response measured with the IgG indirect fluorescent antibody ( IFA ) test . We converted IFA titers to the log10 scale and then estimated mean IFA titre by age separately for intervention and control villages in each measurement round . We compared curves using the difference between age-adjusted means . We repeated the analysis at the village level to make separate comparisons of each individual intervention village against control to examine curves and measures of transmission at smaller spatial scale . The study collected extensive wet season entomological measurements in three of the villages with serological monitoring . The co-located entomological and serological measurements enabled us to compare village-level mean antibody levels and seroprevalence with the wet season entomological inoculation rate ( EIR ) as the transmission intensity changed in the intervention villages . EIR estimates from Table 4 of the original study [18] were used in the analysis . The EIR represents the number of sporozoite positive bites per person over each wet season , and was estimated by multiplying the man-biting rate by the sporozoite positive rate in night-bite collections . Night-bite collections were conducted every 2 weeks using 2 indoor and 2 outdoor stations per village , with 2 human bait collectors in each station throughout the night . We estimated village level mean IFA antibody titers restricted to serum samples collected during the same periods of EIR monitoring , and we measured the association between village level mean antibody titers and the EIR with the Spearman rank correlation coefficient . After completing the primary analysis that estimated age-antibody curves by survey for control and intervention villages , we noticed a reduction in age-adjusted geometric mean antibody titers between wet and dry survey rounds 1–2 . We followed-up this observation with a secondary analysis , restricted to the control villages , that estimated age-antibody curves separately by survey round , which corresponded to wet and dry seasons: 1971 wet ( survey 1 ) , 1972 dry ( survey 2 ) , 1972 wet ( survey 3 ) , 1973 dry ( survey 4 ) , and 1973 wet ( survey 5 ) [18] . Control villages were not measured in survey 6 , and surveys 7–8 took place in the 1974 and 1975 wet seasons; we excluded surveys 7–8 from the secondary analysis because we were interested in comparing transmission in adjacent wet and dry seasons . Our analysis of enteric pathogen antibody measurements relied on two existing data sources . Haiti samples were collected from a longitudinal cohort of 142 children , enrolled between the ages of 1 month and 6 years on a rolling basis from 1991–1999 to monitor lymphatic filariasis , and the selection of samples from the Haiti cohort has been described in detail [19] . Children were followed up to 9 years ( median 5 years ) and each child was measured approximately once per year . At each measurement , the study collected finger prick blood samples . The multiplex bead assay techniques and antibody results for the Cryptosporidium parvum recombinant 17-kDa and 27-kDa antigens [20] , the VSP-5 fragment of Giardia intestinalis variant-specific surface protein 42e [21] , and the Entamoeba histolytica lectin adhesion molecule ( LecA ) [22] have been described [19 , 23] . Enterotoxigenic Escherichia coli ( ETEC ) heat labile toxin β subunit [24] and lipopolysaccharide ( LPS ) from Salmonella enterica serotype Typhimurium ( Group B ) [25] were purchased from Sigma-Aldrich ( St . Louis , MO ) . Purified recombinant norovirus GI . 4 and GII . 4 New Orleans [26] virus-like particles from a baculovirus expression system [27] were kindly provided by J . Vinje and V . Costantini ( CDC , Atlanta , GA ) . Proteins and LPS were coupled to SeroMap beads ( Luminex Corp . Austin , TX ) at 120 μg per 12 . 5 x 106 beads in phosphate-buffered saline at pH 7 . 2 and were included in the multiplex bead assays previously described [19] . As part of a serologic study in the United States ( USA ) [28] , our lab ( JWP , PJL ) had banked 86 anonymous blood lead samples collected in 1999 from children ages 0–6 years . The USA samples were tested contemporaneously with the Haiti longitudinal cohort using the same techniques and bead preparations [19] . We used these anonymous samples from the USA to compare antibody curves with the Haitian children . For each enteric antibody , we estimated separate age-antibody curves in the USA and Haiti using all measurements collected at ages <5 . 5 years ( ages of overlap between the sample sets ) . We then estimated geometric means for each population and differences between means as described in the statistical methods . A cross-sectional survey measures an individual’s quantitative antibody level ( Y ) , age ( A ) , and other characteristics ( W ) . Many surveillance efforts are also interested in differences in antibody levels by one or more exposures ( X ) , which could be confounded by A and W . We assumed the observed data O = ( Y , A , W , X ) ~ P0 arose from a simple causal model ( S2 Text includes additional details ) : W = fW ( UW ) ; A = fA ( UA ) ; X = fX ( A , W , UX ) ; Y = fY ( X , A , W , UY ) . Study protocols for Mauke were approved by the government of the Cook Islands and the NIAID Institutional Review Board , and all adult subjects provided written informed consent . Consent for children was obtained by verbal assent as well as written consent from legal guardians . The Haiti study protocol was reviewed and approved by the Centers for Disease Control and Prevention’s Institutional Review Board and the Ethical Committee of St . Croix Hospital ( Leogane , Haiti ) and all subjects provided verbal consent . Human subjects review boards approved a verbal consent process because the study communities had low literacy rates . Mothers provided consent for young children , and children 7 years or older provided assent .
There was a distinct shift in the W . bancrofti Wb123 age-antibody curve before and five years after a single diethylcarbamazine MDA ( Fig 1a ) , and differences between curves show more gradual antibody acquisition with age in the post-MDA measurement ( Fig 1b ) . As previously noted [16] , mean Wb123 antibody levels declined in individuals who tested positive for circulating filarial antigen before MDA ( a sign of active infection ) but had no detectable circulating antigen post-MDA , as well as among those who tested negative for circulating antigen at both time points ( Fig 1c ) . Together , these results show that slower antibody acquisition combined with antibody loss , presumably a reflection of lowered transmission potential post-MDA , underlie the curve shift . Seroprevalence estimates for Wb123 followed a similar pattern as the quantitative antibody response ( S1 Fig ) . A caveat of the Wb123 seroprevalence analysis was that the seropositivity cutoff , chosen to have near perfect sensitivity and specificity with respect to controls [17] , fell in the center of the Wb123 distribution in the post-MDA measurement ( lower transmission ) ( S1 Fig ) . This observation makes it more difficult to argue that there were two distinct seropositive and seronegative populations-an assumption avoided when relying directly on quantitative antibody levels . Compared to control villages , there was a consistent shift in P . falciparum age-antibody curves with increased length of the insecticide spraying and MDA intervention in the Garki project ( Fig 2a ) . During the active intervention period , children in intervention villages exhibited a sharp drop in antibody levels from birth and a more gradual increase in antibody levels compared with children in control villages . Mean IFA titers demonstrated group comparability before intervention , reduced transmission during intervention , and a transmission resurgence after the intervention period ( Fig 2a ) —a pattern that corresponded closely with rates of patent parasitemia measured in the original study [18] . Age-dependent seroprevalence curves followed a similar pattern to the quantitative antibody results , but changes due to intervention were less pronounced because reductions in seroprevalence were only detectable among children < 5 years old ( Fig 2b ) . When estimated at the finer resolution , village level rather than in aggregate , mean antibody titers more clearly distinguished intervention and control villages compared with seroprevalence ( S2 Fig ) . Village level mean antibody titers correlated strongly with wet season EIR ( Spearman’s ρ = 0 . 75 ) and with seroprevalence ( Spearman’s ρ = 0 . 93 , Fig 3 ) . Malaria transmission was highly seasonal during the study , with more intense vector transmission and incident infections in the wet seasons [18] . In a secondary analysis , we restricted the population to control villages and fit age-antibody curves separately by survey rounds 1–5 , which corresponded to sequential wet and dry seasons . We observed a distinct shift in the age-antibody curve , consistent with lower transmission in the dry season , but only among children <5 years old; older children exhibited far less seasonal variation in mean IFA antibody titers compared with children <5 years ( Fig 4 ) . Age-antibody curves for IgG antibody responses to protozoan , bacterial , and viral enteric pathogens were consistent with lower levels of enteric pathogen transmission in the USA ( Fig 5 ) . The Haiti and USA populations likely illustrate enteric antibody curves near the bounds of high and low transmission environments , and show that as transmission declines the curves flatten . The results illustrate both the consistency of the general pattern across diverse taxa as well as the facility with which the analysis method generalizes to multiplex applications where numerous antibodies can be measured from a single blood spot . In most cases , enteric pathogen antibody distributions did not show obvious seropositive and seronegative subpopulations , and seropositivity cutoff values varied when estimated using different sample sets ( S3 Fig ) . In most cases , seropositivity cutoffs using the Haiti specimens alone fell outside the observed range of the antibody distributions ( S3 Fig ) .
We have shown that diverse , pathogen-specific serum IgG levels follow a characteristic shape with increasing age , and that changes in transmission are reflected in a shift of the age-antibody curve that can be summarized by changes in mean antibody levels . Consistent with our hypothesis , reduced transmission produced age-antibody curves that rose more slowly and plateaued at lower levels . The generality and consistency of the age-antibody relationship across diverse infectious diseases , populations , and diagnostic platforms suggest that this simple , robust methodology constitutes a useful way to measure changes in transmission for pathogens with serum IgG antigen targets . Our results support the use of quantitative antibody levels to measure changes in pathogen transmission as a complement or alternative to seroprevalence and other metrics based on a binary response . For infections that generate lifelong immunity , a characteristic of many vaccine preventable diseases , seroprevalence provides information about population-level immunoprotection and information beyond the first exposure is lost . However , for infections that are partially or transiently immunizing , examples from this study illustrate that mapping a quantitative antibody measurement to seroprevalence can lose substantial information . For example , the Garki project analysis illustrated that in a high transmission setting , the intensive insecticide spraying and MDA intervention reduced P . falciparum antibody titer across ages 0–20 years , but reduced seroprevalence only among children <5 years ( Fig 2 ) . The reduction of antibody levels across a broader age range in the quantitative analysis was presumably caused by less immune system boosting in older individuals living in intervention villages-an effect missed when using seroprevalence . Conversely , in lower transmission settings where seropositive individuals are rare , quantitative antibody levels can still provide information about reduced exposure . Waning W . bancrofti Wb123 antibody levels among individuals in Mauke without circulating antigen ( Fig 1c ) provided another example for how quantitative responses could provide more information about gradations in exposure that are lost with binary , positive/negative assays . These findings are broadly consistent with recent comparisons of quantitative antibody and seroprevalence estimates in the malaria context [9] . Indeed , quantitative antibody levels could provide complementary , high resolution information alongside more traditional metrics of infection to identify heterogeneous transmission in populations-a recent example illustrated the value of using malarial antibody levels directly to identify transmission hotspots in Cambodia [35] , and similar applications could be possible for NTDs and other infectious diseases . Many pathogens whose infections elicit partial or waning immunity have complex immunology that results in a unimodal distribution of antibody levels in a population , which makes it difficult or impossible to identify distinct seropositive and seronegative groups . The W . bancrofti and enteric pathogen analyses provided many examples where seropositivity cutoffs either could not be estimated or fell in the center of unimodal ( rather than bimodal ) distributions ( S1 and S3 Figs ) . In those cases , a comparison based on mean antibody levels obviated the need to choose a cutoff . Mean antibody levels should require fewer observations to estimate precisely than seroprevalence since reducing a quantitative measure to a binary measure results in a theoretical loss of >36% of Fisher’s information [36] . A sample of 20 individuals is unlikely to provide accurate information about seroprevalence or seroconversion rates [6] , but could provide a reliable estimate of mean antibody levels-the village-level analyses in the Garki project showed that use of P . falciparum quantitative antibodies led to larger and more precise estimates of differences between control and intervention groups than seroprevalence when estimated in small samples ( S2 Fig ) . This could be a particular advantage for serological surveillance in population-based surveys where sampling clusters often include fewer than 30 people [37] , and our labs are currently working on more formal guidance for sampling designs based on quantitative antibody levels . The use of data-adaptive , ensemble machine learning to fit antibody curves and compare means has several strengths in the context of developing a generalized methodology for integrated surveillance . The approach is: implemented in open-source software , extremely flexible , easy to adjust for potential confounding covariates , minimally biased , and highly efficient [15 , 30 , 38] . Ensemble approaches have been successful in cases where no single model is likely to be correct across diverse applications-for example , cause of death classification in the Global Burden of Disease studies [39] , mortality prediction in intensive care units [40] , or predicting malaria incidence from diverse antibody panels [41] . An ensemble library can include a range of models or algorithms , and if simpler models perform better they will be upweighted in the estimation [15] . Previous statistical methods have used quantitative antibody levels to measure differences in pathogen transmission by estimating parameters such as infection rates [41–43] , seroconversion rates [3 , 4 , 44] , or antibody acquisition rates [8 , 9 , 44] . Incidence and seroconversion rates are epidemiologically useful , but to estimate them from quantitative antibody levels requires strong modeling assumptions , or well-characterized longitudinal cohorts that directly measure the parameter of interest to train models , or both . Measuring differences in transmission directly from antibody levels with age-antibody curves requires neither modeling assumptions nor well-characterized cohorts to train models or fit parameters . This could be an advantage for integrated surveillance platforms where pathogens vary greatly in their specific immunology and most lack detailed longitudinal cohorts to characterize their antibody infection profiles . The ensemble fits revealed consistent shifts in the age-antibody curve with lower transmission , but individual curves followed age-dependent patterns that varied by pathogen and setting . Data-adaptive , nonparametric algorithms tended to perform better than simpler models in terms of cross-validated R2 , but there was no member of the ensemble that performed best across all pathogens and transmission settings ( S4 Fig ) . We included in the ensemble library an antibody acquisition model developed for malaria [9] , but that particular model underperformed in comparison with more flexible algorithms such as smoothing splines ( S4 Fig ) . This result suggests it may be difficult to develop a single model that describes the full diversity of age-dependent antibody response across very different infectious diseases , and underscores the value of considering an ensemble approach for broad analyses envisioned through integrated surveillance . The specific antibody kinetics and the age range in which the curves are estimated will influence the sensitivity of this approach to detect changes in transmission . Curves fit using antibodies with shorter half-lives should theoretically exhibit shifts more quickly with changes in transmission . Microarray screening efforts to identify malarial antibodies with a range of half-lives [41] open the possibility for discovering antibodies with high sensitivity to measure changes in transmission over short periods . With antibodies measured in multiplex , future work could develop methods to combine multiple antigens expressed by the same pathogen into a single quantitative response-a composite measure could prove more robust to differential immunogenicity arising from differences in host genetics . Our results show that serological surveillance among children captures the period of greatest change in the age-antibody curve , and analyses using children would be less susceptible to longer-term “cohort effects” that could influence the age-antibody relationship for antibodies with long half-lives [45] . Children are likely the most sensitive population to measure reductions in transmission: age-specific immunological profiles of malaria and vaccine response to diverse pathogens show that young children lose antibodies more quickly than adults because short-lived B cells predominate in young children , and antigen presentation and helper T-cell function increase with age [7 , 46 , 47] . Seasonal reductions in P . falciparum antibody titers among children <5 during the dry season when transmission was less intense were consistent with this observation ( Fig 4 ) . Surveillance activities that measure a very narrow age range , such as transmission assessment surveys to monitor lymphatic filariasis elimination programs ( which only measure children ages 6–7 years ) , cannot estimate a full age-antibody curve but the summary mean would still provide a robust measure of adjusted mean antibody levels to compare populations ( Fig 1b ) . Quantitative IgG antibody response integrates information about an individual’s pathogen exposure over time [3] - a characteristic of particular import for community-based surveillance of pathogens with low annual incidence and pathogens that cause many asymptomatic infections . Low incidence and asymptomatic presentation make community-based surveillance of changes in transmission difficult because either scenario requires very large numbers of specimens to be tested to identify incident infections . For example , Cryptosporidium parvum is implicated as a major pathogen of concern due to its contribution to hospitalized cases and prolonged episodes of diarrhea [48] , but community-based studies of Cryptosporidium sp . require the collection of thousands of stool specimens . Large studies are needed because , even in hyper-endemic settings , rates of incident infections fall below a single episode per person-year [49] , and because intermittent shedding of small numbers of oocysts in the stools of some infected individuals can make detection difficult [50] . We have illustrated that full age-antibody curves can be estimated with as few as 100–300 observations spread over different ages , which suggests they could be useful in the surveillance of pathogens with low annual incidence , or asymptomatic infections that clinical surveillance activities typically miss . There are two main limitations of the approach . First , mean antibody levels do not estimate a direct epidemiologic transmission parameter , such as the incidence or force of infection . Thus , while mean antibody levels provide a flexible , sensitive method to measure differences in transmission within- or between populations , they provide only indirect information about the relative importance or health burden of different pathogens . Using the same underlying statistical method with binary outcomes to estimate seroprevalence ( Fig 2 , S1 Fig ) partly addresses this limitation at the cost of losing some information , and our labs are actively working to extend these general methods to estimate a pathogen’s force of infection . A second limitation is that if a quantitative antibody assay has no global reference standard to translate arbitrary units into antibody titers , it will be difficult to make direct comparisons of mean antibody levels across different assays and studies . Until such reference standards exist , direct comparisons based on quantitative age-antibody curves and their summary means are only possible when comparing two or more surveys-or separate groups within a survey-for the same antibody response measured using the same assay . Assay standardization is a common challenge of any serological surveillance , so this limitation is shared by all methods that measure changes in transmission from antibody assays . The development of global reference standards for antibody assays used in infectious disease surveillance [51] , as currently exist for many vaccine-preventable diseases , would facilitate between-study comparisons . This study focused on IgG responses to lymphatic filariasis , malaria , and enteric pathogens measured in blood , but the method should apply to other immunoglobulin isotypes , other specimen types , and other infectious diseases . For example , similar shifts in IgE curves have been documented in populations with different soil transmitted helminth transmission [12] , salivary IgG and IgA norovirus assays have been developed [52] , and NTDs such as trachoma [53] , dengue [54] , and chikungunya [55] all have well-defined antigens that would be amenable to this methodology . Mean antibody response in defined geographic areas over time could translate directly to mapping activities used to target intervention programs and monitor transmission or immunization coverage . The ability to combine dozens of recombinant antigens into multiplex bead assays opens the possibility for high-throughput , integrated infectious disease surveillance that includes pathogens targeted for elimination such as NTDs and malaria alongside newly emerging pathogens , and vaccine preventable diseases [51] . The methods developed here provide a very general tool for integrated surveillance of antibody response from such data . | Global elimination strategies for infectious diseases like neglected tropical diseases and malaria rely on accurate estimates of pathogen transmission to target and evaluate control programs . Circulating antibody levels can be a sensitive measure of recent pathogen exposure , but no broadly applicable method exists to measure changes in transmission directly from quantitative antibody levels . We developed a novel method that applies recent advances in machine learning and data science to flexibly fit age-dependent antibody curves . Shifts in age-dependent antibody curves provided remarkably consistent , sensitive measures of transmission changes when evaluated across many globally important pathogens ( filarial worms , malaria , enteric infections ) . The method’s generality and performance in diverse applications demonstrate its broad potential for integrated serological surveillance of infectious diseases . |
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Human FTO gene variants are associated with body mass index and type 2 diabetes . Because the obesity-associated SNPs are intronic , it is unclear whether changes in FTO expression or splicing are the cause of obesity or if regulatory elements within intron 1 influence upstream or downstream genes . We tested the idea that FTO itself is involved in obesity . We show that a dominant point mutation in the mouse Fto gene results in reduced fat mass , increased energy expenditure , and unchanged physical activity . Exposure to a high-fat diet enhances lean mass and lowers fat mass relative to control mice . Biochemical studies suggest the mutation occurs in a structurally novel domain and modifies FTO function , possibly by altering its dimerisation state . Gene expression profiling revealed increased expression of some fat and carbohydrate metabolism genes and an improved inflammatory profile in white adipose tissue of mutant mice . These data provide direct functional evidence that FTO is a causal gene underlying obesity . Compared to the reported mouse FTO knockout , our model more accurately reflects the effect of human FTO variants; we observe a heterozygous as well as homozygous phenotype , a smaller difference in weight and adiposity , and our mice do not show perinatal lethality or an age-related reduction in size and length . Our model suggests that a search for human coding mutations in FTO may be informative and that inhibition of FTO activity is a possible target for the treatment of morbid obesity .
In genome-wide association studies ( GWAS ) for type 2 diabetes , a single nucleotide polymorphism ( SNP ) within intron 1 of the fat mass and obesity-associated ( FTO ) gene was found to be associated with an increased risk of obesity [1] , [2] . Around 16% of the Caucasian population is homozygous for the risk allele and has an ∼1 . 67-fold increased risk of obesity , weighing ∼3 kg more than controls . The risk allele is not associated with fetal growth but confers an increased risk of elevated body mass index ( BMI ) and obesity [1] that manifests by the age of 7 and persists into adulthood . These results have largely been confirmed in other populations [2]–[7] . FTO is a 9-exon gene located on human chromosome 16 and mouse chromosome 8 . Sequence analyses suggested that FTO has homology with the AlkB family of DNA repair enzymes . Subsequent in vitro biochemical studies revealed FTO to be a member of the Fe ( II ) and 2-oxoglutarate ( 2OG ) dependent oxygenase superfamily [8] . In metazoans these enzymes are involved in diverse processes including oxygen sensing , DNA repair , fatty acid metabolism and post-translational modifications [9] . Recombinant FTO catalyses oxidative demethylation of 3-methylthymine and 3-methyluracil in single-stranded DNA and RNA [8] , [10] , suggesting its physiological role involves nucleic acid modification . In vitro expression of murine FTO results in localisation of the recombinant protein to the nucleus , consistent with a role in nucleic acid modification [8] . FTO is ubiquitously expressed . In the brain , mRNA levels are particularly high within the hippocampus , cerebellum and hypothalamus [8] , [11] . In the hypothalamus , strong expression is seen in the arcuate , paraventricular , dorsomedial and ventromedial nuclei - sites critical for regulating energy balance [12] . The hypothalamic expression of FTO suggests a potential role in the control of food intake and whole body metabolism . Consistent with this idea , several studies have reported higher levels of energy intake in individuals with the at-risk FTO allele [13]–[19] . Although other studies have found no association of variants with energy expenditure measured by calorimetry in adults [16] , [20] or food intake [21] , the balance of evidence from population-based studies suggests that FTO intron 1 SNPs are associated with increased energy intake . GWAS are a powerful way of identifying genes involved in common disease but it is often difficult to translate their findings into an understanding of how the gene products act at the cellular and whole animal levels . This is a particular problem when the disease-associated SNP lies within an intron , as is the case for FTO . One approach to identifying the function of a gene is to mutate it in a model organism . A mouse possessing a 1 . 6 Mbp deletion in mouse chromosome 8 that includes Fto as well as Ftm , Ft1 , Irx3 , Irx5 and Irx6 has been characterised ( fused toes or Ft mice ) [22] . Homozygous Ft mice are embryonic lethal and display neural tube defects , left-right asymmetry and polydactyly . Heterozygous mice have fusion of the forelimb digits and thymic hyperplasia [23] . Recently , a mouse FTO knockout has been described in which exons 2 and 3 of Fto are replaced by a neomycin STOP cassette [24] . This cassette also deletes part of intron 1 although not the position equivalent to the BMI-associated SNPs in human FTO . Homozygous FTO knockout mice were viable but showed significant postnatal death before 4 weeks of age . They also exhibited postnatal growth retardation , a reduction in adipose tissue , a reduction in lean mass , increased energy expenditure , increased sympathetic nervous system activity , relative hyperphagia and reduced spontaneous locomotor activity . Thus this study suggested that Fto is directly involved in energy metabolism and body weight regulation . However , many human SNPs are likely to result in impaired function rather than total loss of function , and mouse knockouts are not always the most informative because they can result in a non-viable or severely compromised organism . Indeed , knockout of the mouse Fto gene is associated with increased postnatal death and growth retardation [24] , which is not observed for human FTO variants . We therefore searched the Harwell mouse N-ethyl-N-nitrosourea ( ENU ) archive for an Fto mutation that produces a partial loss of function . We describe here a dominant missense mutation ( I367F ) in the mouse Fto gene that results in a lean phenotype of reduced body weight and fat mass . Physical activity and food intake is unchanged but metabolic rate is increased . We show by gene expression analysis that fat and carbohydrate metabolism are increased in mutant mice and they possess an improved inflammatory profile . On a high-fat diet mutant mice show a lower fat mass than wild-type mice . These results indicate that our mice provide an improved model for the human phenotype and provide functional evidence that the FTO gene is a causal gene underlying obesity .
In a screen of DNA from 6 , 624 mice from the Harwell ENU-induced mutagenesis archive [25] we identified an adenosine-to-thymidine mutation in exon 6 of Fto leading to substitution of a phenylalanine for isoleucine-367 ( FtoI367F ) in the C-terminal region of murine FTO ( mFTO ) ( Figure 1A ) . Although this substitution lies outside the predicted double-stranded β-helix ( DSBH ) and associated elements that form the conserved “catalytic core” of 2OG oxygenases [9] , the block of ∼20 amino acids surrounding I367 is conserved in FTO throughout vertebrate evolution ( Figure 1B ) , suggesting this region has a physiological role . The C-terminal regions of some 2OG oxygenases are known to facilitate oligomerisation [9] , [26] . We therefore compared expression of full-length and C-terminal truncated ( residues 1–408 , 1–387 and 1–329 inclusive ) mFTO in Escherichia coli . Although wild-type mFTO was efficiently produced in a soluble form , C-terminally truncated forms of wild-type mFTO and full-length mFTOI367F gave only low amounts of largely insoluble protein ( data not shown ) . By contrast , a His-tagged version of the C-terminal domain alone ( residues D329-end ) of wild-type mFTO ( CmFTO ) was efficiently produced in soluble form , CmFTOI367F was largely insoluble . Mutation of residue I367 to alanine ( I367A ) , however , yielded soluble mFTOI367A and CmFTOI367A proteins . The catalytic activity of mFTOI367A in the presence of a 3-methylthymine containing oligonucleotide substrate , as measured by 2OG turnover , was only ∼40% of wild-type mFTO ( Figure 2A ) . As anticipated , because they lack the DSBH core , neither CmFTO nor CmFTOI367A were catalytically active ( Figure 2A ) . We analysed the oligomerisation states of mFTO and human FTO ( hFTO ) by non-denaturing mass spectrometry ( MS , Figure 2B ) and found that mFTO , hFTO and CmFTO exist as a mixture of monomeric and dimeric forms ( Figure S1 and Figure S2 ) . However , the proportion of dimeric mFTOI367A is significantly reduced relative to that in the wild-type mFTO ( Figure S3 ) . We also investigated the oligomerisation state of the proteins in solution using gel filtration chromatography ( Figure 2C ) . This revealed that whereas wild-type mFTO and CmFTO exist in both monomeric and dimeric forms , both I367A variants exist , at least predominantly , as monomers . These results suggest that I367 is involved in dimerisation of FTO ( Figure 2C ) . It was not possible to analyse mFTOI367F or CmFTOI367F because of their insoluble nature . The secondary structure of the C-terminal domain was then investigated using circular dichroism spectroscopy ( Figure 2D ) . In contrast to the full-length proteins , which contain the DSBH “core” domain of the 2OG oxygenases ( comprising 8 β-strands with surrounding loops and α-helices ) , the C-terminal domain proteins are predominantly α-helical ( Figure S4 and Figure S5 ) . A similar structural difference was observed for hFTO and its C-terminal domain ( residues 332-end; ChFTO , Figure S6 ) . The I367A mutation does not appear to alter the overall secondary structure fold of mFTO proteins ( Figure 2D ) , despite preventing dimerisation . This suggests that the I367A proteins are correctly folded , but that isoleucine 367 is involved in dimer formation . Factor Inhibiting Hypoxia Inducible Factor ( FIH ) , a human 2OG-oxygenase involved in the hypoxic response , also exists as a dimer [26] . For FIH , dimerisation is enabled by a C-terminal region . However , sequence comparisons , coupled with structural predictions , suggest that the C-terminal region of FTO and FIH will be substantially different , with that of FTO having a more α-helical character . Overall , these results reveal that FTO can exist in both monomeric and dimeric forms and that the C-terminal domain is involved in dimerisation . The evidence suggests that the I367A ( and presumably I367F ) mutation disrupts dimerisation and results in a reduction of catalytic activity . In addition to the presumed lower activity of mFTOI367F , we cannot exclude the possibility that a reduced stability of mFTOI367F , leading to aggregation and/or proteolytic degradation , contributes to the phenotype . To establish if mFTOI367F is stable in mammalian cells we transfected Cos-7 and PC12 cell lines with a plasmid encoding Fto or FtoI367F N-terminally tagged with yellow florescent protein ( YFP ) , or with YFP alone . Imaging revealed that like mFTO [8] , mFTOI367F localised to the nucleus ( Figure 3A and data not shown ) . However , fewer YFP-positive cells were observed than when mFTO was transfected , suggesting mFTOI367F is expressed at lower levels . This was confirmed by immunoblotting of lysates from cells expressing HA-tagged wild-type or mutant FTO ( Figure 3B ) . Similarly , immunoblotting of heterozygous and homozygous FtoI367F mouse brain and liver with an FTO antibody confirmed that the mutant protein is expressed at lower levels than wild-type ( Figure 3C ) . These data therefore suggest that the I367F mutation results in reduced FTO protein levels and thus is likely to impair FTO function . However , it does not produce a total knockout as some mFTOI367F protein appears to be expressed and correctly targeted to the nucleus . In order to determine if the mutation affects body mass , mice were weighed and measured weekly from 3 to 24 weeks . Both homozygous and heterozygous male mice carrying the dominant FtoI367F mutation exhibited a maturity-onset reduction in body weight ( Figure 4A ) diverging from wild-type at 12 weeks of age ( P = 0 . 01 ) and becoming ∼10% less than wild-type by 24 weeks . There was no significant difference in body weight between heterozygous and homozygous male mice . No difference was observed between wild-type and mutant female mice ( Table S1 ) , and all further experiments described were therefore confined to male mice . Dual Energy X-ray absorptiometry ( DEXA ) analysis revealed a 16–18% reduction in total fat mass between 24-week old FtoI367F and wild-type mice ( Figure 4B ) . The proportion of body mass in fat was reduced by ∼4–7% from 36 . 4% fat in wild-type to 30 . 8–32 . 9% fat in mutant mice . No significant difference in lean body mass was observed ( Figure 4C ) . This suggests that the lower weight of the mutant mice is largely attributable to a decrease in fat mass . Measurement of 24-hour cumulative food intake revealed no statistically significant difference between control and FtoI367F mice on a normal chow diet at either 10 or 20 weeks , even after normalisation for body weight ( Figure 4D ) . To investigate the mechanism underlying the reduced body weight , we measured O2 consumption , CO2 production and respiratory exchange ratio ( RER ) at 18 weeks of age . Heterozygous and homozygous FtoI367F mice showed an increase in O2 consumption ( Figure 5A ) and CO2 production ( Figure 5B ) during both the light and dark phase of activity . As a consequence , RER was increased ( Figure 5C ) . These data indicate the FtoI367F mutation causes an increase in whole body metabolism and a switch to relatively more carbohydrate metabolism . There was no difference in physical activity between wild-type and FtoI367F mice , as assessed by threshold crossing ( Figure 5D ) , during either the light and dark periods of a 22-hour measurement period . An intraperitoneal glucose tolerance test ( IPGTT ) at 12 and 16 weeks of age did not reveal a significant difference between FtoI367F and wild-type mice ( Text S1 and Figure S7A , S7B ) . Fasted serum leptin levels were also not significantly different at 24 weeks ( Figure S7C ) . When circulating leptin levels were plotted against the percentage of body fat we found ( as expected ) a positive correlation for both heterozygous and homozygous FtoI367F mice ( r 0 . 56 , r2 0 . 31 , P = 0 . 0295 and r 0 . 54 , r2 0 . 30 , P = 0 . 0238 , respectively ) . However , circulating leptin as a function of percentage body fat in homozygotes was shifted to the left in comparison to controls suggesting higher leptin secretion per unit of body fat ( Figure S7D ) . Levels of fasting serum glucagon , triglycerides , cholesterol and HDL were increased in 24-week old FtoI367F homozygous mice ( Figure 5E ) . Fasting serum glucose was also increased at 24 weeks ( Figure 5E ) but not at 12 or 16 weeks of age ( Figure S7A , S7B ) . No significant difference was observed in serum insulin or adiponectin ( Figure S7E , S7F ) . A 6-hour cold challenge at 22 weeks did not reveal a significant difference between genotypes ( data not shown ) suggesting that thermoregulation by brown adipose tissue ( BAT ) is unaltered . Urinary catecholamines were analysed at 10 and 20 weeks of age and normalised to urinary creatinine to control for volume . Ten-week old homozygous FtoI367F mice showed significantly higher norepinephrine ( Figure 6A ) and dopamine ( Figure 6B ) , but no difference in epinephrine ( Figure 6C ) , compared to wild-type mice . However , only dopamine was elevated in 20-week old homozygous FtoI367F mice ( P<0 . 05 ) . Sympathetic stimulation of white adipose tissue ( WAT ) , brown adipose tissue ( BAT ) and muscle increases metabolism by stimulating lipolysis and thermogenesis . Expression of β-3 adrenoreceptor mRNA was increased in muscle and WAT , but not BAT , of 16-week old homozygous FtoI367F mice as measured by quantitative reverse transcriptase-polymerase chain reaction ( qRT-PCR ) ( Figure 6D–6F ) . Expression of uncoupling protein 2 ( Ucp2 ) , and the catecholamine degradation enzyme catechol-O-methyl transferase ( Comt ) were also upregulated in muscle of homozygous FtoI367F ( Figure 6D ) . Analysis of 24-week-old mice given a high-fat diet from weaning revealed a significant reduction in fat mass and increased lean tissue mass in FtoI367F mice compared with wild-type mice ( Figure S8A , S8B , S8C ) . There was no difference in food intake between genotypes at 10 and 20 weeks ( Figure S8D ) . Fasting serum glucagon , glucose , triglycerides , and HDL-C levels were all increased in FtoI367F homozygous mice whereas insulin levels were significantly lower ( Figure S8E , S8F , S8G , S8H ) . Fasted serum leptin levels were significantly increased at 8 weeks in FtoI367F mice but not at 24 weeks . A high-fat diet significantly increased oxygen consumption in both wild-type and mutant mice ( Figure S8I , S8J , S8K , S8L , S8M ) , whereas carbon dioxide production increased in wild-type but not FtoI367F mice ( Figure S8J , S8M ) . Interestingly , we did not observe significant differences in either oxygen consumption or carbon dioxide output between mutant and wild-type littermates on a high-fat diet ( in contrast to a normal diet ) . The RER of all mice was significantly reduced on a high-fat diet , reflecting an increase in relative fat metabolism . In the dark phase ( when mice are active ) FtoI367F mice maintained a significantly higher RER than wild-type mice ( Figure S8K ) . To determine if gene expression was altered in FtoI367F mice we carried out microarray analysis of WAT , liver and skeletal muscle from 16-week old wild-type , heterozygous and homozygous FtoI367F mice . The genes that changed in heterozygous mice were largely a subpopulation of those seen to change in homozygous mice: thus we focus here on differences between homozygous and wild-type animals . Significant gene expression changes were found in all tissues , but very few were present in more than one tissue . Only 9 genes were found to change in all three tissues , 11 were common to muscle and liver , 24 to liver and WAT , and 31 to muscle and WAT ( Figure 7A ) . Few of these changes were obviously related to the lean phenotype . Multiple genes involved in inflammation were markedly down-regulated in abdominal WAT ( Figure 7B and Table S2 ) . These include the phospholipase A2 Pla2g7 ( 6 . 2-fold ) , the leukocyte immunoglobulin-like receptor Lilrb4 ( 4 . 7-fold ) , the Integrin beta 2 Itgb2 ( 4 . 3fold ) , the macrophage scavenger receptor 1 Msr1 ( 3 . 6 fold ) , and very many more . A reduction in expression of genes involved in the inflammatory response is consistent with the lean phenotype of the FtoI367F mice as adipose tissue normally contains resident macrophages [27] , [28] , [29] . The array results were in good agreement with those obtained by qRT-PCR analysis ( Figure 7C ) . Significantly , genes involved in the regulation of fat metabolism were also altered . Some genes involved in fatty acid catabolism were upregulated in WAT , as assessed by both microarray and qRT-PCR ( Figure 7B and 7D and Table S3 ) . These include patatin-like phospholipase domain containing 3 ( Pnpla3 ) , a triacylglycerol lipase , and acyl CoA synthetase ( Acss2 ) . Such changes may help explain the lower fat mass and higher RER of FtoI367F mice . Interestingly , Pnpla3 has been associated with fatty liver disease in humans [30]: however the liver triglyceride concentration was unchanged in FtoI367F mice ( Figure S7G ) . Genes involved in fatty acid synthesis were also upregulated , including fatty acid synthase ( Fasn ) , pyruvate carboxylase ( Pcx ) , monoacylglycerol O-acyltransferase ( Mogat1 , Mogat2 ) , and acetyl-Coenzyme A carboxylase ( Acaca , Acacb ) . Given the reduced amount of WAT in FtoI367F mice , upregulation of genes involved in fat synthesis may be a secondary adaptation that attempts to compensate for the lower fat levels . In muscle , fatty acid synthase ( Fasn ) was strongly upregulated in 16- week old homozygous FtoI367F mice , being 10-fold higher by microarray and 3 . 5-fold higher by qRT-PCR ( Figure 7E ) . Some genes involved in carbohydrate catabolism were also slightly upregulated , including glucose-6-phosphate dehydrogenase ( G6pd ) , pyruvate dehydrogenase kinase ( Pdk4 ) and insulin receptor substrate 1 ( Irs-1; Figure 7E , Table S4 ) . These changes are consistent with the idea that carbohydrate metabolism may be increased in muscle of FtoI367F mice . Genes associated with endoplasmic reticulum ( ER ) stress and the unfolded protein response ( UPR ) also showed a small increase ( ≥1 . 3-fold ) in expression in the liver of 16-week old homozygous FtoI367F mice , as assessed by microarray studies ( Figure S9 ) . Such changes are too small to be measured by qRT-PCR . There were no major changes in metabolic genes , consistent with the lack of a change in plasma glucose concentrations at this time point . qRT-PCR analysis of the key hypothalamic neuropeptides Agrp , Npy and Pomc revealed no significant changes in relative mRNA expression between FtoI367F and wild-type mice in the fasted state ( Figure S10 ) . However , a significant reduction in Npy expression was observed in FtoI367F mice in the free-fed state ( Figure S10 ) .
We found that in vitro wild-type mFTO dimerises , probably via its novel helical C-terminal domain , and that it can exist in both monomeric and dimeric forms . Substitution of isoleucine-367 for phenylalanine resulted in insoluble protein when expressed in E . coli and a substantially reduced level of expression in mammalian cells . However alanine substitution at residue 367 resulted in soluble protein . mFTOI367A has a similar secondary structure to mFTO , although the fraction of dimeric protein was significantly reduced and its catalytic activity was decreased . Our results suggest the mFTOI367F allele may exert a heterozygous hypomorphic effect . Heterozygous Fto knockout mice resemble wild-type mice , indicating the presumed FTO reduction is not sufficient to elicit a phenotype ( haploinsufficiency ) . However , because heterozygous and homozygous FtoI367F mice exhibited a similar phenotype , it appears that an mFTOI367F protein can exert a dominant negative effect on mFTO function , possibly by disrupting the wild-type mFTO subunit by formation of a heterodimeric protein complex ( i . e . an antimorphic allele ) . ER stress genes were among those genes whose expression was increased in the liver of 16-week old homozygous FtoI367F mice . It is possible these gene changes are secondary to impaired folding of FTOI367F , as suggested by the fact that the recombinant protein aggregated in solution . However , qRT-PCR analysis did not reach statistical significance suggesting FTOI367F may only be initiating a mild unfolded protein response . Most sick animals will tend to lose weight . However , the fact that food intake and physical activity of FtoI367F mice are normal indicates their lean phenotype is unlikely to be a consequence of illness or general malaise . In contrast , the I367F mutation produced a marked increase in whole body metabolism , with both O2 consumption and CO2 production being stimulated . This argues that the changes in fat mass are produced by differences in metabolism . Interestingly , RER increased from 0 . 87 to 0 . 9 during the dark period ( when the mice are active ) . This is consistent with a switch from fat metabolism ( RER = 0 . 7 ) towards protein ( RER = 0 . 9 ) and/or carbohydrate ( RER = 1 . 0 ) metabolism , which most likely reflects the fact that mutant animals have smaller fat depots . We found some evidence for alterations in catecholamines and sympathetic nervous system activity that are consistent with an increased metabolic rate . The increased glucagon level of FtoI367F mice further supports an increased sympathetic tone ( as this stimulates glucagon secretion ) . On either a standard or a high-fat diet , FtoI367F mice exhibited reduced fat mass , unchanged food intake , and increased serum leptin , glucose , glucagon and lipid serum levels compared with wild-type mice . However the lean mass of FtoI367F mice was greater than that of wild-type on a high-fat diet , whereas no differences were observed on a normal diet . Consequently , overall weight was not significantly different between FtoI367F and wild-type mice on a high fat diet . It is striking that that despite the reduction in protein and carbohydrate , the lean mass of FtoI367F mice increases on the high-fat diet . All mice on the high-fat diet have a higher fat mass than on a standard diet , although the FtoI367F mutation affords some protection against increased adiposity . Finally , we did not observe any differences in metabolic rate between genotypes given a high-fat diet , suggesting that the increased fat metabolism ( particularly in wild-type mice ) masks the differences observed on a standard diet . The RERs on the high-fat diet were all reduced , as expected due to the relatively higher fat metabolism . However , the active ( night ) phase RER was significantly higher in FtoI367F than wild-type mice fed a high-fat ( or standard ) diet , indicating a switch in substrate use towards carbohydrate . How the FtoI367F mutation enhances metabolism requires further study . Nevertheless , it is interesting that other Fe ( II ) and 2OG oxygenases are involved in fat metabolism [34] . Given that the amount of adipose tissue is significantly reduced in FtoI367F mice , the increased expression of genes involved in lipid metabolism pathways can be interpreted to suggest that fatty acid catabolism dominates in WAT and is the primary cause of reduced WAT levels , and that changes in levels of anabolic genes are a secondary effect . Overall , enhanced fat metabolism in WAT may not contribute to RER as much as in wildtype animals because of the reduced fat mass in FtoI367F mice . Gene expression analysis showed a slight increase in carbohydrate metabolic genes in muscle of FtoI367F mice . This might contribute to the phenotype of these mice as they exhibit a higher metabolic rate and higher RER . It is also possible that these changes are secondary to changes in substrate supply to muscle , produced by changes in nutrient storage in WAT and liver . The elevation of fatty acid synthase ( Fasn ) levels suggests that fat synthesis could be enhanced in muscle . However , there was no obvious increase in fat storage in this tissue so this change may be a secondary consequence of a decreased supply of fatty acids to muscle resulting from the lean phenotype . A reduction in expression of genes involved in the inflammatory response is consistent with the lean phenotype of the FtoI367F mice and may reflect concomitant reduction of macrophages residing in WAT . Previous reports have shown inflammation has an important role in obesity and that a lean phenotype results in decreased expression of inflammatory markers [27] , [28] , [29] . We found a significant reduction in Npy expression in FtoI367F mice in the free-fed state ( compared to controls ) . However , we did not observe any significant effect on food intake . Knockout of FTO had no effect on fed levels of Npy expression but produced a small reduction in fasting Npy and Pomc levels [24] . Our data suggest that the I367F mutation leads to a reduction in FTO activity . The mutant protein is expressed at lower levels than wild-type in E . coli , mammalian cell lines , and in tissues isolated from mutant mice . This reduction in FTO protein levels is expected to result in a decrease in total FTO activity . Furthermore , although the activity of mFTOI367F could not be measured that of mFTO1367A was reduced . The FtoI367F mouse shares several features with a mouse in which the Fto gene has been knocked out [24] , which provides additional support for the idea that the FtoI367F mutation causes a ( partial ) loss of function . Thus , in the homozygous state , both types of mice weigh less than wild-type , have a reduced fat mass , exhibit an increased metabolic rate and increased sympathetic nervous system activity and have a stronger phenotype in male than female mice . Collectively , these data suggest that FTO is involved in fat accumulation and that reduction in the activity of its gene product accounts for the lean phenotype of our mutant mice . There are , however , significant differences between FtoI367F mice and the Fto knockout mouse [24] . This is presumably because the latter results in a null allele , whereas the mFTOI367F protein may retain some functional activity , as suggested by the lowered activity of mFTOI367A . The Fto knockout mouse exhibits no significant phenotype in the heterozygous state [24] , whereas heterozygous FtoI367F mice resemble their homozygous littermates . As described above , this most probably results from a dominant negative effect of the I367F mutation . Thus , inhibition of FTO dimerisation may represent a novel strategy for inhibition of FTO activity . We confine our discussion here to a comparison of homozygous animals . The divergence of weight between knockout and wild-type mice has an early onset being apparent from day 2 after birth , and by 6 weeks of age homozygous mice weigh 30–40% less than controls . In contrast , the weight of homozygous FtoI367F mice only diverges after 12 weeks and at its maximum ( 24 weeks ) is only 10% less than wild-type . This smaller weight difference is closer to that observed in humans carrying different FTO SNPs , where the mean difference between at-risk and low-risk alleles is 3 kg for an average adult body weight of 90 kg - or around 3 . 4% [1] . Fto knockout mice are smaller than wild-type ( on average , males are 1 . 5 cm shorter ) [24] . In contrast , FtoI367F mice showed no difference in body length , which is consistent with the lack of association of human FTO SNPs with height [1] , [21] . Fat mass was reduced by 60% and 23% in homozygous male and female Fto knockout mice , respectively . In contrast , we only observed a phenotype in males and the reduction in fat mass was much less , between 16–18% . This is closer to the difference in body fat of 14% found between humans with at-risk and low-risk FTO variants [1] . There are several other differences between FtoI367F and Fto knockout mice . In particular , the latter showed a postnatal increase in mortality suggesting they are less healthy . They also exhibited relative hyperphagia and decreased spontaneous locomotor activity , which we did not detect in FtoI367F mice . These data reveal that the phenotype of FtoI367F mice more closely resembles that of human FTO variants than the knockout mice , and suggests that the low-risk allele does not result in a total knockout of FTO function in humans . The FtoI367F mouse provides a more physiologically relevant model for studying the effects of human FTO variants than a complete gene knockout , where the phenotype is more extreme . Human studies suggest that FTO SNPs are not associated with differences in physical activity , as found for FtoI367F mice [20] . There is strong evidence for increased food intake in humans with the ‘at-risk allele’ . This was not observed in FtoI367F mice although homozygotes showed a trend to towards reduced food intake on both standard and high-fat diets . Unlike humans with FTO gene variants , we observed no differences in homozygous and heterozygous mice . However , this is not unexpected as we have shown that our mutation affects FTO dimerisation , which may lead to dominant negative effects: it is unlikely this occurs with an intronic SNP , which presumably exerts a gene dosage effect on protein levels . Our data provide functional evidence that FTO is a causal gene underlying the association of SNPs within intron 1 of FTO with obesity . They further suggest that the ‘at-risk’ allele may lead to enhanced FTO function - or conversely that the low risk allele may lead to reduced FTO function . The FtoI367F model shows that mutations in this gene are consistent with viable metabolic effects and provides a physiologically more relevant model for further study than does the knockout mutation where the phenotype is more extreme . Our results cannot exclude the possibility that the intronic SNP found in humans may have an additional regulatory role on other genes . Nevertheless , because a missense point mutation is unlikely to affect the expression of adjacent genes , the phenotype of our mice provides strong support for the idea that FTO function does regulate body weight and further reinforces the evidence from the mouse knockout . By showing that FTO affects whole body metabolism , they provide the basis for further studies designed to elucidate the molecular mechanism of how FTO influences body weight . The observation that impaired FTO function results in a lean phenotype also suggests that inhibition of FTO may be of therapeutic interest in relation to morbid obesity .
The Harwell ENU-DNA archive ( http://www . har . mrc . ac . uk/services/dna_archive/ ) was screened by denaturing high performance liquid chromatography ( dHPLC ) using a Transgenomic WAVE system [25] , [35] . Coding sequence from the 9 Fto exons , including flanking ( approximately 70 bp ) splice sites , was tested in DNA derived from 6 , 624 mutagenised animals giving a total of approximately 10 . 29 Mbp screened ( see Text S1 for primer sequences ) . Six ENU induced point mutations ( 2 missense , 3 intronic ( non-splice site ) , 1 silent ) were found . We selected an exon 6 missense mutation ( I367F ) for further work . Fto exon 6 was amplified with the following primers: FTOex6F 5′ ATGTACAGCTGGAAGAGTGC-3′ and FTOex6R 5′-TCCCCTCTGACTAGGATCTC-3′ using Ampli Taq Gold ( Applied Biosystems ) . The mouse ( C57BL/6J×C3H/HeH F1 ) was rederived by in vitro fertilization from frozen sperm using C3H/HeH eggs . Progeny were backcrossed for two generations to C3H/HeH and intercrossed to produce mice heterozygous and homozygous for the mutation . PCR amplification products were purified using a QIAquick PCR purification kit ( Qiagen ) . Sequencing reactions were performed by GATC Biotech ( Jakob-Stadler-Platz 778467 Konstanz , Germany ) and analyzed using Vector NTI advance 10-ContigExpress ( Invitrogen ) . FTO genomic , transcript and peptide sequences were exported from Ensembl ( http://www . ensembl . org/index . html ) . Sequence alignment was performed using NTI advance 10-AlignX software ( Invitrogen ) . cDNA encoding hFTO was synthesised by Geneart and cloned into the pET-28a ( + ) vector to generate a His-tagged fusion protein . The hFTO construct used was produced by deletion of the thrombin cleavage region separating the His-tag from the protein sequence , using the QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) , and confirmed by DNA sequencing . This construct was used to prepare the hFTO 332–505 truncation ( ChFTO ) . The mFTO 329–502 truncation ( CmFTO ) , C-terminal truncations mFTO 1–408 , mFTO 1–387 and mFTO 1–329 , and point mutant I367F mFTO and I367A mFTO constructs were prepared in a similar manner from the wild-type mFTO construct . Further details are provided in the Text S1 . Proteins were prepared as previously reported for full length mFTO [8] . Protein ( 250 µg ) was loaded onto a Tricorn 10/300 GL column packed with Superdex 75 ( CmFTO and CmFTOI367A ) or Superdex 200 ( mFTO and mFTOI367A ) . Chromatographic separation was achieved using one column volume of running buffer , containing 50 mM NaCl , 1 mM DTT , 50 mM Tris pH 7 . 5 . The UV trace of the eluted volume was monitored . Protein standards used to create calibration curves were analysed by the same method . For CmFTO the masses obtained were 23 . 2 kDa and 38 . 2 kDa , consistent with the presence of monomeric and dimeric species ( calculated masses of 21 . 6 and 43 kDa , respectively ) . CmFTOI367A elutes at a volume corresponding to 27 . 8 kDa . Protein ( 0 . 25 mg/ml ) samples were analysed in 50 mM KCl , 20 mM K2PO4 , pH 7 . 0 , on an Applied Photophysics Chirascan machine , with 1 mm pathlength quartz cuvettes . Measurements were taken at 0 . 5 nm intervals between 180 and 260 nm at 4°C . Spectral fitting to determine relative contributions of secondary structure elements was performed using a CDNN deconvolution program ( Chirascan ) . Protein samples were exchanged into 15 mM ammonium acetate , pH 7 . 5 . ESI-MS was carried out on a Waters Micromass Q-ToF spectrometer and an Advion Biosciences NanoMate HD Robot chip-based nano-electrospray device . 10 µM protein samples were sprayed from 15 mM NH4OAc ( pH 7 . 5 ) at a chip nozzle voltage of 1 . 66 kV and cone voltage of 80 V . Samples were also run on a Waters Synapt HDMS , Advion Biosciences NanoMate HD Robot chip-based nano-electrospray device . 15 µM protein samples were sprayed from 15 mM NH4OAc ( pH 7 . 5 ) at a chip nozzle voltage of 1 . 7–1 . 8 kV , a gas pressure of 0 . 25 psi and cone voltage of 80 V . Decarboxylation assays of [1-14C]-labelled 2OG were carried out as reported [36] , and incubated ( 37°C for 10 minutes ) . Assays were carried out with an 18mer methylated oligonucleotide substrate of sequence 5′-GCXAGGTCCCGTAGTGCG-3′ , where X is 3-methylthymine , which was synthesised by ATDBio Ltd . ( University of Southampton , UK ) . Assay mixes contained 4 µM enzyme , 100 µM substrate , 4 mM ascorbic acid , 300 µM 2OG ( 4% [1-14C]-2OG ) , 0 . 3 mg/ml catalase , 1 mM DTT and 25 µM FeSO4 . 7H2O , made to 100 µl with 50 mM Tris pH 7 . 5 . An N-Terminally labelled YFP-Fto fusion construct was generated as described [8] . The I367F variant was created from the wild-type construct using the QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) and confirmed by DNA sequencing . The primers used were ( bold codons indicate the site of mutation ) : mFTOI367F fwd: 5′- AAA CAA GGA GAG GAA TTC CAT AAT GAG GTG GAG -3′mFTOI367F rev: 5′- CTC CAC CTC ATT ATG GAA TTC CTC TCC TTG TTT-3′ . Cell culture and confocal microscopy are described in Text S1 . The HA-Fto chimera was generated by fusing an HA coding sequence to the N-terminus of FTO by PCR . The amplified PCR product was cloned into the pGEM-T Easy vector ( Promega ) , sequenced , and then recloned into the pcDNA3 vector ( Invitrogen ) . The I367F variant was created from the wildtype construct using the QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) . Western blots were performed on 40 µg of total proteins using a rabbit anti recombinant mFTO antibody . Detection was performed using ultrasensitive horseradish Enhanced Chemiluminescence Plus ( ECL plus; Amersham ) . Cos-7 cells expressing HA-FTO or HA-FTOI367F , were lysed with RIPA buffer ( containing 1% NP40 , 0 . 5% DOC and 0 . 1% SDA detergents ) and proteins separated by SDS-PAGE . Blots were probed with an anti-HA antibody ( Roche ) and with an antibody to the TATA-binding protein ( Abcam ) as a loading control for nuclear proteins . All animal studies were carried out using guidelines issued by the Medical Research Council in 'Responsibility in the Use of Animals for Medical Research’ ( July 1993 ) . Mice were kept in accordance with UK Home Office welfare guidelines and project license restrictions under controlled light ( 12 hr light and 12 hr dark cycle , dark 7 pm-7 am ) , temperature ( 21±2°C ) and humidity ( 55%±10% ) conditions . They had free access to water ( 25 ppm chlorine ) and were fed ad libitum on a commercial diet ( SDS Rat and Mouse No . 3 Breeding diet ( RM3 ) ) containing 11 . 5 kcal% fat , 23 . 93 kcal% protein and 61 . 57 kcal% carbohydrate . Alternatively , mice were maintained on a high-fat diet ( D12451 , Research Diets , New Brunswick , NJ , USA ) containing 45 kcal% fat , 20 kcal% protein and 35 kcal% carbohydrate . Phenotyping tests were performed according to EMPReSS ( European Phenotyping Resource for Standardised Screens from EUMORPHIA ) standardized protocols as described at ( http://empress . har . mrc . ac . uk ) . Body mass was measured each week on scales calibrated to 0 . 01 g . Analysis of body composition was performed by DEXA using the Lunar PIXImus Mouse Densitometer ( Wipro GE Healthcare , Madison , WI ) . At 10 and 20 weeks of age mice were placed in metabolic Techniplast cages with free access to water and food . Food consumption was measured by weighing . Urine was collected after 24 hours and urinary catecholamines measured using a 3-CAT Epinephrine , Norepinephrine , Dopamine ELISA ( Demeditec ) according to the manufacturer's instructions . Creatinine was used to standardize between urine samples . Plasma leptin insulin , adiponectin and glucagon levels were measured using a mouse endocrine Lincoplex kit ( Linco research , Missouri , USA ) and a Bio-Plex 200 system ( Bio-Rad , Hemel Hempstead , UK ) , according to the manufacturer's instructions . At 24 weeks of age , fasted mice were anaesthetized , killed by exsanguination and blood collected by cardiac puncture . Plasma concentrations of glucose , triglycerides , total cholesterol , HDL cholesterol and LDL cholesterol and urine creatinine were measured on an AU400 ( Olympus UK ) , as described [37] . Metabolic rate was measured at 18 weeks of age using indirect calorimetry ( Oxymax; Columbus Instruments ) to determine oxygen consumption , carbon dioxide production , respiratory exchange ratio ( RER ) and heat production . Physical activity was assessed using a Threshold load cell system ( Med Associates , VT , USA ) over 22 hours and analysed with Threshold Summary , Version 3 . 0 for upper and lower threshold crossings . Total ribonucleic acid ( RNA ) from WAT , skeletal muscle and liver was labelled and hybridised to Affymetrix Mouse GeneST arrays . Arrays were analysed using GeneSpring GX10 , GenMAPP and Ingenuity pathway analysis tools . All data is MIAME compliant and has been submitted to ArrayExpress under accession number E-MEXP-2201 . Further details of the microarray methodology and SYBR-Green/Taqman ( Table S5 ) PCR assays are described in the Text S1 . Statistical analyses were performed with a Student's t-test for independent samples . Correlation coefficients were obtained by the Pearson method using GraphPad Prism version 5 . 00 for Windows , GraphPad Software , San Diego California USA , www . graphpad . com . Data are expressed as mean±SEM , and P<0 . 05 was considered as statistically significant . For microarray studies , differentially expressed genes were identified ( after RMA normalization ) using an unpaired t-test with a cut-off of P≤0 . 05 . A≥1 . 5-fold change difference between wildtype and FtoI367F mice was taken to indicate expression changes between genotypes . Additional detail on materials and methods is given in Text S1 . | Geneticists have identified many gene regions that cause human disease by using multiple genetic markers in large populations to find gene regions associated with disease . However , it is often not clear precisely which gene in any given region causes the disease or how the gene exerts its functional effect . For example , a gene variant in the non-coding region of FTO enhances obesity risk , but it is not clear if this is an effect of the FTO gene itself or another gene located nearby . We therefore tested whether FTO regulates body weight in the mouse . We found that a single change ( mutation ) in the sequence coding for the mouse FTO protein decreases the functional activity of FTO and causes reduced fat mass and body weight . Food intake and activity were normal , but the mutant mice had a higher metabolic rate . In addition , their fat mass was lower than that of normal mice when both were fed a high-fat diet . Our study provides direct evidence that FTO directly affects fat mass and thus is likely to have a role in human obesity . As reduced FTO function decreases body weight in mice , it is worth exploring if pharmaceutical agents that inhibit FTO activity might help reduce human obesity . |
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Type 2 Diabetes ( T2D ) is a highly prevalent chronic metabolic disease with strong co-morbidity with obesity and cardiovascular diseases . There is growing evidence supporting the notion that a crosstalk between mitochondria and the insulin signaling cascade could be involved in the etiology of T2D and insulin resistance . In this study we investigated the molecular basis of this crosstalk by using systems biology approaches . We combined , filtered , and interrogated different types of functional interaction data , such as direct protein–protein interactions , co-expression analyses , and metabolic and signaling dependencies . As a result , we constructed the mitochondria-insulin ( MITIN ) network , which highlights 286 genes as candidate functional linkers between these two systems . The results of internal gene expression analysis of three independent experimental models of mitochondria and insulin signaling perturbations further support the connecting roles of these genes . In addition , we further assessed whether these genes are involved in the etiology of T2D using the genome-wide association study meta-analysis from the DIAGRAM consortium , involving 8 , 130 T2D cases and 38 , 987 controls . We found modest enrichment of genes associated with T2D amongst our linker genes ( p = 0 . 0549 ) , including three already validated T2D SNPs and 15 additional SNPs , which , when combined , were collectively associated to increased fasting glucose levels according to MAGIC genome wide meta-analysis ( p = 8 . 12×10−5 ) . This study highlights the potential of combining systems biology , experimental , and genome-wide association data mining for identifying novel genes and related variants that increase vulnerability to complex diseases .
Insulin resistance is a common trait present in complex disorders such as type 2 diabetes ( T2D ) , obesity or metabolic syndrome ( MetS ) . Around 340 million people suffer from diabetes worldwide , 90% of whom have T2D ( http://www . who . int/diabetes/facts/en ) . Unlike type 1 diabetes , overt T2D is usually diagnosed several years after its onset due to its milder presenting symptoms , which in part explains why several devastating complications such as cardiovascular related diseases tend to develop soon after or have already arisen at the moment of the initial diagnosis . There has been growing interest in identifying genes and processes that could trigger insulin resistance beyond defects on the insulin signaling cascade itself . As a result , defective mitochondrial activity has been indirectly related to insulin resistance in insulin-targeted tissues , such as skeletal muscle [1] , [2] , [3] and liver [4] . In particular , patients with T2D and , more importantly , non-diabetic subjects with type 2 diabetic relatives showed mitochondrial dysfunction and lower expression of PPAR gamma co-activator 1 alpha and 1 beta ( PGC-1α and PGC1-1β ) , which are key regulators of mitochondrial biogenesis and function . In addition , subjects with early-onset type 2 diabetes typically show defective activation of PGC-1alpha in response to physical activity [5] , and similarly , morbid obese type 2 diabetic patients show a defective activation of mitochondrial gene expression in response to weight-loss surgery [5] . Whether there is a heritable component involved in the alterations in expression of mitochondrial genes/proteins in these common forms of T2D remains to be determined . Despite all of these efforts and lines of evidence , the mechanisms and the molecular contributors to the connection between mitochondria and the insulin signaling and resistance are still unknown . The availability of a wide range of functional interaction data , including metabolomics , genomics , transcriptomics and proteomics and the integration of all these data using systems biology approaches make it now possible to investigate in detail the molecular basis of the interaction between the insulin signaling cascade and mitochondrial biology in healthy and pathological scenarios , particularly in the context of T2D . In addition , and despite substantial progress achieved in the identification of candidate genes involved in specific complex processes or diseases through genome-wide association studies ( GWAS ) , for most diseases , including T2D , less than 10% of the heritability ( percentage of variance attributable to genetic variation ) can be explained by the identified genetic associations [6] . Some hypotheses suggest that a portion of the missing heritability stays behind multiple small effect size variants that have not yet reached genome-wide significance in GWAS meta-analyses when tested individually , due to insufficient sample sizes . If many of the modest effect variants are assumed to implicate genes that function in a limited number of biological processes , collective analysis of variants based on prior biological knowledge could substantially enhance association detection power . In that sense , the application of systems biology approaches to analyze GWAS data may have the potential to increase the chances of unraveling susceptibility genes or biological processes for complex diseases . In this study , we applied systems biology approaches to screen and identify novel candidate T2D genes . The search has been guided by the hypothesis that the functional components of the crosstalk between the insulin signaling pathway and the biology of the mitochondria may play a role in the etiology or the evolution of the disease . We have also generated and analyzed gene expression data on insulin resistance and mitochondria perturbed scenarios to support these candidate genes . We finally tested whether particular genetic variants in loci that contain the identified genes could be collectively associated with T2D .
In order to identify genes specifically involved in the crosstalk between the insulin signaling pathway and the mitochondria , we looked for all possible direct and indirect functional interactions between mitochondria and insulin signaling genes ( Figure 1 ) . We started by building reliable models and parts lists for these two systems . We first explored and manually filtered several public versions of the insulin signaling pathway to end up with a confident collection of 197 proteins/genes ( see Methods ) . At the same time , we extracted data from a database of nuclear and mitochondrial-encoded mitochondrial proteins ( MitoP2 ) to generate the corresponding list of 682 mitochondria genes [7] . Once both parts lists were constructed , we screened several large functional interaction databases to identify direct and indirect connections involving any of the protein/genes of each of the systems . We applied several filters and cutoffs to be able to isolate , from all available interactions , a reliable collection that will be used further in our study . For example , from protein-protein interaction ( PPI ) data , we only considered those protein pairs whose interactions were reported by two or more independent laboratories ( PPIhigh ) and whose pair of genes were reported to be expressed both in any of the insulin-sensitive tissues ( adipose tissue , muscle , liver and heart , [8] ) ; or any other PPI interaction reported only by a single laboratory , simultaneously expressed in any of the insulin-sensitive tissues and that also showed co-expression ( gene-expression correlation ) in a dataset of 427 healthy human liver samples [9] ( these interactions are here termed PPIcorr ) . As a third layer of functional interaction , we also linked those proteins observed to belong to the same protein complex as described in the CORUM protein complex database [10] . The fourth source of interaction consisted of pairs of genes coding for enzymes that participate in linked metabolic reactions , i . e . those reactions that are adjacent in a metabolic reaction map according to the Biochemical Genetic and Genomic ( BiGG_met ) and the Kyoto Encyclopedia of Genes and Genomes database ( http://www . genome . jp/kegg/kegg2 . html; KEGG_met ) [11] , [12] , [13] . Finally , we also included those interactions between genes coding for complexes or genes linked in a signaling pathway , as defined by KEGG ( KEGG_path ) [12] . This final functional interactome comprised 57 , 751 high confidence functional interactions involving 6963 genes , which represent a whole functional network of insulin-targeted tissues or cells . From the pool of selected high quality interactions ( affecting 6963 genes ) , we finally selected those interactions that , either directly or indirectly , provide a link between the mitochondrial and the insulin signaling cascade genes . We defined indirect interactions as those mediated by genes , termed linker or internode genes , that do not belong to either the insulin or the mitochondria parts list , but that are simultaneously connected to both systems . By applying these filters , we finally generated the mitochondria-insulin ( MITIN ) network consisting of 886 genes and a total of 1259 interactions , 70 direct ( Table S1 ) and 1194 indirect . The 70 direct interactions involved 44 insulin genes and 37 mitochondria genes , most of them showing only one evidence of interaction . Both the insulin and mitochondria genes that were directly connected were linked to a median of two genes from the other system . Direct connections showed heterogeneous sources of interaction: PPIhigh , PPIcorr , Corum Complexes , BiGG_met , KEGG_met , Kegg_pathway , contributed 41 , 9 , 13 , 2 , 3 , 12 links , respectively . Indirect interactions involved 286 linker internode genes ( Figure S1 , Dataset S1 , Table S2 and S3 ) . These internodes genes were connected to a mean number of 2 . 1 Insulin and 1 . 7 mitochondria genes and showed a mean of 2 . 6 and 2 . 0 lines of evidence of interaction with insulin and mitochondria , respectively . Regarding the 1194 indirect connections , PPI , PPIcorr , Corum Complexes , BiGG_met , KEGG_met , Kegg_pathway , contributed 570 , 472 , 1263 , 42 , 160 , 169 interactions , respectively . While the majority of the internode genes seem to be novel , as their bridging role connecting both systems has not yet been described , some of them have already been shown to interact with both systems , which constitutes an internal positive control of our underlying search methodology . For example , TRAF2 shows interactions within our MITIN network with four insulin and two mitochondrial genes ( Table 1 ) . Interestingly , other independent studies and approaches also identified five of these interactions . In particular with MAP3K1 ( MEKK1 ) , CAV1 ( caveolin-1 ) and MTOR ( mTOR ) , from the insulin signaling [14] , [15] , [16] and MAP3K5 ( ASK1 ) and CASP8 ( caspase-8 ) from the mitochondria [17] , [18] ( Figure 2 ) . Another example is NFKB1 , for which we found interactions with four insulin signaling and three mitochondrial genes . As above , NFKB1 has been also reported to interact with the IKBKB [19] , [20] , AKT2 [21] , MAP3K1 [22] and SOCS3 insulin genes , as well as to BCL2L1 [22] [23] and BCL2 [24] ( Figure 2 ) . The same MITIN network also allowed us to define which mitochondrial genes are more connected to insulin signaling , and vice-versa , either directly or indirectly . The top five insulin signaling genes most connected to mitochondria are NOLC1 , RPS6 , IKBKB , PKLR , SRC , with a total of 99 , 40 , 31 , 28 and 22 indirect connections with mitochondria , respectively . Similarly , the five most connected mitochondrial genes with the insulin cascade were TUFM , TP53 , SLC25A5 , POLG , ESR1 , with a total of 93 , 36 , 29 , 25 , and 19 indirect connections , respectively ( Table S4 ) . We next explored whether our collection of internode genes where enriched in particular functions or processes by querying the Molecular Signatures Database [25] . We found up to 148 functional signatures for which internode genes were significantly enriched ( 5 . 7×10−107<p value<4 . 41×10−6 , 1 . 94<Odds ratio<20 . 1; Table S5 ) . Besides several enriched categories related to translation , Reactome Regulation of Expression in Beta Cells ( p = 3 . 5×10−87 , Odds Ratio = 15 . 8 ) , Reactome Insulin Synthesis and Secretion ( p = 4 . 46×10−79 , Odds ratio = 14 . 0 ) , and Reactome Diabetes pathways ( p = 1 . 39×10−35 , Odds ratio = 5 . 5 ) were also highly enriched among our set of internode genes . No significant categories were found after correcting for multiple testing in a set of internode genes identified from a simulated network made of randomly generated interactions . In order to facilitate the selection of any of these genes for further studies , we have ranked them according to their number of connections to each of the systems . Hence , we provide a confident subset of 31 genes with at least three lines of evidence linking insulin signaling and mitochondria genes simultaneously ( Table 1 ) . As further support of the functional relationship between internode genes and both , the mitochondria and the insulin signaling pathway , we explored whether the expression of these identified internode genes is modified after perturbing each of the mitochondria or insulin signaling systems independently . To test the effect of the insulin signaling perturbation , we performed gene expression profiling of C2C12 differentiated myotubes that were either left untreated or treated with 100 nM insulin for 2 days in order to induce an insulin resistance state . This treatment resulted in the downregulation of the insulin receptor and subsequently significantly reduced insulin signaling cascades [26] . We used the gene set enrichment analysis method ( GSEA , [25] ) to look for enrichment of differential expression using our set of internode , mitochondria , and insulin genes as molecular signatures . Using the collection of all 6963 genes with identified interactions as a background , we found significant enrichment of upregulation within the internode genes ( Normalized Enrichment Score ( NES ) = 1 . 7; False Discovery Rate ( FDR ) = 0 . 0013 ) , while observed downregulation enrichment within the insulin signaling genes ( NES = −1 . 4; FDR = 0 . 028 ) ( Figure 3a ) . We also explored a second model of insulin signaling cascade perturbation through the analysis of transcriptome data from myotubes treated with RNAi against DOR ( also named Tp53inp2 ) . This gene is dysregulated in muscle of Zucker diabetic rats , participates in the myogenic differentiation and mediates a feed-forward loop between ecdysone receptor and the insulin signaling in flies [27] , [28] . In this model , we also found that there was an enrichment of upregulated internode genes ( NES = 1 . 4; FDR = 0 . 004 ) and enrichment of downregulated insulin ( NES = −1 . 35; FDR = 0 . 007 ) and mitochondrial ( NES = −1 . 36; FDR = 0 . 001 ) genes ( Figure 3c ) . In a parallel experiment we tested how perturbations of mitochondria affect the expression of the MITIN network genes . For this , we analyzed gene expression from the heart of Peroxisome-proliferator-activated-receptor γ coactivator 1 beta ( PGC-1β ) knock-out mice . PGC-1β is a co-activator that regulates mitochondrial biogenesis and function [29] , [30] , [31] , [32] . The analysis of heart gene expression of these mice showed an overrepresentation of upregulated genes within the internodes ( NES = 1 . 3; FDR = 0 . 02 ) , enrichment of upregulated genes within the insulin genes ( NES = 1 . 6; FDR = 0 . 0012 ) , and enrichment of downregulated mitochondria genes ( NES = −2 . 63; FDR = <0 . 0001 ) ( Figure 3b ) . Again , as a control from our experiment , randomly generated internode genes did not show any enrichment in any of these experiments ( Figure 3d ) . We next investigated whether any of these genes has been associated to phenotypes related to insulin resistance or energy metabolism . For this , we searched through the OMIM database ( http://www . ncbi . nlm . nih . gov/omim ) those internode genes that are involved in mendelian and complex disorders [33] . We found that , among all 286 internode genes , 191 ( 66% ) were in genomic loci associated to complex diseases or traits ( SNPs within 250 kb from internode gene were considered ) and 17 ( 6% ) were involved in mendelian diseases . Interestingly 53 of the genes ( 18% ) contained or were near polymorphisms associated to T2D or related traits such as obesity , adiposity , response to glucose challenge , hypertension or coronary artery disease ( Table S6 ) . 10 , 000 random simulations showed that finding 53 genes associated to T2D related traits was modestly more than what expected by chance ( p = 0 . 0535 ) . In contrast , the 10 , 000 random simulations also showed that we did not find more associations with any complex trait ( not restricting to T2D related traits ) , than would be expected by chance , suggesting that the enrichment for associations of the identified internode genes is specific for T2D and related metabolic traits . In order to further investigate the potential involvement of the internode genes in the etiology of T2D , we screened the DIAGRAM consortium GWAS dataset , which consisted on the largest T2D meta-analysis available at the time of the study ( DIAGRAM meta-analysis ) : 8 , 130 cases and 38 , 987 controls [34] . To analyze enrichment of associated genes within the internodes , we used MAGENTA [35] , a software specifically designed for large genome-wide association study meta-analyses , where individual genotypes are typically not available . We found that our internode gene list showed nominal enrichment for modest to strongly associated genes within the top 5% of T2D scores , with 18 genes observed , including three already confirmed T2D associated SNPs [34] , [36] , [37] , compared to the 12 expected by chance ( p = 0 . 0549 , Table 2 ) . These results were robust to the enrichment cutoff used ( p = 0 . 0368 when testing for enrichment above the 97 . 5th percentile of all gene scores; 6 genes expected above cutoff , 11 observed ) . Unlike the collection of internode genes , no significant enrichment for T2D associations was found for gene-sets belonging only to the insulin signaling ( p = 0 . 71 ) or to the mitochondrial ( p = 0 . 52 ) systems . The insulin and mitochondria genes directly interacting with each other were also not enriched for T2D associations ( p = 0 . 53 ) . To further support the involvement of at least some of these 18 internode SNPs in glucose metabolism regulation , we also computed how the best associated SNPs in the 18 regions increased the risk of altered glycemic traits , available from MAGIC consortium datasets [38] , [39] , [40] , [41] , [42] , using an approximation approach developed by Toby Johnson [43] . Among the seven traits tested , we found a significant association risk score for fasting glucose ( p = 8 . 12×10−5 including the 18 top ranked SNPs and p = 0 . 004 including 15 out of the 18 SNPs not previously associated with T2D ) . In order to evaluate the probability of finding such a highly statistical p-value , when using the top T2D associated genes ( and best local SNPs ) we ran MAGENTA on 10 , 000 simulated random gene-sets , and extracted for each simulation the p-values of the most significant SNP per gene for all genes that ranked above the 95th percentile . The empirical p-value was then calculated as the frequency of random gene-sets whose p-values were smaller than the one obtained with the real data and whose effect size was higher than 0 . We found that 8 . 12×10−5 is significantly lower than what one can expect by chance ( p = 0 . 0144 ) , confirming the association of our set of internode genes , not only with T2D , but also to fasting glucose levels . To further explore the involvement of the internode genes associated with T2D ( see above ) in related metabolic traits we explored several available GWA meta-analyses pertaining to obesity-related traits from the GIANT consortium [44] , [45] , seven glycemic traits from MAGIC datasets [38] , [39] , [40] , [41] , [42] , and cardiovascular disease traits from the ICBP consortium [46] . We found that in 10 of the 18 internode genomic loci with modest to strong associations , there was at least one SNP showing association ( p<10−5 ) to one of these metabolic traits . For example , rs6453220 , located in the IQGAP2 intron , was associated to circulating glycated hemoglobin ( p = 4 . 19×10−6 ) and rs13107325 , located upstream of NFKB1 , was strongly associated with diastolic blood pressure ( p = 7 . 53×10−7 ) , body mass index ( p = 1 . 37×10−7 ) , high density lipoprotein levels ( p = 7 . 2×10−11 ) , and systolic blood pressure ( p = 2 . 57×10−7 ) .
Understanding the molecular basis of insulin resistance is essential for the early diagnosis , treatment and prevention of T2D and related co-morbidities , such as hyperlipidemia or cardiovascular disease . In this study we explored the molecular basis of insulin resistance beyond the known role of insulin signaling genes , and , implicitly screened for novel candidate T2D genes . Based on published evidence that connects the function of the mitochondria with insulin resistance and T2D [5] , [47] , [48] , [49] , we hypothesized that there are genes responsible for the crosstalk between the mitochondria and the insulin signaling system , which makes them good candidates for T2D . By screening and filtering a variety of available functional interaction data , we have first generated a conservative network ( MITIN ) containing all genes involved in or connected to the insulin signaling or mitochondrial systems , not only through PPI but also based on interactions of other nature , including co-expression , protein complexes , and signaling and metabolic interactions . From there , we then selected a fraction of 286 internode genes that show connections to genes of both systems and are , therefore , likely to be involved in the functional crosstalk between the insulin signaling cascade and the mitochondria . We have examined these genes at different levels to validate their bridging role and their potential implication in T2D or co-morbidities . In order to provide a more stringent list amenable to low throughput molecular biology experiments in future studies on insulin resistance and diabetes , we ranked these genes on the basis of their level of connectivity to insulin and mitochondrial genes and generated a high confidence subset of 31 genes showing three or more functional connections to each of the systems . While there are no reported confirmatory data for the majority of the 286 internode genes , some have been already found to be linked to both systems , and even to T2D and related metabolic processes . For example TRAF2 [14] , [15] , [17] , [18] , NFKB1 [19] , [20] , [21] , [22] , [23] , [24] ( Figure 2 ) and SMAD3 [50] , which show multiple connections to insulin signaling and to mitochondrial genes in our MITIN network , have also been described elsewhere to interact with genes of both systems . In addition , variants near the NFKB1 gene have been associated to T2D based on the DIAGRAM dataset ( best nearby SNP p-value = 1 . 6×10−5 ) , while SMAD3 has been recently found to protect against diet-induced obesity as well as coronary artery disease [50] , [51] . Other genes that also emerge as connecting internode genes in our MITIN network , such as the chaperone HPSP90AA gene , have not been previously described as linked to the insulin or the mitochondrial systems , but have been linked to insulin resistance conditions and hence to T2D [52] , [53] . On top of the previous knowledge on some of the internode genes , we provide here further evidence that supports the robustness of our search strategy and of this collection of genes as potential molecular connectors of these systems , as well as insulin resistance or T2D candidate genes . First , the 286 internode genes showed significant enrichment of functional categories , like “regulation of beta cell development” ( p = 2 . 1×10−79 ) , “insulin synthesis and secretion” ( p = 3 . 4×10−79 ) and “diabetes pathways” ( p = 1 . 9×10−35 ) . Second , experimental models of mitochondria and insulin signaling perturbation caused a significant upregulation of the internode genes . This could be the result of direct regulation or a mechanism that compensates these perturbed metabolic scenarios . In all cases , the expression analyses helped us to confirm that these genes are indeed functionally connected to both systems . Furthermore , the deregulation of these internode genes under experimental conditions of insulin resistance suggests their involvement in T2D . Encouraged by our positive functional and expression results supporting the connecting role of the internode genes and their impact on T2D , we went one step further and used the MITIN network as a basis for the identification of genetic signatures associated with T2D , contributing to unraveling its missing heritability . We tested for enrichment of T2D associations within the newly identified internode genes , by analyzing the results from the DIAGRAM GWA meta-analyses [34] using MAGENTA to define gene association scores and enrichment of gene associations [35] . We found enrichment of T2D variants within this group of genes , involving 18 associated genes compared to the 12 that were expected by chance ( p = 0 . 0549 ) . Our study also confirms the absence of significant signal when we tested insulin signaling and mitochondria gene-sets for enrichment of T2D associations . This is in agreement with previous studies , where no enrichment was found for mitochondrial or insulin signaling genes [34] , [35] , and suggests that the genes involved on the crosstalk between the insulin and mitochondria networks are more susceptible to harbor T2D risk variants than those that belong to either the insulin cascade or the mitochondria alone . The best local SNP in each of the 18 top ranked regions showed a combined risk score of increased fasting glucose levels according to MAGIC consortium data-sets ( p = 8 . 12×10−5 ) . Also supporting these results , several variants in the internode genomic regions identified by MAGENTA were also associated with many metabolic related quantitative traits , as reported by the MAGIC [38] , [39] , [40] , [41] , [42] , GIANT [44] , [45] and ICBP [46] consortia ( Table 2 ) . Interestingly , the best-associated SNP in four of the 18 genes were among the 43 already validated loci of susceptibility for T2D , which in the former reports were assigned to ZBED3 , BCL11A , PRC1 , and KCNJ11 genes , based only in proximity [34] , [36] , [37] . Taking into account the intrinsic challenge in linking an associated variant to its causal gene , we cannot exclude that these SNPs may be proxies for causal variants affecting our group of identified internode genes . Accordingly , recent findings suggest that a fraction of regulatory variants can be more than 500 Kb away from their regulated gene and that a single locus can expand more than 1 Mb , and even contain more than one independent causal variant [54] , [55] , [56] . Among the top 18 top ranked internode genes identified by MAGENTA analyses of T2D GWAS meta-analysis , there are independent lines of evidence suggesting the involvement on the development of T2D or insulin resistance . For example , two members of the IQ-motif-containing GTPase-activating protein ( IQGAP ) family , scaffold proteins involved in a wide range of cellular and signaling processes , including cytoskeletal organization , cell adhesion , and tumorigenic processes [57] , [58] , appear in the top 95th percentile for association with T2D according to MAGENTA analysis . IQ motif containing GTPase activating protein 2 ( IQGAP2 ) , the second ranked gene according to MAGENTA analysis , contained an intronic low frequency SNP ( rs6453220; MAF = 0 . 05 ) , which was strongly associated with glycated haemoglobin according to MAGIC WGA-meta-analyses ( Hb1Ac; p = 4 . 19×10−6 ) , providing more evidence that variants in IQGAP2 may contribute to insulin resistance . In addition , another gene of the same family , IQGAP1 ( top four according to MAGENTA ) , was recently reported to bind the target of rapamycin complex 1 ( mTORC1 ) having a potential negative feedback loop role upstream mTORC1/S6K1 AKT1 activation [59] . Furthermore , IQGAP1 associates with PKA and AKAP79 in pancreatic Beta cells , suggesting a role in the Beta-cell development and physiology [60] . It is also worth mentioning that IQGAP1 was also found upregulated in our chronic insulin treatment experiment ( fold change 1 . 4; FDR<0 . 01 ) and the Tp53inp2 RNAi treatment in myotubes experiment ( fold change = 1 . 33; FDR = 0 . 1 ) . These results , together with the general role of scaffolding proteins as hubs of signaling pathways further supports the implication of the IQGAP protein family in the insulin signaling and the mitochondrial systems crosstalk and its association to T2D . RAB4A ( Best SNP p value = 3 . 5×10−5 ) is a GTPase that regulates glucose transporter GLUT4 [61] , and is suggested to participate in metabolic remodeling in the diabetic heart [62] . Finally , breast cancer anti-estrogen resistance 1 ( BCAR1 ) , ( Best SNP p value = 6 . 61×10−5 , distance from gene = 16 . 5 Kb ) is another gene that deserves attention , as is connected to 10 insulin genes , according to our network: CRK , SRC , PTPN1 , PTK2 , CRKL , PIK3R1 , GRB2 , PTPRF , RHOA and PTPRA . Interestingly , a SNP in an intronic region 16 Kb upstream this gene was reported to be strongly associated with type 1 diabetes [63] . In summary , this study contributes to untangling the molecular basis linking the mitochondria and the insulin signaling systems and provides a subset of novel T2D candidate genes for further genetic , molecular and clinical studies . This study also constitutes a proof of concept of the utility of combining several integrative systems biology approaches with the analysis of gene expression and large GWA meta-analyses to uncover novel associations with complex diseases of otherwise hidden candidate genes .
We constructed a consensus insulin pathway from several public resources , including ( Biocarta; www . biocarta . com , Kegg [12]; www . genome . jp/kegg/ , and PID; [64]; http://pid . nci . nih . gov/ ) and a commercial resource ( Biobase; www . biobase . de ) . This pathway was manually curated and refined by the participation of molecular biologists in the field . In order to select the parts lists that compose mitochondrial proteins or genes , we have selected a total set of 900 proteins from the mitoP2 database ( www . mitop2 . de/; [7] ) . As it was done for the insulin pathway , the set has been manually curated by the participation of the expert groups in the consortium . To allow for transferability of the results to other species , we have identified each mouse orthologous gene/protein for all involved proteins . To identify protein-protein interactions we used a non-redundant set of 23 protein interaction datasets and only included those interactions reported independently by two different laboratories ( PPIhigh ) [8] . For the gene co-expression analysis , we used the dataset of Schadt et al . [9] , which consists of expression data of 427 healthy human liver samples and constituted the largest insulin-sensitive human transcriptome dataset . We evaluated the overlap between gene co-expression in liver and low confident PPIs ( those reported only by a single lab ) to provide a new source of high confident interactions . Third , we added those interactions that pertained to the CORUM complex database [10] , considering that two genes are functionally linked if they both pertain to a common complex . The fourth source of interaction consisted of pairs of genes coding for those enzymes that participate in linked ( or consecutive ) metabolic reactions as described in KEGG or BiGG databases [11] , [12] , [13] . Finally , we also considered those interactions between genes coding for complexes or genes linked any signaling pathway , as defined by KEGG [12] . We used the Molecular Signatures Database from the Broad Institute ( [25]; http://www . broadinstitute . org/gsea/msigdb ) and for a total of 6770 gene sets , we computed an enrichment score based on a Chi-Square test . The corrected significant p-value after applying Bonferroni's correction for all the tests was 4 . 41×10−6 . We only considered gene sets that had at least 10 genes within the group of internode genes . All statistical analyses were performed using Bioconductor ( Gentleman et al . , 2004 ) . Microarray data was normalized via quantile normalization and summarized to probeset expression estimates via robust multi-array average ( RMA ) ( Irizarry et al . , 2003 ) using the function rma from the oligo package . All the newly generated data was deposited in the Gene Expression Omnibus ( GEO ) ( http://www . ncbi . nlm . nih . gov/geo ) database ( GSE3932 ) . We used gene set enrichment analysis ( GSEA ) ( Subramanian et al . , 2005 ) as implemented in the Bioconductor library phenoTest [65] to assess the degree of association between gene expression and the following signatures: insulin , mitochondria and internodes . As indicated in Subramanian et al . [25] , P-values were computed restricting attention to simulated ES with the same sign as ESobs . All chemicals and reagents were purchased from Sigma-Aldrich , ( Poole , UK ) . Briefly , C2C12 cells were cultured in Dulbecco's modified Eagle media ( DMEM ) supplemented with 10% Fetal bovine serum , and penicillin/streptomycin . To induce differentiation media was replenished by DMEM containing 2% ( v/v ) of horse serum with penicillin/streptomycin . Myotubes between days 4 and 7 following the induction of differentiation were used for experiments . For chronic insulin treatment cells were either left untreated or incubated with 100 nMinsulin in DMEM for 48 h in fusion medium to induce an insulin resistance state . Medium was changed every 24 h . Hearts were quickly collected and snap frozen in liquid nitrogen from wild-type and PGC-1β KO on a mix background ( sv129 and C57BL/6 ) generated as previously reported [31] . Animal procedures were performed in accordance with the UK Home Office regulations and the UK Animal Scientific Procedures Act [A ( sp ) A 1986] . Animals were housed in a temperature-controlled room with a 12-h light/dark cycle . Food and water were available ad libitum . Lentiviruses encoding scrambled or DOR siRNA were used as reported [27] . Fifteen million C2C12 myoblasts grown on 12-well plates were transduced at moi 100 and cells were amplified during 5–7 days . Transduced cells ( GFP-positive ) were then sorted with a MoFlo flow cytometer ( DakoCytomation , Summit v 3 . 1 software ) , obtaining between 93%–99% GFP-positive cells . Confluent C2C12 myoblasts previously infected with lentiviruses encoding scrambled RNA or DOR siRNA were allowed to differentiate in 5% horse serum-containing medium for 4 days . Total RNA was purified and microarrays were performed by using an Affimetrix platform . We used the latest DIAGRAM T2D GWA meta-analysis comprising 8 , 130 cases and 38 , 987 controls [34] and the MAGENTA software was used to test for enrichment of associations in the 286 internode genes [35] . Briefly , we assigned to each gene a set of SNPs that lie within 500 Kb upstream and downstream of the gene's most extreme transcript boundaries . This boundaries were based on the fact that a fraction of regulatory variants can be up to 500 Kb distal to their regulated gene and that a single locus may harbor more than one causal variants , and extend to more than 1 Mb from the locus top hit [54] , [55] , [56] . For each gene , a score was assigned based on the most significant SNP , followed by correction for confounders , including gene size , number of independent SNPs , and linkage disequilibrium-based properties . Once all the association scores were computed , MAGENTA tested for over-representation of genes in a given gene set above a predetermined gene score rank cutoff , which in this case was the 95th percentile of all gene scores . The enrichment is evaluated against a null distribution of gene sets of identical set size that were randomly sampled from the 6963 genes that constitute our complete interactome based on all identified functional interactions . We computed how the best associated SNPs in the 18 regions could collectively increase the risk of altered glycemic traits available from MAGIC consortium datasets [38] , [39] , [40] , [41] , [42] . We used the method described in [43] . An unweighted genetic risk score was defined for each individual as the sum of the number of risk increasing alleles at each of the 18 SNPs of interest . If one had access to individual-level data , association between SNP score and glycemic traits could be tested using the usual approach . However , when the risk score involves SNPs in linkage equilibrium , it was shown [43] that association between risk score and trait can be assessed using meta-analysis results only , without going back to individual-level data . The effect of the risk score on the phenotype is estimated bywhere is the meta analysis effect size for SNP j , and aj is the inverse of the standard error estimate of . The assumption of no Linkage Disequilibrium ( LD ) is required for the contribution of each SNP to be independent and for the standard error estimate to be valid . P-value for the risk score association can be assessed using the ratio of the SNP score effect estimate divided by its standard error , and assessing the significance of the ratio by comparing it to the standard normal distribution . This large sample procedure will result in valid p-values under the null hypothesis of no relationship between the trait and variants included in the risk score . | It has been shown that the crosstalk between insulin signaling and the mitochondria may be involved in the etiology of type 2 diabetes . In order to characterize the molecular basis of this crosstalk , we mined and filtered several interaction databases of different natures , including protein–protein interactions , gene co-expression , signaling , and metabolic pathway interactions , to identify reliable direct and indirect interactions between insulin signaling cascade and mitochondria genes . This allowed us to identify 286 genes that are associated simultaneously with insulin signaling and mitochondrial genes and therefore could act as a molecular bridge between both systems . We performed in vitro and in vivo experiments where the insulin signaling or the mitochondrial function were disrupted , and we found deregulation of these connecting genes . Finally , we found that common variants in genomic regions where these genes lie are enriched for genetic associations with type 2 diabetes and glycemic traits according to large genome-wide association meta-analyses . In summary , we reconstructed the network implicated in the crosstalk between the mitochondria and the insulin signaling and provide a list of genes connecting both systems . We also propose new potential type 2 diabetes candidate genes . |
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Speciation events often occur in rapid bursts of diversification , but the ecological and genetic factors that promote these radiations are still much debated . Using whole transcriptomes from all 13 species in the ecologically and reproductively diverse wild tomato clade ( Solanum sect . Lycopersicon ) , we infer the species phylogeny and patterns of genetic diversity in this group . Despite widespread phylogenetic discordance due to the sorting of ancestral variation , we date the origin of this radiation to approximately 2 . 5 million years ago and find evidence for at least three sources of adaptive genetic variation that fuel diversification . First , we detect introgression both historically between early-branching lineages and recently between individual populations , at specific loci whose functions indicate likely adaptive benefits . Second , we find evidence of lineage-specific de novo evolution for many genes , including loci involved in the production of red fruit color . Finally , using a “PhyloGWAS” approach , we detect environment-specific sorting of ancestral variation among populations that come from different species but share common environmental conditions . Estimated across the whole clade , small but substantial and approximately equal fractions of the euchromatic portion of the genome are inferred to contribute to each of these three sources of adaptive genetic variation . These results indicate that multiple genetic sources can promote rapid diversification and speciation in response to new ecological opportunity , in agreement with our emerging phylogenomic understanding of the complexity of both ancient and recent species radiations .
Speciation—the origin of new species—occurs when diverging lineages accumulate ecological , functional , and/or reproductive differences that result in their evolutionary independence from close relatives . Rates of speciation vary widely among groups , but the underlying causes of this rate variation , especially the conditions that promote bursts of adaptive divergence across short timescales ( “adaptive radiations” ) , are still under debate [1–6] . New ecological opportunity , although likely essential , appears to be insufficient as the sole explanation for many contemporary cases of species radiation [7 , 8] . Instead , intrinsic factors might be more critical , including the availability of sufficient genetic variation to respond to ecological conditions or of novel traits that accelerate rates of diversification [2 , 9] . To understand these factors requires a detailed understanding of both the ecological transitions and the underlying molecular genetic changes that accompany , and potentially facilitate , speciation . Whereas classical genetic studies of speciation were often limited to relatively few loci or genomic regions , modern sequencing can interrogate genome-wide patterns of molecular differentiation during speciation and can potentially reveal the genetic substrate of associated trait changes . Recent studies have begun to uncover several intriguing patterns of phylogenomic divergence , especially in rapidly radiating groups . One such pattern is a persistent discordance among genes for particular phylogenetic relationships , regardless of the quantity or quality of molecular data sampled . This discordance is often caused by incomplete lineage sorting ( ILS ) , in which shared ancestral variation fails to fix between closely timed speciation events [10 , 11] . This sorting of ancestral variation causes conflicting phylogenetic signals , and is especially common in groups radiating both in recent history ( e . g . , African rift cichlids [7] , Drosophila simulans group [12] , platyfish [13] , and horses [14] ) and at deeper timescales ( major land plant families [15] and major bird lineages [16] ) . A second emerging pattern is that postspeciation hybridization ( introgression ) appears to be substantially more commonplace than previously appreciated . A diverse range of animal groups—including butterflies , horses , fish , flies , mosquitoes , and Galápagos finches [8 , 12–14 , 17 , 18]—all show evidence of postspeciation gene flow . The frequency and extent of introgression is remarkable given that introgressive hybridization has played little role in conventional models of animal speciation and diversification [19] . Overall , both ILS and postspeciation introgression contribute to generating more complex evolutionary histories than can be represented by simple bifurcating trees . In response , new approaches are being developed to account for these potential sources of gene tree discordance , including tools that can infer the underlying species tree even when there are high levels of ILS ( e . g . , MP-EST [20] , ASTRAL [21] ) . Accordingly , despite these complexities , several genome-wide studies of diversification have successfully clarified ambiguous species relationships and highlighted loci that might underpin specific functional or ecological changes accompanying rapid phylogenetic transitions . These include loci contributing to ecologically and reproductively significant traits , such as beak size differentiation among Galápagos finches [18] . Here , we examine genome-wide patterns of lineage divergence among all species in the wild tomato clade ( Solanum sect . Lycopersicon ) using whole transcriptome sequencing ( RNA-Seq ) . In addition to domesticated tomato ( S . lycopersicum ) and its conspecific wild relative , the group includes 12 species native to the Galápagos Islands and Andean South America , a biodiversity hotspot ( Fig 1 and S1 Fig ) , and the clade has been estimated to share a common ancestor ~2 million years ago ( Ma ) [22] . All lineages are diploid and chromosomally homosequential , except for a few small rearrangements that distinguish some species [23–25] . Wild tomato species are differentiated for numerous functional , ecological , and reproductive traits , and display different habitat associations at macroecological scales ( Fig 1 ) [26–29] . They also exhibit strong , but often incomplete , reproductive isolating barriers at various pre- and postzygotic stages [30–35] . Nonetheless , efforts to infer species phylogenies and the timing of lineage divergence have met with mixed success and have revealed chronically challenging taxa in this group [23 , 27 , 36 , 37] . Given its ecological and reproductive diversity , the wild tomato group presents a unique opportunity to examine the genome-wide signatures of rapid recent divergence . While the timescale of speciation is comparable to other recent phylogenomic studies of radiating animal clades ( e . g . , [7 , 17 , 18 , 38] ) , it is unclear whether plant clades such as wild tomatoes—equally rapidly radiating , but also classically perceived as having greater tendency to hybridize [39]—will differ in their genomic patterns of diversification and introgression and in the genetic variation on which this diversification is based . The aims of our study were , first , to clarify species phylogenetic relationships; second , to assess postspeciation gene flow between lineages; and third , to investigate the genomic basis of lineage-specific and environment-specific adaptation . We find evidence consistent with at least three genetic sources of adaptive variation: introgression among species , de novo mutation , and recruitment from ancestral variation . Our results indicate that a combination of all three of these evolutionary factors facilitated rapid adaptive expansion in response to ecological opportunity .
We sequenced whole transcriptomes ( mRNAs ) for 29 accessions from 13 tomato species and 2 outgroup species ( Fig 1 and S1 Table ) . Although these sequences came from different species , high sequence similarity allowed us to confidently align ~90% of RNA-Seq read-pairs from all accessions to the reference genome of the domesticated tomato , S . lycopersicum [40] . We aligned an average of 31 . 6 Mb per accession , covering 21 , 896 genes with an average of >26 accessions per gene . This corresponds to an average coverage of 76% of total annotated coding regions per accession , but only 3 . 9% of the full genome due to the high proportion of gene-poor heterochromatin in the tomato genome [40 , 41] . We inferred phylogenetic relationships among species using several data partitions , including: whole transcriptome concatenated , each chromosome concatenated , nonoverlapping 1 Mb and 100 kb genomic windows , and trees inferred from individual genes ( Fig 2 , S2 Fig , and S2 Table ) . We also used a majority rule method ( as implemented in RAxML [42] ) , a coalescent method ( MP-EST [20] ) , and a coalescent-based quartet method ( ASTRAL [21] ) to infer phylogenies using the 100-kb window trees ( S2D–S2F Fig ) . All concatenation , majority rule , and coalescent methods inferred a generally consistent species tree topology ( Fig 2A ) , identifying four main groups that recapitulate relationships found in previous studies [23 , 27 , 36 , 37] . As in other recent analyses , we find that S . habrochaites and S . pennellii are placed together ( the “Hirsutum” group ) and split from the other wild tomatoes at the base of the tree [37] , that S . arcanum groups with other members of our inferred “Arcanum” group rather than with the “Peruvianum” group [43] , and that some members of the “Peruvianum” group have ambiguous taxonomic placement , especially accessions of S . huaylesense [37] . In particular , one of our lineages of S . huaylesense shows evidence of extensive and recent reticulation ( as discussed further below ) , so it was omitted from our reconstructed consensus tree ( Fig 2A ) . Using molecular clock estimates , we dated several nodes that define major groups and distinct species . Our inferred date for the basal node ( 2 . 48 Ma ) agrees well with a recent fossil-calibrated estimate of 2 Ma [22] , and we confirm that some groups within the clade have very recent divergence times ( e . g . , <0 . 5 Ma for the Esculentum or “red-fruited” group; Fig 2 ) . Because of the large amount of sequence used in the concatenated whole transcriptome alignment ( 46 . 5 Mb with at least 10 accessions represented ) and the large number of loci used in coalescent-based methods ( n = 2 , 745 100-kb windows ) , our phylogeny shows strong bootstrap support for almost all nodes ( Fig 2A ) , as expected [44] . However , these summary support measures conceal rampant phylogenetic complexity that is evident when examining the evolutionary history of more defined genomic partitions ( Fig 2B , S2 Fig , and S2 Table ) . Among the 2 , 745 trees generated from nonoverlapping 100 kb segments of the genome , we inferred 2 , 743 different topologies and found wide variation in support for the specific placement of individual accessions and species ( S2 Table ) . For example , the Esculentum group is supported by ~99% of 100-kb trees , while the more diffuse Peruvianum group is supported by only 21 . 3% of trees . Gene trees show discordance both within subclades and across deeper nodes and , when examined spatially within the genome ( using “chromoplots” [17 , 45] ) , discordant topologies are observed to be interdigitated across all chromosomes ( S2 , S4 and S5 Figs ) . None of the trees generated from 100-kb segments ( Fig 2B ) matched the topology of the species tree ( Fig 2A ) . We find that shorter internodes exhibit more discordance ( S3C Fig ) , indicating that homoplasy can be excluded as major contributor to the observed discordance [46] , but consistent with high levels of ILS due to rapid speciation in the group ( S1 Text Section 3 . 1 ) . As such , our results are clearly concordant with several other recent studies of contemporary ( e . g . , [7 , 14 , 18 , 47] ) and more ancient ( e . g . , [15 , 16 , 46 , 48] ) adaptive radiations that also detect abundant evidence for genome-wide ILS . To investigate these patterns of discordance further and to more accurately assess heterozygosity in these wild species , we used a high-depth ( HD ) dataset of 12 . 1 million sites with ≥10X sequencing coverage for all samples . Consistent with very recent divergence , tomato species differ on average by ~1% nucleotide divergence , ranging from 0 . 05% between Galápagos species to 1 . 58% between the most distantly related pairs ( full table in S1 Data 1 . 2 ) . Within-accession variation ranged from 0 . 05%−1 . 1% heterozygous sites ( Fig 3A ) and was higher in outcrossing ( self-incompatible ) lineages compared to more inbreeding ( self-compatible ) lineages , as expected [49 , 50] . In contrast , the proportion of loci that showed shared genetic variation across major subclades was approximately the same across all accessions ( Fig 3B ) ; that is , all lineages appear to exhibit equivalent levels of shared ancestral genetic variation , regardless of their overall proportion of heterozygous sites . Since both ILS and introgression manifest as discordant phylogenetic relationships , distinguishing these two factors is challenging , even with new methods developed specifically to address this issue [51 , 52] . Nonetheless , we detected evidence for a highly variable history of cross-species introgression , including one clearly reticulate lineage , a few cases of clearly demarcated and chromosomally localized introgressions between lineages , and many lineages with little or no evidence of introgression ( S4 and S5 Figs ) . These observations in wild accessions are in addition to observed evidence of intentional introgression of wild alleles into domesticated accessions , which are presumably for crop improvement [53] and well documented in other studies [54 , 55] but excluded here to focus on introgression in nature . In the case of reticulate lineages , hua-1360 ( in particular ) , and hua-1364 and per-2744 ( to a lesser extent ) show extensive phylogenetic conflict and patterns of recent hybridization ( S4 Fig ) . In hua-1360 , 48% of gene trees indicate that this lineage has a closer relationship with the Esculentum group than the Peruvianum group , where it has traditionally been placed based on morphological and reproductive characters [56] . Our finding agrees with another recent study that found this accession to be admixed [43] , and with our analysis indicating that , of all accessions analyzed here , this lineage has the highest taxonomic instability index [57] , a metric of the consistency of topological placement of individual taxa in a phylogeny ( S1 Data 1 . 26 ) . In addition , 40% of heterozygous sites in hua-1360 contain at least one allele that is otherwise Peruvianum- or Esculentum-specific ( S4 Fig ) , indicating that the hybridization event that produced this accession is relatively recent . Unsurprisingly , including this reticulate lineage when inferring the whole-transcriptome phylogeny causes the Peruvianum group to appear to be paraphyletic with respect to the Esculentum and Arcanum groups ( S2A Fig ) . While the level of reticulation observed in these three lineages was surprisingly high , it is consistent with both the history of contested species definitions in the Peruvianum group and the particularly uncertain status of S . huaylasense—a recently described species with populations that have had conflicting taxonomic designations [43 , 58] . To assess introgression across all species in the clade , we calculated genome-wide D-statistics [51 , 59] . In addition , for nonoverlapping genomic windows , we computed D-statistics and DFOIL statistics [52] to identify spatially localized regions of introgression . Because there are 2 , 925 trios of taxa that can be analyzed in the D-statistic framework , we inferred the timing of introgression based on shared signals among related species . Based on genome-wide D-statistics , we inferred that the majority of introgression occurred among relatively ancient lineages rather than across more recent splits ( S5 Fig and S1 Text Sections 1 . 5 and 4 . 2 ) . To estimate the proportion of the euchromatic fraction of the genome exchanged in these ancient events , we calculated the frequencies of discordant gene trees for trios of accessions , using one representative from each lineage that was implicated in introgression by the D-statistics . Other than the reticulate genomes previously described , we noted two likely ancient introgression events ( S1 Text Section 4 . 2 and S4E and S4F Fig ) . First , extrapolating from the frequency of windows with significant D-statistics observed in our transcriptome data , the ancestor of S . habrochaites is inferred to have exchanged 8 . 7% of the euchromatic portion of its genome with the lineage that gave rise to the Esculentum and Arcanum groups . The other ancient introgression involved an estimated 8 . 8% genome exchange between the lineages ancestral to the Esculentum+Arcanum groups and the Peruvianum group , though these patterns are more difficult to interpret because of both ancestral population structure and interbreeding among Peruvianum group species ( S4 and S5 Figs ) . Except in the case of very recent introgression between several Peruvianum group accessions ( S5E Fig ) , evidence of more recent introgression between species or accessions is limited to a few cases and involves <1% of our analyzed loci ( Fig 4A , S5 Fig , and S1 Text Section 4 . 2 ) . In particular , each of the two S . neorickii accessions has a different region introgressed from a red-fruited clade donor ( Fig 4A ) . Another case involves introgression from the red-fruited clade into only one S . pennellii accession ( S1 Text Section 4 . 2 and S5D Fig ) . These cases are particularly interesting because of their recent timing , since population-specific introgressions must postdate the common species ancestor . Based on the function of the genes involved , they may also represent strong candidates for adaptive introgression [60–62] . For example , the two independent introgressions into S . neorickii correspond to different regions within the Cf-4/NL ( “Northern Lights” ) locus that is associated with resistance to the pathogenic leaf mold Cladosporium fulvum [63 , 64] . Because these two accessions of S . neorickii were sampled from ecologically distinct habitats ~1 , 350 km apart , it is plausible that the introgressions occurred in response to different local fungal pathogens . In contrast , the introgression into S . pennellii involved transfer of a gene currently without a described environment-specific adaptive role ( Solyc08g005190; pre-mRNA-splicing factor cwc22 ) . Finally , looking across all branches and all possible trios of species within the wild tomatoes , we can infer a coarse clade-wide estimate of the frequency with which introgression appears in our dataset , if we assume an arbitrary but reasonable general cutoff for inferring significant evidence of introgression . For example , across all 26 lineages that we queried within the wild tomato tree , we found 1 , 147 windows where |D| ≥ 0 . 2 , p < 1 × 10−4 , and |ABBA − BABA| ≥ 10 for any trio of three species ( out of 2 , 596 100-kb windows with 100 or more aligned sites ) . That is , about 44% of windows show some evidence of introgression over at least one branch in the tree , as expected , given that our overall sampling of taxa was found to include admixed taxa . On the basis of these criteria , then , per branch we find that 1 . 76% of our 100-kb windows show evidence of introgression . Note that if we remove the substantially admixed taxa ( hua-1360 , hua-1364 , and per-2744 ) from these calculations , we find 672 windows that are significant over the 20 remaining possible branches , and therefore that an estimated 1 . 29% ( 672 / [ ( 2 , 596 ) ( 20 ) ] ) of windows show evidence of introgression . These calculations rely on several simplifying criteria , but they permit a crude estimate of the genome-wide proportion of 100-kb windows that show evidence of past introgression , and therefore that could contribute to adaptive allele sharing between lineages . Nonetheless , it is clear that a major computational need for future phylogenomic studies is a method to simultaneously integrate data from more than four taxa in order to infer the number and specific timing of introgression events among all members of a clade . Regardless , the substantial but small estimate of clade-wide introgression we infer here also suggests that the pervasive genome-wide discordance we detect across the clade is predominantly due to the effects of ILS . Despite the extensive phylogenetic complexity observed in our genome-wide data , wild tomato species and subclades are separated by clear diagnostic ecological preferences , functional traits , and various pre- and postzygotic isolating barriers [26 , 27] . Therefore , in addition to shared ancestral variation and introgressed alleles , there should also be mutations that uniquely diagnose well-supported groups within the clade , including in loci that confer species- and group-specific traits . To identify candidates for such loci , we examined patterns of protein-coding changes to distinguish genes that showed high rates of group-specific protein-coding changes relative to group-specific synonymous changes . Because the high level of ILS detected here , in addition to lineage-specific introgression , produces highly discordant gene trees , standard approaches for inferring the timing of nucleotide substitutions may be inaccurate [65] . Therefore , we used a more conservative dN/dS-like test to identify genes with high numbers of unambiguously clade-specific sequence changes . This test requires that an amino acid substitution be exclusively observed in a particular group and be common to all members of the group ( S1 Text Section 5 ) . For each of the four main groups within wild tomatoes ( Fig 2B ) , we found hundreds to thousands of genes with protein-coding changes that were unique to all species within a group and not found in other groups ( including the outgroup ) ( S1 Data 1 . 8−1 . 20 ) . These changes are inferred to have occurred exclusively on the ancestral branch of each of our four main clades , and therefore arose during the emergence of that clade . Of these , we detected significant evidence for positive selection ( dN/dS > 1; p < 0 . 01 ) on the Esculentum ( red-fruited ) group ancestral branch in 3 . 08% of genes ( 137 out of 4 , 447 testable genes; False Discovery Rate ( FDR ) = 32 . 5% ) , 4 . 69% in the Arcanum group ( 179 out of 3 , 819 genes; FDR = 21 . 3% ) , and 3 . 96% in the Hirsutum group ( 38 out of 958 genes; FDR 25 . 2%; see S1 Data 1 . 9–1 . 12 for all genes and p-values ) . Due to the variability in the gene tree topologies particular to the Peruvianum group , the ancestral branch appeared in only 10% of genes , so this group was not tested . Results for all genes tested , regardless of the presence of a lineage-specific nonsynonymous substitution , are presented in S1 Text Section 5 . In some instances , there are clear functional consequences for these group-specific amino acid changes . For example , all members of the red-fruited Esculentum group share such changes in 10 enzymes within the carotenoid biosynthesis pathway , which is responsible for red coloration ( Fig 4C ) [40 , 66–68] . Although not all elements of this pathway have been functionally characterized , current estimates are that it contains ~31 enzymes [67]; therefore , we find nearly a third of the enzymes in the carotenoid biosynthesis pathway have novel amino acid changes specific to the group that has evolved red-colored fruits . This includes four amino acid substitutions each in Solyc06g036260 ( β-carotene hydroxylase 1; p = 0 . 005 ) and Solyc04g040190 ( lycopene β-cyclase 1; p = 0 . 043 ) . Other examples of adaptively evolving genes include 10 Arcanum-group-specific amino acid substitutions in Solyc02g067670 , an ortholog of the Arabidopsis gene UVR1 ( Ultraviolet Repair Defective 1 ) , which may be connected to adaptation to increased solar radiation at the high altitudes characteristic of these species ( Fig 1; p < 10−5 ) . We also found many putative species-specific substitutions across the tree , although more extensive intraspecific sampling will be required to confirm species-specificity . For example , both S . chmielewskii accessions shared six nonsynonymous changes in Solyc06g051460 ( ATP-dependent chaperone ClpB ) , a gene implicated in temperature stress response [69] . In addition to genes with obvious phenotypic consequences , these analyses also revealed group-specific loci with many amino-acid changes , but whose ecological functions are less clear . For example , five Esculentum-group-specific amino acid substitutions were observed in Solyc09g082460 ( a homocysteine S-methyltransferase , p = 4 . 64 × 10−4 ) . This and other cases demonstrate the potential of this analysis to discover new candidate genes whose adaptive functional consequences are currently unknown , but that are intriguing targets for follow-up work ( S1 Data 1 . 8–1 . 20 ) . Overall , across all of the loci for which we could test clade-specific sites , we found 3 . 8% of genes had evidence for positive selection ( within PAML at p < 0 . 01 ) on at least one of our three well-supported branches . Though this number includes variable fractions of false positives ( depending upon the branch involved ) , and we have conditioned on seeing lineage-specific nonsynonymous changes , it provides a crude estimate of the potential contribution of de novo mutation to new genetic variation in this clade . In addition to lineage-specific changes , the close genetic relationships among wild tomato species make it possible to conduct a clade-wide , genome-wide investigation of genetic variants associated with broad-scale ecological factors ( Fig 1 ) rather than shared genealogical history . Our expectation for this “PhyloGWAS” approach is that ancestrally segregating variants that confer an advantage to specific ecological conditions will be differentially fixed among current populations that share common environments , regardless of their phylogenetic relatedness . These genes will therefore show polyphyletic topologies that group species or accessions according to common environments . Note that this approach does not aim to detect molecular convergence ( e . g . , [70] ) , instead aiming to identify parallel selection on standing variation ( e . g . , [71] ) . Such surveys have been previously conducted within wild S . lycopersicum populations [72 , 73] , but not among the clade as a whole . While the accessions used in our study were sampled from a broad geographic and environmental range ( Fig 1 ) , these analyses are only informative when ecological conditions are not confounded with phylogenetic relationships ( i . e . , when all members of a clade are not found in similar environments ) . This requirement excludes several broad ecological variables from testing , including variation in salinity , island versus mainland , and East versus West of the Andes . In addition , many potential environmental variables are highly correlated with each other , and data are often only available at relatively coarse environmental scales ( S1 Text Section 6 ) . With these limitations in mind , we examined allelic associations with four ecological factors that were distributed among species within each of the major groups: altitude/temperature , a composite measure of seasonal climate variability , water pH , and soil heavy metal content . These factors capture broad axes of environmental variation among our samples while minimizing strongly correlated environmental variables ( S1 Text Section 6 ) . For all factors except altitude/temperature , we found numerous genes with environmentally associated alleles , and more loci than are expected to be environmentally associated by chance ( see Materials and Methods , S1 Text Section 6 ) , thereby generating a list of genes for which selection has putatively sorted functional allelic variants from variation ancestral to the entire group ( S1 Data 1 . 22–1 . 25 ) . Overall , we found 12 nonsynonymous variants ( in 12 loci ) associated with our second environmental factor ( seasonal climate variation ) , 44 nonsynonymous variants ( in 43 loci ) associated with our third factor ( soil pH ) , and 455 nonsynonymous variants ( in 401 loci ) associated with our fourth factor ( variation in heavy metals ) . None of the loci identified to have nonsynonymous variation uniquely associated with differences in environmental factors is colocalized with a chromosomal region inferred to be introgressed between specific lineages . This indicates that loci putatively subject to selection from standing variation are not associated with inferred cases of cross-species introgression . We found 12 genes with distinguishing amino acid differences between two groups of accessions that are found in distinct categories of seasonal climate variation ( Fig 4C ) described by a composite measure of latitudinal differences in temperature , precipitation seasonality , and the intensity of photosynthetically active radiation ( PAR ) ( p < 2 . 5 × 10−4; S1 Text Section 6 and S1 Data 1 . 23 ) . This list of genes includes several with potential roles in seasonal and latitudinal adaptation , including Solyc02g069460 ( photosystem I reaction center subunit III ) and Solyc12g014040 ( chloroplast protein HCF243 ) . Even more strikingly , using mineral survey data from Peru to identify four populations sampled from habitats with high environmental levels of heavy metals ( As , Cu , Hg , Ni , and Pb ) and four from areas with low levels ( Fig 4C ) , we found 401 genes with protein differences between the high and low metals groups ( p < 2 . 5 × 10−4 ) . These include a likely heavy metal binding/detoxification protein ( Solyc04g015030 ) , and two genes that require copper as a cofactor: Solyc01g005510 ( Laccase-2 ) and Solyc08g079430 ( Primary amine oxidase ) ; these and other detected loci suggest that geographical variation in heavy metals in the Andean region may be a factor in local selection for functionally important ancestral variants . We also found environmentally sorted ancestral allelic variation associated with soil pH ( S1 Text Section 6 ) , enriched above that expected due to random association . Note that , unlike in our cases of lineage-specific de novo adaptive evolution , these genes are generally characterized by only one or few nucleotide differences ( S1 Data 1 . 22–1 . 25 ) , as might be expected of alleles that are recruited from standing ancestral variation; that is , there is little reason to expect that functionally differentiated alleles would be segregating many sequence variants in the ancestral population . This small number of differences also makes it easier to determine whether introgression has contributed to the observed patterns of allele sharing . Under a model of introgression , we expect evidence for a localized block of variants that exhibit discordant phylogenetic signal regardless of whether changes are synonymous or nonsynonymous , whereas this is not expected for selection from standing variation . In our analysis , very few of the loci with environmentally associated nonsynonymous variants also had associated synonymous variants . For environmental factor 2 ( seasonality ) , none of our candidate genes had synonymous variants in addition to the identified nonsynonymous variant ( S1 Data 1 . 23 ) . For factor 3 ( soil pH ) , only 4 of 43 genes also had a single synonymous variant associated with the detected nonsynonymous variant ( s ) . Associations between SNPs within candidate loci were slightly more common for environmental factor 4 ( soil heavy metal content ) : of 401 genes with at least one environmentally associated nonsynonymous SNP , 55 loci also had 1 associated synonymous SNP . Of these 55 loci , 19 had >1 associated synonymous SNP . In these latter cases ( ~5%–14% of the identified candidates for this factor ) , we cannot unambiguously differentiate the relative contributions of standing variation and introgression . However , given the conditions of our tests of environmental association , it is unlikely that our detected candidates are frequently affected by introgression . This is because our tests for selection on standing variation explicitly required that variation in focal environmental factors be distributed among species and clades . For introgression to explain the distribution of these loci across distantly related ( and often geographically distant ) accessions would therefore require a mechanism involving multiple interspecific introgression events across different branches of the phylogeny , and in multiple geographical locations . Similarly , it is also unlikely that these results are generally explained by convergent de novo molecular changes , because each change would have to arise many times in many independent taxa , although we cannot exclude the possibility that some fraction of our loci might have been subject to convergence in one or a few taxa . While the extensive shared variation detected in this group makes phylogenomic reconstruction much more complex , it also provides a novel opportunity to use a genome-wide association approach to identify candidate loci . Accordingly , in addition to lineage specific changes , we can point to potential examples of ecological selection on ancestral alleles as another mode of adaptation in this clade . Overall , across all the loci that could be compared for associations with our four environmental factors , 2 . 6% were found to have at least one nonsynonymous variant in perfect association with at least one of these factors ( S1 Data 1 . 21–1 . 25 ) , providing a provisional estimate of the potential for selection from standing variation across the clade . Lineages of closely related species can occupy diverse ecological roles , but the conditions that promote this rapid adaptive radiation are still under debate . Given multiple examples where only one of two closely related lineages experienced a burst of diversification under the same conditions , new ecological opportunity alone is likely to be insufficient [7 , 74–76] . This suggests that intrinsic factors—such as the availability of appropriate genetic variation—are equally critical for facilitating adaptive responses , although conditions that promote the origin and sharing of this variation remain largely speculative [77 , 78] . Here , we have found evidence for at least three significant sources of genetic variation that might facilitate adaptive diversification in response to ecological opportunity . First , we inferred introgression both between early lineages in the radiation and recently between specific populations . Second , we observed rapid lineage-specific adaptation from de novo mutation in genes related to functional traits that differ between groups . Finally , we find evidence of environment-specific sorting of ancestral variation . Analyses of other rapid radiations have also inferred the role of one or more of these three mechanisms in facilitating rapid diversification . For example , analyses of radiating African cichlids suggest widespread recruitment of potentially adaptive coding and regulatory variants from standing ancestral variation [7] . Clade-wide variation in Equids revealed evidence for both the rapid accumulation of de novo substitutions and for both ancient and recent introgression events between species [14] . In Darwin’s Finches [18] , hybridization appears to play a role both in the origin of new lineages and potentially in the adaptive introgression of functional loci ( e . g . , for beak shape ) between species . Because each of our analyses relies on different assumptions and varies in power , directly comparing the relative contribution of our three detected sources of genetic variation requires caution . Nonetheless , based on our crude estimates within each analysis , we infer that relatively small yet substantial fractions of the euchromatic genome are implicated in each source of genetic variation . We find little evidence that one of these processes predominates in its contribution , although our estimates suggest that de novo mutation might be relatively more influential and cross-species introgression relatively less so . This latter observation is in interesting contrast with several recent studies of animal adaptive radiations , including in Darwin’s Finches [18] , Equids [14] , and fish [13] , where evidence suggests that hybridization and introgression might be much more pervasive and influential than previously suspected , and more abundant than we detect in Solanum . This is despite a greater historical emphasis on the role and importance of post-speciation gene flow in plant groups [79 , 80] and suggests that the dynamics of adaptive radiation might be less shaped by classical expectations of differences between broad taxonomic groups like plants and animals than expected . Rather , as with other studies that also detect one or more of these sources of genetic variation [7 , 14 , 17 , 18] , we detect evidence for all three within the same diversifying clade , suggesting that these mechanisms may be universal in their facilitation of rapid adaptation to diverse environmental niches . Rapid diversification via these three modes within wild tomatoes was likely ecologically driven by the extremely variable environments of the Andes and Galápagos . Notably , most of the significant geo-climatological transitions of this region substantially predate the entire history of wild tomato diversification . These events include major uplifts of the Central Andes [81–83] and the formation of biogeographic zones such as the Atacama Desert ( at least ~14 Ma , though possibly up to ~150 Ma ) and the Peruvian coastal desert [84 , 85] . Therefore , geographical and ecological expansion of wild tomato species was almost certainly due to migration into new environments rather than in situ adaptation during more ancient geological and climatic transitions . The timing of major lineage splits , in addition to the current distributions of extant species , can be used to infer the progression of these migratory steps ( S1 Text Section 7 . 6 and S6 Fig ) . This south-to-north range expansion and diversification has been suggested by phylogenies of other plant and animal groups in the Central Andes [85–89] . More broadly , Solanum is one of the most speciose and widespread angiosperm genera , with ~1 , 500 extant species found on all continents except Antarctica . The last common ancestor of the genus is estimated to be only ~15 . 5 Ma [22 , 90–93] . Therefore , the rapid speciation rates that we see in the tomato clade , and the accompanying genetic and genomic changes , could be symptomatic of the factors facilitating sustained divergence and diversification across the entire Solanum genus around the globe .
Our sampling included 29 accessions from 13 species of tomato and two outgroup species ( representing the entire clade and accepted outgroups; S1 Table ) . Seeds of each accession were obtained from the C . M . Rick Tomato Genetics Resource Center at the University of California , Davis ( http://tgrc . ucdavis . edu ) . Seeds were germinated following standard guidelines ( http://tgrc . ucdavis . edu ) and then transplanted to 7 . 56-L pots containing a 1:1 mix of standard soil and Metro Mix 360 ( http://www . hummert . com/ ) in the Department of Biology greenhouse at Indiana University under supplemental lighting to maintain a constant 14:10 h light:dark cycle . Plants were watered to field capacity daily to prevent drought stress and fertilized weekly . To capture a wide set of transcripts , we harvested RNA from five different tissues: roots , leaf primordia and young/unexpanded leaves , mature leaves ( fully expanded , the fifth leaf from the meristem ) , floral buds , and mature ( open ) unfertilized flowers . Tissue was collected in sterile 15 or 50 mL conical vials ( VWR: 89039–666 , 89039–658 , respectively ) . Floral and leaf tissue was immediately placed into liquid nitrogen . Root tissue was washed in cold water for <60 s to remove large soil particles , blotted with paper towel for 10 s , and then frozen with liquid nitrogen . All tissues were pulverized under liquid nitrogen using a mortar and pestle; 50–100 mg fresh weight of ground tissue was used for total RNA extraction . Extraction of the poly-A fraction of total RNA from ground tissue was performed using RNeasy Plant Mini Kits from Qiagen ( catalog number 74904 ) . Resuspended RNA was stored at −80°C until all samples were collected . Tissue-specific total RNA was equimolar pooled using the RiboGreen RNA quantitation assay ( Life Technologies: R11491 ) and then quality checked using an Agilent 2200 TapeStation System prior to library construction . Stranded , paired-end libraries of total RNA were generated from these pools for each accession using Illumina TruSeq Stranded total RNA HT Sample Preparation Kits ( Illumina: RS-122-2203 ) , these libraries were pooled and distributed evenly ( < 6-fold difference among libraries , S1 Data 1 . 1 ) across three lanes of Illumina HiSeqTM 2000 ( Illumina Inc . , San Diego , CA , US ) . RNA QC , library preparation , and pooling was performed by the Indiana University Center for Genomics and Bioinformatics ( http://cgb . indiana . edu ) . Raw reads were filtered and trimmed using the SHEAR program ( http://www . github . com/jbpease/shear ) . RNA-Seq reads were mapped to the S . lycopersicum reference genome v . SL2 . 50 ( ftp://www . solgenomics . net ) [40 , 41] , reference chloroplast ( NCBI accession NC_007898 . 3 ) , and mitochondrial scaffolds ( http://mitochondrialgenome . org/ ) using STAR [94] . Alignments were processed into multisample Variant Call Format ( VCF ) using SAMtools [95] , then converted/filtered into Multisample Variant Format ( MVF ) using MVFtools ( http://www . github . com/jbpease/mvftools ) [45] . Two primary alignments were filtered: a high-quality ( HQ ) set requiring sequencing depth ≥ 3 and mapping quality ≥ 30 , and a HD set with depth ≥ 10 and mapping quality ≥ 30 ( see S1 Text Section 2 . 1–2 . 3 and S1 Data 1 . 1 for additional details ) . Phylogenies were inferred using several methods ( RAxML [42] , ASTRAL [21] , MP-EST [20] ) and partitions of the data . Using RAxML , whole-transcriptome and whole-chromosome concatenated phylogenies were inferred from all sites with alleles represented in ≥10 accessions . Molecular clock estimates were performed using r8s [96] with calibrated time points from Särkinen , Bohs [22] . RAxML was also used to infer phylogenies for 1 Mb and 100 kb genomic windows , and for annotated reference genes ( ITAG v . 2 . 4 , https://www . solgenomics . net ) with four or more accessions represented ( S1 Text Sections 3 . 1–3 . 3 ) . From 100-kb window trees , a majority rule tree ( S2D Fig ) was computed using RAxML [42] and annotated with percentage of window tree support and IC/ICA scores for each node [44] . Coalescent trees were inferred with ASTRAL [21] and MP-EST [20] ( S2E and S2F Fig ) . MP-EST was run for 100 replicates; the tree with the strongest likelihood score is shown in S2F Fig , as in [16] . Options for all three programs were set to default , and no consensus tree was used as input . RAxML was run with the “-J MRE” option for Majority Rule Extended . Majority rule and coalescent topologies agreed with the consensus phylogeny for the major subclades , with the exception of hua-1364 ( see discussion below on Peruvianum group ) . The proportions of 100-kb trees supporting various nodes are shown in S2D Fig and S2 Table . We calculated the proportion of heterozygous sites sampled from each accession and the patterns of alleles shared among groups from the HD alignment . Pairwise sequence distances between all pairs of the 29 sequenced accessions and the reference ( S1 Data 1 . 2 ) were calculated from the HQ dataset using MVFtools . At heterozygous sites in an accession , one of the two alleles represented was selected randomly; random allele selection was also done for all analyses described below . Accessions in section Lycopersicon differ from accessions in Lycopersicoides by 2 . 10%–2 . 71% sequence divergence . Accessions within Lycopersicon have pairwise distances of 0 . 05%–1 . 7% , with the closest relationships between different accessions within S . galapagense ( gal-3909/gal-0436 ) and within domesticated tomato ( lyc-3475/lyc-ref ) ( S1 Text Section 3 . 2 ) . Using MVFtools [45] , we calculated the D-statistic [51 , 59] for nonoverlapping 1-Mb windows of the HQcomp dataset , for all possible trios of the 27 Lycopersicon accessions and the reference . The consensus tree ( Fig 2A ) was used to determine expected tree topologies and to assign P1 , P2 , and P3 . ABBA and BABA site patterns were combined for all windows to calculate a transcriptome-wide average D-statistic ( S1 Data 1 . 5 ) . Many cases were observed in which transcriptome-wide D values appeared to be driven almost entirely by a small number of 1 Mb windows , consistent with recent introgression at a localized chromosomal location against a background of generally low divergence ( i . e . , few ABBA and BABA patterns genome wide ) . To more directly assess whether the D values observed represented a genome-wide pattern of gene flow , we performed a bootstrap resampling analysis . For each trio of accessions , we randomly resampled 1 Mb windows with replacement and recomputed D ( n = 10 , 000 replicates ) . From the distribution of resulting D-values , we assessed whether the 95% CI of the resampled distribution included D = 0 . From the D-statistics , we inferred putatively introgressing lineages ( S4E and S4F Fig ) . We further investigated cases where trios of accessions showed evidence of widespread or significant amounts of introgression based on D-statistic calculations . For each putatively introgressed trio , we inferred gene trees for each protein-coding region using sequences from the trio and lyd-4126 as the outgroup ( S1 Data 1 . 5 , S4 and S5 Figs ) using RAxML [42] . From these gene trees , we counted the proportion of gene trees of each of the three possible rooted topologies . In each introgression case , we estimated the proportion of genes that were introgressed as the difference in the proportions of trees with the two discordant topologies . In the case of the introgressions involving neo-2133 , neo-1322 , and S . pimpinellifolium , we also calculated DFOIL statistics for 100 kb windows to infer the direction of introgression [52]; these cases involved tree topologies appropriate for the use of this 5-taxon method ( S5A Fig ) . This window size is large enough to avoid problems associated with the sampling of trees from smaller windows [52 , 97] . The high levels of incompletely sorted ancestral variation and variability of the gene-by-gene phylogenies presented a particular challenge to estimating genes with lineage- or species-specific substitutions . A standard tree-based dN/dS model implicitly reconstructs ancestral states , which in our dataset would be subject to high error because of the pervasive background of ILS [65] . Instead , we used a more conservative variant of a dN/dS test to infer which genes show high relative frequencies of nonsynonymous substitutions ( and therefore are likely under positive selection ) for the four well-supported subclades within Lycopersicon ( Esculentum , Arcanum , Peruvianum , and Hirsutum groups ) , as well as some specific species ( below ) . For a given gene , we counted only substitutions that could be placed unambiguously on the branch leading to a particular lineage . For example , when testing the Esculentum group as the target lineage of interest , substitutions were counted as lineage-specific only when the set of sites sampled from Esculentum group and the set of sites sampled from all other accessions ( including the outgroup ) were completely nonoverlapping in identity . These substitutions were tabulated as synonymous or nonsynonymous , depending on whether a change in amino acid occurred . For all tests , the outgroup accessions were included in the nontarget group . Sites were only considered when at least one allele was available for each ingroup species and at least one outgroup accession . We tested for changes on the branch separating section Lycopersicon versus section Lycopersicoides and for all other samples against these groups/species: Esculentum group , Arcanum group , Peruvianum group , Hirsutum group , the Galápagos species , S . pennellii , S . habrochaites , S . chilense , S . chmielewskii , and S . neorickii . The domesticated accessions were not included since they have experienced intentional introgression of wild alleles for crop improvement . hua-1360 and hua-1364 were only included in the Peruvianum group-specific test because of their high incidence of reticulation . We evaluated evidence for positive selection on the set of genes that showed lineage-specific substitutions for each of our well-supported branches ( as outlined above ) using the branch-site test in PAML 4 . 8a [98] . For each protein-coding gene , the codon alignment for that gene was extracted from the MVF-translated alignment file and accepted for testing only if at least one sequence was represented for each species ( not including hua-1360 and hua-1364 ) . To maximize the alignment tested , only the sequence for each ingroup species ( among available accessions ) with the most aligned codons represented was retained . Similarly , only the outgroup accession with the most aligned codons was also retained . Phylogenies for each 14-species gene alignment were then inferred using RAxML v . 8 . 1 . 16 [42] using standard parameters and the GTRGAMMA model . For each of the four major groups ( Esculentum , Arcanum , Peruvianum , Hirsutum ) , we verified in the gene tree that all accessions in the given group are a monophyletic clade ( i . e . , that the gene tree has an appropriate branch ancestral to the group being tested ) . If this ancestral branch was not present , the gene was not tested for that particular group . Otherwise , the ancestral branch was marked as the target “foreground” branch and tested using the branch-site test in PAML . We ran both null and alternative tests , and recorded dN/dS values and likelihood scores . Since branch lengths were fixed at the values provided by the RAxML tree , the null model has four free parameters and the alternative test has five . Therefore , significance was assessed by a likelihood ratio test ( LRT ) assuming a χ2 distribution with one degree of freedom ( see S1 Text Section 5 for full PAML control file parameters ) . From these tests , we calculated the proportion of genes that showed significance under the LRT ( p < 0 . 01 ) both for ( 1 ) a set of genes where we had sampled at least one site where all accessions within the target group had alleles that differed from all accessions outside the target group ( i . e . , an exact allele pattern that indicated a nonsynonymous substitution on the branch leading to the target group; see Results ) and ( 2 ) for all genes containing the target branch according to the RAxML gene trees ( see S1 Text Section 5 ) . Geographical coordinates and sampling location information for each accession were obtained from the TGRC database ( http://tgrc . ucdavis . edu ) . Altitude and temperature for each population location were extracted from the WorldClim database ( www . worldclim . org ) ; because many environmental factors in this database are strongly correlated across the natural range of wild tomatoes [26] , we limited our analyses on WorldClim data to these two broadly representative factors . Soil solution pH data at 1 km resolution was obtained from the ISRIC SoilGrids project ( http://soilgrids . org/ ) . Metal abundances for the Peruvian accessions were estimated from data in GEOCATMIN ( http://geocatmin . ingemmet . gob . pe ) provided by the Instituto Geologico Minero y Metalurgico de Peru . In combination with topographic and hydrological data from the same database , metal abundances ( in ppm ) were averaged for all sample points located within a 100 km2 centroid surrounding each accession’s coordinates , for sites directly upstream or downstream of the population location; this area corresponds to locations within ~11 km of each accession’s geographical location . Metal concentrations were taken from the “Geochemistry: Serie B: Prospecting Geochemistry Sediment ravine” survey data collected between 2002 and 2011 . PAR values for mainland South America were obtained from Insituto Nacional de Pesquisas Espaciais de Brasil ( http://www . inpe . br/ ) . These data are available in units of kWh/m2/d for 40-km resolution in monthly averages from data spanning 1995–2005 . At this resolution , each accession inhabited a unique data cell except hua-1358 and hua-1360 . PAR values were unavailable for Galápagos populations . Seasonality of PAR was estimated as the standard deviation of monthly averages . To identify genomic targets of selection in response to abiotic factors , we treated all the accessions in section Lycopersicon as a population and looked for alleles that differentiated environmentally classified populations in phylogenetic genome-wide association study ( “PhyloGWAS” ) . These tests required that accessions from the same species or group occurred in different ecological categories , thus allowing detection of abiotic effects over lineage-specific effects . In our dataset , some environmental/geographical factors were intrinsically correlated with each other and thus were combined into a single composite environmental axis . Therefore , for our PhyloGWAS analysis , we selected four environmental axes that met the requirements for this approach: ( 1 ) altitude/temperature , ( 2 ) latitude/climate seasonality , ( 3 ) interpolated water pH , and ( 4 ) heavy metal abundance . In our sampled accessions , each environmental axis identified two clearly separable groups of populations ( see S1 Text Section 6 for additional details ) . For each of these four comparisons , we asked whether there were nonsynonymous variants completely correlated with each environmental condition . For instance , at a single position there might be an arginine present in all accessions experiencing high heavy metals , and a glycine present in all accessions in environments with low heavy metal concentrations . We examined all sites with nonsynonymous variants between any of the accessions used in each of the four environmental contrasts . This led to four sets of variants that were queried for environmental-specific changes; the size of the datasets examined were 233 , 567 nonsynonymous variants for abiotic Factor 1 , 253 , 161 for Factor 2 , 160 , 255 for Factor 3 , and 198 , 908 for Factor 4 . We found nonsynonymous variants perfectly associated with our environmental factors for all four contrasts , except for Factor 1 ( altitude/temperature ) , which was nonetheless associated with three synonymous variants . The numbers observed were 0 nonsynonymous variants for Factor 1 , 12 for Factor 2 ( in 12 genes ) , 44 for Factor 3 ( in 43 genes ) , and 455 for Factor 4 ( in 401 genes ) . To assess the significance of observing these patterns , we used the program ms [99] to simulate 109 genes with a single variable site over the consensus phylogeny ( Fig 2A ) using Ne = 105 and 2 . 5 generations per year . For each environmental factor , we determined the number of times we could expect a perfect association between variants and the environment due to ILS alone , out of the specific number of variants examined for that contrast . To do this , we simulated many datasets of the same size as the ones we tested , and for each dataset recorded the number of perfectly associated variants observed . The p-values for all four environmental contrasts are the proportion of simulated datasets that have a greater number of genes perfectly associated with environmental variables than our observed values ( see S1 Text Section 6 for additional details ) . MVFtools is freely available at http://www . github . com/jbpease/mvftools . Plots were generated with the Python matplotlib ( http://www . matplotlib . org ) and Veusz ( http://home . gna . org/veusz/ ) . Phylogenies were prepared with FigTree ( http://tree . bio . ed . ac . uk/software/figtree/ ) . “Cloudogram” diagrams were generated with DensiTree ( https://www . cs . auckland . ac . nz/~remco/DensiTree/ ) . All other analyses were performed with custom Python scripts , using the BioPython , NumPy , and SciPy libraries . Read trimming , mapping , and large-scale file conversions were performed on the Mason High Performance Computing Cluster at Indiana University . All raw sequence reads are available on NCBI SRA at Bioproject PRJNA305880 . VCF , MVF , and phylogeny files are deposited in the Dryad repository http://dx . doi . org/10 . 5061/dryad . 182dv [100] . The tomato reference genome is available from SolGenomics ( http://www . solgenomics . net ) . List of Ultra-Conserved Orthologs can be found at the Compositae Genome Project ( http://compgenomics . ucdavis . edu ) . Additional geographic , ecological , and sampling information on the accessions used in this study is available at http://www . tgrc . ucdavis . edu . | The formation of new and distinct species during evolution often occurs in rapid bursts of diversification in which many species arise within a short time frame . The ecological and genetic factors that promote these radiations are much debated . Here , we examine genome-wide patterns of molecular evolution that accompanied a rapid adaptive radiation among 13 species of wild tomato—the ecologically and reproductively diverse group that gave rise to the domesticated tomato . By analyzing patterns of genetic variation in thousands of expressed genes from multiple populations and species , we identify genome-wide signatures of rapid consecutive speciation events during 2 . 5 million years of diversification in this group . These signatures include pervasive shared ancestral variation and frequently discordant signals of relatedness among different parts of the genome . Our analyses find evidence for three unique sources of genetic variation that fuel adaptive diversification in this group—postspeciation hybridization , rapid accumulation of new mutations , and recruitment from ancestral variation—and identify specific examples of putatively adaptive loci drawn from each source . Recent analyses of other rapid radiations have also inferred a role for at least one of these mechanisms; our finding of all three simultaneously at work within the same diversifying clade suggests that they might be a universal feature of rapid adaptation to diverse environmental niches . |
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γ-herpesviruses ( γHVs ) have developed an interaction with their hosts wherein they establish a life-long persistent infection and are associated with the onset of various malignancies . One critical virulence factor involved in the persistency of murine γ-herpesvirus 68 ( γHV68 ) is the viral homolog of the Bcl-2 protein ( vBcl-2 ) , which has been implicated to counteract both host apoptotic responses and autophagy pathway . However , the relative significance of the two activities of vBcl-2 in viral persistent infection has yet to be elucidated . Here , by characterizing a series of loss-of-function mutants of vBcl-2 , we have distinguished the vBcl-2-mediated antagonism of autophagy from the vBcl-2-mediated inhibition of apoptosis in vitro and in vivo . A mutant γHV68 virus lacking the anti-autophagic activity of vBcl-2 demonstrates an impaired ability to maintain chronic infections in mice , whereas a mutant virus lacking the anti-apoptotic activity of vBcl-2 establishes chronic infections as efficiently as the wild-type virus but displays a compromised ability for ex vivo reactivation . Thus , the vBcl-2-mediated antagonism of host autophagy constitutes a novel mechanism by which γHVs confer persistent infections , further underscoring the importance of autophagy as a critical host determinant in the in vivo latency of γ-herpesviruses .
Apoptosis and autophagy , characterized by distinctive morphological and biochemical changes , are tightly regulated processes essential for homeostasis , development , and human diseases [1] , [2] . Once triggered by internal inducers , such as DNA damage and viral replication , or by external stimuli , such as the engagement of the TNF receptor , apoptosis proceeds through a cascade of programmed internal proteolytic digestion , resulting in the collapse of cellular infrastructure , mitochondrial potential , genomic fidelity , and cell membrane integrity ( for review see [1] , [3] ) . Therefore , apoptosis represents an important effector of host immunity by eliminating virally-infected cells whose survival might otherwise prove harmful to the host [3] . In contrast to the self-destructing apoptotic program , cellular autophagy ( Greek for ‘self-eating’ ) allows cells to engulf cytoplasmic materials , including long-lived proteins or aberrant organelles , into specialized double membrane-bound vesicles and deliver them to lysosomes for degradation and turnover ( for review see [4] , [5] ) . Although originally characterized as a cellular response to nutrient deprivation , autophagy has been increasingly recognized essential for protecting cells against pathogens [6] . Neuronal overexpression of the autophagy protein Beclin1 confers resistance to Sindbis virus infections [7] . Similarly , depletion of beclin1 in plants aggravates the tobacco mosaic virus-induced hypersensitive response ( HR ) [8] . In addition to digesting cellular components , autophagy has been indicated to sequester virions and bacterial components for degradation [9] , [10] . Thus , autophagy constitutes , in addition to apoptosis , an important host antiviral response [10] . However , the relative contributions and coordination of these two important pathways during viral infection remain largely unknown . Yet as distinct as they are , the apoptotic and autophagic machinery converge at a number of points . One direct crosstalk between these two pathways is mediated in part by the functional and physical interaction of Beclin1 , an essential autophagy activator , with Bcl-2 , a prototype apoptosis inhibitor [11] , [12] . Cellular Bcl-2 was originally discovered as an oncogenic protein in B-cell lymphomas , since then a number of proteins belonging to the Bcl-2 family have been identified , each possessing the signature of Bcl-2 homology domain ( BH ) . The Bcl-2 family consists of both anti-apoptotic ( e . g . , Bcl-2 , Bcl-XL , and Bcl-w ) and pro-apoptotic ( e . g . , Bax , Bak , Bid , and Bad ) members , which cooperate by forming homo- or hetero-dimers to regulate the cell's commitment to apoptosis [13] . A major mechanism by which the anti-apoptotic Bcl-2 proteins block apoptosis involves an extended hydrophobic groove on the surface of the proteins that serves as a binding-pocket for the α-helical BH3-domain of the pro-apoptotic Bcl-2 family proteins [14] , [15] . Aside from its ability to interact with and inhibit pro-apoptotic family members like Bax and BH3-only proteins , the hydrophobic pocket of Bcl-2 also binds Beclin1 ( the mammalian ortholog of yeast Atg6 ) , which is part of a class III PI3 kinase complex required for the initiation of autophagosome membrane [16] , [17] . In fact , the anti-autophagic action of Bcl-2 closely mirrors its capacity to bind and inhibit Beclin1 [12] . Intriguingly , structural analysis of Beclin1 revealed that it possesses a putative α-helical BH3 domain , which allows Beclin1 to dock into the hydrophobic pocket of Bcl-2 . As such , Beclin1 is recently considered as a novel BH3-only protein [18] , [19] . Although it remains unknown how Bcl-2 discriminates among its targets , the dual roles of Bcl-2 in apoptosis and autophagy suggest that a coordinated regulation may exist for Bcl-2 to conduct these two activities [20] . Given the important role of Bcl-2 in cell survival , many viruses have evolved to encode structural and functional orthologs of Bcl-2 ( vBcl-2s ) to prevent the premature death of the infected cells from sustained viral replication and associated diseases [21] , [22] . All sequenced γ herpesviruses ( γHV ) encode a homolog of Bcl-2 , including Epstein-Barr virus ( EBV ) , Kaposi's sarcoma-associated herpesvirus ( KSHV ) , herpesvirus saimiri ( HVS ) , and the murine γ herpesvirus 68 ( γHV68 ) [22] , [23] , [24] . The vBcl-2 of γHV68 ( also referred to as M11 ) has been implicated in preventing Bax toxicity in yeast and blocking apoptosis in cultured cells when induced by diverse apoptotic stimuli [25] , [26] . However , γHV68 vBcl-2 appears to be dispensable for acute infection in vivo and lytic replication in vitro , instead it is proved to be essential for efficient viral persistent replication as well as reactivation from latency , characteristic of all γHVs [25] , [27] . Analysis of the three-dimensional structure reveals that vBcl-2 of γHV68 has limited sequence similarity to Bcl-2 but virtually adopts a fold similar to that of Bcl-2 [25] , [28] . The seven-helix bundle ( α1-7 ) of vBcl-2 forms a globular structure , where helices 2 , 3 , 4 , and 5 define an extended hydrophobic surface cleft allowing vBcl-2 to associate with BH3 domains , especially those of Bak and Bax [25] , [28] . Mutations within the BH3-binding groove abolished the ability of γHV68 vBcl-2 to interact with Bax and Bak , block apoptosis , and abrogate vBcl-2 function in persistent replication and reactivation from latency in vivo [25] . Thus , it is generally believed that vBcl-2 functions in vivo predominantly by binding and inhibiting pro-apoptotic Bcl-2 family proteins [25] . Yet , recent evidence favors a central role for vBcl-2s of γHV in blocking autophagy by directly interacting with Beclin1 via the BH3-binding groove of vBcl-2 [12] , [19] , [29] . Furthermore , the binding of purified vBcl-2 protein to the Beclin1-derived peptide appears to be the tightest when compared to peptides from the pro-apoptotic proteins , including BAX , BAK , BIM , PUMA , BID , and Noxa [28] . Unlike its cellular counterpart , this vBcl-2-Beclin1 complex can not be easily displaced by other BH3-only molecules , such as Bid or Bim [15] , [28] . Accordingly , vBcl-2 exhibits an enhanced capacity for autophagy inhibition than cellular Bcl-2 [28] , [30] . These findings raise the possibility that evasion of autophagy might also account for the biological effects of vBcl-2 in viral lifecycle and/or pathology . Nonetheless , due to the engagement of the hydrophobic surface groove of vBcl-2 by both the pro-autophagic BH3 domain of Beclin1 and the pro-apoptotic BH3 domain [28] , mutations of vBcl-2 identified so far that disrupt Beclin1 binding and inhibition of autophagy also abolish its capacity to interact with BH3 peptides and inhibit apoptosis , adding to the complexity of genetically dissecting the in vivo role of vBcl-2-mediated autophagy inhibition and the vBcl-2-mediated antagonism of apoptosis in γHV68 infection . In this study , we used loss of function mutagenesis to determine the role of vBcl-2-mediated anti-autophagy versus vBcl-2-mediated anti-apoptosis in the context of γHV68 infection . We found that a Beclin1-binding-deficient vBcl-2 mutant virus , which is impaired in autophagy inhibition but retains intact anti-apoptotic activity , was compromised in the maintenance of latency , though the initial viral establishment of latency was unaffected . In contrast , anti-apoptosis-defective vBcl-2 mutant virus infection was associated with a normal latent load but was largely impaired in efficient ex vivo reactivation from latency . Our findings thus demonstrate an as yet undefined function of autophagy in controlling viral infections . Unlike what was previously thought that anti-apoptosis features prominently the functions of vBcl-2 in vivo , our study reveals that an evasion of autophagy-mediated host innate immunity serves as a key aspect of γHV68 replication and pathogenesis .
Beclin1 was originally identified as an interactor of Bcl-2 by a yeast two-hybrid screen [7] . The α-helical structure of the N-terminal region of Beclin1 ( residues 88–150 ) mimics the BH3 domain of pro-apoptotic Bcl-2 family members , allowing it to associate with the hydrophobic BH3-binding groove on the surface of vBcl-2 [19] , [28] . However , the Beclin1 peptide ( KD 40 nM ) binds to vBcl-2 with a much higher affinity than is observed for the Bak ( KD 76 nM ) and Bax peptides ( KD 690 nM ) [28] , raising the possibility that Beclin1 may not necessarily share binding sites with the pro-apoptotic Bcl-2 family members for the hydrophobic groove of vBcl-2 . To dissect the specific interacting domain of vBcl-2 for Beclin1 binding , we created a series of N- and C-terminal deletion mutations of vBcl-2 ( Figure 1 ) , and tested for their abilities to associate with the BH3-like domain ( residue 88–150 ) of Beclin1 in the GAL4-based yeast two-hybrid assay [31] . Wild-type ( WT ) vBcl-2 readily interacted with Beclin1 BH3-like domain in the yeast two-hybrid assay . In contrast , a vBcl-2 mutant with a triple alanine substitution at the conserved residues of Ser85 , Gly86 , and Arg87 ( hereafter termed as vBcl-2 AAA ) within the BH3 binding groove that has been shown to abrogate BH3 peptide binding of vBcl-2 , lost the ability to interact with Beclin1 ( Figure 1 ) . This was consistent with previous observations demonstrating that the hydrophobic groove of vBcl-2 is important for this function [25] . When screening our truncation mutants , we found that the C-terminal truncations of vBcl-2 up to α7 helix showed little to no effect on Beclin1 binding; yet a further truncation up to helix α6 ( e . g . vBcl-2 Δα6/α7/TM mutant ) severely impaired Beclin1 interaction in yeast ( Figure 1 ) , suggesting that the C-terminal boundary of vBcl-2 for Beclin1-binding lies within the α6 helix because no deletions from this end was tolerated . Of particular interest , a deletion of the BH2 domain only , which by analogy to the equivalent domain in Bcl-2 and Bcl-xL has been shown to abolish binding and inhibition of Bax [32] , [33] , did not prevent vBcl-2 from binding Beclin1 in yeast ( Figure 1 ) . This data suggests that the BH2 region of vBcl-2 is dispensable for Beclin1 interaction . In contrast to the C-terminal moiety , removal of a small segment of the α1 helix at the N-terminus abrogated the ability of vBcl-2 to interact with Beclin1 ( Figure 1 ) . In fact , any segment truncation from the N-terminus of vBcl-2 resulted in the complete loss of Beclin1 binding ( Figure 1 ) . Thus , our results indicate that the minimal region required for Beclin1 interaction in yeast involves α helices 1-6 of vBcl-2 . Although the N-terminal α1 helix is not part of the core hydrophobic α helices within the BH3-binding groove , it does appear to be critical for mediating Beclin1 interaction in yeast . We next confirmed our yeast two-hybrid results in mammalian cells . 293T cells were transfected with the WT or mutant forms of vBcl-2 and/or V5-tagged Beclin1 , followed by co-immunoprecipitation ( co-IP ) assays . Consistent with the yeast two-hybrid binding data , deletion of the N-terminal α1 helix of vBcl-2 abolished Beclin1 binding , as also seen with the AAA mutant of vBcl-2 ( Figure 2A ) . The loss of binding activity was not due to defects in protein expressions , since both the Δα1 and AAA vBcl-2 mutants were expressed at equivalent levels to WT in transfected cells ( Figure 2A ) . Yet , deletion of the α7 or BH2 , one of the central components of the vBcl-2 hydrophobic cleft , had no significant effect on the interaction between vBcl-2 and Beclin1 , as was seen with the deletion mutation of the C-terminal hydrophobic ‘tail’ ( ΔTM ) ( Figure 2A ) . Similar results were also observed with endogenous Beclin1 in 293T cells , in that removal of the α1 helix but not the BH2 domain abolished endogenous Beclin1 binding ( Figure 2B ) . These data thus indicate that while the BH2 region is structurally important for assembling the hydrophobic core on the surface of vBcl-2 , it is dispensable for vBcl-2-Beclin1 interaction , whereas the N-terminal α1 helix of vBcl-2 serves as a Beclin1-interacting domain ( Figure 2B ) . These data are consistent with those collected from the yeast two-hybrid assay . Although the N-terminal α1 helix deletion has been previously shown not affecting the overall folding of Bcl-2 family proteins [28] , [34] , it remains possible that the inability of the vBcl-2 Δα1 constructs to bind Beclin1 could reflect the loss of proper folding of the protein . To clarify this , we tested whether the mutants of vBcl-2 retain the ability to associate with other BH3-domain-containing molecules . Bak has been previously shown to have the highest affinity to vBcl-2 in vitro among the pro-apoptotic Bcl-2 proteins [28] . We then performed in vitro GST pull-down assay using the bacteria purified GST-fused Bak protein that was incubated with the cell lysates of 293T cells transfected with the HA-tagged WT or mutant forms of vBcl-2 ( Figure 2C ) . The TM domain of Bak was removed ( referred to as GST-BakΔTM ) to increase its solubility in E . coli . In agreement with previous studies [25] , [28] , we observed that Bak was able to associate with the WT and ΔTM mutant of vBcl-2 , but not with the vBcl-2 AAA mutant ( Figure 2C ) . No interaction was detected between the vBcl-2 and purified GST alone , indicating that the vBcl-2-Bak interaction was specific ( Figure 2C ) . Notably , we found that the Δα1 mutant that lacks Beclin1-binding retained its ability to interact with Bak , whereas the ΔBH2 mutant that was able to bind to Beclin1 failed to interact with Bak ( Figure 2C ) . Thus , the loss-of-function phenotype of the vBcl-2 mutants for Beclin1 or Bak binding , particularly that of Δα1 and ΔBH2 , is less likely due to misfolding of the mutant vBcl-2 protein , rather , it implies that the mechanisms of vBcl-2 for binding with Beclin1 and Bak involve distinct contact sites within the hydrophobic groove of vBcl-2 . The interaction of Bcl-2 with Beclin1 largely correlates to its anti-autophagic activity [15] . We then assessed the effects of the vBcl-2 mutants binding to Beclin1 , in particular that of Δα1 and ΔBH2 mutants , on Beclin1-dependent autophagy . NIH3T3 cells stably expressing empty vector ( NIH3T3 . Vector ) , WT vBcl-2 ( NIH3T3 . WT ) , or the mutant forms of vBcl-2 , including the Δα1 , AAA , Δα7 , ΔBH2 and ΔTM mutants , were generated . To measure autophagy levels , we initially used the fluorescent autophagosome marker GFP-LC3 ( a mammalian homologue of the yeast Atg8 ) , which redistributes from a diffused cytosolic/nuclear staining to a punctate pattern in the cytoplasm upon autophagy stimulation [35] . We found that , consistent with their ability to co-IP Beclin1 , the vBcl-2 ΔBH2 , Δα7 , and ΔTM mutants suppressed autophagy in these cells as effectively as WT did under nutrient depletion and rapamycin treatment , the established inducers of autophagy ( Figure 3A and 3B ) . In contrast , the Δα1 mutant and AAA mutant of vBcl-2 , which were unable to interact with Beclin1 , failed to inhibit both starvation- and rapamycin-induced autophagy in the cells ( Figure 3A and 3B ) . In accord , a significantly reduced number of autophagosomes per cell profile was observed in cells expressing WT and the ΔBH2 mutants , but not the Δα1 and AAA vBcl-2 mutants ( Figure S1A ) . Immunoblotting was then performed with an antibody against LC3 to further measure autophagy in vBcl-2-expressing cells . During autophagosome formation , cytosolic LC3 ( LC3-I ) undergoes a covalent conjugation to phosphatidylethanolamine ( PE ) to yield a lipidated form of LC3 , LC3-II , which displays higher electrophoretic mobility [36] . Consistent with the results of the GFP-LC3 puncta assay ( Figure 3A and 3B ) , the conversion of LC3 from LC3-I to LC3-II was much reduced in WT and ΔBH2-expressing cells compared to that in Δα1- and AAA-expressing cells under normal and rapamycin treatment conditions ( Figure 3C ) . It should be noted that the divergent features of the vBcl-2 mutant proteins in autophagy inhibition were not due to their differing protein expression since all tested mutants were expressed at levels equivalent to WT vBcl-2 in stably transfected cells ( Figure S1B ) . Furthermore , all of the vBcl-2 mutants exhibited punctate cytoplasmic staining in the cells , similarly to the WT , except that the ΔTM mutant of vBcl-2 showed modest nuclear staining ( Figure S1C ) . By analogy to the role of the equivalent region in Bcl-2 relatives , this hydrophobic ‘tail’ probably serves as a membrane anchor sequence in vBcl-2 . While the C-terminal hydrophobic tail is not required for the function of vBcl-2 , it may contribute to vBcl-2 by ensuring the proper subcellular localization of the protein . These data collectively demonstrate that the BH2 domain of the hydrophobic groove of vBcl-2 is not essential for suppressing Beclin1-mediated autophagy and its elimination does not affect Beclin1 binding , whereas the elimination of α1 helix leads to the loss of both Beclin1 binding and autophagy suppressing activity , reflecting a striking correlation between the ability of vBcl-2 to bind Beclin1 and its protection from Beclin1-mediated autophagy . To further address whether vBcl-2 inhibits Beclin1-mediated autophagy in virally infected cells , we generated recombinant γHV68 viruses that express HA-tagged WT ( referred to as HA-WT ) or mutant forms of vBcl-2 including AAA , Δα1 and ΔBH2 mutants ( referred to as HA-AAA , HA-Δα1 and HA-ΔBH2 , respectively ) from its normal context in the viral genome , using the bacterial artificial chromosome ( BAC ) system ( for detail please see Material and Methods ) . The genomic integrities of all recombinants were confirmed by restriction enzyme mapping and Southern blot analyses ( Figure S1D ) . The vBcl-2 protein with the predicted molecular weight of 18 kDa was detected by immunoblotting using an anti-HA antibody with all of the recombinant γHV68 viruses in lytically infected 3T3 fibroblast cells ( Figure S2A ) . Furthermore , the genetic manipulation of vBcl-2 did not affect expression of the neighboring v-cyclin ( ORF72 ) in the recombinant viruses ( Figure S2B ) . NIH3T3 cells were then infected with WT γHV68 or the recombinant γHV68 virus expressing HA-tagged WT vBcl-2 or its mutant derivatives . We found that cells infected with the recombinant γHV68 expressing HA-tagged WT vBcl-2 exhibited comparable levels of autophagy to those of WT γHV68-infected cells , suggesting that HA tagging does not affect vBcl-2 function ( Figure 4 ) . Notably , WT γHV68-infected cells exhibited levels of autophagy indistinguishable from those of mock-infected cells ( Figure 4 ) . In contrast , 3T3 cells infected with the Beclin1-binding-deficient vBcl-2 mutant viruses ( γHV68 vBcl-2Δα1 and γHV68 vBcl-2AAA ) showed significantly higher levels of autophagosome accumulation than those infected with the WT and ΔBH2 mutant viruses ( Figure 4 ) . These data indicate that γHV68 infection can trigger cellular autophagy , which is antagonized by vBcl-2 through Beclin1 inhibition . Taken together , vBcl-2 efficiently inhibits Beclin1-mediated autophagy in transfected and virally infected cells , and that this activity requires the α1 helix of vBcl-2 , whereas the BH2 domain is dispensable for the anti-autophagic activity of vBcl-2 . Given the pivotal role of vBcl-2 in apoptosis inhibition , it is important to know if the regions of vBcl-2 , which is required for binding and inhibiting Beclin1 , are equally or differentially required for blocking apoptosis . To this end , we compared the abilities of WT or the vBcl-2 mutants to confer apoptosis resistance . Upon treatment with TNFα and cycloheximide ( CHX ) for 12 h , NIH3T3 cells stably expressing WT , ΔTM , or the Beclin1-binding deficient Δα1 vBcl-2 mutant survived significantly better than those expressing the empty vector , ΔBH2 , or AAA vBcl-2 mutant ( Figure 5A ) . Further quantification of the apoptotic cells via TUNEL staining revealed that the removal of the BH2 domain resulted in the failure of vBcl-2 in inhibiting apoptosis and the accumulation of apoptotic cells ( Figure 5B ) . In contrast , deleting the α1 helix or the TM region did not affect the ability of vBcl-2 in suppressing apoptosis ( Figure 5B ) . Equivalent results were obtained when we used propidium iodide ( PI ) staining to determine the accumulation of sub-G1 cells , which are considered to be apoptotic , via flow cytometry ( Figure S3 ) . Finally , the robust activation of caspase-3 , an early event in apoptosis , was detected in cells stably expressing the vBcl-2 ΔBH2 and AAA mutants , whereas the expression of WT or the vBcl-2 Δα1 mutant significantly blocked caspase-3 activation elicited by TNFα/CHX- ( Figure 5C ) . This further supports the notion that the deletion of the BH2 domain but not the α1 region seriously attenuates the ability of vBcl-2 to suppress caspase-dependent apoptosis . Taken alongside the autophagy analysis data , these results clearly demonstrate that the vBcl-2-mediated inhibition of apoptosis differs in important respects with its anti-autophagic activity . As summarized in Table 1 , the deletion of the α1 helix in vBcl-2 that prevents Beclin1 binding and autophagy inhibition generally has little effects on vBcl-2' anti-apoptotic activity . In contrast , the removal of the BH2 region of vBcl-2 that abolishes vBcl-2's ability to block host-cell apoptosis has minimal effect on vBcl-2-mediated anti-autophagy . Thus , vBcl-2-mediated antagonism of autophagy can be structurally and functionally distinguished from its previously defined apoptosis inhibition activity , which then provides a general basis for evaluating their functional contributions in vivo during viral infections . To determine the role of the vBcl-2-mediated inhibition of autophagy and apoptosis during viral infection , we first examined the in vitro growth properties of the recombinant γHV68 viruses expressing HA-tagged WT and the mutant forms of vBcl-2 in comparison to WT γHV68 in both BHK21 cells and NIH3T3 cells ( Figure 6A and S4 ) . The γHV68 vBcl-2Δα1 and ΔBH2 mutant viruses , as well as the vBcl-2AAA mutant , grew with the same kinetics as WT γHV68 in cultured cells ( Figure 6A and S4 ) , suggesting that the vBcl-2-mediated inhibition of autophagy and apoptosis are not required for lytic replication in vitro , which is consistent with the previous reports that γHV68 does not require vBcl-2 to replicate in vitro [25] , [27] , [37] . In accord with their growth in vitro in fibroblast cells , vBcl-2 mutant γHV68 viruses replicated at levels comparable to WT γHV68 in the lungs of intranasally infected BALB/c mice 5 or 7 days postinfection ( dpi ) , as measured by plaque assay ( Figure 6B , left panel ) and real-time PCR ( Figure 6B , right panel ) . No statistically significant differences were detected among γHV68 WT , the γHV68 mutant lacking vBcl-2 ( vBcl-2-null ) , and the γHV68 recombinants expressing HA-tagged WT or mutant derivatives of vBcl-2 ( Figure 6B ) . Independent isolates of γHV68 containing the Δα1 or the ΔBH2 mutations of vBcl-2 replicated normally in the lungs , arguing against the possibility of chance mutations having occurred elsewhere in the recombinant virus genomes ( Figure S5A ) . These data collectively support a dispensable role for vBcl-2-mediated anti-autophagy and anti-apoptosis in viral lytic replication both in vitro and in vivo . After immune clearance of acute replication , γHV68 establishes latency in splenocytes , macrophages , and dendritic cells [38] , [39] , [40] . Disruption of vBcl-2 has been indicated to abrogate γHV68 from establishment of latency and/or reactivation [25] , [27] . To determine which of the two activities of vBcl-2 , anti-autophagy versus anti-apoptosis , might be primarily responsible for vBcl-2 function in vivo , we next evaluated the capacities of the recombinant γHV68 vBcl-2 mutant to confer chronic infection in mice in comparison to that of WT γHV68 . BALB/c mice were intranasally infected with 5 , 000 PFU of γHV68 WT or mutants . By 12 and 14 days after infection , when WT γHV68 had reached its peak latent load in the spleen , the splenocytes were isolated and the viral latent loads were assessed by an infectious center assay as previously described [41] . Virus-driven splenomegalies were found in all infected mice , but no significant differences in spleen cell numbers were observable among the samples ( data not shown ) . The virus titer of the vBcl-2 mutant including vBcl-2Δα1 , vBcl-2ΔBH2 , vBcl-2AAA , and vBcl-2 null , was similar to the WT in BALB/c mice ( Figure 7A and 7B , left ) . Consistent with the infectious center data , the vBcl-2 mutant viruses showed peak levels of viral DNA loads comparable to the WT γHV68 at 12 and 14 dpi , suggesting the normal amplification of latent viruses in the spleen ( Figure 7A right , 7B right ) . Similar observations could be made with the independently derived Δα1 and ΔBH2 mutant viruses ( Figure S5B ) . Our results thus indicate that the loss-of-function mutations of vBcl-2 in autophagy or apoptosis inhibition , or both , have no appreciable impact on the establishment of viral latency in spleens , consistent with earlier finding that vBcl-2 is not required for the establishment of latency by γHV68 [37] . Given the lack of a role for vBcl-2 during early times of latent infection , we extended our analyses to determine whether vBcl-2-mediated autophagy and/or apoptosis affect viral maintenance of splenic latency at later time points . No significant difference in splenic latency between WT and the vBcl-2 mutant γHV68 was detected at day 21 ( Figure 7C ) . However , by 28 days postinfection , the titers of vBcl-2 mutant viruses , including the independently derived vBcl-2Δα1 and vBcl-2ΔBH2 mutants , were dropped 6- to 10- fold when compared to the WT ( P<0 . 001; Figure 7D up and S5C ) . Notably , this defect was not transient , but persisted 35 days and 42 days postinfection with a substantial , greater than 10-fold reduction in infectious center titers between WT and the vBcl-2 mutant γHV68 , which represents an approximate 90% decrease in the frequency of latent viruses able to reactivate ex vivo . ( Figure 7E and 7F up ) . These results indicate that there was a sustained deficiency of the vBcl-2 mutant viruses in the maintenance of latency after infection . A contraction of latently infected splenocytes was apparent at 42 dpi for both the WT and vBcl-2 mutant viruses ( Figure 7F up ) . In all of the analyzed mice , preformed infectious viruses were undetectable in equivalent , freeze-thawed spleen samples ( data not shown ) . Taken together , these data suggest that although vBcl-2 mutant viruses initially establish latency at levels equivalent to that of WT , there seems to be a steady decline of the latent virus reservoir at later time points in mice infected with the virus lacking a functional vBcl-2 . Since the infectious center assay does not distinguish between reductions in viral latent loads versus a failure of the latent virus itself to reactivate , we quantitated the viral genomes per splenocyte sample by real-time PCR to further measure the degree of viral latency . In agreement with the reduced infectious center titers at later time points , the viral genome load of the vBcl-2Δα1 mutant virus was severely reduced compared to that of WT viruses over the day 28 to day 42 time course in repeated experiments ( Figure 7D , 7E , and 7F down , and S5D ) . In fact , the γHV68 vBcl-2Δα1 mutant , which expresses an anti-autophagy defective vBcl-2 , was nearly as impaired as the AAA and vBcl-2-null mutant viruses ( Fig . 7D , 7E , 7F down ) . The close correlation between viral genome loads and the frequency of latent γHV68vBcl-2Δα1 reactivation ex vivo at later times postinfection substantiates a severe latency defect of the γHV68 vBcl-2Δα1 virus . Since deletion of the α1 helix abolished vBcl-2's anti-autophagic activity but retained its anti-apoptotic function , the impaired latency associated with the vBcl-2Δα1 mutant virus infection at later times thus suggests that autophagy evasion by vBcl-2 plays an important role in maintaining γHV68 latent infection in splenocytes , whereas vBcl-2-mediated anti-apoptosis may not be absolutely required or sufficient for maintaining γHV68 latency . Indeed , the vBcl-2ΔBH2 mutant defective for apoptosis inhibition yet retaining Beclin1-binding and autophagy inhibition intact maintained viral genome loads equivalent to WT virus ( Figure 7D , 7E , 7F down and S5D ) , arguing that the ΔBH2 mutation had no significant impact on viral latency . Yet , in marked contrast to the normal viral genome loads and splenocyte numbers ( Figure S5E ) in the ΔBH2 mutant virus-infected mice , the infectious center titer of the vBcl-2ΔBH2 mutant was significantly lower than WT γHV68 at later times , as previously described ( Figure 7D , 7E , and 7F up ) . The disparity between the viral genome load and the latent viral titer argues that although the γHV68 vBcl-2ΔBH2 mutant virus is capable of maintaining a WT-level viral DNA load , it is unable to efficiently reactivate from latency in the spleen between day 28 and day 42 after infection . Since the BH2 domain is involved in the ability of vBcl-2 to inhibit apoptosis but not autophagy , this result thus suggests that the inhibition of host apoptosis by vBcl-2 is required for efficient ex vivo reactivation from the latent state particularly at later time points after infection , which is consistent with the previous report that the γHV68 vBcl-2 is required for latency reactivation [37] . The viral capacity of latency maintenance is prerequisite for establishing lifelong persistent infection of γHV and is often associated with various malignancies . Our study of the vBcl-2 mutant γHV68 viruses thus indicates that vBcl-2-mediated anti-autophagy and anti-apoptosis may play distinct role in γHV68 persistent infection , in that autophagy evasion by vBcl-2 is particularly required for the maintenance of viral latency , while the vBcl-2-mediated inhibition of apoptosis may play a role during upon viral reactivation .
Here we provide evidence that the vBcl-2-mediated Beclin1 binding and autophagy inhibition is necessary for the maintenance of γHV68 latent infection , whereas the capability of vBcl-2 to antagonize the host apoptosis response is required for efficient viral reactivation from latency ex vivo . Our study thus for the first time indicates that the vBcl-2-elicited anti-autophagy and anti-apoptosis activities are functionally and genetically distinct , also suggesting that the evasion of autophagy represents a critical step in the lifecycle and/or pathogenesis of γHVs . The anti-apoptotic Bcl-2 proteins are structurally characterized by a hydrophobic surface groove that can accommodate the BH3 domain of the pro-apoptotic Bcl-2 family members as well as the BH3-like domain of Beclin1 . Structural alignments of the BH3-like domain of Beclin1 and the BH3 domain of the pro-apoptotic Bcl-2 proteins revealed highly conserved topology and groove contact sites despite overall sequence variability , leading to the conclusion that Beclin1 is a putative BH3-only protein [15] , [18] , [19] . However , no apparent apoptosis induction activity has been found with Beclin1 in an in vivo context [15] . Moreover , our study indicates that despite their structural overlap , Beclin1 and pro-apoptotic Bcl-2 proteins interact with vBcl-2 through two discrete modes of binding that are dependent on a distinct region of vBcl-2 . We show that removing the BH2 domain from vBcl-2 does not affect vBcl-2's capacity to bind and suppress Beclin1 , but it significantly dampens its anti-apoptotic activity . By contrast , deleting the α1 helix does not affect vBcl-2's capacity to suppress apoptosis , yet strikingly impairs its anti-autophagic activity . We thus propose that the anti-apoptotic function of vBcl-2 is not required for its effect on autophagy inhibition and vice versa . Compared to a previous study of the vBcl-2-Beclin1 interaction in vitro [19] , our in vivo data further demonstrates that mutations of vBcl-2 outside the vBcl-2-Beclin1 BH3 domain interface ( e . g . vBcl-2Δα1 ) also affect vBcl-2-Beclin1 binding affinity , probably by altering the conformation of the hydrophobic cleft composed of the BH1 , BH2 , and BH3 domains . It can be speculated that not only do different BH3 domains have distinct binding footprints on the vBcl-2 surface groove as previously described [19] , but that vBcl-2 undergoes different conformational changes when bound to distinct BH3 domain sequences . Thus , our data , in conjunction with recent findings [19] , [28] , provides a molecular explanation for the distinctly different roles of vBcl-2-mediated apoptosis and autophagy regulation in living cells . γHV68 vBcl-2 is required for persistent viral replication and reactivation of the virus from latency [25] , [27] , [37]; two types of biological activities have been described: anti-apoptosis and anti-autophagy . However , it has not been possible to experimentally delineate their relative contributions to overall vBcl-2 functions in vivo . Our studies have allowed us to genetically distinguish the role of vBcl-2-mediated blockage of autophagy in vivo from vBcl-2-mediated anti-apoptosis by constructing a recombinant mutant virus that has the ability to block apoptosis but is unable to inhibit autophagy in infected cells . This mutant virus is highly attenuated in maintaining viral latency , suggesting that the vBcl-2-mediated inhibition of host-cell apoptosis is not sufficient to confer persistent infection , but rather that the vBcl-2-mediated blockage of Beclin1-dependent autophagy is required for the efficient maintenance of viral latency , a prerequisite for subsequent reactivation and transmission . This finding seems to be without precedent because vBcl-2 homologs have not been recognized to directly contribute to viral latent infection by interfering with the host autophagy machinery . Our data , however , do not rule out an important role for apoptosis , likely provided by other viral factors , in maintaining viral latency in vivo but , instead , they identify a vBcl-2-associated autophagy defect in chronic infection of γHV68 . Analogous to γHV68 vBcl-2 , KSHV-encoded vBcl-2 has been found to target Beclin1-dependent autophagy more strongly than cellular Bcl-2 [12] , [30] . Given their poor overall amino acid homology to cellular Bcl-2 family members , the conservation of a mechanism of autophagy inhibition strongly supports the notion that interfering with the host autophagic machinery likely represents a common strategy for latent infection shared by these , and possibly other persistent γHVs . Nonetheless , it remains possible that the impaired latency that we observed with the vBcl-2 mutant γHV68 virus is not simply due to the disabled anti-autophagic activity of vBcl-2 but other as-of-yet undefined mechanisms . Future studies of viral replication and pathogenesis in mice lacking functional autophagy genes should help address this contingency . Notably , two previous studies have also demonstrated the functional role of vBcl-2 during γHV68 chronic infection but with slightly different results [37] . In contrast to our findings that vBcl-2 is required for efficient maintenance of γHV68 latency , Gangappa et al . did not observe evident defects in the splenic latency of the vBcl-2 mutant [37] . As previously noted [27] , [42] , this discrepancy is potentially due to the use of different viral administration routes: intraperitoneal inoculation in Gangappa et al . versus intranasal inoculation in our study . On the other hand , de Lima et al . observed a reduced efficiency in the initial establishment of γHV68 splenic latency in the absence of a functional vBcl-2 as early as day 14 postinfection , which was subsequently recovered 6 months postinfection [27] . However , in our study with the vBcl-2 mutant viruses , the latency defect was not detected until 4∼6 weeks postinfection , the period in which a contraction of latently infected splenocytes was apparently observed . The basis for the differences between these data and our findings is not yet clear . It is possible that the higher dose inoculation ( 2×104 PFU ) used by de Lima et al might provoke stronger proinflammatory responses in the lung , which may presumably affect the initial viral seeding and amplification in the spleen . Alternatively , the vBcl-2 may carry out additional activities other than anti-apoptosis and anti-autophagy that contribute to the chronic infection of γHV68 . Despite the discrepancies revealed in different experimental settings , the lack of absolute ablation of latency upon infection with the γHV68 vBcl-2 mutant suggests a complex nature of γHV68 persistence , involving multiple viral factors and cellular processes . Nevertheless , our studies on the role of vBcl-2 , particularly its anti-autophagy function , in the maintenance of splenic latency highlight the importance of autophagy during γHV68 infection . Despite the fact that vBcl-2 is primarily expressed during the γHV68 lytic cycle and that it efficiently blocks apoptosis in cell culture and in transgenic models [25] , [26] , [37] , γHV68 vBcl-2 is dispensable for the initial evasion of apoptosis in acute infections , as also shown by Gangappa et al . and de Lima et al . [27] , [37] . In this respect , one would expect that other anti-apoptotic lytic γHV68 genes be involved in the acute phase of γHV68 infections . Indeed , recent work by Feng et al . [43] indicates that γHV68 encodes a mitochondrion-associated anti-apoptotic protein ( vMAP ) that effectively antagonizes apoptosis and is required for the lytic replication of γHV68 in cell culture . It will then be of interest to test whether vMAP can also antagonize the host autophagy response and whether autophagy is involved in acute infections of γHV68 . On the other hand , the preferential roles of vBcl-2 at the late stages of latent infection and ex vivo reactivation from latency strongly imply that its expression likely continues through into latent infection [27] , [37] . This view is strengthened by the detection of the vBcl-2 transcript in latently infected cells and/or tissues , albeit in low abundance [41] , [44] , [45] , [46] . However , it should be noted that because the vBcl-2-encoding M11 gene is interposed in the opposite orientation between the v-cyclin gene and LANA/ORF73 , with its transcript overlapping with ORF73 , it remains possible that the RT-PCR signal for vBcl-2 may correspond to an ORF73 mRNA encoded on the opposite strand . Due to the complex nature of transcription across this region , a strand specific transcript mapping may be necessary for clarifying the expression profile of vBcl-2 during different phases of viral infection in mice . While dispensable for lytic replication , vBcl-2 has been previously indicated to be required for ex vivo reactivation [25] , [37] . We further extended this view by showing that the reactivation efficiency of vBcl-2 correlates with its anti-apoptotic ability , not anti-autophagic effect . We found that a mutant strain of γHV68 that is specifically impaired in the apoptosis-inhibitory activity of vBcl-2 , while remaining competent for autophagy inhibition , exhibited normal levels of splenic latency but inefficient ex vivo reactivation of the virus from the latently infected cells , suggesting that apoptosis evasion by vBcl-2 is particularly important during the reactivation process of the virus from latency . Notably , this viral phenotype , associated with the ΔBH2 mutant , was revealed at day 28 dpi but not at earlier time points of latency . This implies that the viral maintenance of latency represents a genetically distinct phase of γHVs infection and involves different viral and/or host factors . In this scenario , it is more likely that additional apoptosis inhibitors of γHV68 and/or the host proteins may be required for , or at least directly involved , for ex vivo reactivation in the early stage of latency , compensating for the anti-apoptotic effect of vBcl-2 . Therefore , our studies do not preclude the importance of apoptosis regulation for ex vivo reactivation at the early stage of latency , but rather have identified a vBcl-2-associated deficit in latency maintenance . Such a deficit would also presumably reduce the virulence of γHV68 . Furthermore , it has been set forth that the poor viability of explanted murine B cells may conceivably affect the efficiency of ex vivo reactivation . It is thus possible that the removal of the BH2 domain , which mitigates the anti-apoptotic properties of vBcl-2 , may affect the survival of explanted B cells , thereby indirectly impacting the efficiency of the ex vivo reactivation of the virus . However , this possibility did not manifest itself in the early time points of viral latency , suggesting that survival of latently infected B cells in culture does not play a critical role in dictating the phenotype of the γHV68 vBcl-2 ΔBH2 mutant and that the vBcl-2-mediated inhibition of host apoptosis may be more directly involved in the reactivation programming of γHV68 . Nevertheless , our study of the requirement of vBcl-2 for both latency maintenance and reactivation process is consistent with vBcl-2 being expressed in latently infected tissues [45] . It also points to the unique protective activities of vBcl-2 mutants in apoptosis and autophagy with respect to distinct phases of viral infection , and also their coordinated effects on γHV68 persistency and/or pathogenesis . γHVs have developed a unique mode of interaction with the host where they establish lifelong latency and may be reactivated throughout the life of the host , which has been associated with the onset of various malignancies . Although it remains to be fully understood what factors govern the establishment and maintenance of latency , our study clearly demonstrates that sustained γHV68 latency in splenocytes requires the vBcl-2-mediated inhibition of the host autophagy machinery . But , how autophagy functions and what accounts for its effect are not yet understood . Given the substantial contributions of autophagy to the quality control of cytoplasmic components in host cell , most simply , autophagy induction may promote the degradation of cytosolic viral protein ( s ) essential for the maintenance of latency . Alternatively , the ‘autophagic digestion’ of viral latent antigens may facilitate its MHC class II presentation and cytotoxic T cells ( CTL ) recognition , as recently exemplified by the nuclear antigen 1 of Epstein-Barr virus ( EBNA1 ) [47] . Additionally , autophagy may help to deliver a ‘viral signal’ to TLR-containing endosomes , thus stimulating the IFNα production that has been proven to be important to the control of acute γHV68 infection as well as latency [48] , [49] , [50] , [51] . It is also possible that the autophagy induced when Beclin1 is unchecked by vBcl-2 can trigger cell death of latently infected cells , such a scenario is supported by the fact that a Beclin1 mutant unable to bind to Bcl-2 induces caspase-independent autophagic cell death [12] . The observations that autophagy may promote the sequestration and digestion of replicating viruses inside the host cell as described in HSV-1 [52] , [53] could also provide an attractive explanation for the restriction of persistent infection of γHV68 , but direct evidence is missing . While it is not yet clear by which mechanism ( s ) autophagy restricts viral persistency , none of these mechanisms are mutually exclusive and there may be other consequences of autophagy function relating to the activation of the host immune responses against γHV68 and latency , as well . Further studies examining the molecular details involved in the vBcl-2-mediated inhibition of autophagy will expand our understanding of both autophagy and γHV-associated pathogenesis and reveal novel targets for antiviral therapy . In conclusion , we have described a crucial role for the viral evasion of autophagy in latent viral infections . Beyond its established anti-apoptotic functions , vBcl-2 targets the host autophagy effector protein Beclin1 and this activity of vBcl-2 is essential to the viral maintenance of latency . Our findings thus indicate that two host innate immune pathways , autophagy and apoptosis , both targeted by vBcl-2 , actually conduct hitherto unexpected and distinctive roles in protecting against viral infections . Future studies will aim to analyze in detail the molecular mechanisms of autophagy that contributes to controlling γHV infection .
All mice handling was performed in accordance with the Animal Research Committee guidelines of the University of Southern California ( USC ) and the University of California , Los Angeles . All methods used herein have also been approved by the USC Animal Research Committee . BALB/c mice were obtained from Charles River Laboratories , Inc . ( Wilmington , MA ) . All mice ( ∼6-week old , n = 5∼8 per pool ) were infected intranasally with 5 , 000 plaque-forming units ( PFU ) of γHV68 viruses under brief halothane anesthesia and the infected mice were sacrificed at 5 and 7 days post-infection ( dpi ) to measure acute infection in the lungs or at 12 dpi , 14 dpi , 21 dpi , 28 dpi , 35 dpi , and 42 dpi to measure viral latent load in the spleen . NIH3T3 , BHK21 and 293T cells were cultured in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal bovine serum , 2 mM L-glutamine , and 1% penicillin-streptomycin ( Gibco-BRL ) . Transient transfection was performed with Fugene 6 ( Roche ) , Lipofectanine 2000 ( Invitrogen ) , or Calcium phosphate ( Clontech ) . NIH3T3 stable cell lines were established using a standard protocol of selection with 2 µg/ml of puromycin ( Sigma-Aldrich ) . The wild-type ( WT ) γHV68 virus , pBAC/γHV68 virus [54] , and its mutant derivatives were all propagated in BHK21 cells for in vitro studies and in NIH3T12 cells for in vivo studies . A DNA fragment corresponding to the γHV68 vBcl-2 coding sequence was amplified from S11 genomic DNA . The PCR-amplified vBcl-2 DNA was then cloned into a modified pEF-IRES-puro vector ( Invitrogen ) encoding an N-terminal HA tag ( pEF-HA-vBcl-2 ) . Mutations in the vBcl-2 gene were generated by PCR ( Hi-Fidelity PCR kit , Roche ) with oligonucleotide-directed mutagenesis . Specifically , vBcl-2 Δα1 ( lacking the N-terminal 21 residues ) and ΔTM ( lacking the C-terminal 20 residues ) deletion constructs were amplified from the pEF-HA-vBcl-2 vector using specific primers; The vBcl-2 ΔBH2 ( lacking residues 129-144 of BH2 domain ) and Δα7 [lacking residues 130–134 ( NHFPL ) ] mutants were created via two-step PCR mutagenesis . The HA-vBcl-2 AAA mutant with alanine substitutions at the Ser85-Gly86-Arg87 residues was created using a Quickchange site-directed mutagenesis kit ( Stratagene ) . All of the PCR products with the indicated vBcl-2 mutations were then cloned in frame into the XhoI/MluI sites of the pEF-IRES-puro vector , for both transient and stable expression . All mutant constructs were completely sequenced to ensure the presence of the desired mutation and the absence of secondary mutations . Constructs expressing the HA-tagged Bcl-2 family proteins were kindly provided by J . Marie Hardwick ( John Hopkins university ) . The Beclin1-V5 plasmid has been described previously [29] . For yeast-two hybrid analyses , vBcl-2 and its mutant derivatives were cloned into the EcoRI/BamHI sites of the yeast plasmid pGBKT7 ( Clontech ) , which carries the S . cerevisiae TRP1 gene as a selectable marker . The BH3-like domain ( residues 88-150 ) of Beclin1 was subcloned into the EcoRI/XhoI sites of the pGADT7 vector ( Clontech ) , harboring the LEU selection marker . To produce a GST fusion protein of Bak ( GST-BakΔTM ) from E . coli , the PCR product of Bak cDNA with a deletion of the C-terminal TM region was subcloned into the EcoRI/XhoI sites of pGEX4T-1 . All constructs were sequenced using an ABI PRISM 377 automatic DNA sequencer . To make specific mutants of γHV68 ( i . e . HA-WT , HA-Δα1 , HA-ΔBH2 , and HA-AAA ) , the two-step bacteriophage lambda Red-mediated homologous recombination method was performed using the γHV68 bacterial artificial chromosome ( BAC ) clone in GS1783 ( an E . coli strain containing an arabinose-inducible I-SceI gene , provided by G . Smith , Northwestern University Medical School ) as previously described [55] . Briefly , PCR was used to generate constructs containing the kanamycin-resistance ( KanR ) gene with the mutated vBcl-2 gene . This kanamycin cassette was then inserted into the γHV68 BAC clone by homologous recombination and a markerless mutation was achieved through the deletion of the kanamycin resistance gene using I-SceI . Consequent mutations in the BAC DNAs were confirmed by DNA sequencing and the genomic integrity of the mutated BAC MHV-68 was investigated by restriction enzyme digestion and southern blot analysis as previously described [54] . vBcl-2-null BAC was generated by the in vitro MuA transposition of signature tagged transposon [54] . All BACs were reconstituted into infectious viruses by transfecting the BAC DNA along with the Cre recombinase-expressing plasmid , which removes BAC vector sequence , into NIH3T12 cells using Lipofectamine Plus reagent ( Invitrogen ) . The produced viruses were purified as single clone by limiting dilution and then amplified in NIH3T12 cells . The purified viral stock was tittered by plaque assays , using a monolayer of Vero cells overlaid with 1% methylcellulose . By 5 days post-infection , the cells were fixed and stained with 2% crystal violet in 20% ethanol . Plaques were then counted to determine infectious titer . To analyze the interactions between the Beclin1 and vBcl-2 mutants , yeast strain AH109 , expressing the BH3-like domain of Beclin1 fused to the Gal4 activation domain in the pGADT7 plasmid , was used to transform pGBKT7 plasmids containing the mutants of vBcl-2 , and the transformants then assayed for α-galactosidase activity , as previously described [56] . Quantitative GFP-LC3 light microscopy autophagy assays were performed in NIH3T3 stable cells expressing the WT or mutant forms of vBcl-2 , then transfected with a GFP-LC3-expressing plasmid [35] . Autophagy was then induced by starvation or rapamycin treatment . For starvation , the cells were washed three times with PBS and incubated in Hank's solution ( Invitrogen ) for 4 h at 37°C . Alternatively , the cells were cultured in DMEM containing 1% FBS and 2 µM rapamycin ( Sigma-Aldrich ) for 6 h . LC3 mobility shift was detected by immunoblotting as previously described [29] . For autophagy levels during viral infection , NIH3T3 cells were transfected with GFP-LC3 , then infected with recombinant γHV68 WT or mutant viruses at an MOI of 5 , and fixed 18 h after infection . NIH3T3 cells stably expressing the WT or mutant forms of vBcl-2 were seeded at 1×106 cells per well into 6-well plates for 24 h . The cells were then treated with fresh medium containing 2 ng/ml tumor necrosis factor alpha ( TNFα ) plus 1 µg/ml cycloheximide ( CHX ) for up to 12 h . For the cell viability assay , the cells were stained with trypan blue for dye exclusion . For the analysis of apoptotic cells , the samples were prepared using an DEADEND™ Fluorometric TUNEL system kit ( Promega ) according to the manufacturer's instructions . Nuclei were counterstained with 4 , 6-diamidino-2-phenylindole ( DAPI ) . Fluorescence microscopy analyses were performed with an Olympus IX-70 microscope . The percentage of TUNEL-positive cells was determined against the number of DAPI-stained nuclei . For the PI staining assay , the cells were collected with the cell dissociation buffer ( Sigma-Aldrich ) and then fixed with 70% of ethanol overnight at −20°C . Fixed cells were washed twice with PBS , and incubated in PBS containing propidium ( PI; 5 µg/ml ) , RNase A ( 1 mg/ml ) , and Triton X-100 ( 0 . 5% ) at room temperature for 30 min . Fluorescence emitted from the propidium–DNA complex was measured using FACScan flow cytometry . Cells containing hypodiploid DNA were considered apoptotic . The data was analyzed using Cell Quest ( BD Bioscience ) . For caspase-3 activity assay , the cells were harvested after treatment , washed three times with PBS and fixed with fixation medium ( Invitrogen , Catalog# GAS001S ) for 15 min , permeabilized with Permeabilization Medium ( Invitrogen , Catalog# GAS002S ) for another 15 min , and then stained with PE-conjugated anti-Caspase3 active form ( BD biosciences #550821 ) for flow cytometry analysis . Data was analyzed by FlowJo-6 . 4 . For apoptotic levels during viral infection , NIH3T3 cells were infected with recombinant γHV68 WT or mutant viruses at an MOI of 5 , and apoptosis was assessed by TUNEL staining and nuclei counterstaining as described above . BHK21 cells and NIH3T3 cells were seeded at 2×105 cells per well into 6-well plates for the single-step growth curve with a multiplicity of infection ( MOI ) of 5 . 0 , or at 1×105 cells per well for multi-step growth curves with an MOI of 0 . 1 . The samples were harvested at various time points post-infection , subjected to three freeze-thaw cycles , then titered by plaque assay in triplicate as previously described [37] . To determine the virus titer in the infected lungs , the lungs were homogenized in 1 ml of DMEM and the infectious viruses in the homogenate supernatants was measured by three independent plaque assays . For infectious center assay , which measures the amount of the latent virus that is able to reactivate from the latently infected B cells , single cell suspensions of splenocytes were prepared from the infected spleens and co-cultivated with a monolayer of Vero cells overlaid with 1% methylcellulose . The Vero cells were incubated further for 5 days , then fixed and stained with 2% crystal violet in 20% ethanol . Plaques were then counted to determine the infectious centers [57] . A majority of the samples in the assay for preformed viruses resulted in no plaque , with a minority of samples displaying 1 to 2 plaques per ∼107 splenocytes . For quantification of viral genome load from the infected cells/tissues , total genomic DNA from the infected organs was prepared and subjected to quantitative real-time PCR , as previously described [54] . Briefly , total genomic DNA from the infected lungs or the spleen tissues was extracted using a DNeasy Tissue Kit ( QIAGEN , Valenia , Calif . ) , according to manufacturer's instructions . γHV68 ORF56-specific primers ( forward primer: 5′-GTAACTCGAGACTGAAACCTCGCAGAGGTCC-3′; reverse primer: 5′-CCGAAGCTTGCACGGTGCAATGTGTCACAG-3′ ) were used in the assay . The DNA templates were mixed with 2× Master mix ( Biorad iQ™ SYBR® Green Supermix ) and PCR was performed at 95°C for 15′ and 45 cycles of 95°C for 30″ , 60°C for 30″ , and 72°C for 30″ , followed by melting curve analyses . 100–500 ng of DNA was analyzed in duplicate for each sample and compared with a standard curve of a BAC plasmid containing the γHV68 genome , serially diluted with uninfected cellular DNAs and amplified in parallel . Amplification and detection were performed using Opticon II ( MJ Research ) . The specificity of the amplified products was confirmed by agarose gel electrophoresis . Quantitative analyses of v-cyclin transcript were performed using SYBR GreenER™ qPCR Kit ( Qiagen ) on a DNA Engine Opticon® 2 continuous Fluorescence Detection System ( MJ Research , Incorporated , Waltham , MA ) . Total RNA was extracted from the infected cells using Trizol ( Invitrogen ) and 100 ng of purified total RNA was reverse transcribed to cDNA using a cDNA synthesis kit ( Invitrogen ) . The PCR reaction was set according to the manufacturer's recommendations . Briefly , after an initial 5 minutes of denaturation at 95°C , thermal cycling was performed at 94°C for 45″ , 57°C for 1′ , and 72°C for 1′ for a total of 40 cycles followed by a melting curve analyses . The amount of RNA was normalized with the quantified β-Actin in each sample . The primer sets for amplification of orf72 were: forward , 5′-GGAGCAACAACAGCTGACAA-3′; reverse , 5′-GTGATTAGCACTGGGCGTTT-3′ . The primer sets for β-Actin were: forward , 5′-CGAGGCCCAGAGCAAGAGAG-3′; reverse , 5′-CGGTTGGCCTTAGGGTTCAG-3′ . Quantitative experiments were performed at least three times , including a no-template control each time . The size of the amplified products was confirmed by agarose gel electrophoresis . For immunoblotting , the polypeptides were resolved by SDS-PAGE and transferred onto a PVDF membrane ( Bio-Rad ) . The membranes were blocked with 5% non-fat milk , and probed with the indicated antibodies . Goat antibodies coupled to horseradish peroxidase specific to mouse or rabbit immunoglobulins were used as secondary antibodies ( diluted 1∶10 , 000 , Sigma-Aldrich ) . Immunodetection was achieved with a chemiluminescence reagent ( Pierce ) and detected by a Fuji Phosphor Imager ( BAS-1500; Fuji Film Co . , Tokyo , Japan ) . For immunoprecipitation , cells were harvested and then lysed in a 1% NP40 lysis buffer supplemented with complete protease inhibitor cocktail ( Roche ) . After pre-clearing with protein A/G agarose beads for 1 h at 4°C , whole-cell lysates were used for immunoprecipitation with the indicated antibodies . Generally , 1–4 µg of the commercial antibodies was added to 1 ml of the cell lysate , which was then incubated at 4°C for 8–12 h . After addition of protein A/G agarose beads , incubation was continued for another 2 h . Immunoprecipitates were extensively washed with an NP40 lysis buffer and eluted with an SDS-PAGE loading buffer by boiling for 5 min . For in vitro GST pull-down assay , GST by itself or a GST-BakΔTM fusion protein was purified from E . coli strain BL21 ( DE3 ) ( Promega ) . 293T cell lysates were incubated with glutathione beads containing the GST fusion protein in a binding buffer ( 20 mM HEPES [pH 7 . 4] , 100 mM NaCl , 1% NP-40 , and protease inhibitors ) for 2 h at 4°C . The glutathione beads were then washed four times with the binding buffer , and the proteins associated with the beads were analyzed by SDS-PAGE and subjected to immunoblot assay with the phosphorimager . NIH3T3 stable cells grown on 8-well chamber slides were fixed with 2% ( w/v ) paraformaldehyde in PBS for 20 min , permeabilised with 0 . 2% ( v/v ) Triton X-100 for 15 min and blocked with 10% goat serum ( Gibco-BRL ) for 1 h . Primary antibody staining was performed using antiserum or purified antibodies in 1% goat serum for 1–2 h at room temperature . The cells were then extensively washed with PBS and incubated with diluted secondary antibodies in 1% goat serum for 1 h . The cells were mounted using Vectashield ( Vector Laboratories , Inc . ) . The confocal images were acquired using a Leica TCS SP laser-scanning microscope ( Leica Microsystems , PA ) fitted with a 100× Leica objective ( PL APO , 1 . 4NA ) and Leica image software . Statistical analyses were performed using unpaired t-tests . Values are expressed as mean±SEM of at least three independent experiments unless otherwise noted . A P value of ≤0 . 05 was considered statistically significant . | Autophagy ( ‘self-eating’ , lysosome-dependent degradation and recycling of the intracellular components in response to stress ) and apoptosis ( ‘self-killing’ , cells commit suicide in response to stress ) are important host defense mechanisms against viral infections . γ-herpesvirus 68 ( γHV68 ) encodes a Bcl-2 family protein , vBcl-2 , that effectively antagonizes both autophagy and apoptosis and is required for chronic viral infection and pathogenesis . However , the relative contributions of the vBcl-2-mediated evasion of autophagy and apoptosis to γHV68 persistent infection remain largely unknown . Here , we characterized a series of vBcl-2 mutants to genetically and functionally distinguish these closely related activities of vBcl-2 in vitro and in vivo . We have found that the inhibition of autophagy by vBcl-2 is important for maintaining latent infections , while the anti-apoptotic activity of vBcl-2 is largely involved in efficient viral reactivation from latency . Our findings thus reveal a novel paradigm for the vBcl-2-mediated evasion of autophagy and apoptosis during chronic viral infection , identifying a vital role for autophagy in controlling γHV68 latent infection . |
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Cholera , an ancient scourge , continues to inflict high rates of mortality today . The rising incidence of epidemics in areas of poor sanitation and crowding highlight the need for better epidemic prevention and early response . Such interventions require the availability of rapid and accurate diagnostic techniques to trigger timely response and mitigate the scale of the outbreak . The current gold standard of bacterial culture is inadequate for rapid diagnosis , highlighting the overarching neglect of field diagnostic needs . This paper was written to support the World Health Organisation's Global Task Force on Cholera Control mandated Cholera and diarrhoeal disease laboratory Network ( CholdiNet ) in devising a protocol for the validation of Rapid Diagnostic Tests ( RDTs ) for Vibrio cholerae . The status of diagnostic tools for Vibrio cholerae is assessed , describing products that have been commercialised over the last two decades and discussing their peer-reviewed evaluation . Review of post-1990 peer-reviewed and grey literature on rapid diagnostic tests for Vibrio cholerae . Since 1990 , twenty four diagnostic tests have been developed for the detection of Vibrio cholerae in human faecal samples . Fourteen of these have also been described in the literature , with rapid chromatographic-immuno assays ( CIA ) featuring strongly . Polymerase chain reaction ( PCR ) assays maintain the ability to detect the lowest amount of bacteria; however CIAs achieve both low detection thresholds and high sensitivity and specificity , making them possible candidates for use in field conditions . Field and laboratory studies were performed in a wide range of settings demonstrating variability in performance , however only a few of these studies were sufficiently stringent , highlighting five RDTs that showed promise in field conditions; COAT , IP cholera dipstick , SMART , IP dipstick and Medicos . In light of non-independent reporting , the authors would like to see these five products undergoing additional studies , with further technical improvements if needed and commercial production . The authors hope that public health use of such a RDT in limited-resource field conditions on stool samples may contribute to effective reduction in cholera epidemic spread .
Cholera is an infectious disease caused by the bacteria Vibrio cholerae O1 and/or O139 . When ingested , its clinical sequelae include the acute onset of severe secretory ‘rice water’ diarrhoea . Within three to four hours of symptom onset , a previously healthy individual may become severely dehydrated and if not treated may die within twenty four hours . This makes cholera one of the most rapidly fatal infectious illnesses known whose clinical management , simple rehydration , can be instituted empirically and remains cheap , safe and life-saving [1] . Cholera inflicts a heavy economic burden in its endemic settings due to its rapid spread and ability to cause large epidemics [2]–[3] . Since the first reported epidemic in the 19th century on the Indian subcontinent , cholera has spread to all inhabited continents and has become endemic in Africa , South and East Asia [4]–[5] . In recent years , reported cholera cases increased steadily reaching more than 300' 000 cases including more than 7'500 deaths during 2010 [6] . During the same year and for the first time since 1995 , the proportion of global cases reported to WHO from the African continent declined from more than 90% to less than 50% , a consequence of the large cholera outbreak which occurred in Hispaniola . Recent trends suggest that the number of outbreaks of cholera will continue to increase in vulnerable areas in the future . As populations of poor countries continue to coalesce in mega-cities with poor sanitation and people move rapidly around the globe , new and more virulent strains of V . cholerae are expected to disseminate more rapidly [7]–[8] . The unpredictable emergence and spread of antibiotic-resistant strains , together with increasing severe weather events and changes in water temperature and nutrient levels means that the occurrence of more frequent cholera outbreaks may continue to occur in the foreseeable future [9] . To combat this threat , attention in clinical and public health circles has expanded to encompass efforts to improve sanitation and utilization of new vaccines to improving epidemic response through early detection . By detecting cholera outbreaks as early in their course as possible additional resources can be more rapidly brought to bear to mitigate the size , scope and duration of the outbreak and subsequent spread of the illness . In addition , using a cheap rapid diagnostic test on watery stool samples , under field conditions with limited resources holds great potential to aid cholera control efforts , not because empirical treatment or later treatment is less effective but rather because decreasing the numbers of cholera infections and aiding in epidemic-preventing surveillance will free up resources that should be directed towards the excessively complex task of fixing the underlying socio-economic and environmental factors that propagate the spread of cholera . This shifts the focus from response measures , which often arrive too late to halt the course of an epidemic . With this approach in mind , the current gold standard for laboratory diagnosis of cholera becomes evidently inadequate due to lengthy culturing on selective growth media . Preliminary identification based on colony appearance on Thiosulfate Citrate Bile Salts Sucrose Agar ( TCBS ) is traditionally confirmed using an array of biochemical tests , taking a few days to confirm a case of cholera and requiring numerous laboratory resources [10] . Such tests still have a role in antibiotic sensitivity surveillance during epidemics , however timely field diagnosis calls for Rapid Diagnostic Tests ( RDTs ) . Most RDTs work by capturing a characteristic component of the cholera bacteria on a solid surface and binding it with specific reagents to produce a visual change , allowing for rapid detection of a cholera infection . Following the principle of commercialised home-pregnancy detection kits , such cassettes and dipsticks ( characterised by quick turnover time , ease of use and accuracy ) are extending their reach outside classical laboratory networks thereby aiding diagnosis in under resourced field settings . The last twenty years have also seen attempts to modify technologies such as Polymerase Chain Reaction ( PCR ) , Enzyme Linked Immunosorbent Assay ( ELISA ) and agglutination methods to make them more applicable as triggers for the timely response against cholera . At the time of writing there is no consensus as to the most efficacious cholera diagnostic for early case detection . Very few diagnostic tests have been well described and throroughly evaluated [11] . This study aims to identify and describe all the commercialised diagnostic tools developed and evaluated since 1990 . It then explores the evidence base for these tests , including their sensitivity , specificity and reliability . In doing so the paper exposes the discrepancies that exist between research and field application of these diagnostic products . The authors hope that this work will facilitate future consensus regarding the best diagnostic for early cholera epidemic detection , and hence begin to address the neglect of cholera .
Systematic searches were conducted using PubMed , SCOPUS , EMBASE , LILACS , ScienceDirect , GoogleScholar , Medline Plus , and ResearchGATE . The following search strategy was used: All published English literature since 1990 focusing on diagnostic tools for human clinical samples of cholera , and the evaluation of diagnostic tests , using the initial terms “Cholera”[Mesh] OR “Vibrio cholerae”[Mesh] OR cholerae OR Choleras OR cholera OR “Cholera Toxin”[Mesh] AND “Sensitivity and Specificity”[Mesh] OR “Diagnosis”[Mesh] OR “diagnosis”[Subheading] OR ( routine AND ( test OR tests or testing ) ) [TIAB] OR ( false AND ( ( positive or positivity ) or negative ) ) [TIAB] OR diagnos* [TIAB] in PubMed . The reference lists of relevant papers were followed and a manual search was conducted of journal titles with multiple publications on the topic , including; Journal of Clinical Microbiology , Transactions of the Royal Society of Tropical Medicine and Hygiene , Biosensors and Bioelectronics , Journal of Microbiological Methods . To elicit information regarding commercialised diagnostic tools , a grey literature search was conducted of manufacturers and governmental regulatory websites , following up manufacturing and product names referred to in the aforementioned literature . The choice to limit the articles to the year 1990 reflects the upsurge of technologies following the South American outbreak of the early 1990s and the discovery of the new strain O139 Bengal in 1992 . Furthermore , papers were excluded if they did not focus on diagnosis of human samples of Vibrio cholerae ( Figure 1 ) . At the analysis stage , the diagnostic technologies were described in terms of: their detection limit , their diagnostic target , the method used ( microscopy , agglutination , ELISA , immunochromatography or PCR ) , turn around time , intended use and settings of use . The sensitivity and specificity of the tests were reported from laboratory evaluation , highlighting field studies ( when performed ) . These field studies were then filtered for having a sample size greater than 100 stool samples and reporting Positive Predictive Value ( PPV ) and Negative Predictive Value ( NPV ) and presented in tabulated form according to the aformentioned values . Following this , a review of the evidence on the validity and reproducibility underpinning the rapid diagnostic tests ( RDTs ) was performed .
In January 2010 , twenty four commercialised diagnostic tests were identified ( Table S1 ) . Ten more received a mention in the literature but due to suspected discontinuation of production or fall-back did not provide sufficient information ( see note under table S1 for details ) . Of the twenty four described in detail below , a clear evolution is portrayed following closely the historical development of diagnostic technologies in this field from cell culture and microscopy methods towards agglutination methods , immunochromatographic assays , and Polymerase Chain Reaction ( PCR ) -based assays . The majority of tests were found to be based on antigen or antibody detection , with a large proportion of the remaining tests being DNA-based . It is important to note that this investigation came across many diagnostic methods that have never been commercialised including those that are currently in the scientific pipeline – technologies such as microarrays and electro-chemiluminescence are being developed as biosensors for cholera toxin and other antigens . Loop-mediated isothermal amplification ( LAMP ) , ELISA and simplified PCR protocols are but a few of the new approaches to the diagnosis of cholera that are likely to be seen in the future , unfortunately these are beyond the scope of this paper . Table S1 summarises the results in two sections , separating DNA from antigen detection . All of the tools are described according to the information provided by the manufacturer; product parameters , intended application and performance . The details provided should allow the reader to compare both the analytical sensitivity , according to the limit of detection , and test utility according to the turnaround time . Since 1990 there have been eighteen laboratory and field evaluations of commercial diagnostic tools for the detection of Vibrio cholerae in clinical samples ( see Tables S2 & S3 ) [12]–[29] . These evaluations have been carried out around the world , with a large proportion taking place in the Indian subcontinent . The largest study enrolled around 400 cases and controls , while most other sample cohorts were quite small , the smallest being just 30 patient samples [30] . Tables S2 and S3 summarise the peer reviewed evaluations of the aforementioned diagnostic tests conducted between 1990 and 2008 . Table S2 highlights those tests conducted in field conditions . Fourteen different diagnostic tests were found to have been evaluated and are thus compared here . Tables 1 and 2 rank those tests that were evaluated under field conditions with over 100 samples , excluding tests where negative and positive predictive values were insufficiently reported . Overall , there appear to be five such tests; coagglutination test ( COAT ) , Institute Pasteur ( IP ) cholera dipstick , Sensitive Membrane Antigen Rapid Test ( SMART ) , IP dipstick and Medicos . Tables 3 and 4 similarily present the sensitivities and specificities of the aformentioned eighteen evaluations by rank of test .
It is important to note that the scientific community remains divided regarding the intended use of rapid cholera tests , failing to provide any clear consensus about the requirements for such a rapid diagnostic test . In trying to formulate such requirements one must recognise the stark difference between individual diagnostic utility and early detection of epidemic or other public health roles . This preliminary step is critical as the characteristics of the tests are expected to differ in each scenario . In the case of PCR technologies , they currently have an important role in individual diagnostic confirmation but also play a role in outbreak detection in combination with RDTs through epidemiology and surveillance . PCR methods have shown to be advantageous at being particularly sensitive to small amounts of infectious bacteria and having the added value of characterising the strains of the epidemic during the same diagnostic investigation . The tests in table S1 detect a range of genes including those that differentiate between biotype El tor and classical cholera , the serotype specific wbe and hemA genes and virulence genes for cholera toxin ( ctx ) , zonula occludens toxin ( zot ) , accessory cholera enterotoxin ( ace ) and a tetracycline resistant genotype ( tetA ) . Such tests could be of great utility if they continue in their adaptation for field use , especially in surveillance programs within the reach of reference laboratories . Their limited value in field settings highlights that a single test cannot serve all contexts and that the end user should be clearly informed about the limitations of each technology . As stipulated in the DEEP guidelines , the study design is the first parameter to consider to ensure that an evaluation study will answer the intended question , be it a lab evaluation or field testing . NPV and PPV can only be derived from field testing when disease prevalence is known . It is therefore critical that studies analysing cholera diagnostic tests mimic the conditions the tests are designed to be used in . Among the rapid diagnostic test evaluations , only a limited number of studies were conducted in field conditions on fresh stool samples with provision of population descriptors . Such demographics may ensure that tests are really applicable to those who suffer from cholera . Therefore it is not unreasonable to suggest that current tests may encounter unexpected results when taken to the field . In the studies analyzed , recurrent conflation of the term ‘sensitivity’ to describe the lowest detectable dilution in the stools made it more difficult to carry out an accurate comparison of the tests . However , overall , some of the new rapid tests ( Smart Q – 105 CFU/mL ) are performing to the same detection standards as the PCR based tests ( BAX - 104 CFU/mL ) . A key study design parameter is the sample size of the investigation . A small sample size limits the validity of the evaluation and the reproducibility of the results . This was the case in most of the evaluations examined , suggesting a direct impact on their specificity and sensitivity measurements . Indeed many of the studies were found to have 100% sensitivity or specificity using approximately 100 participants . In spite of this caveat , it is important to recognize that due to the challenge of long-term preservation of cholera organisms in stools specimen sample archives are hard to come by and conducting the study on larger sample size of fresh faecal samples in a resource-poor setting is challenging . However , in the broader literature covered during the course of this analysis , four larger cohorts were found , each reviewing tests that have not yet to our knowledge become commercialised ( hence being excluded from the final analysis ) [36]–[38] . The largest investigation by far was a collection of 6 , 497 hospital patients by Chaicumpa et al in 1998 [39] suggesting that such numbers are not impossible and that current sub-standard sample sizes should not be allowed to become the acceptable norm . Another point to consider is whether sample archives are ideal for a disease like cholera , and whether greater benefit may be gained from the acquisition and analysis of fresh samples . For example , if fecal material from children was under-represented in the study population , health workers may find that the tests are not as accurate or more prone to diagnostic errors . Such errors relate to our ability to infer test efficacy from the field data . Since positive predictive values and negative predictive values are dependent on the prevalence of the disease in the population , a factor that can differ between different age groups , the utility of these tests may differ for children . As children tend to experience greater mortality in association with severe cholera , it is paramount to diagnose the onset rapidly . Studies lacked a consistent comparison to any one gold standard , many studies neglecting to undertake detection by both test and reference methods across the sample range to confirm the validity of the result . The variability in the use of sample panels and study populations underlined the lack of a standardised case definition . The absence of a clear benchmark for assessing the tests is made worse by the newer tests performing better than the gold standard , leaving no adequate comparator . Furthermore , some of the diagnostic tests were tested against purified cultures of cholera . This fails to assess how they would fare when used with real patient stool samples packed with gut micro flora and likely to have many other contaminating interactions . Therefore , if used , the results of this investigation are only valid for the use of the diagnostic after culturing , hence defeating the purpose of making these into rapid diagnostic tests . Following the same principle , many of the tests appear to be rather specific on first glance , however only few were thoroughly tested against confounding microorganisms commonly found in stool samples which could lead to a similar case presentation of watery stools . Some studies went so far as to not utilise a negative control . Testing performance against a well characterised challenge panels is required . Data showing robustness of the test design are important to support deployment in the areas of highest need . Test descriptors such as a test's reproducibility were markedly underreported; lacking rigorous replication important for assessing the inherent quality of the diagnostic tool , the reliability of different production lots and the ease of replication by different users . This also reflected the lack of important mention of test heat stability . A test's intended use is directly linked to analytical parameters such as heat stability . In the case of cholera much of its endemic settings are affected by harsh temperatures and humidity , factors which must be taken into account for the shelf life and accuracy of the test . It is important to note that this is not a true systematic analysis , nor did the analysis review the entire literature , the authors selected to review only articles written in English from the year 1990 onwards . In spite of the aforementioned limitations of these studies ( see table 5 ) , the authors present here a first attempt to rank the existing tests according to sensitivity and specificity or PPV and NPV ( see tables 1–4 ) . In doing so we have identified that only five tests; COAT , IP cholera dipstick , SMART , IP dipstick and Medicos have been evaluated under field conditions , with a large enough sample size , providing data for essential evaluation parameters . In light of these products' low specificity and sensitivity findings ( some less than 90% ) and non-independent reporting of the data the authors strongly recommend further studies assessing field performance of these promising tests . International collaboration is called for to co-ordinate well designed evaluation studies with standardized protocols , using best gold standard and assess the test performance for the intended use they were built for . From robust data sets , the scientific community can then derive performance benchmarks for each intended use of the available commercial cholera diagnostic tests . Caveats in the available information make it harder to make overarching comparisons between the different diagnostic tools , and so does the redundancy in product re-packaging under different names . The neglected nature of cholera in terms of the ratio of its burden to the relative attention it gets ( funding , awareness , research ) means that most of the diagnostic tools developed thus far have never progressed to mass production and can only be found in small batch numbers for a short period of time . This makes any future evaluation of these tools fraught with difficulties . Much of the information about the respective diagnostic tools has been provided by manufacturers in media releases and product information and is therefore often lacking in reliable information regarding the testing protocol . These are aspects that a future evaluation could address by carrying out independent testing of the products , facilitating an evidence-based revision of the diagnostic strategies for cholera . The authors recommend a prioritization of research and development agenda , and would like to see the five promising field evaluated products highlighted in this article ( table 1 and 2 ) undergoing further independent evaluation , followed by technical improvements if needed and production for use in field condition . Ascertaining the best RDT for early detection of epidemics in this way will maximize the benefits using constrained financial resources available . Despite the limitations of this review , it is the first time a comprehensive picture of the market of diagnostic tests for cholera has been revealed . In doing so , many of the criteria for more thorough investigation have been elucidated and will likely be incorporated into future standard formulations of evaluation guidelines . The authors hope that the availability and use of the resulting reliable and user friendly rapid diagnostic tests will allow for the triggering of timely response to outbreaks and thus limit spread and burden of cholera . | Rising prevalence of cholera outbreaks highlights the need for accurate detection tools . Diagnosing cholera early at the onset of an epidemic , at field level , should allow for a more timely response and a quick containment of the spread and thus a diminished case load . Currently the gold standard to identify the bacteria , Vibrio cholerae , from patient samples remains reliant on lengthy bacterial cultures and an array of biochemical tests . Furthermore , the need for highly-skilled operators and numerous laboratory resources underline the inadequacy of sophisticated tests for use in remote locations . Research to develop more appropriate tools has largely focused on rapid diagnostic tests and attempts to simplify existing technologies . This is yet to deliver evidence-based appropriate tools to address the burden-of-disease cholera inflicts . In light of this neglect we have taken the first step , assessing developments in commercialised diagnostic tools , reviewing previous evaluations undertaken in the literature since 1990 . In doing so , we highlight evaluation study parameters that could benefit from stringent standardisation , and identify five tests that show promise for use in field conditions . The authors recommend an indipendent assessment of these products , including technical improvements as required and production to trigger early detection of cholera epidemics . |
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Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations . Details of this process are however unclear . While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets , the more general application for temporally varying signals has yet to be demonstrated . Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants . The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior . We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells . Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells , variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway . Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network .
Many models of the cerebellum assume that the granular layer recodes its mossy-fiber inputs into a more diverse set of granule-cell outputs [1–4] . It is further assumed that the recoded signals , which travel via granule-cell ascending axons and parallel fibers to Purkinje cells and molecular layer interneurons , are appropriately weighted using plastic synapses and then combined to produce the particular Purkinje cell outputs that are required for any given learning task . Recoding in these models thus enables a given set of mossy-fiber inputs to generate one of a very wide variety of Purkinje cell outputs , giving the model demonstrable computational power ( e . g . [5] ) . Although this framework is seen as plausible in broad outline ( e . g . [6 , 7] ) , the details of its workings are far from established [8] . Relatively simple top-down models have shown that theoretically well-understood recoding schemes such as tapped delay lines , spectral timing , Gaussians , sinusoids , and exponentials can be effective , but do not establish how they could be implemented biologically ( references in [8–10] ) . In contrast , more complex bottom-up models of recurrent inhibitory networks representing the connectivity between granule and Golgi cells are closer to biological plausibility , but have been used for very specific tasks such as eye-blink conditioning so that their general computational adequacy is unknown [11–20] . In part this is because eyeblink conditioning requires a response only at the time the unconditioned stimulus arrives . Eyelid ( or nictitating membrane ) position is not specified either for the period between the conditioned and unconditioned stimulus , or for the period ( possibly some hundreds of milliseconds ) after the unconditioned stimulus has been delivered . In contrast , for a task such as the vestibulo-ocular reflex eye-position is very precisely specified for as long as the head is moving , and afterwards for as long as gaze has to be held constant . Thus , cerebellar output—and hence granular-layer output—is more tightly constrained in motor-control tasks resembling the vestibulo-ocular reflex than in eyeblink conditioning [3] . Here we combine elements of top-down and bottom-up approaches , by investigating whether the outputs of neural networks that incorporate the recurrent inhibition observed in the granular layer can be linearly combined to generate continuous filter functions which are computationally useful for example in vestibulo-ocular reflex adaptation [9] . The split between a complex representation layer ( granular layer ) and a linear reconstruction layer ( perhaps corresponding to the plastic synapses between granule cells and Purkinje cells or molecular-layer interneurons ) is similar to the structure employed in reservoir computing [21] , and it is convenient to use terminology and methods from that field in analyzing these networks ( see Methods ) . We begin by analyzing the case of a one-layer network with recurrent inhibition [15] . This is simpler than the real granular layer in which feedback is provided via a second layer of Golgi cell interneurons , but is worth analyzing separately because it allows us to test the hypothesis , suggested by the reservoir computing metaphor , that the crucial parameter in determining the time extension of responses is the mean amount of feedback in the network , and how closely this parameter is tuned to the edge-of-chaos [22] . This degree of tuning can be measured by the Lyapunov exponent . Generally speaking , if there is very little recurrent feedback in a network , then responses will be highly stable and die away very quickly over time , while for large amounts of feedback the responses can be chaotic or even unstable . The Lyapunov exponent ( see Methods ) is a quantitative measure of stability because it captures the rate of growth or decay of small perturbations . In linear systems negative values imply stability , while positive values imply instability . In non-linear systems , small , negative values of Lyapunov exponents can be especially interesting , since they can signal the ‘edge-of-chaos’ , where there are long-lasting and possibly complex responses to transient inputs . We show that this is the interesting region for our reconstruction problem . One novel feature of this contribution is its use of generic colored noise inputs , rather than the stereotyped pulse or step inputs that are usually considered . These colored-noise inputs are essential for motor control applications such as the VOR , where they are needed to demonstrate that the filter can process generic vestibular signals . A second novel feature is the use of statistical techniques that allow us to evaluate the ability of the network to approximate the range of linear filters required for these applications . While previous work on reservoir networks focused on generic inhibitory and excitatory networks [22–30] this is the first work to systematically examine stability and reservoir performance in networks dominated by recurrent inhibition like the granular layer while also taking into account the effects of cerebellar network properties on filter approximations . To achieve this we extend the model to two populations in order to represent inhibition via Golgi cells . We also test the effect of other non-generic features of the cerebellum such as the newly discovered functional feature of Golgi cell inhibition by mGluR2 receptor activated GIRK channels [31 , 32] and Golgi cell afferent excitation often neglected in cerebellar simulations . Furthermore we also evaluate the effect of output-feedback to the granular layer through the nucleocortical pathway .
The one-population model used in this study ( Fig 1A ) was based on that of Yamazaki and Tanaka [15] . It consisted of Nz = 1000 granule cells , each receiving excitatory afferent inputs Ii ( t ) derived from the external signal x ( t ) , and recurrent inhibitory inputs from other cells . The model neurons were firing-rate ( i . e . non-spiking ) , and the output zi ( t ) of the i-th neuron at time t was given by zi ( t ) =[Ii ( t ) −∑jNzAijwij∑s=1texp ( −t−sτw ) zj ( s−1 ) +nNi ( t ) ]+ ( 1 ) ( here the bracket notation []+is used to set negative values to zero , preventing the firing-rate of a neuron from becoming negative ) . This equation describes ( see Fig 1A ) memory-less rate-neurons connected by single-exponential synaptic process with time constant τw so that neuron i sums past inputs zj ( s − 1 ) , 1≤ s ≤ t from other neurons , exponentially weighted by distance s − t into the past . Neuron j has synaptic weight Aij wij on neuron i where Aij was set to 1 with probability a and 0 otherwise , hence the parameter a controls the sparsity of the connectivity . The connectivity strengths wij were drawn from a normal distribution with mean w and standard deviation vww , normalized by population size Nz , and constrained to be positive , so that wij = 2Nz ( w±vww ) + . Each neuron received an excitatory input Ii ( t ) with additive noise nNi ( t ) ( here Ni ( t ) is a discrete white noise process with std ( N ) = 1/2 so that the added noise is smaller in magnitude than the noise amplitude n 95% of the time ) . In the simulations , unless otherwise specified , we used the following default values for the parameters above . The population size was Nz = 1000 . The probability of connectivity was a = 0 . 4 ( close to the value 0 . 5 in Yamazaki and Tanaka [15] ) , and synaptic variability was set to zero ( vw = 0 ) . The default input noise level was n = 0 . This model had a single time constant which Yamazaki and Tanaka [15] took to be equal to the membrane time constant of Golgi cells in their simulations of granular layer dynamics . However it is not clear that this is the relevant time constant for a firing-rate model since the dynamics of the sub-threshold domain cannot be easily carried over into the supra-threshold ( spiking ) domain and are often counter intuitive . While a still prevailing misconception is that long membrane time-constants are equal to a slow spike response , the exact opposite is the case: integrate-and-fire with an infinite time constant ( perfect integrators ) have the fastest response time to a current step and can respond almost instantaneously [33] . Since temporal dynamics of neurons in a network are primarily determined by the time course of the synaptic currents [33–36] we have ignored membrane time constants in this and following models and instead related τw to the synaptic time constant of recurrent inhibition in the network . We further want to note that the values for the synaptic time constants were not directly adjusted to replicate results for individual electrophysiological studies but rather kept at general values to study the effect on network output of interaction between different magnitudes of time constants . This issue is considered further in the Discussion . To allow for more realistic modeling of the dynamics of the granular layer we extended the one-population network of granule cells above to include inhibition via a population of interneurons corresponding to Golgi cells ( see Fig 1B ) . In this model the firing-rates zi ( t ) of granule cells and qi ( t ) of Golgi cells were given by zi ( t ) =[Ii ( t ) −∑jNqAijwij∑s=1texp ( −t−sτw ) qj ( s−1 ) +L ( t ) ]+ ( 2 ) qi ( t ) =[g⋅Ii ( t ) +∑jNzBij ( uij∑s=1texp ( −t−sτu ) zj ( s−1 ) −mij∑s=1texp ( −t−sτm ) zj ( s−1 ) ) +L ( t ) ]+ ( 3 ) The default sizes for the two populations were Nz = 1000 and Nq = 100 . As before , the excitatory afferent input into a granule cell i was given by Ii ( t ) , however the two-population model also had direct afferent excitation gIi ( t ) of Golgi cells . The factor g setting the level of excitation was set to 0 in the initial simulations , resulting in no afferent excitation for Golgi cells . The output-feedback L ( t ) was 0 until later simulations ( see below ) . The connectivity between the two populations was given by the random binary connection matrices W and U , however in this model the connectivity was not defined by a probability but by the convergence ratios cw = 4 between Golgi and granule cells and cu = 100 vice versa . Thus exactly 4 randomly selected Golgi cells inhibited each granule cell and 100 randomly chosen granule cells were connected to each Golgi cell . The weight of GABAergic inhibition between Golgi and granule cells was drawn from a normal distribution and normalized with wij = 2cw ( w±vww ) + ( default vw = 0 ) and the time constant of inhibition was given by τw . Besides the glutamatergic excitatory connections between granule and Golgi cells with weight uij = 2cu ( u±vuu ) + ( default vu = 0 ) and time constant τu the model was extended to emulate the inhibitory effect of mGluR2 activated GIRK channels [31] with mij = 2cb ( m±vmm ) + ( default m = 0 , vm = 0 ) and time constant τm = 50ms . Note that mGluR2 inhibition was not used until later simulations with m = 0 . 003 . Additional simulations were conducted with only half of the Golgi cells receiving mGluR2 inhibition i . e . Pr ( m = 0 ) = 0 . 5 . In all simulations u was set to 0 . 1 and normalized by the excitatory time constant resulting in u = 0 . 1/τu . All network simulations were written in C and were integrated into Python by transforming them into dynamically linked extensions with the package distutils . The stepsize in all simulations was dt = 1ms . All results were analyzed using Python . All models , methods and simulation results are available from the github repository https://github . com/croessert/ClosedLoopRoessertEtAl . A snapshot of the model code can also be found on ModelDB: https://senselab . med . yale . edu/modeldb/ShowModel . asp ? model=168950 . Computational resources for the simulations were partially provided by the ICEBERG cluster ( University of Sheffield; access granted by the INSIGNEO Institute for in silico Medicine ) . The modulated input to each cell was given by the excitatory input Ii ( t ) = [I0i + f ∙ 0 . 1 ∙ I0i ∙ x ( t ) ]+ . Unless noted otherwise the input I0i was chosen from a normal distribution with mean 1 and default standard deviation vI = 0 . 1 . To test increased input variability , standard deviation was increased to vI = 2 in a later experiment . The factor f , randomly picked as either 1 or -1 defined whether the input was inverted or not . This type of input coding , here termed “push-pull” coding can be routinely found for example in the vestibulo-cerebellum where half of the cells are ipsilateral preferring ( f = 1 , type I ) or contralateral preferring ( f = −1 , type II ) [37] . In order to test the ability of the network to construct a linear filter with a given impulse response it is not sufficient to use impulse inputs alone , since this does not test linearity ( for example the response to two successive impulse inputs may not be the sum of the individual responses ) . For this reason we also used random process inputs that mimic behavioral inputs . The input signal x ( t ) consisted of 3 parts ( see Fig 1C ) . The first part was a training sequence of a 5 second band-passed white noise signal ( low-passed with a maximum frequency of 20 Hz ) [38] chosen to mimic head velocity in the behaviorally relevant frequency range of 0–20 Hz [39] . Additionally a 5 second silent signal ( x ( t ) = 0 ) was added to the training sequence to train a stable response . Training with a segment of null data finds weights which not only give the appropriate impulse response but also produce zero output for zero input data , so that they reject spontaneous modulatory activity in the network . Consecutively the previous signals were repeated with a different realization of the noise signal to test the quality of the filter construction . The third part was an impulse test signal where x ( t ) = 0 apart from a brief pulse of 50 ms where x ( t ) = 1 . The colored noise signal was normalized to std ( x ) = 1/2 which ensured that the amplitude 0 . 1 ∙ I0i included the input 95% of the time . To assess the ability of the network to implement linear filters that depend on the past history of the inputs , the output signals zi ( t ) of all granule cells during the training sequence were used to construct exponential ( leaky integrator ) filters y ( t ) = F * x ( t ) of increasing time constants as linear sums y ( t ) = ∑βizi ( t ) of granule cell outputs . This can be regarded as the output of an artificial Purkinje cell that acts as a linear neuron . In matrix terms ( writing time series in columns ) this expression can be written y −Zβ where the undetermined coefficients β are usually fitted by the method of least squares to minimize root sum square fitting error ∥y−Zβ∥2=∑t ( y ( t ) −Σβizi ( t ) ) 2 ( 4 ) However over-fitting of the data , due to the large output population , can make this method misleading and give excessively high estimates of reconstruction accuracy . To avoid this problem we used the method of LASSO regression taken from the reservoir computing literature . This is a robust fitting procedure that includes a regularization term to keep the reconstruction weights small [40 , 41] . Here , the estimates are defined by β^ = argminβy-Zβ2+αβ1 which is the least-squares minimization above with the additional constraint that the L1-norm ||β||1 = ∑βi of the parameter vector is also kept small . In practice we find that up to about 90% of weights are effectively zero using this method . In contrast to ridge regression that employs a L2 -norm penalty and is commonly used to prevent over-fitting in reservoir computing [27] LASSO regression produces very sparse weight distributions . This corresponds well to the actual learning properties of the Purkinje cell , approximated as a linear neuron , in which optimality properties of the learning rule with respect to input noise force the majority of synapses to silence [42–45] . We fitted three responses yj ( t ) = x ( t ) * Fj ( t ) with j = 1 , 2 , 3 and with Fj ( t ) = exp ( −t/τj ) being one of three exponential filters τ1 = 10ms , τ2 = 100ms or τ3 = 500ms ( see Fig 1D ) . The regularization coefficient was set to α = 1e−4 which gave best maximum mean goodness-of-fit results for the one-population model with τw = 50ms ( not shown ) . LASSO regression was implemented using the function sklearn . linear_model . Lasso ( ) from the python package scikit-learn [46] . In general the estimated weights βi take both positive and negative values , which is not compatible with the interpretation of equation ( 4 ) above as parallel fiber synthesis by Purkinje cells . The use of negative weights is usually justified by assuming a relay through inhibitory molecular interneurons [42 , 44] . To test whether learning at parallel fibers alone is sufficient for the construction of filters from reservoir signals we additionally employed LASSO regression with only positive coefficients ( positive-LASSO ) as a comparison . As a measure of the quality of filter construction , the weights estimated from the training sequence were used to construct the filtered responses in the test sequence and the goodness-of-fit between expected output and constructed output was computed for each filter using the squared Pearson correlation coefficient ( R2 ) [47] ( see Fig 1D ) . For the final goodness-of-fit measure the mean of 10 networks with identical properties but with different random connections was computed . A convenient way to analyze the stability or chaoticity of a dynamic system is the Lyapunov exponent λ . It is a measure for the exponential deviation of a system resulting from a small disturbance [25] and a value larger than 0 indicates a chaotic system . The Lyapunov exponent was measured empirically , similar to Legenstein and Maass [22] by calculating the average Euclidian distance dt = ∑i = 1Nzzit-zi' ( t ) 2 between all granule cell rates zi ( t ) from a simulation where x ( t ) = 0 and the rates zi' ( t ) from a second simulation where the input was disturbed by a small amount at one time step , i . e . x ( 0 ) = 10−14 . This state separation simulation was repeated for 10 randomly connected networks but otherwise identical parameters and λ was estimated from the mean average Euclidian distance d-t with λ = log2mean ( d-t = 2 . 01s:2 . 11s ) /mean ( d-t = 0 . 01s:0 . 11s ) /2s . To estimate the transition between stability and chaos we were mainly interested in the sign of the Lyapunov exponent . Although taking the mean of a 100 ms period and using a relatively large Δt of 2s [24] decreases the accuracy of the Lyapunov estimation , it was used here to prevent errors in the estimation of the sign . The edge-of-chaos was defined as the point where λ crosses 0 for the first time when traversing in the direction of strong inhibition w to weak and therefore from high λ to low . To model putative output-feedback to the reservoir via the nucleocortical pathway the signal L ( t ) = f ∙ oi ∙ −∑βizi ( t ) was injected into 20% of all granule and Golgi cells in the last simulations . The factor f was randomly picked as either 1 or -1 to model 50% excitation and inhibition and the weight was drawn from a normal distribution with oi = [1e−4±1e−5]+ . In these simulations only the case for output-feedback of the slowest filter signal is shown . Thus βi are the weights needed to construct the filter with τ3 = 500ms . As noted in the reservoir computing literature [27 , 48 , 49] output-feedback in general is a very difficult task since it leads to instability . Therefore the weights βi were not learned online but a method called teacher forcing with noise was applied [27] . The weights βi were learned in a prior step by using the teacher signal L′ ( t ) = f ∙ oi ∙ −y3 ( t ) ∙ N ( t ) instead of the feedback signal L ( t ) to uncouple the instable learning . Here y3 ( t ) is the target response for the slowest filter ( Fig 1D ) and N ( t ) is a discrete white noise process that helps to increase the dynamical stability [27] . The quality of filter construction and the Lyapunov exponent were estimated in a second simulation using the previously learned weights βi for filter construction and the feedback signal L ( t ) .
In the first part of this study we focused on the one-population rate-neuron model previously published by Yamazaki and Tanaka [15] . While in this previous study the model was used to represent the passage of time , i . e . an internal clock , we now show that it is also possible to use its output to construct exponential filters with various time-constants . To illustrate the dependence of network stability regime on the amount of feedback we begin by presenting sample impulse responses ( Fig 2 , second row ) for a network ( Fig 2 , top ) with intermediate time constant τw = 50ms and with three values of the recurrent inhibition: w = 0 . 01 , lying in the highly stable region , w = 1 . 4 , close to the edge-of-chaos , and w = 3 , in the chaotic region . When the weight w was low , ( Fig 2A , w = 0 . 01 ) the network was highly stable to perturbations and showed no long lasting responses . Close to the edge-of-chaos ( Fig 2B , w = 1 . 4 ) complex , long lasting responses were present . For larger weights ( Fig 2C , w = 3 ) the network entered a chaotic state in which cells showed random activity without further input modulation . We further illustrate this dependence in the last two rows of Fig 2 which shows filter constructions ( see Methods ) for three target exponential filters with time constants τi of 10 ms ( Fig 2D1 and 2E1 ) , 100 ms ( Fig 2D2 and 2E2 ) and 500 ms ( Fig 2D3 and 2E3 ) ( chosen to cover the range of performance required for e . g . VOR plant compensation [9]; filter construction of intermediate time constants are not shown , but are generally of similar quality ) . It is clear that in the highly stable regime only fast and intermediate time constant responses could be reconstructed ( dotted light lines ) . Near the edge-of-chaos acceptable reconstructions were possible at all three time constants ( dark lines ) , and in the chaotic regime reconstruction was always inaccurate and showed oscillatory artifacts ( solid light lines ) . While Yamazaki and Tanaka [15] argued that this chaotic network state is the preferred network state to implement an internal clock ( compare Fig 2C with Fig 1 from [15] ) these results show that it is disadvantageous when a filter of a continuous signal has to be implemented ( see Discussion ) . We have noted above ( Methods ) that accurate reconstruction of the impulse response of a linear filter does not imply that the output for other inputs is correct; this requires linearity of the reconstructed filter . Linearity of the reconstructed filters is investigated in the second row of Fig 2 by comparing their effects on a band-passed noise signal with that of the exact filter ( plotted in black ) , again for time constants τj of 10 ms ( Fig 2D1 ) , 100 ms ( Fig 2D2 ) and 500 ms ( Fig 2D3 ) , It is clear that the reconstruction in the stable regime or the chaotic regime ( light lines ) were much less accurate than in the edge-of-chaos-regime ( dark lines ) . Note these plots show the response to a test input ( rather than the training input , see Methods ) . The regularized fitting method used ( LASSO regression , see Methods ) tends to use weights that are as small as possible . This property is clear in our example , to construct filters from granule cell signals at the edge-of-chaos only a small subset of granule cell responses were necessary . For the filters with 10 , 100 and 500 ms ( Fig 2D and 2E; w = 1 . 4 ) , the percentage of weights being equal to zero was 90% , 86% and 75% , respectively , and the mean of non-zero weights was 5 . 5 and 11 . 7 and 52 . 6 , respectively . The high proportion of silent synapses is consistent with experimental findings ( see Discussion ) As discussed previously , the value of w corresponding to the edge-of-chaos can be identified using the Lyapunov exponent ( see Methods ) . We illustrate this property by investigating the dependence of filter reconstruction accuracy on the Lyapunov exponent ( Fig 3 ) . Results are shown for three networks with different time constants for the recurrent inhibition: τw = 10ms ( column 1 ) , τw = 50ms ( column 2 ) Fig 2B and τw = 100ms ( column 3 ) approximately corresponding to the ranges of membrane and synaptic time constants present in the granular layer . The top row of Fig 3 shows the Lyapunov exponent of each network plotted against the amount of recurrent inhibition w . In each case there was a point at which the exponent crossed the zero axis , corresponding to the edge-of-chaos value for that network time constant . It can be seen that the amount of recurrent inhibition needed decreased as the time constant increased . The bottom row shows the effect of w on reconstruction accuracy ( measured by R2 goodness-of-fit ) for exponential filters with the three time constants considered previously: τj = 10ms ( blue lines ) , 100ms ( green lines ) and 500ms ( red lines ) for each network . Performance strongly depended on the weight of the recurrent inhibition . The goodness-of-fit was best , especially for filters with time constants longer than the internal inhibitory time-constant , for networks close to the edge-of-chaos , just before the transition from stable to chaotic behavior . Other observations were that while , as expected , the goodness-of-fit for slow filters , e . g . 500ms , increased with the ( inhibitory ) time constant , the performance for fast filters decreased slightly ( Fig 3C2 ) . Furthermore the performance was best if the inhibitory time constant was equal to the time-constant of the filter ( Fig 3A2 , τ1 = 10ms blue line; Fig 3C2 , τ2 = 100ms green line ) . Fig 4 investigates the robustness of the properties described above to moderate levels of additive noise and to variability in input signal levels and synaptic weights . While white noise with amplitude of a = 0 . 01 ( noise amplitude equal to 10% of the input modulation amplitude ) lead to a reduction in goodness-of-fit ( Fig 4A1 ) the principal mechanism of filter construction was not disrupted and the edge-of-chaos was only shifted to larger weights w ( Fig 4A2 ) . Increasing the between-neuron variability of the mean input excitation to a high value of e . g . vI = 2 ( i . e . 95% of constant input increased to 0–5 from 0 . 8–1 . 2 for default value vI = 0 . 1 ) ( Fig 4B1 solid dark lines ) had almost no benefit for the goodness-of-fit while shifting the edge-of-chaos to larger weight values . In contrast , imposing larger variability in the inhibitory weight with vw = 2 ( i . e . 95% of weights between 0 and w+4w ) shifted the edge-of-chaos in the opposite direction—towards lower weights ( Fig 4B2 , dotted lines ) , and the quality of filter construction was increased ( Fig 4B1 , dotted lines ) . This phenomena may be caused by a proportion of input signals or weights being driven to zero due to the positive cut-off which effectively leads to some cells receiving no input and a reduction of connectivity , respectively . To test the effect of reduced connectivity we examined the direct effect of increased sparseness on reservoir performance ( Fig 4C ) . Two methods were used to increase sparseness: the first was to decrease the convergence of inhibition to 40 cells ( Fig 4C1 , dotted lines ) by decreasing the network connectivity from a = 0 . 4 to a = 0 . 04 while keeping the network size at Nz = 1k . The second way was to increase the network size to Nz = 10k while keeping convergence constant at 400 cells ( Fig 4C2 , solid dark lines ) with a = 0 . 04 . While both cases resulted in an improvement of filter quality , a smaller convergence slightly outperformed an increased network size suggesting that a sampling from less cells is more beneficial since it leads to a higher diversity and variability . An important requirement for filter construction turned out to be push-pull coding , found for example in the vestibulo-cerebellum , where half of the input signals are inverted ( see Discussion ) . When the input did not include inverted signals the responses from individual granule cells showed almost no variety in damped oscillations in response to pulse input ( Fig 5A ) . This consequently lead to an impairment of filter construction performance especially for larger filter time-constants and a shift of the edge-of-chaos to lower weights w ( Fig 5B1 , dark lines ) when compared to the control case ( light lines ) . Although filter construction performance was only slightly reduced when using regression with positive coefficients only ( see Methods ) ( Fig 5C , light lines ) when push-pull input was present , without push-pull input filter construction quality was heavily reduced ( Fig 5C , dark lines ) . While the previous model was able to show the principles of filter construction from a simplified model of the granular layer with recurrent inhibition , it did not take into account the fact that inhibition in the granular layer is relayed via a second population of cells , i . e . Golgi cells . To investigate the effects of this arrangement we extended the one-population model to a two-population model . The connectivity of the extended model was based on plausible convergence ratios of cw = 4 between Golgi and granule cells and cu = 100 vice versa [50] . Additional parameters were excitatory time constant τu and the weight of excitation u ( Fig 6 , top ) . Increasing τu while keeping the inhibitory time constant at τw = 50ms showed that the performance of the two-population model was very similar to the one-population model if the excitation is fast ( Fig 6A1 ) . However , increasing the excitatory time constant improved the quality of the constructed slow filter ( τ = 500ms ) at the expense of the faster filters ( τ = 10ms and τ = 100ms ) ( Fig 6B1 and 6C1 ) . Additionally , this leads to a lowered gradient of the Lyapunov exponent ( Fig 6B2 and 6C2 ) . We therefore focus in the following on the best-case scenario of τu = 1ms and τw = 50ms . As in the one-population model before ( Fig 2B ) , responses of single granule and Golgi cells in networks close to the edge-of-chaos featured complex but stable , long lasting damped oscillations ( not shown ) . In Fig 6D we show that increased sparseness , achieved by reducing the convergence onto Golgi cells from cu = 100 to cu = 10 ( light lines ) increased the quality of constructed filters as in the previous model . However , this time , increasing the granular cell population size to Nz = 10k ( dotted lines ) has almost no beneficial effect , which can be attributed to the bottleneck effect of the small Golgi cell population of Nq = 100 ( compare to Fig 4C , dotted lines ) . Here , many granule cell responses converge onto a lower dimension of signals , which decreases the fidelity . On the contrary increasing the granule cell as well as the Golgi cell population size to Nq = Nz = 10k increased the filter construction performance similar to before ( results not shown ) . Equally , further reducing the Golgi cell population to Nq = 10 for the default case ( Nz = 1k , cu = 100 ) enforced the bottleneck effect and strongly decreased the construction quality of slow filters ( results not shown ) . The effects of synaptic-weight variability in the two-population model differed for excitatory and inhibitory weights ( Fig 6E ) . Adding a large variability to excitatory weights vu = 4 increased the goodness-of-fit ( light lines ) just as seen in the model before . However , adding variability to inhibitory weights vw = 4 decreased the quality of constructed filters ( dotted lines ) . This can be explained by the low number of connections between Golgi and granule cells of cw = 4 . Using equal convergence of cw = cu = 20 gave equal effects in increased filter quality with increased variability for excitatory and inhibitory weights ( results not shown ) .
The one-population model demonstrated the properties of a homogeneous reservoir dominated by recurrent inhibition . However in the cerebellum recurrent inhibition is implemented as a relay via a second population of Golgi cells . To test the effect of this layered recurrence we considered a two-population model and found that reservoir behavior was generally preserved . We did observe three differences between the two types of networks: 1 . A slow synaptic time constant for Golgi cell excitation decreases the quality of fast filter construction ( Fig 6C ) . 2 . The lower number of Golgi cells can create a bottleneck in filter fidelity when few Golgi cells sample input from a large number of granule cells ( Fig 4C ) . 3 . Direct afferent Golgi cell excitation can decrease chaotic behavior and is beneficial for filter construction by prolonging the edge-of-chaos regime . These issues are further discussed below . Early models of the cerebellar microcircuit focused on its ability to adaptively process static patterns . Subsequently Fujita [3] described a cerebellar model that could adaptively process time-varying analogue signals , based on the adaptive linear filter used in signal processing and control engineering [61] . In Fujita’s model the granular layer recurrent neuronal network ( RNN ) generated a set of parallel-fiber outputs for a given mossy-fiber input which by linear summation at the Purkinje cell allowed the microcircuit to adaptively transform time-varying inputs into the specific time-varying outputs required for any given signal-processing task . Fujita’s adaptive-filter model required the granular layer to generate computationally adequate sets of parallel-fiber outputs in a biologically realistic way [4] . Subsequent extensions of his work initially focused on computational adequacy , suggesting suitable basis functions for transforming mossy-fiber inputs that included tapped delay lines , spectral timing , and sinusoids ( references in [8–10] ) . However , it was not usually made clear how these functions could be generated by a plausible neuronal network , raising concerns that the approach was unrealistic [11] and excessively ‘top-down’ [14] . The preferred alternative was ‘bottom-up’ modeling in which temporal-processing properties emerged from biologically detailed models of the granular-layer RNN rather than being imposed by a priori theoretical considerations [13 , 14 , 62] . These models successfully learned an eyeblink-conditioning task by generating a suitably delayed output timed to coincide with the arrival of the unconditioned stimulus . Yamazaki and Tanaka [15] investigated the generic time-coding properties of simplified RNNs , with ( usually ) 1000 rate-coding neurons receiving excitatory afferent inputs and recurrent inhibitory inputs from other neurons ( cf . first model here ) . These RNNs could generate a sequence of activity patterns that never recurred , a sequence that could be triggered reliably by a strong transient input signal . Such networks could therefore be used to encode the passage of time for any task that required it . Related results were found for RNNs of integrate-and-fire model neurons [16] , and for a more realistic two-layer RNN embedded in a spiking model of the entire cerebellar microcircuit [63] . The main theoretical conclusion is that with appropriate network parameters the granular-layer RNN can in principle generate the outputs needed for an adaptive filter . It also indicates what those outputs might look like . We now consider experimental evidence bearing on these two points . For convenience , evidence from detailed models of the granular-layer that are concerned primarily with its electrophysiology ( as opposed to the functional models discussed above ) is also included in this experimental section . In all networks and configurations considered the single cell activity of granule and Golgi cells at the edge-of-chaos showed complex but stable , long lasting damped oscillations that were the effect of inhibition and dis-inhibition ( only shown for the one-population model , Fig 2B ) . The similarity between Golgi and granule cell responses is easily graspable when one considers that for the default two-population network Golgi cells merely relay signals from granule cells und thus have to show similar activity . On the other hand for Golgi cells with added mGluR2 dynamics they themselves become prone to inhibition and dis-inhibition . This study thus suggests that one indicator for the presence of reservoir computation in a certain area of the cerebellum would be the similarity in heterogeneity and timing of the responses for granule and Golgi cells . This comparison however must not be made based on the spike activity but on the modulated signals . One explanation for the often-reported bursting responses in granule cells compared to Golgi cells could be the lower baseline/background activity of the former cells [77] . While the signal is carried and hidden by the higher spike rate in Golgi cells , granule cells would ultimately only spike during phases of strong dis-inhibition , which would effectively resemble bursting behavior . This would be even further increased if the operation point of the network is not close to the edge-of-chaos but in the chaotic regime . A further evaluation of these properties will however require the inclusion of spiking neurons in future studies . While the present study only focuses on the interaction between granule and Golgi cells the inclusion of other identified neurons might improve filter construction properties . Glycinergic Lugaro cells which have been found to increase the long-lasting depression of Golgi cells [32] and various other non-traditional interneurons like globular [84] or perivascular neurons [85] might further improve the reservoir performance by contributing to the inhibitory circuit . Furthermore , in some areas of cerebellar cortex , particularly in the vestibulo-cerebellum ( e . g . [86] ) , a substantial proportion of mossy-fiber input is processed and relayed by unipolar brush cells ( UBC ) which are thought to prolong and diversify the input signals [87] . In addition a recently discovered timing mechanism intrinsic to Purkinje cells [88] would potentially further increase the heterogeneity of the granular layer reservoir signals . Although the edge-of-chaos criterion is not a universal predictor of maximal computational performance [22] we find that it applies for most of the configurations considered here . With this requirement however the question arises how the granular layer network can be adjusted to operate in this computationally powerful regime . While the easiest way to achieve this is to change properties inside the loop like weights at Golgi cell—granule cell synapses or the convergence ratio of Golgi cell excitation we show that also external mechanisms like noise , input variability and especially mossy fiber—Golgi cell exaction can shift the network state . The question however remains how and if the cerebellar granule layer network can be automatically tuned to operate close to the edge-of-chaos . Possible mechanism that could help achieve this are synaptic long or short-term plasticity . | The cerebellum plays an important role in the learning of precise movements , and in humans holds 80% of all the neurons in the brain , due to numerous small cells called “granule cells” embedded in the granular layer . It is widely thought that the granular layer receives , transforms and delays input signals coming from many different senses like touch , vision and balance , and that these transformed signals then serve as a basis to generate responses that help to control the muscles of the body . But how the granular layer carries out this important transformation is still obscure . While current models can explain how the granular layer network could produce specific outputs for particular reflexes , there is at present no general understanding of how it could generate outputs that were computationally adequate for general movement control . With the help of a simulated granular layer network we show here that a random recurrent network can in principle generate the necessary signal transformation as long as it operates in a state close to chaotic behavior , also termed the “edge-of-chaos” . |
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Numerous studies are currently underway to characterize the microbial communities inhabiting our world . These studies aim to dramatically expand our understanding of the microbial biosphere and , more importantly , hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora . An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them . We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data ( e . g . as obtained through sequencing ) to detect differentially abundant features . Our method , Metastats , employs the false discovery rate to improve specificity in high-complexity environments , and separately handles sparsely-sampled features using Fisher's exact test . Under a variety of simulations , we show that Metastats performs well compared to previously used methods , and significantly outperforms other methods for features with sparse counts . We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes , COG functional profiles of infant and mature gut microbiomes , and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes . The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study . For the COG and subsystem datasets , we provide the first statistically rigorous assessment of the differences between these populations . The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects . Our methods are robust across datasets of varied complexity and sampling level . While designed for metagenomic applications , our software can also be applied to digital gene expression studies ( e . g . SAGE ) . A web server implementation of our methods and freely available source code can be found at http://metastats . cbcb . umd . edu/ .
The increasing availability of high-throughput , inexpensive sequencing technologies has led to the birth of a new scientific field , metagenomics , encompassing large-scale analyses of microbial communities . Broad sequencing of bacterial populations allows us a first glimpse at the many microbes that cannot be analyzed through traditional means ( only ∼1% of all bacteria can be isolated and independently cultured with current methods [1] ) . Studies of environmental samples initially focused on targeted sequencing of individual genes , in particular the 16S subunit of ribosomal RNA [2]–[5] , though more recent studies take advantage of high-throughput shotgun sequencing methods to assess not only the taxonomic composition , but also the functional capacity of a microbial community [6]–[8] . Several software tools have been developed in recent years for comparing different environments on the basis of sequence data . DOTUR [9] , Libshuff [10] , ∫-libshuff [11] , SONs [12] , MEGAN [13] , UniFrac [14] , and TreeClimber [15] all focus on different aspects of such an analysis . DOTUR clusters sequences into operational taxonomic units ( OTUs ) and provides estimates of the diversity of a microbial population thereby providing a coarse measure for comparing different communities . SONs extends DOTUR with a statistic for estimating the similarity between two environments , specifically , the fraction of OTUs shared between two communities . Libshuff and ∫-libshuff provide a hypothesis test ( Cramer von Mises statistics ) for deciding whether two communities are different , and TreeClimber and UniFrac frame this question in a phylogenetic context . Note that these methods aim to assess whether , rather than how two communities differ . The latter question is particularly important as we begin to analyze the contribution of the microbiome to human health . Metagenomic analysis in clinical trials will require information at individual taxonomic levels to guide future experiments and treatments . For example , we would like to identify bacteria whose presence or absence contributes to human disease and develop antibiotic or probiotic treatments . This question was first addressed by Rodriguez-Brito et al . [16] , who use bootstrapping to estimate the p-value associated with differences between the abundance of biological subsytems . More recently , the software MEGAN of Huson et al . [13] provides a graphical interface that allows users to compare the taxonomic composition of different environments . Note that MEGAN is the only one among the programs mentioned above that can be applied to data other than that obtained from 16S rRNA surveys . These tools share one common limitation — they are all designed for comparing exactly two samples — therefore have limited applicability in a clinical setting where the goal is to compare two ( or more ) treatment populations each comprising multiple samples . In this paper , we describe a rigorous statistical approach for detecting differentially abundant features ( taxa , pathways , subsystems , etc . ) between clinical metagenomic datasets . This method is applicable to both high-throughput metagenomic data and to 16S rRNA surveys . Our approach extends statistical methods originally developed for microarray analysis . Specifically , we adapt these methods to discrete count data and correct for sparse counts . Our research was motivated by the increasing focus of metagenomic projects on clinical applications ( e . g . Human Microbiome Project [17] ) . Note that a similar problem has been addressed in the context of digital gene expression studies ( e . g . SAGE [18] ) . Lu et al . [19] employ an overdispersed log-linear model and Robinson and Smyth [20] use a negative binomial distribution in the analysis of multiple SAGE libraries . Both approaches can be applied to metagenomic datasets . We compare our tool to these prior methodologies through comprehensive simulations , and demonstrate the performance of our approach by analyzing publicly available datasets , including 16S surveys of human gut microbiota and random sequencing-based functional surveys of infant and mature gut microbiomes and microbial and viral metagenomes . The methods described in this paper have been implemented as a web server and are also available as free source-code ( in R ) from http://metastats . cbcb . umd . edu .
To account for different levels of sampling across multiple individuals , we convert the raw abundance measure to a fraction representing the relative contribution of each feature to each of the individuals . This results in a normalized version of the matrix described above , where the cell in the ith row and the jth column ( which we shall denote fij ) is the proportion of taxon i observed in individual j . We chose this simple normalization procedure because it provides a natural representation of the count data as a relative abundance measure , however other normalization approaches can be used to ensure observed counts are comparable across samples , and we are currently evaluating several such approaches . For each feature i , we compare its abundance across the two treatment populations by computing a two-sample t statistic . Specifically , we calculate the mean proportion , and variance of each treatment t from which nt subjects ( columns in the matrix ) were sampled:We then compute the two-sample t statistic:Features whose t statistics exceeds a specified threshold can be inferred to be differentially abundant across the two treatments ( two-sided t-test ) . The threshold for the t statistic is chosen such as to minimize the number of false positives ( features incorrectly determined to be differentially abundant ) . Specifically , we try to control the p-value—the likelihood of observing a given t statistic by chance . Traditional analyses compute the p-value using the t distribution with an appropriate number of degrees of freedom . However , an implicit assumption of this procedure is that the underlying distribution is normal . We do not make this assumption , but rather estimate the null distribution of ti non-parametrically using a permutation method as described in Storey and Tibshirani [21] . This procedure , also known as the nonparametric t-test has been shown to provide accurate estimates of significance when the underlying distributions are non-normal [22] , [23] . Specifically , we randomly permute the treatment labels of the columns of the abundance matrix and recalculate the t statistics . Note that the permutation maintains that there are n1 replicates for treatment 1 and n2 replicates for treatment 2 . Repeating this procedure for B trials , we obtain B sets of t statistics: t10b , … , tM0b , b = 1 , … , B , where M is the number of rows in the matrix . For each row ( feature ) , the p-value associated with the observed t statistic is calculated as the fraction of permuted tests with a t statistic greater than or equal to the observed ti:This approach is inadequate for small sample sizes in which there are a limited number of possible permutations of all columns . As a heuristic , if less than 8 subjects are used in either treatment , we pool all permuted t statistics together into one null distribution and estimate p-values as: Note that the choice of 8 for the cutoff is simply heuristic based on experiments during the implementation of our method . Our approach is specifically targeted at datasets comprising multiple subjects — for small data-sets approaches such as that proposed by Rodriguez-Brito et . al . [16] might be more appropriate . Unless explicitly stated , all experiments described below used 1000 permutations . In general , the number of permutations should be chosen as a function of the significance threshold used in the experiment . Specifically , a permutation test with B permutations can only estimate p-values as low as 1/B ( in our case 10−3 ) . In datasets containing many features , larger numbers of permutations are necessary to account for multiple hypothesis testing issues ( further corrections for this case are discussed below ) . Precision of the p-value calculations is obviously improved by increasing the number of permutations used to approximate the null distribution , at a cost , however , of increased computational time . For certain distributions , small p-values can be efficiently estimated using a technique called importance sampling . Specifically , the permutation test is targeted to the tail of the distribution being estimated , leading to a reduction in the number of permutations necessary of up to 95% [24] , [25] . We intend to implement such an approach in future versions of our software . For complex environments ( many features/taxa/subsystems ) , the direct application of the t statistic as described can lead to large numbers of false positives . For example , choosing a p-value threshold of 0 . 05 would result in 50 false positives in a dataset comprising 1000 organisms . An intuitive correction involves decreasing the p-value cutoff proportional to the number of tests performed ( a Bonferroni correction ) , thereby reducing the number of false positives . This approach , however , can be too conservative when a large number of tests are performed [21] . An alternative approach aims to control the false discovery rate ( FDR ) , which is defined as the proportion of false positives within the set of predictions [26] , in contrast to the false positive rate defined as the proportion of false positives within the entire set of tests . In this context , the significance of a test is measured by a q-value , an individual measure of the FDR for each test . We compute the q-values using the following algorithm , based on Storey and Tibshirani [21] . This method assumes that the p-values of truly null tests are uniformly distributed , assumption that holds for the methods used in Metastats . Given an ordered list of p-values , p ( 1 ) ≤p ( 2 ) ≤…≤p ( m ) , ( where m is the total number of features ) , and a range of values λ = 0 , 0 . 01 , 0 . 02 , … , 0 . 90 , we computeNext , we fit with a cubic spline with 3 degrees of freedom , which we denote , and let . Finally , we estimate the q-value corresponding to each ordered p-value . First , . Then for i = m-1 , m-2 , … , 1 , Thus , the hypothesis test with p-value has a corresponding q-value of . Note that this method yields conservative estimates of the true q-values , i . e . . Our software provides users with the option to use either p-value or q-value thresholds , irrespective of the complexity of the data . For low frequency features , e . g . low abundance taxa , the nonparametric t–test described above is not accurate [27] . We performed several simulations ( data not shown ) to determine the limitations of the nonparametric t-test for sparsely-sampled features . Correspondingly , our software only applies the test if the total number of observations of a feature in either population is greater than the total number of subjects in the population ( i . e . the average across subjects of the number of observations for a given feature is greater than one ) . We compare the differential abundance of sparsely-sampled ( rare ) features using Fisher's exact test . Fisher's exact test models the sampling process according to a hypergeometric distribution ( sampling without replacement ) . The frequencies of sparse features within the abundance matrix are pooled to create a 2×2 contingency table ( Figure 2 ) , which acts as input for a two-tailed test . Using the notation from Figure 2 , the null hypergeometric probability of observing a 2×2 contingency table is: By calculating this probability for a given table , and all tables more extreme than that observed , one can calculate the exact probability of obtaining the original table by chance assuming that the null hypothesis ( i . e . no differential abundance ) is true [27] . Note that an alternative approach to handling sparse features is proposed in microarray literature . The Significance Analysis of Microarrays ( SAM ) method [28] addresses low levels of expression using a modified t statistic . We chose to use Fisher's exact test due to the discrete nature of our data , and because prior studies performed in the context of digital gene expression indicate Fisher's test to be effective for detection of differential abundance [29] . The input to our method , the Feature Abundance Matrix , can be easily constructed from both 16S rRNA and random shotgun data using available software packages . Specifically for 16S taxonomic analysis , tools such as the RDP Bayesian classifier [30] and Greengenes SimRank [31] output easily-parseable information regarding the abundance of each taxonomic unit present in a sample . As a complementary , unsupervised approach , 16S sequences can be clustered with DOTUR [9] into operational taxonomic units ( OTUs ) . Abundance data can be easily extracted from the “* . list” file detailing which sequences are members of the same OTU . Shotgun data can be functionally or taxonomically classified using MEGAN [13] , CARMA [32] , or MG-RAST [33] . MEGAN and CARMA are both capable of outputting lists of sequences assigned to a taxonomy or functional group . MG-RAST provides similar information for metabolic subsystems that can be downloaded as a tab-delimited file . All data-types described above can be easily converted into a Feature Abundance Matrix suitable as input to our method . In the future we also plan to provide converters for data generated by commonly-used analysis tools . Human gut 16S rRNA sequences were prepared as described in Eckburg et al . and Ley et al . ( 2006 ) and are available in GenBank , accession numbers: DQ793220-DQ802819 , DQ803048 , DQ803139-DQ810181 , DQ823640-DQ825343 , AY974810-AY986384 . In our experiments we assigned all 16S sequences to taxa using a naïve Bayesian classifier currently employed by the Ribosomal Database Project II ( RDP ) [30] . COG profiles of 13 human gut microbiomes were obtained from the supplementary material of Kurokawa et al . [34] . We acquired metabolic functional profiles of 85 metagenomes from the online supplementary materials of Dinsdale et al . ( 2008 ) ( http://www . theseed . org/DinsdaleSupplementalMaterial/ ) .
As outlined in the introduction , statistical packages developed for the analysis of SAGE data are also applicable to metagenomic datasets . In order to validate our method , we first designed simulations and compared the results of Metastats to Student's t-test ( with pooled variances ) and two methods used for SAGE data: a log-linear model ( Log-t ) by Lu et al . [19] , and a negative binomial ( NB ) model developed by Robinson and Smyth [20] . We designed a metagenomic simulation study in which ten subjects are drawn from two groups - the sampling depth of each subject was determined by random sampling from a uniform distribution between 200 and 1000 ( these depths are reasonable for metagenomic studies ) . Given a population mean proportion p and a dispersion value φ , we sample sequences from a beta-binomial distribution Β ( α , β ) , where α = p ( 1/φ−1 ) and β = ( 1−p ) ( 1/φ−1 ) . Note that data from this sampling procedure fits the assumptions for Lu et al . as well as Robinson and Smyth and therefore we expect them to do well under these conditions . Lu et al . designed a similar study for SAGE data , however , for each simulation , a fixed dispersion was used for both populations and the dispersion estimates were remarkably small ( φ = 0 , 8e-06 , 2e-05 , 4 . 3e-05 ) . Though these values may be reasonable for SAGE data , we found that they do not accurately model metagenomic data . Figure 3 displays estimated dispersions within each population for all features of the metagenomic datasets examined below . Dispersion estimates range from 1e-07 to 0 . 17 , and rarely do the two populations share a common dispersion . Thus we designed our simulation so that φ is chosen for each population randomly from a uniform distribution between 1e-08 and 0 . 05 , allowing for potential significant differences between population distributions . For each set of parameters , we simulated 1000 feature counts , 500 of which are generated under p1 = p2 , the remainder are differentially abundant where a*p1 = p2 , and compared the performance of each method using receiver-operating-characteristic ( ROC ) curves . Figure 4 displays the ROC results for a range of values for p and a . For each set of parameters , Metastats was run using 5000 permutations to compute p-values . Metastats performs as well as other methods , and in some cases is preferable . We also found that in most cases our method was more sensitive than the negative binomial model , which performed poorly for high abundance features . Our next simulation sought to examine the accuracy of each method under extreme sparse sampling . As shown in the datasets below , it is often the case that a feature may not have any observations in one population , and so it is essential to employ a statistical method that can address this frequent characteristic of metagenomic data . Under the same assumptions as the simulation above , we tested a = 0 and 0 . 01 , thereby significantly reducing observations of a feature in one of the populations . The ROC curves presented in Figure 5 reveal that Metastats outperforms other statistical methods in the face of extreme sparseness . Holding the false positive rate ( x-axis ) constant , Metastats shows increased sensitivity over all other methods . The poor performance of Log-t is noteworthy given it is designed for SAGE data that is also potentially sparse . Further investigation revealed that the Log-t method results in a highly inflated dispersion value if there are no observations in one population , thereby reducing the estimated significance of the test . Finally , we selected a subset of the Dinsdale et al . [6] metagenomic subsystem data ( described below ) , and randomly assigned each subject to one of two populations ( 20 subjects per population ) . All subjects were actually from the same population ( microbial metagenomes ) , thus the null hypothesis is true for each feature tested ( no feature is differentially abundant ) . We ran each methodology on this data , recording computed p-values for each feature . Repeating this procedure 200 times , we simulated tests of 5200 null features . Table 1 displays the number of false positives incurred by each methodology given different p-value thresholds . The results indicate that the negative binomial model results in an exceptionally high number of false positives relative to the other methodologies . Student's t-test and Metastats perform equally well in estimating the significance of these null features , while Log-t performs slightly better . These studies show that Metastats consistently performs as well as all other applicable methodologies for deeply-sampled features , and outperforms these methodologies on sparse data . Below we further evaluate the performance of Metastats on several real metagenomic datasets . In a recent study , Ley et al . [35] identified gut microbes associated with obesity in humans and concluded that obesity has a microbial element , specifically that Firmicutes and Bacteroidetes are bacterial divisions differentially abundant between lean and obese humans . Obese subjects had a significantly higher relative abundance of Firmicutes and a lower relative abundance of Bacteriodetes than the lean subjects . Furthermore , obese subjects were placed on a calorie-restricted diet for one year , after which the subjects' gut microbiota more closely resembled that of the lean individuals . We obtained the 20 , 609 16S rRNA genes sequenced in Ley et al . and assigned them to taxa at different levels of resolution ( note that 2 , 261 of the 16S sequences came from a previous study [36] ) . We initially sought to re-establish the primary result from this paper using our methodology . Table 2 illustrates that our method agreed with the results of the original study: Firmicutes are significantly more abundant in obese subjects ( P = 0 . 003 ) and Bacteroidetes are significantly more abundant in the lean population ( P<0 . 001 ) . Furthermore , our method also detected Actinobacteria to be differentially abundant , a result not reported by the original study . Approximately 5% of the sample was composed of Actinobacteria in obese subjects and was significantly less frequent in lean subjects ( P = 0 . 004 ) . Collinsella and Eggerthella were the most prevalent Actinobacterial genera observed , both of which were overabundant in obese subjects . These organisms are known to ferment sugars into various fatty acids [37] , further strengthening a possible connection to obesity . Note that the original study used Students t-test , leading to a p-value for the observed difference within Actinobacteria of 0 . 037 , 9 times larger than our calculation . This highlights the sensitivity of our method and explains why this difference was not originally detected . To explore whether we could refine the broad conclusions of the initial study , we re-analyzed the data at more detailed taxonomic levels . We identified three classes of organisms that were differentially abundant: Clostridia ( P = 0 . 005 ) , Bacteroidetes ( P<0 . 001 ) , and Actinobacteria ( P = 0 . 003 ) . These three were the dominant members of the corresponding phyla ( Firmicutes , Bacteroides , Actinobacteria , respectively ) and followed the same distribution as observed at a coarser level . Metastats also detected nine differentially abundant genera accounting for more than 25% of the 16S sequences sampled in both populations ( P≤0 . 01 ) . Syntrophococcus , Ruminococcus , and Collinsella were all enriched in obese subjects , while Bacteroides on average were eight times more abundant in lean subjects . For taxa with several observations in each subject , we found good concordance between our results ( p-value estimates ) and those obtained with most of the other methods ( Table 2 ) . Surprisingly , we found that the negative binomial model of Robinson and Smyth failed to detect several strongly differentially abundant features in these datasets ( e . g the hypothesis test for Firmicutes results in a p-value of 0 . 87 ) . This may be due in part to difficulties in estimating the parameters of their model for our datasets and further strengthens the case for the design of methods specifically tuned to the characteristics of metagenomic data . For cases where a particular taxon had no observations in one population ( e . g . Terasakiella ) , the methods proposed for SAGE data seem to perform poorly . Targeted sequencing of the 16S rRNA can only provide an overview of the diversity within a microbial community but cannot provide any information about the functional roles of members of this community . Random shotgun sequencing of environments can provide a glimpse at the functional complexity encoded in the genes of organisms within the environment . One method for defining the functional capacity of an environment is to map shotgun sequences to homologous sequences with known function . This strategy was used by Kurokawa et al . [34] to identify clusters of orthologous groups ( COGs ) in the gut microbiomes of 13 individuals , including four unweaned infants . We examined the COGs determined by this study across all subjects and used Metastats to discover differentially abundant COGs between infants and mature ( >1 year old ) gut microbiomes . This is the first direct comparison of these two populations as the original study only compared each population to a reference database to find enriched gene sets . Due to the high number of features ( 3868 COGs ) tested for this dataset and the limited number of infant subjects available , our method used the pooling option to compute p-values ( we chose 100 permutations ) , and subsequently computed q-values for each feature . Using a threshold of Q≤0 . 05 ( controlling the false discovery rate to 5% ) , we detected 192 COGs that were differentially abundant between these two populations ( see Table 3 for a lisitng of the most abundant COGs in both mature and infant microbiomes . Full results are presented as supplementary material in Table S1 ) . The most abundant enriched COGs in mature subjects included signal transduction histidine kinase ( COG0642 ) , outer membrane receptor proteins , such as Fe transport ( COG1629 ) , and Beta-galactosidase/beta-glucuronidase ( COG3250 ) . These COGs were also quite abundant in infants , but depleted relative to mature subjects . Infants maintained enriched COGs related to sugar transport systems ( COG1129 ) and transcriptional regulation ( COG1475 ) . This over-abundance of sugar transport functions was also found in the original study , strengthening the hypothesis that the unweaned infant gut microbiome is specifically designed for the digestion of simple sugars found in breast milk . Similarly , the depletion of Fe transport proteins in infants may be associated with the low concentration of iron in breast milk relative to cow's milk [38] . Despite this low concentration , infant absorption of iron from breast milk is remarkably high , and becomes poorer when infants are weaned , indicating an alternative mechanism for uptake of this mineral . The potential for a different mechanism is supported by the detection of a Ferredoxin-like protein ( COG2440 ) that was 11 times more abundant in infants than in mature subjects , while Ferredoxin ( COG1145 ) was significantly enriched in mature subjects . A recent study by Dinsdale et al . profiled 87 different metagenomic shotgun samples ( ∼15 million sequences ) using the SEED platform ( http://www . theseed . org ) [6] to see if biogeochemical conditions correlate with metagenome characteristics . We obtained functional profiles from 45 microbial and 40 viral metagenomes analyzed in this study . Within the 26 subsystems ( abstract functional roles ) analyzed in the Dinsdale et al . study , we found 13 to be significantly different ( P≤0 . 05 ) between the microbial and viral samples ( Table 4 ) . Subsystems for RNA and DNA metabolism were significantly more abundant in viral metagenomes , while nitrogen metabolism , membrane transport , and carbohydrates were all enriched in microbial communities . The high levels of RNA and DNA metabolism in viral metagenomes illustrate their need for a self-sufficient source of nucleotides . Though the differences described by the original study did not include estimates of significance , our results largely agreed with the authors' qualitative conclusions . However , due to the continuously updated annotations in the SEED database since the initial publication , we found several differences between our results and those originally reported . In particular we found virulence subsystems to be less abundant overall than previously reported , and could not find any significant differences in their abundance between the microbial and viral metagenomes .
We have presented a statistical method for handling frequency data to detect differentially abundant features between two populations . This method can be applied to the analysis of any count data generated through molecular methods , including random shotgun sequencing of environmental samples , targeted sequencing of specific genes in a metagenomic sample , digital gene expression surveys ( e . g . SAGE [29] ) , or even whole-genome shotgun data ( e . g . comparing the depth of sequencing coverage across assembled genes ) . Comparisons on both simulated and real dataset indicate that the performance of our software is comparable to other statistical approaches when applied to well- sampled datasets , and outperforms these methods on sparse data . Our method can also be generalized to experiments with more than two populations by substituting the t-test with a one-way ANOVA test . Furthermore , if only a single sample from each treatment is available , a chi-squared test can be used instead of the t-test . [27] . In the coming years metagenomic studies will increasingly be applied in a clinical setting , requiring new algorithms and software tools to be developed that can exploit data from hundreds to thousands of patients . The methods described above represent an initial step in this direction by providing a robust and rigorous statistical method for identifying organisms and other features whose differential abundance correlates with disease . These methods , associated source code , and a web interface to our tools are freely available at http://metastats . cbcb . umd . edu . | The emerging field of metagenomics aims to understand the structure and function of microbial communities solely through DNA analysis . Current metagenomics studies comparing communities resemble large-scale clinical trials with multiple subjects from two general populations ( e . g . sick and healthy ) . To improve analyses of this type of experimental data , we developed a statistical methodology for detecting differentially abundant features between microbial communities , that is , features that are enriched or depleted in one population versus another . We show our methods are applicable to various metagenomic data ranging from taxonomic information to functional annotations . We also provide an assessment of taxonomic differences in gut microbiota between lean and obese humans , as well as differences between the functional capacities of mature and infant gut microbiomes , and those of microbial and viral metagenomes . Our methods are the first to statistically address differential abundance in comparative metagenomics studies with multiple subjects , and we hope will give researchers a more complete picture of how exactly two environments differ . |
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Many models of classical conditioning fail to describe important phenomena , notably the rapid return of fear after extinction . To address this shortfall , evidence converged on the idea that learning agents rely on latent-state inferences , i . e . an ability to index disparate associations from cues to rewards ( or penalties ) and infer which index ( i . e . latent state ) is presently active . Our goal was to develop a model of latent-state inferences that uses latent states to predict rewards from cues efficiently and that can describe behavior in a diverse set of experiments . The resulting model combines a Rescorla-Wagner rule , for which updates to associations are proportional to prediction error , with an approximate Bayesian rule , for which beliefs in latent states are proportional to prior beliefs and an approximate likelihood based on current associations . In simulation , we demonstrate the model’s ability to reproduce learning effects both famously explained and not explained by the Rescorla-Wagner model , including rapid return of fear after extinction , the Hall-Pearce effect , partial reinforcement extinction effect , backwards blocking , and memory modification . Lastly , we derive our model as an online algorithm to maximum likelihood estimation , demonstrating it is an efficient approach to outcome prediction . Establishing such a framework is a key step towards quantifying normative and pathological ranges of latent-state inferences in various contexts .
Learning and decision-making are fundamental aspects of day-to-day human life . Indeed , many mental health disorders can be conceptualized from the perspective of biases or errors in learning and decision-making [1] . Accordingly , the study of how humans learn and make decisions is an important topic of inquiry and the past two decades has witnessed a significant surge in the application of computational modeling approaches to the problem of human learning and decision-making [2–5] . One significant insight this literature has made is the differentiation of model-free and model-based learning [6 , 7] . Model-free learning refers to relatively simple updating of cached values based on trial-and-error experience . An early and influential example of model free learning is the Rescorla-Wagner ( RW ) model [8] , which proposed that associative strength ( i . e . the degree to which a cue predicted an outcome ) updates in response to new experiences in proportion to the magnitude of a prediction error ( i . e . , how wrong the current prediction was ) . The RW model , and similar model-free formulations that it inspired [9 , 10] , powerfully explained many aspects of learning . Nonetheless , a notable problem for model-free accounts of learning was the phenomenon of rapid recovery of responding following extinction learning [11] . That is , model-free accounts of learning based on trial-by-trial updating predict that the return of responding following extinction should essentially occur at the same rate as initial learning . This prediction is not supported by a wealth of data demonstrating that extinguished responding can rapidly return via reinstatement ( i . e . , an un-signalled presentation of the outcome ) , context renewal ( i . e . , returning to the initial acquisition learning context ) , or spontaneous recovery ( i . e . , return of responding following the passage of time ) [12] . This rapid return of extinguished responding is an important phenomenon with implications for treatment of clinical disorders such as anxiety disorders and substance use [13 , 14] . One of the first applications of a model-based account of learning that could address rapid renewal of responding following extinction was by Redish and colleagues in 2007 [11] . They argued that an agent performs two concurrent tasks during a learning experiment: 1 ) updating associative strength between a cue and an outcome , and 2 ) recognizing the current state of the experiment and storing separate associative strengths for the acquisition and extinction contexts . Rapid recovery of responding can be explained by the agent inferring that the current state of the experiment has changed to the acquisition phase and therefore responding to the cue with the associative strength stored for the acquisition phase . Though this initial formulation had limitations [15] , it stimulated subsequent research developing latent-state models of reinforcement learning [16–19] . For example , if an agent is assumed to infer latent states to explain observed relationships between stimuli , actions , and outcomes , and if inferring separate latent states for acquisition and extinction phases of an experiment explains rapid recovery of responding , then it follows that blurring the experimental distinction between acquisition and extinction phases would result in less recovery of responding following extinction . That is , if the contingency between cue and outcome ( e . g . , 50% contingency ) during the acquisition phase slowly transitions to extinction , rather than an abrupt change , then an agent is less likely to infer a separate extinction context and more likely to modify the initial acquisition memory . Because the acquisition associative strength is lower , less subsequent recovery of responding would be expected in a recall test . A carefully designed experiment using an animal model demonstrated exactly this observation , providing strong evidence for a latent-state model of learning [20] . The application and utility of latent-state learning models is not confined to explaining recovery of responding following extinction . Tolman’s early theory of cognitive maps [21] posits that an agent forms abstract representations of a task’s state space . There has been a recent resurgence of interest in this concept of cognitive maps in the cognitive neuroscience field informed by latent-state computational models [17 , 22 , 23] . This work , conducted both using animal and human experimental models , suggests that essential functions of the orbitofrontal cortex ( OFC ) and hippocampus are the encoding and representation of latent task space ( i . e . , forming cognitive maps of a task’s state space ) [23] . This theory potentially powerfully describes a superordinate process that explains why the OFC and hippocampus are implicated in such a diverse array of cognitive and emotional functions . This theory also has implications that tie back into extinction learning and clinical disorders and suggest a novel understanding of the role of the OFC in mediating poor extinction learning . However , it is important to recognize that the role of the OFC remains highly debated . As can be seen , latent-state theories have significant implications for our understanding of normative and disordered mechanisms of learning . The purpose of the current work is to introduce a new model of latent-state learning and to verify the suitability and utility of this model for explaining group-level effects of classical conditioning . The introduced model makes six key assumptions about how an agent performs latent-state inferences , notably that a learning agent uses latent states to index disparate associations between cues in an effort to predict rewards and that they assume latent states are relatively stable over time . Altogether , these assumptions differentiate our model from past efforts to describe latent state learning [11 , 15 , 17 , 19] . These differences are highlighted in a series of simulation experiments . In addition to formalizing a new theory of latent-state learning , we were also motivated to provide the research community with a practical model that can be fit to data from a diverse set of experiments probing classical conditioning and latent-state learning . Even though every model has limitations and cannot be applied to every experiment , there is practical significance in being able to compare fitted parameters from a model across experiments .
We present a model of how an agent uses latent states to learn psychological experiments consisting of a sequence of trials , wherein each trial a learning agent is exposed to a collection of cues followed by rewards ( and/or penalties ) . We use the following notation . Cue n on trial t is denoted by cn ( t ) and takes a value of 1 when the cue is present and 0 otherwise . Cues are collected in a vector c → ( t ) . Rewards on trial t are denoted by R ( t ) . After trial t , the strength of the association between cue n and rewards is denoted by Vn ( t ) . An apostrophe denotes transposition , and the notation X ( 1:t ) is shorthand for ( X ( 1 ) , … , X ( t ) ) . Our model assumes a learning agent has a certain world view about how rewards are generated and wants to predict rewards efficiently based on this world view ( Fig 1A ) . Their world view assumes rewards are a function of which cues are presented , a latent state , and a latent error . A latent state is an index to disparate associations between cues and rewards . In order to predict rewards , they must invert their view of the world to infer which latent state is active , how cues are associated with rewards for each latent state , and the expected uncertainty in rewards due to the latent error . We proceed to describe our model for how an agent performs these tasks . We leave details to the Methods section about how our model of latent-state learning can be derived formally from this world view . We used simulation to verify that our latent-state model can reproduce a set of group-level effects observed from classical experiments on learning ( Table 1 ) . Our model was compared to the Rescorla-Wagner ( RW ) model , fRL+decay model of Niv et al [34] and three latent-state learning models: the Redish ( 2007 ) model [11] , the infinite-mixture model of Gershman and Niv in [17] , and the Gershman ( 2017 ) model in [19] . Other model comparisons are considered in S1 Text and S1 and S2 Figs . For each model , we computed associative strength per cue and belief in each latent state . For our model , the infinite-mixture model , and the Gershman ( 2017 ) model , we defined associative strength as the expected reward conditional on the cue being presented alone and belief in a latent state as the estimated probability of the latent state given observations . In order to compare the Redish ( 2007 ) model to other models , we defined associative strength for each cue as average estimated value between reinforcing and not reinforcing the cue presented alone and defined belief in a latent state to be 1 if the state was identified as the current agent state and 0 otherwise . Our model includes pairwise interactions between cues as additional cues and also centers rewards ( i . e . R ( t ) = −1/2 and R ( t ) = 1/2 rather than R ( t ) = 0 and R ( t ) = 1 ) . Additional details about the simulation are found in the Methods section . We first tested whether our model could reproduce learning effects that established the RW model as a powerful model of associative learning ( Fig 2 ) . In a blocking experiment , for instance , a learning agent is conditioned to associate a cue ( Cue A ) with a reward , leading to a high associative strength for Cue A . Afterwards , Cue A is repeatedly presented with another cue ( Cue B ) . The compound is then reinforced with a reward . Even though Cue B is reinforced , Cue B does not acquire the high associative strength as Cue A did . That is , conditioning of Cue A blocked conditioning of cue B . The RW model , our latent-state model , and the Gershman ( 2017 ) model predict blocking because of the way associative strength is updated in each model: updates depend on prediction error and predictions depend on the sum of associative strengths of present cues . Consequently , associative strength of Cue B changes slightly , because the compound of an excitatory Cue A and neutral Cue B yields small prediction errors . Our latent-state model reproduces overexpectation and conditioned inhibition , two other group-level effects explained by the RW model . An overexpectation experiment consists of reinforcing two cues separately with a reward and then later as a compound . When presented as a compound , associative strengths decrease . A conditioned inhibition experiment consists of intermixing the reinforcement of one cue ( Cue A ) with the omission of a reward when the cue is presented with another cue ( Cue B ) . Cue A increases in associative strength while Cue B decreases in associative strength . As with blocking , the RW model , our latent-state model , and the Gershman ( 2017 ) model capture overexpectation and conditioned inhibition because of the way associative strength is updated . For overexpectation , expectations are twice actual rewards when the cues are presented together , yielding negative prediction errors and a decrease in associative strength for each cue . For conditioned inhibition , the negative associative strength of Cue B negates the positive associative strength of Cue A so that the sum of their associative strengths predicts the weaker reward . The Redish ( 2007 ) and infinite-mixture models do not update associative strength in the same way as the RW model , whereas the fRL+decay model decays associative strength of cues that are not presented , leading to different predictions for blocking , overexpectation , or conditioned inhibition experiments . We also tested whether our latent-state learning model could reproduce historically-important learning effects not predicted by the RW model due to its assumption of constant associability ( Fig 2 ) . To complete our simulation study , we tested if our latent-state model could describe more recent experiments that examine latent-state learning . Our model reproduced group-level effects from a series of classical experiments . Table 2 summarizes which effects were captured by which model . Different aspects of the model are important for reproducing different effects . Updating associative strength similar to the RW model , for example , allows our model to capture effects famously reproduced by the RW model ( e . g . , blocking ) . Updating associability based on latent-state beliefs captures sharp increases in associability when a learner shifts their beliefs to a latent state , reproducing experiments from Wilson et al ( 1992 ) [38] and Rescorla ( 2000 ) [39] , and experiments involving partial reinforcement [40–42] . Additionally , updating associability based on history of cue presentation rotates updates in different directions , allowing our model to reproduce backwards blocking . Further , using latent-states to index disparate cue-reward associations allows for a rapid return of prior expectations as a learner recalls a prior latent-state . Changing latent-state beliefs to reflect contextual changes allows our model to capture effects modulated by visual or temporal context such as renewal or spontaneous recovery , and together with rumination steps , allows our model to reproduce memory consolidation experiments [44 , 45] . Finally , our model uses interaction terms and centers rewards , which we found were important for explaining certain group-level effects by determining whether a learning agent would shift to a new latent state .
We presented a computational model of latent-state learning , using the Rescorla-Wagner ( RW ) model as its foundation , and positing that latent states represent disparate associations between cues and rewards . In a series of simulation experiments , we tested the model’s ability to reproduce learning experiments both famously explained and not explained by the RW model and to capture behavior in recent experiments testing latent-state inferences . Lastly , we formally derived our latent-state model under the premise of computational rationality , i . e . a learning agent wants to predict outcomes efficiently . Our goal was to identify a model of latent-state learning that reproduces group-level effects from classical conditioning experiments . The resulting model makes five critical assumptions/predictions about how an agent learns cue-reward associations: an agent learns online , an agent uses latent states to predict rewards , associability depends on beliefs in latent states and cue novelty , latent states are relatively stable between trials , and beliefs are maintained over multiple latent states . Including these features helps to ensure that the proposed model could examine learning in numerous and diverse experiments , from experiments of classical conditioning to more recent experiments on latent-state inferences . We show that most features fall-out naturally when trying to develop an online approach to reward prediction which uses latent-state inferences . Other models of latent-state learning share some , but not all these features [11 , 17 , 19] . For example , our model assumes a learning agent uses latent states to index disparate associations , or mappings , from cues to rewards in an effort to predict cues . By contrast , the Redish ( 2007 ) and infinite-mixture models use latent states to index disparate combined observations of cues and rewards , which can in turn be used to infer rewards from a set of cues . Also , our model assumes latent states evolve according to a Markov chain , whereas other models assume latent states are exchangeable or independent between trials . As a consequence , beliefs in latent states are relatively stable between trials . This stability was observed in certain simulation experiments , in which the predominant latent state would switch only one or two times for our model but over a dozen times for the infinite-mixture model and Redish ( 2007 ) model . Interestingly , our model still updates associative strength in a similar manner to a RW model: change in associative strength is prediction error scaled by associability . In fact , the RW model is a specific case of our latent-state model when there is only one latent state and associability is fixed . For this reason , our latent-state model can reproduce similar learning effects as the RW model such as blocking , overexpectation , and conditioned inhibition . Some latent-state models cannot capture all of these classic learning features [11 , 17] . However , our latent-state model goes beyond the RW model by allowing associability to vary across trials . This feature is thought to be important for describing learning effects not explained by the RW model , such as the Pearce-Hall effect , and is incorporated into newer models , such as the Hybrid RW model [9 , 10 , 49] . Associability in our latent-state model involves two components: effort matrices and beliefs in latent-states . Because associability depends on effort matrices , changes in associative strength are rotated into a direction which has not been previously learned , helping to explain backwards blocking . These matrices approximate Fisher information matrices , which are often incorporated into iterative approaches to maximum likelihood estimation [28 , 50] . Meanwhile , because associability also depends on beliefs , associative strength can be updated quickly when a learning agent infers a sudden shift in experimental conditions . This feature allowed our latent-state model to capture experimental results from Experiment 1 in Wilson et al ( 1992 ) [27] and Experiments 1A–B in Rescorla ( 2000 ) [39] . Similar to other latent-state models , our model also assumes an agent who learns online . Online learning requires only current observations rather than the entire history of observations to update parameters and quantities such as associative strength . Consequently , online approaches have computational and memory requirements that grow in the number of latent states rather than in trials , which could be a more realistic reflection of how humans learn . The infinite-mixture model also uses online updates for all its variables [17] , whereas the Gershman ( 2017 ) model and Redish model ( 2007 ) uses online updates for most variables [11 , 19] . For comparison , an optimal Bayesian approach to latent-state learning would require enumerating over all possible sequences of latent-states , a number that grows exponentially with each trial , imposing a computational and memory burden that is believed to be overly optimistic for human computation [26] . Critically , our latent-state model relies on an approximate Bayesian filter to maintain and update beliefs over latent-states online . Other models of latent-state learning use other approaches to reduce computational burden such serial representations of committed beliefs rather than distributing belief across multiple hypotheses [11] , particle methods [15 , 17 , 19] , or maximum a posteriori estimates [19] . Additional assumptions might also be used such as exchangeable trials and binary outcomes [17] , one latent state [34] , one latent-state transition [27] , or known cue-reward associations [32] . Assumptions , however , limit the ability of any model to describe learning in varied experiments . For example , we can capture renewal , spontaneous recovery , and other learning phenomena that require a more general number of latent states and transitions because of our use of an approximate Bayesian filter [28] . We can also capture experiments such as [32] that require beliefs to be maintained over over multiple latent-states . Notably , the experiment [32] suggested a link between log-beliefs and activity in the orbitofrontal cortex . Many of the assumptions/predictions of the presented model are testable . For example , associability for a latent-state is predicted to be directly dependent on the beliefs in that latent-state . Stronger beliefs lead to faster learning , whereas weaker beliefs lead to slower learning . An experiment could be designed to test , for example , if associability decreases by 50% when beliefs are split between two latent-states relative to when beliefs were concentrated on one latent-state . Further , associability is predicted to depend on whether cues had previously been presented together . New combination of cues can lead to quick learning even when individual cues making up the combination have already been presented . Last , latent-state beliefs are predicted to be updated in a non-optimal manner , suggesting one try to induce sub-optimal behavior in an experiment . Several limitations of the proposed model should be considered . First , we anticipate there are group-level learning effects that our model fails to describe or there are other models that could explain participant behavior . Second , we did not examine to what extent beliefs in latent-states and other variables from our model are encoded in various brain regions . Third , we assume beliefs are maintained over multiple latent states , but some experiments suggest that an agent is committed to a state and alternates between committed states [33] . One possibility is that beliefs in latent states influence which state is committed and over time , the frequency of committed states mirrors these beliefs . Modalities such as fMRI might then suggest beliefs are maintained over multiple states , as in [32] , because they lack the temporal resolution needed to detect the alternating committed states . Allowing beliefs to determine committed states might even lead to better explanations of participant data , as it allows for more flexibility in how states are updated for a given participant . Fourth , there may be better ways to integrate computational mechanisms of how memories are formed into our model [51] . For example , we use online updates in order to avoid memory requirements that grow in the number of trials , but it is possible that humans are either capable of such requirements or use other ways than online updates to integrate past observations . Fifth , some effects ( e . g . , memory modification effect [44 , 45] ) reproduced by our model might be too subtle to ever be detected experimentally . Sixth , we assume latent states are relatively stable between trials . This assumption may be accurate for describing learning in experiments , since experiments often involve blocks of trials in which rewards/cues are generated by the same rules , but may be less accurate for real-world learning environments . Seventh , we included interactions between cues to influence when a learning agent shifts to a new latent state , which we found was necessary to recover one of the Rescorla ( 2000 ) experiments [39] . This inclusion , however , should be investigated further to determine if it reflects how agents actually learn . Finally , we did not study model performance in terms of predicting rewards as a consequence of using an approximate Bayesian filter . It may useful to know how far from optimal our approximate method is for maximum likelihood estimation in various settings . In sum , this work establishes the validity of an online model for latent-state inferences that generalizes across experiments . Establishing such a framework is a necessary step towards quantifying normative and pathological ranges of latent-state inferences ( and mediating neurocircuitry ) across varied contexts . Overall , this work moves us closer to a precise and mechanistic understanding of how humans infer latent-states .
Parameters were fixed throughout the simulation ( Table 3 ) . Parameters for alternative models were chosen from their respective papers , except for the maximum number of latent states which we fixed at 15 to reduce memory requirements . Our model includes cues throughout simulation to represent pairwise interaction terms ( i . e . a binary indicator if a pair of cues are present or absent ) . Our model also centers rewards with R ( t ) = 1/2 when a reward is presented and R ( t ) = −1/2 when a reward is not presented . That way , initial associative strengths give rise to neutral ( i . e . 50-50 ) expectations for reward . Thus , associative strengths are shifted by 1/2 for our model when compared to other models . Accompanying code is publicly-available at https://github . com/cochran4/OnlineLatentStateLearning . S1 Text provides further details on the schedules for how cues and rewards were delivered during each simulation task and examines sensitivity of our model’s predictions to changes in parameters . We justify our choice of model under the premise of computational rationality [52] , i . e . a learning agent wants to predict rewards efficiently . An optimistic strategy for the learning agent would be to use maximize likelihood estimation ( MLE ) to aid prediction . This would entail starting with a probabilistic model of rewards defined up to an unknown parameter θ and finding θ to maximize the log-likelihood of rewards ( scaled by 1/t ) : ℓ ( θ ) = 1 t log f [ R ( 1 : t ) | θ ] . ( 12 ) Treating rewards as a continuous variable , we use f to denote a probability density function . The estimate of θ together with the reward model predict future rewards . With an eye towards efficiency , we focus on online approaches , such as the Rescorla-Wagner model , and refer the reader to the work in [50] and [28] on updating latent-variable models online . | Computational researchers are increasingly interested in a structured form of learning known as latent-state inferences . Latent-state inferences is a type of learning that involves categorizing , generalizing , and recalling disparate associations between observations in one’s environment and is used in situations when the correct association is latent or unknown . This type of learning has been used to explain overgeneralization of a fear memory and the cognitive role of certain brain regions important to cognitive neuroscience and psychiatry . Accordingly , latent-state inferences are an important area of inquiry . Through simulation and theory , we establish a new model of latent-state inferences . Moving forward , we aim to use this framework to measure latent-state inferences in healthy and psychiatric populations . |
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To develop new approaches to control HIV-1 replication , we examined the capacity of recently described small molecular modulators of RNA splicing for their effects on viral RNA metabolism . Of the drugs tested , digoxin was found to induce a dramatic inhibition of HIV-1 structural protein synthesis , a response due , in part , to reduced accumulation of the corresponding viral mRNAs . In addition , digoxin altered viral RNA splice site use , resulting in loss of the essential viral factor Rev . Digoxin induced changes in activity of the CLK family of SR protein kinases and modification of several SR proteins , including SRp20 and Tra2β , which could account for the effects observed . Consistent with this hypothesis , overexpression of SRp20 elicited changes in HIV-1 RNA processing similar to those observed with digoxin . Importantly , digoxin was also highly active against clinical strains of HIV-1 in vitro , validating this novel approach to treatment of this infection .
Current highly active anti-retroviral therapies ( HAARTs ) have successfully delayed the progression of HIV-1-infected individuals to AIDS by targeting viral entry and all HIV-1 enzymes [1] , [2] . However , the clinical application of ARTs is being affected by the spread of drug resistant viral strains [3] , [4] , [5]; detection of drug resistant forms of HIV-1 in newly infected patients has increased ∼3-fold from 2000 to 2007 to 16% [6] , [7] . To overcome these hurdles , more drugs with better profiles , and especially , novel mechanisms of action , are necessary for continued success in combating HIV-1 [1] , [2] , [8] . However , the majority of drugs currently undergoing clinical trials target the same enzymes/proteins for which drugs are already available [1] , [2] , [9] , [10] . In addition , the persistence of virus in reservoirs continues to be a challenge with standard HAART . There are at least 200 host factors required for HIV-1 infection and replication [11] , [12] , [13] . Efforts to understand the role of these factors in the lifecycle of HIV could aid development of future therapies . Among these are the factors regulating RNA processing . HIV-1 requires a balanced regulation of viral RNA processing to generate >40 mRNAs for synthesis of 15 viral proteins , an effect achieved through alternative splicing of a single 9 kb pre-mRNA transcript ( Fig . S1 ) [14] , [15] , [16] , [17] . HIV-1 RNA processing involves the combinatory use of four 5′ splice sites ( splice donors , SD1–4 ) and eight suboptimal 3′ splice sites ( splice acceptors , SA1–7; Fig . S1 ) . Use of 3′ splice sites ( ss ) is regulated by host trans-acting factors that function in an antagonistic fashion by binding to cis-acting elements adjacent to the 3'ss , either impeding ( hnRNPs ) or promoting ( SR proteins ) their use [14] , [16] , [18] , [19] , [20] . Three classes of HIV-1 mRNAs result from HIV-1 RNA splicing ( Fig . S1 ) : unspliced RNAs ( US ) encoding Gag or Gagpol proteins , singly spliced RNAs ( SS ) producing Env , Tat ( p14 ) , Vif , Vpr , or Vpu , and multiply spliced RNAs ( MS ) for synthesis of Rev , Tat ( p16 ) , or Nef [16] , [17] , [21] . Among these , Tat and Rev factors play central roles in HIV-1 replication; Tat activates transcription of all viral RNAs , while Rev transports the incompletely-spliced RNAs ( US , SS ) to the cytoplasm for translation [14] , [22] , [23] , [24] , [25] , [26] . Imbalances in RNA processing can dramatically affect viral replication [27] , [28] , [29]; undersplicing results in the loss of key regulatory proteins such as Tat and Rev ( from MS RNA ) , while oversplicing would reduce incompletely-spliced RNAs ( US , SS ) encoding viral structural proteins ( Gag , Env ) and accessory factors ( Vif , Vpr , Vpu ) . Knowledge of how to manipulate these processes to alter HIV-1 RNA splicing in cells could prove advantageous as a strategy for controlling HIV infection . This hypothesis is supported by studies where modulating SR protein abundance ( by overexpression/depletion ) caused imbalances in HIV-1 splicing , resulting in gross changes in viral protein synthesis [18] , [20] , [30] , [31] . This hypothesis is also supported by the observation that HIV-1 infection leads to a decrease in overall SR protein/activity which can be reversed by increasing SR protein kinase ( SRPK ) 2 function [32] . Consistent with these studies , we have successfully suppressed HIV-1 gene expression through modulation of another family of SR protein kinases , the Cdc2-like kinases ( CLKs ) [33] . While use of small molecular weight ( MW ) inhibitors of SRPK 1 and 2 have met with limited effect against HIV [32] , we recently demonstrated that chlorhexidine ( an inhibitor of CLKs 2 , 3 , and 4 ) is able to alter HIV-1 RNA processing , leading to inhibition of HIV-1 replication [33] . However , the toxicity of chlorhexidine in peripheral blood mononuclear cell ( PBMC ) cultures precludes its systemic use . Further supporting the viability of this approach is recent work demonstrating the suppression of HIV-1 RNA splicing using indole derivatives that function by modulating SR protein function [19] , [34] , [35] . To explore this strategy further , we tested compounds shown to modulate host alternative RNA splicing to identify new inhibitors of HIV-1 replication [36] , [37] . We report here that digoxin , a drug widely used in treatment of congestive heart failure [38] , [39] , is a potent inhibitor of HIV-1 replication . Digoxin treatment drastically reduced HIV-1 gene expression in stably HIV-1 transduced HeLa and SupT1 cell lines and is effective in inhibiting replication of HIV-1 clinical strains in human CD4+ PBMCs . Digoxin accomplishes these effects through two mechanisms: inducing oversplicing of HIV-1 RNA , resulting in an alteration in splice site usage of HIV-1 pre-mRNA as well the loss of the key regulatory protein , Rev . Consequently , this response impairs expression of viral structural proteins . Reduced Rev expression leads to HIV-1 incompletely-spliced RNAs ( US , SS ) being sequestered in the nucleus . Expression of Rev in trans led to a partial rescue of HIV-1 structural protein ( Gag ) synthesis . Coincident with the changes in viral RNA processing , digoxin treatment also induced changes in the modification of a subset of SR proteins ( SRp20 , Tra2β , SRp55 , and SRp75 ) and the activity of the CLK family of SR protein kinases . Our findings support the hypothesis that HIV-1 RNA processing can be effectively targeted without severe toxicity to the host cell . Since this stage of the virus lifecycle is not targeted by current anti-retroviral therapies ( ART ) [1] , [2] , digoxin ( and potentially the cardiac glycoside family of drugs ) represent a novel class of HIV-1 inhibitors with the potential for rapid development into an ART .
In our search for novel HIV-1 inhibitors , drugs with the capacity to alter RNA splicing were screened for antiretroviral activity [36] , [37] . We used a human cell line stably transduced with a modified X4 HIV-1 ( LAI ) provirus regulated by a Tet-ON system that requires addition of doxycycline ( Dox ) for activation of viral gene expression [33] , [40] , [41] . The effects of drugs on HIV-1 gene expression were monitored by treating HeLa rtTA-HIV-ΔMls cells for 4 hours with drugs prior to induction of virus gene expression by Dox ( Fig . 1 ) . After 20 hours , media and cell lysates were harvested for analysis of HIV-1 Gag protein expression by p24CA ELISA ( Fig . 1A ) or Western blots for Gag and Env ( gp120 ) ( Fig . 1B , top and middle , respectively ) . We observed that digoxin ( 100 nM ) caused a 94% inhibition of HIV-1 Gag protein expression relative to DMSO control ( Fig . 1A ) . In contrast , other drugs shown to affect RNA splicing such as clotrimazole and flunarizine ( 10 µM ) showed no significant effects [36] . Western blot analysis of Gag protein expression in cell lysates of digoxin-treated cells ( Fig . 1B , top ) confirms a complete loss of the Gag products , capsid ( CA ) and matrix ( MA ) -CA , and a marked reduction in Gag protein species relative to controls ( untreated and TG009 , + ) . Western blot analysis of Env ( Fig . 1B , middle ) demonstrated a loss in both gp120 and gp160 proteins to near undetectable levels compared to controls . Upon subsequent analysis of the dose response curve ( Fig . 1C ) , digoxin demonstrated potent inhibition of HIV-1 Gag protein expression with an IC50 of ∼45 nM ( IC90 = 100 nM ) . Parallel assessment of the cytotoxicity of digoxin treatment on this cell line ( Fig . 1D ) revealed no significant effects on cell viability at the dose ranges required to inhibit HIV-1 gene expression ( 50–100 nM ) as measured by XTT and Trypan blue ( TB ) exclusion assays ( 0–200 nM ) ( Fig . 1D ) . To validate our findings in a more relevant setting , the ability of digoxin to suppress HIV-1 replication in the context of human CD4+ PBMCs was examined . Isolated PBMCs were infected with a R5 BaL strain of HIV-1 in the presence of increasing doses of digoxin and the extent of virus replication was monitored by p24CA ELISA ( Fig . 2A ) . Analysis of the data revealed a profound suppression of HIV-1 replication upon addition of digoxin ( IC90 = ∼25 nM ) . Parallel examination of the effect of these treatments on cell viability ( Fig . 2B ) determined that negative effects were only discernible at doses of ≥50 nM ( by XTT assay ) , above the dose required to strongly suppress HIV-1 replication . In comparison to the stable cell line , analysis of media from PBMC infections at earlier time points ( day 3; Fig . S2 ) , representing less cycles of replication , demonstrated significant reduction in HIV-1 replication without significant effects on cell viability ( data not shown ) . As a further test of the efficacy of digoxin in suppressing HIV-1 replication , a similar trial was performed using CD8+-depleted PBMCs obtained from treatment-naïve HIV-infected patients . As shown in Fig . 2C and 2E , while Gag accumulated over time in control samples ( DMSO ) , digoxin inhibited HIV-1 replication over the 20 days of the assay to a level comparable to the nucleoside reverse transcriptase inhibitor ( NRTI ) , 3TC ( Fig . 2E and 2F ) . Furthermore , dose response curves ( Fig . 2D ) demonstrate inhibition of HIV-1 replication at an IC90 of 2 nM with no detectable effects on cell viability . To determine the mechanism underlying the response to digoxin , we analyzed its effect on the abundance of all three classes of HIV-1 mRNA by qRT-PCR ( Fig . 3 ) . Using the HeLa HeLa rtTA-HIV-ΔMls cell line , digoxin treatment induced an 84% reduction in US mRNA levels ( encoding Gag and Gagpol ) and a 68% decrease in SS mRNA ( encoding Env , p14 Tat , Vpr , Vif , or Vpu ) . In contrast , digoxin increased MS mRNA ( p16 Tat , Rev , Nef ) by 300% . The effect of digoxin on HIV-1 RNA abundance was also dose dependent ( Fig . S3 ) , in agreement with its effects on the expression of viral structural proteins , Gag and Env ( Fig . 1 ) . These results are consistent with digoxin inhibition being due to the induction of viral RNA oversplicing , which is in contrast to the inhibition of splicing induced by indole derivatives [19] , [35] , [42] . The response to digoxin results in a specific loss of larger , incompletely-spliced mRNA species ( encoded by US and SS ) that , in turn , reduces the synthesis of proteins necessary for virus assembly . To validate that the response observed was not unique to the HeLa cell line , assays were repeated in 24ST1NLESG cells , a human T cell line ( SupT1 ) chronically infected with a HIV-1 variant ( NLE−S-G , a pNL4-3-based virus vector ) [43] . Assays determined that digoxin also suppressed HIV-1 Gag expression in the SupT1 cell line ( Fig . S4C ) , inducing a similar reduction in abundance of incompletely-spliced viral RNAs ( US , SS ) and increasing MS RNA accumulation ( Fig . S4D ) as seen for HeLa rtTA-HIV-ΔMls cells ( Fig . 3 ) . To analyze the effects of digoxin on HIV-1 RNA processing in greater detail , we examined for changes in viral RNA splice site selection ( Fig . 4A–C ) . Using RNA from HeLa rtTA-HIV-ΔMls cells incubated in the presence or absence of digoxin , effects on alternative RNA splicing were analyzed by RT-PCR of the HIV-1 MS ( 2 kb ) mRNA class . Position of the primers is illustrated in Fig . 4A . Upon comparison to control samples ( Fig . 4B ) , we noted that digoxin significantly reduced the level of Rev 2/1 mRNA ( generated by the use of SA4c , a , b ) , while having limited effect on other spliced 2 kb mRNAs . Subsequent densitometry analysis of each MS mRNA species ( Fig . 4C ) revealed that digoxin induced a 73% loss of Rev 2/1 mRNA levels compared to control samples as well as a slight increase in Tat 1 ( generated by the use of SA3 ) . In contrast , other splice modulator drugs such as clotrimazole and flunarizine had no significant effect on HIV-1 MS splice site selection ( Fig . S5 ) . These results reveal that digoxin causes selective alterations in the use of viral MS pre-mRNA splice sites , leading to the specific loss of the mRNA species encoding a key HIV-1 regulatory factor , Rev ( Fig . 4B and C ) . To assess the impact of digoxin's alteration of splice site usage at the protein level , we performed western blots of cell extracts to examine for changes in the viral regulatory factors Rev and Tat . Analysis of Rev ( Fig . 4D , top ) revealed a profound loss of Rev protein expression levels relative to DMSO control ( + ) consistent with the reduced level of the corresponding mRNA ( Fig . 4B and 4C ) . This response was achieved without detectable changes in the level of p16 Tat , a Rev-independent isoform encoded by MS RNA , demonstrating selectivity in the responses observed . However , digoxin did reduce expression of p14 Tat ( Fig . 4D , bottom ) , a Rev-dependent isoform produced from SS mRNA . Reduced p14 Tat levels is consistent with both a decrease in Rev expression ( Fig . 4D , top ) and of SS mRNA ( Fig . 3 ) . These observations confirm that digoxin selectively blocks Rev protein production , leading to impaired export of Rev-dependent mRNAs ( US and SS ) that produce viral structural proteins as well as a subset of regulatory/accessory factors ( illustrated in Fig . S1 ) . As further verification that digoxin results in reduced Rev activity , in situ hybridization was performed to look for changes in HIV-1 US RNA distribution associated with drug treatment . As shown in Fig . 5A , in the presence of doxycycline alone ( DMSO +Dox ) , signal for HIV-1 US RNA is observed throughout the cell with intense staining at the sites of proviral integration . In contrast , addition of both doxycycline and digoxin results in viral US RNA being predominately restricted to the nucleus ( Fig . 5A , Digoxin +Dox , ) . Treatment of cells with the NRTI , 3TC , had no effect on the distribution of the viral US RNA ( Fig . 5A , 3TC +Dox ) . To determine whether reduction of Rev alone was responsible for the loss of HIV-1 structural protein expression , cells were transfected with control ( dsRed ) or Rev ( dsRed-Rev ) expression vectors in the presence of digoxin and Gag protein synthesis monitored ( Fig . 5B , C ) . These assays revealed that trans expression of Rev ( ds Red Rev ) yielded a partial recovery of HIV-1 Gag protein synthesis in comparison to the control vector ( ds Red ) . Digoxin inhibits the function of the sodium-potassium ( Na+/K+ ) ATPase in the plasma membrane resulting in increased intracellular levels of calcium as well as the activation of a number of signaling cascades [38] , [39] , [44] . How events at the plasma membrane ultimately result in altered HIV-1 RNA processing in this system is not immediately apparent . However , many of the kinase cascades affected by cardiac glycosides have been described to influence alternative RNA splicing [45] , [46] , [47] . One hypothesis is that digoxin-induced alteration of cellular signaling cascades ultimately affect the activity of factors , such as SR proteins , known to regulate HIV-1 RNA splicing [16] , [19] . To test whether any alteration in SR protein function occurred in our experimental system , we first examined the effect of digoxin treatment on SR protein kinases belonging to the CLK family ( 1–4 ) [48] , [49] , [50] . As indicated in Fig . 6A and S6 , overexpression of any of these kinases results in a shift in the subnuclear distribution of SR proteins ( such as SC35/SRSF2 ) from being localized to nuclear speckles to being dispersed throughout the nucleus ( compare GFP− with GFP+ cells treated with DMSO ) . Treatment with digoxin reversed the effects of all CLK kinases tested ( Fig . 6A and S6 , Digoxin ) ; SC35 remained in nuclear speckles in the presence of digoxin despite CLK overexpression , consistent with reduced activity of the transfected kinases . Impaired activity of a family of SR protein kinases in response to digoxin addition suggests that an alteration in SR protein function underlies the inhibition of HIV-1 replication . To explore this hypothesis , SR proteins were analyzed by western blot of cell lysates ( Fig . 6B ) for changes in abundance or migration due to drug treatment . Initial analysis of phospho-SR proteins by 1H4 antibody determined that digoxin treatment increased the levels of at least two phospho-SR proteins ( Fig . 6B ) : increasing SRp55 and moderately increasing SRp75 relative to DMSO controls ( +/− ) . No consistent changes in the overall phospho-SR protein levels were observed in the presence or absence of HIV-1 expression by this antibody . To further explore specific members of SR proteins affected by digoxin , we performed western blot analysis on a panel of SR proteins with specific antibodies to SRp20 , Tra2β , 9G8 , and SF2/ASF ( Fig . 6C ) . Recent work [51] demonstrated that treatment with digitoxin ( another cardiac glycoside ) induced marked alterations in SRp20 and Tra2β abundance . Consistent with the selective effect of a cardiac glycoside on a subset of SR proteins , we observed that SRp20 ( Fig . 6C ) underwent a shift to a higher MW species upon digoxin treatment compared to DMSO-treated cells ( +/− ) . Treatment of extracts with alkaline phosphatase confirmed that the shift observed in SRp20 was due to hyperphosphorylation of the protein ( Fig . 6D ) . In the case of Tra2β ( Fig . 6C ) , digoxin treatment increased the level of a high MW form of Tra2β that was reduced upon induction of HIV-1 ( + Dox ) compared to control ( −Dox ) . However , alkaline phosphatase had no effect on the higher MW forms of Tra2β blots induced by digoxin treatment ( data not shown ) . Analysis of other SR proteins , 9G8 and SF2/ASF ( Fig . 6C ) , showed little or no change in levels or MW upon digoxin treatment . These data are consistent with the recent work of Anderson et al . [51] in that only a subset of SR proteins are affected by digoxin treatment , suggesting that at least one or a combination of these splice factors play a critical role in mediating the change in HIV-1 RNA processing or expression . The increased SRp20 phosphorylation or changes in Tra2β modification in response to digoxin raised the possibility that the alterations in HIV-1 RNA splicing could be attributed to increased activity of either factor . To test this hypothesis directly , HeLa rtTA-HIV-ΔMls cells were transfected with vectors expressing these factors and their effects on viral structural protein and RNA accumulation were assessed ( Fig . 7 ) . To ensure that only cells taking up DNA expressed the HIV-1 provirus , cells were also co-transfected with plasmids expressing the TetO activator , tTA , to induce provirus expression , and secreted enzyme alkaline phosphatase ( SEAP ) as an indicator of global effects on gene expression . As shown in Fig . 7C , detection of HIV-1 Gag by p24CA ELISA was dependent upon transfection with tTA ( see −tTA vs . +tTA ) . Transfection of SRp20 or either isoform of Tra2β ( Tra2β1 and Tra2β3 ) resulted in a marked reduction in Gag protein expression with unchanged or increased expression of SEAP . Subsequent analysis of viral RNA accumulation indicated that each factor functioned in a different manner . qRT-PCR of each of the HIV-1 mRNAs ( Fig . 7D ) determined that SRp20 overexpression resulted in reduced accumulation of both US and SS viral RNAs with a trend towards increased MS RNA levels . In contrast , overexpression of either isoform of Tra2β resulted in reduced accumulation of all HIV-1 mRNAs . Subsequent analysis of splice site selection within the MS class of viral RNAs revealed distinct differences in how these factors affected HIV-1 MS RNA splicing ( Fig . 7E , F ) . Similar to digoxin , SRp20 overexpression induced a shift in splice site usage that resulted in increased Tat1 accumulation while reducing Rev1/2 and Nef2 levels . In contrast , Tra2β1 overexpression elicited little change in splice site selection while Tra2β3 overexpression induced a marked accumulation of Nef1 , generated by splicing the first 5'ss of HIV-1 to the last 3'ss of the virus . Taken together , the response to SRp20 overexpression is most similar to that observed upon digoxin treatment .
Despite the success of ART/HAART , there are many caveats with current HIV-1 therapies , including the emergence of drug resistant forms of HIV-1 , high cost , and toxicity [1] , [2] , [10] . New drugs with improvement in these profiles and novel mechanisms of action are necessary [1] , [2] , [9] . A number of strategies have targeted HIV regulatory and accessory proteins to date , but most remain under development [9] , [52] . It is unclear whether disrupting cellular processes essential for HIV-1 replication can yield alternative therapies without significant cellular toxicity . However , a number of existing therapies for other human diseases ( e . g . heart disease , cancer , and dementia ) do work by altering host protein function and are well tolerated [53] , [54] , [55] , [56] . In this report , we demonstrate a novel and alternative use of the FDA-approved cardiovascular drug , digoxin , as an anti-HIV-1 therapeutic ( summarized in Fig . 8 ) . More importantly , digoxin was found to inhibit virus replication by a novel mechanism , inducing oversplicing of HIV-1 RNA ( Figs . 3 , S3 , 4 , and S4D ) —a stage of the virus lifecycle not targeted by current HIV-1 inhibitors and under host cell control . Digoxin achieves this effect by altering the splicing of HIV-1 RNA , reducing accumulation of two classes of viral mRNA ( US and SS; Figs . 3 , S3 , S4D ) that encode structural proteins essential for new virion assembly ( Gag , Gagpol , and Env; Fig . 1 ) . In addition , digoxin selectively inhibits expression of the HIV-1 regulatory factor Rev through specific alteration of viral RNA splice site use without affecting the expression of other viral proteins ( p16 Tat; Fig . 4 ) . While digoxin induced a 73% reduction in Rev2/1 RNA accumulation , it also increased MS viral RNA levels ∼3 fold ( Fig . 3 ) . Combined , these alterations may not account for the complete loss of Rev protein observed , suggesting the possibility that digoxin may have effects beyond the changes in viral RNA processing detected . The loss of Rev further impairs expression of incompletely-spliced viral mRNAs ( US and SS ) by preventing Rev-mediated export of RNAs to the cytoplasm ( Fig . 5A ) for translation into respective viral structural proteins ( Gag , Gagpol , and Env ) and regulatory/accessory factors ( p14 Tat , Vif , Vpr , and Vpu ) ( Figs . 1 , 2 , S2 , 4D ) . Furthermore , the effects were achieved at concentrations of digoxin that did not impact HeLa , SupT1 , and PBMC cell viability relative to control treatments ( Figs . 1 , 2 , and S4 ) . Rev expression in trans ( Fig . 5B–C ) only partially reversed the effects of digoxin , indicating that loss of Rev alone is not sufficient to explain the full effect of digoxin . Rather , in light of the demonstration that Rev acts primarily on newly synthesized viral RNA [57] , the enhanced processing of the viral RNA induced by digoxin may result in the incompletely-spliced HIV-1 RNAs having too short a half-life to be engaged by Rev even when Rev is present . In summary ( Fig . 8 ) , digoxin selectively impairs HIV-1 replication at two levels: ( 1 ) through global alterations in the efficiency of HIV-1 RNA processing and ( 2 ) blocking export of incompletely-spliced viral RNAs to the cytoplasm . Digoxin and other cardiac glycosides are known to bind the Na+/K+-ATPase pump in the plasma membrane , initiating the activation of multiple signaling cascades that result in increased intracellular calcium concentrations as well as signaling of Src , AKT , and MAPK kinases [44] , [58] , [59] . How this response initiated at the cell membrane can alter RNA splicing was not immediately clear . In light of the observed changes in HIV-1 RNA processing , we initially focused on factors known to modulate these events: SR proteins [60] , [61] , [62] , [63] . Consistent with the findings of Anderson et al . [51] , our results reveal that a subset of SR proteins ( SRp20 , Tra2β , SRp55 , and SRp75 ) are altered as well as the function of a number of SR protein kinases ( CLKs 1–4 ) upon digoxin addition ( Fig . 6 ) . In the work of Anderson et al . [51] , only a subset of the exons examined were affected by treatment with digitoxin , suggesting that the response is not a general perturbation of host RNA splicing but is more selective . Since the modifications of SRp20 or Tra2β1 detected might increase their activity , we subsequently examined the impact of overexpression of both factors on HIV-1 RNA processing ( Fig . 7 ) . While the three factors tested ( SRp20 , Tra2β1 , and Tra2β3 ) all elicited a marked reduction in HIV-1 Gag synthesis upon overexpression , analysis of the effects on viral RNA splicing determined that the basis for the response was markedly different . Of the three factors tested , overexpression of SRp20 most closely mimics the changes induced by digoxin; reducing accumulation of US and SS viral RNAs while trending towards increased MS RNA abundance . Furthermore , SRp20 induced increased accumulation of Tat1 and reduced Rev1/2 mRNA levels as observed with digoxin . The response documented here differs significantly from those induced by overexpression of SC35 , SRp40 , 9G8 , and SF2/ASF previously reported [18] , [20] , [31] . In these studies , overexpression of SC35 , SRp40 , or 9G8 resulted in almost exclusive formation of MS RNA encoding Tat ( Tat1 ) , while SF2/ASF increased usage of the splice sites for Vpr . However , the effects of these factors on HIV-1 RNA accumulation and expression differ among published reports: one indicates that SF2/ASF , SC35 , or SRp40 overexpression increases US viral RNA accumulation [18] while another showed marked reduction of all viral RNAs with only SF2/ASF significantly decreasing Gag expression [31] . None of these reports demonstrated selective alterations in Rev1/2 RNA abundance comparable to digoxin or SRp20 overexpression reported here . Future efforts will be focused on understanding how SRp20 achieves this response on HIV-1 , through either direct interaction with sites on the viral RNA and/or manipulation of abundance/activity of other host factors . In contrast to the effects of SRp20 , overexpression of Tra2β1 and Tra2β3 reduced levels of all viral RNAs ( Fig . 7D ) while only Tra2β3 altered splice usage to favor Nef1 ( Fig . 7E , F ) . The difference in activity of Tra2β1 and Tra2β3 is of particular interest since both share a common RRM domain as well as a C-terminal RS domain ( Fig . 7A ) and interact with a common set of SR proteins [64] . However , previously analyses had indicated that Tra2β3 had limited or no ability to modulate splicing of a number of RNA substrates tested [65] . Our demonstration that the two Tra2β isoforms have quite distinct effects on splice usage in the context of HIV-1 RNA splicing suggests that variation in abundance of these two isoforms of Tra2β is likely to yield quite distinct effects on host cell RNA splicing . The response seen upon Tra2β3 overexpression is most similar to alterations induced upon mutation of the exon splicing enhancer ( GAR ) adjacent to SA5 . Previous studies had determined that reduced function of GAR resulted in increased accumulation of spliced RNA corresponding to Nef1 [66] , [67] , raising the possibility that Tra2β3 functions by interfering with GAR function . Our determination that digoxin can alter the equilibrium in viral RNA processing demonstrates that this step of the virus lifecycle can be manipulated to block HIV-1 replication . In principle , targeting host factors essential for HIV-1 replication offers the promise of broad spectrum activity against multiple viral strains and a reduced potential of resistance . Although digoxin has potent effects on HIV-1 in our assays , its use in the treatment of cardiovascular conditions has a narrow therapeutic dose range of 0 . 5–2 . 0 ng/mL ( max . 5 nM ) with higher doses yielding increased toxicity ( including death ) [38] , [39] . Our experiments using the stably transduced HeLa rtTA-HIV-ΔMls and 24ST1NLESG cell lines determined that complete suppression of HIV-1 gene expression requires concentrations of digoxin ( IC90 = 100 nM , Fig . 1C; IC90 = 370 nM , Fig . S4C , respectively ) well above what is compatible for use in humans . However , our subsequent studies using PBMCs showed that reduced doses of digoxin are sufficient to achieve a significant response ( IC90 = 25 nM , Fig . 2A , B ) . In experiments using HIV infected patient PBMCs , doses as low as 2 nM strongly suppressed HIV-1 replication ( Fig . 2C , D ) . The differences in the dose of digoxin required to achieve a measurable response between the various assays might reflect differences in the ability to activate the signaling cascade initiated by the binding of digoxin to the Na+/K+ ATPase at the cell surface [44] . Given the transformed nature of both HeLa and SupT1 cells , it is not unexpected that portions of this cascade may be altered relative to PBMCs . Alternatively , differences in the response of the different cell types ( HeLa/SupT1 vs . PBMCs ) to digoxin may reflect the nature of the assay itself . In the experiments using the stably transduced cell lines ( HeLa/SupT1 ) , >90% of the cells are expressing viral proteins upon induction and , hence , inhibition would require significant alterations in HIV-1 RNA processing/protein synthesis . In contrast , for PBMCs , detection of Gag expression is dependent upon the exponential amplification of the virus in the culture . In this context , even small perturbations in HIV-1 replication will result in significant differences over multiple rounds of replication . The benefit is that doses of digoxin within the therapeutic range were able to suppress HIV infection . Better responses might be achieved using derivatives of digoxin with improved activity and a better therapeutic index [44] , [58] . The determination that digoxin , acting through the Na+/K+-ATPase ( a plasma membrane receptor ) , can suppress HIV-1 gene expression suggests that its downstream effectors might also prove to be therapeutic targets . In addition , compounds which mimic digoxin's effect on CLKs and/or SR protein function could prove equally capable of altering HIV-1 RNA processing . Several compounds affecting CLK function ( TG003 and chlorohexidine ) have already been described [36] , [68] , and we recently demonstrated that chlorhexidine ( but not TG003 ) inhibits HIV-1 gene expression [33] . Recent studies [59] , [69] have identified multiple kinases mediating the effect of cardiac glycosides in transformed cells . Determining which of these kinases is responsible for mediating digoxin's effect on HIV-1 RNA processing would be useful in developing a more targeted approach to manipulating viral gene expression . However , the demonstration that digoxin can inhibit HIV-1 replication through a novel mechanism without significant toxicity to the host cell serves as proof that this strategy is viable and could be used in junction with existing treatments for better control of this infection .
Screening of drugs for effects on HIV-1 RNA processing was performed using the HeLa rtTA-HIV-ΔMls cell line containing an inducible Tet-On HIV-1 provirus [40] , [41] as described in our previous study [33] . Activation of virus gene expression in these cells was achieved by addition of doxycycline ( Dox ) or transfection of plasmid expressing tTA . In drug screens , cells were seeded one day prior in IMDM containing 10% FBS , 1X Pen-Strep , and 1X Amphotericin B ( Wisent Corporation ) while drugs were solubilized to ∼1000X of its final treatment concentration in DMSO . Next , cells were treated for 4 h with 100–200 µL of drugs pre-diluted to ∼25X of its final concentration in Opti-MEM ( Invitrogen , #31985070 ) and HIV gene expression was induced with Dox ( 2 µg/mL ) . After ∼24 h of drug treatment , cells and media were harvested . To monitor effects of drug treatments , p24CA ELISA , western blotting , and RNA analyses were performed as described below . Cell viability was assessed using biochemical ( XTT assay; Sigma-Aldrich , #TOX2 ) and/or physical ( trypan blue exclusion; Invitrogen , #15250-061 ) assays . Written informed consent was obtained from volunteer blood donors in accordance with the guidelines for conduct of biomedical research at the University of Toronto , and all experimental protocols were approved by the University of Toronto institutional review board . Human primary cells were obtained for experiments either from healthy volunteer blood donors ( uninfected with HIV ) or drug-naïve HIV-infected individuals . For infection experiments , PBMCs were isolated , infected with HIV-1 ( BaL ) , and cultured as described previously [33] . Cells were treated with drugs pre-diluted in RPMI in the same manner as described above . Every 3–4 days , 0 . 5 mL of media was harvested for p24CA ELISA and replaced with 0 . 5 mL of fresh R-10 medium containing fresh drug treating 1 mL ( ∼1 . 5X final with fresh and decayed drug ) . The effect of drugs on cell health was assessed in parallel by XTT and/or trypan blue assays . For experiments using HIV-infected patient samples , PBMCs were first depleted of CD8+ T cells using Dynabeads CD8 ( Invitrogen , #111 . 47D ) as outline by manufacturer . Remaining cells were then activated by treatment with anti-CD3 anti-CD28 antibodies ( Bio Legend #302914 and 317304 , respectively; 1 µg/ml of each ) as well as 50 U/ml of IL-2 ( BD Pharmingen #554603 ) in the presence or absence of indicated drugs as mentioned above . Media ( 0 . 5 ml ) was collected every 3–4 days and replaced with fresh media ( 0 . 5 ml ) containing 20 U/ml of IL-2 and fresh drug . Effect of compounds on cell viability was monitored in parallel by XTT assay and expressed relative to control ( DMSO ) treated cells . HIV-1 growth in cultures was monitored by p24CA ELISA of cell supernatants . RNA was extracted from cells by Aurum Total RNA Mini Kits ( Bio-Rad , Cat . #732-6820 ) . Purified RNA was reverse transcribed using M-MLV ( Invitrogen , Cat . No . 28025-013 ) and resulting cDNAs were used to quantitate HIV-1 mRNA levels by qRT-PCR as described [33] . To monitor for changes in HIV-1 US RNA subcellular distribution in response to digoxin , cells were treated with digoxin , 3TC , or DMSO solvent for 4 h and then viral gene expression was induced by addition of Dox . After 20 h , cells were fixed in 3 . 7% formaldehyde-1XPBS . Cells were permeabilized by treatment with 70% EtOH , then rehydrated in hybridization buffer ( 10% formamide , 2XSSPE ) . Hybridization was performed using a mixture of 48 Quasar 570-labelled oligonucleotides spanning the MA , CA , and nucleocapsid ( NC ) regions of HIV-1 as detailed by the supplier ( Biosearch Technologies ) . Following washing to remove unbound probe , nuclei were stained with DAPI and images acquired using a Leica DMR microscope at 630× magnification . The effect of drugs on HIV-1 splice site usage within the 2 kb , MS RNA class was analyzed by performing RT-PCR of cDNA obtained from RNA purified and reverse transcribed as previously described [33] . HeLa rtTA-HIV-ΔMls cells were transfected with vectors expressing GFP-tagged CLK1 , CLK2 , CLK3 , or CLK4 . Twenty-four hours post-transfection , cells were treated with either digoxin or DMSO for 24 h , fixed , processed , and analyzed by immunofluorescence microscopy [33] . Effects on SC35 localization was assessed using a mouse anti-SC35 antibody ( BD Pharmingen , #556363 ) and a secondary Texas Red-conjugated donkey anti-mouse IgG antibody ( Jackson Immunoresearch , #715-075-151 ) , while nuclei were stained with DAPI . To monitor HIV-1 gene expression or virus replication ( Gag synthesis ) , cell culture supernatants were assayed by a HIV-1 p24CA antigen capture assay kit ( AIDS & Cancer Virus Program , NCI-Frederick , Frederick , MD USA ) . Media harvested from PBMC cultures infected with HIV-1 ( BaL ) were diluted ∼250-fold ( or as needed ) prior to performing this assay . For analysis of HIV-1 and SR protein expression by Western blot , cells were solubilized in RIPA buffer , quantitated by Bradford assay , and run on 8 , 10 , or 12% SDS-PAGE under reducing conditions , and then transferred to PVDF . Normally , 25–30 µg of protein was loaded , blots blocked in either 5% Milk-T ( 5% skim milk , 0 . 05% tween-20 , 1XPBS ) or 3% BSA-T for 1 h at room temperature ( RT ) according to the antibody diluent used , and blots incubated with antibody at RT for ∼2 . 5 h , unless otherwise specified . Specific antibodies and conditions used for Tat , anti-tubulin , and isotype-specific HRP-conjugated antibodies were used as described [33] . Additional antibodies and conditions used in this study include a mouse anti-p24 supernatant from hybridoma 183 ( provided by M . Tremblay , Laval University ) : 1/10th dilution in PBS-T incubated for 1 h at RT , blocked in 5% Milk-T overnight at 4°C . Mouse anti-gp120 purified supernatant from hybridoma 902 ( obtained through the AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH: Hybridoma 902 ( anti-gp120 ) from Dr . Bruce Chesebro ) : 1/10th dilution in PBS-T incubated normally or O/N at 4°C , blocked in 3% BSA-T at RT for 2 . 5 h . Mouse anti-Rev ( Abcam , #ab85529 ) : 1/1000th dilution in 3% BSA-T incubated O/N at 4°C , loaded with 30–40 µg of protein . Mouse anti-phospho-SR ( 1H4 ) ( Invitrogen , #33-9400 ) : 1/5000th dilution in 3% BSA-T , blocked for ∼2 . 5 h at RT or overnight at 4°C . Rabbit anti-Tra2β ( Abcam , #ab3135353 ) : 1/10 , 000th dilution in 3% BSA-T incubated for 1 . 5 h at RT . Rabbit anti-9G8 serum ( Znk1 . 4 ) : 1/3000th dilution in 5% Milk-T . Mouse anti-SRp20 ( Invitrogen , #334200 ) , 1/1000th dilution in 3% BSA-T , loaded with 20 µg of protein . Generally , Western Lightning-ECL ( Perkin-Elmer , #NEL101 ) but for anti-Rev , -Tat , and -gp120 blots , Western Lightning Plus-ECL ( #NEL105 ) were used for development of signals onto autoradiography film . In addition , phosphatase inhibitors ( e . g . 10 mM sodium fluoride , 2 mM sodium orthovanadate ) were added to solutions for SR protein analyses . Lastly , SR protein phosphorylation was confirmed through treatment of ∼20 µg of cell lysate with 20 U of calf intestinal alkaline phosphatase ( NEB , #M0290S ) for ∼45 minutes at 37°C prior to western blot analysis . To assess effects of protein overexpression , cells were transfected in the presence or absence of the tTA expression vector , CMV PLAP ( expressing SEAP/alkaline phosphatase ) , and either empty vector ( CMVmyc pA ) , CMVmyc SRp20 , CMVmyc Tra2β1 , or CMVmyc Tra2β3 using polyethylene imine ( PEI ) . At 48–72 h post-transfection , cells and media were harvested . To monitor effects of these manipulations , p24CA ELISA , western blotting , and RNA analyses were performed as described previously [33] . To assess the ability of expression of Rev in trans to rescue the synthesis of HIV-1 Gag in the presence of digoxin , cells were transfected as described above with plasmids expressing either dsRed or a dsRed-Rev fusion . At 24 h post-transfection , cells were treated with digoxin for 4 h then HIV-1 expression was induced for 20 h by addition of doxycycline . Cells were subsequently fixed and examined by immunofluorescence for co-expression of Gag and dsRed signal using a Leica DMR microscope . Data was analyzed using Microsoft Excel and expressed as means ± standard error of the mean ( SEM ) . Differences between two groups of data ( i . e . drug treatment vs . DMSO ( +Dox ) control , drug treatment vs . DMSO ( +HIV ) , or transfected factor vs . mock vector ( +tTA ) were compared by Student's t-test ( two-tailed ) . Statistical significance of results are indicated on each graph as follows: p value<0 . 05 , * , p value<0 . 01 , ** , and p value<0 . 001 , *** , unless otherwise indicated . | Antiretroviral therapies ( ART ) for HIV/AIDS are successful in slowing disease progression by inhibiting viral proteins . However , the ability of HIV to adapt to ARTs has given rise to drug-resistant virus strains that now represent ≥16% of newly infected people . This development calls for the generation of new treatment strategies . Since HIV is dependent upon RNA processing under control of the host , we searched for compounds/drugs that inhibit HIV-1 replication at this step . We identified digoxin as a potent inhibitor of HIV-1 replication . The drug inhibited expression of HIV-1 structural proteins and a key factor involved in viral RNA export . This response was accomplished by altering the efficiency and splicing choices in HIV-1 RNA processing . Since this stage of the virus lifecycle is not targeted by current ARTs , the digoxin family of drugs represent a novel class of HIV-1 inhibitors . Since digoxin targets host factors and is already in clinical use , it and potentially the cardiac glycoside family of drugs has the possibility for swift development into a new ART for HIV-1 infection . |
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Nonalcoholic fatty liver disease ( NAFLD ) is characterized by abnormal accumulation of triglycerides ( TG ) in the liver and other metabolic syndrome symptoms , but its molecular genetic causes are not completely understood . Here , we show that mice deficient for ubiquitin ligase ( E3 ) Smad ubiquitin regulatory factor 1 ( Smurf1 ) spontaneously develop hepatic steatosis as they age and exhibit the exacerbated phenotype under a high-fat diet ( HFD ) . Our data indicate that loss of Smurf1 up-regulates the expression of peroxisome proliferator-activated receptor γ ( PPARγ ) and its target genes involved in lipid synthesis and fatty acid uptake . We further show that PPARγ is a direct substrate of Smurf1-mediated non-proteolytic lysine 63 ( K63 ) -linked ubiquitin modification that suppresses its transcriptional activity , and treatment of Smurf1-deficient mice with a PPARγ antagonist , GW9662 , completely reversed the lipid accumulation in the liver . Finally , we demonstrate an inverse correlation of low SMURF1 expression to high body mass index ( BMI ) values in human patients , thus revealing a new role of SMURF1 in NAFLD pathogenesis .
Nonalcoholic fatty liver disease ( NAFLD ) is a chronic liver condition associated with obesity , non–insulin-dependent diabetes , and hyperglyceridemia [1] . Although presenting few clinical symptoms at early stages , a subset of patients with NAFLD will progress to nonalcoholic steatohepatitis ( NASH ) consisting of hepatic steatosis and inflammation , which can ultimately lead to cirrhosis and even liver cancer [2] . Myriad social–behavioral and genetic causes of NAFLD are now known , but the roles of peroxisome proliferator-activated receptors ( PPARs ) have emerged as crucial molecular underpinnings of these metabolic imbalances and targets of several investigational drugs [3–5] . A thorough understanding of regulatory mechanisms governing PPAR activities will undoubtedly aid in the development of much-needed treatments . PPARs are nuclear hormone receptors that heterodimerize with retinoid X receptors to modulate metabolic transcriptional programs in response to nutritional inputs [6] . Of three PPARs encoded by distinct mammalian genes , PPARα , which is highly expressed in the liver , kidney , and muscle , directs expression of a network of genes that promote utilization of fat as an energy source . PPARγ , on the other hand , is normally expressed in adipose tissues , where it activates target genes involved in fatty acid uptake , transport , and lipogenesis to promote lipid storage . In the liver , PPARγ expression is normally low but becomes drastically induced as hepatic steatosis develops [7] . Reports in the literature have shown that overexpression of PPARγ promotes the accumulation of lipid droplets in the liver , whereas hepatic disruption of PPARγ improves the fatty liver condition in leptin-deficient obese mice or mice that were fed on a high-fat diet ( HFD ) [8 , 9] . In adipose tissues , ligand binding was reported to induce degradation of PPARγ via the ubiquitin-proteasome system , whereas small ubiquitin-like modifier ( SUMO ) ylation of PPARγ was shown to repress its transcriptional activity [10] . However , how steatogenic activities of PPARγ are regulated in the liver remains to be determined . Smad ubiquitin regulatory factor 1 ( Smurf1 ) and its close relative , Smurf2 , are members of homologous to E6-AP carboxyl terminus ( HECT ) domain–containing ubiquitin ligases ( E3s ) , which were initially identified as negative regulators of transforming growth factor-β ( TGF-β ) and bone morphogenetic protein ( BMP ) signaling pathways [11–14] . Subsequent studies broadened the repertoire of Smurf substrates and extended their function to cell differentiation , polarity , and DNA repair [15–18] . During our ongoing quest for physiological functions of Smurfs , we found abnormal accumulation of lipid droplets in the livers of 9–12-month-old Smurf1 knockout ( KO ) mice and other signs that phenocopy NAFLD in human patients . Here , we report that Smurf1 induces non-proteolytic ubiquitination of PPARγ and inhibits PPARγ transcriptional activity in hepatocytes , thereby acting as a critical safeguard against the development of hepatic steatosis .
We previously reported an increased bone density phenotype in aged Smurf1KO mice that were commonly observed under mixed black Swiss × 129/SvEv ( BL ) and C57BL/6N ( B6 ) genetic background [18] . Further analysis revealed a conspicuous accumulation of lipid droplets in the livers of aged Smurf1KO mice that was unique to the BL background ( S1 Table ) . The liver sections of these mice were characterized by large , clear , sharp-bordered cytoplasmic vacuoles upon hematoxylin–eosin ( HE ) staining ( Fig 1A ) . The bright red staining of frozen sections by Oil Red-O confirmed the high fat and neutral lipid content therein ( Fig 1A ) . This phenotype was observed in 12 out of 15 male and female mice examined beyond 9 months of age , implying a 75% penetrance . Microscopic quantification of HE-stained sections reaffirmed the statistically significant increase of steatosis in the livers of Smurf1KO mice compared with that of the wild-type ( WT ) controls ( Fig 1B ) . Surprisingly , this steatosis phenotype was not observed in the livers of Smurf2KO mice ( Fig 1A and 1B ) , suggesting that it is specifically associated with disruption of the Smurf1 function . To determine which lipid fractions were increased , we carried out colorimetric assays in liver lipid extracts prepared from Smurf1KO mice at 9–12 months of age , and the results showed that the level of triglycerides ( TG ) increased more than 3-fold compared with that of WT or Smurf2KO mice ( Fig 1C ) . Moreover , the levels of total cholesterol ( CHO ) and free fatty acids ( FFAs ) were also increased significantly in Smurf1KO livers ( Fig 1C ) . Compared with WT mice , Smurf1KO mice were approximately 30% heavier in body weight , bore more white adipose tissue ( WAT ) , and had a higher liver to body weight ratio ( Fig 1D ) . Nevertheless , despite exhibiting ostensible steatosis , the mutant livers appeared to function normally , as indicated by aspartate transaminase ( AST ) and alanine transaminase ( ALT ) activity measurements ( Fig 1E ) . Because the manifestation of hepatic steatosis is usually accompanied by a constellation of adverse alterations in glucose metabolism , we conducted glucose tolerance and insulin resistance tests . At the fasting state , there was not much difference in plasma glucose levels between aged ( 9–12 months old ) WT and Smurf1KO mice that had developed steatosis; however , following intraperitoneal injection of glucose , the blood glucose level of the mutant mice showed a more dramatic flash increase of the blood glucose level within 30 minutes of injection and more than 100% accumulative gain in the area under the curve ( AUC ) ( Fig 1F ) . On the other hand , after an initial dip following the insulin injection , the blood glucose level in aged mutant mice recovered more rapidly and to a higher extent than that in WT controls ( Fig 1G ) . The AUC of the insulin resistance test of aged Smurf1KO mice was 13 . 5% more compared with that of WT mice . Because young Smurf1KO mice ( at 4–5 months of age ) that had yet to develop steatosis scored no difference from their WT counterparts in both the tests ( S1 Fig ) , the systemic change in glucose metabolism observed in aged mutant mice was most likely associated with the steatosis . Taken together , the phenotypes of hepatic steatosis , obesesity , glucose intolerance , and insulin resistance make these aged Smurf1KO mice a good mouse model of NAFLD . In rodents , difference in genetic background has a well-known influence on the susceptibility to obesity and hepatic steatosis [19–21] . Although the spontaneous steatosis hereto described was only observed at old age , young Smurf1KO mice of the BL background were grossly normal except for a higher body fat content compared with their age- and background-matched WT counterparts and showed no sign of steatosis ( Fig 2A and 2B ) when fed on normal diet ( ND ) . Mice of this strain background are notoriously known for their resistance to HFD-induced obesity , as evident by the lack of apparent gain in body weight and ratio of fat-to-lean mass in young WT mice that were put on a HFD feeding regimen beginning at 10–12 weeks of age and continuing for 8 consecutive weeks ( Fig 2A , S2 Table ) . In contrast , HFD feeding significantly increased fat content in the Smurf1KO mice ( Fig 2A , S2 Table ) . Despite a lack of significant weight gain , HFD feeding did cause mild steatosis ( Fig 2A and 2B ) , as well as an increase in liver TG content in WT mice ( Fig 2C ) ; however , these changes were all dramatically exacerbated in BL-Smurf1KO mice ( Fig 2A–2C ) . As alluded earlier , Smurf1KO mice of the B6 background did not show accumulation of lipid droplets in the liver ( S1 Table ) , and they were not overweight or overtly obese either ( S2A Fig ) . To ascertain that the steatosis pheneotype was not a mere coincidence unique to the BL background , we carried out the HFD feeding study on WT and Smurf1KO mice of the B6 background with the same regimen as for the young BL mice . In contrast to BL mice , B6 mice gained body weight and fat content on HFD as expected , regardless of the presence of Smurf1 gene ( S2A Fig , S2 Table ) . However , the B6-Smurf1KO mice on HFD became ostensibly more obese ( S2A Fig ) and showed more severe lipid droplet accumulation in the liver compared to WT mice of the same background ( S2A and S2B Fig ) . In addition , the increase in the liver TG content was also more pronounced ( S2C Fig ) . Thus , the steatosis associated with Smurf1 loss is likely the result of an overall gain in body fat content in both strain backgrounds , suggesting that Smurf1 may have a systemic role in regulating lipid metabolism . To address if what we learned from the Smurf1KO mice is applicable to human populations , we took the advantage of the non-tumor liver tissue data sets compiled from a cohort of 247 Chinese liver cancer patients from the Liver Cancer Institute ( LCI ) [22] . According to the SMURF1 mRNA expression levels retrieved from the gene expression profile ( GEO: GSE14520 ) , we separated non-tumor liver tissue samples into the high SMURF1 expression ( top 25% ) group ( n = 61 ) and the low SMURF1 expression ( bottom 25% ) group ( n = 59 ) ( Fig 2D , left panel ) . We then graphed the body mass index ( BMI ) of these 120 patients against these two groups of SMURF1 expression , and found that patients with the low SMURF1 expression have a statistically significant higher BMI ( Fig 2D , right panel ) . It is worth noting that the average BMI of the Asian population is lower than that in the United States and European countries , and an Asian with BMI > 27 . 5 is considered obese [23 , 24] . This inverse correlation was further corroborated with non-tumor liver tissue data sets from the cancer genome atlas-liver hepatocellular carcinoma ( TCGA-LIHC ) ( Fig 2E ) . Because there are only 37 cases of non-tumor liver samples that have linked BMI values in the TCGA data set , the median SMURF1 expression level was used as the cutoff to plot BMI values ( Fig 2E ) . Because the BMI is widely used in clinics as a surrogate prognostic indicator for fatty liver [25 , 26] , these results suggest that low Smurf1 expression appears to be associated with high fat accumulation in humans , as well . To investigate the underlying causes of steatosis associated with Smurf1 loss , we compared hepatic gene expression profiles of 11-month-old Smurf1KO , Smurf2KO , and their respective matching WT mice from the BL background , and selected genes that showed either increased or decreased expression by a cutoff of 1 . 5-fold ( false discover rate [FDR] <0 . 1 ) . The results showed that 987 genes in the Smurf1KO livers were differentially expressed over their WT controls , whereas only 13 genes were differentially expressed in the Smurf2KO livers ( Fig 3A , left panel , and S3 Table ) . This result is in line with the notion that Smurf1 plays a more prominent role in the liver than Smurf2 . Many genes that are involved in the lipid metabolism were up-regulated in Smurf1KO livers , and the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway enrichment analysis of differentially expressed genes between Smurf1KO and WT livers revealed a number of metabolically relevant pathways ( Fig 3B ) . We were intrigued by the enrichment of the PPAR signaling pathway that has known strong effects on steatosis [4] . Of the three PPAR genes , Pparγ encodes two protein isoforms , PPARγ1 and PPARγ2 , whose mRNAs are transcribed from two separate promoters [27 , 28] . Quantitative real-time PCR ( qRT-PCR ) analyses showed severalfold increases of both Pparγ isoforms in the livers of aged Smurf1KO but not Smurf2KO mice ( Fig 3C ) . Interestingly , the expression of Pparα was not altered in the liver of any mouse examined ( Fig 3C ) . In young BL mice ( 10–12 weeks of age ) that had yet to develop steatosis , loss of Smurf1 increased the expression of total Pparγ ( about 1 . 57-fold ) when the mice were fed on ND ( S3A Fig ) , suggesting that Smurf1 has a direct causal effect on Pparγ expression . HFD feeding further exacerbated the difference of Pparγ expression to 3 . 42-fold between WT and Smurf1KO livers ( S3A Fig ) . On the other hand , no difference was observed in TNFα and F4/80 expression ( S3B Fig ) , two genes involved in inflammatory response , which is consistent with the absence of any inflammation in Smurf1KO mice ( S1 Table and S1 Data ) . Western blot analyses confirmed the corresponding up-regulation of the PPARγ protein in the livers of aged Smurf1KO mice ( Fig 3D ) . According to data from The Human Protein Atlas ( https://www . proteinatlas . org/ENSG00000198742-SMURF1/tissue ) , Smurf1 protein is highly expressed in visceral organs , but its expression levels in muscle and adipose tissues are extremely low or moderate , respectively . This likely accounts for the dramatic increase of PPARγ in the Smurf1KO livers , where Smurf1 function is expected to be robust . Consistent with tissue distribution of Smurf1 expression , qRT-PCR revealed that total Pparγ expression increased dramatically in the liver and WAT but did not change in the muscle of Smurf1KO mice ( Fig 3E ) . Finally , loss of Smurf1 cast a profound impact on the hepatic expression of PPARγ transcriptional target genes that are involved in fatty acid synthesis , uptake , and transport ( Fig 3F ) , thus lending further support to the activation of PPARγ and its signaling pathway in aged Smurf1KO mice . PPARγ is a strong lipogenic factor essential for steatosis [7] . Although our qRT-PCR analysis alluded that loss of Smurf1 has a direct causal effect on PPARγ1 up-regulation , further evidence is needed to confirm this finding . Toward this end , we silenced Smurf1 using short interfering RNA ( siRNA ) s in human hepatocarcinoma Hep3B cells and mouse normal hepatocyte AML12 cells . Relative to the effect by non-silencing control siRNA ( siNS ) , knockdown by siSmurf1 significantly increased the level of PPARγ but not PPARα or PPARδ in both cell lines ( Fig 4A ) . As expected , siSmurf2 had little effect in either of these two cell lines ( Fig 4A ) . Because Pparγ is a direct transcriptional target of itself in a positive feedback loop [29] , siRNA-mediated silencing of Smurf1 drastically increased the expression level of Pparγ , but not other paralogous Ppars or their transcriptional regulatory partners retinoid x receptor ( Rxr ) α and Rxrβ ( Fig 4B ) . In adipose tissues , transcription of Pparγ genes is under the control of CCAAT enhancer binding protein ( CEBP ) α/β [30 , 31]; however , we were unable to detect any increase of either Cebpα or Cebpβ mRNA by qRT-PCR ( S4A Fig ) , suggesting that the regulation of PPARγ by Smurf1 is by way of a C/EBPα/β-independent mechanism . In line with the low expression of PPARγ in AML12 cells , introducing siPPARγ showed little effect on the expression of PPARγ transcriptional target genes , Fabp1 , Cd36 , Acacb , and Apoc3 , but siSmurf1 significantly increased the expression of these genes ( Fig 4C , and S4B Fig ) . Furthermore , introducing siPPARγ completely blocked the enhancing effect of siSmurf1 ( Fig 4C ) , thus confirming the direct causal relationship between Smurf1 and PPARγ . The fact that up-regulation caused by siSmurf1was particularly pronounced in Fabp1 and Cd36 , two genes that are essential for fatty acid uptake [32 , 33] , suggested a strong connection between Smurf1 and fatty acid uptake . Indeed , using 3H-labelled palmitic acid as a tracer , we observed a 20% increase in fatty acid uptake by AML12 cells upon Smurf1 depletion ( Fig 4D ) . We also measured lipid synthesis in AML12 cells by measuring the incorporation of 3H-labelled acetate into lipids and found it was increased by siSmurf1 as well ( Fig 4E ) . Once again , these two effects of Smurf1 loss were specifically mediated through PPARγ as they were reversed by siPPARγ ( Fig 4D and 4E ) . To further show if Smurf1 actually regulates lipid metablism in vivo , we injected fluorescent 4 , 4-Difluoro-5 , 7-Dimethyl-4-Bora-3a , 4a-Diaza-s-Indacene-3-Hexadecanoic Acid ( BODIPY-FL-C16 ) into the peritoneal cavities of WT and Smurf1KO mice and found that the fatty acid uptake was greatly enhanced in the liver and WAT tissues but not the muscles of Smurf1KO mice compared with that of WT mice ( Fig 4F ) . We also repeated the 3H-labelled acetate incorporation experiment in primary hepatocytes isolated from WT and Smurf1KO mice and confirmed the enhancement effect of Smurf1 ablation on lipid synthesis ( Fig 4G ) . The increased body fat content in aged BL-Smurf1KO mice and HFD-fed young Smurf1KO mice from both background suggests that Smurf1 may also regulate adipogenesis . To determine if this was the case , we took advantage of an in vitro adipogenic differentiation system using 3T3-L1 pre-adipocytes . Following a 6-day differentiation protocol , both PPARγ1 and PPARγ2 as well as their target Cd36 were all induced , as shown by western blot analysis , and the induction was greatly enhanced by siSmurf1 but reversed by the double transfection of siSmurf1 and siPPARγ ( S5A Fig ) . In keeping with the western blot analysis results , Oil Red-O staining of these differentiated 3T3-L1 cells was also enhanced by siSmurf1 and reversed by siSmurf1 and siPPARγ double transfection ( S5B Fig ) . Finally , expression of a cohort of adipogenic target genes of PPARγ also followed the same pattern as influenced by siSmurf1 and siPPARγ ( S5C Fig ) . Taken together , these data indicate that Smurf1 has an intrinsic role in controlling adipogenesis and lipid metabolism through PPARγ . The WW domains of HECT E3 ligases recognize a PPxY ( PY ) motif that is present in the primary sequence of many of their targets [34] . There is one such sequence motif in both human and mouse PPARγ but not in PPARα , which might potentially account for the lack of an effect on this closely related protein by the loss of Smurf1 ( Fig 3C ) . By co-immunoprecipitation experiments , we found that endogenous Smurf1 interacted specifically with PPARγ in the AML12 cells ( Fig 5A ) , and the PY motif of PPARγ contributed to the interaction , because removing it considerably weakened the interaction between Myc-tagged Smurf1 and FLAG-tagged PPARγ , as assayed in transiently transfected AML12 cells ( Fig 5B ) . Also in AML12 cells , Smurf1 but not Smurf2 showed the propensity to ubiquitinate both PPARγ1 and PPARγ2 isoforms ( Fig 5C ) . The substrate and enzyme relationship was further demonstrated in Smurf1KO mouse embryonic fibroblasts ( MEFs ) , in which exogenous Smurf1 but not the catalytically inactive Smurf1 C699A ( CA ) mutant ubiquitinated PPARγ ( Fig 5D ) , as well as in a reconstituted in vitro reaction with recombinant Smurf1 and PPARγ ( Fig 5E ) . Finally , the ubiquitin chain of the modified PPARγ is likely of the K63 linkage , as only the ubiquitin mutant with a single lysine residue at the amino acid residue position 63 supported the polyubiquitination of PPARγ in the reconstituted in vitro reaction , whereas other single-lysine ubiquitin mutants with lysine at other positions did not ( Fig 5F ) . In light of this result and the fact that co-expressing Smurf1 with PPARγ did not alter the stability of the latter ( Fig 5B and 5C ) , we concluded that Smurf1 mediates a non-proteolytic ubiquitin modification of PPARγ . PPARs recognize a consensus sequence of PPAR response element ( PPRE ) that consists of two AGGTCA-like sequences arranged in tandem with a single nucleotide spacer and is present in all PPAR target gene promoters [35 , 36] . In AML12 cells , where PPARγ expression is very low , overexpressing Smurf1 had little effect on a luciferase reporter driven by PPRE , whereas both PPARγ1 and PPARγ2 significantly activated it; however , co-expressing Smurf1 with either PPARγ1 or PPARγ2 severely curtailed their transcriptional activity ( Fig 6A ) . Because Smurf1 has no effect on PPARγ protein levels per se ( Fig 6A , right panel ) , these results suggested that Smurf1 inhibits the transcriptional activity of PPARγ . The regulation by Smurf1 depends on its E3 ligase activity because a ligase-deficient mutant , Smurf1CA , could not reverse the activation of PPRE-luc by PPARγ ( Fig 6B ) . Chromatin immunoprecipitation ( ChIP ) experiments on Pparγ1 , Pparγ2 , and Fabp1 promoters indicated that the binding of PPARγ to these promoters was blocked when it was co-expressed with Smurf1 ( Fig 6C ) . Once again , the E3 ligase activity of Smurf1 is required for its ability to block DNA binding of PPARγ ( Fig 6C ) . ChIP experiments performed in liver extracts isolated from WT and Smurf1KO mice also revealed a much stronger binding of PPARγ to its own Pparγ1 and Pparγ2 promoters , as well as its target Fabp1 promoter in the absence of Smurf1 ( Fig 6D ) , thus lending further support to Smurf1 regulating transcriptional activity of PPARγ . To directly test if the increased PPARγ activity and expression are responsible for steatosis associated with Smurf1 loss , we treated a group of WT and Smurf1KO mice from the BL background with the PPARγ antagonist GW9662 [37] . The compound was administered by intraperitoneal injection starting at 7–9 months of age , and the treatment lasted for 2 months; in this time period , the steatosis was expected to fully develop in Smurf1KO mice . The GW9662 treatment decreased body weight of both WT and Smurf1KO mice ( Fig 7A ) , but because the average beginning weight of Smurf1KO mice was higher , the reduction thereof was more dramatic than that of the WT controls ( about 10% reduction versus about 5% ) . The body fat mass content in Smurf1KO mice was also significantly lowered , to an extent that was comparable to that of the untreated WT mice ( Fig 7B ) . Commensurate to the systemic reduction in obesity , the lipid droplets were essentially cleared from Smurf1KO livers by GW9662 ( Fig 7B and 7C ) . Although the GW9662 treatment caused no significant change in the serum TG and CHO levels ( Fig 7D ) , hepatic contents of TG , CHO , and FFA were all reduced to normal levels ( Fig 7E ) and so was hepatic expression of Pparγ2 , as well as several PPARγ target genes ( Fig 7F ) . These results unequivocally demonstrated that the elevated PPARγ activity and expression account for the NAFLD phenotypes observed in Smurf1KO mice .
PPARγ is a nuclear hormone receptor with principle functions of increasing insulin sensitivity and promoting lipid storage in adipose tissues [6] . In the liver , the physiological function of PPARγ is less clear , although its expression is associated with injury-induced activation of hepatostellate cells and provides an anti-fibrogenic protection [3 , 5] . PPARγ up-regulation is also a known property of steatotic livers , and liver-specific disruption of PPARγ was reported to protect leptin-deficient mice or HFD-fed mice from developing fatty liver [7–9] . Here , we show that mice deficient for HECT-domain E3 ligase Smurf1 in the mixed BL genetic background develop hepatosteatosis spontaneously as they age or are more susceptible to HFD-induced hepatosteatosis . These mutant mice are overweight and obese , as well as glucose intolerant and insulin resistant . These NAFLD phenotypes can be attributed to the heightened transcriptional activity of PPARγ , which in turn increases the expression of itself and genes involved in lipogenesis and fatty acid transport via a positive feedback loop . We further demonstrate that Smurf1 catalyzes the K63-linked non-proteolytic ubiquitination that normally attenuates PPARγ transcriptional activity , and show an inverse correlation between low SMURF1 expression and high BMI values in human patients . This investigation thus reveals a previously unknown mechanism that regulates the lipogenic activity of PPARγ and sheds light on a new role of Smurf1 in NAFLD pathogenesis . Different HECT E3 ligases are known to catalyze ubiquitination with different ubiquitin chains that mark modified protein substrates for different fates [38] . Members of the neural precursor cell expressed developmentally down-regulated protein 4 ( NEDD4 ) family E3 ligases preferentially support monoubiquitin modification or K63-linked chains associated with non-proteolytic functions , but can also assemble lysine 48 ( K48 ) -linked chains that target proteins for proteasome-mediated degradation [39] . As members of this E3 ligase family , Smurf1 and Smurf2 have been shown to target many proteins for K48-linked ubiquitination and degradation [16] . Smurf2 was also shown to induce multi-monoubiquitin modification of Smad3 , thereby inhibiting Smad3 activity [40] , but the K63-linked ubiquitination by Smurfs has not been reported in mammalian species . Recently , NEDD4 itself was shown to induce both K48- and K63-linked ubiquitination of PPARγ in adipocytes , with different functional outcomes [41] . In our study , Smurf1 inhibits PPARγ activity , and deletion of Smurf1 enhances PPARγ activity and up-regulates PPARγ levels through a positive feedback mechanism . In contrast , NEDD4 was shown to stabilize PPARγ , and knockdown of NEDD4 reduced PPARγ expression [41] . Moreover , the PY motif in PPARγ played a role in mediating interaction with Smurf1 , but it was not demonstrated for NEDD4 as such . Perhaps these apparent discrepancies reflect the differences in experimental conditions conducted in different cell types , or the mixed linkages in ubiquitin chains formed by NEDD4 could have compounded the functions of modified PPARγ . In any event , the steatosis observed in Smurf1KO mice is consistent with the heightened PPARγ activity in the liver . Despite the conspicuous steatosis , overweight , and obesity that were present in 75% aged BL-Smurf1KO mice , their liver functions were nevertheless normal . Because these animals were well shielded from inflammatory insults by their accommodative housing facility , it is likely that the elevated PPARγ activity unleashed by the loss of Smurf1 was only sufficient to manifest a restricted impact in bringing about the early-stage NAFLD phenotypes . Future studies are necessary to ascertain the tissue origin of the steatogenic effect of Smurf1 ablation using conditional knockout approaches and to determine if and how BL-Smurf1 mice could be enticed to progress through NASH or even liver cancer to model the entire NAFLD disease spectrum . Regulation of PPARγ by the Smurf1-mediated K63-linked ubiquitin modification centers on its transcriptional activity . Because PPARγ is also a transcriptional target of its own , a disturbance of Smurf1 would create an “all or none” effect: a rise or fall of Smurf1 across a threshold level would either maximize or minimize PPARγ activity . This scenario may normally operate to keep the lipogenic activity of PPARγ to a minimum in the liver but maximized in the adipose tissues . Epidemiology studies indicate that an estimated 27%–34% of the general population within North America have NAFLD [42] , for which there is no approved treatment available at present . Current NAFLD drug developmental effort centers on repurposing fibric acid derivatives , which are lipid-lowering PPARα agonists and insulin sensitivity–improving PPARγ agonists , thiazolidinediones , but the clinical trials yielded mixed results [3 , 4] . Because of the opposite actions of PPARα and PPARγ on hepatic steatosis , the “spillover” effects of these PPAR agonists might prevent a net gain in their ability to reduce TG accumulation in the liver . As to PPARγ agonists , although clinical trials for rosiglitazone in patients with type 2 diabetes reported improvement of steatosis by a median of 20% during the first year , no further improvement was found after 2 additional years of treatment , and the trials exposed severe cardiovascular risks and weight gain [43] . Intuitively , it is possible that the benefit is derived from the systemic lipid clearance by increased fat storage in adipose tissue , because PPARγ is normally expressed in adipose tissues , and its activation in the liver was clearly linked to fatty liver formation . Given our current finding of Smurf1 in protecting the liver from steatosis , a viable strategy to treat NAFLD may be to curtail the transcriptional activity of PPARγ by turning on Smurf1-mediated non-proteolytic ubiquitin modification .
All mice were maintained and handled under protocols ( LCMB-014 , ASP 10–214 , 13–214 , 16–214 ) approved by the Animal Care and Use Committee of the National Cancer Institute , National Institutes of Health ( NIH ) , according to NIH guidelines . Generation of Smurf1KO and Smurf2KO mice in the mixed BL and pure C57BL/6N ( B6 ) background was described previously [18 , 40] . For spontaneous hepatosteosis development , animals were maintained on a ND , monitored weekly , and euthanized and necropsied at 9–12 months of age . For the HFD treatment , male mice were maintained on a ND until 10–12 weeks of age before they were given HFD ( Research Diets , Cat# D12266B ) containing 16 . 8% kcal protein , 31 . 8% kcal fat , and 51 . 4% kcal carbohydrate for 8 weeks . For the GW9662 treatment , a dose of 1 mg/kg of GW9662 dissolved in DMSO was injected intraperitoneally ( i . p . ) into 7–9-month-old BL-WT and BL-Smurf1KO mice for 5 days per week for 2 months . Age and sex of mice used in these studies are listed in S1 Table . Body composition was determined using an EchoMRI mouse scanner ( EchoMRI , Houston , TX ) . Mouse liver and epididymal fat pad were dissected , weighed , then either snap-frozen in liquid N2 or fixed in 10% neutral buffered formalin prior to paraffin embedding . Frozen liver tissues were used for Oil Red-O staining . Liver and fat tissue histology were read by board-certified veterinary pathologists in the Pathology and Histotechnology Laboratory of the Frederick National Laboratory for Cancer Research . Serum TG , CHO , and albumin concentrations , as well as ALT and AST activities were measured by standard methods with a Vitro 250 dry slide analyzer ( Ortho Clinical Diagnostics ) in the Pathology and Histotechnology Laboratory of the Frederick National Laboratory for Cancer Research . Liver TG , CHO , and FFA concentrations were determined using the EnzyChrom TG , CHO , and FFA assay kits ( Bioassay Systems ) after extracting total lipids from 50-mg liver tissues as described [44] . To perform the gluclose tolerance test ( GTT ) or insulin tolerance test ( ITT ) , mice were fasted overnight before receiving an i . p . injection of 20% glucose ( 2 g/kg body weight ) or recombinant insulin ( Humulin R , 0 . 75 U/kg; Lily ) , respectively . Blood samples were collected from the tail 0 , 0 . 5 , 1 , 2 , and 4 hours later , after injection for analysis using the Accu-Chek Compact Plus blood glucose meter ( Roche Diagnostics ) . AML12 cells ( ATCC CRL-2254 ) were cultured in DMEM/F12 supplemented with 10% fetal bovine serum ( FBS ) , 0 . 005 mg/mL insulin , 0 . 005 mg/mL transferrin , 5 ng/mL selenium , and 40 ng/mL dexamethasone . Hep3B cells were cultured in MEM supplemented with 1% Non-Essential Amino Acids ( NEAA ) and 10% FBS . Smurf1KO MEFs were cultured in DMEM supplemented with 10% FBS . Primary hepatocytes were isolated by a two-step collagenase perfusion of the liver and cultured as described [45] . Flag-tagged PPARγ1 , PPARγ2 plasmids , and PPRE-Luc reporter plasmids were obtained from Addgene . Flag-tagged PPARγ2ΔPY plasmid was generated using Site Directed Mutagenesis Kit ( Agilent Technologies ) . Myc-tagged Smurf1 , Smurf2 , and Smurf1CA mutant , HA-tagged Ubiquitin plasmids were described before [13 , 18 , 46] . Anti-Smurf1 ( Novus , 1D7 ) ; anti-Smurf2 ( Abcam , EP629Y3 ) ; anti-PPARγ ( Santa Cruz , sc-7273 ) ; anti-PPARα ( Rockland , 600-401-4215 ) ; anti-PPARδ ( ThermoFisher , PA1-823A ) ; anti-HSC70 ( Santa Cruz , B-6 ) ; Anti-Flag-Peroxidase ( A8592 , Sigma ) ; anti-HA ( Covance , HA11 ) ; and anti-Myc ( Santa Cruz , 9E10 ) were used for western blotting and immunoprecipitation . Knockdown experiments were performed using the following siRNAs: siPPARγ ( J-040712-05 and J-040712-07 , Dharmacon ) . Validated siSmurf1 , siSmurf2 and siNS were previously described [47 , 48] . Lipogenesis assay in AML12 cells and primary hepatocytes were performed using 3H-acetate as described [45] . Fatty acid uptake assay in AML12 cells was performed in 12-well plates . Briefly , AML12 cells were incubated with assay buffer ( Hanks’ balanced buffer containing 1% BSA and 5 μCi/mL 3H-palmitic acid ) for 60 minutes at 37°C . The cells were then washed twice with ice-cold PBS and lysed with 0 . 3 M NaOH . The radioactivity of the cell lysates was measured by liquid scintillation counting . In vivo fatty acid uptake assays were performed as described [49] . Briefly , mice were i . p . injected with BODIPY-FL-C16 ( Life Technologies ) after being fasted for 4 hours , then were euthanized at 5 hours after injection; liver , epididymal fat pad , and skeletal muscle were collected . Fluorescence was analyzed from cleared tissue homogenate using a plate reader and normalized to tissue weight . Preadipocytes 3T3-L1 ( ATCC , CL-173 ) were cultured in basal medium ( DMEM supplemented with 10% FBS ) . Two days after transfection with siRNA , basal media were changed to differentiation media ( day 0 ) , which is DMEM supplemented with 10% FBS , 0 . 5 mM IBMX , 1μM dexamethasone , and 4 μg/mL insulin , for 2 days , then replaced with basal media with 2 μg/mL insulin for another 4 days . After 6 days of differentiation , cells were harvested for protein and mRNA analysis or subjected to Oil Red staining . The purified recombinant PPARγ ( 0 . 25 μg ) ( Abcam , ab81807 ) and His6-Smurf1 ( 1 . 5 μg ) were used in in vitro ubiquitination assay , which was carried out for 1 hour at 37°C in 30 μl reaction buffer supplemented with 2 mM Mg-ATP , 1 μg E1 , 1 μg of recombinant UbcH5c , and 20 μg HA-ubiquitin or HA-ubiquitin variants ( all from Boston Biochem ) . Total RNA from AML12 cells or liver tissues was extracted by RNeasy Mini Kit ( Qiagen ) according to the manufacturer’s instructions . High Capacity Reverse Transcription Kit ( ABI , Life Tech ) was used to generate cDNA from RNA ( 500–2 , 000 ng ) . qRT-PCR was performed with Power SYBR Green PCR Master Mix ( Life Technologies ) using specific oligonucleotide primers as specified ( S4 Table ) . ChIP assays were carried out with an EZ-ChIP Chromatin Immunoprecipitation Kit ( Millipore ) according to the manufacturer’s instructions . Immunoprecipitations were carried out using anti-PPARγ antibody ( Abcam , A3409A ) and an isotype-matched IgG as the control . Reporter assays were performed in 12-well plates using PPRE-Luc ( 0 . 5 μg ) and pRL-TK ( 0 . 2 μg ) reporter plasmids , and the luciferase activities were determined using Dual Luciferase Reporter Assay System ( Promega ) . Microarray experiments for mouse liver tissues were performed on Affymetrix GeneChip Mouse Gene 1 . 0 ST arrays according to the standard Affymetrix GeneChip protocol at the Affymetrix service core in the Frederick National Laboratory for Cancer Research . The raw array data were then analyzed with packages oligo and lima under R platform , as described before [50 , 51] , to identify differentially expressed genes among groups ( fold > 1 . 5 , FDR cutoff = 0 . 1 ) , and results were visualized using VennDiagram ( https://cran . r-project . org/web/packages/VennDiagram ) and gplots ( https://cran . r-project . org/web/packages/gplots ) under R platform . Data were submitted to GEO ( accession number GSE113995 ) . KEGG pathway analysis was performed by gage package , as described [52] , to identify significantly enriched pathway ( FDR q-value cutoff = 0 . 1 ) between Smurf1KO and WT liver samples . The microarray analysis for human liver tissues from the LCI cohort of 247 Chinese patients was previously published [22] and data are accessible through GEO ( accession number GSE14520 ) . TCGA non-tumor liver tissue gene expression data were downloaded from TCGA-LIHC ( https://portal . gdc . cancer . gov ) . Unless indicated in the figure legends , two-tailed Student t test was used for statistical analysis . | Nonalcoholic fatty liver disease ( NAFLD ) is a disease associated with abnormal fat accumulation in the liver and other metabolic symptoms . Among its many social–behavioral and genetic causes , dysregulation of peroxisome proliferator-activated receptor γ ( PPARγ ) is an investigative focal point for therapeutic intervention . This lipid-sensing nuclear receptor plays a major role in promoting lipogenesis in adipose tissues , whereas its expression is low in the liver . We show here that in the absence of ubiquitin ligase ( E3 ) Smurf1 , PPARγ expression increases dramatically in the liver , causing fatty acid uptake and fat accumulation in hepatocytes . We also found that the low SMURF1 expression in human populations correlates with high body mass index ( BMI ) values . We demonstrate that Smurf1 catalyzes the lysine 63 ( K63 ) -linked non-proteolytic modification of PPARγ that suppresses the transcriptional activity of PPARγ and breaks the positive feedback loop governing its own expression . Our data further indicate that treating this mouse model with a PPARγ antagonist , GW9662 , completely reverses the fat accumulation in the liver . |
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We demonstrate that both Hepatitis C virus ( HCV ) and Bovine Viral Diarrhea virus ( BVDV ) contain regions in their 5’ UTRs that stall and repress the enzymatic activity of the cellular 5’-3’ exoribonuclease XRN1 , resulting in dramatic changes in the stability of cellular mRNAs . We used biochemical assays , virus infections , and transfection of the HCV and BVDV 5’ untranslated regions in the absence of other viral gene products to directly demonstrate the existence and mechanism of this novel host-virus interaction . In the context of HCV infection , we observed globally increased stability of mRNAs resulting in significant increases in abundance of normally short-lived mRNAs encoding a variety of relevant oncogenes and angiogenesis factors . These findings suggest that non-coding regions from multiple genera of the Flaviviridae interfere with XRN1 and impact post-transcriptional processes , causing global dysregulation of cellular gene expression which may promote cell growth and pathogenesis .
Hepatitis C virus ( HCV ) is a positive-sense RNA virus of the Hepacivirus genus within the Flaviviridae that chronically infects approximately 130–150 million people worldwide . It has a 9 . 6 kb genome that encodes a single large polyprotein that is processed to form 10 proteins [1] . Chronic HCV infection causes acute liver dysfunction , cirrhosis and is associated with the development of hepatocellular carcinoma ( HCC ) . From a molecular perspective , although several viral proteins have been suggested to contribute to HCV-associated carcinogenesis [2] , it is not clear how infection with a simple RNA virus that does not encode any known oncogenes causes such a complex and heterogeneous cancer . Thus a deeper understanding of the molecular interaction between HCV RNA and protein components with cellular factors is needed to shed light on molecular mechanisms of viral pathogenesis . In addition to encoding a large polyprotein from its single open reading frame , HCV contains complex untranslated regions ( UTRs ) at both the 5’ and 3’ ends . The 3’ UTR is capable of interacting with numerous cellular proteins and contains several regions that are important for viral translation efficiency [3–5] , RNA synthesis [6] and providing communication between the 5’ and 3’ portions of the viral genome [7] . The 5’ UTR is better characterized and harbors four conserved stem loops ( denoted as stem loops I-IV from the 5’-3’ direction ) with several noteworthy features . First , while stem loops I and II are involved in replication [8] , stem loops II-IV of the 5’ UTR ( plus a short region of coding sequence ) form a highly structured internal ribosome entry site ( IRES ) that is required for HCV translation initiation [9] . Second , the cellular small microRNA miR-122 interacts with two regions near the 5’ end of the HCV genomic RNA [10–15] . Instead of repressing gene expression as one might expect from a miRNA , miR-122 actually stimulates viral gene expression and replication . This appears to be due in part to miR-122 interactions with the 5’ UTR that prevent access of the viral RNA to the cellular 5’-3’ exoribonuclease XRN1 [16–18] . Since targeting miR-122 is currently in clinical trials as a potential anti-HCV therapy [19] , it appears that the viral RNA on its own may be highly targeted by XRN1 during infection . The regulation of cellular mRNA abundance is not solely dependent on transcriptional efficiency , but rather also relies on differential mRNA stability to determine the abundances of transcripts in response to cellular and environmental signals [20] . Studies have demonstrated that between ∼20 and 50% of gene expression may be regulated post-transcriptionally at the level of mRNA decay [21–26] . Furthermore , RNA decay also plays a major role in the quality control of cellular gene expression [27] . The major pathway of general mRNA decay is usually initiated by a deadenylation event followed by exonucleolytic decay of the body of the mRNA [28] . In the 5’-3’ decay pathway , deadenylated mRNAs are first decapped and the resulting 5’ mono-phosphorylated mRNA is rapidly degraded by the highly processive 5’-3’ exonuclease XRN1 [29 , 30] . In the alternative 3’-5’ pathway , deadenylated transcripts are attacked by either the exosome complex or the 3’-5’ exonuclease DIS3L2 [31] . The exonucleolytic decay of deadenylated mRNAs is highly efficient in mammalian cells and it is very rare to observe decay intermediates . Given the importance of the cellular mRNA decay machinery in regulating the quality and quantity of gene expression , any perturbation of this machinery could have significant consequences for cellular physiology . Thus viral interference with the decay machinery—perhaps as a natural strategy to stabilize viral transcripts during infection—could cause major disruptions in cellular gene expression patterns . Studies are emerging to suggest that this could be a major strategy employed by a variety of viruses to both maintain efficient viral replication as well as perhaps contribute to viral-induced cytopathology by disrupting cellular gene expression [32] . Arthropod-borne members of the Flaviviridae ( e . g . West Nile virus ( WNV ) and the Dengue viruses ( DENV ) ) have been shown to contain a conserved structure at the beginning of their 3’ UTRs that plays an important role in viral biology [33] . This region of the 3’ UTR folds into an interesting three helix junction that stalls the cellular XRN1 enzyme as it tries to degrade flaviviral transcripts [34 , 35] . The stalling of XRN1 at this structure results in the accumulation of large amounts of a short 3’ UTR-derived subgenomic flavivirus ‘sfRNA’ during infection [36 , 37] . The generation of sfRNA appears to be important for the success of a flavivirus infection as it modulates host RNAi and interferon-responses [38–40] . Furthermore , generation of sfRNA represses the activity of XRN1 both in infected cells and in cell-free assays [41] . The generation of sfRNA and repression of XRN1 in WNV and Dengue virus type 2 ( DENV-2 ) infections is associated with a significant stabilization of cellular mRNAs [41] . Interestingly , HCV and other non-arthropod associated members of the Flaviviridae such as the economically important Bovine Viral Diarrhea virus ( BVDV ) [42] do not generate an sfRNA-like molecule from their 3’ UTRs [36] . However , significant changes in cellular gene expression occur in both HCV and BVDV infections [43] . Thus it is unclear whether or how these viruses interface with XRN1 and the cellular mRNA decay machinery . To address this question , in this study we demonstrate that HCV and BVDV contain structured regions in their 5’ UTR near or including the IRES region that both stall and repress XRN1 activity . XRN1 repression by the 5’ UTRs of these viruses can be demonstrated both in biochemical assays as well as in living cells . Interestingly , HCV or BVDV repression of XRN1 is associated with a dramatic repression of the major 5’-3’ decay pathway and a large increase in the stability and abundance of numerous normally short-lived cellular mRNAs . From a pathogenic standpoint , it is interesting to note that the mRNAs of many cellular oncogenes and angiogenic factors are significantly stabilized and increased in abundance during HCV infection . Therefore we conclude that XRN1 repression is a highly conserved and important facet of infections by disparate members of the Flaviviridae .
Previous studies on arthropod-borne members of the Flaviviridae indicated that XRN1 stalls on specific structures at the proximal side of the 3’ UTR to generate a short RNA referred to as sfRNA [34 , 35] . As seen in Fig . 1A , XRN1 stalling/sfRNA generation can be recapitulated from the 3’ UTRs of Japanese Encephalitis virus ( JEV ) or DENV-2 in cell free systems using either HeLa cytoplasmic extracts ( Fig . 1A upper panel ) or purified recombinant XRN1 enzyme ( Fig . 1A lower panel ) . While the generation of sfRNA by XRN1 in DENV-2 has been demonstrated several times [34 , 36 , 37 , 41] , the biogenesis of sfRNA from JEV by XRN1 has not been formally documented . Importantly , recent collaborative work indicated that XRN1 stalling by these viral 3’ UTRs is due to novel interwoven pseudoknots around a conserved three-way junction [35] . Non-arthropod-borne members of the Flaviviridae , namely the hepaciviruses ( e . g . HCV ) and the pestiviruses ( e . g . BVDV ) do not make a detectable short sfRNA during infection from their 3’ UTRs [36] . Since XRN1 stalling is a conserved property of a large number of other members of the Flaviviridae [33] , we hypothesized that HCV and BVDV might use structures elsewhere in their genomes such as the coding region or the 5’ UTR in order to stall XRN1 . These HCV and BVDV 5’ UTRs , after all , are very highly structured and contain IRES elements that direct initiation of translation [44] . If this hypothesis were correct , XRN1-mediated decay intermediates that result from enzyme stalling on HCV and BVDV mRNAs would be nearly full length in size ( ∼10Kb ) , and thus difficult to detect without specifically assessing the 5’ end of these viral transcripts . Interestingly , a previously reported 5’ end analysis of HCV RNAs during infection by circularization RT-PCR revealed large numbers of HCV RNAs with 5’ ends that were truncated to around 55/56 and 80 nucleotides from the bona fide 5’ end [14] . These nested sets of 5’ shortened HCV mRNAs could be consistent with XRN1 decay intermediates generated by the stalling of the enzyme at structures in the 5’ UTR . To formally test the hypothesis that XRN1 stalls in the 5’ UTR of HCV and BVDV , we created RNA substrates that have a 5’ mono-phosphate ( and are therefore susceptible to XRN1 digestion ) and contained either an HCV/BVDV 5’ UTR or control sequences . These RNAs were incubated in XRN1 exonuclease assays either using HeLa cytoplasmic extract ( Fig . 1B upper panel ) or purified recombinant XRN1 ( Fig . 1B , lower panel ) using incubation times to optimize 5’-3’ exonucleolytic decay . HeLa cytoplasmic extracts were used in these studies due to their well-characterized usage in RNA decay studies ( e . g . [41] ) . However it should be noted that they may also contain XRN2 activity and this 5’-3’ exonuclease may also be contributing to the decay observed in these assays [45] . As seen in Fig . 1B , RNA substrates that contained either the HCV or BVDV 5’ UTRs generated XRN1-mediated decay intermediates . For HCV , three degradation intermediates were identified by cloning and sequencing with 5’ ends at approximately 5 , 45 and 123 bases from the normal 5’ end of the genomic RNA . BVDV RNA degradation yielded two decay intermediates , a strong band at ∼70 nt and a weaker but reproducible stop at ∼137 nt from the 5’ end of the genome . The positions of these XRN1 decay stall sites on the HCV and BVDV 5’ UTRs are indicated in Fig . 2 . Notably , the stall sites are all positioned at or near predicted sites of structural landmarks in both RNAs [46] . Importantly , no XRN1 decay intermediates are seen with numerous control RNAs in these assays ( Figs . 1B , S1A and [47] ) . The observation of decay intermediates when using purified XRN1 and purified RNA implies that the RNA by itself is sufficient to stall XRN1 and no additional RNA-protein interactions are needed . In order to confirm that the HCV or BVDV 5’ UTR by itself in the absence of other components of a viral infection in living cells could stall XRN1 , we inserted the 5’ UTRs into a reporter downstream of a GFP open reading frame and transfected the expression constructs ( or a control reporter that lacked any viral sequences ) into 293T cells . A siRNA complementary to the GFP portion of the reporter RNA was added to cleave the transcripts and generate a large pool of RNA substrates available for XRN1 digestion in transfected cells [48] . As seen in the northern blots in Fig . 1C , while the control GFP construct did not generate any mRNA decay intermediates , both the HCV- and BVDV-containing GFP reporter constructs generated mRNA decay intermediates that contained viral RNA sequences . These intermediates were consistent with XRN1 stalling at or near domain II in the HCV 5’ UTR by RNase protection mapping of the 5’ end . The reason we see only one apparent decay intermediate likely includes gel resolution and/or changes in local RNA structure due to context/protein-RNA interactions on the reporter mRNA . Cell-type-specific alterations in the pattern of sfRNA decay intermediates from XRN1 stalling on the yellow fever virus 3’ UTR [37] . Therefore , we conclude that the 5’ UTRs of both HCV and BVDV transcripts contain sequences and/or structures that are capable of stalling the XRN1 enzyme . Thus , it appears that many if not all members of the Flaviviridae may use a related strategy of XRN1-refractory segments in an untranslated region of their transcripts to stall this highly processive cellular exoribonuclease . In addition to stalling XRN1 , we previously demonstrated that XRN1 enzymatic activity is repressed by the generation of sfRNA from WNV or DENV-2 3’ UTRs [41] . These observations are now extended to include sfRNA generation from the 3’ UTR of JEV in Fig . 3A . Briefly , a radiolabeled reporter RNA containing a 5’ mono-phosphate was efficiently degraded by XRN1 in HeLa cytoplasmic extracts in the absence of any competitor RNA ( ‘none’ lanes ) or in the presence of a control RNA competitor ( ‘Control RNA’ lanes ) . However , RNA competitors containing either the 3’ UTR of JEV or the 3’ UTR of DENV-2 dramatically repressed XRN1 activity and this sfRNA-mediated repression was reversible ( Fig . 3B ) . When the rate of RNA decay was measured relative to increasing concentrations of XRN1 enzyme , the rate of the reaction slowed in the presence of an sfRNA generating competitor RNA relative to control conditions , but the enzyme was not effectively titrated away by the RNA inhibitor as would be expected for an irreversible competitor . To assess whether sequences/structures in the 5’ UTR of HCV and BVDV that stall XRN1 can also repress its activity , similar competition assays were performed as in Fig . 3A . As seen in Fig . 3C , both HCV and BVDV RNAs significantly repressed XRN1 activity in HeLa cytoplasmic extracts compared to control RNA . While the XRN1 repression that was observed with the HCV or BVDV 5’ UTR competitors was substantial , it was consistently approximately half as robust as that observed with DENV-2 . To confirm that XRN1 can be repressed by HCV during infection , the relative levels of uncapped cellular mRNA were measured in naïve Huh7 . 5 cells , cells infected with HCV ( JFH-1 strain ) for 72 hours , or Huh7 . 5 cells that stably harbored an HCV replicon ( with the H77 strain 5’ UTR ) [49] . Uncapped mRNAs serve as a sensitive readout for XRN1 activity since they naturally represent a very small percentage ( ∼3% ) of the total mRNA population in a cell . As seen in Fig . 3D , there was a significant increase in uncapped FOS and TUT1 mRNAs ( two representative short-lived transcripts ) as a consequence of HCV infection . Furthermore , Huh7 . 5 cells harboring the HCV replicon RNA also had significantly more uncapped FOS and TUT1 mRNAs than naïve control cells . Finally , the levels of Xrn1 protein remain unchanged in HCV infected cells ( S1B Fig and [50 , 51] ) . Thus we conclude that XRN1 activity is repressed when it stalls while attempting to degrade the 5’ UTRs of HCV and BVDV . The repression of XRN1 activity by HCV or BVDV infection suggests that cellular mRNA decay may be dramatically dysregulated during viral infection . To test this hypothesis , we measured mRNA half-lives of a representative cellular mRNA ( TUT1 ) in mock infected and HCV or BVDV infected cells . As seen in Fig . 4A & C ( right panels ) , the TUT1 mRNA was significantly stabilized during HCV or BVDV infection . Importantly , TUT1 stabilization was observed in the presence of a truncated sfRNA-like HCV or BVDV RNA as determined by RNase protection assays using a probe that spanned the entire IRES element of each virus ( Fig . 4A &C , left panels ) . To determine whether the stabilization of the TUT1 mRNA could be directly attributed to XRN1 inhibition by the 5’ UTR of HCV or BVDV rather than other viral-specific aspects of infection , we transfected human 293T cells with reporter constructs expressing either GFP mRNA only or a GFP reporter mRNA containing viral 5’ UTR sequences inserted downstream of the open reading frame . We placed the viral 5’ UTR at this downstream location for two reasons . First , a bona fide RNA domain that stalls XRN1 should function regardless of where it is placed in a transcript . Second , placement near the 3’ end provided for easy detection of relatively short decay intermediates on standard acrylamide gels . 293T cells were used in this experiment due to their high transfection efficiency . As seen in Fig . 4B & D ( right panels ) , TUT1 mRNA was significantly stabilized ( approximate two-fold increase in mRNA half-life ) by the reporter RNA containing the HCV or BVDV 5’ UTR sequence in association with the build-up of truncated sfRNA-like reporter RNAs ( left panels ) . Thus , we conclude that the presence of RNAs containing the HCV or BVDV 5’ UTR has a dramatic effect on TUT1 mRNA stability in host cells . Finally , we performed a similar transfection experiment using the GFP mRNA constructs +/− the HCV 5’ UTR in 293T cells that were also transfected with either an empty vector ( pLKO . 1 ) or a vector expressing an shRNA to knock down XRN1 . As seen in Fig . 4E , knockdown of XRN1 on its own results in the significant stabilization of the cellular TUT1 mRNA ( p<0 . 05; see also [41] ) . Furthermore , when XRN1 knockdown cells are transfected with the GFP construct containing the HCV 5’ UTR , we failed to observe any significant further increase in TUT1 mRNA stability as is observed when cells are treated with a control shRNA vector ( pLKO . 1 ) . Collectively , these data suggest that the increase in cellular mRNA stability that is associated with the presence of the HCV 5’ UTR requires XRN1 . In order to expand the observation of viral 5’ UTR-induced repression of XRN1/mRNA stabilization , we measured global mRNA decay rates in mock infected and HCV infected Huh7 . 5 cells after 120 hours at which point the infection rate was ∼70% . One hour prior to RNA extraction , cells were pulse labeled with 4-thiouridine ( 4sU ) to mark nascent transcripts . Three populations were isolated and sequenced: total RNA , 4sU-labeled nascent transcripts and unlabeled transcripts . Categorizing the mapped reads from each population revealed three important features: nascent transcripts were enriched for pre-mRNA , as seen by the increase in the presence of intronic reads ( S2A Fig ) ; HCV infection did not appear to interfere with global mRNA processing in the infected cell ( S2A Fig ) ; and the relative abundances of RNA in each replicate sample clustered well ( S2B Fig ) . These analyses suggest that we generated a high quality data set for further examination . Total mRNA abundances , transcription rates and half-lives were determined as previous described [52 , 53] and compared between mock and HCV infected samples ( S4 Table ) . Importantly , the changes in specific mRNA levels that we observed correlated well with a data set obtained at similar times of HCV infection by Walters et al . [43] ( S2C Fig ) . Interestingly , in both replicates we observed an overall increase in cellular mRNA abundance in HCV infected cells compared to mock infected cells without a concomitant increase in transcription rate prior to normalization ( Fig . 5A ) . Rather , there was a global increase in mRNA stability directly associated with the global increases in mRNA abundance seen in HCV infected cells ( Fig . 5A ) . A broad increase in mRNA abundance caused by XRN1 repression would be expected to affect mRNAs with short half-lives more dramatically than mRNAs with long half-lives . As shown in Fig . 5B , the increase in mRNA abundance scaled directly with the stability of the mRNA prior to infection . For all mRNAs with calculated half-lives of less than 4 hours , each progressive decrease in stability resulted in a significant increase in abundance . This was associated with similar progressive increases in stability but not transcription ( Fig . 5B , center and right panels ) . Thus , we conclude that HCV infection causes a significant dysregulation of mRNA stability whose effects are most strongly observed in mRNAs that have a short half-life in uninfected cells . We further investigated the Gene Ontology ( GO ) of differentially regulated mRNAs in mock versus infected cells using InnateDB [54] . RNAs that increased in abundance were enriched for innate immune responses and transcription factors ( Fig . 5C ) . These functional groups tend to consist of unstable mRNAs [55 , 56] . Interestingly , none of the GO categories that were significantly enriched in the up-regulated mRNA abundance dataset reached significance in the up-regulated mRNA transcription dataset ( Fig . 5C ) . By comparison , all GO categories significantly enriched in down-regulated mRNA abundance were also significant in the down-regulated mRNA transcription set ( Fig . 5D ) . This suggests decreases in mRNA abundance are primarily transcriptionally driven , while increases in mRNA abundance are not . To confirm our observations regarding dramatic increases in mRNA abundance and stabilization in HCV infected cells , we repeated mRNA half-life analysis with a more classical actinomycin D transcriptional shut off approach in mock-treated or HCV infected cells and analyzed changes in RNA abundance over a time course using qRT-PCR assays . We selected unstable innate immunity and transcription factors , focusing on three oncogenes ( MYC , JUN and FOS ) and three angiogenic factors ( VEGFA , HIF1A and CXCL2 ) . Since HCV infection is associated with the development of hepatocellular carcinoma , these factors might contribute to the development of a diseased state . As seen in Fig . 6 and in S1 and S2 Tables in S1 Text , all six of these mRNAs were significantly increased in abundance 2–4 fold ( panel A ) as well as significantly stabilized ( mRNA half-lives increased ∼2-fold ) ( panel C ) in HCV infection after 72 hours . Finally , Gene Set Enrichment Analysis ( GSEA ) demonstrated a significant enrichment in upregulated mRNAs in HCV infection for genes that have promoter binding sites for AP-1 ( JUN and FOS heterodimer ) and MYC ( S4 Fig ) . Thus the stabilization of these oncogenic transcription factor mRNAs also appears to lead to the generation of functional protein which further dysregulates cellular gene expression during viral infection . To further assess whether the increases in abundance and stability we observed reflected functional mRNAs rather than the accumulation of non-functional uncapped mRNA decay intermediates resulting from XRN1 repression , we determined the relative levels of capped and polyadenylated mRNAs in mock versus HCV infected cells . As shown in Fig . 6B , there was a significant 2–3 fold increase in the abundance of intact and presumably fully translatable levels of mRNAs of the six oncogene and angiogenic factors assayed above . Thus , in addition to significant increases observed in the minor population of uncapped mRNAs in infected cells ( Fig . 3D ) , there was also a dramatic increase in intact mRNAs ( the major mRNA species ) due to infection . We therefore suggest that by repressing XRN1 during HCV infection , a feedback mechanism takes place that appears to repress the entire 5’-3’ decay pathway , preventing the initial steps of decay that involve deadenylation and decapping . Finally , to generalize our observations regarding dysregulation of cellular mRNA stability in an HCV infection to another non-arthropod-borne member of the Flaviviridae , we assessed the abundance and stability of select cellular mRNAs in BVDV infected MDBK cells . As shown in Fig . 7A , the abundance of both FOS and JUN mRNAs was significantly upregulated ( three to six fold ) in a BVDV infection . Furthermore , the proteins encoded by these mRNAs were also upregulated in a BVDV infection ( Fig . 7B ) ( and a similar increase in protein expression was seen in HCV infection ( S4 Fig ) . As seen in Fig . 7C , the increase in abundance of the FOS and JUN mRNAs can largely be accounted for by the approximately five and three-fold significant increase in mRNA half-life in a BVDV infection , respectively . Importantly , XRN1 levels are not significantly changed during BVDV infection ( S1B Fig ) . Thus , we conclude that dysregulation of cellular mRNA stability is a common occurrence in hepacivirus and pestivirus infections and may play a heretofore unforeseen role in the success of viral replication as well as viral-induced pathogenesis .
In addition to the well-described role of the 5’ UTR IRES element in translation [9] and the binding of miR-122 to two sites in the HCV 5’ UTR to influence RNA stability [13 , 57] , the data described in this study uncover an additional , unexpected role for the 5’ UTRs of HCV and BVDV in repressing the cellular XRN1 exoribonuclease . These observations indicate that the stalling/repression of XRN1 by a non-coding region in the genomes of members of the Flaviviridae is an evolutionarily conserved strategy for promoting the successful outcome of an infection . The stalling of XRN1 , its subsequent repression and the implications of repressing this major cellular mRNA decay enzyme on gene expression in infected cells is summarized in Fig . 8 . This conserved aspect of flavivirus infections suggests a potentially attractive antiviral target for therapeutics that may have broad-spectrum activity against members of the Flaviviridae . Arthropod-borne members of this virus family use structures in their 3’ UTRs to perhaps generate large amounts of a non-coding sfRNA to provide additional functions such as modulating the invertebrate RNAi anti-viral response [33] . Large amounts of sfRNA produced in these infections could also allow an optimal interaction between the virus and its insect vector , including cellular protein binding/sequestration [58] . The selective pressure from the RNAi pathway , however , is likely not as great for the Flaviviridae members that do not use insect infections as part of their transmission cycle [40] . Thus , viruses like HCV and BVDV can readily use structures in the 5’ UTR instead of structures in their 3’ UTR , resulting in larger , nearly intact XRN1 decay intermediates when compared to the plus-sense genomic RNA . This strategy , particularly with the presence of an IRES element that has its main structural features localized to the distal half of the 5’ UTR [9] , may maintain additional functionality to the XRN1-decay intermediates that are generated , perhaps including translatability . While the unique three-helix junction structure that stalls XRN1 on the 3’ UTR of flaviviruses has recently been solved [35] , the relationship with the structure in the 5’ UTR of HCV and BVDV that blocks the enzyme await further study . The RNA structure around the IRES region is rather complex in both HCV and BVDV , including pseudoknots that are also a hallmark of flaviviral 3’UTR structures [59] . In addition , an extended set of small conserved nucleotide blocks that was noted among XRN1-stalling region of the insect-borne flaviviruses [35] can also be identified in the HCV 5’ UTR . Another interesting question for future analysis is whether stalling of XRN1 is a general property of all or at least some other viral IRES elements . If so , this may reveal an interesting general strategy used by RNA viruses to effectively address two major , often functionally interrelated aspects of post-transcriptional gene expression in the cytoplasm—translation initiation and RNA stability . XRN1 repression , however , is not a strategy used by all RNA viruses . In our previously reported studies with Sindbis virus [60] , we see no evidence of massive stabilization of cellular mRNAs as we do with HCV infections . In fact , we see mRNA destabilization due to the usurping of the cellular HuR factor by Sindbis virus . The presence of miR-122 , which interacts with two regions near the proximal end of the HCV 5’ UTR [13] , could possibly have an influence on XRN1 repression and will require additional experimentation . However based on the ability of the HCV 5’ UTR to repress the activity of XRN1 in HeLa cytoplasmic extracts which do not contain appreciable amounts of miR-122 ( Fig . 3 ) , at this juncture we favor a model based on the available literature that the interaction of miR-122 with the HCV 5’ UTR makes the viral RNA refractory to XRN1 loading ( either directly or by inhibition of an unidentified cellular phosphatase that acts on the 5’ trisphosphate ) , resulting in viral RNA stabilization . In this model , any population of HCV RNA that does not effectively interact with miR-122 , however , would be a substrate for initiation of XRN1-mediated decay and would contribute to the stalling and repression of the cellular enzyme . Thus the HCV 5’ UTR may have a bipartite strategy for interfacing with XRN1—ensuring the availability of intact genomic RNA for replication and packaging through miR-122 interaction while allowing a portion of its RNA to lure the enzyme to initiate decay in order to repress XRN1 activity . Ultimately , this repression of XRN1 dysregulates cellular mRNA decay and likely prevents or significantly alters changes in gene expression which promote the proper cellular response to infection . Repressing XRN1 appears to also repress the deadenylation and decapping aspects of the 5’-3’ mRNA decay pathway , resulting in the increased accumulation of capped and polyadenylated mRNAs during HCV infection . We envision the apparent shut down of the entire decay pathway by the 5’ UTR of HCV/BVDV could be due to at least three mechanisms ( or a combination thereof ) . First , XRN1 has been shown to physically interact with the DCP1 component of the decapping complex [61] . Thus XRN1 repression by stalling on the 5’UTR of viral RNAs could also sequester/alter the localization of the decapping complex in an unproductive fashion . Likewise , deadenylation could be physically co-localized with XRN1 as well through interactions between hPAT1 , the CAF1-CCR4-NOT deadenylation complex and the decapping complex [62] . Second , flavivirus infections , including HCV , have been shown to disrupt P bodies [50] . The disruption of these localized sites of aggregated mRNA decay components could contribute to the shutdown of the entire 5’-3’ decay pathway . Finally , XRN1 has been recently implicated as a key factor in mediating the buffering between the rates of transcription and mRNA decay for genes in order to maintain appropriate levels of gene expression in yeast [63 , 64] . Thus it is possible that by repressing XRN1 , HCV and other flaviviruses are disrupting this natural aspect of buffering in cellular gene expression for at least some transcripts that ultimately contributes to their increased abundance . Did flaviviruses independently evolve the ability to stall XRN1 , or do perhaps select subsets of cellular mRNAs possess XRN1-refractory structures that allow the generation of RNAs with novel 5’ ends that may have independent functions from their parent mRNAs ? Note that the XRN1 decay intermediate observed in Fig . 4 were generated from a bona fide mRNA made from a transfected reporter plasmid . Since most cellular RNAs would be vastly less abundant than viral RNAs in the cell , the amount of repression of XRN1 by the reversible , presumably slow release of the stalled enzyme on these RNAs observed in cells would likely be minimal and perhaps worth the expense in cells to generate novel RNAs . The dysregulation of cellular mRNA stability and the associated increase in the abundance of numerous usually short lived mRNAs has significant implications for pathologies associated with HCV infection . Chronic HCV infection has been strongly linked to development of HCC . Along these lines , it is interesting to note that XRN1 has been previously implicated as a possible tumor suppressor gene in osteosarcoma [65] . While the causes of HCC are likely complex , involving inflammatory and fibrotic responses to the injured liver , dysregulated expression of short-lived mRNAs in HCV-infected cells leading to elevated expression of oncogenic factors may contribute to HCC .
In vitro XRN1-mediated RNA decay assays were performed using internally radiolabeled , 5’ monophosphorylated RNAs as previously described [41] . The 200nt fragment of the JEV 3’ UTR , the 218nt portion of the DENV-2 3’ UTR , the 389nt HCV 5’ UTR , the 440nt BVDV 5’ UTR and control RNAs used in study are described in the Supplementary methods ( S1 Text ) . Huh7 . 5 , MDBK and HEK 293T cells were used in the study for HCV infection , BVDV infection and transfection analyses respectively . For transfections , the HCV or BVDV 5’ UTR were subcloned into the NotI site of peGFP-N1 and introduced into cells using Lipofectamine 2000 . Total RNA was isolated with TRIzol and RNAs were quantified by qRT-PCR , northern blotting or RNase protection as described in detail in the Supplementary methods . mRNA abundance , synthetic rate and stability were determined using actinomycin D shut off and RT-qPCR or metabolic 4sU labeling and RNAseq as described in detail in the Supplementary methods . Total RNA from naïve Huh7 . 5 cells , HCV infections or Huh7 . 5 cells harboring a replicon HCV construct was fractionated into capped and uncapped pools using an antibody that recognizes the methyl-guanosine cap structure ( Synaptic Systems ) as described in Moon et al . [41] . Please see the Supplementary Methods section of S1 Text for additional details . Western blotting was performed as described in Barnhart et al . [60] as outlined in the Supplementary methods . | Understanding how a persistent virus like Hepatitis C Virus ( HCV ) interfaces with the cellular machinery during infection can provide significant insights into mechanisms of pathogenesis . We demonstrate that while trying to degrade HCV transcripts , a major cellular exonuclease called XRN1 stalls and gets repressed in the 5’ noncoding region of the viral mRNA . Interestingly , the region where XRN1 stalls in the 5’ UTR includes the viral IRES that is required for translation initiation , uncovering a novel , unexpected function for this well-studied region . Differential mRNA stability is a major regulator of gene expression in cells . Curiously , repression of the cellular XRN1 exonuclease is associated with a general repression of mRNA decay in general in HCV-infected cells . Thus numerous cellular mRNAs are stabilized and accumulate in a dysregulated fashion during HCV infection . Normally short-lived mRNAs are disproportionately affected—including mRNAs that encode immune regulators and oncogenes . Thus , this study suggests a novel role for the 5’ UTR of HCV in molecular pathogenesis—including hepatocellular carcinoma . Furthermore , the 5’ UTR of Bovine Viral Diarrhea virus also represses the XRN1 enzyme and stabilizes cellular mRNA . Therefore a strategy of 5’ UTR-mediated XRN1 repression appears to be conserved among the vector-independent members of the Flaviviridae . |
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It has long been known that loss of the retinoblastoma protein ( Rb ) perturbs neural differentiation , but the underlying mechanism has never been solved . Rb absence impairs cell cycle exit and triggers death of some neurons , so differentiation defects may well be indirect . Indeed , we show that abnormalities in both differentiation and light-evoked electrophysiological responses in Rb-deficient retinal cells are rescued when ectopic division and apoptosis are blocked specifically by deleting E2f transcription factor ( E2f ) 1 . However , comprehensive cell-type analysis of the rescued double-null retina exposed cell-cycle–independent differentiation defects specifically in starburst amacrine cells ( SACs ) , cholinergic interneurons critical in direction selectivity and developmentally important rhythmic bursts . Typically , Rb is thought to block division by repressing E2fs , but to promote differentiation by potentiating tissue-specific factors . Remarkably , however , Rb promotes SAC differentiation by inhibiting E2f3 activity . Two E2f3 isoforms exist , and we find both in the developing retina , although intriguingly they show distinct subcellular distribution . E2f3b is thought to mediate Rb function in quiescent cells . However , in what is to our knowledge the first work to dissect E2f isoform function in vivo we show that Rb promotes SAC differentiation through E2f3a . These data reveal a mechanism through which Rb regulates neural differentiation directly , and , unexpectedly , it involves inhibition of E2f3a , not potentiation of tissue-specific factors .
The simplicity of the retina makes it an ideal tissue to study neurogenesis . Its development proceeds through three overlapping steps starting with retinal progenitor cell ( RPC ) proliferation , followed by birth of post-mitotic retinal transition cells ( RTCs , also referred to as precursors ) , and ending with terminal differentiation of seven major cell types ( Figure 1A ) [1] . RPCs are multipotent and exit the cell cycle to generate different RTCs at specific time periods in development [1] . This process of RTC “birth” requires coupling of differentiation and cell cycle exit . Once born , post-mitotic RTCs migrate and form different retinal layers . Rods and cones make up the outer nuclear layer ( ONL ) ; horizontal , bipolar , and amacrine cells , as well as Müller glia cell bodies , reside in the inner nuclear layer ( INL ) ; and ganglion and displaced amacrine cells form the ganglion cell layer ( GCL ) ( Figure 1A ) . The outer plexiform layer ( OPL ) and inner plexiform layer ( IPL ) house synaptic connections separating the ONL/INL and INL/GCL , respectively . The retinoblastoma protein ( Rb ) is critical for cell cycle exit during retinal transition cell birth . Rb knockout ( KO ) RTCs continue to proliferate inappropriately and some ( rod , ganglion , and bipolar cells ) die by apoptosis [2 , 3] . Rb controls the cell cycle by binding and inhibiting E2f transcription factors ( E2fs ) ( Figure 1B ) , first defined as transcription factors that bind adenoviral E2 regulatory elements and subsequently shown to be critical cell cycle regulators [4 , 5] . E2fs bind to DNA as heterodimers with proteins of the related Tfdp family . E2f1 , E2f2 , and E2f3a are “activating E2fs” that are required for fibroblast division . They are strong transcriptional activators that can drive G0 fibroblasts into cycle , and are inhibited when bound to Rb [4 , 5] . Ectopic division in Rb KO embryos can be rescued to various extents in different tissues by knocking out E2f1 , E2f2 , or E2f3 [6–9] , but which member ( s ) drive division in Rb KO RTCs is unknown . Other members of the family , such as E2f4 and E2f5 , are known as “repressive E2fs” because they are weak activators and appear to be primarily involved in gene silencing in quiescent or differentiated cells . Activating E2fs may also promote apoptosis in the Rb KO retina ( Figure 1B ) . Originally , E2f1 was considered the primary pro-apoptotic member of the family [10] . However , this view was reevaluated when it was shown that either E2f1 or E2f3 deletion rescues apoptosis in the developing central nervous system ( CNS ) of Rb KO embryos [6 , 11] . Subsequently , CNS apoptosis was shown to be an indirect result of placental defects and probable hypoxia [12–14] . Indeed , E2f3-induced apoptosis in fibroblasts has recently been shown to require E2f1 [15] . Thus , it is controversial whether E2f3 is required for apoptosis of any Rb KO cell type . Determining which activating E2fs promote death in distinct Rb KO tissues requires conditional rather than germ line models of Rb deletion to avoid secondary indirect effects ( such as hypoxia ) . E2f family diversity is expanded by E2f3 isoforms . Alternative promoters generate two forms ( a and b ) that are identical except for distinct first exons [16] . E2f3a is a strong activator , and , like other activating E2fs , its expression is induced when quiescent cells are stimulated to divide [16] . E2f3b , like repressive E2fs , is present in both quiescent and dividing cells , and in quiescent fibroblasts it associates primarily with Rb , suggesting that it mediates repression [16–18] . Indeed , silencing the Cdkn2d ( p19Arf ) locus in unstressed cells relies on E2f3b [19] . Other E2fs may also exist in isoforms since at least two mRNA species have been detected for E2f1 and E2f2 [16] . The roles of E2f isoforms in vivo are unknown . E2fs are also regulated by subcellular localization . Although this feature has been best characterized for repressive E2fs [20–22] , it also affects activating E2fs [23–25] . The distribution of E2f isoforms has never been assessed . It has been known for many years that Rb loss perturbs neuronal differentiation [26–29] . However , prior work could not exclude the possibility that differentiation defects are simply an indirect consequence of abnormal division and death . If Rb does regulate differentiation directly it is unclear whether it does so in all or a subset of neurons . Moreover , the mechanism has never been solved . In other cell types where Rb may promote differentiation directly , such as muscle and bone , it seems to do so through E2f-independent means by potentiating tissue-specific transcription factors ( Figure 1B ) [30–33] . In the retina , others have noted abnormally shaped Rb KO rods and have suggested Rb may directly promote their morphogenesis by activating retina-specific factors [29] . However , differentiation defects in any Rb KO neuron could be an indirect effect of ectopic division and/or apoptosis ( Figure 1B ) . Thus , it is critical to study differentiation of Rb KO cells in the absence of ectopic proliferation and death . Here , we establish that Rb suppresses RTC division and death by inhibiting E2f1 , not E2f2 or E2f3 . When these defects were rescued , most retinal neurons , including rods , survived , differentiated , and functioned normally . Thus , unexpectedly , retina-specific differentiation factors function independently of Rb . However , comprehensive assessment of the Rb/E2f1 double-null rescued retina revealed a differentiation defect in cholinergic starburst amacrine cells ( SACs ) . Recent breakthroughs have revealed that these interneurons are critical for direction selectivity and developmentally important rhythmic bursts [34–36] . However , their differentiation is poorly understood . Contrary to the prevailing view that Rb promotes differentiation through E2f-independent tissue-specific transcription factors , we show that Rb facilitates SAC development through E2f3 . Defects in Rb null SACs correlated with specific E2f3 expression in these cells , and E2f3 expression was absent in neurons that differentiated without Rb . E2f3 is also present in a specific subset of other CNS neurons , implying that this may be a general mechanism by which Rb facilitates neurogenesis . To define the mechanism in even more detail , we determined which E2f3 isoform Rb targets to control SAC differentiation . E2f3b mediates Rb function in quiescent fibroblasts [19] , yet no prior studies to our knowledge have dissected E2f3a or E2f3b functions in vivo . Using an isoform-specific null mouse we show that Rb drives SAC differentiation through E2f3a . Thus , independent of E2f1-mediated effects on division and death , Rb does regulate neuronal differentiation , but only in specific neurons and , unexpectedly , through E2f3a , not tissue-specific differentiation factors .
We used the α-Cre transgene to delete floxed Rb exon 19 at embryonic day ( E ) 10 in peripheral retina [2] . RbloxP/loxP;α-Cre mice were bred with strains lacking E2f1 or E2f2 in the germ line , or a strain carrying a floxed E2f3 allele [5] . RbloxP/loxP;E2f1+/− and RbloxP/loxP;E2f1+/−;α-Cre mice were bred to produce RbloxP/loxP;E2f1−/−;α-Cre mice at a frequency of 1/8 and littermate controls at the same or higher ( 1/4 ) frequency . For simplicity we will refer to the RbloxP/loxP;E2f1−/−;α-Cre peripheral retina as the Rb/E2f1 double knockout ( DKO ) retina . Similar strategies were employed in the case of E2f2 or E2f3 . Cre-mediated excision of Rb and E2f3 alleles in the retina was confirmed by PCR as described previously [2 , 5] . To measure ectopic cell division , mice were pulse-labelled with bromodeoxyuridine ( BrdU ) 2 h before sacrifice and the peripheral retina analyzed for BrdU incorporation by immunofluorescence . As reported before [2 , 3] , Rb KO retinas exhibited both spatial and temporal ectopic DNA synthesis ( Figures 1C and S1A ) . This is easily detected at E14 , E16 , and postnatal day ( P ) 0 in the inner retina where abnormal BrdU+ ganglion and amacrine RTCs are located , or on the outermost region of the P0 retina , where BrdU+ photoreceptor RTCs reside ( Figures S1A and S2 , arrows ) [2] . Ectopic RTC division in Rb KO retinas is even more obvious at P8 or P18 , when division is completed in wild-type ( WT ) retina ( Figures 1C and S1A ) . Strikingly , the ectopically positioned S-phase cells at E14 , E16 , and P0 and all the abnormal division at P8 and P18 were completely suppressed in the Rb/E2f1 DKO retina ( Figures 1C , 1E , 1F , S1A , and S2 ) . In contrast , deletion of E2f2 or E2f3 had no effect at any stage of development . Analysis of mitotic cells with anti–phosphohistone 3 ( PH3 ) –specific antibodies confirmed that loss of E2f1 , but not E2f2 or E2f3 , suppressed ectopic division ( Figure S3 ) . Deleting one E2f1 allele partially suppressed ectopic S-phase and mitosis in Rb KO RTCs ( Figures 1C , 1E , 1F , S1A , S2 , and S3 ) , suggesting that E2f1 drives ectopic division in Rb KO RTCs in a dose-dependent fashion . These data contrast with previous findings in the lens and CNS of Rb KO embryos , where deletion of any activating E2f suppresses ectopic division to some extent [6–9] . Loss of Rb in the retina results in considerable RTC apoptosis , eliminating most bipolar and ganglion cells as well as many rods ( Figure 2A–2D ) [2 , 3] . The loss of Rb KO rods is evident from the thinner ONL , and the death of these cells as well as bipolar and ganglion neurons can be detected directly by double labelling for apoptotic and cell-type-specific markers [2] ( M . P . and R . B . , unpublished data ) . Loss of peripheral Rb KO ganglion cells is also evident from thinning of the optic nerve ( D . C . and R . B . , unpublished data ) . Deleting E2f1 , but not E2f2 or E2f3 , blocked this ectopic cell death in a dose-dependent fashion ( Figures 1D , 1G , and S1B ) . To investigate the molecular mechanism that underlies the unique role of E2f1 , we assessed the expression of known E2f targets as well as other genes that regulate the cell cycle and apoptosis . Numerous positive and negative cell cycle and apoptotic regulators were up-regulated in the Rb KO retina ( Figure 1H ) . Among the E2f family , E2f1 , E2f2 , E2f3a , and E2f7 were induced following Rb loss , but E2f3b , E2f4 , and E2f5 were unaffected . Consistent with the BrdU and terminal dUTP nick-end labelling ( TUNEL ) analyses , E2f1 deletion specifically reversed all these molecular defects , but E2f3 deletion had no effect ( Figure 1H ) . Because E2f1 deletion blocks abnormal division and death in the Rb KO retina , the Rb/E2f1 DKO retina provided a unique opportunity to evaluate whether Rb controls differentiation independent of cell cycle effects . The Rb/E2f1 DKO retina had many Sag+ ( S-antigen/rod arrestin ) photoreceptors , Pou4f2+ ( Brn3b ) ganglion cells , and numerous Prkca+/Cabp5+ bipolar neurons ( Figure 2A–2D ) . In contrast , there was no such rescue of cell types in Rb/E2f2 or Rb/E2f3 DKO retinas ( Figure S4 ) . Analysis with general neuronal markers Mtap2 ( MAP2 ) and Snap25 , as well as other markers expressed in bipolar cells ( Chx10 , Rcvrn , Vsx1 , Tacr3 , and Atp2b1 ) and rod photoreceptors ( Rho and Rcvrn ) confirmed rescue of the Rb/E2f1 DKO retina ( Table S1 ) . Moreover , neurons exhibited the same complex morphology as in WT retina . Bipolar cell bodies were located in the INL and had ascending and descending processes ending in the OPL and IPL , respectively ( Figure 2A ) . In addition , the Rb/E2f1 DKO retina had a healthy appearing ONL consisting of morphologically normal rods with descending processes ending in the OPL and ascending processes that terminated in inner and outer segments ( Figure 2A ) . It was suggested that Rb might regulate photoreceptor differentiation , possibly through rod-specific transcription factors ( Figure 1B ) [29] . However , if Rb does regulate photoreceptor differentiation , it does so by inhibiting E2f1 , not by potentiating rod differentiation factors , such as Otx2 , Crx , or Nrl . It is impossible to tell whether E2f1 perturbs differentiation directly , by affecting the expression of genes that modulate maturation , and/or indirectly through its effects on proliferation and survival ( Figure 1B ) . As with ectopic division and apoptosis ( Figure 1C and 1D ) , the rescue of Rb KO retinal bipolar , ganglion , and rod cells was dependent on E2f1 dose ( Figure 2A–2D ) . Separate from its role in driving ectopic division of Rb KO RTCs , E2f1 also promotes normal RPC division since in its absence RPC proliferation drops ~2-fold ( D . C . and R . B . , unpublished data ) . This modest reduction of RPC numbers in the absence of E2f1 accounts for the slight reduction in the number of ganglion cells at P0 , in the number of bipolar cells at P18 or P30 , and in the thickness of the ONL at P18 or P30 in the E2f1 KO and Rb/E2f1 DKO retina ( Figure 2B–2D ) . The morphology of E2f1 KO neurons was WT ( Figure 2A ) . Despite a slight drop in absolute cell numbers , the proportion of Rb/E2f1 DKO and E2f1 KO bipolar cells was the same as WT ( data not shown ) . Slightly reduced cell numbers were not due to residual RTC death since we have not observed ectopic apoptosis at any embryonic or postnatal stage in the developing Rb/E2f1 DKO retina ( Figures 1D , 1G , and S2 ) . Moreover , deleting Ccnd1 , which acts upstream of Rb proteins , also reduces RPC number , but does not suppress any defect in the Rb KO retina ( D . C . and R . B . , unpublished data ) . Thus , slightly reduced RPC division and dramatic rescue of severe defects in Rb KO RTCs are distinct effects stemming from the deletion of E2f1 . The discovery that E2f1 loss rescues even the morphology of Rb KO neurons is surprising because Rb is thought to regulate differentiation primarily through E2f-independent pathways [30–33] . However , normal morphology may not equate to completely normal differentiation . Thus , we compared the electroretinograms ( ERGs ) of adult WT ( α-Cre ) , E2f1−/− , α-Cre;RbloxP/loxP , and α-Cre;RbloxP/loxP;E2f1−/− mice . ERGs functionally assess visual signalling in the mammalian retina from photoreceptors to amacrine cells ( but usually not gangion cells ) , and are dominated by rod and cone bipolar cells . Typically , an ERG signal begins with a negative deflection initiated by the photoreceptors ( the a-wave ) , which is terminated by a large positive deflection due to the activation of ON bipolar cells ( the b-wave ) . Responses to dim light in dark-adapted ( scotopic ) conditions specifically assess the rod system , and were defective in the Rb KO retina ( Figure 2E ) . The substantial reduction of both a- and b-waves is consistent with rod and bipolar cell apoptosis [2] . The sensitivity of the residual response appeared unchanged , suggesting it arose from the Cre-negative portions of the retina . Responses were about the same in the WT and E2f1 KO retina , and , most importantly , also the Rb/E2f1 DKO response median lay at the lower end of the normal range for most intensities ( Figure 2F ) . Thus , E2f1 deletion almost completely rescued the rod system in the Rb KO retina . Light-adapted ( photopic ) recordings to specifically assess the cone system yielded comparable results . Cones represent only 3% of photoreceptors and , unlike rods , develop without Rb , but they require rods for survival , and in the Rb KO retina , they have abnormal morphology and their synaptic targets , bipolar cells , are much depleted [2] . The photopic response , a product of cone and mainly bipolar activity , was much reduced by Rb loss , but was rescued considerably in the Rb/E2f1 DKO retina ( Figure S5 ) . Again , the median amplitude lay at the lower end of the E2f1 KO range . The photopic response in E2f1 KO mice was slightly reduced relative to WT ( Figure S5B ) , possibly because E2f1 is required for maximal expansion of embryonic RPCs , and the E2f1 KO retina has , as noted earlier , slightly fewer cells than the WT retina , although cell type proportions are unaffected ( D . C . and R . B . , unpublished data ) . Thus , marginally subnormal photopic responses in the Rb/E2f1 DKO retina can be attributed to both a reduction of cone numbers in E2f1 KO mice alone , and a “genuine” slight reduction in cone function attributable to Rb loss relative to WT . This slight effect may relate to a true differentiation defect in a subset of amacrine cells discussed below . This discussion should not obscure the major outcome that E2f1 deletion recovers most of the ERG response . Thus , E2f1 deletion not only rescues morphology but also both rod and cone system function in the Rb KO retina . ERGs primarily assess photoreceptor and bipolar cell function , but may miss differentiation defects in other cells . To test for subtle differences we stained the Rb/E2f1 DKO retina with 43 markers ( Table S1 ) . Thirty-two proteins displayed identical patterns in WT , E2f1 KO , and Rb/E2f1 DKO retina ( Table S1 ) . The other 11 markers revealed a cell-cycle– and apoptosis-independent differentiation defect in SACs . We first studied Calb2 ( calretinin ) , which marks a subset of amacrine and ganglion cell bodies as well as three tracks corresponding to their processes in the IPL ( Figure 3A ) . Normal Calb2 staining was seen in the E2f1 KO IPL ( data not shown ) . However , only one Calb2+ track was evident in the Rb KO IPL , and this defect was not rescued in the Rb/E2f1 DKO retina ( Figure 3A ) . We quantified Calb2+ cell bodies in the Rb KO INL ( corresponding to amacrine cell staining only ) and observed a reduction from P8 onwards ( Figures 3C and S6 ) . Of the three Calb2+ tracks in the IPL , the two outer tracks are from SACs , named after their extensive dendritic-tree-like morphology [37] . SACs are cholinergic , represent ~5 . 2% of amacrine neurons [38] , and are critical for both direction selectivity [34 , 35] and spontaneous rhythmic activity that occurs during normal retinal development [36] . SACs in the INL synapse in the OFF layer of the IPL that responds to decreasing light , while displaced SACs in the GCL have processes that synapse in the nearby ON layer of the IPL that responds to increasing light ( reviewed in [39] ) . Mature SAC processes stain specifically for Slc18a3 ( vesicular acetyl choline transporter , VAChT ) [37] , and , significantly , this marker was absent in the peripheral Rb KO or Rb/E2f1 DKO P18 retina ( Figures 3A and S7B ) . Chat , expressed from the same locus , is also SAC specific , but marks both cell bodies and processes of mature SACs [37] . Chat was seen in fewer cells in the mature Rb KO retina , and was present in the soma but absent from processes ( Figure 3B ) . We obtained similar results for Sv2c , a synaptic vesicle protein found in SACs [40]; Kcnc1b and Kcnc2 , potassium channels expressed on SAC soma and dendrites as well as a very small number of ganglion cells [41]; gamma-aminobutyric acid ( GABA ) , an inhibitory neurotransmitter present in about half of amacrine cells including SACs , as well as horizontal and some bipolar neurons [37]; and Calb1 ( calbindin ) , which is expressed in many amacrine cells and labels SAC process faintly ( Figure S7A and S7B; Table S1; and data not shown ) . Finally , we also examined the effect of Rb deletion on SAC differentiation using a Chx10-Cre transgene that is expressed in a mosaic pattern across the retina , generating patches of Cre-expressing cells [42] . Consistent with the mosaic deletion pattern , we observed markedly reduced Chat/Slc18a3 staining in the IPL of Chx10-Cre;RbloxP/loxP retina compared to WT ( Figure S7C ) . Together , these results suggest a role for Rb in SAC differentiation . The above findings could indicate a defect in SAC specification , SAC survival , or the levels and/or transport of the markers described above . Camk2a marks both SACs and ganglion cells [37] , but because ganglion cells are eliminated in the Rb KO retina , Camk2a is a specific SAC marker in this context . Importantly , Camk2a+ tracks and dendrites were present in both the WT and Rb KO retina ( Figure 3B ) , and the number of Camk2a+ soma was similar in WT and Rb KO retina at P30 and beyond , although fewer cells stained in Rb KO retina at P18 , suggesting a delay in its appearance ( Figures 3C and S6B ) . Thus , Rb is not required for SAC survival or for process outgrowth , but rather it seems to regulate the expression and/or stability of Calb2 , Calb1 , Chat , Slc18a3 , Sv2c , Kcnc1b , Kcnc2 , and GABA in SACs , but leaves Camk2a expression virtually unaffected . The presence of Chat in some cell bodies but never in processes ( Figure 3B ) also suggests a transport defect . The developmental pattern of Slc18a3 expression also supported this notion . In mature WT SACs Slc18a3 is only in processes , but in early postnatal SACs , it is found in the cell body , and moves into emerging processes at approximately P4–P6 . As noted above , Slc18a3 was absent at P18 in the Rb KO retina ( Figure 3A ) ; at P4 or P5 it was in cell bodies , yet was rarely present in Rb KO processes ( Figures 4A and S6 ) . Slc18a3 became virtually undetectable in Rb KO SACs by P8 ( Figures 3C and S6C ) . These data suggest that Rb affects both the synthesis/stability and transport of SAC markers . Rb binds more than 100 proteins [43] and in some non-neuronal cells , such as skeletal muscle , adipocytes , and bone , Rb is thought to bind and potentiate tissue-specific transcription factors that promote differentiation [31–33] . Thus , we expected that Rb might interact with retina-specific factors to facilitate SAC differentiation . A direct role for E2f in mediating Rb-dependent differentiation defects ( independent of cell cycle or death defects ) has to our knowledge not been described , but because E2f can regulate some differentiation genes [44–48] , we first tested whether E2f2 or E2f3 might perturb Rb KO SAC maturation . At multiple time points , E2f1 deletion suppressed ectopic mitosis ( PH3+ cells ) , but did not reverse the SAC defect , and E2f2 deletion had no effect on either defect ( Figure 4A ) . Remarkably , although E2f3 deletion did not reverse ectopic mitosis , it rescued Calb2 , Slc18a3 , Chat , GABA , Kcnc1b , Kcnc2 , and Sv2c staining at multiple times ( Figure 4A and data not shown ) . Rb/E2f3 DKO SAC tracks were slightly more disordered than in WT retina , most likely because of the absence of synaptic partner cells , which are killed by E2f1 . Indeed , this minor defect was rescued in the Rb/E2f1/E2f3 triple knockout retina , where bipolar and ganglion cell death was rescued and SAC differentiation was restored ( Figure 4A ) . E2f3 deletion alone did not affect SAC differentiation ( Figure 4A ) ; thus , it is unleashed E2f3 activity that is detrimental , and the critical role for Rb is to inhibit E2f3 . We quantified the fraction of Camk2a+ SACs in different genotypes and found that 60% of WT P30 Camk2a+ cells expressed Chat and Slc18a3 , which dropped to only 5 . 6% in the Rb KO retina , and remained low at 3 . 7% in the Rb/E2f1 DKO retina , but rose to 91% in the Rb/E2f3 DKO retina ( Figure 4B ) . The latter fraction is higher than WT because ganglion cells , which normally make up ~40% of Camk2a+ cells , are killed by apoptosis . To quantify the effect of different E2fs on ectopic division specifically in SACs , we exploited Isl1 ( Islet1 ) . This marker is expressed in both SACs and ganglion cells , thus Isl1+ cells in the INL are exclusively SACs [49] . We found that 98 . 2% ± 1 . 8% of Isl1+ cells in the forming inner INL at P5 were also Slc18a3+ , confirming that Isl1 is an excellent SAC marker ( Figure 4C ) . Moreover , Isl1 , unlike Slc18a3 , is nuclear , which facilitates scoring of Isl1+/Mki67+ cells . It is also expressed earlier than Slc18a3 , permitting analysis of SACs soon after their birth at ~E15; thus , we could study retina at P0 , a time when ectopic division is high in the inner retina and prior to Rb-independent cell cycle exit associated with terminal differentiation [2] . At P0 , no WT Isl1+ cells in the inner neuroblastic layer ( NBL ) ( which is the future INL ) were dividing , but 57 ± 14 Isl1+/Mki67+ cells were detected in the Rb KO inner NBL ( Figure 4D ) . Indeed , about one-third of all Isl1+ cells in the entire inner NBL were dividing in the Rb KO retina , or ~50% in the periphery where Cre is expressed ( Figure 4E and data not shown ) . This defect was suppressed in the Rb/E2f1 DKO retina , where we detected only 1 ± 1 dividing SAC , but not the Rb/E2f3 DKO retina , where there were 53 ± 8 dividing SACs ( Figure 4D and 4E ) . We observed similar effects at P0 with Calb2 , which marks newborn SACs and other amacrine cells ( data not shown ) . Thus , in Rb KO SACs , E2f1 deletion suppresses ectopic division but not aberrant differentiation , whereas E2f3 deletion suppresses aberrant differentiation but not ectopic division . The unique effect of E2f3 in disrupting the differentiation of SACs but not other retinal neurons might be due to cell-type-specific expression or cell-type-specific activity of E2f3 . Determining between these two possibilities is not easy , as E2f immunostaining in mouse tissues is problematic . We did not solve this issue for E2f1 or E2f2 , but used a modified protocol [50] to successfully track E2f3 expression ( Figure 5 ) . At P0 , E2f3 was detected in RPCs , consistent with a putative role in normal proliferation ( Figure 5A ) . The signal was specific as it was absent in the E2f3 KO peripheral retina ( Figure 5A ) . As the retina differentiated and RPC division diminished , the number of E2f3+ cells also dropped , and by P8 , when division is virtually over , only a subset of post-mitotic cells in the inner retina expressed E2f3 ( Figure 5A ) . By P18 , E2f3 was also detected in two tracks in the IPL ( Figure 5A and 5B ) , reminiscent of SAC markers such as Chat and Slc18a3 ( c . f . Figures 3 and 4 ) . This cytoplasmic E2f3 staining was also specific , as it was absent in the E2f3 KO peripheral retina of α-Cre;E2f3loxP/loxP mice ( Figure 5A ) . Indeed , double labelling with E2f3 ( red ) and Chat plus Slc18a3 ( green ) confirmed that E2f3 is present in both SAC soma and dendrites ( Figure 5B ) . Rb protein was also detected in the inner retina ( Figure 5A ) , and showed a similar distribution as E2f3 in SACs ( Figure 5B ) , and was also present in mature ganglion cells and Müller cells as reported [51] . Rb staining in SAC processes was specific as it was absent in the peripheral retina of αCre;RbloxP/loxP mice ( Figure 5A ) . These data suggest that Rb and E2f3 colocalize in SACs and that E2f3 triggers defects in SAC differentiation because it is specifically expressed in these retinal neurons . We also found that E2f3 is present in a specific subset of mature neurons in various brain regions ( data not shown ) . For example , in the P20 amygdala , E2f3 colocalized with the general neuronal markers Mtap2 and Mecp2 [52] , but not with Calb2 , which marks a subset of neurons , or with the glial marker Gfap ( data not shown ) . Unlike in retinal SACs , E2f3 was not coexpressed in Chat+ or Slc18a3+ cholinergic neurons located in various regions of the brain and spinal cord ( data not shown ) . In agreement , we could not detect defects in cholinergic Rb KO neurons in the developing forebrain , but other Rb KO neurons in this region showed differentiation defects that were rescued by deleting E2f3 [53] . Together , these results suggest that the common mechanism by which Rb promotes neural differentiation is through E2f3 inhibition . As noted above , E2f3 and Rb staining in SACs was both nuclear and cytoplasmic ( Figure 5A and 5B ) . The antibody that worked in immunostaining recognizes a C-terminal region and thus , does not distinguish a/b isoforms . To our knowledge , the subcellular location of E2f3 isoforms has not been determined in any cell type . To verify the dual locations of E2f3 and to determine which isoforms were present in retina , we analyzed nuclear and cytoplasmic fractions by Western blot at different times during development . Analysis with the pan-E2f3 antibody ( sc-878 , Santa Cruz Biotechnology ) detected a 55-kD E2f3a species and a 40-kD E2f3b polypeptide ( Figure 6 ) . To confirm that the upper species in our retinal lysates was E2f3a , we exploited novel mice that lack E2f3 exon 1a and thus express E2f3b exclusively ( R . O . and G . L . , unpublished data ) . The genotyping strategy is discussed in detail later and is outlined in Figure 7A . Western analysis confirmed that the upper band was absent in E2f3a−/− mice ( Figures 6 and S8 ) . Consistent with the drop in E2f3-expressing cells during WT retinal maturation ( Figure 5A ) , the total amount of E2f3a was less at P18 compared to P0 ( Figure 6 ) . E2f3b was present in similar amounts at both time points . At P0 and P18 , E2f3a was present in both nuclear and cytoplasmic fractions , but in marked contrast , E2f3b was exclusively nuclear at both times ( Figure 6 ) . Two closely migrating E2f3a bands were detected , more clearly evident at P18 , of which the faster migrating species was dominant in nuclear and the slower species was dominant in cytoplasm ( Figure 6 ) . The identity of both as E2f3a species was confirmed by their absence in the P18 E2f3a KO retina ( Figure S8 ) . Analysis of Pou4f2 , a nuclear transcription factor expressed in ganglion cells , showed that nuclear proteins had not contaminated the cytoplasmic fraction , and analysis of Slc18a3 , a cytoplasmic SAC marker , confirmed that the reverse had also not occurred ( Figure 6 ) . These data show , to our knowledge for the first time , that E2f3a and E2f3b exhibit distinct patterns of subcellular distribution , and raise the possibility that E2f3a localization may be regulated by as yet unknown post-translational modifications . We also examined the distribution of other cell cycle regulators during retinal development . Like E2f3a , Rb was present in both the WT cytoplasm and nucleus at P0 , but at P18 , when the levels of Rb had increased , it was primarily nuclear ( Figure 6 ) . A very faint cytoplasmic Rb signal was evident at P18 , which is consistent with Rb staining of SAC processes ( Figure 5B ) , and with the very small proportion of SACs in the retina [38] . E2f1 was also detected in both nuclear and cytoplasmic fractions , although unlike E2f3a it was predominantly nuclear both at P0 and P18 ( Figure 6 ) . The E2f dimerization partner , Tfdp1 , which lacks a nuclear localization signal [54] , was primarily cytoplasmic at both P0 and P18 , and the Cdk inhibitors Cdkn1a and Cdkn1b showed a similar pattern of distribution ( Figure 6 ) . Thus , among the cell cycle regulators we examined , most showed bivalent distribution , and E2f3b was unusual in its solely nuclear compartmentalization . To test which E2f3 isoform is responsible for aberrant Rb KO SAC differentiation we exploited E2f3a−/− mice ( Figure 7A ) . The genotyping strategy outlined in Figure 7A was used to distinguish the E2f3a , WT , and null alleles . Reverse transcriptase PCR ( RT-PCR ) confirmed the presence of both E2f3a and E2f3b RNA species in the developing WT retina , and the specific absence of E2f3a RNA in the E2f3a−/− retina ( Figure 7B ) . E2f3a protein was absent in E2f3a−/− retinal lysate ( Figures 6 and S8 ) . Importantly , the levels of E2f3b message were similar in the Rb KO and Rb/E2f3a DKO retina , ruling out the possibility that any effects of E2f3a deletion we might observe were due to down-regulation of E2f3b ( Figure 7C ) . Also , the levels of other E2fs were the same in the Rb KO , Rb/E2f3 DKO , and Rb/E2f3a DKO retina , ruling out any cross-regulatory effects ( Figure 7C ) [55] . E2f3a can trigger cell cycle induction , but because SAC defects are not linked to cell cycle perturbation ( Figures 3A and 4 ) , and in view of the predominant association between E2f3b and Rb in quiescent cells [16 , 19] , we suspected that E2f3b may perturb differentiation in Rb KO SACs . Unexpectedly , however , E2f3a deletion suppressed the Rb KO SAC defect ( Figure 7D ) . Thus , separate from its role in cell cycle control , Rb regulation of E2f3a is critical to ensure proper neuronal differentiation .
Work in the early 1990s showed that Rb loss triggers defects in neuronal cell cycle exit , survival , and differentiation [26–28] . Much of the death is an indirect consequence of probable hypoxia linked to placental defects [12–14] . However , targeted KO and chimeric studies reveal that Rb autonomously promotes cell cycle exit in newborn neurons , and is required for survival of a subset of neurons , particularly in the retina [2 , 3 , 13 , 14 , 56–59] . However , whether Rb also regulates differentiation is obscured by potentially indirect effects of ectopic division and death . Moreover , a mechanism though which Rb may regulate neuronal maturation has not been elucidated . Here , deleting E2f1 specifically rescued ectopic division and death in the Rb KO retina . Importantly , major Rb/E2f1 DKO neurons differentiated normally , and ERGs revealed the rescue of rod- and cone-mediated function , implicating a regular signal flow from photoreceptors to bipolar and amacrine cells . Division and death genes were induced in Rb KO cells , and deleting E2f1 , but not E2f2 or E2f3 , reversed these molecular events . E2f1 may also regulate differentiation targets , but whether this contributes to defects in retinal cell maturation is impossible to separate from potentially indirect consequences of deregulated division and death . In any case , it is clear that in most retinal cells , including photoreceptors [29] , transcription factors that promote differentiation function independently of Rb . We have also found that E2f1 deletion rescues cell-autonomous ectopic division , death , and differentiation defects in sporadic Rb KO clones generated using a Cre retrovirus vector ( M . P . and R . B . , unpublished data ) . These data are consistent with the observation that E2f1 overexpression in newborn photoreceptors drives ectopic division and apoptosis [60] , and add to the growing evidence indicating that E2f1 is the major , and perhaps only , member of the three activating E2fs required to induce apoptosis in Rb KO cells [10 , 15] . Thus , deregulated E2f1 activity in the retina , whether resulting from the inactivation of Rb or from overexpression , promotes unscheduled cell division and triggers apoptosis in susceptible RTCs . E2f1 , rather than other E2fs , may be a potential target for novel therapeutics to prevent retinoblastoma in RB1+/− humans . Our ERG studies revealed rescue of the Rb KO rod–bipolar system , and almost complete restoration of the cone–bipolar system following E2f1 deletion . There was a slightly lower response in the Rb/E2f1 DKO retina relative to the E2f1 KO control retina . This difference might reflect a role for Rb in the development of cones , bipolar cells , or other cells that may contribute to the photopic ERG , including potentially SACs , which do have a serious defect in the Rb/E2f1 DKO retina . Comprehensive marker analysis revealed that , in striking contrast to other retinal neurons , E2f1 deletion did not suppress defects in Rb KO cholinergic SACs . Instead , we observed E2f1-independent defects in the synthesis and transport of a large cohort of SAC proteins . These data expand insight into the development of these important interneurons , but more critically , provide to our knowledge the first unambiguous evidence that Rb regulates neurogenesis beyond terminal mitosis . Rb binds more than 100 factors [43] , and in several non-neuronal cells , such as skeletal muscle , adipocytes , and bone , it binds and potentiates tissue-specific transcription factors that promote differentiation [31–33] . The idea that Rb promotes muscle differentiation by potentiating Myod1 activity was contested [61] , and other mechanisms proposed [62 , 63] , but not involving E2f repression . Strikingly , however , we discovered that Rb promotes SAC differentiation through E2f3 ( Figure 8 ) . Rb regulation of SAC differentiation through E2f3 was independent of its role in controlling division or death: E2f3 deletion rescued Rb KO SAC defects but did not suppress aberrant proliferation or death , whereas E2f1 deletion reversed abnormal proliferation and death but did not rescue SAC differentiation . Double labelling confirmed that E2f1 but not E2f3 deletion reversed Rb KO SAC division . Moreover , deleting E2f1 , but not E2f3 , reversed deregulated expression of cell cycle and apoptotic genes in the Rb KO retina . E2f3 is expressed in a subset of CNS neurons ( this work ) and drives specific cell-cycle–independent defects in Rb KO forebrain neurons [53] . Thus , E2f3 inhibition is the first , and may be the only , mechanism by which Rb participates directly in neuronal differentiation . To further dissect the mechanism of action of Rb in SACs we determined the E2f3 isoform it targets to promote differentiation . E2f3b was the primary candidate , since Rb and E2f3b collaborate to repress targets in quiescent cells in vitro [19] . However , in the first work to our knowledge to examine the function of any E2f protein isoform in vivo , we made the surprising observation that Rb regulates SAC differentiation through E2f3a ( Figure 8 ) . Formally , we cannot exclude the possibility that deleting E2f3b might also rescue SAC differentiation , but definitive proof will require analysis of E2f3b null mice . Nevertheless , our data prove that Rb definitely regulates SAC differentiation through the activating E2f3 isoform . The subcellular location of E2f isoforms has not to our knowledge been addressed before . E2f3a and E2f3b share 110 C-terminal amino acids that encode the NLS , DNA-binding , marked box , transactivation , and Rb-binding domains [16] , yet they exhibit different subcellular distribution in developing retinal cells . E2f3a is both nuclear and cytoplasmic , but E2f3b is always nuclear . The unique 121- and six-residue N-termini of E2f3a and E2f3b , respectively , likely mediate this difference . This region in E2f1 , E2f2 , and E2f3a binds Ccna2 , establishing a negative regulatory loop that deactivates E2fs in mid-late S-phase [64 , 65] . However , even E2f3b , which lacks this domain , binds and is regulated by Ccna2 [18] , so the domain difference may not explain the unique distributions we observed . Rb family and Tfdp proteins can also determine E2f localization [20–22] , and we found that a portion of both Rb and Tfdp1 proteins are cytoplasmic in retinal cells . Indeed , immunostaining revealed that Rb and E2f3 colocalize to SAC processes . The nuclear localization of E2f3b contrasts with that of other repressive E2fs in differentiating muscle , where E2f5 switches from the nucleus to cytoplasm , while E2f4 remains in both compartments [23] . The distinct compartmentalization of E2f3a and E2f3b in the retina suggests temporally and functionally distinct activities . Rb distribution matches that of E2f3a , consistent with its critical role in supporting SAC differentiation through E2f3a . Rb is critical to ensure that many types of terminally differentiating cells leave the cell cycle ( e . g . , neurons , gut and skin epithelia , muscle , and lens fibres ) ( reviewed in [66] ) . Early overexpression studies in vitro suggested Rb might temper expansion of cycling cells , but KO studies in vivo indicate that its major role is to block division in terminally differentiating cells . In its absence , many ( but clearly not all ) aspects of differentiation go ahead relatively unperturbed . In the retina , differentiating transition cells are born in the absence of Rb , migrate to the correct layer , and express appropriate markers ( [2] and this work ) . In brain , Rb KO neurons migrate away from the ventricular zone and switch on Tubb3 ( βIII-tubulin ) , but continue to incorporate BrdU [13] , and in gut epithelia , differentiated enterocytes migrate up the villi and activate expression of serotonin , yet continue to incorporate BrdU [67] . In the case of SACs , the differentiation defects we observed ( e . g . , loss of Slc18a3 and Chat ) were not due to aberrant division , but it is possible there are other problems with these cells that are caused by ectopic division . Nevertheless , it is clear that many aspects of differentiation in multiple cell types are compatible with ectopic division . However , division of terminally differentiating cells is dangerous , since it may facilitate transformation , as is the case in retinoblastoma ( reviewed in [66] ) . E2f3a could disrupt SAC differentiation through its well known role as a transcriptional activator , or , in view of the discovery that it is partially cytoplasmic , E2f3a may affect processes other than gene regulation . Both scenarios are feasible since E2fs regulate differentiation genes [44–48] , and cell cycle regulators , such as Cdkn1b , have cytoplasmic activities that influence differentiation [68 , 69] . Many transcription factors shuttle between nucleus and cytoplasm during neurogenesis ( e . g . , [70] and references therein ) . It may be difficult to identify E2f3a-specific target genes or cytoplasmic proteins in SACs since these neurons are a small proportion ( <1% ) of the total retina and only ~5 . 2% of amacrine neurons [38] . Others have suggested that Rb promotes differentiation in non-neuronal cells through E2f-independent means [31–33] . However , these studies did not assess whether these cell types differentiate normally if Rb is deleted along with one or more E2f family members . One study reported that Rb mutants that do not bind E2f still induce differentiation [30] . However , the binding assays were performed in solution , and we have found that several of these mutants do bind E2f , albeit weakly , on chromatin ( T . Yu and R . B . , unpublished data ) . It is possible that Rb-mediated potentiation of tissue-specific transcription factors may , at least in some cases , be a redundant activity , and that the only critical Rb function is to inhibit E2f . Our study is the first to our knowledge to assess comprehensively whether Rb KO cells can differentiate in the absence of different E2fs . In light of our findings , it will be important to reassess differentiation defects in other Rb KO tissues in the absence of individual and combined activating E2f family members .
Mice were treated according to institutional and national guidelines . α-Cre mice ( P . Gruss ) , Chx10-Cre mice ( C . Cepko ) , RbloxP/loxP mice ( A . Berns ) , E2f1–/– mice , E2f2–/– mice , E2f3loxP/loxP mice , and E2f3a−/− mice were maintained on a mixed ( NMRI × C57/Bl × FVB/N × 129sv ) background . A detailed description of E2f3a−/− mice will be published elsewhere . Mice of different genotypes were compared within the same litter and across a minimum of three litters . We have not noted any phenotypic differences in separate litters . Genotyping was performed as before [2 , 5] , and the primers used for genotyping E2f3a−/− mice were E2f3a KL ( 5′-CTCCAGACCCCCGATTATTT-3′ ) , E2f3a KR1 ( 5′-TCCAGTGCACTACTCCCTCC-3′ ) , and E2f3a KM ( 5′-GCTAGCAGTGCCCTTTTGTC-3′ ) . Eyeballs were fixed in 4% paraformaldehyde for 1 h at 4 °C , embedded in OCT ( TissueTek 4583 , Sakura , http://www . sakuraeu . com ) , frozen on dry ice , and cut into 12-μm sections on Superfrost plus slides ( VWR , http://www . vwr . com ) . For S-phase analysis , BrdU ( 100 μg/g of body weight ) was injected intraperitoneally 2 h prior to sacrifice . BrdU+ cells were detected using a biotin-conjugated sheep polyclonal antibody ( 1:500 , Maine Biotechnology Services , http://www . mainebiotechnology . com ) . All other antibodies are described in Table S1 . For E2f3 , Mki67 , and Rb staining , antigen retrieval was performed by boiling sections in citric acid solution for 15 min according to Ino [50] , except on frozen sections . TUNEL was performed as described [13] . Briefly , sections were incubated for 1 h at 37 °C with 75 μl of mixture solution consisting of 0 . 5 μl of terminal deoxynucleotide transferase , 1 μl of biotin-16-dUTP , 7 . 5 μl of CoCl2 , 15 μl of 5× terminal deoxynucleotide transferase buffer , and 51 μl of distilled water . After three washes in 4× SSC buffer , sections were incubated with Alexa 488– or Alexa 568−streptavidin ( 1:1 , 000; Molecular Probes , http://probes . invitrogen . com ) for 1 h at room temperature . Primary antibodies or labelled cells were visualized using donkey anti-mouse Alexa 488 or Alexa 568 , donkey anti-rabbit Alexa 488 or Alexa 568 , donkey anti-goat Alexa 488 or Alexa 568 , and streptavidin Alexa 488 or Alexa 568 ( 1:1 , 000; Molecular Probes ) . Nuclei were counter-stained with 4 , 6-diamidino-2-phenyindole ( DAPI; Sigma , http://www . sigmaaldrich . com ) . Labelled cells were visualized using a Zeiss ( http://www . zeiss . com ) Axioplan-2 microscope with Plan Neofluar objectives and images captured with a Zeiss AxionCam camera . For double-labelled samples , confocal images were obtained with a Zeiss LSM 5 . 0 laser scanning microscope . The retina was separated into three bins by dividing the ventricular edge of the retina into equal parts and extending a line to the vitreal edge [2] . Bin 1 contains only cells that expressed Cre as progenitors; bin 3 is at central retina and contains cells derived from progenitors that did not express Cre . For cell counts or thickness measurement we used a region 0–100 μm peripheral to the boundary separating bins 1 and 2 . Measurements were performed on an Axioplan-2 microscope using Axiovison software . Quantification of S-phase , M-phase , and apoptotic cells was performed on horizontal sections that included the optic nerve . Quantification of differentiated cell types was performed using horizontal sections at equal distances from the optic nerve . A minimum of three sections per eye and three eyes from different litters were counted . Total RNA was isolated from dissected peripheral retina using TRIzol reagent ( Invitrogen , http://www . invitrogen . com ) followed by digestion with RNase-free DNase ( DNA-free , Ambion , http://www . ambion . com ) to remove DNA contamination . First-strand cDNA was synthesized from 0 . 2–0 . 5 μg of total RNA using the SuperScript II first-strand synthesis system ( Invitrogen ) . PCR primers are listed in Table S2 . Real-time quantitative PCR was performed using an Applied Biosystems ( http://appliedbiosystems . com ) PRISM 7900HT . Tests were run in duplicate on three separate biological samples with SYBR Green PCR Master Mix ( Applied Biosystems ) exactly as we described previously [71] . Briefly , master stocks were prepared such that each 10-μl reaction contained 5 μl of SYBR Green PCR Master Mix , 0 . 1 μl of each forward and reverse primer ( stock 50 μM ) , 0 . 8 μl of blue H2O ( 0 . 73% Blue Food Colour; McCormick , http://www . mccormick . com ) , 2 μl of diluted cDNA template , and 2 μl of yellow H2O ( 0 . 73% Yellow Food Colour ) . PCR consisted of 40 cycles of denaturation at 95 °C for 15 s and annealing and extension at 55 °C for 30 s . An additional cycle ( 95 °C , 15 s , 60 °C ) generated a dissociation curve to confirm a single product . The cycle quantity required to reach a threshold in the linear range was determined and compared to a standard curve for each primer set generated by five 3-fold dilutions of genomic DNA or cDNA samples of known concentration . Values obtained for test RNAs were normalized to Hprt1 mRNA levels . Mouse retinas were homogenized by passing them through a 30-gauge BD 9 http://www . bd . com ) needle 5–10 times in 1× PBS solution . Nuclear and cytoplasmic proteins were extracted using the NE-PER Nuclear and Cytoplasmic Extraction Kit ( Product# 78833 , Pierce Biotechnology , http://www . piercenet . com ) . Proteins were separated by 10% SDS-PAGE and transferred to nitrocellulose . After blocking overnight at 4 °C in 5% skim milk , membranes were incubated in the primary antibody for 2 h at room temperature . After three 10-min washes in TPBS ( 100 mM Na2HPO4 , 100 mM NaH2PO4 , 0 . 5 N NaCl , 0 . 1% Tween-20 ) , membranes were incubated for 30 min at room temperature in the secondary horseradish peroxidase-conjugated antibody ( Jackson ImmunoResearch Laboratories , http://www . jacksonimmuno . com ) . Blots were developed using the ECL-Plus chemiluminescent detection system ( Amersham Pharmacia Biotech , http://www . pharmacia . ca ) , according to the manufacturer's instructions . The following primary antibodies were used: E2f-1 ( SC-193 ) , E2f-3 ( SC-878 ) , Cdkn1a ( p21 , SC-471 ) , Cdkn1b ( p27 , SC-528 ) , Pou4f2 ( Brn3b , SC-6062 ) , and Tfdp1 ( Dp1 , SC-610 ) from Santa Cruz Biotechnology ( http://www . scbt . com ) , pRB ( 554136 ) from BD Science-Pharmingen ( http://www . bdbiosciences . com ) , and Slc18a3 ( VAChT , G448A ) from Promega ( http://www . promega . com ) . ERGs were recorded from dark-adapted mice as described [72] . Briefly , mice were dark-adapted overnight and anaesthetized by subcutaneous injection of ketamine ( 66 . 7 mg/kg body weight ) and xylazine ( 11 . 7 mg/kg body weight ) . The pupils were dilated and single-flash ERG recordings were obtained under dark-adapted ( scotopic ) and light-adapted ( photopic ) conditions . Light adaptation was accomplished with a background illumination of 30 candela ( cd ) per square meter starting 10 min before recording . Single white-flash stimulation ranged from 10−4 to 25 cd·s/m2 , divided into ten steps of 0 . 5 and 1 log cd·s/m2 . Ten responses were averaged with an inter-stimulus interval of either 5 s ( for 10−4 , 10−3 , 10−2 , 3 × 10−2 , 10−1 , and 3 × 10−1 cd·s/m2 ) or 17 s ( for 1 , 3 , 10 , and 25 cd·s/m2 ) . Band-pass filter cut-off frequencies were 0 . 1 and 3 , 000 Hz . Different genotypes were evaluated using analysis of variance ( ANOVA ) followed by the Tukey honestly significant difference ( HSD ) test or Fisher test ( XLSTAT program , http://www . xlstat . com ) .
The GenBank ( http://www . ncbi . nlm . nih . gov/genbank ) accession numbers for the major genes and gene products discussed in this paper are Camk2a ( NM_009792 ) , Chat ( NM_009891 ) , E2f1 ( NM_007891 ) , E2f2 ( NM_177733 ) , E2f3 ( NM_010093 ) , Rb ( NM_009029 ) , and Slc18a3 ( NM_021712 ) . | The retinoblastoma protein ( Rb ) , an important tumor suppressor , blocks division and death by inhibiting the E2f transcription factor family . In contrast , Rb is thought to promote differentiation by potentiating tissue-specific transcription factors , although differentiation defects in Rb null cells could be an indirect consequence of E2f-driven division and death . Here , we resolve different mechanisms by which Rb controls division , death , and differentiation in the retina . Removing E2f1 rescues aberrant division of differentiating Rb-deficient retinal neurons , as well as death in cells prone to apoptosis , and restores both normal differentiation and function of major cell types , such as photoreceptors . However , Rb-deficient starburst amacrine neurons differentiate abnormally even when E2f1 is removed , providing an unequivocal example of a direct role for Rb in neuronal differentiation . Rather than potentiating a cell-specific factor , Rb promotes starburst cell differentiation by inhibiting another E2f , E2f3a . This cell-cycle–independent activity broadens the importance of the Rb–E2f pathway , and suggests we should reassess its role in the differentiation of other cell types . |
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Human herpesvirus 6 ( HHV-6 ) is prevalent in healthy persons , causes disease in immunosuppressed carriers , and may be involved in autoimmune disease . Cytotoxic CD8 T cells are probably important for effective control of infection . However , the HHV-6-specific CD8 T cell repertoire is largely uncharacterized . Therefore , we undertook a virus-wide analysis of CD8 T cell responses to HHV-6 . We used a simple anchor motif-based algorithm ( SAMBA ) to identify 299 epitope candidates potentially presented by the HLA class I molecule B*08:01 . Candidates were found in 77 of 98 unique HHV-6B proteins . From peptide-expanded T cell lines , we obtained CD8 T cell clones against 20 candidates . We tested whether T cell clones recognized HHV-6-infected cells . This was the case for 16 epitopes derived from 12 proteins from all phases of the viral replication cycle . Epitopes were enriched in certain amino acids flanking the peptide . Ex vivo analysis of eight healthy donors with HLA-peptide multimers showed that the strongest responses were directed against an epitope from IE-2 , with a median frequency of 0 . 09% of CD8 T cells . Reconstitution of T cells specific for this and other HHV-6 epitopes was also observed after allogeneic hematopoietic stem cell transplantation . We conclude that HHV-6 induces CD8 T cell responses against multiple antigens of diverse functional classes . Most antigens against which CD8 T cells can be raised are presented by infected cells . Ex vivo multimer staining can directly identify HHV-6-specific T cells . These results will advance development of immune monitoring , adoptive T cell therapy , and vaccines .
Human herpesvirus 6 ( HHV-6 ) may be among the most prevalent persistent viruses in the human population . Antibodies to HHV-6 are present in 95–100% of healthy adults [1 , 2] . Like other herpesviruses , HHV-6 establishes a lifelong infection . HHV-6 is a group of two virus species known as HHV-6A and HHV-6B . Primary infection with HHV-6B , the more widespread species of the two , usually occurs before two years of age , and often causes a common childhood disease known as three-day fever or exanthema subitum [3 , 4] . The first infection with HHV-6A is thought to occur later and appears mostly asymptomatic [5] . Later in life , HHV-6 may be involved in a variety of diseases . HHV-6A is suspected of contributing to the pathogenesis of thyreoiditis Hashimoto [6] and to neuroinflammatory diseases such as multiple sclerosis [7] . HHV-6B is related to severe complications in immunocompromised patients . After allogeneic hematopoietic stem cell transplantation ( allo-HSCT ) , HHV-6B reactivation is associated with increased all-cause mortality , delayed engraftment , graft-versus-host disease , and damaging infection of the central nervous system [8 , 9] . Since no HHV-6-specific antiviral agents are available , treatment of infection after allo-HSCT usually involves drugs approved for use against cytomegalovirus ( CMV ) , but these come along with significant side effects such as kidney failure or bone marrow depression [5] . A potentially more efficacious and tolerable form of therapy aims at restoring antiviral T cell immunity , which is defective in patients who reactivate HHV-6 [10] . For other viral infections after allo-HSCT , many clinical investigations have shown that adoptive transfer of donor-derived virus-specific T cells is safe and effective [11] . Most of these studies focused on the herpesviruses CMV and Epstein-Barr virus ( EBV ) , but some have recently included HHV-6-specific T cells [12] . Further development of such immunotherapies and of HHV-6 vaccines will require a detailed understanding of the virus-specific T cell response in health and disease . Information on HHV-6-specific T cell responses is still limited , in particular regarding CD8 T cells [13] . It was shown early that healthy virus carriers have CD4 T cells that respond to HHV-6 lysate or infected cells [14 , 15] . Target antigens and epitopes of the specific CD4 T cell response were identified first in a study on six selected structural proteins [16] , and more recently by a proteomic approach that has identified ten viral antigens targeted by CD4 T cells [17] . Information on the targets of CD8 T cells has remained much more limited . Responses to five HHV-6B proteins have been investigated so far , and a number of epitopes from these proteins that are presented by infected cells were identified [18–21] . These proteins were chosen because of their ( mostly distant ) homology to CMV proteins that elicit CD8 T cell responses . However , HHV-6B encodes approximately 98 unique proteins [22] , and the hypothesis remains unproven that T cell responses to HHV-6 and CMV are similarly structured or directed to corresponding antigens . The biological differences between these viruses are significant despite their evolutionary relationship as β-herpesviruses , and widespread cross-reactivity of T cells to HHV-6 and CMV seems unlikely considering that most of their proteins have quite divergent sequences [21] . Individual HHV-6 epitope-specific CD8 T cell responses were described to be of low frequency in peripheral blood [18–21] , and it has remained unknown whether stronger responses exist . These open questions prompted us to devise a method to analyse the CD8 T cell response to HHV-6 in a more comprehensive , cross-sectional fashion . Screens with libraries of peptides have been particularly efficient in obtaining copious information on the CD8 T cell repertoire against complex viruses [23–25] . However , due to the large number of possible targets , each such study has necessarily neglected some aspects of analysis , either regarding antigen coverage , HLA allotype coverage , precision of epitope identification , or verification of T cell function in the context of infection . Since detection of ex vivo responses to artificial peptides is not sufficient to prove the presence of T cells that recognize functional viral epitopes [26] , it is of particular importance to verify recognition of infected cells by individual peptide-specific T cells . To obtain a cross-sectional overview of the truly functional repertoire of HHV-6B-specific CD8 T cells and their target antigens and epitopes , we chose to base our approach on the entirety of HHV-6B proteins , but to focus on only one HLA class I allotype . We considered HLA-B*08:01 to be particularly suitable for such a study , because of the clarity of its peptide anchor motif [27] and its tendency to present dominant CD8 T cell epitopes in human viral infections [23 , 28–30] . To verify T cell specificity and function , we established specific T cell clones wherever possible , and used these to verify HLA restriction and recognition of infected cells . Our results show that the HHV-6-specific CD8 T cell repertoire targets multiple epitopes from all phases of the viral life cycle . We identify potent epitopes and track them in patients . We discuss implications for improved immune monitoring , studies of viral pathogenesis , and immunotherapy designs .
We wished to obtain a cross-sectional overview of HHV-6B antigens targeted by CD8 T cells . The reference sequence for HHV-6B strain Z29 contains 98 unique protein-coding genes or annotated ORFs with a total of 43 , 836 amino acids . For reasons of feasibility , we decided to screen the viral proteome for specific T cells with only one representative HLA class I restriction . We chose HLA-B*08:01 , the second most frequent HLA-B allotype in populations of European origin [31 , 32] . We had two more reasons for this choice . First , T cell responses to HLA-B*08:01-restricted viral epitopes are often among the strongest that are observed in a particular virus . Examples of such epitopes are shown in Table 1 . Second , B*08:01-presented peptides recognized by such T cells mostly conform to a clear-cut consensus motif [27 , 33 , 34] . This motif demands basic anchor residues ( arginine or lysine ) in positions 3 and 5 and an aliphatic residue ( leucine , isoleucine , valine , or methionine ) in the C-terminal position of an octameric or nonameric peptide . The HHV-6B reference sequence ( strain Z29 , GenBank NC_000898 ) contains 146 octameric and 153 nonameric peptides with this B*08:01 epitope motif , and 77 of 98 nonredundant ORFs contained at least one candidate ( see Supporting S1 Table for a full list ) . These peptides were synthesized and used in the following experiments . First , we attempted to determine the frequency of T cells specific for these HHV-6B-derived peptides in peripheral blood of healthy carriers . We stimulated PBMCs ex vivo with pools of the 146 octameric or the 153 nonameric peptides in an IFN-γ ELISPOT assay . Responses to HHV-6B peptide pools were much weaker than those to an EBV peptide pool , and generally below 1 / 10 000 PBMCs ( Fig 1A ) . Therefore , we decided to enrich HHV-6B-specific T cells from peripheral blood by peptide stimulation . PBMCs from four B*08:01-positive healthy HHV-6B carriers were initially stimulated with octamer or nonamer peptide mixes , and then restimulated every week with autologous CD40-activated B cells loaded with the same peptide mixes . Fig 1B shows analyses of such cultures from donor 1 after six to eight weeks of cultivation . Specific reactivity was observed against five subpools of the octameric peptides and at least seven subpools of the nonameric peptides , suggesting the presence of T cells specific for at least twelve HHV-6B peptides in this donor . After six to eight weeks of cultivation , limiting dilution of the peptide-stimulated cultures was performed to generate T cell clones . Between 6% and 23% of T cell clones were specific for the HHV-6B peptide pool that was used for expansion ( Table 2 ) , as demonstrated by their specific IFN-γ secretion in response to peptide-loaded B cells ( Fig 1C ) . Most of these clones could be sufficiently expanded to determine their precise peptide specificity by testing with peptide subpools ( Fig 1D ) and individual peptides in IFN-γ ELISA assays . Collectively , T cell clones recognized 25 HHV-6B peptides from 19 proteins or open reading frames ( Fig 2 ) . For seven specificities , we verified restriction through HLA-B*08:01 by tests with B*08:01-transfected , peptide-loaded 293T cells and , for comparison , with peptide-loaded B*08:01-matched B cells . The very clear patterns of IFN-γ secretion indicated that all T cell clones tested were restricted through HLA-B*08:01 , as shown for four clones in Fig 3 . We analyzed whether specific T cell clones were able to recognize their cognate antigen on HHV-6B-infected cells . Primary CD4 T cells from B*08:01-positive donors were activated with phytohemagglutinin ( PHA ) and infected with HHV-6B strain HST . Infected cultures were combined with peptide-specific CD8 T cell clones to test for specific IFN-γ secretion . T cell clones with 17 peptide specificities could be tested . For 13 of these , we observed specific recognition of HHV-6B infection at day 6 , as shown in Fig 4A and summarized in Fig 2 . Since most of the HHV-6B peptides recognized by T cells were fully or closely homologous to corresponding sequences in HHV-6A ( Fig 2 ) , we also tested the reactivity of T cell clones with six specificities against HHV-6A-infected cells . All six T cell clones recognized infected cells ( Fig 4B ) . For three epitopes , we could demonstrate presentation by both HHV-6B-infected and HHV-6A-infected cells . Three additional specificities could only be tested against HHV-6A , due to limited cell numbers . Four of the six HHV-6A epitopes were identical to their HHV-6B counterparts , two differed in only one conservatively exchanged amino acid . Overall , these experiments demonstrated that 16 of the 25 candidate peptides against which T cell clones could be established ( Fig 2 ) were bona fide epitopes processed and presented by cells infected with HHV-6B or 6A . Four candidates were not recognized , and five candidates could not be tested because T cell clones did not sufficiently expand and survive . We also tested cytotoxic reactivity against HHV-6B-infected target cells , focusing on CD8 T cells specific for the DFK peptide from U86 . Both a DFK-specific CD8 T cell clone and a polyclonal CD8 T cell line , obtained by PBMC stimulation with the peptide DFK , displayed strong cytotoxic activity against HHV-6B-infected cells , but not non-infected cells; HLA-B8 expression of the target cells was required for this activity ( Fig 4C–4E ) . We proceeded to analyze recognition of target cells over a period of 12 to 18 days of infection with HHV-6B ( Fig 5 ) . Some T cell clones reached a maximum of reactivity at three days of infection , others at six days . Timing of maximal recognition did not appear to correlate with the described expression kinetics of HHV-6B antigens . For example , the SPR epitope from immediate-early ( IE ) antigen U86 was maximally recognized on day 3 , but other IE antigens [44] such as U79 and B4 reached a maximum of recognition on day 6 . Presumably , time to completion of antigen processing differed between antigens , or potential secondary cycles of virus production and re-infection within the infected CD4 T cell culture augmented the presentation of some antigens to specific CD8 T cells . We performed time-course T cell recognition assays with T cell clones of additional specificities , including an analysis of recognition of HHV-6A and an additional control condition in the presence of ganciclovir , an inhibitor of HHV-6 replication ( Fig 6 ) . Presentation of various IE , E , and L antigens showed distinct peaks of recognition on days 3 , 6 , or 9 . Recognition of all epitopes was partially or fully inhibited by ganciclovir . Taken together ( Fig 2 ) , a majority of the HHV-6B peptide-specific T cell clones tested against infected cells recognized their endogenously processed target epitope in the context of infection . Epitopes from antigens of all kinetic categories ( IE , early , late ) and of diverse functional roles ( regulation , DNA synthesis , virus assembly , structural proteins ) were presented by infected cells . No particular class of antigens appeared to be excluded from presentation . At least three epitopes were from proteins with unknown function or putative proteins; our results provide evidence that ORFs including U7 , U26 , and B4 are translated in infected cells . Multiple epitopes were found in antigens U38 ( the DNA polymerase ) , U41 ( the major DNA-binding protein ) , and U86 ( the transcriptional regulator IE2 ) . A panel of thirteen HLA-B*08:01/peptide multimers ( dextramers ) was commercially synthesized . For inclusion in this panel , 11 of the 16 epitopes in their HHV-6B variants were arbitrarily chosen . For two of these epitopes , multimers loaded with their variant HHV-6A peptide were also synthesized; these included the HHV-6A variant of EGR ( from U79 ) and the "EFK" variant of DFK ( from U86; compare Fig 2 ) . As a positive control , a multimer for the Epstein-Barr virus epitope RAKFKQLL ( RAK ) from the BZLF1 antigen was synthesized in parallel . All these multimers were used to stain PBMCs from eight healthy donors for analysis in flow cytometry , to determine ex vivo frequencies of HHV-6-specific CD8 T cells ( Fig 7 ) . As the examples in Fig 7A show , T cells that bound a multimer DFK/B*08:01 , carrying the DFK peptide from U86 , often had an elevated frequency and appeared as clearly distinct populations . T cells that bound other HHV-6 multimers were usually much less frequent . Overall , DFK-specific T cells were detectable in 7 of 8 healthy donors , with a median frequency of 0 . 09% of CD8+ T cells ( 0 . 005%– 1 . 11% ) . The second most frequent population were T cells specific for the SPR epitope , also from U86 ( median 0 . 025% of CD8+ T cells; 0 . 007%– 0 . 07% ) . Thus , while T cells specific for many HHV-6 epitopes were in most donors not detectable above a standard baseline of 0 . 01% , we identified an HHV-6B epitope , DFK , that regularly allowed clear ex vivo detection of specific T cells by multimer staining . In contrast , T cells specific for the HHV-6A variant of this epitope , EFK , were of low frequency or absent . DFK-specific T cells displayed a mixed phenotype ex vivo with respect to markers of central memory , effector memory or terminal differentiation ( S1 Fig ) . The median number per donor of different HHV-6 epitope specificities with a frequency higher than 0 . 01% was four ( Fig 7C ) , and the highest number was seven . However , this number is likely to underestimate the overall number of specificities including those of lower frequency that are present in a donor , considering that the number of different specificities in donor 1 and 2 that could be obtained as T-cell clones after specific expansion was 17 and 12 , respectively ( Table 2 ) . There were three HHV-6 epitopes that elicited responses higher than 0 . 01% in more than half of the donors ( Fig 7D ) . A complete set of FACS plots is provided as supporting information ( S2 and S3 Figs ) . We analyzed the frequency of HHV-6-specific multimer staining-positive CD8 T cells in peripheral blood of three patients after HLA-B*08:01-positive allo-HSCT from unrelated HLA-matched donors . Patient 1 had detectable HHV-6 in throat swabs at the time of transplantation . In the third to fifth week after transplantation , while in aplasia , this patient underwent an episode of HHV-6 reactivation , detectable in gastric biopsy and throat swabs , with symptoms of skin rash and nausea . Treatment with foscarnet was initiated . At day +54 , EBV reactivation was detected , and was treated with cidofovir and rituximab . Probably due to viral infection , engraftment was delayed until day +105 . Samples were available for analysis of specific T cells on days +57 and +68 , at a time when HHV-6 reactivation had subsided ( Fig 8A ) . DFK-specific T cells were detected at both times at similar levels , while QTR-specific T cells were increasing ( Fig 8A and 8B ) . Patient 2 showed HHV-6 reactivation at day +29 after allo-HSCT with detection of the virus in bronchoalveolar lavage ( BAL ) , performed due to a CT scan showing pneumonia . A concurrent infection with Aspergillus fumigatus was found , as well as EBV and adenovirus reactivation . Patient 2 developed a histologically proven post-transplant lymphoproliferative disorder ( PTLD ) at day +94 , and treatment with cidofovir and rituximab was performed . HHV-6-specific T cells targeting four of four different epitopes were detected in patient 2 at moderate frequencies in two of two samples after resolution of HHV-6 reactivation ( Fig 8C ) . Patient 3 , who suffered from severe aplastic anemia , was admitted to allo-HSCT with ongoing detection of HHV-6 in throat swabs after immunosuppressive treatment consisting of anti-thymocyte globulin ( ATG ) , corticosteroids , and cyclosporine A . No specific HHV-6-related symptoms were observed . During aplasia , a concurrent enteral adenovirus reactivation occurred , and progressed to disseminated adenovirus disease . The patient was treated with cidofovir and adenovirus-specific T cells , and fully recovered . Viral infection probably contributed to delayed engraftment . HHV-6-specific T cells could be analyzed in an early sample concurrent with ongoing HHV-6 reactivation ( day +56 ) and a late sample ( day +1221 ) . Only DFK and the EBV epitope RAK could be studied in the early sample due to a shortage of material . DFK-specific CD8 T cells were absent at the time of reactivation ( Fig 8D and 8E ) , but were well reconstituted at the late time point ( Fig 8D , 8F and 8G ) . In addition , there was evidence for low-frequency establishment of CD8 T cells specific for various other HHV-6 epitopes ( Fig 8F and 8G ) at the late time point in this patient , who has remained alive and well until now . Taken together , these data provide tentative evidence that reconstitution of CD8 T cells specific for HLA-B*08:01-restricted HHV-6 epitopes , notably DFK , may be associated with control of viral reactivation in patients after allo-HSCT . However , all patients were treated with cidofovir , which has high activity against HHV-6 . Studies in larger patient cohorts will be necessary to establish an association of particular HHV-6 T-cell specificities and control of infection . Our study identified a total of 16 HLA-B*08:01-restricted epitopes that were presented by infected cells to specific CD8 T cells , based on a set of 299 peptides as epitope candidates . This test set consisted of all HHV-6B peptides that conformed to a simplified HLA-B*08:01 motif ( Table 1 ) defined by the presence of three anchor residues , while any amino acid was allowed in other positions of the peptide . To find out if other internal or flanking sequences were non-randomly enriched for preferred residues or motifs , we aligned our 16 epitopes and their flanking regions in their proteins of origin ( Fig 9A ) and analyzed their amino acid content in each position , subdividing amino acids into broad categories according to their chemical characteristics ( Fig 9B ) . In our set of 16 confirmed epitopes , there were nine arginines and seven lysines each in anchor positions N3 and N5 , suggesting that there was no strong preference for either of these two . Each of the four permitted aliphatic residues was found in the C-terminal anchor position ( C1 ) of the nonameric epitopes , with a preference for leucine , which may simply mirror the higher frequency of this amino acid in the viral proteome ( L , 10 . 1%; I , 6 . 4%; V , 6 . 2%; M , 2 . 4% in the HHV-6B GenBank reference sequence NC_000898 ) . All six octamers had a leucine in C1 . A tendency for leucine to be enriched in N7 was noted . Other than that , there was no strong enrichment of particular amino acids within the peptide other than in the three pre-defined anchor positions , and at least five of the six chemical categories were represented in each internal non-anchor position . Somewhat more conspicuous patterns were seen in the regions flanking the peptide . N2' was often an uncharged polar amino acid ( C , S , T , N , Q ) or a basic amino acid ( R , K , H ) , C1' was often serine or another uncharged polar amino acid , and C2' was often a basic amino acid . We calculated the likelihood that such enrichments occurred by chance using Fisher's exact test , comparing the 16 epitopes to the rest of the 299 peptide candidates ( Table 3 ) . The lowest probabilities of enrichment by chance were calculated for uncharged polar or basic amino acids in position N2' ( p = 0 . 0010 ) , serines in C1' ( p = 0 . 0016 ) , polar uncharged amino acids in C1' ( 0 . 0006 ) , and lysine in C2' ( p = 0 . 0013 ) . Thus , the strongest tendency in HLA-B*08:01-restricted T cell epitopes to follow conserved motifs ( apart from the three pre-defined anchor residues ) was not found for peptide-internal positions , but for certain flanking positions .
PBMCs from anonymized healthy adult donors were purchased from the Institute for Transfusion Medicine , University of Ulm , Germany . PBMCs from patients after allo-HSCT were obtained at the Department of Internal Medicine III , Hematopoietic Stem Cell Transplantation , Klinikum der Universität München , Munich , Germany , with written informed consent . Anonymized cord blood samples were collected at the Department of Obstetrics and Gynecology ( Klinik und Poliklinik für Frauenheilkunde und Geburtshilfe , Klinikum der Universität München , Munich , Germany ) . The institutional review board ( Ethikkommission , Klinikum der Universität München , Munich , Germany ) approved these procedures ( project no . 071–06–075–06 , project no . 17–455 ) . Standard cell culture medium was RPMI 1640 ( Life Technologies/Invitrogen , Karlsruhe , Germany ) supplemented with 10% FCS ( Biochrom , Berlin , Germany ) , 100 U/ml penicillin , 100 µg/ml streptomycin ( Life Technologies/Invitrogen ) , and 100 nM sodium selenite ( ICN Biochemicals , Aurora , CO ) . 293T cells were cultivated in DMEM ( Invitrogen ) with the same supplements . Cells were all cultivated at 37°C and 5% CO2 . PBMCs were obtained by centrifugation on Ficoll/Hypaque ( Biochrom ) . High-resolution HLA typing was performed by PCR-based methods ( MVZ , Martinsried , Germany ) . HHV-6-specific , HLA-B*08:01-restricted T cell lines and clones were derived from four HHV-6 IgG-positive donors . Their full HLA class I types are as follows: donor 1 , HLA-A*02:01 , A*68:01 , B*07:02 , B*08:01 , Cw*07:01 , Cw*07:02; donor 2 , HLA-A*01:01 , B*08:01 , B*15:01 , Cw*03:03 , Cw*07:01; donor 11 , HLA-A*01 , A*11 , B*08 , B*15:01 , Cw*03:03 , Cw*07:01; donor 12 , HLA-A*01:01 , A*02:01 , B*08:01 , B*40:01 . In healthy donors , HHV-6 IgG was determined by immunofluorescence test at Max-von-Pettenkofer Institute , Munich , Germany , with the exception of donors 3 , 4 , 7 , and 8 , in whom it was determined using the HHV-6 IgG ELISA kit ( Abnova ) . Cell lines and cultures from these and other HLA-typed donors were used as antigen-presenting cells in T cell assays . Mini-lymphoblastoid cell lines ( mLCLs ) were generated by infection of PBMC with mini-Epstein-Barr viruses as described [47] . CD40-activated B-cell cultures were established as described [48] and maintained by weekly replating on irradiated ( 180Gy ) LL8 stimulator cells in the presence of 2 ng/ml rIL-4 ( R&D Systems ) . LL8 cells are murine L929 fibroblasts stably transfected with human CD40L [49] . Human embryonic kidney cells 293T ( partial HLA type: HLA-A*02:01 , B*07:02 ) were obtained from ATCC ( CRL-11268 ) . HHV-6-specific T cells were analyzed in peripheral blood samples from three adult patients after allo-HSCT . Transplant indication was severe aplastic anemia ( SAA ) in patients 1 and 3 , and acute myeloid leukemia ( AML ) in patient 2 . G-CSF-mobilized peripheral blood stem cells from an HLA-matched unrelated donor were used in patients 1 and 2; bone marrow donated by an HLA-matched unrelated donor was used in patient 3 . GvHD prophylaxis consisted of cyclosporine A plus sirolimus ( n = 2 ) or mycophenolate mofetil ( n = 1 ) . All patients and donors were HLA-B*08:01-positive and CMV-seronegative . Patients received standard antiviral prophylaxis with acyclovir . Viral infection/reactivation was monitored weekly by quantitative PCR in peripheral blood , including HHV-6 . Other specimens like stool , urine and throat swab samples were monitored for virus reactivation weekly on a routine basis as indicated . A detailed overview of the characteristics of patients , donors , and transplant procedures is provided ( Supporting S2 Table ) . Peptide sequences adhering to the HLA-B*08:01 anchor motif were extracted from the HHV-6B strain Z29 reference sequence ( GenBank NC_000898 ) using the text editor Tex-Edit Plus and a script in the AppleScript language . The 299 peptides of the HLA-B*08:01 candidate library were synthesized by JPT ( Berlin , Germany ) in a "Research Track" format . Each peptide was analyzed by liquid chromatography–mass spectrometry . Median purity of peptides was 77% . Nineteen peptides had a purity below 50% ( minimum 25 . 5% ) , none of these was recognized by any T cell clone . Peptides were reconstituted in 100% dimethyl sulfoxide ( DMSO ) and stored at –20°C . DMSO concentration in all T cell effector assays was kept below 0 . 1% ( vol/vol ) . PBMCs from HHV-6-positive donors were enriched for HHV-6B-specific T cells by stimulation with a mix of 146 octameric peptides or 153 nonameric peptides that represent HLA-B*08:01 candidate epitopes from HHV-6B , using a protocol employing autologous CD40-activated B cells . For peptide loading , PBMC ( first stimulation ) or CD40-activated B cells ( all later stimulations ) were coincubated with octamer or nonamer peptide pool ( 1 µg/ml for each peptide ) at 37°C for 2 h , and washed three times with PBS . The T cell stimulation protocol was initiated by peptide loading of PBMC , which were then plated at 5×106 cells in 2 mL per well of a 12-well plate . Ten to 14 days later , cells were pooled , counted using trypan blue staining , and restimulated at 3×106 cells in 2 mL medium per well with freshly irradiated ( 50 Gy ) autologous CD40-activated B cells , previously loaded with peptides , to reach an effector:stimulator ratio of 4:1 , in the presence of 25–50 U/mL recombinant IL-2 ( „Proleukin S“ , Novartis ) . Cells were restimulated every following week with peptide-loaded CD40-activated B cells in the same manner , with the exception that the IL-2 concentration was successively increased to 100 U/mL . Between stimulations , the T cell cultures were expanded using fresh IL-2-containing culture medium as seemed necessary , judging from the visual appearance of the cultures . For cytotoxicity analysis , PBMCs were stimulated with a single peptide ( DFK from U86 ) and autologous CD40-activated B cells in an analogous manner , and tested at day 29 of cultivation . For single cell cloning of polyclonal T cell cultures , 0 . 7 or 2 . 5 T cells/well were seeded into 96-well round-bottom plates , together with 2×104/well irradiated ( 50 Gy ) HLA-B*08:01-positive mini-LCLs loaded with the octameric or nonameric HHV-6B peptide pool , 3×105 cells/well of a mixture of irradiated ( 50 Gy ) allogeneic PBMCs from three donors , and 1000 U/mL IL-2 . Outgrowing T cell clones were expanded in 96-well round-bottom plates by restimulating every 2 weeks under equivalent conditions . Later , clones with known peptide specificity were restimulated in an analogous manner but using only the single specific peptide . HLA/peptide multimers in the form of phycoerythrin- ( PE ) -labeled HLA-B*08:01/peptide dextramers were purchased from Immudex , Copenhagen , Denmark . Dextramers contained one of thirteen HHV-6 peptides or the peptide RAK ( full sequence RAKFKQLL ) from the BZLF1 protein from Epstein-Barr virus . Dextramers covered the epitopes EAR , RSK , FEK , QTR , VVK , NVK , MAR , whose peptide sequences are identical in HHV-6A and HHV-6B; the epitopes TNK , EGR-6B and DFK from HHV-6B , which differ from their HHV-6A counterparts in one to three amino acids; the epitopes EGR-6A and EFK from HHV-6A ( EFK being the HHV-6A version of DFK ) ; and the HHV-6B epitope SPR , which has no HHV-6A counterpart . The sequences of HHV-6 peptides are provided in Fig 2 . For quantification of antigen-specific CD8+ T cells in peripheral blood from healthy donors using dextramers , a median of 7x105 PBMCs per staining was treated as follows . Cells were stained for 10 minutes at room temperature with 1 μl PE-labeled HLA/peptide dextramer . For negative controls , cells were processed identically , but dextramer was not added . After washing with PBS supplemented with 2% FCS , cells were counterstained on ice for 15 minutes with anti-CD4-FITC ( clone RPA-T4 ) , anti-CD3-PE-Cy5 ( clone HIT3a ) , and anti-CD8-APC ( clone RPA-T8 ) antibodies ( all BioLegend ) . Cells were then washed with PBS/FCS and resuspended in 1 . 6% formaldehyde ( Carl Roth ) in PBS for fixation , stored at 4°C , and analyzed within one day on a Becton Dickinson FACSCalibur flow cytometer . Data analysis was performed using FlowJo 9 . 5 . 3 software ( Tree Star ) : lymphocytes were gated in a forward/sideward scatter dot plot , then CD3+CD4– cells were analyzed for the proportion of multimer-positive cells within CD8+ T cells . For healthy donors 4 , 6 , 10 and transplantation patients , a variation of this protocol was used . Instead of anti-CD4-FITC , a "dump channel" mix of FITC-labeled antibodies anti-CD14 ( clone TÜK4 , Miltenyi Biotec ) , anti-CD19 ( clone LT19 , Miltenyi Biotec ) , and anti-TCR-γδ ( clone 5A6 . E9 , Life Technologies ) was used . Viable lymphocytes were gated according to forward/sideward scatter , and FITC-positive cells were excluded . For patient samples , only 3x105 PBMCs were used per staining . A range of differentiation markers was analyzed on DFK-specific T cells in donor 3 and 6 . Staining with dextramer DFK was combined with FITC-labeled anti-CD14 and anti-CD19 antibodies as above ( dump channel ) and CD3-Alexa Fluor 700 ( clone HIT3a , BioLegend ) ; additional antibodies in panel A were CD8-APC ( clone RPA-T8 ) , CCR7-PE-Cy7 ( clone G043H7 ) , and CD45RA-Pacific Blue ( clone HI100; all BioLegend ) ; additional antibodies in panel B were CD8-APC-H7 ( clone SK1 , BD ) , CD27-APC ( clone O323 , BioLegend ) , CD28-PE-Cy5 ( clone CD28 . 2 , BD Pharmingen ) , and CD57-Pacific Blue ( clone HCD57 , BioLegend ) . Dot plots displaying flow cytometry data in Figs 7 and 8 , S2 and S3 Figs span , in both dimensions , a range from 1 to 10000 arbitrary fluorescence units in a logarithmic scale . Data in S1 Fig are presented in a biexponential scale spanning a range from 10−3 to 105 arbitrary units in both dimensions . To verify the HLA restriction of HHV-6B-specific T cell clones , 293T cells were transfected with a HLA-B*08:01 expression plasmid ( kindly provided by Josef Mautner , Munich ) by calcium phosphate precipitation . Twenty-four hours later , cells were harvested , washed with PBS , loaded with single peptides at 1 µg/ml , washed three times , and used as targets in T cell assays . HHV-6B-specific T cell lines and T cell clones were analyzed for antigen-specific IFN-γ secretion in ELISA or ELISPOT assays . Effector cells ( 104 , unless noted otherwise ) were cocultivated overnight ( 16–18 h ) with target cells ( 2x104 , unless noted otherwise ) in 200 μL per well of a 96 V-well plate at 37°C and 5% CO2 . Then supernatants were harvested , and an IFN-γ ELISA was performed according to the manufacturer’s protocol ( Mabtech , Nacka , Sweden ) . IFN-γ ELISPOT assays were used to determine the frequency of specific T cells in freshly isolated PBMCs and polyclonal T cell lines . They were performed according to the reagent manufacturer’s protocol ( Mabtech , Nacka , Sweden ) in 96-well MultiScreen-HA plates ( Millipore ) in 200 μL medium per well , with an overnight incubation period of 16–18 hours at 37°C and 5% CO2 . To analyze PBMCs , 250 , 000 cells were distributed to each well and directly loaded with antigenic peptide . To analyze T cell lines , autologous CD40-stimulated B cells were loaded with antigenic peptides , washed , and co-incubated at 5x104/well together with the T cells at 10 , 000 cells/well . Spots were developed using the AP Conjugate Substrate Kit from Bio-Rad . Spots were counted in an automated ELISPOT reader ( CTL ) . To determine the cytotoxic activity of HHV-6B-specific T cells against HHV-6B-infected cells , calcein release assays were performed . HHV-6B-infected target cells ( see below ) were loaded with Calcein AM ( 5 μg/ml , Molecular Probes ) for 30 min at 37°C in standard medium . Cells were washed three times and resuspended in RPMI medium without phenol red supplemented with 5% FCS . Effector T cells were washed once and resuspended in the same medium . T cells and target cells were combined in V-bottom 96-well plates ( 200 μl total volume per well ) , with 5 , 000 target cells per well and 5 , 000–80 , 000 T cells per well ( effector: target ratio 1:1 to 16:1 ) , in four replicates of each condition . After 3 . 5 hours at 37°C and 5% CO2 , supernatants ( 100 μl per well ) were collected , and fluorescence was measured ( excitation 485 nm , emission 535 nm ) . Specific lysis was calculated relative to maximal lysis ( 100% , targets incubated with 0 . 5% Triton X-100 ) and minimal lysis ( 0% , targets incubated in the absence of T cells ) . HHV-6A ( strain U1102 ) and HHV-6B ( strain HST ) were purchased from NCPV , UK , and serially propagated on phytohemagglutinine ( PHA ) -activated cord blood mononuclear cells . Fresh or cryoconserved cord blood cells at 2x106 cells in 2 ml per well of a 24-well plate were stimulated with 5 μg/ml PHA-M ( Calbiochem ) . Three days later , cells were infected with virus suspension from previous passages ( 230 μl/well ) . After 5–7 days , when the cytopathic effect appeared maximal , cell cultures were harvested , cells were pelleted by centrifugation at 300 g for 10 min , and supernatants were stored in aliquots at -80°C . Peripheral blood cells from adult donors with known HLA types were used to prepare HHV-6A/B-infected target cells for the analysis of T cell recognition . CD4 T cells were positively isolated from PBMC using anti-CD4-coupled paramagnetic beads ( Miltenyi Biotec ) , and 2x106 CD4+ cells were activated in 2 mL per well of a 24-well plate using 5 μg/mL PHA . After 3 days , the cells were pooled , counted , replated at 2x106 cells/well , and infected with 230 μl/well of HHV-6A or HHV-6B virus stocks . Thereafter , infected T cell cultures were resupplied with fresh medium every 3 days on average . At different time points after infection , cells were used as targets for HHV-6-specific T cell clones in cytokine secretion assays . At every time point , infected cells were harvested , washed and counted in Trypan Blue solution immediately before they were combined with HHV-6-specific T cells at constant numbers ( 104 effector T cells , 2 × 104 infected cells or control targets ) . In selected experiments as indicated , ganciclovir ( 20 μg/mL , Roche ) was added immediately before infection .
Here we present a cross-sectional analysis of the CD8 T cell response to HHV-6 , and an overview of antigens recognized by this response . For one exemplary HLA class I molecule , HLA-B*08:01 , we identified candidate epitopes all across the HHV-6B proteome , and tested which of these represent bona fide epitopes . A large set of T cell clones was established to assess and correlate epitope specificity and antiviral reactivity with precision . Frequencies of specific T cells in healthy donors and allogeneic transplant patients were determined by multimer staining . A majority of peptides against which we could raise T cells were presented by infected cells , and epitopes from all classes of viral antigens were presented . Ex vivo frequencies of specific T cells were low for most epitopes . However , U86-specific T cells were readily detectable ex vivo in most donors and patients . U86 is thus a candidate for an immunodominant CD8 T cell antigen of HHV-6 . Moreover , we describe the presence of HLA-B*08:01-restricted HHV-6-specific T cells in patients who were able to control episodes of HHV-6 reactivation after stem cell transplantation . Taken together , the present work provides a cross-sectional overview of the structure of the HHV-6-specific CD8 T cell response at two levels . It shows that multiple viral antigens of different functional and kinetic classes furnish epitopes for T cell recognition; and it describes the quantitative contributions of the different specificities to the T cell repertoire , including identification of a prominent antigen . Our study extends earlier investigations of the HHV-6-specific CD8 T cell response that were limited to the analysis of responses to five pre-chosen proteins: four virion proteins ( U11 , U14 , U54 , U71 ) and the IE-1 transactivator U90 [18–21] . The motivation to choose those antigens was their correspondence to immunogenic proteins of human CMV . The present work employed a method that was independent of such criteria and targeted CD8 T cell epitopes across the viral proteome . HLA-B*08:01-restricted epitopes were identified in 12 proteins of varied function and from all phases of the viral replication cycle . No epitope was derived from any of the five antigens studied earlier , although 12 candidates from these proteins were included in our analysis . This suggests that immunity to HCMV antigens has limited power to predict the specificity of CD8 T cell responses to HHV-6 . We cannot exclude , however , that CD8 T cells that target additional epitopes , including such from the five proteins mentioned , may exist in the T cell repertoire . Of note , U86 attracted the strongest T cell responses among the antigens described here , and its CMV counterpart IE-2/UL122 is a strong CD8 T cell antigen [25] . This suggests that certain commonalities between recognition patterns of CMV and HHV-6 antigens may exist . However , the overall composition and diversity of the HHV-6-specific CD8 T cell repertoire , as characterized here , appears to stand in marked contrast to the best-studied herpesviruses , CMV ( a β-herpesvirus like HHV-6 ) and EBV ( a γ-herpesvirus ) . Contrary to HHV-6 , CMV elicits very large CD8 T cell responses , amounting to an average of 10% of the peripheral CD8 T cell repertoire of healthy carriers [25] . HLA-B*08:01-restricted T cells make a strong contribution to this response [23 , 50] . EBV-specific CD8 T cells account for a smaller proportion of total CD8 T cells in healthy donors [51] , but , for example , the HLA-B*08:01-restricted RAKFKQLL epitope ( Table 2 ) is recognized by a median of about 2% of CD8 T cells , and frequencies above 5% are no rarity [29 , 52] . CD8 T cell responses to CMV further increase in the elderly [53 , 54] , and EBV-specific CD8 T cells are strongly elevated in patients with symptomatic primary EBV infection [55] . On the other hand , the diversity of the CD8 T cell response to CMV or EBV appears restricted . For example , the database IEDB [56] currently lists only three HLA-B*08:01-restricted epitopes from CMV and four from EBV , counting strain variants as one epitope . In CMV carriers , a median of eight out of 213 ORFs is recognized by CD8 T cells [25] , and a majority of EBV antigens appear exempt from CD8 T cell recognition [51] . Thus , it appears that the diversity of epitopes and antigens available for presentation by infected cells is distinctly larger in HHV-6 than in the two paradigmatic human herpesviruses . However , it cannot be excluded that more diverse repertoires of low-frequency CD8 T cell specificities in EBV or CMV have so far escaped detection , possibly because their responses were masked by more dominant CD8 T cell populations . In contrast , CD8 T cells specific for other herpesviruses such as varicella-zoster virus ( VZV ) or herpes simplex virus ( HSV-1 ) are maintained at relatively low frequencies in healthy carriers [57–59] . In HSV-1 , CD8 T cells appear to target multiple antigens from different phases of infection , whereas IFN-γ responses to individual epitope peptides ex vivo have frequencies of 1 in 104 PBMCs or lower . A large number of potential epitopes from HSV-1 were described , with up to 13 sharing the same HLA class I restriction [57] , although it is not clear yet whether a majority of these is presented by infected cells . This structure of the T-cell repertoire appears comparable to the one described here for HHV-6 . Less is known about the VZV CD8 epitope repertoire , but available data are compatible with a highly diverse repertoire which is in part shaped by cross-reactivity of CD8 T cells to HSV and VZV [60] . Potential reasons for differentially structured antiviral CD8 T cell repertoires may be sought in the patterns of cellular tropism of these viruses . CMV resides latently in monocytes and myeloid precursors and is reactivated upon their differentiation to dendritic cells [61] , whereas EBV infects B cells in diverse activation states [62] . Infection of professional antigen-presenting cells by CMV and EBV may favour competitive clonal expansion and selection of immunodominant T cells into the repertoire [63 , 64] . In other herpesviruses , tropism for professional antigen-presenting cells is less predominant [5 , 65]—although HHV-6 was shown to infect monocytes and other antigen-presenting cells in vivo [5] . Also , it appears that the repertoire of viral immunoevasive molecules that directly interfere with steps in the HLA class I presentation pathway is larger for CMV or EBV [49 , 66 , 67] than for other herpesviruses [58 , 68] . Co-expression of many immunoevasive functions in CMV and EBV may limit the number of epitopes that escape such regulatory mechanisms [69 , 70] , and this may lead to competitive advantage and immunodominance of T cells that recognize their epitopes in the context of infection . Our analysis of T cell epitopes was limited to only one allotype , HLA-B*08:01 , and extrapolations to the CD8 T cell repertoire in general must be made with caution . More comprehensive studies on the entire CD8 T cell repertoire to HHV-6 will be necessary to strengthen our present suggestions . However , available information on CD8 T cell responses to other complex viruses indicates that HLA-B*08:01 , wherever studied , rarely fails to be an effective presenter of epitopes , as shown by the examples in Table 1 . The groove of MHC class I molecules accomodates peptides for presentation to CD8 T cells [71–73] . Particularly important for stable binding are certain anchor residues [74] whose side chains reach into dedicated pockets in the peptide-binding groove . Allelic variants of MHC class I demand anchor residues required for peptide binding that can differ in their chemical nature and their position in the peptide [27 , 33 , 74] . Our identification of HLA-B*08:01-restricted T cell epitopes consisted in a functional screen of all HHV-6B-derived peptides that contained a motif of three required anchor residues [27 , 75] , as depicted in Table 1 , while any amino acid was permitted in other positions of the peptide . Application of such a simple anchor-motif based algorithm ( SAMBA ) is supported by the observation that a majority of well-characterized , independently verified , and potent CD8 T cell epitopes from infectious pathogens perfectly adhere to this motif , whereas amino acid usage in all other positions is more variable ( see Table 1 and the references therein ) . Full conformity to this motif was also shown for abundant self-derived peptides eluted from HLA-B*08:01 in a seminal study [75] . Subsequent studies have , however , increasingly identified B*08:01-binding self peptides that partially deviated from the motif [76 , 77] . In these cases , peptides were eluted from cells co-expressing several HLA class I molecules , and their HLA-B*08:01 restriction was retrospectively inferred from the peptide sequence . Deviation from the classical motif was also observed when predicted B*08:01-restricted epitopes were validated in ex vivo ELISPOT assays with blood cells loaded with peptide [78] . However , such approaches carry the risk of identifying responses to peptides that are not endogenously processed [26] or not presented by the predicted HLA allotype [69] , if those aspects are not independently tested . Prediction of MHC class I epitopes currently relies on machine-learning algorithms trained on ever increasing datasets of MHC binders or epitopes [79 , 80] . To the extent that such datasets may contain a growing number of candidates whose HLA restriction and qualities as epitopes have not been verified , further progress in predicting optimal epitopes may be difficult to achieve . Therefore , in our view it is as important as ever to rigorously verify HLA restriction and endogenous presentation , optimally with target cells infected with the pathogen of interest . Reliance on T cell clones increases the accuracy of epitope identification and validation , since this ensures that the very same T cells recognize peptide and infected cells , and minimizes the likelihood of accidental cross-reactivities . Peptide-based functional screens of epitope candidates ex vivo have been successful in identifying CD8 T cell reactivities that were later confirmed to be viral epitopes , for example in CMV [23 , 54] , but such approaches are likely to be less robust when proportions of specific T cells are low , such as for HHV-6 . This study identified sixteen HLA-B*08:01-restricted HHV-6 epitopes–defined as peptides presented by infected cells–out of 299 candidates . We took advantage of this dataset to compare amino acid usage in internal and flanking sequences of epitopes and non-epitopes . In the C-terminal anchor position ( C1 ) , Leu appeared to be favoured among admitted aliphatic residues , clearly so in octameric epitopes . Leu was also enriched in the C2 position . Otherwise , no restrictions of amino acid usage in non-anchor positions in HHV-6 epitopes were apparent , which is in line with the idea of distinct functional roles for anchors and non-anchors , and retrospectively supported our use of a SAMBA approach . However , stronger enrichment of certain amino acids was found in peptide-flanking positions ( N2' , C1' , and C2' ) . The C terminus of most MHC I ligands is generated by the proteasome [81] . Cut site preferences of human proteasomes have been identified by in vitro digestion of model proteins [82–85] , and coincide well with the requirements of many MHC I allotypes ( such as HLA-B*08:01 ) to bind peptides with a bulky hydrophobic residue in the C-terminal position . Downstream of the cut site , amino acid preferences partially diverge between model proteins [82–85] . We found uncharged hydrophilic amino acids , particularly Ser , to be enriched in the C1' position ( called P1' in analyses of proteasome function ) . Ser in this position was also enriched after degradation of HIV Nef by the constitutive proteasome [83] and of prion protein by the immunoproteasome [84] . Increased frequency of Arg [83 , 85] and depletion of bulky hydrophobic amino acids [82 , 83 , 85] also agreed with our findings , whereas enrichment of Ala or Pro [82–85] did not . Although limited in size , our dataset suggests an influence of amino acid identity in C1'/P1' on effective proteasomal processing of HHV-6 epitopes . In the C2'/P2' position , we observed basic amino acids to be enriched; no clear tendency in that regard is found in the literature [84 , 85] . Formation of the N terminus of MHC I ligands is in many cases a complex multistep process comprising proteasomal degradation , processing by cytosolic aminopeptidases , TAP-mediated transport to the ER , and final trimming by ER aminopeptidases [86] . Nontheless , an N-terminal processing motif of MHC I ligands could be defined [86] . Consistent with our findings , this motif has the basic amino acids Lys and Arg somewhat enriched in the N2' position [86] . Basic amino acids in N-terminal overhangs of MHC I ligand precursors may favour processing by cytosolic or ER aminopeptidases [86] , although the ER peptidase ERAP1 does not appear to have this preference [87] . Moreover , basic amino acids close to the N terminus of MHC I ligand precursors may support effective TAP-mediated transport to the ER [88] . Thus , amino acid usage in regions flanking HHV-6 epitope peptides is compatible with some of the described N- and C-terminal MHC I processing preferences . However , such motifs represent tendencies rather than strict criteria , so improving epitope prediction by considering processing motifs remains difficult [79] . Nontheless , we speculate that simplified peptide-flanking motifs may be useful to design screening approaches that prioritize efficiency over completeness . Identification of multiple CD8 target antigens and epitopes as undertaken here will advance immune monitoring and immunotherapy of HHV-6 . Since our study identifies an epitope ( DFK from U86 ) that allowed detection of specific T cells ( sometimes at high frequencies of up to 1 . 1% ) in 7/8 healthy carriers and 3/3 patients after allo-HSCT , multimer staining based on this epitope will be a convenient tool for monitoring and monospecific approaches to antiviral T cell therapy [89 , 90] . HHV-6-specific T cell transfer after allo-HSCT is attractive and feasible . In patients who received allo-HSCT , HHV-6 reactivation and disease is associated with a lack of virus-specific T cells [10 , 91] and the use of transplantation procedures that lead to imperfect T cell reconstitution [92] . In a first clinical application of HHV-6-specific T cell transfer to allo-HSCT patients , T cells specific for U11 , U14 , and U90 were part of a protocol that employed multivirus-specific peptide-stimulated T cells derived from the transplant donor [12] . In two patients , HHV-6 reactivation was cleared after transfusion of multivirus-specific T cells , in connection with an emergence of HHV-6-specific T cells in peripheral blood [12] . Partial remissions of HHV-6 infection were also observed in a third-party approach based on similarly prepared T cells [93] . These promising initial results encourage further application and development of HHV-6-specific adoptive immunotherapy . Since multiple epitopes are targeted by HHV-6-specific CD8 T cells , a multiepitope approach [94] may be particularly promising for selection of effective HHV-6-specific T cells for immunotherapy . If TCR-transgenic T cell therapy [95] is considered , HHV-6 antigens from diverse functional classes may be suitable targets . | This paper deals with the immune response to a very common virus , called human herpesvirus 6 ( HHV-6 ) . Most people catch HHV-6 in early childhood , which often leads to a disease known as three-day fever . Later in life , the virus stays in the body , and an active immune response is needed to prevent the virus from multiplying and causing damage . It is suspected that HHV-6 contributes to autoimmune diseases and chronic fatigue . Moreover , patients with severely weakened immune responses , for example after some forms of transplantation , clearly have difficulties controlling HHV-6 , which puts them at risk of severe disease and shortens their survival . This can potentially be prevented by giving them HHV-6-specific "killer" CD8 T cells , which are cells of the immune system that destroy body cells harboring the virus . However , little is known so far about such T cells . Here , we describe 16 new structures that CD8 T cells can use to recognize and kill HHV-6-infected cells . We show that very different viral proteins can furnish such structures . We also observe that such T cells are regularly present in healthy people and in transplant patients who control the virus . Our results will help develop therapies of disease due to HHV-6 . |
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Bacterial genome evolution is characterized by gains , losses , and rearrangements of functional genetic segments . The extent to which large-scale genomic alterations influence genotype-phenotype relationships has not been investigated in a high-throughput manner . In the symbiotic soil bacterium Sinorhizobium meliloti , the genome is composed of a chromosome and two large extrachromosomal replicons ( pSymA and pSymB , which together constitute 45% of the genome ) . Massively parallel transposon insertion sequencing ( Tn-seq ) was employed to evaluate the contributions of chromosomal genes to growth fitness in both the presence and absence of these extrachromosomal replicons . Ten percent of chromosomal genes from diverse functional categories are shown to genetically interact with pSymA and pSymB . These results demonstrate the pervasive robustness provided by the extrachromosomal replicons , which is further supported by constraint-based metabolic modeling . A comprehensive picture of core S . meliloti metabolism was generated through a Tn-seq-guided in silico metabolic network reconstruction , producing a core network encompassing 726 genes . This integrated approach facilitated functional assignments for previously uncharacterized genes , while also revealing that Tn-seq alone missed over a quarter of wild-type metabolism . This work highlights the many functional dependencies and epistatic relationships that may arise between bacterial replicons and across a genome , while also demonstrating how Tn-seq and metabolic modeling can be used together to yield insights not obtainable by either method alone .
The prediction of genotype-phenotype relationships is a fundamental goal of genetic , biomedical , and eco-evolutionary research , and this problem underpins the design of synthetic microbial systems for biotechnological applications [1] . Recently , there has been a shift away from the functional characterization of single genes towards whole-genome , systems-level analyses ( for recent reviews , see [2 , 3] ) . Such studies have been facilitated by methods allowing for the direct interrogation of a genome to determine all genetic elements required for adaptation to a given environment . Two primary methods are in silico metabolic modeling [4 , 5] , and massively parallel sequencing of transposon insertions in bacterial mutant libraries ( Tn-seq ) [6 , 7] . In silico genome-scale metabolic modeling attempts to reconstruct all cellular metabolism , including all biochemical reactions and the genes encoding the participating enzymes , thereby linking genetics to metabolism [8] . Next , mathematical models such as flux balance analysis ( FBA ) are used to simulate the flux distribution through the reconstructed network [9] , allowing predictions of how environmental perturbations or gene disruptions would influence growth . This approach allows for phenotypic predictions of all single , double , or higher-order gene deletion mutations within a matter of days [10 , 11] , which is infeasible using a direct experimental approach . However , the quality of the predictions is highly dependent on the accuracy of the metabolic reconstruction . Outside of a few model species like Escherichia coli , experimental genetic and biochemical data are not available at the resolution necessary to provide accurate assignment of all metabolic gene functions . Tn-seq involves the generation of a library of hundreds of thousands of mutant clones , each containing a single transposon insertion at a random genomic location ( refer to [12] for a review on this method ) . The library of pooled clones is then cultured in the presence of a defined environmental challenge . Insertions resulting in altered fitness in the environment under investigation become under- or over-represented in the population . Deep sequencing is used to identify the genomic location and frequency of all transposon insertions , which is then used as a measure of the growth phenotype brought about by specific mutations: a less than expected number of insertions within a gene is interpreted to reflect that mutation of the gene impairs growth . This approach is imperfect , as important biochemical functions may be redundantly encoded [13–15] , or replaced by compensatory alternative processes [16] . Moreover , fitness changes brought about by mutation in one gene may require mutation of a second gene bearing no resemblance to the first—a phenomenon known as a genetic interaction [17 , 18] . Such genetic interactions may cause the apparent functions of some genes to be strictly dependent on their genomic environment [19] . In other words , a gene may be essential for growth in one organism , but its orthologous counterpart in another organism may be non-essential . This significantly complicates efforts to generalize genotype-phenotype relationships [20] . Resolving the problem of genome-conditioned gene function is of broad significance in the areas of functional genomics , population genetics , and synthetic biology . For example , the ability to design and build optimized minimal cell factories on the basis of single-mutant fitness data is expected to present numerous complications [21] , as evidenced by the recent effort to rationally build a functional minimal genome [22] . Tn-seq studies suggest there is as little as 50% to 25% overlap in the essential genome of any two species [23–25] . As a striking example , 210 of the Tn-seq essential genes of Pseudomonas aeruginosa PA14 are not even present in the P . aeruginosa PAO1 genome [26] . Comparison of Tn-seq data for Shigella flexneri with deletion analysis data for the closely related species E . coli suggested only a small number of genes were specifically essential in one species; however , mutation of about 100 genes appeared to result in a growth rate decrease specifically in E . coli [27] . Similarly , comparison of Tn-seq datasets from two Salmonella species revealed that mutation of nearly 40 genes had a stronger growth phenotype in one of the two species [28] . Overall , these studies suggest that the genomic environment ( here defined as the genomic components that may vary from organism to organism ) influences the fitness contributions of a significant proportion of an organism’s genes . However , no large-scale analysis has been performed to directly examine how the phenotypes of individual genes are influenced after large-scale genomic manipulation . Here , we provide a quantitative , genome-scale evaluation of how large-scale genomic variance influences genotype-phenotype relationships . The model system used is Sinorhizobium meliloti , an α-proteobacterium whose 6 . 7-Mb genome consists of a chromosome and two additional replicons , the pSymA megaplasmid and the pSymB chromid [29 , 30] . The pSymA and pSymB replicons constitute 45% of the S . meliloti genome ( ~2 , 900 genes ) ; yet , by transferring only two essential genes from pSymB to the chromosome , both pSymA and pSymB can be completely removed from the genome , yielding a viable single-replicon organism [31] . We report a comparison of gene essentiality ( via Tn-seq ) for wild-type S . meliloti and the single-replicon derivative . This experiment was designed to evaluate interactions between individual chromosomal genes and the secondary genome ( pSymA/pSymB ) as a whole , recognizing that the pSymA/pSymB deletion , while consistent with cell viability , is certainly pleiotropic [32 , 33] . This enables us to detect the chromosomal side of inter-replicon genetic interactions with single-gene precision , but unable to discern the extrachromosomal side of the interactions . This high-throughput genetic analysis is supplemented by an in silico double gene deletion analysis of a S . meliloti genome-scale metabolic network reconstruction . We further examine how integration of Tn-seq data with in silico metabolic modeling , through a Tn-seq-guided reconstruction process , overcomes the limitations of using either of these approaches in isolation to develop a consolidated view of the core metabolism of the organism . This process produced a fully referenced core S . meliloti metabolic reconstruction .
To interrogate the S . meliloti genome using a Tn-seq approach , we first developed a new construct based on the Tn5 transposon as described in the Materials and Methods . The resulting transposon ( S1 Fig ) contains constitutive promoters reading out from both ends of the transposon to ensure the production of non-polar mutations; without such promoters , non-essential genes at the beginning of an operon may be incorrectly classified as essential if a downstream operon gene is essential . However , the inclusion of outward facing promoters means that surrounding genes are constitutively expressed , potentially resulting in growth impairments , and this must be kept in mind during examination of the data . Analysis of the insertion site locations validated that the transposon performed largely as expected . Gene disruptions caused by transposon insertions were confirmed to be non-polar as illustrated by the case reported in Fig 1 , and there was no strong bias in the distribution of insertions around the chromosome ( Fig 2A , S2 Fig ) . However , there did appear to be somewhat of a bias for integration of the transposon in GC-rich regions ( S3 Fig ) . As there was little relationship between GC content and Tn-seq derived gene essentiality scores ( S4 Fig ) , it is unlikely that this moderate bias had a discernable influence on the results of this study . Our Tn-seq experiments were undertaken with two primary aims: i ) to identify the core set of genes contributing to S . meliloti growth under laboratory conditions , and ii ) to determine the extent to which the phenotypic consequence of a gene deletion is influenced by the genomic environment ( i . e . presence/absence of the secondary replicons ) . To accomplish this , Tn-seq libraries were prepared for two isogenic S . meliloti strains: RmP3496 ( ΔpSymAB ) , which lacks the pSymA and pSymB replicons; and RmP3499 ( wild type ) , resulting from the restoration of pSymA and pSymB into RmP3496 [34] ) . Strain RmP3499 was used as the wild-type as whole-genome sequencing revealed only three chromosomal polymorphisms between RmP3496 and RmP3499; in contrast , there are 23 polymorphisms between the chromosomes of RmP3496 and the wild-type Rm2011 [34] . Transposon library sizes were skewed to compensate for the difference in genome sizes ( ~ 1 . 8-fold more insertion mutants were collected for the wild type ) , resulting in nearly identical insertion site density for each library ( S1 Table ) . Both libraries were passed in duplicate through selective growth regimens in either complex BRM broth ( rich medium containing yeast extract , tryptone , and sucrose ) or minimal defined broth ( a medium containing a minimal set of salts required for growth , including sucrose , ammonium , sulfate , and phosphate ) . These media were chosen as both were found to support growth of both strains , and they represent two very different nutritional environments . Following approximately nine generations of growth , the locations of the transposon insertions in the population were determined , a gene essentiality index ( GEI ) was calculated for all chromosomal genes , and each gene was classified into one of five fitness categories ( Table 1 ) using the procedure described in the Materials and Methods . Four genes that were found to be essential in the Tn-seq data ( pdxJ , fumC , smc01011 , smc03995 ) were independently tested in the wild-type background by targeted knock-out . In all cases , the mutations yielded the expected no-growth phenotype ( S5 Fig ) , supporting the accuracy of the Tn-seq output . All Tn-seq data are available as S1 Dataset . Data for the pSymA and pSymB replicons in the wild-type strain are also provided in S1 Dataset , but these data will not be considered further in this manuscript . A strong correlation was observed between the number of insertions per gene in each set of duplicates ( S6 Fig ) , indicating that there was high reproducibility of the results and that differences between conditions were unlikely to reflect random fluctuations in the output . On average , insertions were found in 190 , 000 unique chromosomal positions with a median of 39 unique insertion positions per gene ( S1 Table ) . The similarity in the number of unique insertion positions between samples confirmed that equal coverage of the two strains was obtained , and suggested that differences in the Tn-seq outputs were unlikely to be an artifact of the quality of the libraries . The growth phenotype of mutating each of the S . meliloti chromosomal genes was inferred based on the density of transposon insertions within the gene , with fewer insertions suggesting a larger growth impairment when the gene is mutated . Based on this approach , 307 genes were classified as essential independent of growth medium or strain ( Fig 2B ) . This set of 307 genes includes those encoding functions commonly understood to be essential: the DNA replication apparatus , the four RNA polymerase subunits , the housekeeping sigma factor , the general transcriptional termination factor Rho , 40 out of 55 of the annotated ribosomal protein subunits , 18 out of 20 of the annotated aminoacyl-tRNA synthetases , and 6 out of 10 of the annotated ATP synthase subunits . Considering genes classified as essential plus those genes whose mutation resulted in a large growth defect ( Groups I and II in Table 1 ) , a core growth promoting genome of 489 genes , representing ~ 15% of the chromosome , was identified ( Fig 2B ) . This expanded list includes 51 out of 55 of the annotated ribosomal protein subunits , 19 out of 20 of the annotated aminoacyl-tRNA synthetases , and 9 out of 10 of the annotated ATP synthase subunits . These 489 genes appeared to be mostly dispersed around the chromosome , although there was a bias for these genes to be found in the leading strand ( Fig 2A ) , and many ribosomal and RNA polymerase genes are grouped together in one locus ( the 5 o’clock position in Fig 2A ) . Based on published RNA-sequencing data for S . meliloti grown in a glucose minimal medium , these 489 genes tend to be highly expressed , with a median expression level above the 90th percentile ( S7 Fig ) . Compared to the entire chromosome ( Fisher exact test , p-value < 0 . 05 following a Bonferroni correction for 18 tests ) , this set of 489 genes was enriched for genes involved in translation ( 5 . 2-fold ) , lipid metabolism ( 2 . 7-fold ) , cofactor metabolism ( 3 . 3-fold ) , and electron transport ( 2 . 1-fold ) , whereas genes involved in transport ( 2 . 1-fold ) , motility/attachment ( 9 . 4-fold ) , and hypothetical genes ( 2 . 7-fold ) were under-represented ( Fig 2C ) . A clear influence of the growth medium on the fitness phenotypes of gene mutations was observed . Focusing on the wild-type strain , a core of 519 genes were identified as contributing equally to growth in both media ( Fig 2D ) . Forty genes were identified as more important during growth in rich medium than in defined medium , and these genes had a median GEI fold change of 7 . Only translation functions ( 5 . 8-fold ) displayed a statistically significant enrichment in these genes , which may reflect the faster growth rate in the rich medium ( S8 Fig ) , while there was also a non-statistically significant enrichment in signal transduction ( 5 . 1-fold ) ( Fig 2C ) . The extent of specialization for growth in the defined medium was more pronounced; 93 genes were more important during growth in the defined medium with a median GEI fold change of 20 . These genes were enriched ( statistically significant ) in amino acid ( 9 . 0-fold ) and nucleotide ( 6 . 7-fold ) metabolism presumably due to the requirement of their biosynthesis , and carbohydrate metabolism ( 3 . 6-fold ) likely as the sole carbon source was a carbohydrate ( Fig 2C ) . The same overall patterns were observed between media for the ΔpSymAB strain ( S9 Fig ) . The Tn-seq datasets for the wild-type and the ΔpSymAB strains were compared to evaluate fitness phenotypes across the two genetic backgrounds . Similar global trends were observed for both growth media ( rich and defined ) , suggesting that the results were generalizable and not medium-specific . Mutation of 488 ( rich ) or 484 ( defined ) chromosomal genes decreased growth in both genetic backgrounds . However , we also observed striking strain-dependent phenotypic effects . In ΔpSymAB cells , mutations of 250 ( rich ) or 251 ( defined ) genes led to stronger growth impairments than in wild-type cells; and conversely , in wild-type cells , mutations of 81 ( rich ) or 89 ( defined ) genes led to stronger growth impairments than in ΔpSymAB cells ( Fig 2E and 2F and Table 2 ) . Only minor functional biases were observed in the genes associated with larger fitness defects in the ΔpSymAB background ( Fig 2C ) ; in both media , only electron transport ( 3-fold ) and oxidoreductases ( 9 . 5-fold ) were over- and under-represented , respectively . Similarly , few functional biases were detected in genes associated with larger fitness defects in the wild-type background ( Fig 2C ) ; in both media , lipid metabolism ( 4 . 5-fold ) and hypothetical genes ( 2-fold ) were over- and under-represented , respectively , while nucleotide metabolism ( 5 . 5-fold ) was also enriched in the rich medium data . Overall , these results are consistent with pervasive functional connections between the chromosome and the secondary replicons , showing no strong bias toward specific biochemical pathways . Of 16 arbitrarily chosen genes predicted to be important for growth specifically in one of the two strains , nine of them , when disrupted in a targeted manner , led to the expected strain-specific strong growth inhibition ( S10 Fig ) . Of the other seven genes , three ( cbrA , ppk , tig ) were non-lethal but displayed growth rate defects or extended lag phases during liquid culture experiments ( S2 Table and S11 Fig ) . The remaining four genes may represent false positives from the Tn-seq screen; alternatively , the contrasting results may reflect differences in the growth conditions of the Tn-seq experiment ( competitive growth in a genetically complex population ) compared to the validation experiment ( monoculture ) . The results for the feuQ gene may be representative of how the failure to validate a result may be due to difference in experimental set-ups and not due to a false positive in the Tn-seq data ( see the Supplementary Results in S1 File ) . Regardless , the observation that at least 75% of the selected genes were confirmed to have a genome content-dependent fitness phenotype indicate that the large majority of the strain specific phenotypes observed in the Tn-seq screen represent true differences . Several recent studies have used Tn-seq to study the essential genome of Rhizobium leguminosarum [35–37] . We compared our Tn-seq datasets with those reported by Perry et al . [36] to examine the conservation of the essential genome of these two closely related legume symbionts . Putative orthologs for ~ 75% of all S . meliloti chromosomal genes were identified in R . leguminosarum via a Blast Bidirectional Best Hit ( Blast-BBH ) approach ( S2 Dataset ) . Much higher conservation of the growth promoting genome was observed; 97% of the 489 core growth promoting genes and 99% of the 307 core essential genes had a putative ortholog in R . leguminosarum . However , conservation of the gene did not necessarily correspond to conservation of the phenotype . Considering only the 303 core essential S . meliloti genes with a putative ortholog in R . leguminosarum , 8% ( 25 of 303 ) of the R . leguminosarum orthologs were classified as having little contribution to growth on defined medium ( Fig 3A ) . Two genes ( fumC , pdxJ ) identified as essential in S . meliloti but non-essential in R . leguminosarum based on the Tn-seq data were mutated by targeted disruption in S . meliloti . In both cases , the genes were confirmed to be essential in S . meliloti ( S5 Fig ) , supporting the Tn-seq data . A similar pattern is observed when starting with the R . leguminosarum genes classified as essential in both minimal and complex medium by Perry et al . [36] . Of the 241 core essential R . leguminosarum genes with an ortholog on the S . meliloti chromosome , 21 ( 9% ) of the orthologs were classified as having no contribution to growth in defined medium in S . meliloti ( Fig 3B ) . Whether these species-specific essentiality phenotypes are unique to the tested environment , or if they would also be observed in a more ecologically relevant environment , remains unclear . To further test the species-specificity of the above-mentioned genes , the mutational analysis was replicated in silico , based on metabolic reconstructions . Fifteen of the 25 orthologs specifically essential in S . meliloti were present in our existing S . meliloti genome-scale metabolic model [38] as well as in a draft R . leguminosarum metabolic model ( see Materials and Methods ) . Flux balance analysis was used to examine the in silico growth effect of deleting these 15 pairs of orthologs . Three pairs were classified as essential in both models , five were classified as non-essential in both models , and seven were classified as essential specifically in the S . meliloti model ( S3 Dataset ) . The analysis was repeated using a more rudimentary draft S . meliloti model to see if the results were strongly influenced by the curation level of the models . In this case , three pairs of orthologs were classified as essential in both models , seven were classified as non-essential in both models , and five were classified as essential specifically in the S . meliloti model ( S3 Dataset ) . Thus , even with the draft metabolic models , the in silico metabolic simulations corroborate at least some of the inter-species gene essentiality differences observed in the Tn-seq data . The results of the previous two sections are consistent with a strong genomic environment effect on the phenotypic consequences of gene mutations . One possible explanation is the presence of widespread genetic redundancy , at the gene and/or pathway level . In support of this , ~ 14% of chromosomal genes had a significant Blast hit when the chromosomal and pSymA/pSymB proteomes was compared against each other ( S4 Dataset ) . Therefore , this phenomenon was further explored using a constraint-based metabolic modeling approach . We first tested the in silico effect of chromosomal single gene deletions on growth rate in the presence and absence of pSymA/pSymB ( Fig 4A ) . This analysis identified 67 genes ( ~ 7% of all chromosomal model genes ) as having a more severely impaired growth phenotype when deleted in the absence of pSymA/pSymB genes , 38 of which were lethal . This appeared to be due to a combination of direct functional redundancy of the gene products as well as through metabolic bypasses , as deletion of 50 reactions dependent on chromosomal genes had a more severe phenotype in the absence of pSymA/pSymB , 42 of which were lethal ( S12 Fig ) . However , there was little overlap between the in silico and Tn-seq data ( S13 Fig ) ; in cases of differences , we would consider the Tn-seq data to be more accurate . Differences between datasets may be a result of errors in the model , or could reflect that the model does not account for regulation of gene expression; i . e . , two genes may be correctly predicted as functionally redundant , but this is not observed experimentally as they are expressed in unique conditions . Next , a double gene deletion analysis was performed to examine the effect on growth rate of deleting every possible pair of genes in the model . This analysis suggested that 75 chromosome-pSymAB gene pairs ( encompassing 49 chromosomal genes ) had a more significant impact on growth than expected based on the combination of predicted single-mutant phenotypes ( Fig 4B ) . Additionally , synthetic negative phenotypes were observed when simultaneously deleting 111 chromosome-chromosome gene pairs ( totaling 97 chromosomal genes ) ( Fig 4C ) . Overall , 14% of chromosomal genes were predicted to have a synthetic negative phenotype when co-deleted with a second gene . Even if metabolic modeling over-predicts the presence of functional redundancy in a specific environment ( for example , due to regulatory differences ) , these results are consistent with a high potential for metabolic robustness being encoded both within and between replicons in the S . meliloti genome . The results described in the previous sections made it evident that a Tn-seq approach alone is insufficient to elucidate all processes contributing to growth in a particular environment . This is especially true if also considering non-essential metabolism that is nevertheless actively present in wild-type cells , such as exopolysaccharide production . Moreover , it is difficult to fully comprehend the core functions of a cell by simply examining a list of essential genes and their predicted functions . We therefore attempted to overcome these limitations by using the Tn-seq data to guide a manual in silico reconstruction of the core metabolic processes of S . meliloti . A detailed description of this process is provided in the Supplementary Methods of S1 File . In brief , we began with a reconstruction that consisted of only one reaction , the biomass production reaction that combined all biomass components ( e . g . , protein , DNA , RNA , lipids , cofactors , etc . ) into ‘biomass’ . Initially , only protein was included as a biomass component . Pathways required to produce protein were built reaction by reaction in this new reconstruction , drawing from the reaction pool present in the existing genome-scale metabolic reconstruction , where possible . At the same time , the genes associated with each reaction were compared to the Tn-seq data and published literature ( see S5 Dataset ) to confirm the linkage of the correct gene ( s ) to each reaction . Only when all reactions necessary for production of protein were added to the reconstruction , as determined by the model being able to produce protein and convert it to biomass in FBA simulations , was the next biomass precursor ( e . g . , DNA ) added to the biomass reaction . This process was repeated until all biomass components ( S3 Table ) could be produced by the model and combined into biomass . As the final model was required to grow , where necessary , reactions required to complete essential pathways were added to the core model even if the associated gene ( s ) were not essential . The resulting core model , termed iGD726 and included in SBML format in S2 File , is summarized in Fig 5 and Table 3 , and the entire model , including genes , reaction formulas , and references is provided as an easy to read Excel table in S5 Dataset . The process of integrating the Tn-seq data with in silico metabolic reconstruction resulted in a major refinement of the core metabolism compared to the existing genome-scale model , while also improving predictions of gene essentiality ( S13 Fig ) : 228 new reactions were added , 115 new genes were added , and the genes associated with 135 of the 432 reactions common to the existing genome-scale reconstruction and the new core reconstruction were updated . In addition to improving the metabolic reconstruction , this process significantly expanded the view of core S . meliloti metabolism compared to that gained solely through the application of Tn-seq . The genes associated with approximately one third of the reactions in the core model were not detected as growth promoting in the Tn-seq datasets ( Fig 5 , Table 3 ) . While many of the additional reactions present in the core model are due to the inclusion of non-essential biomass components , which are part of the wild-type cell but are nonetheless dispensable for growth , others are from essential metabolic pathways ( Fig 5 , S14 Fig ) . Overall , the combined approach of integrating Tn-seq data and in silico metabolic modeling allowed for the development of a high-quality representation of core S . meliloti metabolism in a way that neither approach alone was capable of accomplishing . Initially , over 20 of the reactions in the core metabolic reconstruction could not be associated with a particular S . meliloti gene . Similarly , many genes with no clear biological function were found to be essential in the Tn-seq screen . By attempting to fill the gaps in the in silico model with the uncharacterized essential genes , we were able to assign putative functions to eight previously uncharacterized genes , of which five have good support for their new annotation ( S4 Table ) . Two of these genes were chosen for further characterization: smc01361 and smc04042 . The smc01361 gene was annotated as encoding a dihydroorotase , and targeted disruption of smc01361 resulted in pyrimidine auxotrophy ( S15 Fig ) . Given its location next to pyrB , and the presence of an essential PyrC dihydroorotoase encoded elsewhere in the genome ( S1 Dataset ) , we propose that smc01361 encodes an inactive dihydroorotase ( PyrX ) required for PyrB activity as has been observed in some other species including Pseudomonas putida [39 , 40] . The essential smc04042 gene was annotated as encoding an inositol-1-monophosphatase family protein . It was previously observed that rhizobia lack a gene encoding a classical L-histidinol-phosphate phosphohydrolase , and it was suggested an inositol monophosphatase family protein may fulfill this function instead [41] . Targeted disruption of smc04042 resulted in histidine auxotrophy ( S15 Fig ) , consistent with this enzyme fulfilling the role of a L-histidinol-phosphate phosphohydrolase . It is likely that this is true for most rhizobia , as putative orthologs of this gene were identified in all 10 of the examined Rhizobiales genomes ( S2 Dataset and S5 Dataset ) . These examples illustrate the power of the combined Tn-seq and metabolic reconstruction process in the functional annotation of bacterial genomes .
In this study , we developed a new variant of the Tn5 transposon for construction of non-polar insertion mutations that should be readily adaptable for use with other α-proteobacteria . Although Tn5 is generally considered to be non-specific with respect to insertion site , the consensus sequence of ~ 190 , 000 unique insertion locations revealed a bias for a particular GC rich motif ( S3 Fig ) , largely consistent with previous studies [42–44] . While this bias appeared to have little effect in mutagenesis of the high-GC genome of S . meliloti ( S4 Fig ) , accounting for this bias may be more important when applying Tn5 mutagenesis to species with low-GC genomes . In S . meliloti , each replicon appears to have a distinct evolutionary trajectory [45] . The pSymA replicon is a much more recent addition to the genome and is present only in a few closely related species , such as Sinorhizobium medicae [46] . Although present in all sequenced S . meliloti isolates , there is high genic variation between the pSymA replicons of individual strains , indicative of continual gene gain and gene loss [45 , 47] . This replicon is required for the symbiotic interaction with plants [48] , but few other functions can be attributed to it . On the other hand , the pSymB replicon is thought to be an old addition to the genome , acquired prior to the split from Brucella and sharing common ancestry with the second chromosome of the genus Brucella and the linear chromosome of the genus Agrobacterium [49] . The pSymB replicon is present in all sequenced S . meliloti isolates , and generally shows high synteny between isolates [45 , 47] . Functionally , pSymB is notable for controlling exopolysaccharide biosynthesis [50] and for encoding numerous solute transporters and a broad range of accessory metabolic pathways [31 , 51] , and it is likely specialized for adaptation to the rhizosphere environment [38] . Greater than 10% of bacterial species with a sequenced genome contain a genomic architecture similar to S . meliloti , that is , with at least two large DNA replicons [30 , 52] , including many plant symbionts ( e . g . , the rhizobia ) and plant and animal pathogens ( e . g . Burkholderia and Vibrio ) . Several studies have revealed that , in many ways , each replicon acts as a functionally and evolutionarily distinct entity ( for a review , refer to [52] ) ; yet , there can also be regulatory interactions [53] , as well as the exchange of genetic material between the replicons [54] . The Tn-seq analyses reported here provide new insights into the functional integration among replicons in a compound bacterial genome . The prevailing view is that secondary replicons such as pSymA and pSymB encode few to no essential genes . However , a large number of chromosomal genes—across many functional groups ( Fig 2 ) —became conditionally essential following the removal of pSymA and pSymB . This demonstrates that secondary replicons actually encode many proteins able to fulfill essential cellular functions in concert with chromosomally encoded proteins . Gene functions on secondary replicons may thus remain cryptic due to functional redundancy across replicons . We hypothesize that complete functional redundancy ( e . g . via gene duplication ) would be a transient phenomenon , and that on an evolutionary time-scale , either i ) one of the genes would be lost , ii ) functional divergence would occur , or iii ) regulatory divergences may occur that result in differential expression of the genes as for proline biosynthesis in Brucella [55] . Thus , secondary replicons have the potential to fine-tune the functionality or regulation of genes to help an organism adapt to new environments , leading to a robust genetic network encoded by both the chromosome and the secondary replicons . Previous studies have illustrated that the fitness phenotypes of orthologous genes in related species may differ [21 , 23–28 , 56] , and even that intercellular effects within microbial communities can modify the essential genome of a species [57] . The data reported here more directly address the topic of how a gene’s genotype-phenotype relationship is influenced by its genomic environment , by comparing the fitness phenotypes of mutating the exact same set of ~ 3 , 500 genes in two very different genomic environments . It was found that the non-essential genome had a remarkable influence on what was classified as a growth-promoting gene , with 10% of S . meliloti chromosomal genes exhibiting fitness-based genetic interactions with the non-essential component of the genome ( Fig 2 ) . This observation was not growth medium-dependent , was not unique to a specific gene functional class , and was not simply due to an overall reduced fitness of the ΔpSymAB strain as the findings could be largely replicated in silico ( Fig 4 ) . The majority of the genes whose fitness phenotype was dependent on the genomic environment became more important for fitness following the genome reduction . In many cases , this may reflect a loss of functional redundancy . For example , the increased importance of the chromosomal cytochrome genes ( see Fig 6 ) likely reflects a compensation for the loss of the pSymA/pSymB encoded cytochrome complexes . In other cases , increased gene essentiality after genome reduction may reflect pathways that must compensate for loss of a non-identical housekeeping pathway . Proline and histidine biosynthesis during growth in the rich medium was specifically essential in the ΔpSymAB strain ( Fig 6 ) , possibly to compensate for the inability of this strain to transport these metabolites [33] . Similarly , glycolysis appeared specifically essential in the ΔpSymAB strain in rich medium ( Fig 6 ) , likely as the reduced metabolic capacity of this strain [31] led to a greater reliance on catabolism of the abundant sucrose in the medium used for these experiments . Specific gene essentiality in the ΔpSymAB background may also occur as a result of synthetic negative interactions not associated with direct redundancy; for example , synthetic effects of disrupting two independent aspects of the cell envelope . This is evident in the ΔpSymAB-specific essentiality of the feuNPQ and ndvAB genes involved in production of periplasmic cyclic β-glucans ( Fig 6 ) [58–61] , as the cell envelope of the ΔpSymAB strain is expected to be significantly altered relative to the wild-type [33 , 50 , 62] . Somewhat surprisingly , approximately a quarter of the genes with a genomic environment effect had a greater fitness defect in the wild-type strain . In some cases this may have been due to the reduced nutrient demand of the ΔpSymAB strain as a result of the smaller genome content . For example , mutations of genes for arginine biosynthesis and the biosynthesis of common purine and pyrimidine precursors ( AICAR [5-Aminoimidazole-4-carboxamide ribonucleotide] and UMP ) led to fitness defects in rich medium specifically in the wild type ( Fig 6 ) . Potentially , the uptake of these nutrients is growth-limiting to the wild type in the absence of their de novo synthesis , whereas this is not the case in the ΔpSymAB strain due to the reduced genome size , and thus lower nutrient requirement , and the already reduced growth rate ( S8 Fig ) . Another possibility is that removal of pSymAB evokes phenotypes that are epistatic to many of those brought about by chromosomal mutations . For example , effects of impairing biosynthesis of the lipopolysaccharide core oligosaccharide ( Fig 6 ) may be phenotypically masked in the absence of pSymB due to the existing cell membrane alterations brought about by removal of pSymB [33 , 50 , 62] . Our work in integrating the Tn-seq data with in silico metabolic modeling made it evident that Tn-seq alone is insufficient to identify the entire core metabolism of an organism . Almost a third of the reactions present in the core metabolic reconstruction were not supported by Tn-seq data ( Fig 5 , Table 3 , S14 Fig ) . Conversely , the Tn-seq data supported substantial refinement of gene-reaction relationships in the model . In some cases , the gaps in the Tn-seq data were due to genomic environment effects; in other cases it was due to the inclusion of non-essential reactions that are nonetheless part of ‘wild-type’ metabolism , and sometimes the gene associated with a reaction is simply unknown . Furthermore , as Tn-seq involves growth of a complex population of mutants , there can be phenotypic complementation through cross-feeding if the metabolite is secreted and transferred to the mutant from the rest of the population . That the Tn-seq approach fails to identify many central metabolic reactions could have a significant practical impact in synthetic biology . The results of Tn-seq studies may guide engineering of designer microbial factories with specific properties [63] , or assist in the identification of putative new therapeutic targets [25 , 64] . While Tn-seq studies undoubtedly give invaluable information to be used toward these goals , such studies alone are insufficient as evidenced in the recent efforts to design and synthesize a minimal bacterial genome [22] . Importantly , this limitation can be overcome by combining Tn-seq with metabolic modeling . Only a few other studies have used both Tn-seq data and metabolic reconstruction [65–69] , which almost always focus on using the Tn-seq data to refine the metabolic reconstruction . As illustrated here , combining an experimental Tn-seq approach with a ground-up in silico metabolic reconstruction strategy can also improve the interpretation of Tn-seq data . A Tn-seq-guided reconstruction forces the identification of missing essential reactions , improves gene-reaction associations , and can facilitate functional annotation of uncharacterized genes . This process allows one to obtain a very high-quality representation of the metabolism ( and underlying genetics ) of an organism in the given environment . The resulting model can serve as a blueprint for understanding the workings of the cell in its native state , and for engineering new cell-based factories .
The wild-type and ΔpSymAB strains used throughout this work are the RmP3499 and RmP3496 strains , respectively , whose construction was described previously [34] . All E . coli or S . meliloti strains used in this study are described in S5 Table and were grown at 37°C or 30°C , respectively . BRM medium was used as the rich medium for growth of the S . meliloti strains , and it consisted of 5 g/L Bacto Tryptone , 5 g/L Bacto Yeast Extract , 50 mM NaCl , 2 mM MgSO4 , 2 μM CoCl2 , 0 . 5% ( w/v ) sucrose , and supplemented with the following antibiotics , as appropriate: streptomycin ( Sm , 200 μg/ml ) , neomycin ( Nm , 100 μg/ml ) , gentamycin ( Gm , 15 μg/ml ) . The defined medium for growth of S . meliloti contained 50 mM NaCl , 10 mM KH2PO4 , 10 mM NH4Cl , 2 mM MgSO4 , 0 . 2 mM CaCl2 , 0 . 5% ( w/v ) sucrose , 2 . 5 μM thiamine , 2 μM biotin , 10 μM EDTA , 10 μM FeSO4 , 3 μM MnSO4 , 2 μM ZnSO4 , 2 μM H3BO3 , 1 μM CoCl2 , 0 . 2 μM Na2MoO4 , 0 . 3 μM CuSO4 , 50 μg/ml streptomycin , and 30 μg/ml neomycin . E . coli strains were grown on Luria-Bertani ( LB ) medium supplemented with the following antibiotics as appropriate: chloramphenicol ( 30 mg/ml ) , kanamycin ( Km , 30 μg/ml ) , gentamycin ( Gm , 3 μg/ml ) . Overnight cultures grown in rich medium with the appropriate antibiotics were pelleted , washed with a phosphate buffer ( 20 mM KH2PO4 and 100 mM NaCl ) , and resuspended to an OD600 of 0 . 25 . Twelve μl of each cell suspension was mixed with 288 μl of growth medium , without antibiotics , in wells of a 100-well Honeycomb microplate . Plates were incubated in a Bioscreen C analyzer at 30°C with shaking , and OD600 recorded every hour for at least 48 hours . Single-gene knockout mutants were generated through single cross-over plasmid integration of the suicide plasmid pJG194 [70] . Approximately 400-bp fragments homologous to the central portion of the target genes were PCR amplified using the primers listed in S6 Table . PCR products as well as the pJG194 vector were digested with the restriction enzymes EcoRI/HindIII , or SalI/XhoI , and each PCR fragment was ligated into appropriately digested pJG194 using standard techniques [71] , and all recombinant plasmids were verified by Sanger sequencing . Recombinant plasmids were mobilized from E . coli to S . meliloti via tri-parental matings as described [60] , and transconjugants were isolated on BRM Sm Nm agar plates . All S . meliloti gene disruption mutants were verified by PCR . Transduction of the integrated plasmids into the S . meliloti wild-type and ΔpSymAB strains was performed using bacteriophage N3 as described elsewhere [72] , with transductants recovered on BRM medium containing the appropriate antibiotics . The plasmid pJG714 is a variant of the previously reported mini-Tn5 delivery plasmid , pJG110 [70] , with the primary modifications being removal of the bla gene and pUC origin of replication , and introduction of the pir-dependent R6K replication origin . A map of pJG714 is given in S1A Fig , and the complete sequence of the transposable region is provided in S1B Fig . This delivery plasmid is maintained in E . coli strain MFDpir [73] , which possesses chromosomal copies of R6K pir and RK2 transfer functions . MFDpir is unable to synthesize diaminopimelic acid ( DAP ) , thus disabling growth on rich or defined medium lacking supplemental DAP . The MFDpir/pJG714 strain was cultured on rich medium containing kanamycin and 12 . 5 μg/ml DAP . Transposon mutagenesis was accomplished in the wild-type and ΔpSymAB strains in parallel . Flask cultures of MFDpir/pJG714 and the two S . meliloti strains were grown overnight to saturation , and pellets were washed and suspended in BRM to a final OD600 value of approximately 40 . Equal volumes of each suspension were mixed as bi-parental matings , to accomplish mobilization of the transposon delivery vector into the S . meliloti recipient strains . These cell mixtures were plated on BRM supplemented with 50 μg/ml DAP and incubated at 30°C for 6 h . Mating mixtures were collected in BRM with 10% glycerol , and cell clumps were broken up by shaking the suspended material for 30 min at 225 rpm . Aliquots were stored at -8°C . For selection of transposants , mating mixes were thawed and plated at a density of 15 , 000 cfu/plate ( 150-mm plates ) on BRM supplemented with Sm and Nm . To accomplish equivalent coverage of each genome with transposon insertions , 675 , 000 and 360 , 000 colonies were selected for the wild-type and ΔpSymAB strains , respectively . For each recipient , transposon mutant colonies were collected and cell clumps were broken up as described above . The selected clone libraries were aliquoted and stored at -80°C . For whole-population selection and massively parallel sequencing of transposon ends , 1x109 cells from each of the two clone libraries were transferred into 500 ml of either BRM or defined medium , allowing approximately 8–10 generations of growth at 30°C before reaching saturation . At this stage , cells were pelleted , DNA was extracted using the MoBio Microbial DNA isolation kit ( #12255–50 ) , and the resulting DNA was fragmented with NEB fragmentase ( #M0348S ) to an average molecular weight of 1000 bp . After clean-up ( Qiagen #27106 ) , the resulting DNA fragments were appended with short 3’ homopolymer ( oligo-dCTP ) tails using terminal deoxynucleotidyl transferase ( NEB #M0315S ) , and this sample was used as the template for a two-round PCR process that gave rise to the final Illumina-ready libraries . In the first round , a transposon end-specific primer ( 1TN ) and oligo-G primer ( 1GG ) were used ( all primer sequences can be found in S6 Table ) . After clean-up , a portion of the first-round product was used as the template for the second-round reaction employing a nested transposon-specific primer ( 2TNA-C ) and a reverse index-incorporating primer ( 2BAR01-08 ) . A series of three 2TN primers ( A-C ) were designed to incorporate base diversity in the opening cycles of Illumina sequencing , and a series of eight 2BAR primers were designed to uniquely identify each experimental condition in a single multiplexed sequencing sample . After PCR amplification of transposon-flanking sequences with concomitant incorporation of Illumina adapters and barcodes , the samples were size-selected for 200-600-bp fragments , and sequenced on an Illumina Hi-Seq instrument as 50-bp single-end reads . Raw sequencing data was deposited to the Sequence Read Archive ( SRA ) as part of a Bioproject ( accession: PRJNA427834 ) . Raw DNA-sequencing reads were used as input into a custom-built Tn-seq analytical pipeline , which was recently described [64] . In brief , the pipeline first processed the fastq files and discarded reads not containing the transposon sequence . It then aligned reads to the genome with Bowtie2 [74] , counted the number of reads to each annotated gene in the genome , and normalized the results based on reads mapping to intergenic regions . Reads mapping to the last 5% of the gene were discarded [75] , and all other options were left at their default settings . The normalized read counts were used as a proxy of the transposon insertion density within each gene . To calculate the Gene Essentiality Index ( GEI ) scores , a pseudo count of one was first added to all normalized gene read counts for each replicate . GEI scores were then calculated by summing the number of reads that mapped to the gene in both replicates , and dividing this number by the nucleotide length of the gene . GEI scores were calculated for each gene separately in each medium and in each strain . All GEI values are available in S1 Dataset , as are fold changes between conditions . The output of the Tn-seq analysis pipeline for the chromosomal genes was used in the fitness classification of genes as detailed further in the Supplementary Methods of S1 File . Briefly , all genes with no observed insertions ( i . e . , no reads ) were classified as essential . Although this step may result in small genes ( that lack insertions by chance ) being falsely annotated as essential , manually checking these genes showed many are expected to be truly essential ( e . g . ribosomal proteins ) . Next , GEI scores were imported into R version 3 . 2 . 3 and log transformed . Initial clustering of the data was performed through the fitting of an optimal number of overlapping Guassian distributions to the log transformed GEI scores ( S16 Fig ) , using the Mclust function of the Mclust package in R [76] . Clusters were then refined through the use of the affinity propagation statistical approach , implemented in the apcluster function of the apcluster package of R [77] . Genes with GEI scores significantly different between conditions were determined through clustering of the log transformed fold changes and the fitting of overlapping Guassian distributions with Mclust in R ( S16 Fig ) . Subsequent to the above analyses , the validity of the clustering-based approach in identifying essential genes was examined by re-analyzing the data with the recently published TSAS pipeline [69] . The pipeline was run independently for each sample ( both replicates were considered together ) using the one-sample analysis setting and the default parameters . The TSAS pipeline uses binomial probability to identify the probability that a gene has fewer insertions than expected by chance , but does not distinguish between essential and growth defective genes . Output from this tool is included in S1 Dataset , and overall , the analysis i ) supported the gene classifications determined using the clustering approach above , and ii ) supported that the general conclusions drawn from this work are unlikely to be significantly impacted by choice of analysis pipeline . Additional description of the comparison of the methods can be found in the Supplementary Results of S1 File . Assignment of chromosomal genes into specific functional categories was performed largely based on the annotations provided in the S . meliloti Rm1021 online genome database ( iant . toulouse . inra . fr/bacteria/annotation/cgi/rhime . cgi ) . This website pulls annotations from several databases including PubMed , Swissprot , trEMBL , and Interpro . Additionally , it provides enzyme codes , PubMed IDs , functional classifications , and suggested Gene Ontology ( GO ) terms for most genes . The numerous classifications were simplified to 18 functional categories , designed to adequately cover all core cellular processes , as indicated in the Supplementary Methods of S1 File . In cases of ambiguous or conflicting annotations , the annotations were refined through an approached based on BLASTp searches , as described in the Supplementary Methods of S1 File . The functional annotations of all chromosomal genes are provided in S6 Dataset . Tn-seq results were visualized using the Integrative Genomics Viewer v2 . 3 . 97 [78] . Scatter plots , functional enrichment plots , box plots , and line plots were generated in R using the ggplot2 package [79] . Venn diagrams were produced in R using the VennDiagram package [80] . The genome map was prepared using the circos v0 . 67–7 software [81]; the sliding window insertion density was calculated with the geom_histogram function of ggplot2 , and the GC skew was calculated using the analysis of sequence heterogeneity sliding window plots online webserver [82] . The metabolic model was visualized using the iPath v2 . 0 webserver [83] . The logo of the transposon insertion site specificity was generated by first extracting the nucleotides surrounding all unique insertion sites in one replicate of the wild type grown in rich medium using Perl v5 . 18 . 2 , followed by generation of a hidden Markov model with the hmmbuild function of HMMER v3 . 1b2 [84] and visualization with the Skylign webserver [85] . Putative orthologous proteins between species were identified with a Blast-BBH approach , implemented using a modified version of our in-house Shell and Perl pipeline [86] . Proteomes were downloaded from the NCBI repository , and the Genbank annotations were used . To limit false positives , Blast-BBH pairs were only maintained if they displayed a minimum of 30% amino acid identify over at least 60% of the protein . To identify putative , functionally duplicated proteins in S . meliloti , the same Blast approach was employed to compare the S . meliloti chromosomal proteome with the proteins encoded by pSymA and pSymB . The Blast-BBH approach was used to identify putative orthologs of all S . meliloti proteins in 10 related species within the order Rhizobiales . All results are given in S2 Dataset and S5 Dataset in order to facilitate easy identification of the Tn-seq data and metabolic reconstruction data for a S . meliloti ortholog of a gene of interest in these other species . All simulations were performed in MATLAB 2017a ( Mathworks ) with scripts from the COBRA Toolbox ( downloaded May 12 , 2017 from the openCOBRA repository ) [87] , and using the Gurobi 7 . 0 . 2 solver ( gurobi . com ) , the SBMLToolbox 4 . 1 . 0 [88] , and libSBML 5 . 13 . 0 [89] . Boundary conditions for simulation of the defined medium are given in S7 Table . Single and double gene deletion analyses were performed using the singleGeneDeletion and doubleGeneDeletion functions , respectively , using the Minimization of Metabolic Adjustment ( MOMA ) method . All MATLAB scripts used in this work are provided as S3 File . For all deletion mutants , the growth rate ratio ( grRatio ) was calculated as: growth rate of mutant / growth rate of wild-type . Single gene deletion mutants were considered to have a growth defect if the grRatio was < 0 . 9 . For the double gene deletion analysis , if the grRatio of the double mutant was less than 90% of the expected grRatio ( based on multiplying the grRatio of the two corresponding single mutants ) , the double deletion was said to have a synthetic negative phenotype . In silico analysis of redundancy in the S . meliloti genome was performed using the existing S . meliloti genome-scale metabolic network reconstruction [38] , modified as described in the Supplementary Methods of S1 File . Removal of pSymA and pSymB in silico is described in the Supplementary Methods of S1 File . A draft , fully automated model containing no manual curation for R . leguminosarum bv . viciae 3841 was built using the KBase webserver ( kbase . us ) , based on the Genbank file of the R . leguminosarum genome [90] . Similarly , a draft S . meliloti Rm1021 model was built using KBase , starting with the Genbank file for the S . meliloti genome [29] . Draft model reconstruction is described further in the Supplementary Methods of S1 File . All metabolic reconstructions used in this work are provided in SBML and MATLAB format in S2 File . The iGD726 core model was built from the ground-up using the existing genome-scale model as a reaction and GPR database , and with the Tn-seq data as a guide . This process is described in detail in the Supplementary Methods of S1 File . Briefly , iGD726 began with no reactions except for a biomass reaction that contained only a single substrate ( e . g . , protein ) . All pathways required to produce protein were then added to the core model , using the Tn-seq data as a guide and drawing reactions from the original genome-scale model , or when necessary , from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) database [91] . When all reactions necessary for the production of protein were present in the model , as confirmed by the ability of the model to produce biomass in FBA simulations , the next biomass component was added to the biomass reaction . This process was repeated until the core model could produce all biomass components ( S3 Table ) . As the original model is a full genome-scale metabolic reconstruction , encompassing core and accessory metabolism , not all reactions were transferred to the core model; only those essential for biomass production or to accurately capture the Tn-seq data were included in iGD726 . Throughout the above process , the Tn-seq data were used to refine the gene-reaction associations . Each time a new reaction was added to the core reconstruction , the genes associated with the reaction were checked against the Tn-seq data , and a literature search for each associated gene was performed . The gene associations were then modified as necessary to ensure the model accurately captured the experimental data . Additionally , putative functions were assigned to uncharacterized genes during model construction by trying to link essential Tn-seq genes to essential reactions lacking an associated gene , as described in the Supplementary Methods of S1 File . The final model contained 726 genes , 681 reactions , and 703 metabolites , and is provided in SBML and MATLAB format in S2 File , and as an Excel file in S5 Dataset . The Excel file contains all necessary information for use as a S . meliloti metabolic resource , including the reaction name , the reaction equation using the real metabolite names , the associated genes/proteins , and references . Additionally , for each reaction , the putative orthologs of the associated genes in 10 related Rhizobiales species are included , allowing the model to provide useful information for each of these organisms . | S . meliloti , which has traditionally facilitated ground-breaking insights into symbiotic communication , is also emerging as an excellent model for studying the evolution of functional relationships between bacterial chromosomes and anciently acquired accessory replicons . Multi-replicon genome architecture is present in ~ 10% of presently sequenced bacterial genomes . The S . meliloti genome is composed of three circular replicons , two of which are dispensable even though they encompass nearly half of the protein-coding genes in this organism . The construction of strains lacking these replicons has enabled a straightforward , genome-wide analysis of interactions between the chromosome and the non-essential replicons , revealing extensive functional cooperation between these genomic components . This analysis enabled a substantial refinement of a metabolic network model for S . meliloti . The integration of massively parallel genotype-phenotype screening with in silico metabolic reconstruction has enhanced our understanding of metabolic network structure as it relates to genome evolution in S . meliloti , and exemplifies an approach that may be productively applied to other taxa . The combined experimental and computational approach employed here further provides unique insights into the pervasive genetic interactions that may exist within large bacterial genomes . |
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A recent study has shown that treatment of visceral leishmaniasis ( VL ) with the standard dose of 15 mg/kg/day of paromomycin sulphate ( PM ) for 21 days was not efficacious in patients in Sudan . We therefore decided to test the efficacy of paramomycin for a longer treatment duration ( 15 mg/kg/day for 28 days ) and at the higher dose of 20 mg/kg/day for 21 days . This randomized , open-label , dose-finding , phase II study assessed the two above high-dose PM treatment regimens . Patients with clinical features and positive bone-marrow aspirates for VL were enrolled . All patients received their assigned courses of PM intramuscularly and adverse events were monitored . Parasite clearance in bone-marrow aspirates was tested by microscopy at end of treatment ( EOT , primary efficacy endpoint ) , 3 months ( in patients who were not clinically well ) and 6 months after EOT ( secondary efficacy endpoint ) . Pharmacokinetic data were obtained from a subset of patients weighing over 30 kg . 42 patients ( 21 per group ) aged between 4 and 60 years were enrolled . At EOT , 85% of patients ( 95% confidence interval [CI]: 63 . 7% to 97 . 0% ) in the 20 mg/kg/day group and 90% of patients ( 95% CI: 69 . 6% to 98 . 8% ) in the 15 mg/kg/day group had parasite clearance . Six months after treatment , efficacy was 80 . 0% ( 95% CI: 56 . 3% to 94 . 3% ) and 81 . 0% ( 95% CI: 58 . 1% to 94 . 6% ) in the 20 mg/kg/day and 15 mg/kg/day groups , respectively . There were no serious adverse events . Pharmacokinetic profiles suggested a difference between the two doses , although numbers of patients recruited were too few to make it significant ( n = 3 and n = 6 in the 20 mg/kg/day and 15 mg/kg/day groups , respectively ) . Data suggest that both high dose regimens were more efficacious than the standard 15 mg/kg/day PM for 21 days and could be further evaluated in phase III studies in East Africa . ClinicalTrials . gov NCT00255567
According to the WHO estimates , visceral leishmaniasis ( VL ) is a parasitic disease that affects more than 500 , 000 people globally each year [1] , and has a fatality rate of up to 100% if left untreated [2] . 90% of cases occur in five countries: India , Bangladesh , Nepal , Sudan , and Brazil [1] , with the affected communities mostly located in remote regions of these endemic areas without ready access to treatment . Although drugs ( mainly antimonials such as sodium stibogluconate [SSG] ) currently exist to treat this parasitic infection , their use has been limited because of high cost , toxicity , or development of parasite resistance [3]–[5] . A multi-center phase III study in India showed that PM is a very efficacious , affordable , and safe treatment [6] , and is now registered for VL treatment in India . In an effort to identify an effective treatment for VL in East Africa , we had previously initiated a multi-center phase III study in Sudan , Ethiopia , and Kenya comparing the efficacy of PM alone at the dose shown to be efficacious in India ( 15 mg/kg/day for 21 days ) against SSG alone ( 20 mg/kg/day for 30 days ) and against a combination treatment of SSG and PM ( same dose of individual treatments but for 17 days ) . PM monotherapy did not show adequate efficacy , particularly in Sudan where parasite clearance was below 50% in patients at 6 months after end of treatment ( EOT ) , and the study had to be prematurely stopped [7] . In the current study , we sought to find an efficacious dose of PM for the treatment of VL in Sudan and to explore possible reasons for the failure of the drug at the previous dose studied of 15 mg/kg/day for 21 days . In our previous study using this dose , conducted in 5 sites in Ethiopia , Kenya and Sudan , we found an overall end of treatment cure of 67 . 4% and 6-month post-treatment cure of 63 . 8% [7] . Cure at both sites in Sudan was below 50% [7] . The cure rate in this study of SSG was 92 . 2% at 6 months post-treatment [7] . Therefore a total dose increase of 33% was attempted through two possible regimens- an increased dose of 20 mg/kg for 21 days or a prolonged course of 15 mg/kg for 28 days . The former regimen has been evaluated in some clinical trials in India [8] , [9] . There was no previous clinical experience with the 15 mg/kg dosage given for 28 days . The rationale was that the longer course of treatment would provide additional time for the patient's general condition to improve , and for their immunological response to develop , and that this might translate into a better clinical response without increasing the daily dosage .
The main objective was to assess the efficacy of two dosing regimens of PM monotherapy for the treatment of VL: 20 mg/kg/day for 21 days and 15 mg/kg/day for 28 days . Secondary objectives were to assess the safety of PM and compare the pharmacokinetic ( PK ) profiles of the two groups in a subset of patients . Patients with clinical symptoms and signs suggestive of VL and confirmed by visualization of parasites in bone-marrow aspirates were eligible for enrollment according to the National VL guidelines for Sudan for treatment and control . To be included in the study , patients had to: be between 4 and 60 years of age; be able to comply with the protocol ( Protocols S1 , S2 and S3 ) ; and provide written informed consent signed by themselves or by parents or legal guardians . Patients were excluded from the study if they: had negative bone-marrow smears; were clinically contraindicated to having a bone-marrow aspirate; received any anti-leishmania drug in the past 6 months; had severe protein or caloric malnutrition ( Kwashiorkor or marasmus ) ; had previous hypersensitivity reaction to aminoglycosides; suffered from a concomitant severe infection , ie tuberculosis , HIV , or any other serious underlying disease ( cardiac , renal , hepatic ) ; suffered from other conditions associated with splenomegaly such as schistosomiasis; had previous history of cardiac arrhythmia or an abnormal electrocardiogram ( ECG ) ; were pregnant or lactating; or had pre-existing clinical hearing loss . If tuberculosis or schistosomiasis were suspected , these were screened through laboratory testing . Additionally , patients with the following laboratory values were excluded: hemoglobin less than 5 g/dL; white blood cell less than 103/mm3; platelets less than 40 , 000/mm3; liver function test values more than three times the normal range; and serum creatinine values outside the normal range for age and gender . This was a two-arm , randomized , open-label , dose-finding study done at a single site in Sudan ( Kassab Hospital , Ministry of Health , Gedaref State ) . This site participated in the previous study conducted on PM [7] . Eligible patients were randomly assigned to 20 mg/kg/day PM for 21 days ( n = 21 ) or 15 mg/kg/day PM for 28 days ( n = 21 ) , and started a treatment regimen upon allocation to their treatment . Restricted-block randomization was done for the two groups . Randomization was done using sequentially numbered sealed envelopes that were prepared according to a centrally generated randomization list . Treatment was administered via daily intramuscular injection , and patients remained in the hospital for the duration of treatment . Patients were followed up at 3 and 6 months after treatment as outpatients . Parasitological assessments ( bone-marrow aspirates only ) were done at baseline , end of treatment ( EOT ) , 3 months ( only on patients who were not clinically well ) and 6 months after treatment . Safety and clinical laboratory assessments were done at baseline , day 7 and day 14 of drug administration , EOT , and at 3 and 6 months follow-ups . These included a clinical assessment , ( clinical symptoms , vital signs , weight , spleen and liver size ) , ECG , HIV testing ( at baseline only ) , hemoglobin , white cell count , platelets , urea , creatinine , liver function tests ( bilirubin , aspartate aminotransferase , alanine aminotransferase , alkaline phosphatase ) , urinalysis and audiometry . Audiometry was performed using a standardized procedure by site investigators who were trained by a qualified audiometrist and recorded as hearing levels in dB at 0 . 25 , 0 . 5 , 1 , 2 , 4 and 8 kHz frequencies [10] . All reported abnormal audiometric readings were reviewed by the audiometrist . An audiometric shift was defined in patients for whom there was one of the following: an increase in hearing level between baseline and EOT of ≥25 dB at ≥1 threshold frequency; an increase in hearing level between baseline and EOT of ≥20 dB at ≥2 adjacent threshold frequencies . Disabling hearing impairment was determined as an average of at least 41 dB across 0 . 5 , 1 , 2 and 4 kHz frequencies in adults ( ages 15 years and above ) and at least 31 db across 0 . 5 , 1 , 2 and 4 kHz frequencies in children ( less than 15 years of age ) [10] . Parasitology slides were prepared from bone-marrow aspirates , read , and reported according to a standardized , approved WHO method [11] , [12] . Standardised parasitology readings were done from freshly prepared bone-marrow aspirates taken directly from the patients to the laboratory . Slide fields were examined and counted for parasites under oil emersion 100× magnification for 30 minutes ( timed ) before being declared negative ( absence of parasites on microscopy slide ) . All parasitology was performed by a trained laboratory technician . For the PK analysis , the first six consenting patients weighing 30 kg or more were selected from each treatment group and had additional venous blood and urine samples on day 1 and day 14 in the 20 mg/kg/day group , and on day 1 and day 26 in the 15 mg/kg/day group . The timing for blood sampling was 0 ( before treatment ) and at 0 . 25 , 0 . 5 , 1 , 2 , 4 , 6 , 8 , 12 , and 24 hours after administration of the drug , and at 0–2 , 2–4 , 4–6 , 6–8 , 8–12 , and 12–24 hours after administration of the drug for urine sampling . The trial was done in accordance with the Declaration of Helsinki ( 2002 version ) for the conduct of research on human subjects and followed the International Committee for Harmonization guidelines for the conduct of clinical trials . All trial site personnel received relevant training in Good Clinical Practices . The Ethics Committee of the Institute of Endemic Diseases , University of Khartoum , and the Directorate of Health Research , Federal Ministry of Health , Sudan approved the study protocol ( July 8 , 2005 ) , which was submitted as a protocol amendment ( Protocol S3 ) to our previous study [7] . All participants or their parents or legal guardians gave their written informed consent before entry into the trial . Children were included in this study because they represent more than 50% of VL cases in this endemic area , and were included in the PK sampling if they met the weight criteria ( >30 kg ) . This study was registered at ClinicalTrials . gov ( registration number NCT00255567 ) . The study medication was 1 g/2mL paromomycin sulphate ( Gland Pharma , India ) . Doses in the study groups were 20 mg/kg/day paromomycin sulphate ( equivalent to 15 mg/kg/day of paromomycin base ) and 15 mg/kg/day paromomycin sulphate ( equivalent to 11 mg/kg/day paromomycin base ) . The rescue medication was AmBisome ( a liposomal formulation of amphotericin B , Gilead , USA ) , which was reconstituted according to the manufacturer's instructions for a dosage of 3 mg/kg/day for 10 days .
104 patients with suspected VL were screened for entry into this study . Of these , 42 patients were enrolled in the study ( 21 per group; figure 1 ) . Demographics and baseline characteristics were similar in the two groups ( table 1 ) . One patient in the 20 mg/kg/day PM group was considered lost by the 6-month follow-up . The first patient was recruited in October 2005 and the last patient followed-up in October 2006 . Data were available for all patients at EOT ( figure 1 ) . 18 patients in the 20 mg/kg group and 19 in the 15 mg/kg group had parasite clearance at EOT , indicating an efficacy of 85 . 7% ( 95% CI: 63 . 7% to 97 . 0% ) and 90 . 5% ( 95% CI: 69 . 6% to 98 . 0% ) , respectively ( table 2 ) . At 3-months follow-up , two patients had relapsed in the 20 mg/kg/day for 21 days regimen and three in the 15 mg/kg/day for 28 days regimen; however , there were no additional relapses at 6 months . At 6-months follow-up , the complete-case analysis efficacy in both groups was similar ( 80 . 0% in the 20 mg/kg/day group versus 81 . 0% in the 15 mg/kg/day group ) ( table 2 ) . All treatment failures were given rescue medication . An exception was one patient in the 20 mg/kg/day PM group who was parasite positive at EOT but clinically responded . This patient was lost to follow-up at 6 months , leading to a lower efficacy estimate in the worst-case analysis ( table 2 ) . There was one slow responder ( ie , parasite-positive patient at EOT , but clinically well and ultimately recovered ) in the group treated with 15 mg/kg/day PM for 28 days . PM was well tolerated in this study . 48 AEs were reported in total; 20 in the 20 mg/kg for 21 days group and 28 in the 15 mg/kg for 28 days group ( table 3 ) , and none was regarded as serious . This gives an AE rate of 0 . 05 per person-day on treatment in both groups . All AEs , except diarrhea and malaria , were judged to be related to the treatment . The most frequent AE was injection site pain ( n = 33 ) . Audiometric shifts were seen in five patients at EOT ( n = 3 in the 15 mg/kg group and n = 2 in the 20 mg/kg group ) , but completely resolved by 6 months follow-up . Disabling hearing impairment , detected at EOT , which improved but persisted at 6 months ( ie still met the criteria for audiogram shift ) , occurred in one patient in the 20 mg/kg group . Although six patients from each group should have taken part in the PK study , only data from three patients in the 20 mg/kg/day PM group and six in the 15 mg/kg/day PM group were obtained . Only one patient was a child ( age of 12 years and weight of 39 kg in the 15 mg/kg group ) . The others were aged between 17 and 28 years . Mean plasma PM concentrations at the earlier time points were similar between the two treatment groups ( figure 2 ) . Nevertheless , the peak mean plasma PM concentration on day 1 was slightly higher in the 20 mg/kg/day group compared with that in the 15 mg/kg/day group ( 7 . 8±4 . 9 µg/mL versus 5 . 6±4 . 2 µg/mL ) . Six hours after administration , PM was not detected in the plasma of patients receiving 15 mg/kg/day PM but was seen at concentrations slightly lower than peak in the plasma of patients receiving 20 mg/kg/day PM ( figure 2 ) .
To date , the standard treatment for VL in East Africa still consists of antimonials . This study is part of the first large-scale multi-centre clinical trial to assess the efficacy of PM for the treatment of VL for the East African region . The initial study [7] showed poor efficacy results when 15 mg/kg/day PM was administered for 21 days to VL patients . This finding is in contrast to an earlier phase III study in India [6] . The results of this study show that increasing the total dose of PM from 15 mg/kg/day for 21 days to 15 mg/kg/day for 28 days or 20 mg/kg/day for 21 days improves efficacy in VL patients in Sudan . However , it should be cautioned that the results found in this study apply to one site only and might not apply to the whole East African region . Although efficacy is normally assessed as parasite clearance at 6 months in trials for VL , in this study we chose to use parasite clearance at EOT as the primary endpoint because a chance of loss to follow-up of just a few patients would significantly affect the result . In addition to the small sample size , another potential limitation is the use of bone-marrow aspiration for diagnosis and test of cure . However , spleen aspiration remains contraindicated in rural hospitals in Sudan , making bone marrow the best viable alternative . At 6 months after treatment , efficacy was 80 . 0% ( 95% CI: 56 . 3% to 94 . 3% ) and 81 . 0% ( 95% CI: 58 . 1% to 94 . 6% ) in the 20 mg/kg/day and 15 mg/kg/day groups , respectively , compared with less than 50% ( in Sudan ) at 6 months observed in the previous study [7] . This result shows that efficacy improved to levels closer to those obtained in trials in India ( ∼95% ) [6] . Serious safety issues that would limit the evaluation of PM at high doses were not identified in this study . Otoxicity , which has been seen as a transient side-effect of PM in other studies [6] , was also identified as a potential issue in this study because one patient had audiometric shift at 6 months . This shift occurred at high frequencies , as expected with aminoglycosides [6] . We suggest that this adverse event needs to be monitored closely in subsequent studies . PK analyses showed that peak plasma PM concentration occurred 1–2 hours after administration and suggest that , at the high daily dose of 20 mg/kg , elevated plasma PM concentrations may be maintained for a longer period of time ( up to 8 hours ) . Unpublished data ( Mahmoud Mudawi , personal communication ) of PM administration ( 15 mg/kg ) to healthy Sudanese volunteers showed peak PM plasma concentrations similar to those in American volunteers who received a similar dose [14] . Sudanese VL patients had a much lower plasma concentration ( 30–40% ) than that of healthy Sudanese ( 19 . 5±7 . 6µg/mL; n = 6 ) and American volunteers . Therefore , Sudanese VL patients may have different PK characteristics from both Sudanese and American healthy volunteers , and Indian VL patients . However , PK data were very limited and derived from only a small subset of patients . A PK study with more patients is currently underway as part of the larger phase III study . Even though interpretation of our results is limited because of the small sample size , we identified what seems to be a more efficacious dose of PM than the one previously used in Sudan [7] . A meeting of the principal investigators was held to discuss the PM efficacy and PK dose-finding results . The group chose to use in the large multi-center phase III study , a dose of 20 mg/kg/day for 21 days for a comparison with the previously used doses of SSG and SSG and PM in combination . Our initial study [7] showed that efficacy of PM can vary greatly between geographical regions , and in addition to this study , suggests that different doses may be required to obtain similar levels of efficacy . If confirmed , these results emphasize the importance of considering regional differences in the treatment of VL and show that drugs of proven efficacy in Asian patients might not have the same efficacy in African patients . | Visceral leishmaniasis ( VL ) is a parasitic disease transmitted through the bite of sandflies . The WHO estimates 500 , 000 new cases of VL each year , with more than 90% of cases occurring in Southeast Asia , East Africa , and South America . If left untreated , VL can be fatal . We had previously conducted a large multi-center study in Sudan , East Africa , to assess the efficacy of paromomycin ( PM ) alone or in combination with sodium stibogluconate . Clinical studies in India have shown that 15 mg/kg/day PM for 21 days was an effective cure . However , the same treatment regimen was not efficacious in two study sites in Sudan . Here , our aim was to assess two high-dose regimens of PM in Sudan: 15 mg/kg/day for 28 days and 20 mg/kg/day for 21 days . The results suggest that , at these total doses , PM is more efficacious than when given daily at 15 mg/kg for 21 days , and that high doses are required to treat VL in Sudan . Efficacy of 20 mg/kg/day PM for 21 days is currently being evaluated in a prospective , comparative phase III trial in East Africa . |
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Weight control diets favorably affect parameters of the metabolic syndrome and delay the onset of diabetic complications . The adaptations occurring in adipose tissue ( AT ) are likely to have a profound impact on the whole body response as AT is a key target of dietary intervention . Identification of environmental and individual factors controlling AT adaptation is therefore essential . Here , expression of 271 transcripts , selected for regulation according to obesity and weight changes , was determined in 515 individuals before , after 8-week low-calorie diet-induced weight loss , and after 26-week ad libitum weight maintenance diets . For 175 genes , opposite regulation was observed during calorie restriction and weight maintenance phases , independently of variations in body weight . Metabolism and immunity genes showed inverse profiles . During the dietary intervention , network-based analyses revealed strong interconnection between expression of genes involved in de novo lipogenesis and components of the metabolic syndrome . Sex had a marked influence on AT expression of 88 transcripts , which persisted during the entire dietary intervention and after control for fat mass . In women , the influence of body mass index on expression of a subset of genes persisted during the dietary intervention . Twenty-two genes revealed a metabolic syndrome signature common to men and women . Genetic control of AT gene expression by cis signals was observed for 46 genes . Dietary intervention , sex , and cis genetic variants independently controlled AT gene expression . These analyses help understanding the relative importance of environmental and individual factors that control the expression of human AT genes and therefore may foster strategies aimed at improving AT function in metabolic diseases .
Obesity is characterized by an excess of fat deposited in adipose tissue ( AT ) as triglycerides . An increase in adiposity is associated with increased risk of cardiovascular disorders and metabolic abnormalities , including hypertension , insulin resistance , type 2 diabetes , obstructive sleep apnea and cancers . Diet-induced weight loss prevents risk for type 2 diabetes and metabolic syndrome [1] , [2] , emphasizing the pivotal role of AT in obesity-related complications . As a key target tissue of dietary intervention and a node of integration between metabolism and immunity , adaptations occurring in AT are likely to have a profound impact on the whole body response [3] , [4] . Obesity is a complex disorder with numerous contributing environmental and genetic factors . A multidisciplinary research effort involving a combination of clinical , biochemical and omics approaches appears mandatory to increase knowledge in the complexity of biological traits and processes associated with obesity [5] . Through probing of the transcriptional activity of tissues , the techniques allowing systematic analysis of AT gene expression have proved useful at identifying master genes [6] and regulatory networks involved in human obesity and related disorders [7] . Moreover , mRNAs are molecular species easily and evenly amplified . Hence , mRNA profiling remains one of the most powerful methods to comprehensively explore minute amounts of tissue . Real-time PCR , which provides great dynamic range and sensitivity , is a low throughput and time-consuming technology . DNA microarray analysis allows genome-wide profiling often applied to small subsets of samples . Combining benefits of both approaches recently became possible through the emergence of microfluidic-based technologies that use very limited sample and reagent quantities [8] . Moreover , large-scale investigation of gene expression in small AT samples obtained from microbiopsy has been impaired by poor yield of total RNA due to the richness in lipid . Optimization of AT biopsy handling and total RNA extraction is thus an essential step to profitably use AT samples for gene profiling applications . The DiOGenes trial is one of the largest longitudinal dietary interventions worldwide consisting in an 8-week weight loss diet and a 26-week weight control phase with different dietary regimes [9] , [10] . The prospective long-term , randomized , controlled study design offered a unique opportunity to apply genomics technology to dietary intervention aimed at maintaining weight loss . In this study , we applied an improved total RNA preparation from AT to the thousands of samples available during the DiOGenes study . Using a novel microfluidic technology , quantitative expression analysis of AT genes was performed in individuals from this cohort . The relationship between mRNA levels and bio-clinical and genetic data was investigated . These integrative analyses provide evidence of composite control of AT gene expression by nutrition , metabolic syndrome , body mass index ( BMI ) , sex and genotype .
Despite recent development in single-step techniques dedicated to lipid-enriched samples , total RNA extraction from AT had to be improved before application to AT analysis in the DiOGenes clinical trial . Each step of total RNA extraction from small amounts of human AT samples was optimized in order to prevent the loss of precious samples ( Table S1 , Figure S1 ) . In the context of large scale clinical programs , we also investigated whether long term storage of fat samples may have negative impact on total RNA integrity . AT samples frozen in liquid nitrogen can be stored at −80°C up to 3 years without affecting total RNA yield ( Figure S1 ) or quality ( data not shown ) . Flash freezing in liquid nitrogen before storage proves as efficient as soaking the samples in preservative solutions . This is a critical point as it allows use of fat samples for other applications than transcriptomics . Different approaches were used for real time qPCR data normalization . Use of the simple 2−ΔCt method with GUSB as a reference transcript proved to be the best for normalization in human subcutaneous AT ( Figure S2 ) . The DiOGenes dietary intervention consisted of two phases [9] , [10] . The first phase was an 8-week low-calorie diet ( LCD ) with the objective of ≥8% weight loss . In the second phase , the successful patients were randomized into one of five ad libitum weight maintenance diets ( WMD ) : four diets combining high and low protein content with high and low glycemic index of carbohydrates and a control diet according to National dietary guidelines on healthy diets . Clinical investigations including subcutaneous AT microbiopsies were performed before and at the end of each phase . Five hundred sixty eight obese individuals , age 24 to 63 ( mean weight: 99 . 6±17 . 1 kg ) had clinical data available and good quality AT RNA samples . Two groups of patients were defined ( Figure S3 ) . The first group , group A , included 311 obese individuals ( 107 men and 204 women ) with gene expression data available at each clinical investigation day . The second , group B , had 204 individuals with gene expression data available at baseline and after LCD . Subjects were also categorized according to the occurrence of metabolic syndrome at baseline [11] . Group A had 125 metabolic syndrome and 186 non-metabolic syndrome individuals at baseline . Group B had 81 metabolic syndrome and 123 non-metabolic syndrome individuals at baseline . All baseline anthropometric and plasma characteristics are described in Table S2 . In both men and women , blood pressure , triglycerides , HDL-cholesterol , C reactive protein , adiponectin , fasting glucose and insulin were significantly different in metabolic syndrome compared to non-metabolic syndrome individuals . In addition , women with metabolic syndrome had higher weight , BMI , fat mass and waist circumference . Massive parallel reverse transcription quantitative PCR ( RT-qPCR ) was performed on AT from the DiOGenes study using a microfluidic qPCR device [8] . AT expression data from 271 genes of interest ( Table S3 ) was investigated on 1341 samples from 515 subjects . The genes were selected from our previous published and unpublished DNA microarray analyses on a limited number of individuals . The choice was made using the following criteria: regulation during dietary weight loss programs [12]–[14] , including the DiOGenes trial [14] , and differential expression according to the presence or absence of obesity and metabolic syndrome [15] , [16] . Forty percent of these genes encoded proteins involved in metabolism and 23% participated in immune response . This list encompassed 38 AT macrophage [15] , [16] and 84 adipocyte markers [12] , [15] , i . e . genes expressed in these cell types at much higher levels than in any other AT cell type . Controlling for weight variation , a majority of genes were regulated in both men and women by the dietary weight management program . The main pattern observed on 175 genes was an opposite regulation of AT gene expression between LCD and WMD phases ( Figure 1 , Table S4 ) . Genes downregulated during LCD and upregulated during WMD ( n = 158 ) were mostly associated with metabolic functions ( n = 110 ) , including 72 genes defined as adipocyte markers . The top ranking genes included SCD , FADS1 and FADS2 encoding enzymes involved in unsaturated fatty acid synthesis . An inverse trend was seen for 17 genes including 9 immunity-related genes . Four of those genes were AT macrophage markers . Most of the genes had similar expression at the end of the intervention compared to baseline . Forty three genes showed variations in expression at the end of WMD compared to baseline ( Table S5 ) . The majority showed decreased expression compared to baseline . LEP showed a downregulation during calorie restriction that persisted until the end of the intervention . As shown in Figure 1 , this pattern is superimposable with the evolution of HOMA-IR , an index of insulin resistance . The macronutrient composition of the diet during the WMD phase had no effect on AT gene expression . We also looked at genes related to weight changes during the ad libitum WMD by comparing changes in mRNA levels between the end of LCD and the end of WMD in women who lost and those who regained at least 50% of the weight lost during calorie restriction . The changes in mRNA levels of 16 genes differed between the two groups of women ( Table S6 ) . CIDEA which is involved in fat cell lipid droplet metabolism was the best marker for weight loss ( Figure 2 ) . FADS1 , encoding a fatty acid desaturase , and BCAT1 , encoding a branched-chain amino acid aminotransferase , were the best markers for weight regain . Principal component analysis of gene expression data from group A and group B subjects at baseline indicated that the major component explaining AT gene expression data distribution was sex . Figure S4a depicts partial least square-discriminant analysis of AT genes with sex specificity . To list the AT genes with sex-biased expression , a mixture model controlling for centre was first built with data from group A . One hundred and eighty six genes exhibited sex specificity . The same model was then run with data from group B , giving a list of 158 genes . Sex specificity for 109 transcripts persisted during the dietary intervention ( Table S7 ) . Higher expression in female AT was found for all genes except for CCL19 , which showed higher expression in male AT ( Figure 3 ) . Fat mass being higher in women than in men could possibly explain this marked sexual dimorphism . However , 88 genes remained different when controlling for fat mass . Only 5 genes were located on sex chromosomes ( Table S7 ) . SAA4 , AZGP1 , CDKN2C and CES1 were the highest ranked genes with a more than two-fold higher expression level in female than in male AT ( Figure 3 ) . Exploratory analysis of AT gene expression also indicated a discriminatory effect according to the presence or absence of metabolic syndrome ( Figure S4b ) . Because the clinical presentation of metabolic syndrome is different in men and women and might be at least in part originating in the AT [16] , [17] , we separately analysed the 2 populations to assess the molecular characteristics of AT from patients with metabolic syndrome . A metabolic syndrome signature was found for 22 genes ( Table S8 ) . CCL3 and AZGP1 showed two-fold higher and lower expression , respectively , in women with metabolic syndrome compared to women without metabolic syndrome ( Figure 4 ) . The difference , albeit less pronounced , was also present in men . To assess the contribution of obesity to AT gene expression , the impact of BMI was studied in men and women separately at baseline and along the dietary intervention . In women , 51 genes showed significant BMI dependency that persisted during the whole dietary intervention ( Table S9 ) . In men , a single gene , AZGP1 , was dependent on BMI at each time point of the intervention ( data not shown ) . To elucidate the relationship between AT gene expression and related phenotypes at a greater depth , we used a co-correlation network-based approach . Interactions between the two matching bio-clinical and AT gene expression data sets were modeled using partial correlations . By eliminating over-estimation of co-correlations due to correlation with a third variable , partial correlations measure direct correlation between two variables with control for confounding variables . First , in addition to bio-clinical parameters ( Table S2 ) , we selected 38 metabolism and immunity related genes that showed regulation in the present analysis ( Table S10 ) . Since sex appeared to control AT gene expression variance more than other parameters , we used the network approach on the 180 men and 335 women separately . Figure S5 displays the regulatory networks in men and women . Edges represent direct and strong correlations and thickness connection strength between variables . Each node represents a variable . Node degree refers to the number of edges attached to the node . High degree indicates hubs which are the most connected variables . Betweenness centrality quantifies the importance of a variable within network . Nodes with highest betweenness centrality are those providing the strongest network connection and show key variables in the network . The topology of the male and female networks was similar with 75% of edges in common between men and women . A majority of highly connected co-expression networks consisted of the same clinical parameters and genes that clustered together in both men and women . Several macrophage markers ( Table S3 ) showed strong connection . A module consisting of the same group of correlated genes in both the male and female networks encompassed genes involved in de novo lipogenesis such as FASN , SCD , FADS1 , FADS2 and ELOVL5 . In men and women , CIDEA and AZGP1 , two cachexia markers , were connected . Next , the network-based approach was used on 204 women from group A ( Table S2 ) to expand the analyses to functional interconnections during the dietary intervention . The list of genes consisted of the lipogenesis module observed at baseline ( Figure S5 ) extended to related glycolysis and glucose metabolism genes ( Table S11 ) . Networks were built during LCD ( Figure 5a ) and from baseline to the end of the WMD ( Figure 5b ) . The two data-driven dependency networks in AT showed 62% of shared edges . HK1 appeared as an important hub , with 10 connections , including another hub , SCD , ACACB , ELOVL5 and fasting glucose ( Figure 5a ) . Notably , two components of the metabolic syndrome , waist and triglycerides , were highly connected to the most important node , SCD , which was also highly connected to glycolytic genes ( SLC2A4 and PCK1 ) during LCD . From baseline to the end of the WMD ( Figure 5b ) , fat mass , waist and triglycerides were associated with the network key node ALDOC , which was connected to several lipogenic genes ( FASN , ACACB , SCD , FADS2 and ELOVL5 ) . We identified 2953 single nucleotide polymorphisms ( SNPs ) which were in the close proximity of 252 genes . At baseline , 118 SNPs representing 46 genes showed association with AT gene expression ( Table S12 ) . The strongest associations ( P<10−10 ) were found for ALDOB , MARCO , MMP9 and HLA-A ( Figure 6 ) . Four SNPs located in the intronic regions of MARCO , which encodes an AT macrophage-specific marker regulated by obesity [15] and dietary intervention ( Table S5 ) , showed associations with P<10−20 . A moderate effect of sex and BMI was observed for 3 and 13 SNPs , respectively . However , these effects were not consistent among SNPs with significant associations with AT gene expression in the corresponding genes . The majority of the associations observed at baseline remained significant when expression after LCD and WMD were considered ( Table S13 ) . Of note , no SNP showed association with diet-induced variations in mRNA levels ( P>0 . 5 ) .
Carefully monitored weight-control diets favorably affect parameters of the metabolic syndrome and delay the onset of diabetic complications [1] . AT is a key player in the homeostatic control of whole body metabolism . Besides the more recent DNA methylome and microRNA arrays , gene expression profiling is one of the most comprehensive omics technologies , which permits parallel quantitative measurements of a large number of targets . However , a minimal amount of high quality total RNA is required . To study large intervention programs , we optimized the entire process , from needle biopsy of human subcutaneous AT to long term storage of samples . A single needle biopsy allows fast and painless AT sampling that may be easier to perform than blood sampling in morbidly obese subjects . Biopsies of about 200 mg are easily obtained in large scale intervention studies and can also be obtained from lean individuals . From this amount of tissue up to 34 , 100 different transcripts can be quantified using the Fluidigm Biomark Dynamic Array technology . This high-throughput technology has been successfully applied to many biological fields [18]–[20] . It allows reduction in cost and time , and improvement of accuracy , throughput and performance compared with conventional instruments . We show here its potencies to study AT expression of multiple genes in large-scale population-based interventions . Normalization is an essential step to correct for systematic bias in transcriptomic data . In microfluidic RT-qPCR assays , systematic errors due to sampling , reverse transcription and preamplification steps as well as set and plate spreading must be eliminated . Numerous different methods can be used for data normalization , including those used for transcriptome analyses [21] . When analyzing real-time PCR data , the most widely used is the 2−ΔCt method using a reference gene [22] . The ideal reference , also referred to as a house-keeping gene , should be constantly transcribed in all cell types and tissues regardless of internal and external influences . However , the expression of house-keeping genes may vary considerably . GAPDH is one of the most commonly used house-keeping genes . However , GAPDH expression was shown to be regulated during dietary intervention in this study and previous work [12] . The ribosomal 18S is another common reference gene . However , because of its very high expression level , this transcript shows such a strong expression after the preamplification step that it overflows the detection system . Here , the use of GUSB as a reference gene in the easy-to-use 2−ΔCt method proved to deliver the best normalization for human subcutaneous AT . AT gene expression profiling combined with clinical investigations has opened a novel approach to decipher physiological and pathophysiological processes [5] . Most previous studies have aimed at comparing obese and lean individuals and observing the effects of altered body weight during dynamic weight change . The highly clinically relevant weight stabilization phase has rarely been studied . We have previously investigated AT gene expression during multiple phase dietary interventions on a limited number of individuals [12] . The DiOGenes intervention study was designed to investigate diet-induced changes on a much larger scale [10] . This trial focused on identifying key dietary and genetic factors as a basis for predicting whether individuals may reach and maintain healthy weight . The number of participants and the complete bioclinical characterization combined with AT biopsy offered a unique opportunity to study the interactions between sex , metabolic status , dietary phases and genetic factors . Unexpectedly , a striking sex effect on AT gene expression that persisted during the dietary intervention for most of the sex-specific genes was found . As a prototypical example , it has repeatedly been shown that leptin AT mRNA level is higher in females than in males even after correction for the degree of body fat mass [23] . Similar sexual dimorphism has previously been reported for 45 of the genes described in the present study in mouse AT [24] . Higher expression in female than in male AT was found for all but 1 gene . However , to ascertain that the majority of human AT genes showing sex differences have female-biased expression , a systematic analysis would be required . Higher fat mass in women than in men could possibly explain this marked sexual dimorphism . However , 80% of the genes remained different when controlling for fat mass . Indeed , SAA4 and CDKN2C AT expression are increased and decreased , respectively , in morbidly obese subjects whereas the two genes show higher expression in women than men [25] , [26] . The contribution of gonadal hormones and sex chromosomes has been investigated in mouse models . Sexual steroids play a strong role in sex-biased gene expression in various tissues with minor sex differences explained by direct effects of the sex chromosome in liver genes [27] . Here , less than 5% of the human AT sex-related genes were located on sex chromosomes . The large number of subjects with a wide range of adiposity allowed testing the effect of BMI on AT gene expression at baseline and during the dietary intervention . In women , 51 genes show BMI dependency which persists at each time point of the dietary intervention indicating adiposity-dependent control of gene expression that is not influenced by diet-induced changes in weight . AZGP1 was the only common gene to men and women and among the top ranking less expressed genes in the morbidly obese patients ( BMI>40 ) . It was recently shown as down regulated with fat mass expansion in obesity in both visceral and subcutaneous fat with positive association with adiponectin [28] . Of note , the network approach also showed such connection in the male network . The lower number of significant genes in men may be related to real sex differences but may also be due to the lower number of men in the cohort . A metabolic syndrome signature was also found in AT . As a top ranking gene , CCL3 encodes macrophage inflammatory protein 1α , a CC chemokine involved in the interactions between immune cells and regulated by insulin resistance in AT [29] . AZGP1 appears as a marker of sexual dimorphism , obesity and metabolic syndrome encodes an adipokine with putative antidiabetic properties [30] . When looking along the DiOGenes dietary program , the main pattern was an opposite regulation of AT gene expression between LCD and WMD phases . Genes downregulated during LCD and upregulated during WMD were mostly adipocyte genes associated with metabolic functions [12] . The top ranking genes encoded enzymes involved in fatty acid desaturation [31] . An inverse trend was seen for immunity-related genes . As a result of the opposite regulation between LCD and WMD , most of the genes had similar expression at the end of the intervention compared to baseline . However , a subset of genes showed downregulation at the end of the dietary intervention . The list included several genes previously characterized as human AT macrophage-specific markers ( CD68 , CD163 , CD209 , IL10 , LIPA , MARCO , MS4A4A , PLA2G7 , SPP1 ) [12] , [15] . This coordinated downregulation most likely reflects a decrease in AT macrophage number as observed in a 6-month weight reducing intervention [32] . Leptin mRNA levels were also lower at the end of the dietary intervention . The superimposition of LEP and HOMA-IR data lend support to our hypothesis that variation in LEP expression contributes to the improvement in insulin sensitivity observed during diet-induced weight loss [3] . Genes related to weight changes during WMD include AGPAT9 , presumably involved in the biosynthesis of triacylglycerol and phospholipid , ALOX12 , encoding the arachidonate12-lipoxygenase involved in production of inflammatory and adipogenesis mediators , and the proangiogenic VEGFA , which all showed robust overexpression in individuals who regained weight . PKM2 , AP2M1 , ACTR3 and CES1 had been shown in microarray experiments to have higher expression in individuals who failed to control their weight [14] . FADS1 , encoding a fatty acid desaturase , and BCAT1 , encoding a branched-chain amino acid aminotransferase , were the best markers for weight regain . Interestingly , branched-chain amino acid catabolism is down-regulated in obese individuals [33] . CIDEA , which plays a critical role in fat cell lipid droplet metabolism [34] , showed decreased expression in individuals continuing to lose weight after LCD , supporting a role for the encoded protein in the adaptation of subcutaneous AT to body weight changes as over-feeding-induced weight gain induces its expression [35] . The macronutrient composition of the diet ( i . e . , protein content and glycemic index ) during the WMD phase had no effect on AT gene expression in agreement with previous data on energy-restricted diets differing in fat and carbohydrate content [13] . Therefore , during dietary weight management programs , energy balance and fat mass variations rather than the composition of the diet is a determinant of AT gene expression . During LCD , network analysis of gene expression and clinical parameters showed that the top associations function as part of a major hub gene , the stearoyl CoA desaturase SCD , which is highly connected to components of the metabolic syndrome and the gene encoding a glycolytic enzyme , hexokinase HK1 , connected to glycemia . These genes are targets of ChREBP , a transcription factor involved in glucose-mediated control of de novo lipogenesis gene expression [36] , [37] . These connections suggest that ChREBP target genes are regulated during LCD . This transcription factor was not studied here . The link between ChREBP and metabolic improvements along the dietary intervention requires further investigation [38] . Along the dietary intervention , clinical data and gene co-expression network analysis also revealed ALDOC , an aldolase involved in glycolysis , and fat mass as key nodes . Both hubs were connected to components of the metabolic syndrome . The ALDOC-centered module included key genes for de novo lipogenesis , illustrating the common transcriptional control of glycolysis and fatty acid synthesis [37] . The fat mass centered module was composed of glycolytic genes , indicating a direct link between change in fat mass and aerobic glycolysis , which seems to be related to the connection between de novo lipogenesis gene expression and metabolic features [39] . Human AT gene expression is under strong genetic control [40] . Recent genome-wide gene expression and genotyping analysis identified 10 , 000 cis SNPs associated to gene expression in subcutaneous AT [41] . The number of cis expression SNPs ( eSNPs ) was much higher than the number of trans eSNPs . In the present study , more than 80% of the genes with eSNPs had not previously been reported [41] . This high level of detection was related to several factors . First , we selected SNPs located in the immediate vicinity of the genes that allow capture of significant associations with our sample size [42] . Second , we investigated a carefully selected population enrolled in a multicentric dietary intervention [9] , [10] . Thereby , we could control for biological and non-biological confounders such as center , sex , fat mass and diet . Highly significant associations were found for MARCO and MMP9 . MARCO encodes a class A scavenger receptor shown to be specific of AT macrophages compared to other human AT cell types [15] . AT macrophages also specifically produce metalloproteinase 9 , a key enzyme involved in remodeling processes [43] . Of 46 genes with eSNPs , 19 were directly related to immunity and inflammation and were highly expressed in human AT , in agreement with the existence of an AT macrophage gene network module with tight cis genetic control [40] . Strikingly , the eSNPs identified here were not influenced by sex and diet-induced changes in AT gene expression . We found no evidence of association between cis SNPs and variations in mRNA levels during the dietary protocol , suggesting that cis genetic control operates at baseline and is preserved during the dietary intervention but does not influence the response to the diet . As prototypical examples , ACSL1 , ECHDC3 and HSDL2 mRNA levels were influenced by all the investigated factors ( Figure 7 ) . SNPs did show associations with AT mRNA levels as transcript abundance varied during the dietary intervention . It also differed according to sex and metabolic syndrome . Dietary intervention did not alter the sexual dimorphism in gene expression . ACSL1 , which catalyzes the conversion of long chain fatty acids into acyl-CoAs , is the most abundant ACSL isoform expressed in AT . AT-specific ablation of Acsl1 in mice shows that the enzyme plays a crucial role in directing acyl-CoAs towards β-oxidation in fat cells [44] . Here , ACSL1 gene expression was lower in individuals with metabolic syndrome . An association between ACSL1 gene polymorphisms and the metabolic syndrome has recently been reported [45] . These data suggest that impaired adipocyte fatty acid oxidation due to ACSL1 defect may constitute a feature of the metabolic syndrome . The present data provide evidence for control of AT gene expression by nutrition , sex , metabolic status and genotype . A main feature was a major effect of sex , which was independent of sex chromosomes , fat mass and dietary intervention . Another characteristic was that the control of gene expression by genetic elements appeared unaffected by nutritional status . Altogether , the effects of the investigated factors were most often independent of each other . Understanding the relative importance of environmental and individual factors that control the expression of human AT genes may help in deciphering strategies aimed at improving AT function in metabolic diseases .
Fat samples used for the optimization of total RNA extraction were obtained using abdominal dermolipectomy from the plastic surgery department of the Toulouse University Hospitals . The patients were not included in a weight reduction program . DiOGenes was registered in ClinicalTrials . gov ( NCT00390637 ) . Briefly , 932 overweight and obese adults in 8 European centers participated in a dietary program with a 8-week 3 . 3 MJ/day LCD ( Modifast , Nutrition et Santé , France ) . Subjects achieving 8% of initial body weight loss were randomized to a 6-month ad libitum WMD consisting in one of four low-fat diets that differed in glycemic index and protein content , or a control diet as described in [9] . Abdominal subcutaneous AT biopsies from the DiOGenes protocol were obtained by needle aspiration under local anesthesia after an overnight fast at baseline , at the end of LCD and at the end of WMD [14] . All clinical investigations were performed according to standard operating procedures . Analysis of blood samples was performed at the Department of Clinical Biochemistry , Gentofte University Hospital , Denmark as described in [9] . The study was performed according to the latest version of the Declaration of Helsinki and the Current International Conference on Harmonization ( ICH ) guidelines . All subjects gave verbal and written informed consent . Applications were submitted to the regional Ethics Committees from the participating centres and the study was not undertaken without a positive statement from the committee regarding the study . Five single-step methods for total RNA extraction were evaluated on 6 human subcutaneous AT samples ( Table S1 ) . Among the 5 extraction methods , 2 yielded very few or partly degraded total RNA and one low purity RNA . The QIAGEN methods ( RNeasy Mini and RNeasy Lipid Tissue Mini ) yielded sufficient amount of good quality total RNA that appeared to be free of genomic DNA contamination . Based on the QIAGEN RNeasy Lipid Tissue Mini Kit , an in-house optimized total RNA preparation protocol that uses chloroform delipidation and phenol/guanidine isothiocyanate-based ( QIAzol ) extraction , silica-gel membrane purification and microspin technology was set up ( see below ) . This adapted protocol provided a higher mean total RNA supply with more consistent yield than other methods . Human AT samples of weights ranging from 0 . 04 g to 1 . 5 g were collected , flash frozen in liquid nitrogen and stored at −80°C . Figure S1a was drawn from 84 AT samples . It shows a positive correlation between the amount of total RNA extracted and the weight of the fat biopsies up to 0 . 2 g . Above 0 . 5 g of fat , the amount of total RNA per g of tissue becomes more variable . The data reveal that 0 . 3 to 0 . 5 g of fat are enough for substantial total RNA recovery . Such an amount may yield a minimum of 5 µg of total RNA which is sufficient for both microarray application and RT-qPCR . Besides sample preparation , storage conditions are a major concern because of the instability of mRNA due to contaminating RNases . In order to prevent total RNA degradation , commercial RNA stabilization reagents are available . Figure S1b shows adipose tissue total RNA yield and quality using alternative protocols . Samples of about 0 . 5 g of human fat tissue were collected and stored at −80°C following 5 different protocols: 1 ) immediate storage of the freshly cleaned fat sample at −80°C 2 ) flash-frozen in liquid nitrogen and stored at −80°C 3 ) stored overnight in RNAlater RNA Stabilization Reagent ( QIAGEN ) at 2–8°C , then removed from the RNAlater and stored at −80°C 4 ) freshly cleaned fat sample stored at −80°C in QIAzol Lysis Reagent ( QIAGEN ) 5 ) homogenized in QIAzol Lysis Reagent with ultra-turax homogenizer and stored at −80°C . These samples were extracted after short term , 1 month , and after long term , 1 year , storage . The 5 protocols gave similar total RNA yield and quality . The frozen AT sample was homogenized in QIAzol ( QIAGEN ) ( 2 . 5 ml of QIAzol for 500 mg of tissue , 5 ml for 1 g of tissue , 1 ml for ≤200 mg of tissue ) using a rotor-stator homogenizer until homogeneity ( 20–40 s; longer time may lead to overheating ) then incubated at room temperature for 5 min . Two hundred µl of chloroform was added for 1 ml of QIAzol ( otherwise the volume of chloroform was adjusted to QIAzol volume with a 1∶5 ratio ) and vigorously shaked for 15 s using a vortex then incubated at room temperature for 3 min . After centrifugation at 4000 rpm for 15 min at 4°C the upper aqueous phase was transferred to a new tube . One volume of 70% ethanol was added and mixed by vortexing . Seven hundred µl of this sample was pipetted onto an RNeasy Mini Spin Column ( QIAGEN ) in a 2 ml tube and centrifuged at ≥10 , 000 rpm for 15 s at 25°C . Flow-through was discarded . This step was repeated using the reminder of the sample . Seven hundred µl of Buffer RW1 ( QIAGEN ) was added to the column and centrifuged at ≥10 , 000 rpm for 15 s at 25°C . Flow-through was discarded . The column was transferred into a new 2 ml tube . Five hundred µl of Buffer RPE ( QIAGEN ) was added to the column and centrifuged at ≥10 , 000 rpm for 15 s at 25°C . Flow-through was discarded . Another 500 µl of Buffer RPE was added to the column and centrifuged at ≥10 , 000 rpm for 2 min at 25°C . The column was transferred into a new 2 ml tube and centrifuged at ≥10 , 000 rpm for 1 min at 25°C . To elute , the RNeasy Mini Spin Column was transferred to a 1 . 5 ml tube and 30 µl of preheated RNAase-free water at 50°C directly pipetted onto the column then centrifuged at ≥10 , 000 rpm for 1 min at 25°C . For the optimization of total RNA extraction from AT samples ( Table S1 ) , total RNA quality was checked using ethidium bromide-stained agarose gels . Concentration was determined using a Nanodrop spectrophotometer , Illkirch , France ) . For AT biopsies from the dietary program , total RNA concentration and quality were estimated by capillary electrophoresis using the Experion analyzer ( BioRad , Marnes-la-Coquette , France ) . The amount of total RNA from the DiOGenes study was 25 . 3±9 . 3 µg/g of AT ( n = 1363 ) validating the RNA extraction and purification method in a large multicenter study . Total RNA was of good quality and free of genomic DNA . Genomic DNA was extracted from the buffy coats with a salting out method . Genomic and amplified DNA samples were quality-checked , quantified and normalized to approximately 100 ng/ml and 2 . 0 µg before genotyping . High throughput SNP genotyping was carried out using the Illumina iScan Genotyping System ( Illumina , San Diego , CA , USA ) . Seven hundred forty eight individuals were genotyped using the Illumina 660W-Quad SNP chip . SNP genotyping was done in accordance with manufacturer's protocols . The Integrated mapping information is based on NCBI's build 37 . The coding sequences were investigated 15 kb downstream and 10 kb upstream . All SNP with a genotype frequency ≥95% and in Hardy-Weinberg equilibrium ( P>0 . 05 ) were selected for further analyses . Among 3965 SNPs related to 252 genes , 2953 SNPs remained after data filtering . | In obesity , an excess of adipose tissue is associated with dyslipidemia and diabetic complications . Gene expression is under the control of various genetic and environmental factors . As a central organ for the control of metabolic disturbances in conditions of both weight gain and loss , a comprehensive understanding of the control of adipose tissue gene expression is of paramount interest . We analyzed adipose tissue gene expression in obese individuals from the DiOGenes protocol , one of the largest dietary interventions worldwide . We found evidence for composite control of adipose tissue gene expression by nutrition , metabolic syndrome , body mass index , sex , and genotype with two main novel features . First , we observed a preeminent effect of sex on adipose tissue gene expression , which was independent of nutritional status , fat mass , and sex chromosomes . Second , the control of gene expression by cis genetic factors was unaffected by sex and nutritional status . Altogether , the effects of the investigated factors were most often independent of each other . Comprehension of the relative importance of environmental and individual factors that control the expression of human adipose tissue genes may help deciphering strategies aimed at controlling adipose tissue function during metabolic disorders . |
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Chronic parasitic infections are associated with active immunomodulation which may include by-stander effects on unrelated antigens . It has been suggested that pre-natal exposure to parasitic infections in the mother impacts immunological development in the fetus and hence the offspring’s response to vaccines , and that control of parasitic infection among pregnant women will therefore be beneficial . We used new data from the Entebbe Mother and Baby Study , a trial of anthelminthic treatment during pregnancy conducted in Uganda , to further investigate this hypothesis . 2705 mothers were investigated for parasitic infections and then randomised to albendazole ( 400mg ) versus placebo and praziquantel ( 40mg/kg ) during pregnancy in a factorial design . All mothers received sulfadoxine/pyrimethamine for presumptive treatment of malaria . Offspring received Expanded Programme on Immunisation vaccines at birth , six , 10 and 14 weeks . New data on antibody levels to diphtheria toxin , three pertussis antigens , Haemophilus influenzae type B ( HiB ) and Hepatitis B , measured at one year ( April 2004 –May 2007 ) from 1379 infants were analysed for this report . Additional observational analyses relating maternal infections to infant vaccine responses were also conducted . Helminth infections were highly prevalent amongst mothers ( hookworm 43 . 1% , Mansonella 20 . 9% , Schistosoma mansoni 17 . 3% , Strongyloides 11 . 7% , Trichuris 8 . 1% ) and 9 . 4% had malaria at enrolment . In the trial analysis we found no overall effect of either anthelminthic intervention on the measured infant vaccine responses . In observational analyses , no species was associated with suppressed responses . Strongyloidiasis was associated with enhanced responses to pertussis toxin , HiB and Hep B vaccine antigens . Our results do not support the hypothesis that routine anthelminthic treatment during pregnancy has a benefit for the infant’s vaccine response , or that maternal helminth infection has a net suppressive effect on the offspring’s response to vaccines . ISRCTN . com ISRCTN32849447
There is substantial evidence that pre-natal exposures are important in shaping immunological development [1] . This includes strong evidence that prenatal exposure and sensitisation to parasite antigens determines susceptibility to the same parasite in the offspring [1] and that immunisation during pregnancy influences the infant response to the same vaccine [2] . There is also evidence that prenatal exposures may influence the offspring’s response to unrelated antigens [1] . It is important to better understand such effects since they are likely to be important in broadly determining susceptibility to infectious diseases , either directly or through responses to immunisation , as well as determining susceptibility to other immunologically mediated conditions ( such as allergy-related disease [3 , 4] ) . Vaccines provide an example of a standardised immunological challenge given at a standardised time and hence an opportunity to evaluate the effects of pre-natal exposures on infant immune responses . Recently , Malhotra and colleagues reported a study among children of mothers infected or uninfected with malaria and helminths in a coastal region of Kenya which suggested that infants of parasite-infected mothers had a reduced ability to develop antibody responses to Haemophilus influenzae type B ( HiB ) immunisation and diphtheria toxoid ( DT ) , but showed no effect on responses to hepatitis B ( Hep B ) immunisation or tetanus toxoid ( TT ) [5] . If there is a causal association between prenatal parasitic exposure and infant vaccine responses , then treatment of maternal parasitic infections might be expected to remove parasite-associated effects . We conducted a randomised controlled trial ( the Entebbe Mother and Baby Study , ISRCTN32849447 ) to investigate whether anthelmintic treatment of pregnant mothers improved the vaccine response amongst their children [6] . We have previously reported the effects of treatment on cellular responses following BCG and tetanus immunisation , and on tetanus and measles antibody concentrations: there were no overall effects but , in planned subgroup analyses , albendazole treatment of mothers with hookworm was associated with reduced T-helper 2 cytokine responses to TT in their infants , and ( unexpectedly ) albendazole treatment of mothers without hookworm resulted in increased interferon-γ ( IFN-γ ) responses to mycobacterial antigen; otherwise no effects of maternal treatment on responses to BCG , TT or measles were observed [7] . Here , we report on the effects of maternal anthelminthic treatment on a further six serological responses ( DT , pertussis [pertussis toxin ( PT ) , filamentous haemagglutinin ( FHA ) and pertactin] , Hep B and HiB ) . In addition to the trial results , we also present an observational analysis of associations between multiple maternal infections and infant immunological responses , analogous to the observational analyses reported by Malhotra and colleagues , using our existing dataset to investigate whether similar associations are also present in our cohort of mothers and babies from rural and urban areas of central Uganda .
Healthy pregnant mothers in their second or third trimester were enrolled as part of the Entebbe Mother and Baby Study ( EMaBS; ISRCTN32849447 ) between 2003 and 2005 , described elsewhere [6 , 7] . Briefly , this was a randomised , placebo-controlled , factorial study of the effect of single-dose albendazole ( 400 mg ) and praziquantel ( 40 mg/kg ) given during the second or third trimester of pregnancy on postnatal outcomes . Mothers were enrolled at their first antenatal visit unless they attended in the first trimester , in which case enrolment was postponed to minimise risk of teratogenicity . After enrolment they continued to receive standard antenatal care , including intermittent presumptive treatment for malaria with sulfadoxine/pyrimethamine and tetanus immunisation , and intrapartum and neonatal single-dose nevirapine for prevention of mother-to-child HIV transmission for the minority in whom it was required . Infants received the routine EPI ( Expanded Programme on Immunisation ) vaccines ( BCG and polio at birth; DT , pertussis toxin , TT , Hep B and HiB at age six , 10 and 14 weeks , measles at nine months ) . All mothers gave informed written consent on behalf on themselves and their children . We have previously reported on responses to vaccines against tuberculosis , TT and measles [7] . Here we assess six immunological responses amongst children at age one year: DT , Hep B , pertussis , FHA , pertactin , Hep B , and HiB . Only children who received all three doses of pentavalent vaccine are included in this analysis . Ethical consent was granted for the original trial and for subsequent analysis from UVRI ( GC/127/12/07/32 ) , the Uganda National Council for Science and Technology ( MV625 ) , London School of Hygiene & Tropical Medicine ( 790 , A340 ) , and the Oxford Tropical Research Ethics Committee ( 39–12 ) . At screening during pregnancy , and at delivery , or as soon as possible after delivery ( for those whose children were born outside hospital ) , blood samples were obtained from each woman to test for presence of malaria parasites by thick film and for microfilariae of Mansonella perstans using a modified Knott’s method [8]; a single stool sample was obtained for diagnosis of intestinal parasites including hookworm ( Necator americanus in this area [9] ) , Schistosoma mansoni , Trichuris trichiura and Ascaris lumbricoides using the Kato Katz technique [10] and for Strongyloides stercoralis by culture [11] . In case of multiple births just the first child was considered for inclusion in this analysis . Mother-baby pairs were excluded if the infant did not receive the standard three doses of EPI vaccines before samples were collected . At one-year of age a blood sample was obtained from infants . Plasma and serum were separated and stored at -80°C until processing . Plasma or serum were assessed for antibody concentrations against DT , pertussis antigens and HiB using a Luminex bead-based multiplex immunoassay described in detail elsewhere[12 , 13] . Antibody concentrations against Hep B were measured using the ABBOTT Architect i2000 with their anti-HBs kit ( Abbott Laboratories , Chicago IL , USA ) using the recommended protocol . We elected to measure the unstimulated serological response to vaccination in order to maintain consistency with other published reports of helminth-vaccine response associations . In the cases of DT and HiB these measures are likely correlated with protection against disease . In the case of pertussis it remains unclear which antigen is responsible for inducing protection and whether serological levels are sufficient correlates , whereas for hepatitis B , there is evidence that measuring peak response of antibody ( approximately 6 weeks post final vaccination ) is the optimal correlate of protection although practically this is very difficult to achieve [14] . Mothers were categorised by parasite exposure in three ways , following the approach of Malhotra 2015 for comparability [5] . First , mothers were grouped according to the total number of infections ( helminths and malaria: 0; 1; 2; ≥3 ) . Second , mothers were grouped as no infection; malaria only; malaria plus one helminth infection; malaria plus two or more helminth infections . Third , mothers without malaria were grouped as follows: no helminth infections; one infection and no malaria , two infections and no malaria; three or more infections and no malaria . The vaccines examined here were not the primary outcomes for this trial , so sample size calculations were not based on these responses . In a post-hoc evaluation of power , based upon the standard deviations we observed , we had 80% power to detect differences ranging from 1 . 18 ( FHA ) to 1 . 34 ( HiB ) . A complete case analysis was done where possible , and imputation of missing data was not performed . Vaccine responses from the included and excluded records were compared with t-tests or Mann-Whitney tests , as appropriate . As this was a factorial trial , comparisons were made between all those randomised to albendazole versus those randomised to matching placebo , and between praziquantel versus placebo . Linear regression was used to assess associations between exposures ( albendazole and praziquantel ) and outcomes ( infant vaccine responses ) . Outcome variables were transformed onto the log ( base 10 ) scale to reduce skew; hence reported coefficients represent geometric mean ratios ( GMR ) . Regression was performed with a bias-corrected bootstrap using 100 replicates . Covariates were selected a priori and included maternal baseline characteristics of age , parity ( 1; 2–4; ≥5 ) , education level ( none; primary; secondary; tertiary ) , and household socio-economic group ( on a six point scale , with six representing the highest group ) ; infant covariates were sex , infant malaria and time ( in days ) since the third EPI vaccination . Pre-planned sub-group analysis was carried out to examine the effect of albendazole on children of mothers who had a hookworm infection , and separately for the effect of praziquantel on the children of mothers with S . mansoni infection . This was performed using the same regression technique as described above and allowing an interaction between randomised treatment and infection . Interaction effects between the two randomised treatments were tested in a similar way . A similar approach was used to assess individual infections ( including binary indicator variables for malaria , hookworm , S . mansoni , M . perstans , Ascaris , Trichuris and strongyloidiasis in one linear regression model ) and the exposure categories defined above . In each case we also adjusted for randomised treatment . Exposure groups were treated as categorical variables designed to allow for a comparison with the previously published results from mothers in Kenya [5] . No adjustment was made for the numerous testing caused by assessing the effect of multiple exposures on six outcomes . Stata version 14 . 1 was used for all analyses .
A total of 2507 mothers were enrolled into the trial [7] . We had complete vaccine response data ( excluding hepatitis B ) for 1379 ( 55% ) mothers and first-born babies: 348 were randomised to albendazole + praziquantel , 346 to albendazole + placebo , 336 to praziquantel + placebo , and 349 to placebos only . Due to limited serum and plasma from infants , samples were unavailable for Hepatitis B assay for 374 of these infants ( Fig 1 ) . The demographic characteristics of these 1379 mothers were similar to those who were missing from our analysis . The biggest difference was in maternal malaria infection: this was 9 . 4% in those included in the analysis and 12 . 9% in those excluded . Other characteristics were broadly similar: average age was 23 . 9 years ( included ) and 23 . 4 years ( excluded ) ; education was “none” or “primary” in 54 . 7% in the included group and 54 . 5% in the excluded group . Hookworm infection was 43 . 2% and 45 . 3% , and S . mansoni infection was 17 . 3% and 19 . 7% in the included and excluded groups respectively . The sex of the babies was also similar between the included ( 49 . 6% female ) and excluded groups ( 47 . 3% female ) , as was the parity of the mothers ( mean 2 . 8 in both groups ) . Baseline characteristics were broadly balanced between the randomised arms ( Table 1 ) . The most common maternal infection among the mothers with vaccine response data was hookworm ( 43 . 1% ) , followed by Mansonella ( 20 . 9% ) , S . mansoni ( 17 . 3% ) , Strongyloides ( 11 . 7% ) , malaria ( 9 . 4% ) , Trichuris ( 8 . 1% ) and Ascaris ( 2 . 1% ) . We had stool samples for 1235 ( 89 . 6% ) of one-year olds . The most common parasites detected were Ascaris ( n = 16 ) and Trichuris ( n = 12 ) . We found very low levels of hookworm ( n = 4 ) and S . mansoni ( n = 1 ) . We found no evidence of an effect of randomised treatment on any of the infant vaccine responses ( Table 2 ) , nor any evidence for treatment interaction ( p>0 . 1 for all outcomes , S1 Table ) . In pre-planned sub-group analysis , the only evidence of a differential treatment effect was on DT response in children of mothers who received albendazole: in mothers with hookworm the adjusted geometric mean ratio ( aGMR ) for albendazole was 0 . 89 ( 95 CI% 0 . 74–1 . 08 ) and in mothers without hookworm it was 1 . 24 ( 95% CI 1 . 04–1 . 47 ) . The p-value for interaction was p = 0 . 01 ( Table 3 ) . For the observational analysis , a total of 1286 mothers-baby pairs had known status for all seven infections of interest and five of the six vaccines ( Hepatitis B , n = 940 ) . We found no evidence of different vaccine response results in the excluded records . Similar to the trial analysis , the most common maternal infection in this group was hookworm ( 43 . 1% ) , followed by Mansonella ( 21 . 2% ) , S . mansoni ( 16 . 7% ) , Strongyloidiasis ( 11 . 6% ) , malaria ( 9 . 2% ) , Trichuris ( 8 . 5% ) and Ascaris ( 1 . 9% ) . We found no evidence that maternal infections were associated with infant vaccine response except for maternal strongyloidiasis . The aGMR of HiB response for children of mothers with strongyloidiasis was 1 . 51 times greater ( 95% CI 1 . 11–2 . 01 ) than children of mothers who were uninfected . For Hep B the increase was by a factor of 1 . 47 ( 95% CI 1 . 11–1 . 94 ) and in pertussis response the aGMR was 1 . 41 ( 95% CI 1 . 06–1 . 88 ) . We found no evidence of an association between any of the maternal exposure groups ( number of infections , number of infections alongside malaria , number of infections among mothers without malaria ) and any infant vaccine response at one year . Full results are in Table 4 .
We found no evidence of enhanced vaccine responses among infants of infected mothers who were treated for helminths during pregnancy , nor evidence of a suppressive effect of prenatal exposure to maternal parasitic infections on infant vaccine responses , in this cohort of mothers and infants in Uganda . Such possible effects as were observed were indicative of enhanced responses for a number of vaccines in the infants of mothers identified as having strongyloidiasis , and of reduced DT responses in the infants of mothers with detectable hookworm infection who were treated with albendazole . The primary strength of our study is the randomised , controlled intervention during pregnancy . Hookworm infection was treated effectively by albendazole ( declining from over 40% prevalence before treatment to 5% after delivery among albendazole treated women ) and schistosomiasis was treated effectively by praziquantel ( declining from about 18% to 5% ) while Mansonella and Strongyloides were unaffected by the treatment [15] . From a simplistic perspective , the lack of effect of maternal treatment on vaccine responses among infants of women infected with hookworm and S . mansoni implies either that prenatal exposure to these helminth species has no important effect on the infant response to unrelated vaccines ( and hence perhaps to unrelated infections ) , or that the impact of prenatal exposure is established prior to the second trimester and cannot be reversed thereafter . Of note , Malhotra and colleagues treated all mothers with albendazole ( for nematodes ) during pregnancy; we , and Malhotra and colleagues , treated all mothers with sulfadoxine / pyrimethamine ( for malaria ) ; the effects described in each study were necessarily those that occur despite , or in the context of , these interventions [5] . However , the truth of the matter seems to be that the impact of prenatal parasitic infections on infant vaccine responses is complex and depends at least on characteristics of both the parasitic infection and the vaccine , and on the nature of the desired , protective vaccine response . A study from Ecuador , in accord with our results , showed no association between maternal geohelminths and infant IgG responses to DT , TT , PT , measles , rubella or HiB [16] , but several studies have now indicated a net enhancement of infant vaccine responses following exposure to certain maternal infections , including Trypanosoma cruzi for BCG , DT , TT and Hep B [17] , maternal intestinal helminth infections and the IgA response to rotavirus and polio ( in the Ecuador study [16] ) , as well as our result for strongyloidiasis and PT , HiB and Hep B . Meanwhile , Malhotra and colleagues have shown that , for malaria and lymphatic filariasis ( and , in an earlier study , schistosomiasis [18] ) , the impact of maternal infection on infant vaccine response depends upon whether or not the infant was sensitised to the parasitic infection in utero: compared to unexposed infants , malaria sensitised infants showed an increase , and malaria tolerised infants a decrease , in the response to DT [5]–this may contribute to a neutral net effect in studies which do not make the same distinction . Like us , Malhotra and colleagues observed no effect of pre-natal exposure to parasitic infections on infant responses to most of the EPI vaccines . The principal exception was HiB and , interestingly , although individual maternal infections were associated with reduced responses , additional infections tended to reverse this effect . Our findings for HiB followed a similar pattern , although the associations were not statistically significant . Our observation of an enhanced response to DT amongst infants of mothers without hookworm who received albendazole was surprising , and may be a chance finding given that subgroup analyses were conducted and multiple comparisons were made , with no formal adjustment in statistical interpretation . However , this result accords with our previous findings of an enhanced IFN-γ response to BCG , an enhanced IL-13 response to TT , and an enhanced risk of infantile eczema in the same group [7 , 19] and suggests a pro-inflammatory effect of albendazole , in the absence of maternal hookworm , which may be a direct effect of the drug , or mediated by effects on other co-infections . We think it unlikely that these results represent an effect of albendazole on light , undetected hookworm infections: as we have previously reported , a proportion of mothers in the albendazole placebo group had three samples examined before treatment was given post-delivery , evaluation of which increased the prevalence of hookworm in this group by only 6% ( from 45% to 51% ) [19] . A net adverse effect of prenatal exposure to maternal parasitic infections on the induction of immune responses by vaccines given to the offspring would imply a net adverse effect on the infant’s ability to respond to pathogens , also . This would be expected to result in increased neonatal or infant mortality . However , an initial result suggesting that anthelminthic treatment during pregnancy had benefits for infant mortality [20] has not been substantiated in controlled trials [7 , 21] . By contrast , there is considerable evidence that intervention against malaria during pregnancy has benefits for infant mortality [22] . While this may be mediated largely by prevention of the major effects of malaria on placental function and fetal growth , and by effects on infant susceptibility to malaria itself [23 , 24] , an impact on vaccine responses and on susceptibility to heterologous infections may contribute . This accords with the relatively prominent suppressive effect of maternal malaria described in Malhotra’s study , with a reported suppressive effect of prenatal exposure to malaria on the infant response to BCG , described in The Gambia [25] and with our own previous finding of an association between maternal malaria and reduced infant antibody response to measles immunisation [26] . A limitation of our study was that we included just 55% of eligible infants due to incomplete data . However , the mother-baby pairs that were excluded were similar in known characteristics to those included in the analysis . Further infants were missing from the Hep B analysis . A limitation of the observational component of our study was the classification of maternal infection status based on a single blood or stool sample . This would substantially underestimate and misclassify malaria exposure , which is best assessed by placental histology , and more sensitively assessed by polymerase chain reaction ( PCR ) assays . The use of a single stool result would also result in misclassification for hookworm or S . mansoni exposure [27 , 28] and hence may have obscured relevant associations . We have previously reported that in this study the sensitivity of one stool sample compared with three stool samples was 89% for hookworm infection and 66% for schistosomiasis [7] . This limitation does not apply to evaluation of Mansonella exposure which showed 96% agreement between samples taken in pregnancy and after delivery in this cohort ( 2077 of 2162 mothers for whom samples were available at both time points ) . Our classification of exposure differed markedly from the classification used by Malhotra and colleagues who included microscopy and PCR on placental and cord blood for malaria , and assays of circulating antigen and IgG4 for Schistosoma haematobium and Wuchereria bancrofti . The use of IgG4 detection as a marker of active infection is of possible concern as levels may be higher among individuals with a regulatory bias in their immune response [29] . Contrasting with the time-course described by Malhotra and colleagues , we had data on vaccine-specific antibody responses only at age one year , but this was a time point at which many of the effects observed by Malhotra and colleagues were evident , so comparable results might have been expected . Although other markers or measured timepoints may be more desirable in terms of assessing true protection against disease ( such as neutralisation or functional opsonophagocytic assays [14] ) , many of these other assays are more prone to inter-observer error . Our analysis of the unstimulated antibody concentrations maintains consistency between studies and provides observations relating to immunological responses rather than chances of protection . Furthermore , we had data on a different set of helminth infections to those used by Malhotra . It could be that particular helminths are more important in determining vaccine responses of infants , or that there are interaction effects which we have not explored . This paper contains a large number of estimates , confidence intervals and hypothesis tests . Due to these multiple comparisons , it is possible that some associations which appear statistically significant are in fact due to chance alone . These results should therefore not be considered definitive , but should instead be seen as evidence to be considered alongside other studies in this field . From a public health perspective , the additional results that we contribute here accord with our previous findings [7 , 26] and suggest that , whatever its benefits , routine anthelminthic treatment during pregnancy is not likely to result in improved infant vaccine responses . | Parasitic infections , such as worms and malaria , have potent effects on the human immune system . These effects include modification of immune responses in the fetus and infant if a mother has a parasitic infection during pregnancy . These immunological changes can influence the way a child responds to the same infection when exposed in later life . It has been suggested that the immunological changes might also influence how the child responds to the vaccines given in infancy , and that treating mothers for parasitic infections when they are pregnant might be helpful . In this study we compared responses to vaccines between infants of mothers who had , or had not , been treated for worms while they were pregnant . We found no overall differences . We also compared vaccine responses between groups of mothers with and without parasitic infections . We found no evidence that the parasitic infections were associated with reduced responses in the children . This means that , although treating worms during pregnancy may have some benefits , improvements in the children’s responses to vaccines are not likely to be among them . |
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As many as 59% of the transcription factors in Escherichia coli regulate the transcription rate of their own genes . This suggests that auto-regulation has one or more important functions . Here , one possible function is studied . Often the transcription rate of an auto-regulator is also controlled by additional transcription factors . In these cases , the way the expression of the auto-regulator responds to changes in the concentrations of the “input” regulators ( the response function ) is obviously affected by the auto-regulation . We suggest that , conversely , auto-regulation may be used to optimize this response function . To test this hypothesis , we use an evolutionary algorithm and a chemical–physical model of transcription regulation to design model cis-regulatory constructs with predefined response functions . In these simulations , auto-regulation can evolve if this provides a functional benefit . When selecting for a series of elementary response functions—Boolean logic gates and linear responses—the cis-regulatory regions resulting from the simulations indeed often exploit auto-regulation . Surprisingly , the resulting constructs use auto-activation rather than auto-repression . Several design principles show up repeatedly in the simulation results . They demonstrate how auto-activation can be used to generate sharp , switch-like activation and repression circuits and how linearly decreasing response functions can be obtained . Auto-repression , on the other hand , resulted only when a high response speed or a suppression of intrinsic noise was also selected for . The results suggest that , while auto-repression may primarily be valuable to improve the dynamical properties of regulatory circuits , auto-activation is likely to evolve even when selection acts on the shape of response function only .
Many transcription factors ( TFs ) in Escherichia coli regulate the transcription rate of their own gene . In fact , 59% of the TFs are known to auto-regulate and the list is growing [1] , [2] . Negative auto-regulation ( auto-repression ) occurs more frequently than positive auto-regulation ( auto-activation ) , but both are very common: 71 auto-repressors and 34 auto-activators are found in the current databases ( including 9 TFs that have binding sites for auto-activation as well as for auto-repression ) . This suggests that auto-regulation has one or several important functions [3] , [4] . In this paper , one possible function is explored . In general , the expression level of a gene is a function of the concentrations of the TFs that regulate its transcription rate . We propose that auto-regulation can naturally be used to optimize the shape of this response function . Auto-regulating transcription factors are typically regulated by other TFs too . In fact , 23 auto-regulating TFs in E . coli are known to respond to at least two additional regulators [1] , [2] . In such cases , the response of the regulated TF to changes in the “input” TF concentrations must reflect an interplay between regulation and auto-regulation . Conversely , this suggests that auto-regulation could emerge as a result of natural selection on the shape of these responses . In the past years , several other functions of auto-regulation have been proposed . Negative auto-regulation has been shown to decrease the sensitivity of expression levels to intrinsic fluctuations in the transcription rate under certain conditions [5]–[7] and to mitigate variations due to changes in the bacterial growth rate [8] . In addition , auto-repression can speed up the response of expression levels after a sudden change in conditions [9] , [10] . In the presence of time delays , it can also create oscillations [11] . On the flip side , negative auto-regulation tends to reduce the sensitivity of the expression level to input signals [12] , [13] . The effects of positive auto-regulation are usually opposite to those of auto-repression: it slows down responses and tends to amplify intrinsic fluctuations . At first sight , these qualities may not seem very desirable . Yet , a slow response can be beneficial if a sensitive response to persisting signals is desired while fast fluctuations in the input signal should be ignored [12] . Occasionally the fact that auto-activation can provide bi-stability may also be useful [14] . Each of these qualities could be relevant in some cases; our new suggestion does not contradict or replace any of them . To study the benefits of auto-regulation we use a computational approach that we developed recently [15] . In this approach , an evolutionary algorithm and a physical–chemical model of transcription regulation are integrated to design in silico cis-regulatory regions with predefined response functions . The evolutionary algorithm subjects a population of model cis-regulatory regions to rounds of mutation and selection . The mutations are introduced at the level of base-pair sequences while the selection step is based on the emerging network properties calculated using the model of transcription regulation . In the course of these simulations complex promoter designs develop that perform the desired function; these designs often reveal new design principles . In earlier work , auto-regulation was not included in this method . In contrast , we now use an extended version of the method to design cis-regulatory constructs that can exploit feedback . Many cis-regulatory regions in real cells essentially implement logical decisions [15]–[17] . We therefore study the class of response functions that can be interpreted as analogue equivalents of logic gates . Gates are computational devices that produce an output signal depending on one or more input signals; logic gates are gates that implement a binary ( Boolean ) decision rule . For example , a transcriptional AND gate would be a gene whose expression ( the “output” ) is regulated by two TFs ( the “inputs” , TF1 and TF2 ) such that it is transcribed only if both TF1 and TF2 have a sufficient expression level [16] . We refer to Table 1 for the definitions of other logic gates . Even though it has proven fruitful to think of promoters as analog approximations of logic gates , we stress that gene expression levels are of course not actually binary and that we do not treat them as binary in the models below . We analyze the cis-regulatory sequences resulting from the simulations by calculating DNA footprints for the resulting transcription factors and promoter sequences . These footprints show that auto-regulation—in particular auto-activation—is often used in these cis-regulatory regions; indeed , further analysis shows that auto-activation can be used to construct “better” transcriptional logic gates by allowing for more switch-like , “steep” response functions . However , the use of auto-regulation in shaping response functions is not limited to creating switch-like functions . To demonstrate this , we also applied our method to the design of cis-regulatory constructs that respond in a linear fashion to input concentrations . Again we find that auto-activation emerges spontaneously in the results . Finally , we also performed simulations in which we selected for designs with desirable dynamical qualities . First , we adjusted the method to select for gates with a short response time . Second , we performed selection against intrinsic fluctuations . In agreement with earlier results [5]–[7] , [9] , [10] , auto-repression evolved in both cases , demonstrating how auto-repression can be used to speed up response times or to reduce intrinsic fluctuations . Before describing the results we first provide a detailed description of the model and the algorithm used .
We consider one “output” gene , tf3 , and at most two “input” transcription factors , TF1 and TF2 . The gene tf3 codes for another transcription factor called TF3 . All three TFs can regulate the transcription rate of tf3 by binding to its cis-regulatory region . ( See Fig . 1 for an illustration of the model . ) The cis-regulatory region and the TFs are represented as nucleotide sequences and amino-acid sequences respectively . All TFs can bind anywhere on the cis-regulatory region , but the affinity of a TF for a particular site depends on the sequences of the TF and the site . For our purpose , it is sufficient to only model the DNA-binding domains of the TFs explicitly . We assume that amino-acids in these domains are responsible for the DNA-binding specificity and therefore represent each TF as an amino-acid sequence of length . We choose in our simulations because known binding sites in E . coli typically have length 6 to 15 and usually one base pair interacts with amino acid in TF–DNA binding [2] . The cis-regulatory region of tf3 is a base-pair sequence of length ; we take because in E . coli most transcription factors bind within from the start of transcription [18] . By the rules specified below all interactions between TFs , RNA polymerase ( RNAP ) and the cis-regulatory region can be deduced from these sequences; therefore each transcriptional gate is completely specified by them . The various molecules interact in the following ways: We model the dynamics of the concentration of TF3 , , by the following ordinary differential equation: ( 4 ) Here is the maximal production rate of TF3 , and is the degradation rate constant of TF3 . The function was defined above . In this simplified description , transcription and translation are concatenated and translational regulation is not included . The concentrations and of TF1 and TF2 are considered the inputs of the gate . Assuming that the system is mono-stable ( bi-stability is discussed below ) equation 4 defines a unique steady state for each set of input concentrations in which has a value . This steady-state concentration is considered the output of the gate . Because time delays between transcription initiation and translation are ignored , oscillations are excluded and can be calculated by propagating the dynamics numerically from any initial condition until the steady state is reached . ( If a gate has only one input , the dependence on is simply dropped . ) We choose the constants and such that ; this ensures that stays within the range . Apart from this ratio the values of and are irrelevant because in this work we are not interested in absolute time scales of the dynamics; for simplicity we use a time unit such that . In order to design networks with a prescribed function an evolutionary algorithm was used . A population of 200 transcriptional gates was subjected to 1000 cycles of mutation , selection and replication . Initially , all gates had random sequences . Auto-regulation was not imposed , but the system was free to exploit it by evolving binding sites for TF3 . The details of the evolutionary algorithm were chosen to combine an effective optimization of the gates with computational efficiency; we emphasize that we do not intend to faithfully mimic biological evolution . Several types of mutations were included . First , a base substitution could occur in cis-regulatory sequences ( with probability per cis-regulatory region ) . If this happened , a base pair was selected at random from the cis-regulatory sequence and substituted by a randomly chosen nucleotide . Second , insertions or deletions of a random base pair occurred in cis-regulatory regions ( with probability ) . Third , we applied point mutations to the sequences of the TFs ( with probability per TF ) , in which case one randomly chosen amino acid in the sequence was replaced by a random alternative . The exact mutation rates are not crucial for the results , as long as the rates are ( i ) high enough to generate significant variation and ( ii ) low enough to allow high-quality gates to persist in the population . For the selection step a fitness score was used . Here ( where RF stands for Response Function ) measures the deviation of the response function from a predefined goal function ( the desired response function ) . It was computed as follows . When evaluating gates with two inputs , and , the output level was computed for 16 combinations of the input concentrations: ( see the red dots in Fig . 2B ) . Next , the differences between these output levels and the goal function were computed . was defined as the sum of the squares of these deviations . If the gate had only one input , the definition was analogous , except that seven input values were used , equally spaced in the interval . The constant is required to make dimensionless ( for simplicity , ) and is an arbitrary constant large enough to ensure . Based on the fitness scores , the top 20% of the population were selected and the remaining gates discarded . Subsequently the population was brought back to its initial size by duplicating gates randomly chosen from the survivors of the selection process . If auto-activation evolved , the system could become bi-stable . In bi-stable systems , given the inputs two different values of are stable under the dynamics of the system ( equation 4 ) so that the output concentration is not uniquely defined by the input concentrations . Even though bi-stability is likely to occur in some real transcription networks we decided that such systems do not qualify as gates , since gates by definition map input states to a uniquely defined output state . The fitness function therefore contained an additional term that was designed to penalize bi-stability . When evaluating the fitness of a gate , we always computed the steady-state value twice for input values : once by propagating the differential equation 4 using initial condition and once using . If the results were different , the difference squared was added to the fitness function , which was sufficient to assure that the particular gate was eliminated by the selection process . However , because this method did not exclude bi-stability for all possible input values we also checked afterward whether the results were bi-stable . All gates are defined for input concentrations in the domain only . The logic gates are specified in Table 1 . In addition we define LACT , LIN , MEAN and NMEAN gates . A LACT ( linear activate ) gate has one input , , and the output responds as . A LIN ( linear inhibit ) gate also has one input , but responds according to . A MEAN gate is linear in two inputs , and , and obeys . Lastly , we define NMEAN to have the following linearly decreasing response function: . We repeated the simulations for each of the gates 20 times with different random seeds . In order to quantify the importance of auto-regulation in a particular design we defined the measure ( referred to as the “feedback measure” ) . First , we calculated the response function for the particular design . Then we artificially removed all possible binding sites for TF3 by setting the affinity of TF3 for all sites on the cis-regulatory region to zero and calculated the response function again; we call the result . In the absence of auto-regulation one should find , but if auto-regulation does play a role the two functions differ . Therefore the difference between these functions is a measure of the degree of auto-regulation; we define as the mean of the squared differences over 16 combinations of the input concentrations ( again , the red dots in Fig . 2 ) . If auto-regulation is not being exploited by a certain design is generally small ( ) . If , on the other hand , auto-regulation is used the resulting value is typically in the range . ( For instance , if all 16 points shift by 100nM when auto-regulation is removed . ) During the simulations , binding sites emerge in the initially random cis-regulatory promoter sequences . However , since the equilibrium binding constants have continuous values there is no fundamental distinction between binding sites and non-binding sites . Recognizing binding sites is further complicated by the fact that , in particular in the presence of cooperativity , weak binding sites can be important . Nevertheless , in order to understand the design principles of a particular gate we wish to identify which binding sites are necessary and sufficient to explain the observed promoter response . This problem is not an artifact of our models: the exact same conceptual problems occur whenever one tries to identify the binding sites of real TFs by experiment . Since a direct cutoff in terms of the equilibrium constants would eliminate possibly important weak sites we use computational “DNA footprints” ( analogous to experimental techniques such as DNase I footprinting ) to select those sites that are likely to be important . For each TF and each site , we calculate the steady-state occupancy for four sets of input concentrations . Sites that influence the response of the gate should have a significant occupancy in at least one of these digital footprints . We define to be the maximal occupancy of site by TF over the four conditions . Figure 4 in Text S1 shows a histogram of these occupancies for all TFs and all sites using data gathered from the results of 200 simulations . This histogram is bi-modal . The vast majority of the maximal occupancies have negligible values but a second peak occurs at . This peak is the result of selection pressure and is associated with functional binding sites . Based on this histogram , we use a rather stringent cut-off at to separate binding sites from pseudo binding sites . Simplified models that only take into account these selected binding sites and assume that all other binding affinities are zero usually accurately reproduce the response function of the full , unsimplified system . In rare cases where this is not the case the threshold can be lowered to obtain more accurate but more complex models; this was not necessary for the examples presented below . ( See the Text S1 for more details and examples of footprinting profiles . )
The first mechanism that our scheme elucidated , we called conditional auto-activation . This mechanism occurred in AND and ACT ( activation ) gates ( see Table 1 for the definitions ) , in which cooperative activation plays a key role . In those gates , conditional auto-activation is used to create a steep , switch-like response . As an example , we first discuss the design of AND gates . In simulations in which auto-regulation was excluded by the method , the resulting AND gate designs always consist of a tandem array of binding sites to which TF1 and TF2 bind cooperatively ( see Fig . 2A ) [15] , [16] . We called this a hetero-cooperative module . This design functions as follows . Crucially , the binding site from which RNAP is recruited ( the site directly next to the core promoter ) is too weak to considerably activate transcription on its own . As a result , only when TF1 and TF2 are both present at sufficient concentrations they bind cooperatively and activate transcription , as the definition of an AND gate requires . In the new simulations , in which auto-regulation can evolve , this design still emerged in 14 out of 20 simulation runs . Each of these gates has a feedback measure , proving that auto-regulation does not play any role . The remaining 6 simulation runs resulted in conditional auto-activation . In these gates the feedback measure was high , in the range . The new design looks very similar to the old one ( see Fig . 2A and B ) . However , the hetero-cooperative module now also contains a binding site for TF3 , which leads to a positive feedback loop . Importantly , TF3 bound at its binding site cannot recruit RNAP directly; instead , it interacts with the hetero-cooperative activation module . As a result , the auto-activation is conditional on the presence of TF1 and TF2 . As the concentrations of TF1 and/or TF2 increase , the auto-activation is gradually turned on , leading to a sudden ( steep , switch-like ) response . The exact same mechanism is exploited by some ACT gates . Out of the 20 simulations of ACT gates , 3 resulted in conditional auto-activation ( values were , and ) , while the other 17 did not use auto-regulation ( ) . The basic mechanism can be studied in minimal models inspired by the simulation results . In Fig . 2C and D , we compare three activation mechanisms . The first scenario is conventional activation by a single TF1 binding site . In the second scenario only a homo-cooperative activation module is present , consisting of two binding sites for TF1 . In the third scenario , the auxiliary TF1 site is replaced by a binding site for TF3 , introducing conditional auto-activation . In all designs we chose the binding site affinities such that they maximize the fitness function for the ACT gate . Conditional auto-activation indeed produces a response that is steeper than the one resulting from the design with a single activator binding site ( Fig . 2D ) . However , the conventional cooperative design with two binding sites gives an even steeper result . The results imply that , after one binding site has evolved for the activator TF1 , the response can be improved in two ways: by adding an additional site for TF1 ( leading to cooperative activation ) or by adding a site for TF3 ( resulting in conditional auto-activation ) . Which design emerges therefore depends critically on the actual sequences and mutations occurring in the population . This explains why cooperative activation and conditional auto-activation show up as alternatives in the simulations . The effect of conditional auto-activation can be understood quantitatively by studying the minimal model mathematically . The response function for the minimal model follows from the condition and is given by ( 5 ) withHere and denote the dissociation constants for TF1 and TF3 binding to their respective operator , is the concentration of RNAP normalized by the dissociation constant of RNAP binding to the promoter , and . We first consider the limit of and . In this limit is small as long as . The numerator of 5 can then be approximated by . As a result we can distinguish two regimes depending on the sign of : ( 6 ) where is the border between the two regimes , implicitly given by : ( 7 ) Note that provided ; under this condition approximation 6 holds around the transition . As both and are linear functions of , the second regime has the form of a Hill function with Hill coefficient . Therefore equation 6 shows that equation 5 behaves like a sharp threshold response . This threshold effect is responsible for the increased steepness of the response due to conditional auto-activation . The maximal expression following from equation 5 , at full activation , is . This demonstrates that in the limit of the maximum expression level becomes very low ( for a given value of ) . On the other hand , if is increased the term becomes more and more significant and the transition between the two regimes in equation 6 becomes more and more gradual . Consequently , in the optimized functions plotted in Fig . 2 the values of reflect a compromise between the opposing requirements of having a high maximal expression ( requiring a large ) and a sharp threshold response ( requiring a small ) . The steepness of a function in the point can be formalized by the sensitivity , defined by . The sensitivity of a Hill function is limited by the Hill coefficient , which is equal to the number of cooperatively interacting binding sites for the input TF . We therefore ask if a similar limitation applies to the minimal model of conditional auto-activation . From equation 5 the sensitivity function can be derived straightforwardly . The result is rather cumbersome and therefore an exact expression for the maximal sensitivity is hard to obtain . However , since the most sensitive part of the function is in the region where ( i . e . , close to ) , the maximal sensitivity can be approximated by . In the limit of large this expression converges to ( 8 ) Importantly , since we evaluated the sensitivity in a point close to but not exactly at the maximum , this approximate result is a conservative estimate: the true maximum cannot be lower than this . In the limit of small the maximum sensitivity diverges as , which proves that the sensitivity of response function 5 does not have a theoretical upper limit , unlike those of Hill functions . So far we have neglected the dynamical properties of the designs because the current model only considers the steady-state response of the system . As we mentioned , auto-activation tends to slow down the response time of the system . Therefore , in systems where the speed of response is of great importance cooperative regulation is expected to outperform conditional auto-activation . Selection on response speed is discussed in more detail below . A second feedback pattern emerges in logic gates in which repression is important , notably the NAND , NOR and IN ( inhibition ) gates . As it turns out , whenever steep repression is required , we also find strong auto-activation; this occurred in every simulation run for our NAND , NOR and IN gates ( 20 repeats each ) , with in all cases . We present the NAND gate as an example . Fig . 3A shows the cis-regulatory region of a typical NAND gate using auto-activation . The corresponding response function plotted in Fig . 3B indeed shows an excellent NAND-like behavior . As quantified below , in fact it performs better than the design without auto-activation reported earlier and reproduced for comparison in Fig . 3A and B [15] . The design that resulted when auto-activation was excluded is composed of a hetero-cooperative repression module ( a tandem series of repressor sites to which both input TFs bind cooperatively ) . The function of this module is to repress transcription only when both TF1 and TF2 are present in sufficiently high concentrations , as required of a NAND gate . In Ref . [15] we pointed out that in the simulation results such a repression module was always accompanied by strong activation sites for both input TFs . This counter-intuitive feature turned out to enhance the sharpness of the response . At low TF concentrations , the activation sites counter-act the repression module , so that the expression stays high . At higher TF concentrations , however , the repression module dominates and represses transcription . Under the parameters used designs of this type reached a modest fold-change of and a deviation measure ( see section “Evolutionary algorithm” in the methods section for the definition of ) . In the new results ( Fig . 3 ) the activation sites for TF1 and TF2 have disappeared , but instead we find auto-activation . In the absence of TF1 and TF2 , tf3 is highly expressed , aided by auto-activation . As the concentrations of TF1 and TF2 are increased , the repression module starts to compete with the auto-activation module . Quite suddenly , the repression module wins this competition and displaces RNAP from the promoter . The strong , cooperative repression module now leads to a rather complete inhibition . The new design can lead to fold-changes of and a deviation measure of . To study the mechanism responsible for the steepness of the response function we again analyzed a minimal model . In Fig . 3C and D two scenarios for an IN gate are compared . In the first scenario , a transcription factor TF1 cooperatively binds to a pair of repressor sites to inhibit the gene tf3 . In the second scenario we use the same configuration , but add an activator site for TF3 . Thus , auto-activation competes with cooperative repression . The fitness of each design is optimized using the fitness function for the IN gate . As can clearly be seen in Fig . 3D the second scenario , using conditional auto-activation , results in a steeper and more complete repression . Figure 1 in Text S1 shows plots of the sensitivity as a function of for the response plots in Fig . 3D and clearly demonstrates that auto-activation enhances the sensitivity . Does the sensitivity of the response function 9 have an upper bound , as is the case for Hill functions ? To answer this question we again study the minimal model mathematically . The response function of the minimal model is given by ( 9 ) with ( 10 ) ( 11 ) ( 12 ) We first describe the limit in which and . The form of this equation is obviously similar to equation 5 for conditional auto-activation . However , here is large and negative ( ) when . In this regime , , so that the numerator is approximated by . As increases , decreases while the denominator increases; therefore the expression is rapidly repressed . This regime ends suddenly as reaches zero , at ; at this point the expression is almost fully repressed . The sensitivity function can be derived from equation 9 . The exact expression is again too cumbersome to derive the maximal sensitivity analytically . However , the most sensitive region of the response plot is again expected around so that we estimate the maximal sensitivity as ( 13 ) For large this converges to ( 14 ) which for large approaches . Numerical tests demonstrate that this conservative approximation becomes excellent for ( data not shown ) . In the absence of auto-regulation , the sensitivity cannot exceed 2 , the number of repressor sites . Equation 14 demonstrates that in the presence of auto-regulation the sensitivity can easily exceed 2 but is nevertheless limited given . We note that the sensitivity is optimal for . Hence , unlike the case of conditional auto-activation the requirements of a high maximal expression and a high sensitivity do not contradict . In the simulations as well as in reality , however , the promoter strength is bounded by other factors . Clearly the binding affinity of RNAP for the promoter is bounded by the physics of RNAP–DNA binding . A less obvious constraint follows from the fact that the expression switches from high to low around ; if the repression is to occur at reasonable TF1 concentrations ( the simulations impose the interval ) high values of require low values of the dissociation constant ( i . e . , strong repression ) . Finally , a high sensitivity in one point does not guarantee that the response function switches from high to low in a narrow interval as is required by the fitness function; this explains why in the plots in Fig . 3 the maximal sensitivity is not optimal ( ) . In both previous cases , auto-regulation was used to obtain the steep or switch-like behavior required to approximate the binary responses of logic gates . Indeed , sharp responses are observed and probably required in many real examples; nevertheless many genes respond in a more gradual manner to their input signals ( Ref . [17] provides examples of both sharp and gradual responses ) . Is auto-regulation also useful in cases where a gradual response is required ? To test this , we now turn to the results of simulations with linear goal functions . Indeed , simulation results for linear repression ( i . e . , the LIN and NMEAN gates ) always use auto-regulation , with in the range . As can be seen in Fig . 4 , approximately linear repression can be obtained when repression is combined with auto-activation; the deviation measure for the simulation result shown is . The same figure shows results of simulations in which auto-regulation is excluded . In that case a large cooperative repression module results , which leads to a less linear result ( ) . Again , we analyzed the mechanism through a slightly simplified model presented in the same figure . The promoter design of the simplified model is identical to the one presented in Fig . 3C , where it was used to demonstrate how auto-activation can provide sharp responses . In essence , the difference between the two cases is that in the IN gate the two repressor sites have the same affinity , whereas in the LIN case they do not: one of them is many times weaker than the other ( vs . . As a result , the repression is introduced gradually as the repressor concentration increases . Linear repression requires that in the domain . Since is defined as the solution of , we can take the total derivative of this relation to arrive at ( 15 ) In the absence of auto-regulation the denominator equals 1 . In this case is a Hill-type function of and therefore its derivative is not constant . In the presence of auto-regulation the denominator can be used to correct some of the variation in the numerator . ( See Text S1 . ) In contrast , in the simulation results for linear activation ( both the LACT and the MEAN gates ) auto-regulation is never used . To test if these results are an artifact of the algorithm , we studied a series of models ( see Text S1 ) . Each model is a possible layout of transcription factor binding sites and includes auto-regulatory sites . For each of the models , we optimized the affinities of all binding sites with respect to the fitness score , using a standard Nelder–Mead optimization routine . Consistent with our simulations , in the solutions for all models the affinities of the auto-regulatory sites vanished . Even though the list of models tested is not exhaustive , this suggests that auto-regulation is not helpful in constructing LACT or MEAN gates . To illustrate an important difference between linear activation and linear repression we provide the following general argument . Suppose that an accurate LACT gate can be constructed using auto-regulation . By definition the response function should then be in the interval . Consequently , in this interval . Interestingly , this shows that if all TF3 binding sites in the cis-regulatory region are replaced by binding sites for TF1—resulting in a gate without auto-regulation—the exact same response function should be obtained . Even though this argument does not prove that auto-regulation cannot be used to construct LACT gates , it does show that if a high-quality LACT can be constructed with auto-regulation a similar response can always be obtained without it as well . This is in stark contrast with the linear repression case , where . Surprisingly , auto-repression does not show up in any of the simulations described so far , whereas auto-activation features regularly . As we mentioned in the introduction , previous studies have shown that negative auto-regulation can be used to diminish intrinsic noise and to speed up response times . In the simulations presented so far , such qualities were not rewarded . Therefore we asked if auto-repression would emerge if we did select for such dynamic properties on top of our usual selection criteria . First , we used a heuristic measure ( where RT stands for Response Time ) to select for a quick response to changes in the input parameters; it was computed as follows . For 16 combinations of input concentrations ( corresponding to the red dots in Fig . 2 ) we numerically solved the differential equation 4 with two different initial conditions: and . The solutions were used to measure the time it took for the system to approach the steady-state value up to a small distance . The measure was defined as the sum of all 32 response times . The total fitness function , combining selection on the response function with selection on the response time , was , where the factor was used to tune the relative strength of the selection on the response time . Again , is an irrelevant constant ensuring that . In fact , the initial condition of the simulations , in which the gate is completely dysfunctional , is a local optimum of this fitness score . This is because initially the steady-state expression level is negligible so that the response time for initial condition is practically zero . Even though mutations that increase the constitutive promoter activity improve both the response function and the response time for initial condition , the concomitant increase in the response time for initial condition dominates . To ensure that the simulation was not trapped in this local optimum was increased slowly from 0 to in the course of the simulations , according to: ( 16 ) where is the simulation time ( i . e . , the cycle number of the evolutionary algorithm ) , and . Second , we selected against intrinsic noise , i . e . , fluctuations in the concentration of due to the stochasticity of the processes involved in the production and degradation of TF3 . This type of noise should be contrasted with extrinsic noise , which here is understood to be the noise due to fluctuations in the input concentrations and or due to changes in RNAP concentrations [12] , [27] . Even though extrinsic noise is generally important too [28] , the treatment of extrinsic fluctuations involves subtleties that are beyond the scope of this work , such as the question which changes in and should be considered changes in the input signal and which should be considered noise . We therefore only consider intrinsic noise . In order to treat intrinsic fluctuations in a tractable manner we now replaced the ordinary differential equation 4 by the following stochastic differential equation: ( 17 ) The term represents Gaussian white noise and is characterized by ( see Text S1 ) : ( 18 ) The first term on the right-hand side describes the noise in the production of TF3 while the second term describes the stochasticity in the degradation of TF3 . Both terms depend explicitly on the volume because , at constant concentration , the copy number of TF3 scales with which affects the variance in . In Text S1 we show that the standard deviation of the concentration can be approximated as: ( 19 ) with ( 20 ) where and are the first and second partial derivatives of with respect to , and is the steady-state solution of the deterministic equation 4 . We computed the right-hand side of equation 19 numerically for 16 input values ( again , corresponding to the red dots in Fig . 2 ) and treated the sum of the results as an additional fitness measure ( where N stands for Noise ) . The strength of selection against noise was again increased gradually during the simulations ( analogous to equation 16 ) . The total fitness function thus became . We performed simulations with several values for and : ( where is the arbitrary unit of time , see Methods ) , and . Indeed , in these simulations auto-repression emerged . In activating gates ( ACT , AND , OR ) auto-repression resulted in all simulation runs with or . The auto-repression was invariably strong , with , and mediated by multiple cooperative binding sites . If or were further increased eventually the resulting cis-regulatory regions became completely dysfunctional; this can be understood from the fact that both the response time and the noise reduction can be optimized by abolishing expression altogether . Figure 7 in Text S1 demonstrates how the properties of resulting OR gates changed as a function of . As is increased the deviation of the response from the ideal OR gate , measured by , increases , while the noise , measured by , decreases . As we explained , the response functions of the NAND , NOR and IN gates benefit from auto-activation; in those gates auto-activation occurred unless the selection pressure on the dynamical properties dominated ( i . e . , if or were large ) , in which case the quality of the response functions was negatively affected . Fig . 4 shows results from simulations selecting for NAND gates at various values of . As the selection pressure on response time was increased the response functions became more and more compromised . Auto-activation resulted for and ; in the former case the promoter designs were of the type shown in Fig . 3A , while in the latter case only one auto-activation site remained . Interestingly , in most of the simulation runs ( 18 out of 20 ) at a weak auto-repression site shows up in conjunction with the auto-activation site . These weak auto-repression sites are incorporated in the hetero-cooperative repression module and have a high occupancy only at high concentrations of TF1 and TF2; analogous to conditional auto-activation , this effect could be called conditional auto-repression . At the auto-activation was replaced by strong auto-repression mediated by a single or multiple binding sites and the sites for the input TFs were very weak . Finally , at the resulting gates became completely dysfunctional and no significant binding sites remained . The results above suggest that auto-activation and auto-repression have very different functions . We therefore wondered whether in the known transcription regulatory network of E . coli the auto-activators and auto-repressors have different statistical properties . Surprisingly , we found that auto-activators are more often regulated by other TFs than auto-repressors . According to the data in RegulonDB [2] , 18 of the 25 auto-activating TFs in E . coli are regulated by at least one additional TF ( 72% ) versus 30 out of 62 auto-repressing TFs ( 48% ) ; this indeed suggests that auto-activators are more likely to have additional inputs ( ) . The difference becomes more convincing if we look at the total number of inputs for the two sets . The 25 auto-activators have , in total , 52 inputs ( i . e . , an in-degree of 2 . 08 on average; the auto-regulation is not counted as an input ) while the 62 auto-repressors have 50 inputs in total ( 0 . 81 on average ) . Evidently , auto-activators have significantly more inputs than auto-repressors ( ) . Since auto-regulation can potentially have many functions and most of the auto-regulators are poorly characterized , we can only speculate about the origin of this difference . One possible explanation would be the following . If a common function of auto-activation is to shape response functions , as suggested by our analysis , then auto-activation should evolve preferentially for TFs that are regulated by one or more input TFs . In that case one would expect the average in-degree for auto-activators to be high . The same argument does not hold for auto-repression: our results suggest that auto-repression typically evolves for different reasons . Some of the functions of auto-repression suggested in the literature , such as its tendency to decrease intrinsic noise and to mitigate the effect of changes in the bacterial growth rates on gene expression , do not require additional input TFs . It is therefore not too surprising that for many auto-repressors ( 32 out of the 62 ) no input TF is known .
Our results shed new light on the use of auto-regulation . We described three situations in which auto-activation can be used to improve the response function of promoters . First , if auto-activation is conditional on the presence of other TFs , it can give rise to sensitive responses that otherwise require multiple cooperative binding sites of the input TF . Presumably , not all input TFs can bind cooperatively to multiple binding sites; in those cases conditional auto-activation can serve as an alternative . Secondly , auto-activation can strongly contribute to the sensitivity of the response of repression systems . Whenever sharp repression is required , auto-activation can have a selective advantage . Thirdly , we showed that auto-activation is also useful if a linearly decreasing response function is desired . Together , such mechanisms may help explain the large number of auto-activators present in E . coli . Auto-repression never appeared in the simulation results if selection was based on the response function of the gates only . Most likely , the limited use of auto-repression in shaping response functions is due to its general tendency to decrease the fold-change and sensitivity of the response . A low fold-change or sensitivity can typically also be achieved without auto-repression by tuning both the promoter strength and the affinities of the TF binding sites . Nevertheless , we cannot exclude the possibility that auto-repression would show up in simulations selecting for response functions different from the ones presented here . If the fitness function was altered to favor a fast response or suppression of intrinsic transcriptional noise , auto-repression did emerge . It has been suggested before that the function of negative auto-regulation is to regulate such dynamic properties [5] , [9] , [10]; our results support this view . In retrospect , the emergence of auto-regulation is hardly surprising . The evolution of cis-regulatory regions can be perceived as adaptive curve fitting . Allowing for auto-regulation gives gene-regulatory systems additional degrees of freedom to optimize their performance , and it would perhaps be more surprising if this freedom were not exploited . We therefore expect that the conclusions based on the idealized gates studied in this work are also relevant for real biological systems requiring more complex response functions . We have seen that in some cases the advantage of using auto-regulation is large ( e . g . when sensitive repression is required ) whereas in other cases there is only a small difference between the quality of the response function for designs with or without auto-regulation . This leads one to wonder whether in the latter case natural selection on the shape of the response would be large enough to evolve and maintain auto-regulation , in particular in the presence of noise . This is largely an open question; yet , the fact that some E . coli promoters contain a large number of TF binding sites many of which contribute only marginally to the expression ( see for instance [29] ) suggests that , at least in some cis-regulatory regions , natural selection is strong enough to fine-tune the response function in great detail . The results presented are quite insensitive to the parameter values chosen . The value of influences important properties such as the maximum fold change in activation systems , but as long as it is chosen within the biological range 10–100 the designs of the gates do not seem to depend qualitatively on the value chosen . To verify this , we performed simulations with for AND , NAND , NOR and OR gates ( without selection against noise or response speed ) and found the results to be qualitatively the same as those presented . The value of influences the spacing of binding sites within a module , but not the basic designs properties , as long as so that overlapping modules can be constructed that bind independently . The results are also insensitive to the length of the binding sites ( we tested this with simulations for AND , NAND , NOR and OR gates with ) and the matrix elements of the binding energy matrix; essential is only that the evolutionary algorithm can tune the dissociation constants of the binding sites to a wide range of values ( 1–10000nM ) , as in reality . The length of the cis-regulatory region , , determines the maximum number of tandem binding sites that fit on the regulatory region; larger values of therefore ultimately lead to larger tandem arrays . However , since tandem arrays of five or more binding sites can form in the simulations , we believe that is large enough to accommodate typical E . coli promoters . Even though in eukaryotes the mechanisms of gene regulation are generally different and various additional layers of regulation exist , recent work has shown that many basic principles of prokaryotic gene regulation—in particular the interplay between cooperative binding and competitive inhibition—are equally important in eukaryotes ( see for instance [30] about repression and inhibition in yeast and [31] about enhancers in Drosophila ) . Auto-regulation is also widespread in eukaryotes [32]; therefore , our findings could also be relevant for gene regulation in eukaryotes . As we mentioned , auto-activation is known to reduce the response speed in some situations and to increase the amplitude of fluctuations . Clearly , those issues may be problematic in some real-life situations . On the other hand , a slow response can be a positive feature as well if it is applied as a filter of high-frequency noise ( a low-pass filter ) . Fluctuations may in some cases be beneficial or even necessary . For instance , when cells respond to a fluctuating environment via the strategy of stochastic switching , fluctuations are essential [33] . But even when cells cope with a fluctuating environment via the strategy of deterministic switching , fluctuations may be beneficial , since they can increase the population's growth rate when the response function is suboptimal [34] . Indeed , the fact that auto-activation is found so often in E . coli demonstrates that the associated reduction of the response speed and the amplification of fluctuations can apparently be circumvented , tolerated or put to use . | Bacteria adjust which proteins they make , and how many copies of each kind , depending on their environment . The production rate of each regulated protein is controlled by a special class of proteins called transcription factors . The rate at which a certain protein is produced usually depends on the cellular concentrations of a few such transcription factors . When circumstances change , the concentrations of these transcription factors alter too and consequently the production rates of all proteins regulated by them are adjusted . Interestingly , many transcription factors also regulate their own synthesis rate . This suggests that this self-regulation must have one or more important functions . In this article we study one possible function . In order for cells to function properly each protein concentration has to respond in a particular way to changes in transcription factor concentrations . We have studied how bacteria can optimize and fine-tune these responses . To this end , we formulated a physical model of the regulation by transcription factors and performed computer simulations . These simulations show that self-regulation—and in particular self-activation—is often a useful tool to achieve the prescribed response . Therefore we conclude that natural selection on the regulation of protein levels could naturally lead to self-regulation . |
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Coral reefs are in global decline , with coral diseases increasing both in prevalence and in space , a situation that is expected only to worsen as future thermal stressors increase . Through intense surveillance , we have collected a unique and highly resolved dataset from the coral reef of Eilat ( Israel , Red Sea ) , that documents the spatiotemporal dynamics of a White Plague Disease ( WPD ) outbreak over the course of a full season . Based on modern statistical methodologies , we develop a novel spatial epidemiological model that uses a maximum-likelihood procedure to fit the data and assess the transmission pattern of WPD . We link the model to sea surface temperature ( SST ) and test the possible effect of increasing temperatures on disease dynamics . Our results reveal that the likelihood of a susceptible coral to become infected is governed both by SST and by its spatial location relative to nearby infected corals . The model shows that the magnitude of WPD epidemics strongly depends on demographic circumstances; under one extreme , when recruitment is free-space regulated and coral density remains relatively constant , even an increase of only 0 . 5°C in SST can cause epidemics to double in magnitude . In reality , however , the spatial nature of transmission can effectively protect the community , restricting the magnitude of annual epidemics . This is because the probability of susceptible corals to become infected is negatively associated with coral density . Based on our findings , we expect that infectious diseases having a significant spatial component , such as Red-Sea WPD , will never lead to a complete destruction of the coral community under increased thermal stress . However , this also implies that signs of recovery of local coral communities may be misleading; indicative more of spatial dynamics than true rehabilitation of these communities . In contrast to earlier generic models , our approach captures dynamics of WPD both in space and time , accounting for the highly seasonal nature of annual WPD outbreaks .
The study site was located at the shallow water reef ( depth of ca . 1 . 5 m ) off the shore of the Interuniversity Institute ( IUI ) in Eilat . The reef is relatively uniform with respect to bathymetry and is situated on a gentle slope ( ca . 3° ) on flat beach-rock . The reef did not appear to exhibit any particular dominant water-flow direction due to the high impact of the erratic and changing wave action . The reef is characterized by relatively high coral density , which allows for a relatively large number of infection cases per unit area . Thus the IUI site is particularly suitable for studying the spatial distribution and the dynamics of coral diseases . A 10×10 m plot was surveyed once a month , from June 2006 until May 2007 providing twelve “snapshots” in total . The size of the plot and the period of time between snapshots were based on a preliminary survey where we roughly assessed the clustering size of infected corals , and the development time of new infections . The four corners of the plot were marked in the field , and a grid made of ropes and elastic bands was placed on the plot dividing the plot to 100 subunits of 1×1 m . Using photography ( photoquadrats ) , all 2 , 747 susceptible corals within this area were mapped and an X-Y coordinate of the coral’s centre within the plot was allocated , following the “center rules” of Zvuloni et al . [59] . Once a month , the grid was placed precisely on the same area and the locations of infected corals were recorded . Corals were classified in the field as infected if they showed typical signs of the disease—a sharp line between apparently healthy tissue and a thin zone of bleached tissue grading into exposed coral skeleton ( Fig 1 , [50] ) and some level of progression ( i . e . , increased severity ) relative to the previous snapshot . The Israel National Monitoring Program of the Gulf of Eilat provided continuous measurements of sea-surface temperature ( SST ) , ca . 20 m away from the plot , as obtained from two temperature probes ( Campbell Scientific , Temperature Probe Model 108; accuracy of ±0 . 1°C within the range of 20–30°C and resolution of 0 . 1°C ) . The 12 spatial snapshots of the reef-section were organized as eleven pairs of sequential snapshots , where in each pair infected corals were partitioned into two groups: Newly-Infected Corals ( NICs ) —those corals that had signs of infection in the current snapshot , but not in the previous one . Previously-Infected Corals ( PICs ) —those corals that were infected in the previous snapshot . Our conjecture was that if inter-colonial transmission is significant for the spread of the disease , NICs should develop in closer proximity to PICs than would be expected at random . To test this hypothesis , we developed a simple , but novel , spatiotemporal index , which is based on Ripley’s K-function [60 , 61] . While the K-function tests the spatial pattern of a single group of events , our spatiotemporal index n ( r ) was designed to test the spatial relations between two groups of events , in this case two groups of infected corals—the NICs and the PICs . This index is defined as the mean number of NICs in a given month within a radius r from a PIC of the previous month , and is calculated as: n ( r ) =1m∑i=1m∑j=1kIr ( dij ) . ( 1 ) Here , m and k are the numbers of PICs and NICs , respectively , in the tested pair of sequential sampling dates , dij is the distance between any PIC i and NIC j . The indicator variable Ir ( dij ) indicates whether or not NIC j is located within radius r from PIC i . Thus , Ir ( dij ) receives a value of 1 if dij < r and zero otherwise . In contrast to the nearest-neighbor approach used by Zvuloni et al . [16] to identify whether NICs form aggregations in the vicinity of PICs , the n ( r ) index also quantifies the spatial scale of aggregation , as it is calculated for a range of distances r ( similarly to Ripley’s K function; see Ripley [60 , 61] ) . Using a null model approach , which bases the null expectation on the spatial distribution of the entire pool of susceptible corals , we ascertained whether the k NICs found in the field were significantly aggregated around the PICs ( see Material and Methods ) . We model disease transmission by using a stochastic spatiotemporal model similar to Zvuloni et al . [16] , but with a new maximum-likelihood fitting procedure to estimate model parameters from the field-data . The analysis that follows is based on the classical Susceptible-Infected-Susceptible ( SIS ) model of epidemiology [62 , 63] . Corals are classified as either susceptible or infected . A susceptible coral can become infected when the disease is transmitted from a ( usually ) neighboring PIC , and an infected coral can return to be susceptible if the disease stops showing clinical signs . The model assumes transmission is via local waterborne infections ( i . e . , susceptible corals are infected by suspended infectious material originating from diseased corals within the study site ) . The assumptions underlying the construction of the model are that: ( i ) there is a higher probability that infection events take place in close proximity to existing infections; and ( ii ) there is a cumulative impact of multiple infections on a single susceptible coral , such that the more infected neighbors a susceptible coral has , the more likely it is to become infected itself . More specifically , the model determines the probability of each susceptible coral being infected and thus becoming a Newly Infected Coral ( NIC ) . The probability of being infected by any Previously Infected Coral ( PIC ) within the study site is assumed to be inversely proportional to the distance ( d ) of the PIC . In addition , a susceptible coral can be infected by any of the PICs present . Thus , we define the probability of a coral i ( from all susceptible corals within the study site ) to become infected during a month t ( 1≤t≤11; in total there are eleven sequential sampling dates ) as: pt ( i ) =ct∑j∈PICt1dijα , ( 2 ) where PICt is the set of all PICs in month t and dij is the Euclidean distance between coral-i and PIC-j . The exponent α characterizes the decay of the transmission probability with distance . In this way , infections are preferentially passed to neighboring susceptible corals . Another special feature of the model is the inclusion of seasonal drivers [64] through the constants ct that characterize the transmission strength of WPD in each month t . These constants presumably depend on environmental factors that change in accordance to the season ( e . g . , seawater temperatures ) , and therefore may link between the spatiotemporal model and these factors . Note that all PICs within the study site influence the probability of any susceptible coral to become infected . The definition ensures the probability is inversely proportional to the coral’s distance from any PIC . In addition , the probability increases with the number of PICs and the increase will be largest for neighboring PICs ( where the distances dij are small ) . Model parameters that need to be estimated are: ( i ) the exponent α that characterizes the decay of the transmission probability with distance , and ( ii ) the constants ct that characterize the transmission strength in each month t . In order to find the best fitting parameters α , c1 , … , c11 , we define a likelihood function and then maximize it with respect to these parameters . Given PICt ( the set of PICs in month t ) , the probability that the set of corals infected during this month is precisely the set NICt of NICs is: p ( NICt|PICt , α , ct ) =∏i∈NICtpt ( i ) ×∏i∉NICt∪PICt ( 1−pt ( i ) ) . ( 3 ) Here , the first term on the RHS is the probability that all the corals in the set NICt are infected , and the second product is the probability that all the corals , which are neither in the set NICt , nor in the set PICt , are not infected . The total probability of obtaining the empirical results given the model , that is the likelihood function , is thus given by: L ( α , c1 , … , c11 ) =∏t=111[∏i∈NICtpt ( i ) ×∏i∉NICt∪PICt ( 1−pt ( i ) ) ] , ( 4 ) and the log-likelihood is given by: LL ( α , c1 , … , c11 ) =∑t=111[ ∑i∈NICtlog ( ct∑j∈PICt1dijα ) +∑i∉NICt∪PICtlog ( 1−ct∑j∈PICt1dijα ) ] ( 5 ) The maximum-likelihood estimate for the parameters is obtained by maximizing the function in Eq 5 . The procedure described below reduces the multi-variable optimization problem to a series of one-dimensional problems . We note that since each of the variables ct appears in only one of the summands , we find that: maxα , c1 , … , c11LL ( α , c1 , … , c11 ) =maxαM ( α ) ( 6 ) where: M ( α ) =∑t=111maxct[∑i∈NICtlog ( ct∑j∈PICt1dijα ) +∑i∉NICt∪PICtlog ( 1−ct∑j∈PICt1dijα ) ] . ( 7 ) The profile likelihood function M ( α ) is the maximum of LL with respect to c1 , … , c11 with a fixed α . In order to maximize LL , we proceed as follows in our numerical algorithm: ( i ) We step α incrementally through a certain interval in small steps . For each of the α values we run over t from 1 to 11 ( the number of pairs of sequential sampling dates ) , and for each of the values of t we numerically find ct = ct ( α ) that maximizes: M˜t ( α , ct ) =∑i∈NICtlog ( ct∑j∈PICt1dijα ) +∑i∉NICt∪PICtlog ( 1−ct∑j∈PICt1dijα ) . ( 8 ) Two approaches were used to test the null hypothesis that the observed data is generated by the SIS epidemic model driven by Eq 2: The number of NICs observed in the field ( k ) in each month was compared to the distribution of the simulated number of NICs generated from 1 , 000 model realizations using the best-fitting parameters α , c1 , … , c11 . The model fit was tested by comparing the spatiotemporal index n ( r ) ( Eq 1 ) calculated for the actual data with that generated by repeated model realizations using Eq 2 . For further details see Material and Methods . We link the model to seawater temperatures and test possible effect of increasing temperatures on disease dynamics . By controlling the temperature we can test different climate change scenarios . Our model differs from the usual mean-field SIS models in which susceptible individuals and infectives mix randomly and in a uniform manner; here an explicit spatial component is incorporated through the use of Eq 2 . For all future projections , we use the last month of the real data as initial conditions . Then , at each monthly time step , the susceptible corals that become infected over the coral network are stochastically determined according to Eq 2 , given the spatial compositions of the sampled community . The computations keep track of which of all the corals become infected and which remain susceptible . Two different demographic assumptions were applied in the simulations— ( i ) constant influx of recruits , and ( ii ) free-space regulation of recruitment ( see Material and Methods ) .
Based on our analysis with the sptiotemporal index n ( r ) ( eq 1 ) , we found that in all cases Newly-Infected Corals ( NICs ) appeared to form aggregations around Previously-Infected Corals ( PICs ) over distance scales of up to 4 . 5 m ( see e . g . , Fig 2A for June-July 2006 , and S2a Fig for all eleven sequential snapshots ) . This is because the index n ( r ) of the observed data sits almost always above the Monte Carlo 95% CI envelope generated by the null test ( see Materials and Methods ) . That is , in all cases the hypothesis that the NICs were infected by a random process of disease transmission , independent of the spatial location of the PICs , was rejected . In S3 Fig we provide spatial illustrations of the disease dynamics over the studied year showing the spatial relation between PICs and NICs . Using the maximum likelihood fitting procedure , the best-fitting exponent α , which in Eq 2 expresses the decay of the transmission probability with distance , was found to be α^ = 1 . 9 ( Fig 3 ) . The maximum-likelihood estimates for the best-fitting parameters ct , constants that express the transmission strength of the disease during month t ( c1 , … , c11 ) and presumably depend on environmental factors , are given in S1 Table . For all pairs of sequential sampling dates , the number of NICs observed in the field ( k ) was within the 95% confidence interval ( CI ) envelope of the simulated number of NICs obtained from the model realizations ( Fig 4 ) . We thus could not reject the hypothesis that the observed NICs were produced according to Eq 2 . ( Note that here we are essentially testing the model’s “goodness of fit” to the data , and thus there is no need to use the first half of the time series to predict the second half . ) Additional support for the validity of the spatiotemporal model is that in nearly all cases the observed n ( r ) was purely within the null expectation of the model for all distance scales r ( e . g . , Fig 2B ) . However , in a few cases the observed n ( r ) was found to be greater than the upper bound of the 95% CI envelope generated by the model realizations for certain distance scales ( see , for example , August-September 2006 in S2b Fig ) . The number of infected corals observed within the study site ranged from a low of 11 infected corals during June 2006 to a peak of up to 36 infected corals in November 2006 ( Fig 5A ) . The disease prevalence lagged ca . 3 months behind the sea surface temperature ( SST ) that reached its seasonal peak of 27 . 7°C at the end of August 2006 . On the other hand , we found a high association between SST and ct ( see Fig 5B; Adjusted r2 = 0 . 88 , goodness of fit is SSE: 3 . 02e-07 , RMSE: 0 . 0001943 ) , which is expressed by the polynomial relationship: ct=p1⋅SST2+p2⋅SST+p3 ( 10 ) having coefficients [with 95% CI]: p1 = 2 . 968e-05 [-6 . 95e-06 , 6 . 631e-05] , p2 = -0 . 001216 [-0 . 002972 , 0 . 0005402] and p3 = 0 . 01267 [-0 . 008197 , 0 . 03353] . We calculated the epidemiological reproductive number R0 [65] for the time period between June and August 2006 , when the cumulative incidence of infections grows approximately exponentially with time ( see Material and Methods ) . The result shows that the development of the disease within the coral community resulted in an epidemic-like growth with R0 = 1 . 2 ( r = 0 . 35; TG = 0 . 53 ) . The unexpected high association found between SST and the transmission strength ct of WPD ( Adj . r2 = 0 . 88; see Fig 5B ) extracted from fitting the spatiotemporal model to the data allows us to assess the potential long term impact of WPD on the local coral community under different climate change scenarios . We first examine model projections assuming that there is no climate change and that the seasonal cycle of SST temperature repeats in exactly the same way from year to year . Projections of the disease 80 years into the future under these conditions ( see Material and Methods ) show seasonally driven annual cycles ( Fig 6A and 6D ) . Indeed , each year the transmission strength of the disease increases as SST rises from March to August , and then rapidly decreases from September to February ( Fig 5B ) . We then considered the impact of a general mean increase in SST assuming a scenario of constant influx of recruits ( “recruitment limited” ) . Fig 6B shows the effects of increasing SST by 0 . 5°C while Fig 6C shows the effect of only a 1°C increase . We find multi-annual cycles , in which severe epidemics take place every few years when the density of susceptibles corals build up to relatively high levels ( Fig 6A , 6B and 6C ) . The intensity of these epidemics increases with increasing SST , but their frequency is still restricted by the rate at which corals are replenished . The same simulations were examined under an assumption that the coral community is governed by space limitation and is thus “free-space regulated , ” or dependent on the level of free substrate available in the local patch . This follows from the hypothesis that space is a limiting resource in many marine benthic populations [66–69] . Fig 6D , 6E and 6F show that under a scenario of free-space regulation of recruitment , a mean increase of only 0 . 5°C can cause epidemics to double in size , while a mean rise of 1°C can cause increases scaling in orders of magnitude . Finally , we point out that these model “forecasts” should not be viewed as accurate predictions of monthly changes but more as qualitative guidelines as to what might be expected should there be a future long-term trend in SST temperatures . This corresponds to the “strategic” approach suggested by May [70] , which “sacrifices precision in an effort to grasp at general principles . Such general models , even though they do not correspond in detail… provide a conceptual framework for discussion and further exploration” .
Our work offers the very first model fit of any coral disease epidemic , over the timescale of the epidemic , to be found in the literature . Other attempts failed to succeed either because they did not have the fine resolution data ( e . g . , 12 monthly sampling points ) over the timespan of the epidemic , and/or because they did not have a modelling formulations to conduct parameter estimates and model fits . At best other modelling attempts have only taken into account the total annual numbers of infected corals , which is the coarsest of descriptors when characterizing epidemic dynamics . In the beginning of the transmission season , the spread of the disease in the local community exhibited epidemic-like growth motivating us to study R0 , the epidemiological reproductive number . R0 was estimated ( see Material and Methods ) for the time period between June and August 2006 ( the development period of the disease within the community ) and was found to be greater than unity ( R0 = 1 . 2; r = 0 . 35 , TG = 0 . 53 ) . This value of R0 was lower than these calculated for BBD for the outbreaks of 2006 and 2007 ( R0 = 1 . 6 and 1 . 7 , respectively; [16] ) . In BBD , both the exponential growth rate ( r ) and the mean generation interval of the epidemic ( TG ) were greater than these calculated for WPD . Although the observed seasonal outbreak generated an epidemic-like growth , the disease did not spread over a large fraction of the susceptible corals ( see Fig 5 ) . Our model simulations suggest that seasonality and low R0 are not the only factors responsible for this restriction in disease spread , and in particular , that the spatial component of the system may also play a significant role . The spatial scale of aggregations of NICs in the vicinity of existing infected corals indicates that small-scale inter-colonial transmission is significant within the community under study ( see Figs 2 and S2 ) . That is , infected corals are ‘hotspots’ of potentially infectious material , being transmitted to nearby susceptible corals on the reef ( see S3 Fig ) . We find that the larger the number of infected corals in proximity to a susceptible coral , and the closer they are , the higher the likelihood of this coral becoming infected itself . Similar results were found in previous studies for WPD in the Florida Keys [50] , for BBD in the Red Sea [16] and for aspergillosis in the Caribbean [71] . These findings are in contrast to a recent study by Muller & van Woesik [72] which suggests that coral diseases in the Caribbean do not follow a contagious-disease model . One possible explanation for the inconsistency in the results between these studies , is that there are differences in the infection process of the two identified pathogens ( i . e . , the causative agents are known to be different between regions ) . In addition , coral communities across the Red Sea are much denser than in the Caribbean; while in the present study 2 , 747 corals susceptible to WPD were recorded within a 10×10 plot , Muller & van Woesik [72] recorded only 78±12 ( mean±SE ) susceptible corals within the same plot size . Hence , the average distance among susceptible corals in the Caribbean is much greater than in Eilat , making the probability of identifying inter-colonial transmission significantly lower than in Eilat . As such , the findings of Muller & van Woesik [72] would not necessary contradict the findings of our transmission model . In a similar spirit , Bruno et al . [13] also argue that high coral cover and/or density increases the occurrence for horizontal transmission of White Syndrome between corals across the Great Barrier Reef in Australia . Testing the goodness of fit of our spatiotemporal model ( Eq 2 ) in two different ways [e . g . , distribution of NICs and clustering index n ( r ) ] reveals that in all cases the model could effectively predict the number of NICs and in nearly all cases it could simulate the actual spatial patterns of new infections . However , in a few cases the observed n ( r ) was found to be greater than the upper bound of the 95% CI envelope generated by the model realizations for certain distance scales . These deviations suggest that there may be mechanisms involved in the transmission process that are not fully captured by our simple model . However , by comparing the results obtained from the random simulated infections ( S2a Fig ) and those obtained from the spatiotemporal model ( alongside with S2b Fig ) , it is clear that the spatiotemporal model always outperformed the random transmission model . The unexpectedly high association found between SST and the transmission strength ct of WPD ( Adj . r2 = 0 . 88; see Fig 5B ) extracted from fitting the model to the data indicates the power of the modeling approach . This association strongly suggests that SST is the seasonal driver behind the WPD dynamics , and might well be explained by the response of the host and/or pathogen to seasonal thermal fluctuations . High seawater temperatures may cause stress to coral hosts and increase their susceptibility to disease infections [73] , while at the same time they may increase the virulence of the pathogen [74] . Previous studies from other locations have also identified clear seasonal patterns of various coral diseases , such as white syndrome [13 , 32] , BBD [15 , 22] , ulcerative white spots [46] , aspergillosis [47] and white pox [48] , related particularly to warm seawater temperatures . In this study , the seasonal patterns of the transmission strength of WPD ( ct ) preceded the seasonal patterns of the disease prevalence by ca . three months ( see Fig 5B vs . Fig 5A , respectively ) . This suggests that the high seawater temperatures may directly affect the susceptibility of the corals and/or the virulence of the pathogen , but indirectly affect the prevalence of WPD . That is , the impact of the disease on the reef might be the lagged response ( ca . three months ) to processes that advance the progression of the disease within and among coral colonies . The strong coupling of the transmission strength of the disease ( measured by ct ) and the seasonal variation in SST , forms the basis for our forecasts of future global warming scenarios . The association suggests that the higher seawater temperatures associated with future global warming will intensify the impacts of WPD on reefs . Our future predictions verify that in a demographic scenario , when recruitment is purely free-space regulated , such that the coral community density is relatively constant in steady-state conditions , a mean increase of only 0 . 5°C can cause epidemics to double in size . Likewise , a mean rise of 1°C can even lead to increases in several orders of magnitude . However , in reality , the influx of recruits is likely to be limited to some extent and located along a continuum between the two extremes ( i . e . , constant influx vs . free-space regulation ) . Thus , it is reasonable to assume that during an intense epidemic , when many susceptible corals will be removed through death , the spatial component of the disease will play a role in the disease dynamics . Indeed , our future predictions confirm that the spatial component of the disease transmission system has , to some extent , a protective effect that restricts the magnitudes of annual epidemics . Under a demographic scenario of constant influx of recruits , the mean coral community densities decrease as the SST increase ( Fig 6A , 6B and 6C ) . In this case the intensity of the disease does not change with increased SST scenarios . We suggest that this is because the decrease in density discounts for the increase in the transmission strength of the disease ( i . e . , each of these parameters work in a different direction ) . In practice , the decrease in coral density increases the mean distance between infected and susceptible corals within the community and thus decreases the potential for disease transmission [13] . Such a positive relationship between host density and disease transmission has been demonstrated in many host-pathogen systems [75–78] , and is considered as an important property of the infectious process [79] . Specifically with infectious coral diseases , high coral density may have similar effects to that of high coral coverage; effectively this reduces the mean distance between neighboring corals , and as with our spatiotemporal epidemic model , increases the likeliness of inter-colonial transmission . Indeed , Bruno et al . [13] demonstrated that for white syndrome outbreaks to occur in the Great Barrier Reef in Australia , in addition to thermal stress , coral coverage must be relatively high ( 50% or higher ) . Our model suggests that an infectious disease , such as WPD in the Red Sea , cannot lead to a complete destruction of the coral community , due to the spatial nature of the disease transmission and its protective effect . However , this also implies that signs of recovery of local coral communities may be misleading , and are not truly indicative of their rehabilitation ( see for example the sharp fluctuations in the disease prevalence in Fig 6C ) . In addition , environmental changes , such as increasing levels of SST , can shift the nature of recruitment on local scales , altering the way in which the spatial component of the system restricts or enhances local disease dynamics . In addition , note that the remarkable transition in disease prevalence , which is observed when recruitment is free-space regulated ( Fig 6D , 6E and 6F ) , may indicate that the interaction of the seasonal driving force and the spatial nature of the system has higher levels of complexity , beyond those described here . These more complex aspects of this system are beyond the scope of the present paper . To summarize , we have addressed some fundamental questions regarding the dynamics of WPD in the Red Sea . Spatiotemporal statistics combined with null hypothesis approaches proved to be effective tools for understanding epizootiological processes in coral reef communities . The new spatiotemporal index , n ( r ) , proved to be specifically tuned to detect the localized transmission dynamics among the infected corals . Previous approaches for modeling coral disease have not used powerful statistical inference methodologies to estimate parameters and for choosing the best model structure . Neither have they attempted to model the epidemic curve as it changes over a single season . In this study , however , a specially formulated maximum-likelihood fitting procedure , enabled us to estimate the most likely parameters in the model ( α and ct ) , based on the disease dynamics in space and time . It also allowed us to link the spatiotemporal dynamics of the disease to seawater temperature ( see ct in Eqs 2 and 10 ) and gave us an opportunity to generate future projections that assess the impact of increasing SST on coral communities . Over any season , the spatial model revealed that as the temperature increases , the spread of WPD on corals looks similar to the spread of forest fires , where dense forests tend to burn completely while less dense forests are relatively resistant because the fire can hardly spread [80 , 81] . Current assessments on the future of these reef-building corals are still relatively uncertain , being hindered by a lack of knowledge and understanding . In this context , our study exposes the critical importance of conducting additional multi-annual surveys on local spatial scales , for deepening our insights into these unique systems , and for supporting our efforts to successfully design effective conservation policies .
Using a null model approach , which bases the null expectation on the spatial distribution of the entire pool of susceptible corals , we ascertained whether the k NICs found in the field were significantly aggregated around the PICs . We used n ( r ) ( Eq 1 ) as a statistical index , defined as the mean number of NICs in a given month within a radius r from a PIC of the previous month . The non-aggregated null distribution of the NICs , and thus n ( r ) , was generated as follows . Infected corals from the first month in each pair of sequential sampling dates defined the m fixed PICs . Then , via computer simulation , a group of k simulated NICs was randomly chosen from the entire pool of susceptible corals without any discrimination as to whether individuals were healthy or infected . n ( r ) was then determined for different radii r . This was repeated 1 , 000 times so that n ( r ) could be calculated for each group of k NICs for any value of r . These results made it possible to generate a 95% confidence interval ( CI ) envelope for n ( r ) under the null hypothesis of no aggregation of the NICs . We then calculated n ( r ) using only the k observed NICs found in the field . If the observed n ( r ) was found within the envelope , then the null hypothesis could not be rejected and the spatial distribution of NICs was considered independent of the spatial distribution of the PICs . Otherwise , if the observed n ( r ) was found outside the 95% CI envelope , the null hypothesis was rejected and the spatial distribution of NICs was considered significantly dependent on that of the PICs at α = 5% level ( that is , the null hypothesis was rejected ) . NICs are considered spatially aggregated around PICs where the observed n ( r ) is greater than the null expectation , indicating inter-colonial ( i . e . local ) infections . On the other hand , NICs are considered over-dispersed in relation to PICs , if n ( r ) is smaller than the null expectation . This test was carried out for all pairs of sequential sampling dates . To test whether the spatiotemporal model describes suitably the transmission pattern of the disease , we simulated the infection process at the studied site based on a given set of PICs for a particular date , using the most likely parameters ( α^ , c1 ( α^ ) , … , c11 ( α^ ) ) . Thus , infected corals from the first month in each pair of sequential sampling dates define the m fixed PICs . Then , for a simulation that required a generation of new infections , we simply chose NICs at random from the entire pool of corals , assuming that coral-i has a probability pt ( i ) of being chosen ( Eq 2 ) . We repeated this process 1 , 000 times . Then , the model was tested for each pair of sequential sampling dates in two different ways: ( i ) the number of NICs observed in the field was compared with the distribution of the number of NICs obtained from the 1 , 000 random realizations; and ( ii ) the spatiotemporal index n ( r ) ( Eq 1 ) that was calculated for the real data was compared with the distributions of n ( r ) that was calculated for any distance scale r , for the 1 , 000 random realizations . We tested whether the observed number of NICs and n ( r ) were significantly different from the null distribution of those simulated under a two-tailed test of 5% significance level . If this occurred it implied that the results found in the field are inconsistent with the proposed null model . In the beginning of the transmission season , the spread of the disease in the local community exhibited epidemic-like growth . The epidemiological reproductive number , R0 [65] , was calculated for the time period between June and August 2006 ( the development period of the disease within the community ) , using the approximate relationship R0≈erTG[82] ( cf . , Zvuloni et al . [16] for black-band disease ( BBD ) ) . The exponential growth rate is governed by the parameter r , which is estimated by fitting an exponential function to the ( cumulative ) incidence of the infective numbers . The parameter TG is the observed mean generation interval , i . e . , the interval between a coral becoming infected and its subsequent infection of another coral ( see Zvuloni et al . [16] ) . R0 measures the epidemic potential of a pathogen and is defined as the mean number of secondary infections caused by a typical single infectious individual in a wholly susceptible coral community . When R0 ≤ 1 , the introduction of an infected individual will fail to result in an outbreak . If , however , R0 > 1 , then the introduction of the disease is likely to result in an epidemic that persists for extended periods . Linking the spatiotemporal model ( Eq 2 ) to seawater temperatures allows us to assess the potential future impact of WPD on the local coral community . We calculated the probability of each susceptible coral to become infected according to Eq 2 , where ct in this equation was determined by fitting a quadratic model to fit SST according to the SST in that month ( Eq 10 ) . We set α = 1 . 9 , which was found to be the best fitting exponent . The use of Eqs 2 and 10 for future predictions ensures that the probability of any susceptible coral to become infected has both spatial and seasonal/environmental components . In accordance with our data , simulations are carried out in discrete time steps from month to month . For all simulated projections , we use the last month of the real data as initial conditions for the future projections , and SST is based on a time series measured between June 2006 and May 2007 , which we assume repeats yearly . In light of global change , there is also obvious interest in trying to assess long-term effects of variations in SST , and we do this by varying the levels of SST in our simulated projections . Each year in the beginning of the infection period we randomly infected one of the corals . This insured that the local population did not stay infection free due to stochastic fadeouts in the previous season . Clearance and death rates were month specific and calculated based on collected data , i . e . , the probability of death , recovery , or remaining infected is determined by the fraction of infections that died , cleared , or stayed infected in the same month in the original data . The locations for new recruits in the 10×10 m plot are randomly chosen anywhere on the plot whenever a recruitment event takes place . This approach sets no spatial restrictions on coral settlement , and as such does not constrain the topological distribution of the corals . We assume that the per capita recruitment is either: ( i ) “recruitment limited”—independent of local community density by assuming a constant influx of recruits per year . Alternatively , we assume recruitment is ( ii ) “free-space regulated”—dependent on the level of free substrate in the local patch; following from the hypothesis that this is a limiting resource in many marine benthic populations [66–69] . Here it is assumed that following a coral’s death , a healthy recruit instantaneously replaces it . In the first scenario ( i ) , due to the spatial component of the model , the coral density may play a significant role in the transmission probability of the disease . On the other hand , in the second scenario ( ii ) , the coral community density remains constant , and the role of the spatial component in the model is also expected to be relatively constant . In reality , coral recruitment is likely to lie somewhere between these two extremes , with variations in the location of different reefs along this continuum ( for further reading on these assumptions , see [66–69] . | Coral reefs are deteriorating at alarming rates , with coral disease outbreaks increasing in prevalence and in space . Anomalously high ocean temperatures are thought to significantly contribute to this problem . We collected a unique and highly resolved dataset of a White Plague Disease ( WPD ) outbreak from the coral reef of Eilat ( Israel , Red Sea ) . By fitting a novel epidemiological model to the data , we characterize the dynamics of WPD , and study the possible effects of future increasing sea-surface temperatures ( SST ) on disease dynamics . In contrast to earlier studies , our approach captures the dynamics of coral disease both in space and time , and accounts for the highly seasonal nature of the annual outbreaks . We also apply a novel combination of spatiotemporal statistics and null hypothesis approaches to study the disease progression . Model forecasts into the future show that for some scenarios even an increase of only 0 . 5°C in SST can cause epidemics to double in magnitude . Since the probability of infection is found to be negatively associated with coral density , this implies that the spatial nature of disease transmission can both enhance and restrict the magnitude of annual epidemics . The results have implications for designing management policies appropriate for coral reef conservation . |
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Exogenous Interleukin-7 ( IL-7 ) , in supplement to antiretroviral therapy , leads to a substantial increase of all CD4+ T cell subsets in HIV-1 infected patients . However , the quantitative contribution of the several potential mechanisms of action of IL-7 is unknown . We have performed a mathematical analysis of repeated measurements of total and naive CD4+ T cells and their Ki67 expression from HIV-1 infected patients involved in three phase I/II studies ( N = 53 patients ) . We show that , besides a transient increase of peripheral proliferation , IL-7 exerts additional effects that play a significant role in CD4+ T cell dynamics up to 52 weeks . A decrease of the loss rate of the total CD4+ T cell is the most probable explanation . If this effect could be maintained during repeated administration of IL-7 , our simulation study shows that such a strategy may allow maintaining CD4+ T cell counts above 500 cells/µL with 4 cycles or fewer over a period of two years . This in-depth analysis of clinical data revealed the potential for IL-7 to achieve sustained CD4+ T cell restoration with limited IL-7 exposure in HIV-1 infected patients with immune failure despite antiretroviral therapy .
Human Immunodeficiency virus ( HIV ) infection is characterized by a profound depletion of CD4+ T cell numbers and function . Immune restoration with combination antiretroviral therapies ( cART ) has substantially improved patients' outcomes . Unfortunately , this restoration may be delayed , notably in patients starting treatment late , and/or incomplete , despite control of the viral replication [1] . Hence , immune therapy may be a complementary intervention to accelerate or improve immune restoration . Interleukin-7 ( IL-7 ) is a cytokine produced by non–marrow-derived stromal and epithelial cells and is required for the development and persistence of T cells in the periphery [2] , [3] . IL-7 may enhance thymopoiesis [4]–[6] , as well as thymic-independent peripheral proliferation of recent thymic emigrants [7]–[9] and of more mature T cells [7] , [9] even in the absence of cognate antigen [9]–[11] . Improved cell survival has also been shown in vivo [11]–[15] . In HIV-infected patients , a strong inverse correlation has been observed between plasma IL-7 levels and CD4+ T cell numbers as well as with CD4+ T cell reconstitution after initiation of antiretroviral therapy [16] , [17]–[19] . Increased levels of IL-7 during lymphopenia are thought to be mainly the consequence of a decreased receptor-mediated clearance of IL-7 as the availability of receptors diminishes [20] . In addition , the IL-7 signaling on IL-7 receptor-a-positive ( IL-7Ra+ ) dendritic cells in lymphopenic settings may diminish the homeostatic proliferation of CD4+ T cells [21] . A recent study has suggested that the remaining chronic inflammation in treated HIV-infected patients due to exposure to IL-1β and IL-6 may decrease T-cell sensitivity to IL-7 and therefore a reduced CD4+ T cell reconstitution [22] . Recent analyses of lymph node tissues have shown that collagen deposition may restrict T-cell access to IL-7 , resulting in apoptosis and depletion of T cells [23] . This in turn leads to decreased production of lymphotoxin B , a trophic factor for reticuloendothelial cells , leading to their demise and loss of IL-7 producing cells . In summary , the IL-7 effect on CD4+ T-cell homeostasis is highly compromised in HIV infection [24] . The beneficial effect of administration of IL-7 on T cell homeostasis in patients with refractory cancer [9] , in SIV infected macaques [15] , [25] , [26] and HIV infected individuals has been shown through several early trials [27] , [28] and observational studies [29] . However , before proceeding with phase II and III trials , several questions remain . From a mechanistic standpoint , the respective contributions of thymic production [9] , peripheral proliferation and survival [11] in the observed increase of CD4+ T cell count in HIV-infected patients are unclear . Also , the schedule of IL-7 administration , notably the frequency of cycling needed to reach optimal and durable CD4+ T cell restoration is not defined . Finally , the long-term effects of IL-7 therapy and repeated IL-7 cycles on T cell homeostasis in HIV-infected patients are unknown . To address these questions , we have developed a mathematical model to approximate the effect of IL-7 on CD4+ T cell homeostasis to fit the data from two phase I trials of IL-7 intervention in HIV-infected patients . This analysis is most consistent with a significant additional biological effect ( on cell survival and/or thymic production ) to the observed transient increase in peripheral cell proliferation . The predictions from the model have been used to explore the feasibility of repeated “maintenance” cycles of IL-7 administration with the aim of maintaining a given level of circulating CD4+ T cells .
Chronically HIV-1 infected patients with CD4+ T cell counts between 100 and 400 cells/µL and plasma HIV RNA<50 copies ( c ) /mL while on antiretroviral therapy were studied in three phase I/II trials ( see Methods and Table 1 for characteristics ) . In Study I and II , there was a dose-dependent increase of CD4+ T cell count peaking between 14 and 21 days after the initial injection and followed by a steady decline . The peak increase ranged between 152 and 1202 CD4+ T cells/µL in the two studies [28] , [30] . A significant increase compared to baseline ( and placebo group in Study II ) persisted until 12 weeks in the first study ( Figure S1 ) and 52 weeks in the second ( Figure 1 ) . The main contributors to CD4+ T cell increase were naive and central memory cells [28] , [30] . There was a transient increase of Ki67 expression ( a marker of proliferation; see Methods ) in all CD4+ T cell subsets during IL-7 administration ( Figure 2 ) . The peak of Ki67 expression was observed at the first available measurement after the initiation of IL-7 therapy , which is 14 days in study I and 7 days in study II . At 28 days , Ki67 expression returned to baseline in both studies . This increase in cell proliferation , observed in parallel with the increase in CD4+ T cells , might be the only significant effect in vivo of the injection of exogenous IL-7 . Indeed , IL-7 induces an acute cellular proliferation during a short time period leading to a rapid CD4+ T cell increase , followed by a slow return to baseline levels as CD4+ T cells die , explaining the observed dynamics . However , additional effects , especially on thymic output or cell survival , might exist and slow down the decline of CD4+ T cells . Recent thymic emigrants ( defined as CD45RA+CD31high ) and the sj/β T cell receptor excision circles ( TREC ) ratio ( in Study II ) , which are both an indirect measure of thymic output [31] , are significantly increased after IL-7 injections [28] , [30] . In Study II , we also observed a decrease in PD-1 expression ( a marker of cell exhaustion ) by CD4+ T cells [30] suggesting an increased cell survival . Although these observations gave some insight in potential effects of IL-7 on T cell homeostasis , they do not quantify the respective contribution of these mechanisms to the observed CD4+ T cell dynamics in blood in terms of input and output of cells . This is why we embarked on a mathematical analysis to test whether the observed peripheral proliferation could explain the CD4+ T cell dynamics after IL-7 injections or if other additional biological mechanism played a significant role . We used a simple mathematical model to investigate mechanistically the effect of IL-7 on total CD4+ and naive CD4+ T-cell dynamics ( see Methods and Figure S2 ) . Modeling CD4+ dynamics and Ki67+ expression by changing the proliferation rate during IL-7 administration provided a fair fit of the data of study II ( Figure 3A , plain lines ) . Interestingly , there was a significant linear increase of estimated proliferation rates according to the dose group ( p<0 . 0001; Figure 4A and 4B ) . However , we found a better fit of CD4+ dynamics with Model 2 ( LCVa −0 . 173 vs . 0 . 937; Figure 3A , dashed lines ) that includes an effect of IL-7 on proliferation rate during IL-7 administration and on loss rate after IL-7 administration . In addition to the significant dose-dependent increase of proliferation during IL-7 , we estimated a decrease of the loss rate of quiescent cells from 0 . 061 to 0 . 044–0 . 049 per day corresponding to an improvement of the life span of about 25% from 16 . 4 days to 20 . 4–22 . 7 days ( likelihood ratio test p-value<0 . 001; Figure 4 , Table 2 and Table S1 ) . This result was found with both formulation of IL-7 ( with either rh-IL-7 or glycosylated rh-IL7; Table S2 ) . Adding a modification of the constant production of CD4+ ( Model 3 ) rather than a modification of loss rate ( Model 2 ) did not substantially improve the fit to total CD4+ T cell dynamics as shown in Figure 3A where the fits from the two models overlap ( see also Figure S3 and S4 ) . In other words , although the Model 2 that includes a modification of quiescent cell loss rate was better from a statistical point of view ( LCVa = −0 . 131 vs . −0 . 173 ) , it was difficult to distinguish the fits of the two models . Interestingly , all models described correctly the initial increase of CD4+ T cells and thereafter , Model 1 predicted a slower decline of CD4+ T cells than Model 2 and 3 . This poorer long-term fit might be explained by a transient effect on the proliferation rate ( until day 16 ) that altered only briefly the equilibrium while the lingering effect on the production or loss rate changed it in the long-term . To further analyze the potential effect of IL-7 on thymic output , we explored the effect of IL-7 on the naive ( CD45RA+CD27+ ) CD4+ cells ( either Ki67+ or Ki67− ) using the available data for this subset ( until 12 weeks ) . Here again , we found that an additional effect of IL-7 after its administration either on the thymic production or the loss rate significantly improves the fits compared to a model including only an effect on the proliferation rate ( Table S3 ) . Interestingly , we found that the best model was the one including an effect on the thymic production rate of naive cells after IL-7 administration in addition to the proliferation rate ( LCVa = 1 . 705 vs . 1 . 760 for the model including an effect on the loss rate after IL-7 administration; Table 2 and Table S3 ) . Both models including an additional effect of IL-7 were better than the model with an effect on proliferation only ( LCVa = 1 . 832 ) . However , a change in loss rate of quiescent cells led to a good fit as well and individual fits from Model 2 and 3 were very close as shown in Figure 3B and S5 . Finally , we were interested in the ability of Model 2 ( with the effect of IL-7 on proliferation and loss rates ) to predict individual responses to IL-7 . We made use of data from 12 additional patients ( from INSPIRE 2 , Table 1 ) treated with a 20 µg/kg dose as per the INSPIRE study . We used only the first two measurements of total CD4+ T cells and Ki67+ cells to compute the Empirical Bayes estimates for each parameter that could vary between patients . The other population parameters were fixed according to the previous estimations ( Table S1 ) . We then predicted the individual CD4+ T cell dynamics until week 12 . Most of the observed total CD4+ T cell counts were in the prediction interval ( Figure S6 ) . Therefore , the model that includes an effect of IL-7 on proliferation and loss rates led to a good description of the total CD4+ T cell dynamics and a fair predictive ability at the individual level . To our knowledge , no data exists yet on the effect of repeated cycles of IL-7 in vivo . Therefore , to investigate to what extent IL-7 administration might sustain CD4+ T-cell restoration , we artificially created data and compared different scenarios allowing the IL-7 effect to wane after subsequent injections compared to the initial one ( see Methods ) . In this part , we considered an extended version of the mathematical model that incorporates a homeostatic proliferation ( see Methods ) . This model gave similar results as presented in the previous section ( not shown ) but more realistic long-term dynamics for repeated IL-7 administrations ( total CD4+ T cell counts staying below 1500 cells/µL ) . As the dose 20 µg/Kg was the one recommended for further phase II/III studies [30] , we simulated CD4+ T cell dynamics with hypothetical repeated cycles of IL-7 administration for each patient who received this dose in the Study I ( INSPIRE ) : namely 14 patients . The parameter κ controlling the proliferation rate was fixed to the same value for each patient ( see Methods ) . The initial CD4+ dynamics was the one predicted by the Model 2 as presented in the previous section . CD4+ T cell count were assumed to be measured every three months and when it dropped below 500 cells/µL a cycle of IL-7 administration was simulated ( one injection per week for 3 weeks ) . The dynamics were therefore based in part on fit to observed data and in part on a predicted response to repeated therapy . We analyzed two primary outcomes: the time spent above 500 cells/µL and the number of cycles ( including the first cycle ) needed to maintain CD4+ T cell counts above 500 cells/µL over 2 years . A secondary outcome was the median time between two successive cycles . Simulations were performed according to several scenarios varying from a constant effect after each cycle and decreasing effects on loss and proliferation rates . At each cycle , we assumed that the effect of IL-7 on the loss rate of CD4+ T cells started to decrease 3 ( or 9 ) months after the last injection and disappeared after 1 ( or 2 ) year . Table S3 shows some scenarios ordered from the best to the worst according to the primary outcomes . Where all the IL-7 cycles were assumed to keep 100% of their effects on CD4+ T cell counts ( Scenario A-1 and A-2; Figure 5A and Table S4 ) , the intervention was highly effective . CD4+ T cell counts were maintained above 500 cells/µL between 84% and 91% of the time compared to 11 . 5% when no new IL-7 cycle was administrated during the 24 months of follow-up . Moreover , the time between two cycles of IL-7 was estimated to be greater than 6 months . We also investigated reduced effect of IL-7 on peripheral proliferation and/or loss rate of non-proliferating cells during subsequent cycles . The scenario assuming that during repeated cycles only 50% of the effect on proliferation was effective while the effect on the loss rate was conserved ( scenario B-1 and B-2; Figure 5B and Table S4 ) gave similar results as the scenario assuming a full and constant effect of proliferation and loss rate ( Scenario A-1 and A-2; Table S4 ) . Moreover , we observed that the more the effect of IL-7 on the loss rate was reduced , the more often cycles have to be administrated and the shorter the time between two successive cycles ( Figure S7 ) . These results suggest that it is more important to keep a strong effect on the loss rate of non-proliferating cells than on proliferation . As shown in Figure S7 , when the effect of IL-7 is only maintained on the proliferation rate ( i . e . no more effect in the loss rate of non-proliferating cells: αμQ = 0 ) , we predicted the highest number of injections , whatever the size of the effect on the proliferation rate , to sustain CD4 count above 500 cells/µL . Overall , in comparison to no repeated cycle ( Reference scenario in Table S4 ) whatever the assumed effect of IL-7 during successive cycles , the CD4+ T cell count may be durably increased with clinically realistic administration schedules . Therefore , IL-7 repeated cycles seems feasible and efficient .
We report here a mathematical analysis of total and naive CD4+ T cell dynamics in HIV-1 infected patients treated with antiretrovirals who experienced a significant increase of CD4+ T cell counts while receiving IL-7 therapy . We confirm , once again , that IL-7 induces a significantly increased peripheral proliferation of CD4+ T cells as measured by Ki67 expression . However , results presented here extend our knowledge on the in vivo effects of IL-7 by showing that this increased peripheral proliferation alone could not explain the long-term changes in CD4+ T cell number that were observed . An increase of the production rate of naive CD4+ T cells and a decrease of the loss rate for total CD4+ T cells might also contribute to T-cell homeostasis during IL-7 therapy . Importantly , baseline parameters such as naive T cell production ( around 9×108 naive cells/day ) [32] , loss rate of proliferating cells ( 0 . 08 day−1 ) [33] or reversion rate ( accounting for duration of division and duration of Ki67+ expression ) were in agreement with current knowledge . Furthermore , our mathematical model shows good performance for individual predictions and provides insights on the feasibility of repeated cycles of IL-7 ( or long-lasting formulation ) for maintaining CD4+ T cell counts in HIV-infected patients . Predictions from the mathematical model underline the importance of an additional effect of IL-7 beyond peripheral cell proliferation for long-term CD4+ T cell responses . HIV infection leads to a profound disturbance of T cell homeostasis with an increased turnover of these cells [33]–[35] . The naive T cell pool is replenished mainly by post-thymic proliferation in adults [36] , [37] but the observed proliferation of naive CD4+ T cells is not enough to prevent the slow decline of these cells in HIV infected patients . Augmenting immunity with exogeneous cytokines has been attempted in HIV infection; and although CD4 T cell numbers were increased with administration of IL-2 , two large clinical trials failed to show any evidence of clinical benefit [38] . The failure of IL-2 therapy to confer clinical benefit despite CD4 T cell increases could be attributed at least in part to the regulatory phenotype of the expanded cells [39] and the possibility of enhanced inflammation and coagulation during administration [40] . The effects of IL-7 on the other hand are fundamentally different [13] and the defined role of IL-7 in maintaining T cell homeostasis in health provides rationale for testing its therapeutic administration in HIV infection complicated by immune failure [41] . Our data argue for an increase in cell survival after IL-7 administration . Our results show that CD4+ T cell dynamics are better explained by a decrease of cell loss in addition to the transient peripheral proliferation . This finding is consistent with previous findings that increasing cellular survival through up-regulation of bcl2 expression is a physiological function of IL-7 [13] , [42] . Moreover , increases in T cell survival after IL-7 injection have been demonstrated in monkeys using BrdU labeling [15] . Our findings warrant further study to define the precise mechanisms of IL-7 induced cell expansion in HIV infection . Improvement of thymopoiesis has been reported during exogenous IL-7 administration [5] , [6] . However , there is some controversy on the importance of this effect in vivo [9] , [43] . Sportes et al . showed a modest increase of absolute numbers of TRECs and a major dilution of TREC content due to peripheral cell proliferation leading to the conclusion that the increase of TCR repertoire diversity is mainly due to the proliferation of recent thymic emigrants . The effects of IL-7 on thymopoiesis may be dependent , on one hand , on the underlying disease ( HIV-1 infection , cancer and chemotherapy ) and , on the other hand , on the duration of cytokine therapy [9] . In our simulations of repeated IL-7 cycles , we did not consider any effect of IL-7 on thymopoiesis because we favoured the hypothesis of an effect on cell survival according to the rationale above and the slightly better fit . Therefore , if IL-7 administration improved thymopoiesis in addition to peripheral proliferation and enhanced cell survival , the CD4+ T cell response should have surpassed our predictions . For long-term predictions using simulations , the initial model was extended by adding a homeostatic control of proliferation after IL-7 administration . This can be related to the modulation of IL7Ra expression that prevents uncontrolled proliferation [44] , [45] . Furthermore , the effect on T cell loss was also modeled to wane over time . Strikingly , the estimation of the duration of the effect of IL-7 on cell survival was prolonged up to 2 years . Likely , this could not be explained by the pharmacokinetics of exogenous IL-7 that was administrated during two weeks only . However , exogenous IL-7 is known to bind to components of the extracellular matrix resulting in saturation of this tissue compartment followed by a slow release of IL-7 , which may exert long-lasting effects [3] . Also , IL-7 could have persistent effects on cellular homeostasis by normalizing tissue architecture through decreasing fibrosis in the gut [46] and in lymph nodes [23] , [47] , [48] , [49] and thus improving cellular access to survival signals . Other potential activities such as effects on cell trafficking [27] , [50] , IL-7 antibody formation , switch to memory phenotypes [14] , [51] or impact on proviral HIV DNA content [52] have not been taken into account in this model . Redistribution of CD4+ T cells to tissues , leading to a transient decrease of CD4+ T cells levels in blood , is mainly observed in the first days after IL-7 administration and should not affect measurements made thereafter . Although neutralizing anti-IL-7 antibodies were not observed in patients following the first cycle of IL-7 [28] , their induction after repeated administration could attenuate the effects that we modeled here . For these reasons , new clinical trials are needed to help distinguishing the persistent IL-7 effect ( s ) involved in CD4 recovery in HIV-infected patients and to propose personalized therapy in the future [53] . Furthermore , we assumed repetition of cycles ( i . e . three injections over two weeks ) but the repetition of single injections could lead to similar results shown in the simulations if the effect of one injection respect the assumptions made in some scenarios . For instance , the repetition of a single injection may have a reduced effect on proliferation and cell survival compared to a whole cycle but this would still lead to a good maintenance of CD4+ T cell counts . There are limitations to this study that include the restricted number of harvest times and the lack of validated markers for cellular lifespan . One way to overcome these limitations and to help distinguishing between increased production and increased survival is to perform studies that include in vivo labeling with deuterium and TREC content measurements . Indeed , deuterium labeling is a recent and powerful tool to estimate cell turnover [54] and used in combination with TREC content allows estimation of thymic output [37] , [55] . Also , it may have been relevant to distinguish the dynamics in lymph node tissue to better capture long-term effect of IL-7 although data on lymph node tissue would be difficult to obtain . Despite these caveats , the goodness of fit and the predictive capacity of the model provide important insights for further development of IL-7 treatment strategies . We have learned that IL-7 administration leads to a burst of peripheral proliferation that is likely associated with a lingering effect beyond the period of IL-7 administration . We surmise that a durable effect of IL-7 on T cell homeostasis could be achieved after repeated administration but safety and activity need to be confirmed [2] , [3] .
Data were generated in three phase I/II studies ( Table 1 ) . All participating institute's Institutional Review Boards approved the studies and the procedures and all participants provided written informed consent before study participation . The rh-IL-7 study ( referred to as Study I ) [28] evaluated Recombinant Human Interleukin 7 ( rh-IL-7 ) , a nonglycosylated protein composed of 153 amino acids , and included 14 HIV-infected patients receiving antiretroviral therapy whose CD4+ T cell counts were between 100 and 400 cells/µl and whose plasma HIV RNA levels were less than 50 copies/ml . Patients received a total of 8 subcutaneous injections of 2 different doses of recombinant human IL-7 ( 3 or 10 µg/kg , dose 1 and 2 , respectively ) 3 times per week over a 16-day period . Eleven repeated measurements of total CD4+ T cells up to 48 weeks and four measurements of Ki67+ positive T cells among CD4+ T cells up to 12 weeks were performed . The INSPIRE Study ( referred to as Study II ) [30] evaluated 3 weekly subcutaneous ( SC ) injections of a purified glycosylated 152 amino acid rhIL-7 ( CYT107 over a period of 2 weeks ) . Three doses were tested: 10 , 20 and 30 µg/Kg/week . Seven , 8 and 6 patients received three injections ( one per week ) in each dose group , respectively . Two HIV-infected patients were randomized per dose level and received a placebo ( NaCl ) . Visits for safety and immunologic evaluation were performed at days 7 , 14 , 21 , 28 , 35 , week 9 and week 12 and then quarterly up to week 52 . In the INSPIRE 2 Study ( referred to as Study III ) , 12 patients received 3 subcutaneous injection of 20 µg/Kg/week CYT107 ( one per week over 2 weeks ) and were followed up to week 52 . Absolute CD4 T-cell counts , T cell expression of Ki67 , and the proportions of naive subsets defined by expression of CD45RA and CD27 ( naive: CD45RA+CD27+ ) were measured in whole blood by flow cytometric assays within 6 hours of blood draw in the Rh-IL7 study [28] . In INSPIRE , naive cells in cryopreserved samples were identified by expression of CD45RA , CD27 and CCR7; in INSPIRE 2 naive cells were enumerated in cryopreserved PBMC by expression of CD45RA and CCR7 . Ki67 is a cellular marker of proliferation [56] and is associated with cell proliferation . It is present during all active phases of the cell cycle ( namely G1 , S , G2 and M ) and it is absent from resting cells ( phase G0 ) . Therefore , some of the cells expressing Ki67 are actually in the division phase M and the rest are “on their way” to this phase or very recently in this phase . In this model , we assume that proliferating cells express Ki67 whereas non-proliferating ( i . e . resting ) cells do not; this is a relatively good approximation . We consider the following mathematical model including two populations of cells ( see Figure S2 for a general cartoon of the model ) : non-proliferating cells ( Ki67− , denoted Q ) and proliferating cells ( Ki67+ , denoted P ) : Non-proliferating cells ( Q ) are produced at a constant rate λ . They become Ki67+ at rate π and die at rate μQ . Proliferating cells ( P ) die at rate μp and lose their proliferation marker Ki67 at rate ρ ( cells express Ki67 for 1/ρ days ) . We assumed that 2ρP cells enter the Q compartment that is two daughter cells are produced after one single cell cycle . The loss rates ( μP and μQ ) are influenced by cell survival but also by any redistribution between blood and other tissues . Before the first injection at t = 0 , both populations are assumed to be at equilibrium ( i . e . and ) . This model was used for total CD4+ T-cells where Q and P include both naive and memory CD4+ T-cells . Similarly , we used this general model to describe naive CD4+ T-cell dynamics adding a superscript ‘N’ to all parameters ( λN , πN , μQN , μPN , ρN ) and where QN represent non-proliferating naive CD4+ T-cells and PN proliferating naive CD4+ T-cells . To model the effect of IL-7 , we considered different models allowing some parameters to change over time ( i . e . during or after the IL-7 administration ) . We distinguished three models: Model 1 includes only an effect on the proliferation rate π during the IL-7 administration Model 2 includes an effect on π and an additional effect on the loss rate of non-proliferation cells μQ ( effect tested during or after the IL-7 administration ) Model 3 includes an effect on π and an additional effect on the production rate λ ( effect tested during or after the IL-7 administration ) . Models including an effect of IL-7 on the duration of Ki67 expression or on a direct production of Ki67+ cells from the thymus were also tried but did not improve the fit of the data ( not shown ) . Each parameter is assumed equal to a baseline value ( denoted π0 , μQ0 , λ0 ) and we tested a possible effect of IL-7 and a possible dose effect . For instance , the proliferation rate π was assumed equal to before ( t = 0 ) and after IL-7 administration ( t>τ ) and to during IL-7 administration ( 0<t≤τ ) . The variable trt indicates if an individual received the placebo ( trt = 0 ) or IL-7 injections ( trt = 1 ) , hence the parameter η0 represents the possible effect of IL-7 on the parameter π . Similarly , we tested a possible dose effect via the parameter η1 and the variable dose; dose = 0 if the individual received the placebo and 0 . 3 , 1 , 2 or 3 according to the received dose of IL-7 . The additional dose effect is here assumed to be linear: the dose 30 µg/Kg will have 3 times more effect than the dose 10 µg/Kg and 10 times more than the dose 3 µg/Kg . The time τ ( time of IL-7 effect on the proliferation rate π ) was fixed to 16 days and robustness analyses with values between 14 and 16 days have been performed leading to similar results but slightly worse likelihood ( not shown ) . For the simulations of repeated administrations of IL-7 , a homeostatic control of proliferation was incorporated in order to constrain the CD4+ T cell level in a credible range ( below 1500 cells/µL ) . The newly defined rate of proliferation , denoted π* , is defined as follows:where N0 is the baseline CD4 T cell count , Nt is CD4 T cell count at time t and κ is a number lower than 1 estimated on the data . As N0 is relatively low due to lymphopenia , cells will be allowed to proliferate more while the circulating number of CD4+ T cells is still relatively low but as soon as this number deviates too much from the baseline value the proliferation rate is reduced [57] . Several formulations have been tested , including one with Nmax = 1000 referring to a normal healthy value rather than N0 and all formulations led to similar conclusions due to different estimates of the parameter κ ( not shown ) . The parameter κ was estimated by profile likelihood because it was difficult to estimate it at the same time as the other parameters . The parameter κ was therefore fixed at different plausible values and the model was estimated for each value of κ and we kept the value of κ for which the model had the lowest likelihood . Here , we only considered Model 2 including the effect of IL-7 on the proliferation rate and the loss rate of non-proliferating cells as it was the best model according to real data . Moreover , in this model , the loss rate of non-proliferating cell ( μQ ) was assumed decreased ( to a constant value ) after day 16 and during ( Tfull-16 ) days after the injection and then linearly increased to its baseline value after Tend days ( see Figure S2 ) . As subsequent cycles of IL-7 might have reduced effects compared to the first one , the proliferation rate ( resp . loss rate ) of the subsequent cycle is denoted by πsub ( resp . μQsub ) and keep π ( resp . μQ ) for the first injection . These rates are defined as πsub = αππ and μQsub = αμQμQ , where απ and αμQ represent the strength of new IL-7 administration ( i . e . the percentage of effect of πsub and μQsub compared to the initial injection ) . Therefore , we assumed for all subsequent cycles a similar reduction of IL-7 effects compared to the first cycle . Parameters were estimated using maximum penalized likelihood that takes into account unbalanced data due to sparse missing values and the availability of Ki67+ staining up to week 12 ( no measurements were available beyond that time ) . This method can be viewed either as a version of maximum likelihood allowing taking into account prior knowledge ( from previous estimates found in published studies ) , or as an approximation of Bayesian inference [58] , [59] . The Ordinary Differential Equations ( ODE ) system was solved with dsolve from the ODEPACK [60] for stiff system using Backward Differentiation Formula ( BDF ) methods ( the Gear methods ) . Each parameter ( θi ) was modeled as the sum of a population ( fixed ) parameter ( β ) and a random effect ( bi ) allowing the parameter to be different from one patient to another: Each random effect was assumed to be normally distributed with a variance to be estimated: . A stepwise selection procedure was used and when the variance of a given random effect was not significant , the parameter was considered as fixed in the next model . We observe the number of proliferating cells ( P ) and the total number of cells ( P+Q ) plus a measurement error adding two unknown parameters and . The final models included only two random effects , one for λ ( production rate ) and one for ρ ( the rate at which proliferating cells go back to rest ) . The best model was selected using an approximation of the likelihood cross-validation criterion ( LCVa , [58] , [59] ) that is based on the likelihood weighted by the number of parameters estimated like the Akaike Criteria ( AIC ) . The lower is the value of the criteria the better is the model . Individual predicted trajectories were computed using the Parametric Empirical Bayes ( PEB ) for all parameters having a random effect [61] . | HIV infection is characterized by a decrease of CD4+ T-lymphocytes in the blood . Whereas antiretroviral treatment succeeds to control viral replication , some patients fail to reconstitute their CD4+ T cell count to normal value . IL-7 is a promising cytokine under evaluation for its use in HIV infection , in supplement to antiretroviral therapy , as it increases cell proliferation and survival . Here , we use data from three clinical trials testing the effect of IL-7 on CD4+ T-cell recovery in treated HIV-infected individuals and use a simple mathematical model to quantify IL-7 effects by estimating the biological parameters of the model . We show that the increase of peripheral proliferation could not explain alone the long-term dynamics of T cells after IL-7 injections underlining other important effects such as the improvement of cell survival . We also investigate the feasibility and the efficiency of repetitions of IL-7 cycles and argue for further evaluation through clinical trials . |
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The use of antibiotics targeting the obligate bacterial endosymbiont Wolbachia of filarial parasites has been validated as an approach for controlling filarial infection in animals and humans . Availability of genomic sequences for the Wolbachia ( wBm ) present in the human filarial parasite Brugia malayi has enabled genome-wide searching for new potential drug targets . In the present study , we investigated the cell division machinery of wBm and determined that it possesses the essential cell division gene ftsZ which was expressed in all developmental stages of B . malayi examined . FtsZ is a GTPase thereby making the protein an attractive Wolbachia drug target . We described the molecular characterization and catalytic properties of Wolbachia FtsZ . We also demonstrated that the GTPase activity was inhibited by the natural product , berberine , and small molecule inhibitors identified from a high-throughput screen . Furthermore , berberine was also effective in reducing motility and reproduction in B . malayi parasites in vitro . Our results should facilitate the discovery of selective inhibitors of FtsZ as a novel anti-symbiotic approach for controlling filarial infection . The nucleotide sequences reported in this paper are available in GenBank™ Data Bank under the accession number wAlB-FtsZ ( JN616286 ) .
Filarial nematode parasites are responsible for a number of devastating diseases in humans and animals . These include lymphatic filariasis and onchocerciasis that afflict 150 million people in the tropics and threaten the health of over one billion . Unlike other nematodes , the majority of filarial species are infected with an intracellular bacterium , Wolbachia [1] . In the human filarial nematode Brugia malayi , these obligate α-proteobacterial endosymbionts have been detected in all developmental stages [2]–[4] . Moreover , their presence is essential for the worm , as tetracycline-mediated clearance of bacteria from Brugia spp . leads to developmental arrest in immature stages and reduction in adult worm fertility and viability [5]–[10] . These findings have pioneered the approach of using antibiotics to treat and control filarial infections . However , in humans , tetracycline therapy is not ideally suited for widespread use because several weeks of treatment are required and the drug has contra-indications for certain individuals . Therefore , there is considerable interest in identifying new endosymbiont drug targets and other classes of compounds with anti-Wolbachia activity . Importantly , the completed genome sequence of the Wolbachia endosymbiont of B . malayi ( wBm ) [11] now enables genome-wide mining for new drug targets [11]–[14] and a foundation for rational drug design . These approaches should lead to the discovery of new classes of compounds with potent anti-Wolbachia/antifilarial activities targeting essential processes that are absent or substantially different in the mammalian host . Bacterial cytokinesis has emerged as a major target for the design of novel antibacterial drugs [15]–[17] since several of the components that are essential for multiplication and viability are absent from mammals . The bacteria-specific “filamenting temperature sensitive” protein , FtsZ , plays a central role during bacterial cytokinesis . In Escherichia coli , temperature sensitive mutations in the ftsZ gene cause blockage in cell division with limited cell growth and the generation of long filaments . FtsZ assembles into the contractile Z-ring and coordinates more than a dozen other cell division proteins at the midcell site of the closing septum [18]–[21] . Formation of the septal Z-ring requires two important functional properties of FtsZ , namely , polymerization of the FtsZ monomers into protofilaments and GTPase activity . Since inhibition of either function is lethal to bacteria , both GTP-dependent polymerization [22]–[27] and enzymatic [27]–[28] activities of FtsZ have been targeted for the identification of new antibacterial agents . Several inhibitors have been discovered including synthetic compounds [17] , [29] and natural products [17] , [30]–[33] . In the present study , we identify the cell division machinery present in wBm and characterize the FtsZ protein ( wBm-FtsZ ) . Using quantitative real time RT-PCR , Wolbachia ftsZ was found to be expressed throughout the life cycle , but up-regulated in fourth stage larvae and adult female worms . Recombinant wBm-FtsZ was shown to possess a robust GTPase activity , which was inhibited by the natural plant product berberine . Berberine was also effective in reducing motility and reproduction in B . malayi parasites in vitro . A library of small molecules was also examined for its inhibitory activity against the wBm and E . coli FtsZ proteins . Several compounds were identified as potent inhibitors , and structure-activity relationship studies revealed a derivative with selectivity for wBm-FtsZ . Thus , our results support the development of wBm-FtsZ as a promising new drug target in an anti-symbiotic approach for controlling filarial infection .
Living B . malayi adult female worms were purchased from TRS Laboratories , Athens GA . Genomic DNA and RNA were isolated following the protocols developed by Dr . Steven A . Williams ( http://www . filariasiscenter . org/molecular-resources/protocols ) . To clone full-length wBm-ftsZ for expression studies , forward 5′ ( GAGAGCTAGCATGTCAATTGACCTTAGTTTGCCAG ) 3′ ( NheI site underlined ) and reverse 5′ ( GAGACTCGAGTTACTTCTTTCTTCTTAAATAAGCTGG ) 3′ ( XhoI site underlined ) primers were designed according to the wBm-ftsZ sequence ( accession number: YP_198432 ) in order to amplify the gene from B . malayi genomic DNA . The PCR product was then cloned into the NheI and XhoI sites of pET28a ( + ) ( Novagen ) to generate a fusion protein with a His6 tag at the N terminus . The authenticity of the insert was verified by sequencing . Total RNA supplied by the Filariasis Research Resource Center ( FR3 ) was treated with RNase-free Dnase ( New England Biolabs , Cat# M0303S ) and purified using the RNeasy Kit from Qiagen . cDNA was obtained using random primers and the ProtoScript® AMV First Strand cDNA Synthesis Kit ( New England Biolabs , Cat# E6550S ) . Forward primer 5′ ( AACAAGAGAGGCAAGAGCTGGAGT ) and reverse primer 5′ ( CGCACACCTTCAAAGCCAAATGGT ) were utilized to amplify a 102 bp Wolbachia ftsZ amplicon . Wolbachia 16S rRNA amplified with forward primer 5′ ( TGAGATGTTGGGTTAAGTCCCGCA ) and reverse primer 5′ ( ATTGTAGCACGTGTGTAGCCCACT ) was utilized for bacterial total RNA quantification . B . malayi 18S rRNA amplified with forward primer 5′ ( ACTGGAGGAATCAGCGTGCTGTAA ) and reverse primer 5′ ( TGTGTACAAAGGGCAGGGACGTAA ) was utilized as a total worm RNA control . Quantitative PCR was performed using the DyNAmo™ HS SYBR® Green qPCR Kit ( Thermo Fisher ) and a CFX-96 Real Time PCR instrument ( Bio-rad , Hercules , CA ) . Relative levels of ftsZ expression ( ratio of ftsZ to 16S rRNA ) , and abundance of Wolbachia in B . malayi ( ratio of Wolbachia 16S to B . malayi 18S rRNA ) were calculated for each RNA sample . Experiments were performed twice with triplicate samples . Controls consisting of samples processed in the absence of reverse transcriptase were included in qPCR and no DNA contamination was detected . To determine the sequence of the ftsZ gene from the Wolbachia endosymbiont wAlB present in the insect cell line Aa23 [34] , multilocus sequence typing ( MLST ) ftsZ forward 5′ ( TGTAAAACGACGGCCAGTATYATGGARCATATAAARGATAG ) and reverse 5′ ( CAGGAAACAGCTATGACCTCRAGYAATGGATTRGATAT ) [35] primers were utilized to obtain a PCR fragment . Using BLAST analysis , the sequence of the PCR product was compared to the corresponding region of known full-length ftsZ sequences and their conserved downstream and upstream sequences and 6 additional primers 5′ ( TCTATTTTTAATTCTTTTAGAGAAGCATT ) , 5′ ( CGTTCGGTTTTGAAGGTGTGC ) , 5′ ( ACCGTTGTGGGAGTGGGTGGT ) , 5′ ( TTATTTTTTTCTTCTTAAATAAGCTGGTATATC ) , 5′ ( GGAATGACAATAAGTGTATCTACGTA ) , and 5′ ( TGCATTTGCAGTTGCTCATCC ) were designed to obtain a complete wAa-ftsZ sequence . Phusion® High-Fidelity DNA Polymerase ( New England Biolabs , M0530 ) was utilized for all PCR reactions according to manufacturer's instructions . wBm-ftsZ and E . coli ftsZ ( Ec-ftsZ ) were amplified using genomic DNA isolated from B . malayi and E . coli wild-type strain MG1655 respectively , and were then cloned into the pET28a plasmid to generate fusion proteins with a N-terminal His tag . Each protein was expressed in the Escherichia coli strain C2566 ( New England Biolabs ) . Optimum conditions for production of soluble recombinant wBm-FtsZ involved co-transformation with the pRIL plasmid isolated from BL21-CodonPlus ( DE3 ) cells ( Stratagene ) together with the pET28a-ftsZ plasmid . Cultures were grown at 37°C till the OD600 reached 0 . 6 , before induction with 0 . 1 mM IPTG overnight at 16°C . Both Ec-FtsZ and wBm-FtsZ were purified using a similar method . The cells expressing the recombinant proteins were suspended in lysis buffer ( 20 mM NaPO4 , 500 mM NaCl , 10 mM imidazole , pH 7 . 4 ) plus 1 mg/mL lysozyme and protease inhibitor cocktail ( Roche ) and incubated on ice for 30 min , followed by sonication . The lysate was then cleared by centrifugation at 12 , 500 rpm , 4 °C for 30 min . The His-tagged proteins were purified on a 5 mL HiTrap chelating HP column ( GE Healthcare ) using an AKTA FPLC following manufacturer's instructions . After application of the sample , the column was washed with 5 column volumes of buffer A ( 20 mM NaPO4 , 500 mM NaCl , 10 mM imidazole , pH 7 . 4 ) followed by 10 column volumes of 92% buffer A:8% buffer B ( 20 mM NaPO4 , 500 mM NaCl , 400 mM imidazole , pH 7 . 4 ) . Protein was then eluted using a linear gradient ( 8–100% ) of buffer B equivalent to 40–400 mM imidazole . Fractions containing wBm-FtsZ or Ec-FtsZ were pooled , dialyzed against dialysis buffer ( 40 mM Tris-HCl , 200 mM NaCl and 50% glycerol , pH 7 . 5 ) and stored at −20°C prior to use . Purity of the proteins was estimated by 4–20% SDS-PAGE and the protein concentration was determined using the Bradford assay . GTPase activity was measured using an enzyme-coupled assay [36] . Activity was determined by measuring the consumption of NADH , which is monitored by absorbance at 340 nm . The amount of NADH oxidized to NAD corresponds to the amount of GDP produced in the reaction . Reactions were optimized for a 96-well format to enable compound screening . The 100 µL reaction mixture containing 50 mM MOPS ( 4-morpholinepropanesulfonic acid ) pH 6 . 5 , 50 mM KCl , 5 mM MgCl2 , 1 mM PEP , 500 mM NADH , 0 . 1% Tween-20 , 20 units/mL of L-lactate dehydrogenase ( Sigma L2518 ) and pyruvate kinase ( Sigma P7768 ) , 1 mM GTP and 5 mM FtsZ was distributed into 96-well plates . The plate was incubated at 30 °C for 45 min with data collected at 20 second intervals using a SpectraMax® Plus 384 ( Molecular Devices ) spectrophotometer . Control assays without FtsZ were performed to provide a baseline and with GDP to ensure the function of the coupling enzymes . For inhibitor screening , 100 µL of reaction mixture was added to each well of a 96-well plate and 1 µL of compound dissolved in DMSO , or berberine sulfate ( MP Biomedicals ) in water , in varying concentrations were added . The reaction was initiated at 30 °C by adding 1 mM GTP . Experiments were performed in triplicate . Living B . malayi adult female and male worms were washed extensively with RPMI1640 medium supplemented with 2 mM glutamine , 10% Fetal Calf Serum ( Gibco ) and 100 U/mL streptomycin , 100 mg/mL penicillin , 0 . 25 mg/mL amphotericin B ( Sigma ) . Three worms of either gender were distributed into each well of a 6-well plate and incubated at 37 °C , 5% CO2 . After overnight recovery , motility and microfilaria production were recorded . Worms were then transferred to a new well containing varying amounts of berberine sulfate dissolved in water , namely 40 µM , 20 µM , 10 µM and 5 µM . Control wells containing either no drug or 10 µM doxycycline , were also included . Culture media were replaced with fresh medium containing drug daily . Adult worm and microfilaria motility production were recorded daily as described [37] . Motility was scored as described [38] and expressed as % of motility relative to motility scored on day 0 of the experiment . Microfilaria production was counted in 10 µL of either diluted or concentrated culture medium using a hemocytometer . The results were presented as the number of microfilaria released in 1 mL of medium from each well on the indicated day . Each treatment was performed in triplicate and the experiment was repeated several times . Berberine sulfate ( MP Biomedicals ) was added at a final concentration of 0–400 µM to growth medium containing E . coli ER1613 ( acrA13 Δ ( top-cysB ) 204 gyrB225 IN ( rmD-rmE ) mcrA ) ( New England Biolabs ) and growth determined during 5 h or 20 h of incubation . For the 5 h evaluation , an overnight culture of E . coli ER1613 ( acrA13 Δ ( top-cysB ) 204 gyrB225 IN ( rmD-rmE ) mcrA ) ( New England Biolabs ) was diluted 100-fold and 1 mL volumes were dispensed into a 48-well deep well plate ( Axygen Scientific ) containing various concentrations ( 0–400 µM ) of berberine sulfate ( 10 µL of serial diluted berberine sulfate in water ) . The plate was then incubated at 30 °C with shaking . After 90 min of initial growth , bacterial growth was determined every 30 min for 5 h by monitoring absorption at 600 nm using a microtiter plate reader ( Spectramax M5 , Molecular Devices ) . Alternatively , an overnight culture of E . coli was diluted 1∶1000 fold and incubated with varying amounts of berberine sulfate for 20 h before growth was determined . All experiments were performed at least twice . Viability of berberine sulfate-treated ( 24 h ) cells was evaluated by spotting 3 µL serial dilutions ( 10−2–10−7 ) of bacteria on a petri dish and incubation overnight at 30 °C . Bacterial morphology was visualized using a Zeiss AxioVert 200 microscope and images were obtained using a 20× objective . Reactions were carried out under a nitrogen atmosphere with dry , freshly distilled solvents under anhydrous conditions , unless otherwise noted . Yields refer to chromatographically and spectroscopically homogenous materials , unless otherwise stated . Reactions were monitored by thin-layer chromatography ( TLC ) carried out on 0 . 25-mm EMD silica gel plates ( 60F-254 ) using UV-light ( 254 nm ) . Flash chromatography separations were performed on Silicycle silica gel ( 40–63 mesh ) . Purity analyses were performed using HPLC ( 254 nm ) . A stirring solution of aldehyde ( 1 . 0 equiv ) in MeOH at 25°C was treated with carboxylic acid ( 2 . 0 equiv ) , amine ( 2 . 0 equiv ) and isonitrile ( 2 . 0 equiv ) . The solution was heated to reflux , and stirred for 24 h . The solution was then cooled to 25°C and concentrated in vacuo . The crude residue was purified via flash column chromatography ( 10–50% EtOAc in hexanes ) to afford the purified product . For characterization data , see references [39]–[40] .
The bacterial cell-division pathway has been extensively studied in E . coli and several essential proteins have been identified [17] , [19] . Many of the genes encoding putative orthologs of these proteins are also present in wBm ( Table 1 ) . A total of 18 major cell division genes were identified in wBm genome ( Table 1 ) , including ftsZ , ftsA , ftsI , ftsK , ftsQ and ftsW , which are known to be essential for cell division [17] . These wBm genes were mapped and found to be more scattered throughout the genome , in comparison with their E . coli homologs . In E . coli the majority of genes were found in one major operon , with the remaining 5 genes distributed randomly . Of these , FtsZ was one of the most highly conserved essential proteins possessing 43% identity to Ec-FtsZ ( Table 1 ) . Wolbachia ftsA , ftsI , ftsK , ftsQ and ftsW were less related ( 13–34% ) to the E . coli homologs . Some previously described essential cell division genes in E . coli ( including ftsB , ftsL , ftsN and ZipA ) were not found in wBm , indicating that there are differences in the cell division machinery present in free living E . coli and intracellular Wolbachia . wBm-ftsZ exists as a single gene on the chromosome and is 1182 bp in length . It encodes a 394-amino acid protein with a predicted molecular mass of 42 kDa containing four distinct domains characteristic of FtsZ proteins . These comprise the variable N-terminal domain , a highly conserved core region , variable spacer , and a C-terminal conserved domain . The core region contains the highly conserved catalytic aspartate residue [41]–[42] and the GGGTGTGA motif ( 8 residues see [41] , [43] ) , which are responsible for GTP hydrolysis and required for polymerization of the protein . The C-terminal region is not required for assembly , but is essential for interactions with the cell division proteins FtsA , FtsW and ZipA [17] . A similar organization was also found in the insect Wolbachia , wMel-FtsZ ( NP_966481 ) and wAlB-FtsZ ( JN616286 ) . The FtsZ proteins of Wolbachia from different hosts share 89–91% identity and 43% identity to E . coli FtsZ proteins , with a substantially lower level at the carboxyl-terminal region ( 17 . 2% identity ) . Wolbachia have been identified in all developmental stages of B . malayi , from studies on individual worms and isolates from regions endemic for lymphatic filariasis [2]–[4] . To determine the relative expression of wBm-FtsZ throughout the parasite life cycle and validate its suitability as a drug target , wBm-ftsZ mRNA expression was analyzed by quantitative real-time reverse transcription polymerase chain reaction ( qRT-PCR ) . Relative levels of ftsZ expression ( ratio of Wolbachia ftsZ to 16S rRNA ) and abundance of Wolbachia in B . malayi ( ratio of Wolbachia 16S to B . malayi 18S rRNA ) were calculated for each RNA sample . wBm-ftsZ was found to be expressed throughout all stages examined ( adult female and male worms , microfilariae , third- and fourth-stage larvae ) . Moreover , wBm-ftsZ/16S ratios were found to be increased substantially following infection of the mammalian host since levels were significantly higher ( p value<0 . 001 ) in fourth-stage larvae and adult female worms compared to the vector-derived infective third-stage larvae . The wBm-ftsZ/16S ratio was also higher in microfilariae compared with the vector-derived third-stage larvae , but was significantly lower than the ratios obtained for fourth-stage and adult female worms . Of the various developmental stages examined , the lowest level of wBm-ftsZ expression was found in male worms ( Figure 1A ) . No DNA contamination was detected in controls consisting of samples processed in the absence of reverse transcriptase . Wolbachia 16S rRNA/B . malayi 18S rRNA ratios were also determined to measure the relative abundance of bacteria in different stages of B . malayi ( Figure 1B ) . Wolbachia was found to be most abundant in fourth stage larvae and adult female worms and least abundant in infective third stage larvae , indicating a massive multiplication of Wolbachia soon after infection of the mammalian host . Taken together , these data indicate that while wBm-ftsZ is expressed in all stages , gene activity and bacterial multiplication is most pronounced in fourth-stage larvae and adult females . Recombinant wBm-FtsZ was expressed in E . coli with a His-tag at the C-terminus and purified by nickel-affinity chromatography ( Figure 2A ) . Optimum conditions for production of soluble recombinant wBm-FtsZ involved growth of cultures at 37°C until the OD600 reached 0 . 6 , followed by induction with 0 . 1 mM IPTG overnight at 16°C . Purified protein was eluted with 100 mM imidazole . The apparent molecular weight of 43 kDa ( Figure 2A ) was consistent with the predicted molecular size of wBm-FtsZ with an N-terminal His-tag . For comparative studies , E . coli FtsZ ( 41 kDa ) was also expressed and purified in a similar manner ( Figure 2B ) . GTPase activity was measured using an enzyme-coupled assay involving pyruvate kinase and lactate dehydrogenase [36] . GTP hydrolysis was determined by measuring the decrease in fluorescence emission following oxidation of nicotinamide adenine dinucleotide ( NADH ) to NAD ( Figure 3A ) . As Figure 3B shows , recombinant wBm-FtsZ was found to possess GTPase activity . Moreover , the specific activities for wBm-FtsZ and Ec-FtsZ were comparable ( 0 . 18±0 . 012 µmolµmin−1mg−1 and 0 . 22±0 . 015 µmol min−1mg−1 , respectively ) . Berberine , an alkaloid natural product , is a known inhibitor of the GTPase activity of FtsZ in E . coli [33] , [44] . Thus , we were interested in examining the generality of berberine's GTPase inhibitory activity against wBm-FtsZ . As Figure 4 shows , dose-dependent inhibition ( 25–1000 µM ) was found with an IC50 value of 320 µM . E . coli FtsZ [33] , [44] was included for comparison , and an IC50 value of 240 µM was observed ( Figure 4 ) . Since wBm-FtsZ possesses all but one of the key residues proposed in the binding of E . coli FtsZ to berberine ( lysine instead of glycine at position 183 of Ec-FtsZ ) , this may account for the higher concentration of berberine required to inhibit 50% of wBm-Ftsz's GTPase activity . Since filarial Wolbachia remain unculturable , we were unable to evaluate the direct effect of berberine on the endosymbiont . Therefore , we examined the indirect effect of the drug on adult female worm . As Figure 5A shows , berberine ( 10–40 µM ) had adverse effects on the motility of adult female B . malayi worms , as well as microfilariae production ( Figure 5B ) when compared to untreated controls . Two days after treatment with berberine ( 40 µM ) , female worms showed almost no movement and the production of microfilaria had virtually ceased . Berberine at 20 µM was comparable to 10 µM of doxycycline in terms of effect on female worm motility . Reduction in adult female motility coincided with a decrease in microfilariae production . Similarly , motility of the freshly released microfilaria was decreased when berberine was present , with some effect observed at the lowest concentration ( 5 µM ) tested ( Figure 5C ) . On the other hand , male worms were more resistant to the effects of the drug with limited reduction in motility observed following treatment with berberine ( 5–40 µM ) for 6 days ( Figure 5D ) . However , treatment with 100 µM berberine for 24 h did completely paralyze male worms ( data not shown ) . Doxycycline ( 10 µM ) had a comparable affect on the motility of male and female worms . To demonstrate that berberine's in vitro GTPase inhibitory activity and anti-parasitic activity correlates with its known antibacterial activity , studies were performed on E . coli strain ER1613 . Berberine is known to act as a substrate for the multi-drug resistance efflux pumps and ER1613 contains a mutation in the acrA gene , which inactivates the multidrug efflux pump [45] . Overnight incubation of ER1613 with 0–100 µM berberine showed a dose-dependent effect with complete inhibition of bacterial growth observed at 60 µM ( Figure 6A ) . Similarly , no growth was evident when experiments were initiated with greater bacterial densities and the cells were treated with 50 µM berberine for up to 5 h ( Figure 6B ) . Treatment with berberine resulted in the filamentous phenotype ( Figure 5C ) typically observed in ftsZ mutant strains [46] , indicating that berberine was inhibiting cell division . Moreover , the presence of elongated bacteria also correlated with decreased growth and viability . Viability was also evaluated by ability to form colonies on an agar plate . Berberine sulfate-treated ( 24 hours ) cells produced substantially fewer colonies ( Figure 6D ) , compared to untreated controls . Untreated bacteria had approximately 4×105 - fold growth in 24 h , whereas bacteria treated with 40 µM berberine had 4×102 - fold growth . At concentrations of 80 µM and higher , the treated bacteria failed to produce viable colonies ( Figure 6D ) , demonstrating that without active replication E . coli die . To initiate a campaign to identify molecularly unique inhibitors of wBm-FtsZ GTPase activity , a library of small molecules based on naphthalene , quinoline and biphenyl core scaffolds were examined [39]–[40] ( Figure 7A ) . The library was constructed using Ugi multicomponent reaction chemistry , and each compound consists of a flat aromatic scaffold for enhanced π-stacking interactions decorated with varying diversity elements ( R1–R4 in Figure 7A ) . Importantly , these scaffold motifs are also found in berberine ( Figure 7B ) and known FtsZ inhibitors [17] , [29]–[33] . The ∼500-member library was screened using the wBm-FtsZ GTPase assay , and 13 compounds with greater than 30% inhibition at 100 µM were identified . From these screening efforts , compounds AV-C6 and N938 ( Figure 7C ) emerged as leading hits , and each showed dose-dependent inhibition of wBm-FtsZ ( Figure 8A ) . AV-C6 and N938 were also examined for inhibition of the E . coli FtsZ enzyme ( Figure 8A ) . As shown in Figure 8A , both compounds inhibited Ec-FtsZ activity although each was slightly less potent compared to the inhibitory activity against wBm-FtsZ . Structure-activity relationship ( SAR ) studies were then performed on N938 as this compound showed the most potential in dose response experiments . In addition to identifying compounds with enhanced potency , we were also interested in exploring the possibility of tuning down any inhibitory activity against Ec-FtsZ in order to obtain a more specific Wolbachia FtsZ inhibitor . A series of analogues were synthesized with varying aromatic side chains ( R3 in Figure 7A ) . As shown in Figure 8B , both goals were met: N982 with an ortho-chloro substituent ( Figure 7D ) showed enhanced potency in the wBm-FtsZ assay and N983 with a para-cyano substituent ( Figure 7D ) showed some specificity for wBm-FtsZ over that from E . coli . Future SAR studies should enable the discovery of compounds with both enhanced inhibitory properties and specificity . Finally , as the solubility of these compounds is poor , 100% inhibition of FtsZ with this scaffold was not possible and true IC50 values could not be obtained . Scaffold modification and/or hopping strategies will be investigated in the future to afford enhanced solubility .
The use of antibiotics targeting the Wolbachia endosymbionts of filarial parasites has been validated as an approach for controlling filarial infection in animals and humans . As a result , there is considerable interest in identifying new compounds that specifically target the obligate bacterial endosymbiont . In the present study , we investigated the cell division pathway in wBm to identify new drug targets that may be exploited for the development of new antifilarial therapies . Filamenting temperature sensitive ( fts ) genes produce many of the proteins essential for cell division in E . coli [17] . In wBm , we identified the majority of core genes that are indispensable to cytokinesis including ftsA , ftsI , ftsK , ftsQ , ftsW and ftsZ . Interestingly , ftsB , ftsL , ftsN and ZipA were not found in wBm . ZipA is a bitopic membrane protein with a large cytoplasmic domain that binds and bundles FtsZ protofilaments in vitro and helps to stabilize the Z ring in vivo . FtsN is a core component of the divisome that accumulates at the septal ring at the initiation of the constriction process . The C-terminal SPOR domain specifically recognizes a transient form of septal murein , which helps trigger and sustain the constriction process . However , in E . coli , it has been found that alterations in FtsA can compensate for the absence of ZipA , FtsK [47] and FtsN [48] and a gain-of-function FtsA variant , FtsA* ( R286W ) , efficiently stimulates cell division in the complete absence of ZipA [47] . Thus , Wolbachia FtsA may function like the mutant FtsA , as an alanine residue is present in the same position . ftsB , ftsL , ftsN and ZipA are also absent in some important bacterial pathogens including certain Gram-negative ( Neisseria spp . , Bordetella pertussis , Helicobacter pylori , Chlamydia spp . ) and Gram-positive ( Mycobacterium tuberculosis ) bacteria and cell wall-lacking ( Mycoplasma pneumoniae ) organisms [17] . It is likely that this reflects the reduced genome size present in these intracellular bacteria . FtsZ is the most highly conserved essential bacterial cell division protein and is present in all bacteria except Chlamydia spp [17] . We determined that wBm-FtsZ shares substantial similarity ( 43% identity ) to the highly characterized E . coli FtsZ protein and is highly similar ( ∼90% identity ) to insect Wolbachia FtsZ proteins . While the majority of wBm genes are expressed in a stage-specific manner [49] , wBm-ftsZ was found to be expressed in both male and female worms as well as in all larval stages examined . It was not surprising to find wBm-ftsZ expressed throughout the entire lifecycle of the parasite since the bacterial Z-ring is known to exist in a state of dynamic equilibrium in order to fulfill its many roles in the cell . Using fluorescence recovery after photo bleaching ( FRAP ) , the E . coli Z-ring was found to continually remodel itself with a halftime of 30 seconds with only 30% of cellular FtsZ present in the ring with continuous and rapid exchange of subunits within a cytoplasmic pool [17] . E . coli ftsZ transcription analysis has revealed that the rate of ftsZ expression is constant with a sudden doubling at a specific cell age , suggesting that ftsZ expression is regulated [50] . Similarly , we observed up-regulation of wBm-ftsZ gene expression in fourth-stage larvae and adult female worms with microfilariae likely contributing to the increased expression in the latter case . While the lowest levels of gene expression were evident in adult males , FtsZ protein was easily detected in proteomic analyses of male worms [49] . In general , the gene expression pattern of ftsZ correlated with bacterial multiplication . The increased bacterial multiplication in the worm during early infection of the mammalian host and embryogenesis is in agreement with an earlier study [4] . These data are consistent with the third- and fourth-stage larval stages , and embryogenesis being particularly sensitive to the effects of antibiotic treatment [4] , [51] . This result indicates that ftsZ gene expression could be used as a marker to monitor Wolbachia multiplication in the filarial parasite much like the ftsZ gene in the intracellular bacterium Candidatus Glomeribacter gigasporarum that resides in the mycorrhizal fungus Gigaspora margarita [43] . Molecular studies have established the importance of conserved amino acids in the FtsZ protein that when changed results in ftsZ mutants blocked at different stages of cell division [42] , [46] , [52]–[55] . wBm-FtsZ possesses the key residues and conserved GTP-binding pocket required for GTPase activity . Our functional analysis revealed that the GTPase activities of recombinant wBm-FtsZ and Ec-FtsZ are similar , and both proteins are sensitive to the plant alkaloid berberine . Most of the residues in Ec-FtsZ that are thought to bind berberine and inhibit FtsZ GTPase activity are also present in wBm-FtsZ . An earlier detailed study in E . coli determined that the target of this commonly used compound is FtsZ [33] . Plants containing berberine have been used in traditional Chinese and Native American medicine to treat many infectious diseases and the sulfate , hydrochloride and chloride forms are used in Western pharmaceutical medicine as antibacterial agents [56] . It is active against a number of Gram-positive and Gram-negative pathogenic bacteria , including drug resistant Mycobacterium tuberculosis [57] and Staphylococcus aureus [58] . Our experiments in E . coli demonstrate that berberine has both bacteriostatic and bacteriocidal effects . Since filarial Wolbachia remain unculturable , we were unable to evaluate the direct effect of berberine on the endosymbiont . However , following berberine treatment , we did observe reductions in adult female worm and microfilariae motility and microfilariae production . On the other hand , we did not see any effect on male worms , which had the lowest level of wBm-ftsZ gene expression . We examined berberine- and doxycycline-treated worms for Wolbachia load by qPCR analysis and did not observe a significant difference between control and treated parasites . A similar result was also found in a study evaluating the effects of globomycin and doxycycline on filarial Wolbachia , and the authors [59] suggested several possibilities which can also apply to our study , namely: the Wolbachia qPCR assay may not have sufficient sensitivity to detect effects on Wolbachia load over this time frame in nematodes , inhibition of FtsZ is sufficient to affect nematode motility and viability independent of or prior to any effect on Wolbachia load , and/or a direct effect of berberine on nematode motility and viability and alternative mechanisms of action . Nonetheless , our results suggest that FtsZ inhibitors that operate via inhibition of enzyme activity including natural products [28] , [30]–[33] , [53] and synthetic molecules [29] , [60] may have also activity against wBm-FtsZ . To complement the berberine studies , a library of naphthalene- , quinoline- and biphenyl-based compounds constructed using Ugi multicomponent reaction chemistry was examined for the discovery of new and ultimately highly specific antagonists of either E . coli or Wolbachia FtsZ . Of interest , compounds based on similar scaffolds have already been demonstrated as potent FtsZ inhibitors [17] , [29]–[33] . From our screening efforts , the ( 6-{butylcarbamoyl-[ ( aryl ) - ( butylcarbonyl ) -amino]-methyl} ) -naphthen-2-ol scaffold ( Figure 7A , C ) emerged as an antagonist of both E . coli and Wolbachia FtsZ . Interestingly , from basic SAR studies it appears that modification of the aryl substituent on the scaffold may afford selectivity for Wolbachia FtsZ , a key element of our initial goal . Additional compounds are currently being prepared to examine this possibility . Although not discussed here , compounds based on our lead scaffold had no effect on growth or viability in E . coli . Based on these findings and their potency in the in vitro assays , it is plausible that penetrability or metabolism issues are to blame for their attenuated activity . Finally , the solubility of these compounds is also poor precluding measurement of true IC50 values . Further iterations of chemical synthesis will be necessary to address these potential liabilities . While we have focused on assaying the GTPase activity of wBm-FtsZ using a medium- to high-throughput coupled enzyme assay for the discovery of inhibitors that target cell division in Wolbachia , it is also possible to screen for compounds that would target wBm-FtsZ via other mechanisms of action . FtsZ is considered a distant functional relative of the mammalian cytoskeletal protein β-tubulin [61]–[63] . Microtubule formation is a major target in cancer chemotherapy and the anticancer drug Taxol binds to β-tubulin and blocks cell division by interfering with microtubule formation . Interestingly , the FtsZ inhibitor PC190723 [60] operates by a similar mechanism and more recently , novel inhibitors of B . subtilis cell division have been identified in an in vitro FtsZ protofilaments polymerization assay [64] . Importantly , significant differences exist in the active sites in tubulin and FtsZ polymers , and several small molecule inhibitors of FtsZ have been identified [65] that do not inhibit tubulin [66]–[67] . Tubulin is also the target of the broadly anti-parasitic benzimidazole drugs [68]–[69] , which have been used extensively to control soil-transmitted nematodes [70]–[71] . FtsZ is also responsible for recruiting and coordinating more than a dozen other cell division proteins at the midcell site of the closing septum [18]–[19] , [21] , [72] . Many of these interactions are essential and it has been suggested that they might also be useful targets , particularly in light of developments in the discovery of small molecule inhibitors of protein-protein interactions [17] , [73]–[74] . Therefore , it might be feasible to screen for inhibitors of the interactions between wBm-FtsZ and its various binding partners that modulate its polymerization . Another Wolbachia cell division protein worth considering for drug discovery is FtsA , as this protein also possesses enzymatic activity and contains an ATP-binding site that might be targeted with drug-like molecules . Moreover , this protein is essential in E . coli [75] and Streptococcus pneumoniae [76] . In summary , we have investigated the cell division pathway in wBm and determined that it possesses a FtsZ protein with GTPase activity . We demonstrated that the activity is inhibited by berberine and identified small molecule inhibitors in a high-throughput screen . Furthermore , berberine was found to have adverse affects on B . malayi adult worm and microfilariae motility , and reproduction . Our results support the discovery of selective inhibitors of Wolbachia FtsZ as a new therapeutic approach for filariasis . | Filarial nematode parasites are responsible for a number of devastating diseases in humans and animals . These include lymphatic filariasis and onchocerciasis that afflict 150 million people in the tropics and threaten the health of over one billion . The parasites possess intracellular bacteria , Wolbachia , which are needed for worm survival . Clearance of these bacteria with certain antibiotics leads to parasite death . These findings have pioneered the approach of using antibiotics to treat and control filarial infections . In the present study , we have investigated the cell division process in Wolbachia for new drug target discovery . We have identified the essential cell division protein FtsZ , which has a GTPase activity , as an attractive Wolbachia drug target . We describe the molecular characterization and catalytic properties of the enzyme and demonstrate that the GTPase activity is inhibited by the natural product , berberine , and small molecule inhibitors identified from a high-throughput screen . We also found that berberine was effective in reducing motility and reproduction in B . malayi parasites in vitro . Our results should facilitate the discovery of selective inhibitors of FtsZ as a novel antibiotic approach for controlling filarial infection . |
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DNA sequence and local chromatin landscape act jointly to determine transcription factor ( TF ) binding intensity profiles . To disentangle these influences , we developed an experimental approach , called protein/DNA binding followed by high-throughput sequencing ( PB–seq ) , that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin . We applied our methods to the Drosophila Heat Shock Factor ( HSF ) , which inducibly binds a target DNA sequence element ( HSE ) following heat shock stress . PB–seq involves incubating sheared naked genomic DNA with recombinant HSF , partitioning the HSF–bound and HSF–free DNA , and then detecting HSF–bound DNA by high-throughput sequencing . We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment . We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity . We also investigated the extent to which DNA accessibility , as measured by digital DNase I footprinting data , could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs , and found GAGA element associated factor ( GAF ) , tetra-acetylation of H4 , and H4K16 acetylation to be the most predictive covariates . Lastly , we generated an unbiased model of HSF binding sequences , which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE . These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity .
Binding of transcription factors ( TFs ) to DNA elements is necessary to establish and maintain functional changes in gene expression levels . The mechanism by which these factors seek out and bind to their cognate motif elements remains an area of active investigation ( reviewed in [1] ) . TFs are present at cellular concentrations that allow binding to sites that are degenerate from the consensus sequences , and genomes of eukaryotes are littered with potential degenerate binding sites; however , only a small fraction of potential binding sites are recognized in vivo . Moreover , TF binding sites vary dependent upon cell type and cellular conditions . In vivo , TF binding is potentially dependent upon motif accessibility and the surrounding chromatin landscape . Therefore , determining a comprehensive set of potential genomic binding sites and quantifying the joint effects of DNA sequence and chromatin landscape upon binding intensity remains a challenge . Experimental approaches to characterize TF binding sites include assays such as ChIP-seq , protein binding microarrays ( PBM ) [2] , iterative rounds of protein-DNA binding and selection with a complex oligonucleotide library [3] , or extrapolation from DNase I hypersensitivity regions [4] . However , perhaps the most direct way to determine all potential TF binding sites within a genome is to incubate purified TF and naked sheared genomic DNA in vitro , and then specifically quantify the TF-bound DNA [5] . This in vitro method allows binding sites to be interrogated in their native sequence context without the confounding effects of chromatin and cooperation between chromatin-bound factors . It is challenging to predict in vivo TF binding accurately even when all potential in vitro binding sites have been characterized , because the chromatin landscape dramatically influences binding and it changes dynamically with development and with alterations in cellular nutrition and environment [6] , [7] . Recent TF binding site modeling efforts have considered genomic nucleosome occupancy or DNase I hypersensitivity data to account for the effect chromatin has upon in vivo TF occupancy [8]–[11] . However , these models are limited in that they rely upon genomic accessibility data and TF binding data produced under the same conditions . To date there are no data sets that describe the full set of potential TF binding sites , the chromatin state data prior to binding , and occupied binding sites in vivo , in a single inducible system . Integration of these three data sets would allow one to decouple the effect TF binding has upon chromatin state from the effect pre-existing chromatin state has upon induced TF binding . The heat shock response of Drosophila is a model system extensively used to study the general functions of sequence specific activators and how they function to regulate transcription ( reviewed in [12] ) . The master regulator of the heat shock genes , Heat Shock Factor ( HSF ) , has a modest affinity for DNA under non-stress conditions [6] , [13] , [14] , and upon stress , HSF homotrimerizes and inducibly binds to a conserved consensus motif at over 400 sites in the Drosophila genome [6] , [14] . While over 95% of the HSF binding sites contain an underlying HSF sequence motif element ( HSE ) , the vast majority of predicted genomic HSEs remain HSF–free following heat shock . Therefore , the chromatin landscape most likely plays a prominent role in determining binding of HSF . Here , we describe an experimental technique , protein/DNA binding followed by high-throughput sequencing ( PB–seq ) , to quantify the binding potential of all binding sites within a genome . We then develop a quantitative model that incorporates HSF PB–seq data , together with HSF ChIP-seq in Drosophila S2 cells [6] and S2 cell chromatin data , that accurately predicts observed in vivo HSF binding profiles . Moreover , our model allows us to quantify the relative importance of the chromatin features influencing HSF binding intensity . Finally , we develop a sequence model that uses HSF PB–seq data to characterizes the relationship between positions within the HSE and provide biophysical insight into the mechanisms by which HSF interacts with its cognate element .
We performed an in vitro binding experiment with purified HSF ( Figure S1 ) and naked , sheared genomic Drosophila DNA , to derive an accurate set of potential HSF binding sites in the Drosophila genome . HSF–bound DNA was specifically eluted and detected by high throughput sequencing ( Methods ) . The HSF PB–seq experiment yielded 68% of the sequence tags within peaks . In contrast , typical ChIP-seq protocols are more inefficient and the majority of DNA ( 60% to >99% ) sequenced is uninformative background DNA [15] . Peak calling revealed 3952 HSF–binding peaks ( p<0 . 01; 2848 peaks were common to both experimental replicates ) , which include 60% of the previously identified high-confidence HSF binding peaks in vivo [6] . The naïve expectation is that every in vivo HSF peak should have a corresponding in vitro peak , but it is not surprising to observe an incomplete overlap of in vivo by in vitro peaks , for various reasons . As will be discussed , binding sites detected in vivo but not in vitro tend to be more degenerate and have higher DNase I accessibility . Additionally , in vivo binding sites that are dependent upon cooperative interactions with pre-bound chromatin factors , long range DNA interactions , post-translational modifications of HSF [16] , higher-order chromatin structure , or bridging protein interactions [17] will not be detected in the current form of PB–seq . Underlying the in vitro binding peaks , we detected 3735 clusters of HSF binding site HSE sequences ( 2896 in peaks common to both replicates ) at 20% HSE False Discovery Rate ( FDR ) . We used clusters of co-occurring sites due to the uncertainty in HSE detection ( see Methods ) . Furthermore , the majority , 3389 clusters ( 2586 in peaks common to both replicates ) are not detectably bound in S2 cells in vivo . Figure 1 shows two examples of in vitro binding sites flanking the Cpr67B gene that are not bound in vivo . Moreover , the in vitro binding data quantifies differences in the in vitro and in vivo HSF binding intensity , such as the peaks within each of the promoters for Hsp23 and Hsp26 ( Figure 1 ) . The PB–seq experiment allows for an estimate of the relative binding intensity of each HSE , based on the number of sequence tags associated with it . To compute the dissociation constant ( Kd ) values it is necessary to have estimates for both the fraction of bound and free HSE in the PB–seq experiment . Since the PB–seq data only provides information on the bound fraction , we needed to determine the absolute Kds for two HSEs that are found within the PB–seq data in order to provide enough information to estimate the free fraction ( see Methods ) . To generate the HSF/HSE Kd measurements , we performed electrophoretic mobility shift assays ( EMSA ) . The EMSAs were performed with purified HSF and HSEs that are only modestly degenerate from the consensus . We found that HSF binds to the first HSE with ∼42 . 6 pM interval: 36 . 8–49 . 4 pM; Figure 2A and 2C ) and the second HSE with ∼224 pM affinity ( 95% confidence interval: 181–276 pM; Figure 2B and 2D ) . The resulting two absolute Kd values enabled us to transform PB–seq read depths into absolute Kd values ( Figure 2E and Methods ) . We confirmed the transformation of the relative Kd values to absolute Kds by performing band shifts with genomic HSEs of different predicted Kd values ( Figure S2 ) . The experimental verifications of the measurements are within the estimated error of the EMSA confidence interval and the variability between PB–seq replicates ( Figure S3 ) . Taken together , these measurements allow us to characterize the binding energy landscape for HSF across the entire Drosophila genome , in the absence of chromatin . Our estimated Kd values for isolated HSEs in the Drosophila genome ranged from 40–400 pM ( Figure 2E ) . These in vitro binding results demonstrate the feasibility and efficiency of combining high-throughput detection methods with classic EMSA and competition experiments to quantify the binding energy for the comprehensive set of potential genomic binding sites for a sequence-specific TF . Our data reveals substantial differences between in vivo and in vitro binding intensities ( Figure 3A ) , underscoring the role of chromatin in determining in vivo binding site selection and affinity . We found DNase I hypersensitivity was the most important predictor of HSF binding; therefore , we scaled the in vivo and the in vitro read counts so that they were approximately equal at in vivo sites with high DNA accessibility ( Methods , Figure S4 ) . After this normalization , we partitioned the binding sites that were detectable in vitro into classes: “unaffected” sites , bound at comparable affinities in vivo and in vitro ( 55 red points in Figure 3A; 2% of all sites ) ; “suppressed” sites , with reduced , but detectable , in vivo intensity ( 365 green points; 13% ) ; and “abolished” sites , below the in vivo threshold for detection ( 2223 blue points; 76% ) . In addition , sites not detectable in vivo or in vitro were labeled “background” ( 249 gray points; 9% ) , and sites with stronger relative in vivo intensity compared to in vitro were labeled “enhanced” ( 4 black points; 0 . 1% ) . PB–seq data reveals potential HSF binding sites , providing the opportunity to model the effect that non-stressed chromatin landscape has upon induced HSF binding intensity . There is a wealth of chromatin data available for S2 cells during unstressed conditions [18] , [19] , and heat-shock induced binding sites of HSF in S2 cells are also known [6] . We used DNase I hypersensitivity data [18] , MNase data [19] and ChIP-chip data for 9 factors and 21 histone modifications for unstressed Drosophila S2 cells ( Table S1 ) [18] to predict the intensity of inducibly bound in vivo HSF–bound sites ( Figure 4A , Figure S5 and Figure S6 ) . For our statistical model , we selected a rules ensemble [20] , a linear regression model in which some terms are combinations of covariates known as “rules” . This approach allowed us to capture fairly complex interactions between covariates . For example , a rule might apply when H3K27ac and DNase I hypersensitivity both exceeded designated thresholds ( value ranges can also be expressed ) . Each rule's coefficient is added to the predicted value if , and only if , the rule applies . When there is only one covariate , the model reduces to a linear regression . The Pearson's correlation coefficient between HSF ChIP-seq data for the model incorporating all these data sets was r = 0 . 62 ( Figure S6 and Figure S7 ) . As the large number of covariates brings with it some danger of overfitting , we tested combinations of the four classes of covariates: DNase I hypersensitivity , MNase , histone modifications/variants , and non-histone factors ( Figure 4B , Figure S6 , Figure S7 ) . Of notice , the correlation of the linear regression model that incorporates DNase I data was r = 0 . 64 on the test data ( Figure 4B and Figure S7B ) . Our study is consistent with a previous study that obtained r = 0 . 65 for actual and inferred TF binding intensities using a DNase I dependent model [8] . Other covariate classes produce similar , but lower , correlations . The rules model using histone modifications and histone variants yielded r = 0 . 57 ( Figure 4B and Figure S7 ) , while a rules model incorporating non-histone bound chromatin factors yielded r = 0 . 58 ( Figure 4B and Figure S7 ) . Combining covariate classes further improves the correlation to as much as r = 0 . 70 ( Figure S6 and Figure S7 ) . We also examined the Receiver Operator Curves ( ROC ) for the different covariate combinations ( Figure S8 ) and found concordant results . If we assume that the PB–seq , genomic ChIP , DNase I-seq , and MNase-seq experiments are maximally resolved and sensitive , with no experimental noise , an approximate upper bound is given by r = 0 . 90 , as observed for two HSF–ChIP-seq replicates [6] . Notably , the higher resolution of the DNase I-seq data , compared to the ChIP-chip data , may be why DNase I-seq alone is strongly predictive in the linear regression model and most influential in the rules ensemble models . Notably , we used the chromatin landscape prior to induced TF binding to predict binding intensity , whereas previous models have used the chromatin landscape present when the TF is bound in order to infer binding intensity [8] or infer binary binding events [10] , [11] ( see Discussion ) . Our data and modeling indicated that the presence of active chromatin features , such as histone acetylation and DNase I hypersensitivity , had a significant influence on the predictive power of the model , while repressive features had minimal influence ( Figure S9 ) . DNase I hypersensitivity was a strongly predictive covariate in the model when used in a simple linear regression model ( Figure 4 ) , or in combination with histone modification and non-histone factor covariates in the rules ( Figure S9E–S9G , S9J , S9K , and S9M ) . Tetra acetylation of H4 and H3K9ac were the most informative histone marks in the model that used histone variants and histone modifications as covariates ( Figure 5A ) . GAGA associated factor ( GAF ) , which has a proposed role in permitting HSF binding [21] , was the most influential factor in the HSF binding prediction model that considered all chromatin-binding factors ( Figure 5B ) . The analysis above indicates that DNA accessibility , as measured by DNase I hypersensitivity , is a primary determinant of binding intensity . Previous studies have similarly shown that TF binding sites correlate strongly with DNase I hypersensitive sites [8] , [10] , [11] , [22] . For instance , histone acetylation causes local chromatin decondensation by reducing the ionic interactions between lysine residues and DNA and promotes accessibility , but the extent to which combinations of histone marks and TFs act together to dictate chromatin accessibility is not known . Therefore , it is of interest to see whether DNA accessibility can be predicted from specific features of the chromatin landscape , such as histone modifications and non-histone chromatin bound factors . In addition , accurate predictions of DNA accessibility would be of practical use , because direct measurements are often not available . To address this question , we applied our rules ensemble framework to predict DNase I hypersensitivity ( the best available proxy for DNA accessibility ) from ChIP-chip data for histone features , non-histone chromatin bound factors , MNase data and combinations of these covariate pools ( Figure 6 ) . Tetra-acetylation of H4 and H3K9 acetylation were most influential in the model that uses histone modifications , bulk histone and histone variant intensities ( Figure S10E ) ; the correlation coefficient for this model using the test data is 0 . 51 ( Figure S11B ) . The model that uses non-histone factor ChIP-chip data obtains a correlation of 0 . 52 ( Figure S11B ) , which is consistent with TFs having characteristic DNase I hypersensitivity footprints [10] , [11] . The model that combines both histone data and non-histone data into a rules model performs the best on the test set , with a correlation of 0 . 60 ( Figure S11B ) . Repressive histone marks appear to contribute little to generating the DNase I hypersensitivity pattern ( Figure S10 ) and the lack of active chromatin marks appears to be sufficient to package DNA into inaccessible units . These models reinforces the notion that the biochemical composition of chromatin permits DNase I hypersensitivity and quantifies the contributions individual modifications , and combinations thereof , make to DNase I hypersensitivity ( Figure S11 ) . As more and higher-resolution genome-wide data becomes available , these models will be refined . PB–seq provides the opportunity to model the sequence-dependent binding preferences of a purified TF genome-wide and independent of chromatin or other factors . In the case of HSF , the consensus binding site is well characterized and consists of three pentamers , ÒAGAAN NTTCT AGAANÓ , ( here denoted pA , pB , and pC ) , each bound by a monomer of the HSF homotrimer . Note that the consensus sequences for pA and pC are identical , while the one for pB is their reverse complement . Of course , the consensus HSE is a crude summary that ignores subtleties in the base preferences at each position . A position-specific scoring matrix ( PSSM ) provides a somewhat improved description but still ignores dependencies between positions within the binding site . We sought to use genome-wide binding sites from PB–seq to produce an improved model for the sequence preferences at HSEs . We began by computing the mutual information for all pairs of HSE positions based on the identified in vitro binding sites . We found negligible evidence of correlated base preferences between positions , but we did observe that some pentamers within PB–seq peaks adhered closely to the consensus motif while others did not . This led us to formulate a probabilistic model that allows each pentamer in an HSE to closely match the consensus ( “strict” ) or diverge from it more substantially ( “relaxed” ) , and considers all possible combinations of pentamer composition ( Figure S12 ) . More specifically , we described each of the three pentamers using a two-component mixture model , with a latent variable indicating “strict” or “relaxed” binding preferences , and estimated the joint distribution of these three latent variables from the data . The model parameters—the position-specific nucleotide probabilities and prior distribution for the combinations of strict/relaxed pentamers—were estimated from the data by maximum likelihood using an expectation maximization algorithm ( see Methods ) . In fitting the model , we considered only the 1309 isolated HSEs , sequence elements that were at least 200 base pairs away from any other degenerate HSE motif , to avoid complications arising from overlapping HSEs . The model fit the data substantially better than did a simple PSSM ( lnL = −15442 vs . lnL = −15673 for the PSSM; Akaike information criterion [AIC] = 15636 vs . AIC = 15763 for the PSSM ) , suggesting that it effectively captures important dependencies between positions . Based on the estimated model parameters , we computed a posterior probability distribution over all combinations of pentamer stringency and order for each HSE ( Methods; Figure 7B ) . These values were averaged across HSEs to obtain expected genome-wide fractions of HSEs having each of the strict/relaxed pentamer combinations . We found that binding sites with strict pB and pC , and relaxed pA , were most frequent ( an expected 38% of sites ) , indicating that this configuration is preferred ( Figure 7B ) . The next most frequent configurations were a relaxed pB flanked by a strict pA and pC ( 33% ) , and a strict pA and pB combined with a weak pC ( 29% ) . Interestingly , combinations of three strict pentamers occur at negligible frequency . Indeed , only 5 out of 1309 isolated genomic HSEs matched the consensus sequence exactly , while 148 differed from it by a single mismatch . Configurations with at most one strict pentamer were also rare . Together , these results indicate that the biophysical interactions of the pentamers within the binding sites are critically dependent upon their composition and position relative to the other pentamers in an HSE . While the three estimated strict pentamer matrices were similar ( Figure 7A top ) , the relaxed matrices showed substantial differences with respect to each other ( Figure 7A bottom ) . For example , the relaxed pA matrix indicates that 70–80% of HSEs containing a weak pA have the consensus base at positions two , three and four . In contrast , position 12 in pC ( the analog of position 2 in pA ) almost invariably contains a G in all HSEs , while positions 7 and 8 in pB ( analogous to positions 3 and 4 in pA ) have only modest base preferences in HSEs containing a weak pB . This analysis indicates that each monomeric HSF/pentamer interaction has distinct biophysical properties within the context of the broader HSF/HSE interaction . We also devised a simplified model , with a single strict matrix shared by all three pentamers , and a single relaxed matrix obtained by applying a “dampening” factor to the strict matrix ( Figure S13 , Methods ) . This model further supports the strict/relaxed pentamer split ( lnL = −15908 vs . lnL = −16048 for a single-monomer PSSM; and AIC = 15952 vs . AIC = 16078 ) , although both the full model and the full PSSM fit the data better ( lower AIC ) . Moreover , not only was the simplified model still able to reproduce the posterior distributions over pentamer configurations of the full model , but it was also able to replicate synthetic patterns from simulated data ( Figure S14 ) . Finally , the preference for single pentamer degeneracy was also observed independently by comparing the pentamer-specific KL-divergence in PSSMs obtained from subsamples of HSF bound peaks ( Figure S15; Methods ) .
The PB–seq technique combined with EMSA and competition assays provides a straightforward , yet versatile and powerful framework for characterizing all potential binding sites in a genome , regardless of tissue specificity , developmental stage , or environmental conditions . Comparing in vitro and in vivo binding profiles , in the context of pre-induction genomic chromatin landscape , revealed DNase I hypersensitivity , H4 tetra-acetylation , and GAF as critical features that modulate cognate element binding intensity in vivo . Furthermore , DNase I sensitivity was found to be strongly influenced by high GAF occupancy and histone acetylation , while repressive factors were minimally influential in the statistical models . Finally , the full set of potential genomic binding sites provided a rich data set that was used to build more detailed sequence models , which tease apart substructure and features that are lost with traditional PSSM models . One initially surprising observation from our study was that 40% of the in vivo HSF peaks were not found in vitro . We believe that the limited dynamic range for quantifying in vitro binding affinity may be responsible for the lack of detectable in vitro peaks . Although we quantify in vitro binding over an order of magnitude ( 40–400 pM ) , the experimental concentrations of HSF and genomic DNA and our depth of sequencing do not permit the detection of lower affinity HSF binding sites . For instance , only eleven sequence tags would be predicted to underlie a hypothetical 5 nM HSF binding site , and these would not be distinguishable from background . Upon further examination , we find that the composite HSE representing those in vivo binding sites that were not found in vitro is more degenerate than those found using both assays ( Figure S16A ) . Moreover , the in vivo sites that were not found using PB–seq were also more accessible in vivo ( Figure S16B ) , in support of our hypothesis . Performing PB–seq at a range of protein and DNA concentrations , or increasing sequence coverage would expand the dynamic range of quantification by PB–seq . Other possible explanations for this observation include cooperative interactions with pre-bound chromatin factors , long-range DNA interactions , post-translational modifications of HSF , higher-order chromatin structure , or bridging protein interactions . The influence of DNA modifications and immediate flanking sequence do not contribute to this disparity , since we use large fragments of purified genomic DNA . Bridging protein interactions [17] , which do not involve HSF directly binding to DNA , appear not to be responsible for our results because 95% of in vivo peaks encompass at least one HSE near the peak center [6] . However , if other proteins were cooperating with HSF in vivo to enhance HSF binding intensity at low affinity binding sites , then some of these peaks may not be observed in vitro . Since our PB–seq experiment used recombinant HSF in the binding experiments , we would also not capture differences in binding site affinities that are due to post-translational modifications of HSF [16] . To overcome these potential limitations , PB–seq could be adapted to include known bridging/cooperative factors and proteins could be purified from in vivo sources to capture indirect or modification-dependent interactions . The notion that motif accessibility is driving inducible TF binding in vivo is supported by independent studies of distinct TFs: STAT1 , HSF , glucocoticoid receptor ( GR ) , and GATA1 [6] , [22]–[24] . These studies show that the chromatin landscape prior to TF binding influences inducible TF binding . In the first study , it was found that a large fraction of STAT1 induced binding sites contained H3K4me1/me3 marks prior to interferon-gamma ( IFN-γ ) induced STAT1 binding [23] . Our group previously found that inducible HSF binding sites are marked by active chromatin compared to sites that remain HSF–free [6] . A more recent study has shown that inducibly bound GR sites are marked by DNase I hypersensitive chromatin prior to GR binding [22] . Likewise , the permissive chromatin state at GATA1 binding sites is established even in GATA1 knock out cells [24] . While these correlations are instructive , no previous attempt has been made to model inducible TF binding using biological measurements of chromatin landscape present prior to TF binding . Recent models have successfully inferred TF binding profiles using DNA sequence and chromatin landscape data , generated at the same time the TF is bound [8]–[11] . However , these models do not distinguish between the influence TFs have upon local chromatin and the chromatin features that permit TF binding . In contrast , we modeled the changes between HSF in vitro binding ( PB–seq ) and in vivo binding ( ChIP-seq ) landscapes as a function of the non-heat shock chromatin state . This produced a quantitative model describing the important features that modulate the in vivo HSF binding intensity . Moreover , the use of our rules ensemble model enabled the capture of potential interactions between these chromatin features . Our study reveals that DNase I hypersensitivity and acetylation of H4 and H3K9 are strong predictors of inducible HSF binding intensities , however the molecular events and factors that precede TF occupancy to maintain accessible chromatin remain poorly characterized . For instance , the degree to which pioneering factors or flanking DNA sequence , individually or in combination , maintain or restrict accessibility remains unclear . A recent study highlights the biological consequences of maintaining the inaccessibility of TF binding sites , in order to repress expression of tissue-specific transcription factors in the wrong tissues . The authors found that ectopic expression of CHE-1 , a zinc-finger TF that directs ASE neuron differentiation , in non-native C . elegans tissue is not sufficient to induce neuron formation [25] . However , combining ectopic CHE-1 expression with knockdown of lin-53 did modify the expression patterns of CHE-1 target genes in non-native tissue , effectively converting germ line cells to neuronal cells [25] . LIN-53 has been implicated in recruitment of deacetylases , and deacetylase inhibitor treatment mimics lin-53 depletion , suggesting that LIN-53 is actively maintaining CHE-1 target sites inaccessible in germ cells . Alternatively , functional TF binding sites could be actively maintained in the accessible state . HSF binding within ecdysone genes has a functional role in shutting down their transcription [14] , and activating ecdysone-inducible genes containing inaccessible HSEs causes chromatin changes that are sufficient to allow HSF binding [6] . In this special case of HSF–bound ecdysone genes , active transcription and the corresponding histone marks are mediating access to HSEs , in order for HSF to bind and repress transcription upon heat shock . A more recent study has shown that activator protein 1 ( AP1 ) actively maintains chromatin in the accessible state , so that GR can bind to cognate elements [26] . Although TF accessibility to critical genomic sites appears to be actively maintained , many binding sites may be a non-functional result of fortuitous TFBS recognition . It has long been hypothesized that the binding affinities for TF/DNA interactions are sufficiently strong to allow promiscuous binding at the cellular concentrations of TFs and DNA [27] , [28] . There are roughly 32 , 000 HSF molecules per tetraploid S2 cell [29] and the dissociation constants for trimeric-HSF/HSE interactions are in the picomolar range ( Figure 2E ) ; therefore much of the in vivo HSF binding may be non-functional promiscuous binding . Additional investigation will further illuminate the role of chromatin context in TF binding and the mechanisms by which programmed developmental or environmental chromatin changes permit or deny TF binding . Elucidating the rules that govern accessibility is essential for predicting in vivo occupancy of TFs . Diverse transcription factors [7] , from a broad spectrum of organisms [22] , bind their sequences based on site accessibility . We found that chromatin accessibility as measured by DNase I hypersensitivity could be inferred using ChIP-chip data for various histone modifications and transcription factors . Although our model can infer accessibility based on chromatin composition , the mechanism by which accessibility originates is not addressed . Previous studies have shown that activators , such as HSF , glucocorticoid receptor , and androgen receptor bind to their cognate sites and direct a concomitant increase in local acetylation , DNase I hypersensitivity , and nucleosome depletion [6] , [22] , [30] , [31] . Androgen receptor also acts to position flanking nucleosomes marked by H3K4me2 [31] . These post-TF binding chromatin changes that occur are the result of acetyltransferase and nucleosome remodeler recruitment , both of which functionally interact with activators . For instance , both GR and GATA1 interact with the nucleosome remodeling complex Swi/Snf [32] , [33] . Concomitant increases in locus accessibility likely allow large molecular complexes such as RNA Pol II and coactivators to access the region that in turn can reinforce and maintain active and accessible chromatin . Thorough biophysical characterization of TF binding site properties is critical for accurate predictions of TF binding sites , underscoring the need for more complete models of TF binding . While the commonly used PSSM model makes the assumption of base independence , recent work has revealed that richer models providing for interactions between positions are necessary [34] , [35] . Our model captures critical features of the HSF/HSE interaction that are lost with simpler computational models , namely the interdependencies between the sub-binding sites of each HSF monomer . Consistent with our model , a series of in vitro experiments with S . cerevisiae , D . melanogaster , A . thaliana , H . sapien and D . rerio HSFs indicate that HSF from each of these species can bind to discontinuous HSEs containing canonical pentamers that contain intervening five base pair gaps [36] , [37]; interestingly , however , C . elegans HSF strictly binds to continuous HSEs that do not contain gaps [36] . The complex interactions between positions within a binding site are a critical aspect of inferring whether a polymorphism or mutation affects TF binding . These features should prove useful in providing degenerate HSE sequences for optimal co-crystallization of trimeric HSF and DNA and inferring changes in DNA sequence that affect HSF binding within and between species . In conclusion , the data and models presented here reinforce both the importance of chromatin landscape in modulating in vivo TF binding intensity and how genome wide , chromatin free , binding assays contribute to the understanding of TF sequence binding specificity .
Drosophila HSF was N-terminally tagged with glutathione s-transferase and a tobacco etch virus ( TEV ) protease cleavage site . The C-terminus of the recombinant HSF was fused to the 3xFLAG epitope . Recombinant HSF was purified from E . coli with glutathione resin as previously described [38] , with the following modifications: HSF–3xFLAG elution was achieved by addition of 6xHistidine tagged TEV protease and TEV protease was cleared from the HSF preparation using a Nickel-NTA column . Densitometry was used to show that the HSF protein preparation was 40% full length HSF–3xFLAG , and known amounts of bovine serum albumin ( BSA ) were used to quantify the HSF ( Figure S1 ) . Serial two-fold dilutions of recombinant HSF , from 3 nM ( 1 . 5 nM for the 221 pM HSE ) to 23 . 3 pM , was incubated with 200 attomoles of radiolabeled dsDNA containing modestly degenerate HSEs ( chrX:3380775–3380824 ( 224 pM ) , chr2L:5009892–500994 ( 42 . 7 pM ) , chr2R:3529792–3529841 ( 308 pM ) , chr3L 13470978–13471009 ( 221 pM ) , and chr3L:4073542–4073591 ( 97 . 5 pM ) ) and allowed to come to equilibrium for 30 minutes in a total of 10 µl of 1xHSF binding buffer ( 20 mM HEPES pH 7 . 9 , 10% glycerol , 1 mM EDTA , 4 mM DTT , 3 mM MgCl2 , 100 mM NaCl , 0 . 1% NP-40 , and 300 µg/ml BSA ) at room temperature . Binding reactions were loaded in a 3% agarose TBE ( 10 mM Tris-HCl pH 8 . 0 , 25 mM boric acid , and 1 mM EDTA ) gel and electrophoresed at 50 Volts for 2 hours . The HSF–bound probe and free probe were quantified by densitometry and the dissociation constant , Kd = ( [A][B] ) /[AB] , was estimated using a non-linear least squares method on the function [AB]/[A]total = [B]/ ( [B]+Kd ) where [AB]/[A]total is the measured shifted fraction and [B] is the free HSF trimer concentration . We incubated 600 pM HSF and 2500 ng genomic DNA ( sonicated to 100–600 bp fragment size as previously described [6] ) in 1500 µl final volume of 1xHSF binding buffer and let it come to equilibrium for an hour at room temperature . We added 20 µl ANTI-FLAG M2 affinity gel for 10 minutes and washed 8 times with 1xHSF binding buffer to remove unbound DNA , 3xFLAG peptide was added to a final concentration of 200 ng/µl to specifically elute HSF and HSF–bound DNA . The mock IP was done in the absence of recombinant HSF . We attribute the in vitro binding assay's low background to the design of the experiment . Since recombinant C-terminally 3xFLAG tagged HSF was used , the HSF–associated DNA could be specifically eluted by the addition of excess 3xFLAG peptide . In contrast , standard ChIP protocols rely on non-specific elution of all protein and DNA that binds the resin . The sample preparation was as previously described [6] , except that 15 rounds of amplification were performed in this case . The PB–seq reads were aligned to the Drosophila Genome ( BDGP R5/dm3 ) using BWA ( v 0 . 5 . 8c ) [39] . We obtained 5 , 052 , 425 uniquely aligned reads for replicate one , 4 , 694 , 846 for replicate two and 5 , 410 , 049 for the mock . Files that contain raw sequence data and uniquely aligned reads were deposited into NCBI's Gene Expression Omnibus ( GEO ) [40] , accession number GSE32570 . We called peaks using MACS ( v 1 . 3 . 7 . 1 ) [41] , both for each individual replicate and for the merged set , using a tag size of 55 bp , a starting bandwidth of 100 bp and an appropriate genome size . After experimenting with several p-value thresholds , we selected a value of p = 0 . 01 , which achieved a good tradeoff between maximizing the number of called peaks and ensuring consistency between replicates . Our results were largely unaffected by the ‘mfold’ parameter ( the threshold for fold enrichment relative to background for inclusion in the peak model ) , so we left this parameter at its default value . To improve our sensitivity in binding site detection , we made use of an ensemble of position weight matrices ( PSSMs ) , rather than a single matrix . We sampled 10 , 000 sets of 100 peaks and used the program MEME [42] for motif discovery in each set . As input , MEME was given the 100 bp sequence centered at each peak summit . We used a fixed motif width of 14 bp , a second order background Markov model estimated from the entire peak set , and the ‘zoops’ model ( zero or one site per sequence ) with the restriction that at least 75% of the sequences must contain a site . The resulting PSSMs were compared by KL-divergence against the canonical monomer PSSM ( four base pair unit with consensus AGAA ) estimated from the previously published in vivo high-confidence HSF binding sites detected by ChIP-seq [6] . In each PSSM , one of the three monomers had on average about twice the KL-divergence as the other two . Figure S15 shows a scatter plot of the KL-divergence of the PSSMs in the ensemble Each peak was scanned for matches to all PSSMs in the ensemble , allowing for overlapping sites . The score at each position was taken to be the maximum score across the ensemble . Peaks were split into three groups by GC% quantile , and for each group a 10 kbp sequence was simulated from a second order Markov model , which was then used to estimate the FDR associated with the score . In our context , an appropriate FDR threshold should strike a balance between recapitulation of in vivo results and limiting the number of spurious binding sites . In vivo results are defined by high-confidence peaks , which are ChIP-seq peaks that were called by two peak calling programs and have a corresponding binding site sequence underlying the peak [6] . Whereas , spurious sites are accounted for by limiting the average number of HSE clusters per peak ( set of potentially overlapping HSE no more than 10 bp apart from each other ) . Due to the repetitive nature of the HSE , a cluster is a better representative than a single site of a functional binding locus . We chose a 20% FDR threshold , which maximizes the fraction of peaks having a single HSE cluster while ensuring that a large fraction ( 97% ) of the high-confidence in vivo peaks contain HSEs . This threshold resulted in 3735 clusters ( 71% with a single HSE , 20% with two HSEs overlapping by 10 bp , ∼5% with two HSEs overlapping by 5 bp; see Figure S17 ) . The final set of HSE clusters was obtained by combining data from the two experimental replicates . First , a set of genomic regions was identified by intersecting the peaks from the two experimental replicates , and retaining only those peaks for which the two replicates were in close agreement ( >80% of reads fall in the overlapping region ) . We then identified the 2896 HSE clusters that fell in these regions ( ∼77% of all clusters ) . The problem of measuring the intensity of each peak is complicated by the fact that some peaks contain multiple , closely spaced clusters , whose contributions are difficult to disentangle . Furthermore , peaks often include trailing edges that are dominated by the background signal . To address these concerns we experimented with various measures of intensity based on the output produced by MACS ( wig files giving shifted read counts in 10 bp windows ) as well as the reported ‘bandwidth’ B . We considered three measures , applied to a window of radius B centered at each cluster: maximum read count , read count sum , and an “integrated” read count based on a biweight kernel ( which produces a curve at each peak that is similar to the one implied by the peak model used by MACS ) . We selected the biweight kernel measure , which does the best job of handling closely spaced clusters ( see Figure S18 ) . We assume that each HSE site i is at approximately the same initial concentration in the experiment ( [HSEi]initial = C ) . Furthermore , all sites compete to bind a shared amount of free HSF , with the remaining unbound concentration denoted by [HSF] . At the end of the experiment , a fraction of site i is bound , with concentration [HSEi : HSF] , and the remainder is unbound , with concentration [HSEi] . The dissociation constant for a particular HSE site is therefore given by: The bound HSE concentration is measured by the PB–seq experiment in terms of the number of reads at element i ( Ri ) . This leaves two unknown quantities , [HSF] and [HSEi] , in units of read counts . The first of these unknowns , [HSF] , can be eliminated by considering instead the relative Kd with respect to a known reference value ( for an HSE present in the experiment ) . To solve for [HSEi] , we express this quantity as the difference between the initial concentration C and the measured bound concentration:By substituting the expression for Kdi ( above ) and dividing by the Kd value for the reference HSE , Kdref , we obtain an expression with a single unknown , C:With the use of a reference dissociation value for a second HSE , we can solve for C and obtain estimates of the dissociation constants for all other HSE sites for which read counts are available . Replacing Kdi and Ri by the corresponding values for the second reference HSE and solving for C: Our probabilistic model for HSEs was designed to capture interactions among the binding preferences of the three monomers that form the HSF homotrimer . The model consists of three PSSM-based submodels corresponding to the three 5 bp sequences ( pentamers ) that are bound by the HSF monomers . Each of these submodels is defined by two PSSMs , one ‘strict’ and one ‘relaxed’ . These three submodels allow for eight possible combinations of strict and relaxed pentamer binding . Within each pentamer the positions are considered independent , as in standard PSSM models . Formally , let a candidate 15 bp HSE sequence Xk be composed of random variables Xi , jk where i is the pentamer index and j is the base position within that pentamer . Additionally , let each sequence have an associated unobserved random variable Yk which indicates which of the eight combinations of strict/relaxed distributions are applied the corresponding Xi , jk ( Figure S12 ) . For simplicity , our model definition assumes that the middle monomer sequence has been reverse complemented and is therefore in the same orientation as the outer monomer binding sequences . We considered two versions of the model: a sparsely parameterized ‘constrained’ version and a more parameter-rich ‘expanded’ version , as described below . The chromatin effect and DNase models are rule ensemble models , estimated using the RuleFit R package . This package was also used to estimate the relative importance of the model covariates . The covariates were obtained from modENCODE tracks , taking the mean value over a 200 bp window centered on the target point . Furthermore , these data were filtered to contain only points that had a value for every covariate used . | Transcription factors ( TFs ) bind DNA to modulate levels of gene expression . TF binding sites change throughout development , in response to environmental stimuli , and different tissues have distinct TF binding profiles . The mechanism by which TFs discriminate between binding sites in a context dependent manner is an area of active research , but it is clear that the chromatin environment in which potential binding sites reside strongly influences binding . This study used the Heat Shock TF ( HSF ) to study the effect chromatin has upon induced HSF binding . We implemented an experimental technique to quantify all potential HSF binding sites in the genome . These data were incorporated into a modeling framework along with chromatin landscape information prior to HSF binding to accurately predict the intensities of inducible HSF binding sites . DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in the model . The binding data enabled the development of a more complete HSF/DNA interaction model , providing insight into the biophysical interaction of HSF trimer subunits and target DNA pentamers . |
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In Africa , many areas are co-endemic for the two major Schistosoma species , S . mansoni and S . haematobium . Epidemiological studies have suggested that host immunological factors may play an important role in co-endemic areas . As yet , little is known about differences in host immune responses and possible immunological interactions between S . mansoni and S . haematobium in humans . The aim of this study was to analyze host cytokine responses to antigens from either species in a population from a co-endemic focus , and relate these to S . mansoni and S . haematobium infection . Whole blood cytokine responses were investigated in a population in the north of Senegal ( n = 200 ) . Blood was stimulated for 72 h with schistosomal egg and adult worm antigens of either Schistosoma species . IL-10 , IL-5 , IFN-γ , TNF-α , and IL-2 production was determined in culture supernatants . A multivariate ( i . e . multi-response ) approach was used to allow a joint analysis of all cytokines in relation to Schistosoma infection . Schistosoma haematobium egg and worm antigens induced higher cytokine production , suggesting that S . haematobium may be more immunogenic than S . mansoni . However , both infections were strongly associated with similar , modified Th2 cytokine profiles . This study is the first to compare S . mansoni and S . haematobium cytokine responses in one population residing in a co-endemic area . These findings are in line with previous epidemiological studies that also suggested S . haematobium egg and worm stages to be more immunogenic than those of S . mansoni .
Schistosomiasis is a parasitic disease of major public health importance . Schistosoma mansoni and S . haematobium are the main human species . Both species are endemic in Africa , where their distributions show a great overlap [1] . Schistosomes are known to down-regulate host immune responses and to induce so-called modified Th2 responses . The exact phenotype of the induced response depends on a complex immunological ‘dialogue’ that involves cytokines and immune cells of Th2 , but also Th1 , Th17 and regulatory components of the immune system [2] . So far , little is known about differences in host immune responses to schistosomes and possible immunological interactions between S . mansoni and S . haematobium in humans . Yet , epidemiological studies have suggested that host immunological factors may play an important role in co-endemic areas . Interspecies differences in immunogenicity for example , may explain why infection-age curves and morbidity patterns differ between S . mansoni and S . haematobium . Also , immunological interspecies differences and/or immunological interactions between S . mansoni and S . haematobium may explain differences in morbidity levels between single and mixed Schistosoma infections . Cheever et al . reported a more pronounced reduction of S . haematobium than S . mansoni worm loads with age [3] . Similarly , in a mixed focus in northern Senegal , we found the age-infection curve of S . haematobium to decline more steeply after adolescence than that of S . mansoni [4] , indicating that protective immunity against S . haematobium may develop more rapidly . In addition , we found that mixed S . mansoni and S . haematobium infection as compared with single S . haematobium infection tended to decrease the risk of S . haematobium-specific urinary tract pathology [5] . This appeared mainly due to ectopically excreted , possible hybrid eggs [6] . Others also found S . mansoni to affect S . haematobium-specific morbidity and vice versa [7] , [8] , indicating that the two infections may have different effects on the egg-induced immune responses that provoke morbidity . The present study set out to compare Schistosoma-specific cytokine responses induced by S . mansoni and S . haematobium antigens , and to relate these to Schistosoma infection in a S . mansoni and S . haematobium co-endemic area . Schistosoma infection status ( single and mixed ) and infection intensities as well as Schistosoma-specific cytokine responses were determined in residents from a co-endemic focus in northern Senegal . A multivariate ( i . e . multi-response ) approach was used to allow a joint analysis of multiple cytokine responses ( interleukin ( IL ) -10 , IL-5 , interferon ( IFN ) -γ , tumor necrosis factor ( TNF ) -α , and IL-2 ) [9] .
This study was part of a larger investigation on the epidemiology of schistosomiasis and innate immune responses ( SCHISTOINIR ) for which approval was obtained from the review board of the Institute of Tropical Medicine , the ethical committee of the Antwerp University Hospital and ‘Le Comité National d'Ethique de la Recherche en Santé’ in Dakar . Informed and written consent was obtained from all participants prior to inclusion into the study . For minors , informed and written consent was obtained from their legal guardians . All community members were offered praziquantel ( 40 mg/kg ) and mebendazole ( 500 mg ) treatment after the study according to WHO guidelines [10] . This study was conducted in Ndieumeul and Diokhor Tack , two neighboring communities on the Nouk Pomo peninsula in Lake Guiers . Details on the study area have been described elsewhere [4] , [5] . Between July 2009 and March 2010 , parasitological data were collected from 857 individuals [4] . A random subsample of 200 subjects was followed up immunologically . These subjects were between 5 and 53 years of age . Individuals who had lived in an urban area in the 5 years preceding the study ( n = 7 ) , had taken praziquantel within the last year ( n = 2 ) , or had clinical signs of malaria ( recruited upon recovery ) , and pregnant women ( n = 18 ) were excluded from the immunological study . Two feces and two urine samples were collected from each participant on consecutive days . Infection with Schistosoma spp . was determined quantitatively ( by Kato-Katz and urine filtration ) , and infection with soil-transmitted helminths ( STHs ) Ascaris lumbricoides , Trichuris trichiura and hookworm , was assessed qualitatively ( by Kato-Katz ) , as described elsewhere [4] . Aliquots of the first fecal samples were preserved in ethanol to confirm microscopy results by multiplex PCR ( A . lumbricoides , hookworm and Strongyloides stercoralis ) ( n = 198 ) [11] . Infection with Plasmodium was determined by Giemsa-stained thick blood smears . Five hours after venipuncture , heparinized blood was diluted 1∶4 in RPMI 1640 ( Invitrogen ) supplemented with 100 U/ml penicillin , 100 µg/ml streptomycin , 1 mM pyruvate and 2 mM glutamate ( all from Sigma ) . This mixture ( 200 µl sample volume ) was incubated in 96-well round bottom plates ( Nunc ) at 37°C under 5% CO2 atmosphere for 72 h , together with one of four schistosomal water-soluble antigen preparations at a final concentration of 10 µg protein/ml: Medium ( see above ) without stimulus was used as a negative control . After harvesting , supernatants were stored at −80°C . Schistosoma eggs and adult worms were isolated from either S . mansoni- or S . haematobium-infected golden hamsters . SEAm , SEAh , AWAm and AWAh were prepared from this material using identical procedures . In brief , eggs or worms were freeze-dried and then homogenized in phosphate-buffered saline ( PBS ) with 10% n-octyl-β-D-glucopyranoside . Subsequently , this mixture was sonicated , frozen , thawed and washed with PBS . The resulting pellet was dialyzed and filter-sterilized . While AWAm and AWAh batches were lipopolysaccharide ( LPS ) -free , SEAm and SEAh antigens contained equivalent amounts of LPS ( final concentrations of 1–5 ng/ml ) . IL-10 , IL-5 , IFN-γ , TNF-α , and IL-2 in culture supernatants were analyzed simultaneously using custom Luminex cytokine kits ( Invitrogen ) according to the manufacturer's instructions . Samples with concentrations below the detection limit were assigned values corresponding to half of the lowest value detected . Lowest values detected were 0 . 063 pg/ml for IL-10 , 0 . 044 pg/ml for IL-5 , 0 . 090 pg/ml for IFN-γ , 0 . 051 pg/ml for TNF-α , and 0 . 063 pg/ml for IL-2 . Results were considered significant when the p-value was <0 . 05 . The Pearson Chi-square test was used to determine the association between infection status on the one hand , and age and gender on the other . Nonparametric techniques were chosen because cytokine concentrations were not normally distributed . Univariate statistics were used to compare single antigen-induced responses within individuals ( IBM SPSS 21 . 0 ) . McNemar's tests were used to compare cytokine response frequencies between S . mansoni and S . haematobium antigen-induced responses within individuals ( e . g . SEAm- versus SEAh-induced responses ) . Similarly , Wilcoxon Signed Rank tests were used to compare cytokine response levels between S . mansoni and S . haematobium antigen-induced responses within individuals . Multivariate ( i . e . multi-response ) statistics were used to collectively analyze multiple cytokine responses – i . e . cytokine profiles - in the study population , and to investigate interrelationships between these responses [9] . We chose the nonparametric technique nonmetric multidimensional scaling ( nMDS; in R with the ‘Vegan’ package [12] , [13] ) . This is a variant of the parametric principal component analysis ( PCA ) , but with fewer assumptions about the nature of the data and the interrelationship of the variables [14] . This is important because cytokine response levels were not normally distributed , even after log-transformation . Also , levels of different cytokines typically correlate with one another . Upon computation of the cytokine profiles , associations between these cytokine profiles and Schistosoma infection were assessed . The approach is illustrated in Supporting Information S1 . Before nMDS , cytokine concentrations in the negative control were subtracted from those in antigen-stimulated samples to obtain net cytokine responses . Negative values were set to zero . Net cytokine responses were normalized by log ( base 10 ) -transformation after adding 1 pg/ml to allow for zeroes . Schistosoma infection intensities were normalized after adding half of the detection limit ( i . e . 5 eggs per gram of feces and 0 . 5 eggs per 10 ml of urine for S . mansoni and S . haematobium , respectively ) . One nMDS was performed for each of the four Schistosoma-specific whole blood stimulations ( either SEAm , SEAh , AWAm or AWAh ) using the ‘metaMDS’ function [13] . Each nMDS was repeated several times to assess the robustness of the resulting pattern [14] . The Euclidean dissimilarity index was used [13] , and cytokine profiles - i . e . the matrix of IL-10 , IL-5 , IFN-γ , TNF-α , and IL-2 - were plotted in three dimensions ( 3D ) to adequately represent the variation in the data [14] . Afterwards , gradients of the separate cytokine responses , on which the nMDS was based , were fitted using the ‘envfit’ function [13] . The same function was used to fit infection intensities onto each 3D nMDS , and to statistically test associations of antigen-induced cytokine profiles with Schistosoma infection intensity or infection status , i . e . uninfected , single S . mansoni , single S . haematobium , versus mixed S . mansoni and S . haematobium infection . The ‘ordiellipse’ function was used to fit average group scores - with their 95% confidence intervals ( CIs ) - for different infection statuses [13] . In contrast to individual S . mansoni- and S . haematobium-induced cytokine responses which can be compared quantitatively within individuals as described above ( univariate statistics ) , qualitative differences between S . mansoni- and S . haematobium-induced cytokine profiles could only be assessed visually by nMDS , not by formal statistical testing .
The study population consisted of 88 males and 112 females with a median age of 16 ( range 5–53 ) years . Malaria and STHs T . trichiura and hookworm were absent in this population , and A . lumbricoides and S . stercoralis rare ( n = 3 and 2 , respectively , with 100% concordance between microscopy and PCR ) . In contrast , 137 ( 69% ) subjects were infected with S . mansoni , and 116 ( 58% ) with S . haematobium . Sixty percent ( 95/158 ) of all Schistosoma infections were mixed S . mansoni and S . haematobium infections ( Table 1 ) . The distributions of S . mansoni and S . haematobium infections in the study population according to age and gender are shown in Table 2 . Both Schistosoma infections peaked in adolescents ( 10 to 19 year-olds ) , but gender differences were not statistically significant . Epidemiological patterns of infection have been described in more detail elsewhere [4] . Insight into the different antigen-induced cytokine responses relative to one another was obtained by nMDS . Figure 1 and 2 show the variation in multivariate cytokine responses in the study population , with dots representing individuals . Distances between dots approximate inter-individual dissimilarities in cytokine responses with stress values ( i . e . discrepancies ) of 0 . 051 for SEAm , 0 . 041 for SEAh , 0 . 058 for AWAm , and 0 . 061 for AWAh . Red arrows indicate increasing gradients of IL-10 , IL-5 , IFN-γ , TNF-α and IL-2 responses , respectively . The level of a cytokine response increases in the direction of the corresponding arrow ( see also Supporting Information S1 ) . The length of a cytokine arrow indicates the goodness of fit of that arrow ( or cytokine gradient ) . The nMDS outcomes for the first axis ( nMDS1 ) show that for each of the four antigen stimulations , all cytokine responses point to the left . Individuals plotted on the left produced consistently higher levels of all cytokines measured than those on the right . In other words , nMDS1 indicates a gradient of high ( left ) to low ( right ) cytokine responses . In analogy , the second axis ( nMDS2 ) , indicates a gradient of Th1-like ( IFN-γ and TNF-α , top ) to Th2-like ( IL-5 , bottom ) phenotypes for each of the antigen stimulations . In contrast to SEA-induced IL-5 , AWA-induced IL-5 was not accompanied by production of IL-10 . IL-2 levels increased with Th1 cytokines , except for SEAm . The third axis ( nMDS3 ) indicates a gradient of TNF-α and IL-2 ( left ) to IFN-γ and IL-10 ( right ) . In contrast to antigen-induced cytokines , spontaneously induced levels of cytokines in the control ( medium only ) , did not show significant gradients , except for IL-5 on the third nMDS axis ( stress = 0 . 11 , data not shown ) . Figure 1 and 2 indicate that S . mansoni and S . haematobium antigens induced very similar cytokine profiles; cytokine profiles differed more between adult ( AWA ) and egg ( SEA ) life stages of the parasite than between the two Schistosoma species . Within individuals , S . haematobium-induced cytokine response levels were higher than those induced by S . mansoni ( Table 3 ) . This was statistically significant for all SEA- and AWA-induced cytokine responses that were measured , except for SEA-induced IFN-γ and IL-10 . Subsequently , we related the above-described variation in cytokine responses in the study population ( i . e . plotted cytokine profiles ) to infection intensity . Table 4 shows that all associations between Schistosoma antigen-induced cytokine profiles and infection intensity were statistically significant . In Figure 1 , the direction of the black arrows represents the increasing gradients of S . mansoni and S . haematobium infection intensity , respectively ( see also Supporting Information S1 ) . On the first axis , which indicates cytokine response levels ( see above ) , these arrows generally point into the opposite direction of cytokine responses . This indicates that people with elevated Schistosoma infection intensities are more likely to have lower cytokine responses , and vice versa . On the second axis , which indicates the Th1 versus Th2 response phenotype ( see above ) , infection intensity generally increases with IL-5 and decreases with Th1 cytokines TNF-α , IFN-γ , and IL-2 ( except for SEAm-induced IL-5 which decreases with increasing infection intensity ) . Briefly , as infection intensity increased , cytokine response levels decreased and the Th2 phenotype became more pronounced . The association between infection intensity and reduced cytokine responsiveness was more pronounced for SEA than for AWA stimulation . Schistosoma infection intensity increased with AWA-induced IL-5 , but decreased with SEA-induced IL-5 levels , indicating that people with higher infection intensities produced more of a Th2-like response against AWA and more of a suppressive response ( i . e . with low cytokine response levels ) against SEA than people with lower infection intensities , and vice versa . We did not observe differences in induced cytokine profiles between the two Schistosoma infections . Associations between cytokine profiles and infection intensity were comparable for S . mansoni and S . haematobium infections ( Figure 1 ) . Table 4 shows significant correlations between cytokine profiles and Schistosoma infection intensity for homologous combinations ( i . e . infection intensity and antigen stimulation of the same species ) as well as for heterologous combinations ( i . e . infection intensity of one and antigen stimulation of the other species ) . Schistosoma antigen-induced cytokine profiles were significantly associated with Schistosoma infection status , except upon stimulation with AWAm ( Table 4 ) . Figure 2 shows how antigen-induced cytokine profiles differed according to infection status ( except for AWAm , which was not significantly associated with infection status ) , with 95% CI ellipsoids indicating the average nMDS scores per infection group: uninfected ( ‘N’ ) , single S . mansoni ( ‘M’ ) , single S . haematobium ( ‘H’ ) , versus mixed ( ‘MH’ ) Schistosoma infection group . In analogy with Figure 1 , uninfected individuals had higher cytokine responses than Schistosoma-infected subjects , and their cytokine profiles were skewed more towards the Th1 phenotype . On the whole , there was a gradient in cytokine profiles from uninfected individuals , to people with single and then mixed Schistosoma infections ( Figure 2 ) and these profiles were in the same direction as the gradient of infection intensity ( Figure 1 ) . In other words , people with low cytokine responses of the Th2 phenotype tended to have both mixed and heavier infections , people with strong Th1 responses tended to be uninfected , and those with an intermediate cytokine profile tended to have both single and lighter Schistosoma infections . For the SEAm-induced cytokine profile , there was a clear difference ( i . e . separation between ellipsoids ) between S . mansoni-infected individuals ( with either single or mixed S . mansoni ) , and those without S . mansoni ( no Schistosoma infection , or single S . haematobium infection; Figure 2A ) . There were no significant differences in this cytokine profile between single and mixed S . mansoni infections , or between uninfected individuals and those with single S . haematobium infections . This indicates that , in contrast to S . mansoni , S . haematobium infection status was not associated with SEAm-induced cytokine profiles . Schistosoma haematobium-induced cytokine profiles on the other hand , showed similar relationships with S . mansoni as well as with S . haematobium infection status . Cytokine profiles of people with single and mixed infections differed significantly from those of uninfected people , and cytokine profiles did not appear to differ between single S . mansoni and single S . haematobium infections .
In conclusion , this is the first study to comprehensively investigate S . mansoni- and S . haematobium-induced cytokine responses in a S . mansoni and S . haematobium co-endemic area , and to relate these cytokine responses to Schistosoma infection . The present study demonstrates that nMDS can be used successfully as a tool for the joint analysis of multiple cytokine responses in relation to Schistosoma infection . We showed strong associations between Schistosoma infection and Schistosoma-induced cytokine profiles , and provided a first insight into potential differences and interactions between human S . mansoni and S . haematobium infections . This knowledge will contribute to an improved understanding of the mechanisms underlying Schistosoma infection and morbidity in co-endemic populations . | In the developing world , over 207 million people are infected with blood-dwelling parasitic Schistosoma worms . Schistosoma haematobium and S . mansoni are the most widespread species . In Africa , they often occur together in the same area , with many people carrying both species . Yet , little is known about the differences in immune response that the human host develops against these two species . It is also unknown whether the presence of one species may affect the immune response to the other . We here investigated 200 people from an area in the north of Senegal where both species occur . They were examined for Schistosoma infections , as well as for immune responses to the two species . We observed that both infections were characterized by very similar cytokine responses . However , S . haematobium antigens induced higher levels of cytokines than S . mansoni . This suggests that S . haematobium may give rise to stronger immune responses , and may help to explain differences between the two most important Schistosoma species regarding the occurrence of infection and morbidity . |
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Prions arise when the cellular prion protein ( PrPC ) undergoes a self-propagating conformational change; the resulting infectious conformer is designated PrPSc . Frequently , PrPSc is protease-resistant but protease-sensitive ( s ) prions have been isolated in humans and other animals . We report here that protease-sensitive , synthetic prions were generated in vitro during polymerization of recombinant ( rec ) PrP into amyloid fibers . In 22 independent experiments , recPrP amyloid preparations , but not recPrP monomers or oligomers , transmitted disease to transgenic mice ( n = 164 ) , denoted Tg9949 mice , that overexpress N-terminally truncated PrP . Tg9949 control mice ( n = 174 ) did not spontaneously generate prions although they were prone to late-onset spontaneous neurological dysfunction . When synthetic prion isolates from infected Tg9949 mice were serially transmitted in the same line of mice , they exhibited sPrPSc and caused neurodegeneration . Interestingly , these protease-sensitive prions did not shorten the life span of Tg9949 mice despite causing extensive neurodegeneration . We inoculated three synthetic prion isolates into Tg4053 mice that overexpress full-length PrP; Tg4053 mice are not prone to developing spontaneous neurological dysfunction . The synthetic prion isolates caused disease in 600–750 days in Tg4053 mice , which exhibited sPrPSc . These novel synthetic prions demonstrate that conformational changes in wild-type PrP can produce mouse prions composed exclusively of sPrPSc .
Prions are infectious proteins that cause heritable , sporadic , and transmissible disease in humans and other mammals [1] . The molecular basis of prion disease is a conformational change in the normal , cellular prion protein , denoted PrPC , to a disease-causing form , denoted PrPSc [2] , [3] . This conformational change has often been detected by measuring the extent to which PrP resists digestion by proteases , such as proteinase K ( PK ) , because most naturally occurring prion strains are partially resistant to digestion [4] , [5] , [6] , [7] . However , a substantial portion of some prion strains is comprised of protease-sensitive ( s ) PrPSc; for example , over 90% of PrPSc in the brains of some sporadic Creutzfeldt-Jakob disease ( sCJD ) cases is sensitive to PK digestion [8] . Importantly , cases of fatal neurological disease have been reported with neuropathology typical of sCJD but harboring no protease-resistant ( r ) PrPSc [9] , [10] , and the PrP ( H187R ) mutation gives rise to neurological disease with an abnormal PrP conformer that is sensitive to protease digestion [11] . Atypical strains causing scrapie , a prion disease in sheep , have also been reported with a high proportion of sPrPSc [12] , [13] , [14] . Transgenic ( Tg ) mice expressing mouse ( Mo ) PrP with the P101L mutation corresponding to the human P→L mutation causing Gerstmann-Sträussler-Scheinker ( GSS ) disease also harbor protease-sensitive prions . Tg ( PrP , P101L ) mice expressing high levels of mutant PrP spontaneously develop prion disease and generate a mutant form of PrPSc that is resistant only to mild PK digestion [15] , [16] , [17] . Tg ( PrP , P101L ) 196 mice expressing low levels of mutant PrP were inoculated with brain extracts from ill Tg mice overexpressing mutant PrP or a synthetic , 55-residue PrP ( P101L ) peptide refolded into a β-rich conformation [18] , [19] . In the inoculated Tg196 mice , both the brain extracts and the synthetic peptide hastened the development of neurodegenration [15] , [16] , [20] . Interestingly , prions with the P101L mutation were not transmissible to mice expressing the wild-type ( wt ) PrP sequence; whether this was due to the protease sensitivity of the prions or the presence of the P101L mutation was not clear . Inoculation of seeded and unseeded preparations of recMoPrP ( 89–230 ) amyloid fibers into Tg9949 mice , which express a similar , N-terminally truncated PrP at 16–32× the levels of PrP in Syrian hamster brain [21] , generated prions [22] . The brains of mice that had been inoculated with the seeded PrP amyloids produced a synthetic prion strain denoted MoSP1 , which exhibited protease resistance and shortened incubation periods upon serial passage to both wt and Tg lines of mice [22] , [23] , [24] . The extent to which the brains of mice that had been inoculated with unseeded fibers harbored protease-resistant PrP was unclear [22] . We hypothesized that Tg9949 mice inoculated with the unseeded amyloid fibers , as described in our initial report [22] , may contain protease-sensitive prions since their brains exhibited all the neuropathological features of prion disease . At that time , the most reliable method of detecting sPrPSc was the conformation-dependent immunoassay ( CDI ) [7] , [25] , which consists of selective precipitation of PrPSc by phosphotungstate ( PTA ) followed by immunodetection . However , the CDI proved unreliable in detecting sPrPSc due to the high levels of the transgene product N-terminally truncated PrPC . For this reason , we sought an alternative method for detecting sPrPSc; we called this new procedure the amyloid seeding assay ( ASA ) . The ASA employs PTA precipitation , similar to the CDI , but detects prions based on their propensity to hasten the formation of PrP amyloids . We found that prions could be detected using the ASA in brain samples from Tg9949 mice inoculated with the unseeded fibers [26] . Recently , several new strains of protease-resistant synthetic prions have been created from amyloid generated under a variety of conditions and inoculated into mice that overexpress full-length PrP [27] . These findings expand the original report of synthetic prions [22] to a second line of transgenic mice and confirm the ability to create protease-resistant synthetic prions . To extend our discovery that truncated wt mammalian prions could be produced synthetically [22] , [27] , we performed a large series of experiments with various recMoPrP amyloid fibers in Tg9949 mice . We sought conditions to produce synthetic prions with abbreviated incubation times . While we investigated numerous variations in the preparation of recMoPrP amyloids , none resulted in a shortening of the incubation times . However , most of the amyloid preparations caused prion disease in Tg9949 mice as demonstrated by neuropathological changes and the presence of sPrPSc . These protease-sensitive prions transmitted disease to two different Tg lines of mice . Unexpectedly , control , uninoculated and mock-inoculated Tg9949 mice were prone to late-onset neurological dysfunction that was indistinguishable clinically from Tg mice inoculated with protease-sensitive prions . But the ill , control Tg9949 mice did not develop neurodegeneration , form sPrPSc or transmit prion disease . The studies reported here not only demonstrate the validity of the experimental systems reported earlier but they also extend our understanding of synthetic prions . Moreover , our findings establish that wt sPrPSc alone , in the absence of detectable rPrPSc , is sufficient to cause neurodegeneration .
To determine if Tg9949 mice generate prions spontaneously , 96 uninoculated Tg9949 mice and 78 Tg9949 mice inoculated with bovine serum albumin ( BSA ) were monitored twice weekly for signs of neurological dysfunction . We found that a cumulative incidence of 85% of these control Tg mice developed late-onset ataxia at approximately 600 d ( Fig . 1A and Table S1 ) . The most common clinical observations of aged Tg9949 mice were ataxia , circling , and a dull coat . Mice inoculated with BSA were no more likely than uninoculated mice to develop neurological dysfunction ( Fig . 1A and Table S1 ) . We compared the probability of these Tg9949 mice developing ataxia in old age to the probability that other Tg and wt mice develop ataxia . We found that Tg9949 mice are significantly more likely to develop ataxia than wt FVB mice ( n = 12; p = 0 . 03 ) and Tg mice that express full-length PrP at 4–8 times wt levels ( Tg4053 mice , n = 62; p<0 . 001 ) [17] , [27] , [28] . Older Tg4053 and FVB mice had comparable rates ( p>0 . 30 ) of neurological dysfunction . We used four different methods to determine if Tg9949 mice suffering from neurological dysfunction had spontaneously generated prions: bioassay , neuropathology , Western blotting for rPrPSc , and ASA for sPrPSc [26] . For bioassays , brains from three Tg9949 mice exhibiting neurological dysfunction were homogenized and inoculated intracerebrally ( ic ) into weanling Tg9949 and Tg4053 mice . Inoculation of these brain homogenates neither hastened the onset of neurological dysfunction in Tg9949 mice nor resulted in neurological dysfunction in Tg4053 mice ( Fig . 1B ) . In contrast , inoculation of Rocky Mountain Laboratory ( RML ) prions into Tg9949 and Tg4053 mice mice resulted in disease in 161 d and 50 d , respectively [21] . For neuropathological analyses , we examined more than 20 brain samples from Tg9949 mice exhibiting neurological dysfunction ( from both the uninoculated and BSA-inoculated groups; Fig . 1A ) . Typically , neuropathologic features of prion disease include the formation of vacuoles , proliferation of astrocytes , and deposition of PrP aggregates [29] . We found no evidence of prion disease pathology in any of the brains taken from aged Tg9949 mice ( a representative specimen is shown in Fig . 1C ) . Occasional vacuoles and mild astrocytic gliosis of the cerebellar white matter were observed , but these findings were consistent with aging ( for comparison with aged , healthy Tg9949 , wt , and Tg4053 mice , see Fig . S1 ) . Neuropathological analysis did not indicate the cause of neurological dysfunction in older uninoculated or BSA-inoculated Tg9949 mice . To determine whether Tg9949 mice suffering from neurological dysfunction harbored protease-resistant PrP , we performed Western immunoblotting of brain samples . In over 100 mouse brains from uninoculated and BSA-inoculated Tg9949 mice , we found no PK-resistant PrP signal . Six independent samples are shown in Fig . 1D . Next , we subjected the brain homogenates of Tg9949 mice to the ASA ( Fig . 1E ) [26] . This assay is based on the observation that prions , partially purified from brain homogenates by PTA precipitation [7] , accelerate the conversion of recPrP into a conformation that favors amyloid assembly [26] . We incubated PTA-precipitated brain homogenates with recMoPrP ( 89–230 ) for 6 h and monitored amyloid formation by measuring the fluorescence emission of Thioflavin T ( ThT ) [30] . As depicted , samples from RML prion-inoculated animals were active in the ASA whereas samples from BSA-inoculated Tg9949 mice were not ( Fig . 1E , top ) . Because amyloid seeding is a kinetic process , we wanted to be certain that none of the samples had an intermediate effect on amyloid formation that did not register on the time scale of the initial measurement ( 6 h ) . We measured the mean lag phase for amyloid formation for all of the samples , and found that all uninoculated and BSA-inoculated Tg9949 samples showed lag times similar to uninoculated FVB control brains ( Fig . 1E ) , indicating that aged Tg9949 mice do not spontaneously form protease-sensitive prions . In contrast , the brains of RML prion-inoculated mice were able to reduce the lag phase for amyloid formation ( Fig . 1E , bottom ) . We inoculated Tg9949 mice with recPrP ( 89–230 ) in α-helical ( monomeric ) , β-rich oligomeric and amyloid forms . In addition to the two amyloid inoculations previously reported [22] ( Amyloids 1 and 2; Table S2 ) , we made 24 independent amyloid preparations by systematically varying the conditions used for amyloid formation including: ( 1 ) the initial conformation of recMoPrP , ( 2 ) the composition and concentration of denaturant , ( 3 ) the number of times the seeding procedure was repeated , ( 4 ) use of multiple freeze-thaw cycles , and ( 5 ) the method used to purify the fibers prior to inoculation ( Amyloids 3–4 , Amyloids 14–35 , Table S2 ) . We inoculated monomeric recMoPrP ( 89–230 ) , oligomeric recMoPrP ( 89–230 ) , and each of the 24 new amyloid preparations into groups of at least eight Tg9949 mice . All inoculated Tg9949 mice developed neurological dysfunction between 500 and 650 days ( Table S3 ) . Tg9949 mice inoculated with protease-sensitive synthetic prions had clinical presentations that were indistinguishable from control mice as they aged , specifically , ataxia , circling , and a dull coat . To determine if the brains of inoculated Tg9949 mice harbored prions , we analyzed brain samples by Western immunoblotting and the ASA ( Fig . 2A–C and Table S3 ) . In the brains of mice that had been inoculated with Amyloids 2 , 3 , or 4 , no PK-resistant PrP was detected using any of three different antibodies ( P , D18 , and R2 , which bind to the N-terminal , middle , and C-terminal regions of PrP ( 89–230 ) , respectively; immunoblot probed with P is shown in Fig . 2A ) . Likewise , no PK-resistant PrP was detected in the brains of mice inoculated with Amyloids 14–35 by immunoblotting with the antibody D18 ( Table S3 ) . However , brain samples from mice that had been inoculated with 21 of the 24 new amyloid preparations showed substantial activity in the ASA , indicating the presence of prions; for the remaining three amyloids , no prions were detected ( Figs . 2B , S2 , and Table S3 ) . We found that the brains of Tg9949 mice inoculated with PrP in an α-helical , monomeric conformation [31] and those inoculated with PrP in a β-rich oligomeric form [32] did not contain PrP in a conformation that was active in the ASA ( Fig . 2C , top ) . We also measured mean lag phases in the ASA to be certain that no intermediate seeding effect had occurred ( Fig . 2C , bottom ) . Examination of the brains of ill , amyloid-inoculated animals by histopathology revealed the hallmarks of prion disease , including extensive vacuole formation and PrP deposits , either lining the vacuoles or as punctate aggregates near the vacuoles ( Fig . 2D and Table S3 ) . Tg9949 mice inoculated with the α-helical or β-oligomeric recPrP had normal brains histologically with no evidence of prion disease . Based on the ASA activity and neuropathology , we conclude that 21 of the 24 new amyloid preparations resulted in the formation of protease-sensitive prions , which were transmissible to Tg9949 mice . We chose three brain isolates for further study and designated the resulting prion strains MoSP2 , MoSP3 , and MoSP4 , respectively . Because this was the first time that the ASA has been applied to a large number of unknown samples , we analyzed the correlation of this method to neuropathological analysis . Forty-six samples were analyzed for the presence of prions both by neuropathology and the ASA; of these , 34 were positive by both methods , 11 were negative in both , and 1 was positive in the ASA but negative by neuropathology ( Table S4 ) . Thus , results by the ASA correlated with neuropathologic assessment for 98% of samples ( p<0 . 001 ) . Brain homogenates from ill Tg9949 mice containing MoSP2 , MoSP3 , and MoSP4 prions were inoculated ic into Tg9949 mice . Brain homogenates from aged Tg9949 mice with neurological dysfunction were used as controls . Serial transmission ( or second transmission , 2T ) of all three protease-sensitive synthetic prion strains in Tg9949 mice resulted in neurological dysfunction within a timeframe comparable to uninoculated , control mice ( Table S5 ) . A third transmission ( 3T ) of MoSP2 into Tg9949 mice gave similar results ( Table S5 ) . We wished to determine whether the protease sensitivity and ASA activity of MoSP2 , MoSP3 , and MoSP4 were maintained upon serial passage in Tg9949 mice . Western blots of brain samples from Tg9949 mice serially infected with MoSP2 , MoSP3 , and MoSP4 were probed with anti-PrP antibody P and revealed no protease-resistant PrP fragments ( Fig . 3A ) . MoSP1 was used as a PK-resistant positive control . Employing lower concentrations of PK ( 1 , 3 , and 10 µg/ml ) revealed no difference between Tg9949 mice inoculated with MoSP2 and uninoculated Tg9949 controls ( Fig . S3 ) . We next subjected brain homogenates of mice that had received serial transmission of MoSP2 , MoSP3 , and MoSP4 to the ASA . PTA-purified brain homogenates were incubated with recMoPrP ( 89–230 ) for 6 h , and amyloid formation was monitored by ThT fluorescence . MoSP2 , MoSP3 , and MoSP4 serially passaged in Tg9949 mice exhibited consistent activity in the ASA ( Fig . 3B ) . In contrast , brain homogenates from control mice inoculated with a mock inoculum ( Tg9949 brain homogenate ) did not seed amyloid formation . Next , we analyzed brain sections of Tg9949 mice serially infected with MoSP2 , MoSP3 , and MoSP4 . Serial passage of each protease-sensitive synthetic prion strain resulted in substantial vacuolation and formation of PrP deposits ( Fig . 3C ) . Vacuolation scores , or the area of a region occupied by vacuoles , were tabulated for various brain regions from the initial transmission , second transmission , and third transmission of MoSP2 in Tg9949 mice ( Fig . S4 ) . Vacuolation in Tg9949 mice infected with MoSP2 by serial passage was similar to that in Tg9949 mice originally inoculated with amyloid fibers , indicating that the strain characteristics of MoSP2 were conserved upon passage . Finally , brain sections of mice inoculated with MoSP2 were subjected to histoblot analysis with and without PK digestion ( Fig . S5 ) , which confirmed that PrP deposits in the brains of mice inoculated with MoSP2 are protease-sensitive . Tg9949 brain homogenates containing MoSP2 were inoculated ic into Tg4053 mice , which overexpress full-length MoPrP-A . Additionally , two Tg9949 brain homogenates inoculated with Amyloid Prep 19 ( Table S3 ) were passaged to Tg4053 mice . In contrast to Tg9949 mice , Tg4053 mice are not prone to developing late-onset ataxia ( Fig . 1A ) . Transmission of MoSP2 and the other protease-sensitive synthetic prion isolates to Tg4053 mice resulted in prion disease with incubation periods of 600–750 d ( Fig . 4A ) . Tg4053 mice inoculated with protease-sensitive synthetic prion isolates were significantly more likely to develop neurological dysfunction than Tg4053 mice inoculated with brain homogenate from uninoculated aged Tg9949 mice ( p<0 . 001 ) . Brain samples of Tg4053 mice inoculated with protease-sensitive synthetic prion isolates showed no rPrPSc in Western blots ( MoSP2 shown in Fig . 4B ) , but substantial activity in the ASA ( MoSP2 shown in Fig . 4C ) . In contrast , Tg4053 mice inoculated with control Tg9949 brain homogenates had neither rPrPSc nor sPrPSc . To detect trace quantities of rPrPSc in Tg4053 mice inoculated with MoSP2 , we subjected 1 ml of 5% brain homogenate to PK digestion ( 20 µg/ml ) , followed by PTA precipitation ( Fig . S6 ) . The PTA pellet was resuspended in 100 µl of 10% SDS and boiled . Thirty microliters of the resulting product was then analyzed by Western immunoblotting , approximately 10-fold more material than used elsewhere in this work for Western blots and 1000-fold more material than used for the ASA . Even under these conditions , no rPrPSc could be detected . Neuropathology consistent with prion disease was observed in brain sections from MoSP2-inoculated Tg4053 mice ( Fig . 4D ) . Punctate PrP deposits and vacuolation were widespread , but most severe in the CA1 region of the hippocampus and in the cerebellum ( Fig . S4 ) . From these data , we conclude that protease-sensitive synthetic prions in the brains of Tg9949 mice were transmitted to Tg4053 mice , and the resulting prions were composed of sPrPSc and produced neuropathologic changes typical of prion disease . Notably , MoSP2 produced no clinical or pathologic evidence of prion disease in wt FVB mice ( Table S6 ) .
Encouraged by the production of prion infectivity by polymerizing recMoPrP ( 89–230 ) into amyloid fibers [22] , [23] , we undertook a study aimed at identifying conditions that would shorten incubation times for synthetic prions in Tg mice . We explored an array of variables , including the composition and concentration of denaturant , the number of seeding rounds , and the number of freeze-thaw cycles , none of which modified experimental outcomes . Twenty-five preparations of recMoPrP ( 89–230 ) polymerized into amyloid were inoculated into 204 Tg9949 mice . Eighty percent ( or 164 ) of the Tg9949 mice were found to have sPrPSc and neuropathology typical of experimental prion disease . Three of the amyloid preparations failed to produce measurable sPrPSc and neuropathology while six other preparations showed incomplete transmissions ( Table S3 ) . Three of the 22 infectious , recMoPrP amyloid preparations were studied in detail; these were designated MoSP2 , MoPSP3 and MoSP4 . Each of these synthetic prion isolates transmitted disease upon serial passage in Tg9949 mice ( Fig . 3 ) . In addition , MoSP2 and two other protease-sensitive synthetic prion isolates transmitted disease to Tg4053 mice overexpressing MoPrP ( Fig . 4 ) . Our creation of these novel protease-sensitive prions challenges the accepted definition of what constitutes a prion . Mammalian prions have been most closely associated with PrP that resists protease digestion [4] , [5] , [6] , [7] . Additionally , mammalian prions typically cause disease that shortens the lifespan of the animal . While the novel synthetic prions reported here do not have either of these characteristics , they share four traits common to all mammalian prions: ( 1 ) they possess an alternatively folded isoform of PrP ( Fig . 2B ) ; ( 2 ) they cause neurologic dysfunction in animals ( Fig . 4A ) ; ( 3 ) they cause profound neuropathologic changes ( Fig . 2D and 4D ) ; and ( 4 ) they are transmissible ( Figs . 3 and 4 ) . We suggest that these four traits define mammalian prions . Many prions observed in nature appear to be composed of mixtures of rPrPSc and sPrPSc [7] , [8] , [12] , [13] , [14] , [25] , though the relationship between the two is unclear . The creation of synthetic prions composed solely of sPrPSc offers new insight into this relationship and the role of sPrPSc in disease . Our results demonstrate that sPrPSc is transmissible and causes neurodegeneration in the absence of rPrPSc . Our findings also suggest that sPrPSc does not arise as an off-pathway product during the replication of rPrPSc . Examples of natural prion diseases that feature sPrPSc predominantly are rarely reported [9] , [10] . In the work reported here , it was necessary to use genetically modified lines of mice to make this unusual prion phenotype more readily accessible . Notably , inoculation of wt FVB mice with the amyloid fibers used in these studies did not result in prion disease ( Table S7 ) . It is intriguing that MoSP2 remained protease-sensitive even after repeated serial passage . The protease-sensitive prion fraction isolated from Syrian hamsters infected with 263K prions was shown to give rise to rPrPSc in the protein misfolding cyclic amplification assay [33] . Our findings indicate that infection with sPrPSc does not necessarily lead to rPrPSc generation . Because some lines of Tg mice overexpressing wt PrP develop spontaneous neurological dysfunction [34] , we observed 96 uninoculated , control Tg9949 mice and ic inoculated 78 control Tg9949 mice with BSA in PBS . Unexpectedly , most of these control Tg9949 mice developed late-onset , spontaneous neurological dysfunction . All the ill , control Tg9949 mice showed no neuropathological changes typical of prion disease . Additionally , no sPrPSc or rPrPSc was detected in the brains of these control Tg9949 mice . These studies established the validity and limitations of transmitting prions to Tg9949 mice . In our initial report of synthetic prions , we described the onset of neurological dysfunction in Tg9949 mice between 380 and 660 days after inoculation [22] . Three sets of Tg9949 mice were used as controls . In the first set , 10 of 12 healthy , uninoculated Tg mice were terminated at 574 days of age; the other two Tg9949 mice developed signs of neurological dysfunction at 564 and 576 days of age but had neither rPrPSc nor neuropathology typical of prion disease . In the second set of control mice , eight Tg9949 mice were inoculated with Syrian hamster Sc237 prions and were healthy at 525 days of age when they were sacrificed . Third , seven Tg9949 mice were inoculated with PBS and remained healthy at 672 days of age when they were sacrificed . In light of the current work , the first and second control groups were terminated too early to observe neurological dysfunction and the third group appears to be an outlier . Our discovery that Tg9949 mice develop late-onset neurological dysfunction does not undermine the key finding of the earlier work [22] , which demonstrated that prions could be generated de novo from recombinant protein , but it does raise the possibility that the incubation period for the initial transmission may have been longer than reported . Incubation periods for some prion strains in Tg9949 mice cannot be determined when they approach or exceed the age of onset of spontaneous neurological dysfunction in these mice . Despite the observation that uninoculated , control Tg9949 mice were prone to ataxia in old age , we found no evidence of prions in these mice by biochemical means , by histopathology , or by attempted serial transmission of their brain homogenates ( Fig . 1 ) . Neuropathological analysis of the brains of these mice excluded that neurologic dysfunction was caused by the spontaneous generation of prions . It is noteworthy that neurological deficits in Tg mice overexpressing PrP are not uncommon and are distinct from those caused by prion infection . Tg mice overexpressing wt MoPrP-B , Syrian hamster PrP , or ovine PrP develop disease featuring hindlimb paralysis , tremors , and ataxia , with mean ages of onset at ∼550 days [34] . Deletion of specific N-terminal segments of PrP results in fatal ataxia accompanied by degeneration of the cerebellum at 90–275 days of age [35] . Deletions of helical regions near the C-terminus result in CNS illnesses similar to neuronal storage diseases [36] . Like Tg9949 mice , none of these neurologically compromised mice spontaneously generated prions . Evidence of prion disease was observed in 22 of 25 amyloid inoculations in Tg9949 mice , but was not observed from any of 7 control inoculations , including PBS , BSA , α-helical recPrP , β-oligomeric recPrP , and 3 uninfected Tg9949 brain homogenates . These results exclude the possibility that the observed neuropathology resulted from contamination of the inocula . It is possible that a small titer of rPrPSc that eluded detection is responsible for the disease observed in these studies . Given the extensive neurodegeneration observed in the brains of infected Tg9949 mice ( Fig . S4 ) , this possibility seems unlikely . In fact , the vacuolation profile generated by inoculating the protease-resistant MoSP1 strain into Tg9949 mice was much less severe than that observed for MoSP2 prions , which lack protease-resistance [22] . Furthermore , despite its tendency to accumulate , no rPrPSc could be detected even upon serial passage ( Fig . 3 ) . Nonetheless , it is conceivable that some rPrPSc may be detectable under conditions not yet explored , for example , using alternate proteases . This would not alter our conclusions , however , that such protease-sensitive prions would be overlooked using the standard conditions used to detect prions . Whereas protease-sensitive prions composed of mutant PrPSc ( P101L ) in Tg mice have been studied extensively [15] , [16] , [17] , wt sPrPSc has been less well investigated . While rPrPSc is clearly transmissible , it is unknown what role , if any , rPrPSc plays in the pathogenesis of prion disease . From the studies reported here as well as other investigations , sPrPSc is clearly pathogenic . The pathogenicity of sPrPSc calls into question the adequacy of some terms used to describe different isoforms of PrP , such as PrPres and PrPsen [37] . PrPres is often equated with PrPSc , and PrPsen with PrPC . From the work presented here , we contend that PrPSc can be both protease-resistant and protease-sensitive , rendering terms that describe only the protein's response to limited protease digestion as ambiguous . Therefore , the use of terms describing both infectivity and resistance to protease digestion ( i . e . , sPrPSc , rPrPSc , and PrPC ) is necessary in order to avoid confusion . While inoculation ic of recMoPrP ( 89–230 ) amyloid did not shorten the lives of Tg9949 mice ( Table S2 ) , the amyloid preparations provoked severe neurodegeneration ( Fig . 2 ) . Serial transmission of protease-sensitive prions MoSP2 , MoSP3 , and MoSP4 in Tg9949 mice did not alter the incubation periods ( Table S5 ) , suggesting that these prion isolates encipher long incubation times . Because the formation of rPrPSc has been used as an operational assay for the identification of prions , protease resistance has been often viewed as an intrinsic and obligatory feature of prions [38] . The results reported here extend our more recent findings that challenge the notion that protease resistance is an obligatory feature of PrPSc that is required for the transmission of prions [4] , [16] . The production of synthetic prions , which are sensitive to proteolysis but cause transmissible disease , is an important step toward understanding the role of protease-sensitive forms of PrPSc in the pathogenesis of prion disease . Recent reports suggest that prions with low levels of rPrPSc occur naturally in sheep [14] and humans [9] . Our results show the importance of using alternate methods for detecting PrPSc , rather than employing only the presence of PK-resistant PrP . Exclusive reliance on the detection of rPrPSc as a surrogate marker for prion infectivity may overlook the contribution of sPrPSc to prion infectivity and the pathogenesis of prion disease [39] .
All animal procedures were performed under protocols approved by the Institutional Animal Care and Use Committee at the University of California San Francisco . RecMoPrP ( 89–230 ) was made as previously described [22] , [40] . For inoculation into Tg9949 mice , recMoPrP ( 89–230 ) was refolded into an α-helical conformation at 0 . 5 mg/ml [31] , a β-rich oligomer at 1 . 0 mg/ml [32] , or into amyloid fibers at 1 . 0 mg/ml [22] . For recPrP used in the ASA , lyophilized protein was dissolved in 6 M Gdn at 5 mg/ml , aliquotted , and stored at −80°C . Tg9949 mice [also referred to as Tg ( MoPrP , Δ23–88 ) 9949/Prnp0/0 mice] were bred in-house and express MoPrP ( 89–231 ) on a knockout background at 16–32× compared to PrP in Syrian hamsters [21] . Tg4053 mice [also referred to as Tg ( MoPrP-A ) 4053 mice] [28] were bred in-house and express full-length PrP at 4–8× the levels in wt , FVB mice [17] . FVB mice were obtained from Charles River Laboratories ( Wilmington , MA ) . To prepare 10% ( w/v ) brain homogenates , 9 volumes of ice-cold PBS were added to brain tissue in a 50-ml tube . Brain tissue was homogenized on ice , using either needle extrusion through progressively smaller needles , or , for samples used in the ASA , by bead beating ( FastPrep FP120 , Qbiogene ) . The sample was centrifuged at 500× g for 5 min at room temperature ( RT ) to clarify samples . The supernatant was collected , the pellet discarded; aliquots were keep frozen at −80°C until use . RecPrP was inoculated following dialysis against PBS to remove toxic buffer components; alternatively , the fibers were washed 3× in PBS to remove toxic buffer components . Each time , fibrils were spun down at maximum speed in a tabletop centrifuge and resuspended in PBS as indicated in Table S2 . For serial passage experiments , 10% brain homogenates from Tg9949 mice were diluted 1∶10 in 5% BSA in PBS . Approximately 30 µl of recPrP , PBS ( with or without 5 mg/ml BSA ) , or diluted brain homogenate were inoculated intracerebrally into mice of either sex , aged 7 to 10 weeks . Inoculation was carried out with a 27-gauge , disposable hypodermic needle inserted into the right parietal lobe . Mice were examined twice weekly for neurological dysfunction . Animals were assessed using standard diagnostic criteria for prion disease [41] , [42] . If neurological dysfunction was evident , mice were sacrificed and their brains were removed for biochemical and histological analysis . Brain homogenates were adjusted to 1 mg/ml total protein; 20 µg/ml PK ( Boehringer Mannheim ) was added for a final volume of 0 . 5 ml . Following a 1-h incubation at 37°C , digestion was stopped by addition of phenylmethylsulfonyl fluoride ( PMSF; 2 mM final concentration ) . Digestion products were precipitated by centrifugation at 100 , 000× g for 1 h , resuspended in SDS loading buffer , and run on 12% polyacrylamide gels . Western blotting was carried out as previously described [41] using anti-PrP HuM-D18 , P , or R2 . The ASA was performed as described elsewhere [26] , except that PTA pellets were prepared on 1/5 scale ( 100 µl of 5% BH was used as starting material , and all volumes scaled down proportionally ) . Briefly , brain homogenates in Sarkosyl were precipitated with PTA to purify prions . Two µl of PTA-purified brain homogenates were diluted into 400 µl water , then tested as seeds in amyloid formation reactions . A 96-well plate was prepared with 180 µl/well of recPrP solution ( 50 µg/ml recMoPrP ( 89–230 ) , 0 . 4 M GdnHCl , 1× PBS , 10 µM ThT ) . Twenty µl of diluted PTA-precipitated brain homogenate were added to each well , with each sample tested with six replicates . ThT fluorescence measurements were taken at 444/485 nm excitation/emission spectra on an M2 Spectramax fluorescence plate reader ( Molecular Devices ) after 6 h of continuous shaking at 37°C . Each sample was measured in six independent replicates . MoSP1 used as a PK-resistant control in these experiments was passaged in either Tg9949 or Tg4053 mice [22] . Brains were fixed immediately upon being harvested by immersion in 10% buffered formalin . Following paraffin embedding , 8-µm-thick sections were stained with H&E to visualize vacuoles . Reactive astrocytic gliosis was visualized by peroxidase immunohistochemistry with an antibody against glial fibrillary acidic protein . The antibody R2 was used to visualize PrP deposits [43] . Distributions of neuropathological lesions were estimated as the percentage of tissue occupied by vacuoles . These estimates were confirmed by a second , independent technician . For survival analysis , STATA software ( StataCorp , College Station , TX ) was used to calculate p-values based on cumulative survival . Microsoft Excel ( Microsoft Corp . , Redmond , WA ) was used to calculate standard deviations and standard errors . | Prions are infectious proteins that cause heritable , sporadic , and transmissible diseases in humans and other mammals . These infectious proteins arise when the normal form of the prion protein ( PrP ) adopts a self-perpetuating conformation . This disease-causing PrP form is frequently distinguished from normal PrP by its resistance to digestion by proteases although considerable evidence shows that protease-sensitive prions occur naturally in humans and sheep . Here we describe the generation of novel protease-sensitive synthetic prions . After producing recombinant PrP of the wild-type mouse sequence in Escherichia coli , we polymerized the protein into an amyloid fiber conformation . Mice inoculated with these amyloid fibers developed extensive neurodegeneration characteristic of prion disease , but did not generate protease-resistant PrP . Prions from sick animals were transmitted to healthy animals , which likewise developed neurodegeneration but not protease-resistant prions . These novel synthetic prions demonstrate that truncated wild-type PrP can undergo a conformational change that becomes infectious yet the protein remains protease sensitive . |
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Broadly-neutralizing monoclonal antibodies ( bNAbs ) may guide vaccine development for highly variable viruses including hepatitis C virus ( HCV ) , since they target conserved viral epitopes that could serve as vaccine antigens . However , HCV resistance to bNAbs could reduce the efficacy of a vaccine . HC33 . 4 and AR4A are two of the most potent anti-HCV human bNAbs characterized to date , binding to highly conserved epitopes near the amino- and carboxy-terminus of HCV envelope ( E2 ) protein , respectively . Given their distinct epitopes , it was surprising that these bNAbs showed similar neutralization profiles across a panel of natural HCV isolates , suggesting that some viral polymorphisms may confer resistance to both bNAbs . To investigate this resistance , we developed a large , diverse panel of natural HCV envelope variants and a novel computational method to identify bNAb resistance polymorphisms in envelope proteins ( E1 and E2 ) . By measuring neutralization of a panel of HCV pseudoparticles by 10 μg/mL of each bNAb , we identified E1E2 variants with resistance to one or both bNAbs , despite 100% conservation of the AR4A binding epitope across the panel . We discovered polymorphisms outside of either binding epitope that modulate resistance to both bNAbs by altering E2 binding to the HCV co-receptor , scavenger receptor B1 ( SR-B1 ) . This study is focused on a mode of neutralization escape not addressed by conventional analysis of epitope conservation , highlighting the contribution of extra-epitopic polymorphisms to bNAb resistance and presenting a novel mechanism by which HCV might persist even in the face of an antibody response targeting multiple conserved epitopes .
Hepatitis C virus ( HCV ) infects over 170 million people worldwide [1] and kills more people in the United States annually than HIV [2] . Appalachian regions of the United States saw a >350% increase in the number of new HCV infections from 2009–2012 [3] and recent outbreaks in the United States have been attributed to the rapid increase in injection drug use [4] . While direct-acting antiviral ( DAA ) therapy has revolutionized care for patients with HCV , control of the HCV pandemic remains challenging due to frequent reinfection in high-risk individuals who have achieved a sustained virologic response after DAA therapy [5] , transmission of NS5A inhibitor-resistant HCV variants from individuals failing DAA therapy [6] , and the high proportion ( ~50% ) of infected individuals who are unaware asymptomatic carriers [7] . A major goal for the development of a prophylactic vaccine against HCV is stimulation of an immune response that is protective against a wide range of naturally occurring viral variants [8 , 9] , which is a daunting challenge given the enormous genetic diversity of HCV [10–18] . Broadly neutralizing antibodies ( bNAbs ) are a useful guide for vaccine development , since they bind to relatively conserved viral epitopes , prevent successful entry of diverse HCV isolates , and have been associated with spontaneous clearance of HCV [19] . Despite the relative conservation of bNAb epitopes , polymorphisms conferring resistance to various bNAbs have been identified [20–24] , and increasing evidence has shown that polymorphisms distant from bNAb binding sites can modulate E1E2 resistance [20 , 22 , 24] . BNAb resistance polymorphisms have been identified by various methods , including alanine-scanning mutagenesis , mapping of longitudinal sequence evolution in infected humans [22] , and passage of replication competent virus ( HCVcc ) in vitro in the presence of bNAbs [21 , 23] , but an efficient method to identify common naturally-occurring resistance polymorphisms in circulating E1E2 variants has not been available . Recently , we and others have observed significant variation in sensitivity of natural E1E2 variants to a diverse panel of monoclonal bNAbs and HCV-infected sera [24 , 25] . When we compared the rank orders of neutralization of a diverse array of 19 genotype 1 HCVpp by individual bNAbs , distinct relationships between antibodies were observed , allowing grouping of all bNAbs into three distinct clusters of functionally-related antibodies , and suggesting that common E1E2 determinants of neutralization sensitivity are shared between bNAbs within each cluster [24] . In that study , we identified E2 polymorphisms conferring resistance to most antibodies falling in a group we called neutralization cluster 1 , which included bNAbs that are known to target the CD81-binding site of E2 . We were previously unable to explain the functional relationship between antibodies in a second group that we called neutralization cluster 2 . Surprisingly , this cluster includes the potent human bNAbs HC33 . 4 and AR4A , although their described binding epitopes are at opposite termini of the E2 protein [26 , 27] . HC33 . 4 is a human monoclonal antibody that binds to a continuous epitope near the N-terminus of E2 , at amino acids ( aa ) 412–423 , commonly known as ‘epitope I’ [28] . Recently , aa 408 was also shown to be a HC33 . 4 binding residue [29] . AR4A , also a human monoclonal antibody , binds to a conformational epitope including the C-terminal , membrane proximal region of E2 as well as residues on E1 . BNAbs like HC33 . 4 targeting ‘epitope I’ have been shown to neutralize HCV by blocking E2 interaction with CD81 [30–35] , while AR4A does not appear to interfere with this interaction [26] . Despite the clearly distinct binding epitopes of these two bNAbs , and possibly differing mechanisms of neutralization , we hypothesized that shared E1E2 resistance polymorphisms to these antibodies would explain the unexpected correlation between neutralization profiles of HC33 . 4 and AR4A . No obvious polymorphisms mediating this effect were identified in a small panel of E1E2 variants with varying HC33 . 4 and AR4A sensitivities , so we developed a larger panel of more than 100 E1E2 variants as well as a statistical approach to identify natural polymorphisms that were associated with resistance to each bNAb . Using these tools , we identified polymorphisms conferring resistance to HC33 . 4 and AR4A individually , as well as polymorphisms outside of either binding epitope that confer resistance to both bNAbs by modulating binding to the HCV co-receptor , scavenger receptor B1 ( SR-B1 ) .
To construct a library of E1E2 genes to predict relationships between amino acid sequence and neutralization sensitivity , we cloned more than 700 naturally occurring HCV genotype 1 E1E2 genes . Of these cloned E1E2s , 113 produced HCV pseudoparticles ( HCVpp ) that were functional in repeated tests when co-transfected with an HIV NL4 . 3Δenv-Luc reporter genome , as previously described [24] . The resulting library includes 71 subtype 1a and 42 subtype 1b E1E2 variants isolated from a total of 27 unique donors . It is not known why the majority of cloned E1E2 variants were nonfunctional in HCVpp , but this has also been observed in other studies [36] . We analyzed genetic variation in our functional E1E2 panel to confirm that it is representative of circulating strains . Loci of greatest amino acid variation of the panel across E1E2 mirror those of a reference panel of 643 genotype 1 HCV isolates from GenBank , with the greatest amino acid diversity observed in hypervariable region 1 ( HVR1 ) of E2 ( Fig 1A ) . As many as 9 possible amino acids are represented at some E1E2 loci . Overall , this neutralization panel of functional E1E2 variants contains 97% of amino acid polymorphisms present at ≥5% frequency in the Genbank genotype 1 reference panel . We first assessed variation at the known binding epitopes of the two bNAbs across the HCVpp panel . The HC33 . 4 epitope varied at position 408 , while the AR4A epitope was 100% conserved across the panel at all known binding residues ( Fig 1B ) . We then quantitated the fraction unaffected ( Fu ) of each HCVpp in the presence of each bNAb by measuring hepatoma cell entry of HCVpp in the presence of 10 μg/mL HC33 . 4 or AR4A relative to entry in the presence of nonspecific human IgG ( Fig 1C ) . Neutralization was assessed at a single concentration of bNAb rather than with serial bNAb dilutions in order to increase the throughput of the assay and to minimize the quantity of bNAb required . We have previously shown that Fu values measured by this method are reliably quantitative , as they correlate significantly with IC50 values calculated from neutralization curves of the same HCVpp/bNAb combinations as well as with IC50 values calculated from neutralization curves of replication competent virus ( HCVcc ) [24] . Another recent study also confirmed strong concordance between neutralization results obtained using HCVpp or HCVcc [36] . Each bNAb showed a more than 100-fold variation in neutralization across the HCVpp panel , which was surprising given the conservation of the bNAb binding epitopes . HC33 . 4 and AR4A were associated with a median ( min-max ) Fu of 0 . 22 ( 0 . 003–1 . 1 ) and 0 . 17 ( 0 . 01–1 . 15 ) , respectively . Using a cutoff of Fu<0 . 5 , which is roughly equivalent to an IC50 cutoff of 10 μg/mL , HC33 . 4 and AR4A neutralized 88% and 85 . 8% of the HCVpp panel , respectively . As we previously observed using a panel of 19 HCVpp [24] , there was a significant positive correlation between the rank order of sensitivity of the 113 HCVpp in this panel to HC33 . 4 and AR4A ( r = 0 . 44 p = 7e-7 ) ( Fig 2A ) . We identified E1E2 variants with exquisite sensitivity to both bNAbs ( Fu < 0 . 02 with either bNAb ) as well as variants with high level resistance to both bNAbs ( Fu >1 ) , with other E1E2 variants distributed between those extremes . This correlation between neutralization profiles of the two bNAbs was surprising , given that they do not share binding residues ( Fig 1B ) . To further confirm that the two bNAbs bind to distinct epitopes , we performed binding competition assays between the two bNAbs . E1E2 protein-coated ELISA wells were pre-incubated with a high concentration of either HC33 . 4 or AR4A ( blocking bNAbs ) , followed by biotinylated HC33 . 4 or AR4A at a concentration selected to give 50% of maximal binding ( EC50 ) , with binding of the biotinylated bNAb detected using streptavidin-horseradish peroxidase . A ratio of binding of each biotinylated bNAb in the presence of blocking bNAb divided by binding in the absence of blocking bNAb was calculated ( Fig 2B ) . As expected , each bNAb competed for binding with itself , but HC33 . 4 and AR4A showed minimal competition for E1E2 binding with each other , confirming that the bNAbs bind to distinct epitopes . In spite of their shared resistance pattern , HC33 . 4 more potently neutralized subtype 1a than subtype 1b HCVpp ( Fu median = 0 . 17 for 1a vs . 0 . 27 for 1b , p = 0 . 002 ) , while AR4A displayed minimal difference in subtype neutralization ( Fig 3A ) . The significant positive correlation between HC33 . 4 and AR4A neutralization profiles observed with the full HCVpp panel was also observed on analysis of the subtype 1a-only ( r = 0 . 39 , p = 7e-4 ) and subtype 1b-only ( r = 0 . 69 , p = 1e-6 ) subsets of the panel ( S1 Fig ) We used novel ( SNAPR ) and established ( LASSO ) methods to identify E1E2 sequence determinants of resistance to HC33 . 4 and AR4A . An E1E2 amino acid alignment was generated including sequences of each of the 113 variants in the panel . For the SNAPR method , due to the higher degree of similarity among E1E2 variants originating from the same HCV-infected donor , and a variable number of E1E2 variants contributed by each donor , neutralization and sequence data from variants from underrepresented donors were randomly selected for replication until the number of data points in the analysis representing each individual donor was identical . For each amino acid position across E1E2 , all HCVpp were divided into groups according to the amino acid occupying that position , and the amino acid associated with lowest median Fu ( greatest neutralization sensitivity ) for each bNAb was identified . For each bNAb , at each position , a nonparametric ( Wilcoxon rank-sum ) test was used to compare the neutralization values of all HCVpp with E1E2 carrying the amino acid associated with the lowest mean Fu with the neutralization values of all of the other variants , generating a “SNAPR value” for each position ( Fig 3B ) . While the replication of some sequence and neutralization data reduces the effect of over-representation of some donors , the method also artificially inflates the sample size with data that are not independent , so calculated p-values are artificially low . Therefore , the p-values themselves cannot be used to determine whether variation in neutralization sensitivity at an individual position is statistically significant . Rather , these "SNAPR-values" provide a metric for comparison between effects at different polyprotein positions . Because of the potential for subtype differences to dominate findings , grouped genotype ( S2 Fig ) and subtype 1a only analyses were performed separately for further focused investigation . SNAPR-values spanned approximately 10 and 20 orders of magnitude for HC33 . 4 and AR4A , respectively ( Fig 3C ) . We also re-analyzed the neutralization and sequence data using a method that considers multiple residues in combination—LASSO , without the replication of data required for the SNAPR analysis [37] . Of the 20 most likely resistance polymorphism position predictions from SNAPR for HC33 . 4 and AR4A ( Table 1 ) 5 positions ( for HC33 . 4 ) and 5 positions ( for AR4A ) were also among the 20 and 18 most likely LASSO predictions , respectively . Of note , position 408 was predicted by SNAPR to modulate resistance to HC33 . 4 but not AR4A , which supports sensitivity and specificity of the SNAPR analysis since lysine ( K ) 408 is a known binding residue for HC33 . 4 but not AR4A , and it is the only known binding residue for either bNAb that varies significantly across the neutralization panel . Position 408 was not among the top 20 LASSO predictions for HC33 . 4 . Given the distinct binding epitopes of HC33 . 4 and AR4A ( Figs 1B and 3C ) , and the imperfect correlation between neutralization profiles of the bNAbs ( Fig 2A ) , it is not surprising that many of the loci predicted to modulate sensitivity to HC33 . 4 and AR4A are not shared . Interestingly , SNAPR predicted positions 242 , 403 , and 438 as determinants of neutralization sensitivity for both HC33 . 4 and AR4A . Positions 242 and 403 were also predicted by LASSO to be determinants of sensitivity for both bNAbs ( Table 1 ) . To further investigate the 8 most likely SNAPR predictions for HC33 . 4 and AR4A , we compared Fu values of HCVpp grouped by the amino acid present at each position , without the replicated neutralization data included in the SNAPR analysis ( Fig 4 ) . In the analysis for HC33 . 4 , E1E2 variants with methionine ( M ) vs . valine ( V ) at position 242 showed significant differences in neutralization sensitivity ( median Fu 0 . 10 vs . 0 . 25 , p = 0 . 02 ) ( Fig 4A ) . For AR4A , M vs . V at position 242 were also associated with significant differences in neutralization sensitivity ( median Fu 0 . 12 vs . 0 . 27 , p = 0 . 02 ) ( Fig 4B ) . Variation at the 242 position resulted in the 13th and 8th most extreme LASSO coefficients of any position in E1E2 for HC33 . 4 and AR4A , respectively ( Table 1 ) . Variants with leucine ( L ) vs . phenylalanine ( F ) at position 403 showed significant differences in neutralization sensitivity to HC33 . 4 ( median Fu 0 . 042 vs . 0 . 22 , p = 1E-3 ) . ( Fig 4A ) . For AR4A , L vs . F at position 403 were also associated with significant differences in neutralization sensitivity ( median Fu 0 . 08 vs . 0 . 20 , p = 0 . 01 ) ( Fig 4B ) . Variation at the 403 position resulted in the most extreme LASSO coefficients of any position in E1E2 for HC33 . 4 and AR4A ( Table 1 ) . For HC33 . 4 , E1E2 variants with leucine ( L ) vs . valine ( V ) at position 438 also showed significant differences in neutralization sensitivity ( median Fu 0 . 096 vs . 0 . 39 , p = 3E-3 ) . ( Fig 4A ) . For AR4A , L vs . V at position 438 were also associated with significant differences in neutralization sensitivity ( median Fu 0 . 11 vs . 0 . 44 , p = 0 . 01 ) ( Fig 4B ) . To test SNAPR predictions , putative resistance polymorphisms at the 8 positions with the lowest SNAPR-values for each bNAb were introduced individually by site directed mutagenesis into 2–3 distinct wild type ( WT ) E1E2 variants in which they were not naturally present . These WT variants were each from subtype 1a and differed from each other prior to mutagenesis by an average of 42 amino acids ( 7% ) . Mutated E1E2 and corresponding WT variants were used to produce HCVpp , which were tested for neutralization by HC33 . 4 ( Fig 5A ) or AR4A ( Fig 5B ) . To control for experimental variation between HCVpp neutralization experiments , neutralization of each mutated and WT HCVpp pair was tested in at least 2 independent experiments . Experiments were considered independent only if independently produced HCVpp preparations ( transfections ) were used and independent neutralization assays were performed . M242V , L403F and L438V were predicted to modulate resistance to both HC33 . 4 and AR4A , so these mutations were tested for effect on each bNAb . Four of the 8 polymorphisms predicted by SNAPR to confer resistance to HC33 . 4 showed statistically significant effects after introduction by site directed mutagenesis . Notably , mutation of lysine ( K ) 408 to methionine ( M ) led to an increase in resistance ( WT mean Fu of 0 . 09 vs . K408M mean Fu of 0 . 72 , p<0 . 0001 ) , which was expected since K408 was recently identified by alanine scanning as a binding residue for HC33 . 4 [29] . Mutation of leucine ( L ) 403 to phenylalanine ( F ) also led to a significant increase in resistance to HC33 . 4 . Curiously , mutation of L438 to valine ( V ) , which was predicted by SNAPR to confer resistance to HC33 . 4 , instead conferred significantly increased sensitivity to the bNAb ( WT mean Fu 0 . 22 vs . L438V mean Fu 0 . 04 , p = 0 . 004 ) . Four of the 8 polymorphisms predicted to confer resistance to AR4A also showed statistically significant effects after introduction by site directed mutagenesis . As with HC33 . 4 , L403F conferred significant resistance to AR4A neutralization ( WT mean Fu 0 . 08 vs . L403F mean Fu 0 . 23 , p = 0 . 007 ) , and L438V conferred increased AR4A sensitivity ( WT mean Fu 0 . 20 vs . L438V mean Fu 0 . 06 , p = 0 . 03 ) . Notably , mutation of serine ( S ) 686 to threonine ( T ) and valine ( V ) 720 to isoleucine ( I ) also conferred significant resistance to AR4A . Neither S686 nor V720 fall at a known binding residue for AR4A , but they are 6 and 22 amino acids from known AR4A binding residues , respectively . Though polymorphisms at 242 were also predicted by SNAPR and LASSO to be determinants of resistance for AR4A and HC33 . 4 ( Fig 3C; Table 1 ) , mutagenesis at this position did not confer a significant change in sensitivity to either antibody . Taken together , these results confirm that L403F and L438V modulate sensitivity to neutralization by both HC33 . 4 and AR4A . All resistance polymorphisms except V720I that had been validated by introduction into neutralization sensitive E1E2 variants were reverted in 2–5 distinct E1E2 variants where they were naturally present ( Fig 6 ) . Mutated E1E2 variants and corresponding WT variants were used to produce HCVpp , which were tested for neutralization by HC33 . 4 ( Fig 6A ) or AR4A ( Fig 6B ) . Three of four polymorphisms that showed an effect on HC33 . 4 when introduced into neutralization sensitive E1E2 variants also showed a significant effect when reverted in E1E2 variants where they were already naturally present . Mutation of M408 to the known HC33 . 4 binding residue , K , resulted in significantly increased sensitivity to HC33 . 4 neutralization ( Wild type mean Fu 0 . 75 vs . M408K Fu 0 . 10 , p = 0 . 001 ) . Mutation of F403 to L also increased sensitivity to HC33 . 4 . Mutation of V438 to L also conferred a small but significant increase in HC33 . 4 sensitivity ( Wild type mean Fu 0 . 43 vs . V438L Fu 0 . 38 , p = 0 . 02 ) . This was unexpected because in other E1E2 variants , the reciprocal mutation of L438 to V had also conferred increased neutralization sensitivity , but the magnitude of the effect of V438L was very small ( mean Fu fold change of 0 . 9 ) relative to the magnitude of the effect of L438V ( mean Fu fold change of 0 . 2 ) Two of three polymorphisms that showed an effect on AR4A when introduced into neutralization sensitive E1E2 variants also showed a significant effect when reverted in E1E2 variants where they were already naturally present . Most notably , mutation of F403 to L and mutation of V438 to L conferred increased sensitivity to AR4A , just as they had for HC33 . 4 . Taken together , these results show that mutation of L403 to F and mutation of F403 to L confer reciprocal neutralization resistance and sensitivity effects on both HC33 . 4 and AR4A , while mutation of L438 to V in some E1E2 variants and V438 to L in others confers increased sensitivity to neutralization by both HC33 . 4 and AR4A . To measure the magnitude of the effect of L403F and L438V mutations on neutralization sensitivity , we measured neutralization of wild type 1a154 ( H77 ) , 1a154_L438V , and 1a154_L403F HCVpp by serial dilutions of HC33 . 4 and AR4A ( Fig 7A ) . As expected , the 1a154_L438V HCVpp variant was most sensitive to neutralization . Wild type 1a154 HCVpp showed a 3-fold increase in IC50 relative to 1a154_L438V for both antibodies , and 1a154_L403F HCVpp showed a 24-fold increase in IC50 relative to 1a154_L438V HCVpp for HC33 . 4 and a 90-fold increase in IC50 for AR4A . We also confirmed the resistance phenotypes of the mutations using replication competent cell culture virus ( HCVcc ) ( Fig 7B ) . Wild type 1a154 , 1a154_L438V , or 1a154_L403F E1E2 genes were cloned into a J6/JFH-1 HCVcc genome lacking E1E2 [38] , and replication competent virus was produced from each chimeric strain . HCVcc neutralization results mirrored those observed with HCVpp very closely , with 1a154_L438V most sensitive to neutralization by each bNAb , wild type 1a154 8-fold more resistant to HC33 . 4 and 7-fold more resistant to AR4A , and 1a154_L403F 40-fold more resistant to HC33 . 4 and 24-fold more resistant to AR4A . To understand whether these changes in neutralization sensitivity were mediated by changes in binding of the bNAbs to E1E2 , we performed an ELISA to measure binding of serial dilutions of the bNAbs to 1a154_L438V , 1a154 , and 1a154_L403F E1E2 proteins ( Fig 7C ) . No significant difference in binding of either bNAb to the E1E2 variants was observed , suggesting that differences in bNAb binding to E1E2 are likely not the mechanism by which L403F and L438V modulate resistance to neutralization by HC33 . 4 and AR4A . Using Chinese hamster ovary ( CHO ) cells stably expressing either human CD81 or human SR-B1 [33] , we investigated relative binding of wild type 1a154 ( H77 ) , 1a154_L403F , and 1a154_L438V E2 proteins to these HCV receptors . We used previously described methods to clone these variants without E1 and with replacement of their transmembrane domain with a histidine tag , allowing their expression as soluble E2 ( sE2 ) [39] . Serial dilutions of these soluble proteins were incubated with CD81-CHO , SR-B1-CHO , or wild type CHO cells , then labeled with anti-HIS and fluorescent secondary antibodies to allow detection of binding of sE2 on the cell surface . We were able to quantitate dose-dependent binding of sE2 to both CD81 and SR-B1 using this technique . Fig 8A shows flow cytometry histogram plots of binding of serial dilutions of 1a154 sE2 to CD81-CHO cells , relative to background binding to wild type CHO cells without CD81 or SR-B1 . After normalizing for total sE2 protein input ( S3 Fig ) , we compared binding of serial dilutions of 1a154 , 1a154_L403F , and 1a154_L438V sE2 proteins to SR-B1 and CD81 ( Fig 8B ) . Remarkably , we saw a clear increase in binding of 1a154_L403F to SR-B1 relative to binding of wild type 1a154 , and we saw a decrease in SR-B1 binding of 1a154_L438V , matching the hierarchy of neutralization resistance of these E2 variants . In comparing binding of the same variants to CD81 , we observed a small decrease in binding of 1a154_L403F relative to 1a154 , and a large decrease in binding of 1a154_L438V , confirming that the differences between 1a154 and 1a154_L403F binding to SR-B1 are not likely due to differences in protein input . We next compared binding of a matched , fixed concentration of 1a154 and 1a154_L403F sE2 to SR-B1 and CD81 in the presence of increasing concentrations of nonspecific IgG or HC33 . 4 ( Fig 8C ) . The 1a154_L438V sE2 variant did not have high enough baseline binding to allow accurate measurement of percent inhibition by HC33 . 4 , and we were also not able to study AR4A in this manner because it requires both E1 and E2 for binding . HC33 . 4 reduced binding of 1a154 and 1a154_L403F variants to both SR-B1 and CD81 in a dose-dependent manner . It is not surprising that HC33 . 4 inhibits both SR-B1 and CD81 binding , since a prior study of HC33 . 4-like antibodies showed that some could block binding to both receptors [33] . The concentrations of HC33 . 4 inhibiting 50% of binding to SR-B1 or CD81 ( IC50 values ) of 1a154 and 1a154_L403F sE2 were nearly identical , suggesting that the differing affinities of these proteins for SR-B1 and CD81 did not alter the percent binding inhibition of each by equivalent concentrations of mAb . Unlike HCVpp in neutralization assays , the sE2 variants are normalized for protein concentration , so it is also informative to consider absolute sE2 binding in the presence of mAb . To determine whether modulation of SR-B1 binding could mediate mAb neutralization resistance , we analyzed absolute SR-B1 binding of a fixed concentration of sE2 in the presence of varying concentrations of HC33 . 4 ( Fig 8D ) , and binding of varying concentrations of sE2 in the presence of a fixed concentration of HC33 . 4 ( Fig 8E ) . Comparison of sE2 binding of 1a154 and 1a154_L403F in the presence of increasing concentrations of HC33 . 4 ( Fig 8D ) , showed that 1a154_L403F sE2 bound more SR-B1 than an equivalent concentration of 1a154 sE2 at inhibitory but non-saturating concentrations of HC33 . 4 . We also measured binding of serial dilutions of 1a154 , 1a154_L403F , and 1a154_L438V sE2 proteins to SR-B1 after preincubation with a fixed concentration of HC33 . 4 ( 40 μg/mL ) ( Fig 8E ) . As observed in the absence of antibody , multiple concentrations of 1a154_L403F sE2 incubated with a high concentration of HC33 . 4 showed greater binding to SR-B1 relative to 1a154 sE2 , and 1a154_L438V sE2 showed consistently less binding . Together , these results suggest that , in the presence of inhibitory but non-saturating concentrations of HC33 . 4 , 1a154_L403F sE2 binds more SR-B1 than an equivalent concentration of 1a154 sE2 , and 1a154_L438V sE2 binds less , providing a likely mechanism by which these polymorphisms could confer increased resistance or sensitivity , respectively , to mAbs whose mechanism of neutralization is interference with the E2-SR-B1 interaction .
We have developed a high-throughput platform for measurement of neutralizing antibody breadth and prediction of HCV neutralizing antibody resistance polymorphisms . Despite the relative conservation of HC33 . 4 and AR4A binding epitopes , with 100% conservation of known AR4A binding residues across the panel , we identified E1E2 variants with resistance to one or both bNAbs . We also identified amino acid polymorphisms in E2 conferring resistance to each bNAb individually , as well as polymorphisms outside of both binding epitopes that modulate resistance to both bNAbs . We determined that two of these polymorphisms , L403F and L438V , modulate resistance of both HCVpp and HCVcc to both HC33 . 4 and AR4A . These mutations increase or reduce E2 binding to SR-B1 , identifying a novel mechanism of broad bNAb resistance . It is interesting that HC33 . 4 IC50 values calculated from inhibition of binding of 1a154 and 1a154_L403F to SR-B1 were nearly equivalent , despite the apparent differences in affinity of the two sE2 variants for SR-B1 ( Fig 8C ) . This could be a limitation in the sensitivity of the binding assay , or alternatively could suggest that HC33 . 4 binding affinity for sE2 is significantly higher than the affinity of even the 1a154_L403F-SR-B1 interaction . These binding inhibition IC50 values were higher than the neutralization IC50 values measured for the same variants , likely due to differences in the assays , such as the amount of E2-receptor interaction necessary to generate a detectable signal above background . We show that , despite the equivalent binding inhibition IC50 values of 1a154 and 1a154_L403F , differences in sE2 binding to SR-B1 are a likely mechanism of neutralization resistance , since the neutralization resistant variant , 1a154_L403F , binds more SR-B1 than the same amount of 1a154 sE2 in the presence of non-saturating concentrations of HC33 . 4 ( Fig 8D and 8E ) . As our ability to query larger sets of naturally occurring HCV isolates for their sensitivity to bNAbs increases , so does our understanding of determinants of bNAb resistance—a key barrier to developing an effective prophylactic vaccine against HCV . In a previous report , we found that bNAbs cluster into functional groups with respect to the HCV variants that they neutralize most and least potently . This clustering of bNAbs is determined at least in part by shared or overlapping binding epitopes , but we and others have shown that polymorphisms distant from known binding epitopes can also confer bNAb resistance [20 , 22 , 24] . This study provides evidence that these extra-epitopic polymorphisms play an important role in neutralization resistance of natural E1E2 isolates . Mutations arising within mAb binding epitopes tend to be an antibody-specific resistance mechanism , and cannot confer resistance to bNAbs with epitopes that are 100% conserved . Here we describe a novel mechanism that can confer resistance to multiple anti-HCV bNAbs , even if the bNAb binding epitopes are completely intact , by modulating E2 binding to SR-B1 . To our knowledge , L403F and L438V are the first examples of a naturally-occurring mutations that confer resistance or sensitivity to bNAbs by this mechanism . L438 falls near the CD81 binding site of E2 [40] , which is consistent with our finding that mutation at this site reduced sE2 binding to CD81 , possibly also contributing to the increased bNAb sensitivity of L438V mutants . Introduction of L438V significantly decreased E1E2 fitness to mediate HCVpp or HCVcc entry into hepatoma cells ( S5 Fig ) , which is consistent with the observed reduction in binding of 1a154_L438V sE2 to CD81 and SR-B1 . We found that L403F increased binding to SR-B1 but decreased binding to CD81 . Notably , despite these opposing binding effects , we observed a net increase in E1E2 resistance to neutralization by HC33 . 4 and AR4A after introducing this mutation . The effect of L403F on SR-B1 binding may be dominant over the CD81-binding effect because the interaction of E2 with SR-B1 most likely occurs before binding of CD81 during HCV entry [41 , 42] , or because of differences in relative expression of SR-B1 and CD81 on the surface of hepatocytes . This warrants further study , as it has potentially interesting implications for strategies to inhibit HCV entry with antibodies or small molecules . The binding epitope of HC33 . 4 has been mapped in prior studies , and L403 and L438 were not found to be binding residues [28] . The binding epitope of AR4A is less clearly defined , but L403 and L438 were also not among probable AR4A binding residues [26] . Notably , L438 was identified as a contact residue for mAb AR3C in the crystal structure of AR3C/strain H77 E2 described by Kong et al [39] , and another study showed that AR3C and AR4A do not compete for binding to E2 [26] , Together , these data are all consistent with our finding that L403 and L438 are extra-epitopic for HC33 . 4 and AR4A . Recent crystallization of the E2 protein core in complex with a bNAb has been informative [39] . However , large deletions in E2 to facilitate crystallization preclude analysis of many bNAb epitopes , including the HC33 . 4 and AR4A epitopes . Given the difficulty and limitations of co-crystallizing HCV bNAbs with HCV E2 , much of what we know about bNAb-E1E2 interactions will need to be inferred by a comprehensive approach including binding studies with alanine-scanning mutants as well as binding competition assays . This study shows the utility of an additional , complementary approach that can be used to measure neutralizing breadth , group functionally similar bNAbs , and identify bNAb resistance polymorphisms that may fall within or outside of known binding epitopes . These data are particularly relevant given studies in animal models suggesting that combinations of bNAbs may be necessary to provide sterilizing protection against HCV infection [43 , 44] . Based on their distinct binding epitopes , it would have been reasonable to assume that neutralizing breadth of bNAbs like HC33 . 4 and AR4A would be greater if they were used in combination . That may still be true , but this study shows that an unexpectedly high proportion of HCV variants with resistance to one bNAb may also have resistance to the other , which could reduce the efficacy of this bNAb combination . While we were able to identify polymorphisms modulating resistance to multiple bNAbs , there were limitations to the study design and approach . We only sampled 97% of the naturally occurring polymorphisms that exist at a ≥5% threshold in a large set of Genbank HCV genotype 1 sequences . When the frequency threshold for polymorphism prevalence is reduced to ≥1% , the coverage is reduced to 78% . While SNAPR correctly predicted the 438 locus as a modulator of HC33 . 4 and AR4A resistance , it incorrectly predicted that L438V would confer bNAb resistance , when in fact this mutation confers increased sensitivity to both bNAbs . The error may have arisen due to genetic linkage between the 438 locus and other resistance-determining loci in E1E2 , since the LASSO algorithm , which adjusts for linkage , did not predict that the 438 locus is a determinant of neutralization sensitivity . Notably , the SNAPR algorithm accurately predicted position 408 , a known binding residue , as a mediator of HC33 . 4 resistance , while LASSO did not . Further testing would be necessary to more clearly determine whether SNAPR , LASSO , or a combination of the two methods would be best suited to predict resistance polymorphisms in HCV E1E2 . While the effects of the L403F and L438V polymorphisms are significant , they are small in magnitude relative to the large variation in neutralization sensitivity observed between natural isolates , suggesting that combinations of polymorphisms likely play an important role in bNAb resistance . Even larger , more diverse E1E2 panels are required to reduce confounding from genetic linkage , to probe rarely occurring natural polymorphisms , and to better define the influence of combinations of polymorphisms on neutralization resistance . In conclusion , we have developed a large , diverse HCV neutralization panel and a statistical approach using amino acid sequence variation and neutralization sensitivity to identify bNAb resistance polymorphisms in E1E2 . Despite conservation of HC33 . 4 and AR4A binding epitopes across the E1E2 panel , we discovered variants with resistance to both bNAbs , identifying polymorphisms conferring resistance to each bNAb individually , as well as polymorphisms outside of either binding epitope that modulate resistance to both bNAbs . We determined that two of these polymorphisms , L403F and L438V , modulate resistance to HC33 . 4 by increasing or decreasing E2 binding to SR-B1 , which is a novel mechanism of bNAb resistance . This study highlights the important contribution of extra-epitopic polymorphisms to bNAb resistance , presenting a potential mechanism by which HCV might persist even in the face of an antibody response targeting multiple conserved epitopes . This diverse viral panel and novel computational pipeline are broadly applicable to future studies to define neutralizing antibody breadth , identify functionally-related bNAbs , and define mechanisms of bNAb resistance .
HC33 . 4 [29] was a gift of Steven Foung ( Stanford University School of Medicine , Stanford , California . AR4A [27] was a gift from Mansun Law ( The Scripps Research Institute , La Jolla , California , USA ) . Plasma samples obtained from HCV infected subjects in the BBAASH cohort [15 , 16 , 45] , Irish Anti-D cohort [46] , and Swan Project [47] were used to construct a library of genotype 1 E1E2-expressing lentiviral pseudoparticles using a high-throughput production and screening approach . The E1E2 region was PCR amplified from cDNA reverse transcribed from viral RNA purified from subject plasma and cloned into the expression vector pcDNA3 . 2/V5/Dest ( Invitrogen ) using Gateway technology in a one-tube BP/LR reaction , as previously described [19] . HCVpp were produced by lipofectamine-mediated transfection of HCV E1E2 and pNL4-3 . Luc . R-E- plasmids into HEK293T cells ( ATCC ) in 96-well plates as previously described [19] . Hep3B cells were exposed to transfected 293T supernatants in order to test for the presence of infectious HCVpp , as previously described [19] . HCVpp were considered infectious in the initial screen if infection of Hep3B cells ( ATCC ) in a 96 well format resulted in greater than 200 , 000 RLU of luciferase activity , which is >10X typical values obtained from infection with mock pseudoparticles . E1E2 variants included in the panel differed by at least one amino acid from every other clone contained in the library . Envelopes that displayed enhanced infection in the presence of neutralizing bNAbs ( Fu >1 . 2 with either bNAb ) were not included in the analysis or in the description of library meta-data as these values most often resulted from HCVpp with poor infectivity . 18 of the 113 E1E2 variants in the final panel were previously described: 1a38 , 1a53 , 1a72 , 1a80 , 1a114 , 1a123 , 1a129 , 1a142 , 1a154 , 1a157 , 1b09 , 1b14 , 1b20 , 1b34 , 1b38 , 1b52 [19] and 1a116 , 1b21 [24] . Sequences of the remaining 95 E1E2 clones have been submitted to GenBank accession numbers KY565136—KY565230 . Sanger sequencing of the entire length of the cloned E1E2 region was performed . Amino acid sequences from a nucleic acid MUSCLE alignment [48] were used to build a phylogenetic tree . Initial tree ( s ) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model , and then selecting the topology with superior log likelihood value [49] . All trees are drawn to scale , with branch lengths measured in the number of substitutions per site , and all positions containing gaps and missing data were eliminated . Evolutionary analyses were conducted in MEGA6 [50] . Sequence logos were generated using VisSPAv1 . 6 ( http://sray . med . som . jhmi . edu/SCRoftware/VisSPA/ ) . Polymorphisms associated with bNAb resistance or sensitivity were introduced into at least two independent E1E2 clones . Mutants were created using the QuikChange Lightning Multi Site-Directed Mutagenesis Kit ( Agilent ) and Sanger sequencing was performed to verify that all mutants differed from parent clones at only the desired locus . For infectivity and neutralization testing of the panel of 113 HCVpp , 2 , 000 Hep3B cells per well were plated in 384-well white flat bottom tissue culture plates . For infectivity and neutralization testing of site-directed mutants 8 , 000 Hep3B cells per well were plated in flat bottom 96-well tissue culture plates and incubated overnight in a humidified CO2 incubator at 37°C . Media was removed from the cells the following day and replaced with 50μL of culture supernatant containing HCVpp ( 96-well plates ) or 25μL of HCVpp supernatant ( 384 well plates ) . The plates were placed in a CO2 incubator at 37°C for 5 hours , after which the HCVpp were removed and replaced with 100μL of phenol-free Hep3B media ( 96-well plates ) or 50μL of phenol-free Hep3B media ( 384-well plates ) and incubated for 72 hours at 37°C . For 96-well plate infections , media was removed from the cells and 50 μL of 1x Cell Culture Lysis Reagent ( Promega ) added and left to incubate for >5 minutes then 45μL from each well were then transferred to a white , low-luminescence 96-well plate ( Berthold ) and read in a Berthold Luminometer ( Berthold Technologies Centro LB960 ) . Each sample was tested in duplicate . For 384-well plate infections , cells were lysed directly in the culture plate with 25μL of lysis buffer and luciferase activity measured using a BMG Labtech Fluostar Omega luminometer . A mock pseudoparticle ( no envelope ) was used as a negative control . The same procedure used to measure infectivity was employed , except that HCVpp were incubated with 10μg/mL of bNAb or serial dilutions of bNAb at 37°C for 1 hour prior to addition to the Hep3B target cells . Infections were performed in duplicate with the test antibody and nonspecific human IgG , the negative control . Murine Leukemia Virus ( MLV ) was used as a control for nonspecific neutralization . Fraction Unaffected ( Fu ) was calculated as RLU in the presence of test antibody/RLU in the presence of nonspecific human IgG . Each replicate RLUmAb value was divided by the average of two replicate RLUIgG values . % Neutralization was calculated as ( 1-Fu ) x100% . To estimate the precision of Fu neutralization measurements , we compared Fu neutralization values of HCVpp measured in independent replicate experiments and observed a highly significant correlation ( S6 Fig ) Amino acid alignments were assembled as described in the phylogenetic analysis . To account for the uneven number of the infectious clones per human donor , neutralization values and corresponding E1E2 sequences were selected at random from each human donor and added to the initial data set until all donors were represented by an equal number of isolates . For each position in the alignment , HCVpp were grouped according to the amino acid encoded at that locus . The amino acid at each position associated with greatest bNAb sensitivity was identified by comparing median Fu values of the HCVpp in each amino acid group . Fu values for HCVpp in the most sensitive amino acid group were then compared to the Fu values of HCVpp with any other amino acid at the same position using a Wilcoxon rank-sum test . The resulting p-value is the SNAPR-value associated with that locus . Analysis was implemented using code developed for R , which is freely available upon request . The LASSO combines a prediction error term ( the least squares error ) with a model complexity penalty , which regularizes the model coefficients to perform variable selection and prevent over-fitting [37] . The two replicate fraction unaffected values were square root transformed , and the mean of these used as the outcome variable , which the model aims to explain using the amino acid sequence . The amino acids at each site were our explanatory variables , and these were encoded as indicator variables . Leave-one-out cross validation was used to select the optimal LASSO penalty ( with the lowest mean-squared error ) , which gives the coefficients for each amino acid at each site , which were used as the LASSO results throughout . This was performed in R using the Lasso implementation from the package "glmnet" ( https://cran . r-project . org/web/packages/glmnet/ ) . BNAb binding to E1E2 was quantitated using an enzyme-linked immuosorbent assay ( ELISA ) as previously described [51] . 293T cells were transfected with E1E2 expression constructs . 48 hours post-transfection cell lysates were harvested . Plates were coated with 500ng Galanthus nivalis ( GNA ) lectin ( Sigma-Aldrich ) and blocked with phosphate-buffered saline containing 0 . 5% Tween 20 , 1% non-fat dry milk , and 1% goat serum . E1E2 cell lysates were added . BNAbs were assayed in duplicate 2 . 5-fold serial dilutions , starting at 10 μg/ml . Binding was detected using HRP-conjuagated anti-human IgG secondary antibody ( BD Pharmingen no . 555788 ) . For binding competitions ELISA , E1E2 protein-coated ELISA wells were pre-incubated with 20 μg/ml of either HC33 . 4 or AR4A ( blocking bNAbs ) , followed by biotinylated HC33 . 4 or AR4A at a concentration selected to give 50% of maximal binding ( EC50 ) , with binding of the biotinylated bNAb detected using streptavidin-horseradish peroxidase . A ratio of binding of each biotinylated bNAb in the presence of blocking bNAb divided by binding in the absence of blocking bNAb was calculated . HCVcc chimeras were generated as previously described [38 , 52] . Briefly , after digestion of the HCVcc backbone with AfeI ( New England Biolabs ) , 1a154 ( H77 strain ) , 1a154_L438V , and 1a154_L403F E1E2 genes amplified from library plasmids were inserted in frame using In-Fusion cloning ( Clontech ) . 2μg of plasmid DNA was linearized using XbaI ( New England Biolabs ) then used for in vitro RNA transcription using the T7 MEGAscript kit ( Ambion ) . RNA clean-up was performed using RNeasy mini kit ( Qiagen ) , quantified using a NanoDrop 1000 spectrophotometer ( Thermo Scientific ) , and stored at –80°C . 10μg of RNA was transfected into 1 . 8e6 Huh7 . 5 . 1 cells ( a gift of Charles Rice , The Rockefeller University , New York City , New York , USA ) using Nucleofector Kit T ( Amaxa ) and plated in a 6-cm plate . Transfection supernatants were collected 4–11 days later and stored at -80°C . Supernatants were titered by serial dilution and infection of Huh7 . 5 . 1 cells . HCVcc neutralization assays were performed in triplicate as described elsewhere [38 , 52] . Briefly , human hepatoma Huh7 . 5 . 1 cells were maintained in DMEM supplemented with 10% fetal bovine serum and nonessential amino acids . 10 , 000 Huh7 . 5 . 1 cells per well were plated in flat bottom 96 well tissue culture plates and incubated overnight at 37°C . The following day , HCVcc were mixed with mAb ( 3-fold dilutions started at 50μg/mL ) then incubated at 37°C for 1 hour . Media was removed from the cells and replaced with 50 μL of HCVcc/antibody mixture . The plates were placed in a CO2 incubator at 37°C overnight , after which the HCVcc were removed and replaced with 100μL of Huh7 . 5 . 1 media and incubated for 48 hours at 37°C . Medium was then removed and cells were fixed with 4% formaldehyde then stained for HCV NS5A using primary anti-NS5A antibody 9E10 ( a gift of Charles Rice , The Rockefeller University , New York City , New York , USA ) at 1:2 , 000 dilution for 1 hour at room temperature . Cells were washed twice with PBS and stained using secondary antibody Alexa Daylight 488–conjugated goat anti-mouse IgG ( Life Technologies ) at 1:500 dilution for 1 hour at room temperature . Cells were washed twice in PBS and then stored in 100μl PBS at 4°C . Images were acquired and spot forming units were counted for infection in the presence of mAb ( HCVccSFUtest ) or PBS alone ( HCVccSFUcontrol ) using an AID iSpot Reader Spectrum operating AID ELISpot Reader version 7 . 0 . Percent neutralization was calculated as 100% x [1- ( HCVccSFUtest /HCVccSFUcontrol ) ] . A truncated , soluble form of the 1a154 ( H77 ) strain E2 ectodomain ( sE2 ) that retains antigenticity and function as previously described [39] , encompassing residues 384–645 , was cloned into a mammalian expression vector ( phCMV3_Ig Kappa_HIS , a gift of Leopold Kong , The Scripps Research Institute , La Jolla , California , USA ) from plasmids containing H77 structural proteins . The vector allows expression of E2 protein with a C-terminal His tag as well as an N-terminal murine Ig Kappa leader signal for efficient protein secretion . H77 mutants , L403F and L438V , were created as described above and verified by Sanger sequencing . Each E2 construct was co-transfected with pAdvantage ( Promega ) into HEK293T cells and incubated for 72 hours at 37°C . Supernatant was collected at 48 and 72 hours , passed through a 0 . 2μm filter , and concentrated using a regenerated cellulose centrifugal filter with a 10kDa cutoff ( Amicon ) . Serial 6 . 25 fold dilutions of each sE2 supernatant beginning with a 1 to 40 dilution were immobilized onto ELISA wells pre-coated with 500 ng Galanthus nivalis lectin ( Sigma-Aldrich ) and blocked with PBS containing 0 . 5% Tween 20 , 1% nonfat dry milk , and 1% goat serum . Wells were probed with 0 . 5 μg of a mouse monoclonal anti-6x His-tag antibody ( ab18184 , Abcam ) and quantified using a HRP-conjugated goat anti-mouse IgG secondary antibody ( ab97265 , Abcam ) . The EC50 for each sE2 construct was calculated by nonlinear regression analysis and fold differences in EC50 used to normalize sE2 concentration in subsequent experiments . CHO-CD81 and CHO-SR-B1 binding experiments were carried out as previously described [33] . CHO cells expressing recombinant human CD81 or SR-B1 ( a gift from Dr . Matthew Evans , Icahn School of Medicine , Mount Sinai , New York ) were detached using PBS supplemented with 4mM EDTA and 10% FBS and washed in PBS containing 1% BSA . Cells ( 2E+05 ) were pelleted in a 96-well u-bottom plate and re-suspended in 2 fold serial dilutions of each sE2 construct ( H77 , L403F , and L438V ) . Following 30 minutes incubation on ice the cells were washed twice and incubated with 0 . 5 ug of anti-6x His-tag antibody for another 20 minutes on ice . The cells were then washed again , re-suspended in an Alexa fluor 647-labeled goat anti-mouse IgG secondary antibody , and incubated on ice for 15 minutes . After a final wash , the cells were fixed with 1% paraformaldehyde and analyzed on a LSRII ( Becton-Dickinson ) using FloJo software ( Tree Star ) . For mAb binding-inhibition experiments , sE2 was normalized for protein concentration , then diluted 1:32 for SR-B1 binding , 1:16 for CD81 binding . sE2 was preincubated with serial dilutions of HC33 . 4 or nonspecific human IgG , then used to stain CHO cells as above . | Generation of an immune response that is protective against a wide variety of naturally occurring isolates is necessary for vaccines against highly variable viruses like hepatitis C virus ( HCV ) . Two broadly neutralizing human monoclonal antibodies , HC33 . 4 and AR4A , neutralize multiple highly divergent HCV isolates , raising hope that a vaccine against HCV is possible . Previous reports have defined the distinct , highly conserved sites on the viral envelope proteins where these antibodies bind . However , little is known about naturally occurring variation in sensitivity of different HCV isolates to these antibodies . We developed a high throughput assay and computational algorithm to evaluate over 100 naturally occurring HCV variants for their sensitivity to these two antibodies , identifying several resistance polymorphisms to each antibody which do not fall within their mapped binding sites . Furthermore , two of these polymorphisms modulate resistance to both antibodies by enhancing or reducing envelope protein binding to HCV co-receptor scavenger receptor B1 ( SR-B1 ) . By developing this broadly applicable platform , we have shown the important neutralization resistance conferred by changes distant from antibody binding sites , presenting a potential mechanism by which HCV might persist even in the face of an antibody response targeting multiple conserved sites . |
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During cell entry of flaviviruses , low endosomal pH triggers the rearrangement of the viral surface glycoproteins to a fusion-active state that allows the release of the infectious RNA into the cytoplasm . In this work , West Nile virus was complexed with Fab fragments of the neutralizing mAb E16 and was subsequently exposed to low pH , trapping the virions in a pre-fusion intermediate state . The structure of the complex was studied by cryo-electron microscopy and provides the first structural glimpse of a flavivirus fusion intermediate near physiological conditions . A radial expansion of the outer protein layer of the virion was observed compared to the structure at pH 8 . The resulting ∼60 Å-wide shell of low density between lipid bilayer and outer protein layer is likely traversed by the stem region of the E glycoprotein . By using antibody fragments , we have captured a structural intermediate of a virus that likely occurs during cell entry . The trapping of structural transition states by antibody fragments will be applicable for other processes in the flavivirus life cycle and delineating other cellular events that involve conformational rearrangements .
West Nile Virus ( WNV ) , a member of the Flaviviridae family , is closely related to other arthropod-borne and medically relevant viruses , such as dengue , tick-borne , Japanese encephalitis , and yellow fever viruses . Flaviviruses are enveloped viruses that enter host cells by receptor-mediated endocytosis . The outer surface of a flavivirus is formed by an icosahedral scaffold of 90 envelope glycoprotein ( E ) homodimers [1] . The E glycoprotein of flaviviruses has three domains , DI , DII and DIII , with a flexible ‘hinge’ between DI and DII [2]–[4] . An ∼50 amino acid , partially alpha-helical “stem” region connects the E ectodomain with its C-terminal transmembrane anchor . In the mature virion , the stem region lies essentially flat against the viral membrane [5] . Conformational and oligomeric reorganization of E into a fusion-active state occurs during cell entry upon exposure of the virion to the mildly acidic pH in the early endosomes , allowing release of the RNA genome into the cytoplasm . Based on the pseudo-atomic resolution structure of the mature virus at neutral pH [1] and the crystal structure of the solubilized E ectodomain post-fusion trimer [6] , [7] , mechanistic proposals have been interpolated between these end states as to the rearrangement processes that lead to exposure of the fusion loop on the distal end of the E ectodomain [1] , [7]–[9] . From experiments at alkaline pH , a lipid-binding monomeric intermediate form of E has been suggested to precede trimerization [10] . However , no structural intermediates of the fusion process have been captured to date under physiological conditions . The E glycoprotein is the principal antigen that elicits neutralizing antibodies against flaviviruses [11] . The monoclonal antibody ( mAb ) E16 neutralizes WNV primarily at a post-attachment stage , probably by interfering with the pH-induced reorganization of E prior to fusion [12]–[15] . In the present study , cryo-electron microscopy ( cryoEM ) was used to examine WNV complexes with E16 antigen binding fragments ( Fab ) after exposure to low pH . The virions were trapped irreversibly as a pre-fusion intermediate with the E glycoprotein/Fab layer expanded radially outwards leaving an ∼60 Å-wide gap between the lipid bilayer and the outer protein shell . These structural data suggest that the low pH-triggered formation of fusion-active E homotrimers on the viral surface is preceded by the outward extension of the E stem region .
Based on crystallographic data and cryoEM reconstructions at neutral pH , we had proposed that E16 neutralizes WNV infection by locking the viral particle in a pre-fusion intermediate state during the structural reorganization of surface proteins that is triggered by endosomal acidification and required for fusion [13] , [14] . To evaluate this hypothesis and capture a fusion intermediate , WNV was complexed with E16 Fab ( Figure 1A ) and shifted from pH 8 to pH 6 in vitro , mimicking the low pH environment during cell entry ( Figures 2A and 2B ) . The virions were trapped irreversibly as a pre-fusion intermediate with the E glycoprotein/Fab layer expanded radially outwards by ∼60 Å ( Figures 2B and 3 ) . In contrast , the outer radial limits of the nucleocapsid core and lipid bilayer were comparable to those of the mature virus at neutral pH . These results were substantiated when analyzing a complex of WNV with a single-chain antibody derivative ( scFv ) of E16 . At pH 8 , the WNV/scFv complex corroborated the E16 binding pattern observed for Fab fragments [14] ( Figure 1B ) . More importantly , exposure to pH 6 resulted in a radial density distribution similar to that of the low-pH structure of the WNV/Fab complex ( Figure 3 ) . The expanded outer protein layer of the low pH WNV/scFv complex was found to be thinner than in the Fab complex , consistent with scFv and Fab being external to E and scFv being only about half the size of a Fab fragment . Virion expansion was also observed when WNV was exposed to pH 6 in the absence of bound Fab molecules ( Figures 2C and 2D ) . However , the high degree of aggregation prevented detailed structural analysis . The reduction of particle aggregation at low pH in the presence of Fab is consistent with the hypothesis that E16 blocks the fusion process prior to exposure of the fusion loop [13]–[15] . The density distribution within the outer density shell was not sufficiently resolved to be interpreted in detail . This may be due to heterogeneity of the sample , for example the icosahedral symmetry could be partially lost during the low pH-induced rearrangement and/or the particles might have been captured at different stages of the radial expansion process ( Figure 4 ) . Nevertheless , the well-defined ∼60 Å-wide gap between the lipid bilayer and the outer protein layer is presumably traversed by the linearly extended E stem region ( Figures 3 and 5 ) . This distance is similar to the ∼58 Å length of the stem helices [4] , [5] and the ∼50–60 Å stem length predicted from the post-fusion E trimer structure [6] , [7] . The extension of the stem region as being an early event in the membrane fusion process was previously only speculated [7] , [16] . This conformational change gives the previously tightly packed E molecules greater lateral freedom for their rearrangement into fusion-active homotrimers . By using fragments of a neutralizing antibody , we captured a structural intermediate that likely occurs during cell entry when flaviviruses transit through the acidic pH environment of early endosomes . To our knowledge , this is the first structural data on a cell-entry intermediate state of a virion . The low pH-triggered formation of fusion-active E homotrimers on the viral surface is preceded by the outward extension of the stem region ( Figure 5 ) . The trapping of structural transition states by antibody fragments may be applicable in the search for other intermediates in the flavivirus life cycle as well as other dynamic processes .
WNV was propagated in Vero cells using MEM media supplemented with 10% fetal calf serum , non-essential amino acids and glutamine according to standard cell culture procedures . Confluent cells were infected with WNV ( New York 1999 ) at a multiplicity of infection of 0 . 5 in the presence of 2% fetal calf serum . Cell culture supernatant was harvested 30 h after infection and the virus was concentrated by polyethylene glycol precipitation using PEG 8 , 000 at a final concentration of 8% . Further purification was achieved by sedimentation through a 20% sucrose cushion , followed by density gradient centrifugation using a 10–30% tartrate step gradient ( 125 , 000×g for 2 . 5 h at 4°C ) or by tartrate gradient centrifugation only . The virus fraction was recovered from the gradient , transferred into NTE buffer ( 12 mM Tris-HCl , pH 8 . 0 , 120 mM NaCl , 1 mM EDTA ) , and concentrated using Amicon centrifugal filters ( Millipore , Billerica , MA , USA ) . The protein concentration and purity of the final virus preparations were estimated by SDS gel electrophoresis with Coomassie Blue-staining . Fab fragments of the mouse mAb E16 were generated after papain digestion and purified as described [14] . A plasmid encoding the mouse E16 mAb sequence [12] was used as template for the construction of the E16 scFv . Primers H9 ( 5′ CGAGCTAGCT GAGATCACAG TTCTCTCTAC 3′ ) and Igh283R ( 5′ gaaccgccac ctccagaacc gcctccacca ctagcTTTCA GCTCCAGCTT GGTCCCAGC 3′ ) were used to amplify the recombinant signal sequence and variable domain of the mouse E16 light chain ( VL ) , and primers Igh282F ( 5′ ggttctggag gtggcggttc tggcggtggt ggatctCAGG TTCAGCTGCA GCAGTCTGG 3′ ) and Igh284R ( 5′ tatcaaatgc ggccgcTGAG GAGACTGTGA GAGTGGTGCC 3′ ) were used to amplify the variable domain of the mouse E16 heavy chain gene sequence ( VH ) . Subsequently , the VL and VH sequences were linked together by overlapping PCR . The product was cloned using NheI and NotI sites into a pCI-neo vector , which contains C-myc and S-tag coding sequences . The mouse E16 scFv contains C-terminal C-myc and S-tags and was expressed in 293H cells . E16 scFv was purified sequentially by DIII ( amino acids 296–415 of WNV E protein ) -Sepharose affinity and size exclusion chromatography . Briefly , E16 scFv conditioned media was loaded directly onto a DIII-Sepharose column at a flow rate of ∼50 cm/h . The column was equilibrated and washed in phosphate buffered saline , pH 7 . 2 . The protein was eluted in 50 mM glycine , pH 3 , and neutralized immediately with 1 M Tris-HCl at pH 8 . 0 . E16 scFv was further purified by size exclusion chromatography ( Superdex 75 H/R 10/30 , GE Healthcare ) . The eluate was concentrated using a centrifugation type concentrator ( Vivaspin 20 , 10 kD molecular weight cutoff ) to less than 0 . 5 ml prior to loading the size exclusion column equilibrated in PBS . Samples were run at a linear velocity of 10 cm/h and the E16 scFv peak was collected , filtered ( 0 . 2 mm ) and stored at 4°C . Purified WNV particles were incubated with E16 Fab or E16 scFv in the presence of 100 mM NaCl at 4°C overnight using a ratio of ∼5 Fab or scFv fragments per E molecule . The pH of the sample was lowered to pH 6 ( “low pH” ) using MES-HAc buffer ( 0 . 1 M , pH 4 . 5 ) and incubated at room temperature for 5–15 min before flash-freezing . A portion of the low-pH sample was back-neutralized to pH 7 . 5 using 1 M HEPES buffer , pH 8 . 0 . Small aliquots of sample were applied to 400 mesh copper grids coated with a holey carbon film and rapidly frozen by plunging into an ethane slush . Micrographs of the frozen-hydrated samples were recorded on Kodak ( Rochester , NY ) SO-163 films with a CM300 FEG transmission electron microscope ( Philips , Eindhoven , The Netherlands ) at a total electron dose between 17 . 2 and 23 . 4 e−/Å2 . Image analysis and 3D image reconstructions were performed independently for each dataset . The structure of the WNV/scFv complex at pH 8 . 0 was determined as described previously for the WNV/Fab complex [14] . A total of 1 , 139 particles were selected from 53 micrographs ( defocus level range 2 . 74–1 . 35 µm ) and , of those particles , 783 images were used to calculate the final 3D reconstruction with an estimated resolution of 22 . 8 Å ( Figures 1B and 3A ) . The glycoprotein shell and the two membrane leaflets were clearly resolved , an indication of the good quality of the map . The cryoEM density map was deposited in the EM databank under accession number EMD-5115 . Reconstructions of the low pH complexes , assuming icosahedral symmetry , were computed from several independent datasets ( Table 1 ) as described previously [14] or using a modified version of XMIPP [17] . Starting models for orientation and center search were produced either by assigning random orientations to a subset of images or from best five- , three- and twofold rotational symmetry views using the program starticos of the EMAN package [18] . Iterative processing through search and reconstruction cycles was continued on low-pass filtered ( 15 Å ) images until no further improvement of the resulting map was achieved . The resolution of maps , estimated by comparing structure factors for the virus shell computed from two independent half-data sets , ranged from 30 to 50 Å at a Fourier shell correlation coefficient of 0 . 5 . For display , maps were computed using data to a resolution of 25 Å ( Figure 3 ) . Of all the low-pH reconstructions , only the maps of the WNV/scFv complex showed the separated leaflets of the lipid bilayer . The lateral density distribution in the expanded outer protein shell of the resulting low-pH structures varied depending on data set , initial model , processing parameters , and reconstruction program used , whereas the radial density distributions were consistent with one another . The lack of consistency of the density distribution in the outer protein layer may be due to heterogeneity of the sample , for example the icosahedral symmetry could be partially lost during the low pH-induced rearrangement and/or the particles might have been captured at different stages of the radial expansion process . To investigate the presence of the latter , radial averages of the particle images were produced and subsequently subjected to a reference-free classification procedure using the program startnrclasses of the EMAN package . The classification produced a total of 17 classes for the WNV/Fab complex at pH 6 . 0 , which could be combined into 4 classes ( IM-1 through 4 ) by visual inspection ( Figure 4 ) . Of 1 , 832 particles , 10 . 8% , 26 . 5% , 25 . 9% , and 36 . 9% were classified into classes IM-1 , IM-2 , IM-3 and IM-4 , respectively . These classes appeared to represent successive stages of a radial particle expansion . Fewer classes were observed for WNV/scFv at pH 6 . 0 , with the majority of images being combined into a class similar to IM-4 of the WNV/Fab complex ( data not shown ) . However , 3D image reconstructions of the individual classes did not lead to a consistent , interpretable density of the outer protein layer , suggesting that the radial expansion is accompanied by the destruction of icosahedral symmetry . | West Nile Virus ( WNV ) and other related viruses such as dengue virus enter their host cell by a process that involves fusion between the viral membrane and the membrane of cellular vesicles ( endosomes ) resulting in the release of the viral genome into the cytoplasm of the cell . This fusion event is initiated by low pH in the endosomes . Little is known regarding structural changes within the viral particle that render the viral surface proteins capable of fusion . In the present study , we used antibody fragments as a tool to trap virions in a pre-fusion intermediate state and examined these particles by cryo-electron microscopy . We showed that low pH triggered a radial displacement of the virion's external protein layer . The surface proteins moved away from the viral membrane , a shift made possible by the outward extension of a small alpha-helical region of the surface protein . The process gives the proteins greater sideways freedom for their reorganization into the fusion-active state . Our results provide a first structural glimpse into the low pH-induced conformational rearrangement of the flavivirus particle that occurs prior to fusion of viral and endosomal membranes . Such dissection of the fusion process highlights targets for the development of antiviral strategies . |
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Understanding the molecular pathways by which oncogenes drive cancerous cell growth , and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies . In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers , and define biomarkers predictive of pathway dependencies . A panel of 18 HER2+ ( ERBB2-amplified ) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer . PI3K and MAPK-pathway dependencies were quantified by measuring in vitro cell growth responses to combinations of AKT ( MK2206 ) and MEK ( GSK1120212; trametinib ) inhibitors , in the presence and absence of the ERBB3 ligand heregulin ( NRG1 ) . A combination of three protein measurements comprising the receptors EGFR , ERBB3 ( HER3 ) , and the cyclin-dependent kinase inhibitor p27 ( CDKN1B ) was found to accurately predict dependence on PI3K/AKT vs . MAPK/ERK signaling axes . Notably , this multivariate classifier outperformed the more intuitive and clinically employed metrics , such as expression of phospho-AKT and phospho-ERK , and PI3K pathway mutations ( PIK3CA , PTEN , and PIK3R1 ) . In both cell lines and primary patient samples , we observed consistent expression patterns of these biomarkers varies by cancer indication , such that ERBB3 and CDKN1B expression are relatively high in breast tumors while EGFR expression is relatively high in other indications . The predictability of the three protein biomarkers for differentiating PI3K/AKT vs . MAPK dependence in HER2+ cancers was confirmed using external datasets ( Project Achilles and GDSC ) , again out-performing clinically used genetic markers . Measurement of this minimal set of three protein biomarkers could thus inform treatment , and predict mechanisms of drug resistance in HER2+ cancers . More generally , our results show a single oncogenic transformation can have differing effects on cell signaling and growth , contingent upon the molecular and cellular context .
The elevated rate of proliferation and apoptotic-resistance characteristic of cancer cells depends on the activation of oncogenic signaling pathways . Such oncogenic pathway dependence creates molecular vulnerabilities , which can be exploited by targeted therapies . The effectiveness of such drugs however requires prospectively identifying which specific pathway ( s ) among many possibilities a given tumor is dependent on . This is a non-trivial task given the molecular and genetic heterogeneity of the disease , and the complexity of cell signaling networks . As a result , the majority of patients treated with targeted anti-cancer drugs fail to respond , and those that do often develop resistance over time [1] . The receptor tyrosine kinase HER2 is prototypic of oncogene addiction and a target for personalized anti-cancer therapy [2] . Overexpression of the receptor via amplification of the gene ERBB2 results in ligand-independent homo-dimerization and constitutive signaling [3] primarily through the phosphoinositide 3-kinase ( PI3K ) cascade [4 , 5] . The monoclonal antibody trastuzumab ( Herceptin; Genentech ) is standard of care therapy for HER2+ disease . While its use has significantly reduced mortality from HER2+ breast cancer since approval in 1998 [6] , many patients do not respond to treatment , particularly those with metastatic disease [7] . While subsequent HER2-targeted agents lapatinib , pertuzumab , and ado-trastuzumab-emtansine ( T-DM1 ) have improved survival as components of combination regimens , patients still progress on these therapies [8] . Mutational activation of the PI3K pathway ( via PIK3CA point mutations or PTEN deletions ) is known to mediate resistance to HER2-targeted therapies in both pre-clinical models and through retrospective analysis of clinical data [9] . Consequently , many small molecules targeting components of the PI3K cascades , including PI3K , AKT , and mTOR inhibitors , are currently undergoing clinical trials in combination with HER2 therapy [10] . The mitogen activated protein kinase ( MAPK ) signaling cascade is another pathway hyper-activated in a large number of cancers , and many small molecule inhibitors targeting its pathway components such as BRAF [11] and MEK [12] are approved or in clinical development . While critical for transducing signals emanating from oncogenes such as KRAS [13] and other receptor tyrosine kinases including ErbB-family receptors [14] , the pathway is not known to play a critical role in HER2-amplified cancers . On the other hand , the dual inhibition of PI3K and MAPK cascades can result in synergistic effects on cell proliferation and apoptosis in multiple cancer models [15] , including HER2+ breast cancer [16 , 17] , suggesting a potential role of MAPK signaling in the growth and survival of HER2+ cancers . Many combinations of targeted therapies are currently undergoing clinical evaluation for treating trastuzumab-refractory HER2+ disease , including small molecule inhibitors of HER2 , histone deacetylases ( HDAC ) , heat shock proteins ( HSP ) , insulin-line growth factor-1 receptor ( IGF-1R ) , and the HER2 binding partner ERBB3 [8] . However , the molecular and genetic determinants of sensitivity to these agents , let alone their combinations , remain largely obscure . Rational strategies to functionally classify tumors by dependence on oncogenic signaling pathways using minimal sets of biomarkers would thus be highly valuable in designing improved treatment strategies . The goal of this study was to characterize the dependence of HER2+ cancers on two such pathways , the canonical PI3K and MAPK cascades . Further , we explored whether such dependence can be predicted from phenotypic , proteomic , or genomic biomarkers that could ultimately be used to stratify patients and inform treatment strategies .
Amplified HER2 is known to signal predominantly through the PI3K/AKT pathway in breast cancers [4] . However , it is unclear whether different indications with this genomic alteration are wired similarly downstream of the receptor . Also , it is well established that the HER3 ligand heregulin ( HRG ) stimulates PI3K signaling through induction of HER2/HER3 hetero-dimerization [14] . Yet the degree to which this ligand affects MAPK signaling downstream of the ErbB receptors in different cellular contexts is unclear . To answer these questions , we examined whether dependence on the PI3K and MAPK signaling cascades varies across HER2+ cancers , both in the presence and absence of heregulin . Specifically , a panel of 18 HER2+ , but otherwise diverse cell lines was assembled , including breast , lung , gastric/esophageal , and ovarian cancer models . To characterize pathway dependence , each cell line was treated with a full 5x6 dose combination matrix of AKT and MEK inhibitors MK-2206 and GSK-1120212 ( trametinib ) . In vitro cell proliferation was then quantified via video microscopy over 96 hours . All cell lines tested displayed some sensitivity to at least one of the inhibitors used . To characterize the shapes of these response surfaces , quantitative logic-based models of cell growth kinetics were parameterized for each cell line . These phenomenological models characterize the balance of cell proliferation vs . cell death as functions of drug concentration ( and by extension , pathway dependence ) using combinations of quantitative logic gates . While nine alternate model variations were assessed ( S1 Table ) , a logical OR-Gate regulating cell survival as a function of active ( phosphorylated ) AKT and ERK was found to perform optimally across the panel ( S1–S3 Figs , S4 Fig ) . With only six parameters , it has the additional benefit of easy interpretation for comparison between cells . The six model parameters characterizing each cell consist of the maximal proliferation rate and cell death rates ( µMAX , δMAX ) , EC50 and Hill coefficients characterizing inhibitor dose-responses ( τ , k ) , and empirical weights toward PI3K and MAPK dependence ( wAKT , wERK ) ( see Materials and Methods and S2 Table ) . To develop a single metric of relative PI3K vs . MAPK pathway dependence , we define Pathway Bias as the normalized difference of the weighting parameters , where a value of 1 signifies complete PI3K-dependence ( wAKT >> wERK ) , 0 dual-dependence ( wAKT ~ wERK ) , and -1 complete MAPK-dependence ( wAKT << wERK ) . As shown in Fig 1A , 5/18 cell lines are classified as PI3K-dependent , 9/18 as MAPK-dependent , and unexpectedly 4/18 switch from PI3K to MAPK-dependence upon HRG stimulation . Error bars shown represent 95% confidence intervals ( 2 standard deviations ) from 100 parameter estimation runs . The Bias estimates are very well constrained , with a median coefficient of variation of 3 . 7% ( S5 Fig , part B ) . Only the SKOV3 cells would be considered undetermined ( 95% CI cross the axis ) , a result of the uniquely profound synergistic response of these cells to dual pathway inhibition ( S2 Fig ) . Our models implicitly assume the PI3K/AKT and MAPK pathways function independently ( i . e . no “cross-talk” ) , a shortcoming revealed by this specific case . Our primary motivation with the models were for data compression; reducing a 30-point response surface to three intuitive parameters ( rates of cell proliferation , cell death , and pathway bias ) . While complexities could be added to the current models to better capture this phenomenon with SKOV3 cells , this is beyond our primary motivation . Representative surface responses for each class are shown below in Fig 1B . Heregulin stimulation reduced the sensitivity of all cells to AKT inhibition , and correspondingly increased relative sensitivity to MEK inhibition , though the effect was much more pronounced in the switching class ( S5 Fig ) . Heregulin is known to desensitize cells to PI3K inhibitors [18] , however the converse increased relative sensitivity to MEK inhibition was unexpected . Overlaying information on basal proliferation , mutational status of three PI3K pathway key genes , and tissue source reveals some interesting patterns . First , consistent with the canonical classification of the MAPK pathway as mitogenic [14] , we observed that proliferation rate ( population doubling; PD ) correlates with MAPK-dependence . Mutational status of the PI3K pathway , while correlated with PI3K-dependence , is not a predictive classifier . That is , while PI3K-biased cells are enriched for PIK3CA , PIK3R1 , and PTEN mutations , some MAPK-dependent cells harbor PIK3CA mutations . These genetic metrics alone ( HER2 amplification and PI3K pathway mutations ) are thus insufficient for determining dependence of the tumor cells on PI3K vs . MAPK signaling . Most interestingly , our results show that , whereas breast cancers cover all three functional classes , all of the non-breast indications are MAPK-dependent . These are clinically significant findings , suggesting that current use of PI3K/AKT inhibitors in either unselected HER2+ cancer patients , or based on PIK3CA and PTEN mutations may be sub-optimal [8] , and some HER2+ patients may benefit from treatment MEK inhibitors . To better define the HER2+ patient sub-populations that could respond to PI3K/AKT or MEK inhibitors , we sought to identify molecular features of the cells that are predictive of dependency on the PI3K/AKT and MAPK signaling . Applying a targeted proteomics approach , the same panel of cell lines was profiled for ErbB receptor expression , total and phosphorylated forms of ERK and AKT , and the cell cycle regulator CDKN1B ( P27 ) using quantitative Luminex assays ( S6 Fig; raw data provided in S1 Data ) . The relationships between protein expression and cellular functional properties were then analyzed by computing Spearman’s rank correlation coefficients between protein measurements and the characteristic model parameters across the panel of cell lines ( Fig 2A ) . Some of the protein species were quantified with more than one detection antibody ( annotated a , b , c ) as a quality control check . The effect of heregulin stimulation was accounted for solely by its effect on cell signaling , as each cell line +/- heregulin was treated as two independent samples . For our analysis of the proteomic and cellular response data , we treated the same cell line +/- heregulin treatment as independent samples , thus producing 36 ( 18x2 ) samples . Functional relationships are revealed from these correlations; highly proliferative cells express increased levels of EGFR and are MAPK-signaling dependent , while slowly proliferating cells have higher levels of ERBB3 , CDKN1B ( p27 ) , and are PI3K-dependent . This is consistent with the canonical association of PI3K and MAPK pathways with regulating cell survival and proliferation , respectively [14] , but to our knowledge the first instance of this functional partition revealed in a purely data-driven manner . To further explore this relationship , we created a tenth model variant ( M10 ) which explicitly encodes proliferation and cell survival as separately regulated by MAPK and PI3K/AKT signaling , respectively ( see Methods ) . The model parameters were estimated from the surface response data as with the previous nine , and parameter estimates , simulations , and goodness of fit metrics ( MSE and AIC values ) are shown in S1 Data for all . In comparison to the chosen model ( M4 ) , this variant produced a better fit to the data in the majority ( 21/36 ) of samples , with average MSE slightly reduced by 0 . 5% . This further supports the observed relationship between cell proliferation and MAPK signaling , and cell survival with PI3K/AKT signaling . It is notable that neither the phosphorylated or total amounts of AKT or ERK proteins correlated with pathway dependence , confounding our naïve expectations . This non-intuitive finding is nevertheless consistent with previous studies examining biomarkers of PI3K/AKT and MEK inhibitor sensitivity in different cancer models [19–21] . To assess whether these molecular correlations were predictive , logistic regression models were parameterized to classify cells as PI3K vs . MAPK-dependent using different sets of input features ( protein expression , PI3K pathway mutational status , the phenotypic properties of proliferation rate and tissue origin [breast vs . non-breast] , or all features combined ) . Models were evaluated for predictive accuracy via leave-one-out cross validation ( LOOCV ) , and compared against 10 , 000 random permutations to assess statistical significance ( Fig 2B ) . Consistent with the correlation analyses , the most intuitive biomarkers , pAKT and pERK , were in fact no better predictors of Pathway Bias than random chance ( Accuracy = 50% , P = 0 . 75 ) . Accuracy of the “full” model ( containing all molecular , genetic , and phenotypic features ) , the protein-based model , and the genetic model were quite poor ( 67% , 72% , and 72% corresponding to P-values of 0 . 45 , 0 . 18 , and 0 . 055 ) . In contrast , model predictions based solely on “phenotype” ( tissue and proliferation rate ) were quite accurate ( 81% , P = 0 . 006 ) . To assess whether a subset of the protein measurements could provide a molecular explanation for this result , a model was built using the 3 proteins best correlated with pathway bias: EGFR , ERBB3 , and CDKN1B . The accuracy of this 3-biomarker model matched the phenotype-based predictions ( 81% , P = 0 . 0002 ) , and was statistically superior to all alternatives assessed ( S7 Fig ) . Combining the 3 protein biomarkers and phenotypic features did not improve accuracy , demonstrating redundancy between these measurements . That is , the observed association between tissue , proliferation rate , and pathway dependence can be explained solely by differential expression of these three proteins . Relative importance of the three protein features can be inferred from the normalized regression coefficients ( Fig 2C; raw data provided in S3 Table and S4 Table ) , in order of descending importance EGFR , ERBB3 , and CDKN1B . The ability of heregulin to shift pathway dependence from PI3K to MAPK is an unexpected observation , given that this growth factor is commonly associated with PI3K signaling . Consistent with this established role , pAKT ( pS473 and pT308 ) was induced in the majority of cells treated with the ligand ( S8 Fig ) . And while pAKT induction was greater in the PI3K-depdendent cells , this nor any other single protein change consistently correlated with pathway dependence switching . Including heregulin treatment as an additional discrete feature ( 1/0 ) in addition to the three protein biomarkers did not improve model accuracy ( 78% , P = 0 . 001 ) . The context-dependent Bias of the four cell lines which switch dependence was poorly predicted ( Fig 2D ) . In fact , the majority of the error in model ( 4 of 7 misclassifications ) is attributable to its inability to predict the switching behavior as the AU565 , HCC419 , and ZR751 cell lines are classified as PI3K-depdendent , and SKBR3 as MAPK-dependent , regardless of heregulin . The 3 protein biomarkers are thus able to accurately predict intrinsic dependence on PI3K/AKT vs . MAPK signaling . However , the shift in dependence induced by heregulin stimulation may occur through alternative mechanisms not accounted for in our panel of protein measurements . We observed that all non-breast cell lines are MAPK-dependent , and this is explained by expression of the three protein biomarkers ( Fig 3A ) . Thus , if cell lines are representative of the derivative disease , one would expect to see higher levels of EGFR and lower levels of ERBB3 and CDKN1B in non-breast indications vs . breast tumors . To test this hypothesis , we next queried available clinical gene expression data from The Cancer Genome Atlas ( TCGA ) for expression of EGFR , ERBB3 , and CDKN1B by indication . RNAseq profiling data ( V2 RSEM ) was extracted for all indications available , and classified as HER2+ vs . HER2- sub-classes for analyses based on ERBB2 gene expression ( S9 Fig ) . Consistent with molecular profiles of the cell lines , in all 10 indications with significant numbers of HER2+ samples , EGFR expression was relatively higher and/or ERBB3 and CDKN1B lower in in comparison to breast cancers ( Fig 3B ) , and these patterns hold for both the HER2+ and HER2- subsets . The expression patterns observed in our panel of immortalized cell lines are thus consistent with their derivative indications , suggesting that non-breast HER2+ cancers are likely to be dependent on MAPK rather than PI3K signaling . We speculate this may arise though differential hetero-dimerization , with ERBB2-EGFR complexes preferentially activating MAPK signaling , and ERBB2-ERBB3 PI3K/AKT signaling . To validate the functional utility of the three protein biomarkers ( EGFR , ERBB3 , and CDKN1B ) , we assessed whether they could predict pathway dependence using data from independent experiments . First , we sought to determine whether protein expression of EGFR , ERBB3 and CDKN1B , could be used to predict differential sensitivity to anti-cancer drugs in HER2+ cancers . An ELISA-based protein profiling dataset across 90 cancer cell lines [22] was intersected with the Genomics of Drug Sensitivity in Cancer database ( GDSC; 714 cell lines screened for sensitivity to 138 cancer drugs [23] ) . While CDKN1B measurements were not available , the relevance of EGFR , ERBB3 , pAKT , and pERK as predictive biomarkers were evaluated by focusing on the eight PI3K/AKT/MTOR inhibitors and four MEK inhibitors in the GDSC database ( S6 Table ) . Within the HER2hi population ( the 67th percentile , corresponding to 22 intersecting cell lines ) , differential sensitivity ( IC50s ) to each of the agents was evaluated between the biomarker-high vs . low group , defined by median cuts and statistical threshold of P < 0 . 1 ( rank-sum test ) . Consistent with the logistic model , EGFR was the best single marker , identifying 1 of 8 PI3K/AKT/MTOR and 2 of 4 MEK inhibitors . While neither pAKT nor pERK expression predicted differential sensitivity to any of the drugs ( S6 Table ) , examining combinations of the biomarkers , comparing EGFRloERBB3hi ( PI3K-bias ) vs . EGFRhi ( MAPK-bias ) yielded 2 of 8 PI3K/AKT/MTOR and 2 of 4 MEK inhibitors as differentially sensitive between the two groups ( Fig 4A ) . Based on this EGFRloERBB3hi ( PI3K-bias ) vs . EGFRhi ( MAPK-bias ) classification scheme , thirteen drugs were found to display differential sensitivities between the groups ( Fig 4A ) . This includes the AKT inhibitor used in our studies ( MK2206 ) , and the MEK inhibitors CI1040 and RDEA119 . The PI3K-predicted subset was also increasingly sensitive to AZD8055 , an agent targeting MTOR , a canonical downstream effector of this pathway . Examining properties of the cell lines in each cohort , it is notable that the PI3K-predicted subset was relatively enriched in breast cancers ( 55% vs . 0% ) , and PI3K pathway mutations ( PIK3CA , PTEN , or PIK3R1; 64% vs . 44% ) as compared to the MAPK-predicted subset ( Fig 4B ) , consistent with the characteristics of our internal 18 cell line panel . It was however unclear as to why only a subset of the AKT and MEK inhibitors came up in this analysis . To explore the reason for this discrepancy , we examined patterns of sensitivity to the drugs across all 714 cell lines in the database by computing pair-wise Spearman correlation coefficients between their IC50 values . As depicted in the correlation matrix in S11 Fig , sensitivity to inhibitors of the same pathway , and even the same target across cell lines are often poorly correlated . For example , correlation coefficients between the AKT inhibitor we employed ( MK-2206 ) and the two other AKT inhibitors in the dataset ( AKT Inhibitor VIII and A-443654 ) are 0 . 22 and 0 . 08 . Correlations between sensitivity to MK-2206 and the four PI3K inhibitors vary between 0 . 23 ( AZD6482 ) and 0 . 55 ( GDC0941 ) , and the four MTOR inhibitors from 0 . 16 ( Rapamycin ) to 0 . 42 ( AZD8055 ) . Correlations between the four MEK inhibitors are significantly better ( 0 . 61 to 0 . 75 ) but still not nearly as tight as would be expected for inhibitors of the same target . These discrepancies may be attributable to different mechanisms of action , off-target specificities between the alternate inhibitors , or other technical issues . Regardless of the underlying reason , this could explain why our biomarker stratification scheme only identified a subset of the MEK and PI3K/AKT/MTOR inhibitors . We next sought to validate our predictions using functional genomics data . Here , we utilized two cancer cell line data repositories; mRNA expression profiles from the Cell Line Encyclopedia ( CCLE; [24] ) , and functional genomics data from Project Achilles , which catalogues vulnerabilities of cancer cell lines to shRNA or Cas9/sgRNA-mediated gene silencing [25] . We first assessed whether mRNA expression of the biomarkers could substitute for protein . EGFR , ERBB2 , and CDKN1B gene expression correlated well with protein levels across the 18 HER2+ cell line panel ( Spearman ρ = 0 . 84 , 0 . 67 , 0 . 77 ) but ERBB3 less so ( Spearman ρ = 0 . 50; S10 Fig , part A ) . The poorer correlation between ERBB3 mRNA transcript and protein may be attributable to the multiple feedback circuits regulating expression of this receptor [26] . Also consistent with protein expression patterns , CDKN1B transcript expression is positively correlated with ERBB3 and anti-correlated with EGFR ( Spearman ρ = 0 . 27 and -0 . 23 respectively; S10 Fig , part A ) . Using the CCLE mRNA expression data , we classified cell lines in the dataset as HER2+ and HER2- based on the 80th percentile of ERBB2 expression , and examined differences in sensitivities to gene knockdowns between cell lines based on expression of EGFR , ERBB3 , and CDKN1B mRNAs . Among the 43 HER2+ cell lines found in the Achilles portal , 11 were predicted to be PI3K-dependent ( EGFRloERBB3hiCDKN1Bhi ) and 9 to be MAPK-dependent ( EGFRhiERBB3loCDKN1Blo ) based on median cuts of the 3 biomarkers . Of the 5711 genes tested for growth dependence , 781 showed differential sensitivity between the two sets of cell lines ( P < 0 . 05 , rank-sum test ) . This is almost 3-fold more than expected by chance , suggesting real biological differences between the biomarker-defined subsets . The PI3K-predicted cells were significantly more sensitive towards silencing of three canonical PI3K/AKT signaling nodes , PIK3CA , AKT1 and MTOR . Within the MAPK-predicted set , while MEK1 ( MAP2K1 ) did not come up as a differentially sensitive target , the main MAPK effector ERK2 ( MAPK1 ) did . A full list of the genes and associated statistics is provided in S1 Data , both for the combined three biomarker results , and each biomarker in isolation . To evaluate the relative utility of this three-gene classifier , we performed the same analysis using PIK3CA mutation status as a predictor of PI3K pathway dependence . This is the most widely used clinical biomarker associated with the use of PI3K/AKT inhibitors , and thus could be considered the gold standard comparator , despite being a poor predictor of clinical activity in actuality [27] . PIK3CA-mutant cells are indeed significantly more sensitive to knock-down of PIK3CA itself , as well as MTOR within the HER2+ population . However , AKT1 dependence was not associated with PIK3CA mutants , nor was either of the two MAPK targets ( MAP2K1 and MAPK1 ) associated with the PIK3CA-wildtype cells ( Table 1 ) . Applying the same analyses to HER2- cells reveals that the relationship between expression of the three genes and pathway dependence is specific to HER2+ cancers; only AKT1 comes up as differentially sensitive target using the three biomarker combination , while PIK3CA , MTOR and MAPK1 do not , as in the HER2+ population . The predictive utility of PIK3CA mutations however appears independent of HER2 status , as the same target ( PIK3CA ) comes up in both HER2+ and HER2- groups . The identification of 4/5 canonical gene targets ( PIK3CA , AKT1 , MTOR , MAP2K1 , and MAPK1 ) as differentially sensitive using the three biomarker enrichment strategy in HER2+ cells is highly unlikely to be due to chance alone ( P = 1 . 5 × 10−3 , Hyper-Geometric test ) , much more so than the 2/5 targets uncovered using PIK3CA mutational status . The three-biomarker set thus appears a better differentiator of PI3K/AKT vs . MAPK/ERK pathway dependence in HER2+ cells as compared to commonly used genetic marker PIK3CA . To interrogate the molecular mechanisms underlying this relationship , we mapped the differentially sensitive genes on to the NCI pathway interaction database ( NCI-PID ) , a curated resource of cancer-associated signaling pathways [28] . To limit the network size and enrich for biologically meaningful components , we further filtered for genes with median differential effects ( ATARiS scores [29] ) > 0 . 75 and with at least one interaction annotated in the NCI database , and included EGFR , ERBB3 , CDKN1B , and MAP2K1 . The resulting network , consisting of 41 nodes and 56 edges , is represented in Fig 4C . Besides direct protein-protein interactions , an edge in the network could represent transcriptional and translational regulation , as well as a macroprocess whose internal composition is not included [28] . Many core components of PI3K/AKT signaling and downstream effectors are connected ( i . e . E1F4E , RHEB , FOXA1 , MAX , CCND1 , AKT2 , TSC1 ) in a giant component associated with PI3K-pathway dependence , while the MAPK-predicted genes are more diffuse , and cover diverse signaling pathways and mechanisms ( such as components of the WNT signaling pathway ) . The three biomarkers ( EGFR , ERBB3 , and CDKN1B ) are connected with each other , both directly and through the intermediary network hub AKT1 . These connections suggest that the three biomarkers are functionally linked to AKT and MAPK signaling , and their relative expression levels thus could induce differential dependence on the two signaling cascades . Together , these results support our initial finding that that EGFR , ERBB3 and CDKN1B expression predict differential dependence on PI3K/AKT and MAPK signaling in HER2+ cancer cells .
Our finding that many HER2+ cancer cell lines are dependent on MAPK signaling contrasts with canonical view of HER2 signaling predominantly through the PI3K pathway [4 , 5] . We believe this novelty is due to our profiling of a larger and more diverse panel of HER2+ cell lines than any previous study to our knowledge , and the fact that MEK inhibitors are typically not examined in these cells as a result of this established dogma . The finding is however not completely unprecedented; a recent study describing the construction of Boolean network models using proteomic data from HER2+ cell lines revealed that the cell lines varied in their intrinsic bias toward PI3K vs . MAPK signaling [30] . If results translate beyond in vitro cell culture , this finding has implications for the design of treatment strategies in HER2+ cancers , as multiple PI3K/AKT/MTOR inhibitors are being tested in HER2+ cancers , and MEK inhibitors are being tested in a variety of other tumor types [12] . Combined measurement of these three proteins in tumor biopsies could thus inform the use of PI3K/AKT or MEK inhibitor treatments . It is worth noting that mutations in any of these three genes may affect their predictive utility in this context , however this rarely occurs in HER2+ cancers ( less than 10% harbor mutations in EGFR , ERBB3 , or CDKN1B [31] ) . Our results predict that MAPK pathway-activating mutations ( such as KRASG12V ) may be genetic mechanisms of resistance to HER2-direted therapy in indications outside of breast cancer , with higher EGFR and lower ERBB3 and CDKN1B expression [32] . While clinical data supporting this prediction are lacking , mechanistic model simulations are consistent with the role of KRAS mutations as dominant mechanisms of resistance in MAPK-dependent HER2+ cancers [33] . As with all molecularly targeted agents , predictive biomarkers are needed to realize the utility of PI3K/AKT and MEK inhibitors . Our results highlight the difficulty in identifying such predictive markers , as the most intuitive protein ( pAKT and pERK ) , and genetic ( PIK3CA ) candidates turned out to be largely uninformative and surpassed by a fairly non-intuitive multivariate classifier . These findings are consistent with clinical experience to date with PI3K/AKT/MTOR inhibitors [34] and MEK inhibitors [12] , in that mechanistically intuitive genetic markers have proven poor predictors of activity . Results from large cell line-based functional genomics projects [35 , 36] more broadly support this finding . Despite substantial efforts to find robust genetic predictors of drug sensitivity , these have proven largely disappointing [37] . We believe the root of this challenge lies in two related sources . First , cellular dependence on a given signaling pathway may arise through multiple mechanisms , such as expression patterns of regulatory ligands , receptors and downstream effectors , in addition to mutations in core signaling nodes . Predictors of sensitivity to pathway targeted inhibitors are thus expected to be necessarily multivariate , and often non-genetic , which would favor the use of proteomic technologies for predictive biomarker discovery [38] . Second , the very properties of oncogenic signaling networks that confer robustness to therapeutic intervention ( adaptive feedback circuits and redundancies ) also obscure predictors of responsiveness to such interventions . In addition to our data , recent functional proteomic studies support these conclusions . Phospho-kinase expression has been proven a poor predictor of cellular sensitivity to inhibitors targeting those kinases and the cascades in which they are embedded , including pAKT and pERK in relation to PI3K/AKT and MEK inhibitors [20 , 39] . Similarly in line with our results , PI3K inhibitor sensitivity across a panel of breast cancer cell lines was predicted by responsiveness to the ERBB3 ligand heregulin much better than by pAKT expression level or PIK3CA mutations [19] . We speculate that differential HER2-heterodoimerization accounts for the association between ERBB3 vs . EGFR receptor expression and PI3K vs . MAPK pathway dependence . The EGFR cytoplasmic domain contains multiple binding sites for the adaptor proteins Growth-factor-Receptor-Bound 2 ( GRB2 ) and Src-homology-2-containing ( SHC ) which activate the MAPK cascade , while ERBB3 has six binding sites for PI3K and only one SHC site [40] . Competition for binding to HER2 between the receptors could thus shift receptor complex formation to favor one pathway over the other . CDKN1B ( p27 ) is likely a functional surrogate of pathway activity , rather than a causal regulator . As a negative regulator of cell cycle progression , its expression level may be indicative of the intensity of pro-proliferative MAPK signal flux . These results also demonstrate that the functional effect of an oncogene can be context-dependent . In this case , ERBB2-amplification can result in either PI3K or MAPK-signaling-dependent cell growth , depending on molecular context . Consistent with the “ERBB network theory” [14 , 41] , signal output depends upon the composition of surface receptors and presence of extracellular ligands . Whether such context-dependent signaling effects are confined to the ErbB-family , or shared by other oncogenes is an open question . It is however clear that cancers harboring the same oncogenic driver can respond very differently to targeted inhibitors based on their tissue of origin , the most notable example being vemurafenib responses in BRAFV600E-mutant melanoma vs . colorectal cancers [42] . Context-dependent signaling differences may play a role in such cases . The finding that PI3K vs . MAPK pathway dependence is not genetically hardwired into cells , and can be affected by exposure to the ligand heregulin was somewhat unexpected . However , there is precedent for observations of cellular plasticity with respect to reliance on oncogenic signals . Growth factor stimulation is known to mediate resistance to many kinase inhibitors through the activation of alternate but functionally redundant pathways [43 , 44] . PI3K/AKT inhibitors and MEK inhibitors themselves can also induce compensatory signaling though alternative pathways via the relief of negative feedback regulatory controls on cell surface receptors [16 , 45–47] . Many cancer cells are thus endowed with the capacity for using alternate pathways in response to environmental changes . While we have focused solely on the role of the ERBB3 ligand heregulin , other growth factors and cytokines may have similar effects . This is important to consider when interpreting biomarker-response relationships . If both molecular profiles and drug response patterns are fluid , such relationships would be amenable to shift under different experimental [48] , and possibly pathophysiological conditions . In light of all the aforementioned challenges , it is perhaps unsurprising that despite all the resources and efforts committed date , a very limited number of predictive biomarkers have proven clinical utility [49] . Though counter-intuitive , strong relationships between inhibitor sensitivity and target expression appear to be the exception rather than the norm . We were able to support our initial findings from the 18-cell line panel using both chemical genomic and functional genomic data from independent sources . Besides expected hits in the PI3K/AKT and MAPK/ERK signaling pathways , our three biomarker stratification scheme revealed differential sensitivity towards additional small molecules and shRNAs . It is likely that some of these are false positives . However , there appear to be mechanistic connections between the targets of these compounds and shRNAs , the three biomarkers , and PI3K/AKT and MAPK signaling . For example as shown in Fig 4A , cells predicted to be PI3K or MAPK dependent are also differentially sensitive towards an AMPK inhibitor ( AICAR ) , consistent with known biological functions of PI3K/AKT signaling in metabolism [50] . In addition , histone deacetylase ( HDAC ) inhibitors are known to mediate at least part of their effects through suppression of PI3K/AKT signaling [51] . Elesclomol induces apoptosis through disruption of mitochondria metabolism and in cancer cells upregulates AKT signaling to promote survival [52] . BMS708163 targets presenillin1 as a NOTCH-sparing gamma-secretase inhibitor [53] and regulates 4ICD release upon NRG1 binding to ERBB4 [54] . While the exact mechanistic connections between these small molecules and EGFR/ERBB3 expression are unclear , these results are likely to be biological meaningful and not merely random . In addition , we performed a GO enrichment analysis [55 , 56]on biological processes for the genes from the Achilles’ analysis using genes tested in this dataset as background . We found that the shRNA targets are enriched for regulators of mRNA transcription , mRNA transport , translation , and mitotic progression . Consistently , the small molecules that we found in addition to direct inhibitors of PI3K/AKT/MTOR and MEK target components of the transcriptional , translational and cell cycle machinery . For example , vorinostat is known to cause abnormal mitosis through inhibition of HDAC [57] . Vinblastine has been shown to block mitosis through inhibiting microtubule dynamics indirectly or directly [58] . We also observed differential proliferation rates between AKT and MAPK-dependent cell lines experimentally ( Fig 1A ) , consistent with differential sensitivity to knockdowns of cell cycle regulators . These results suggest that the hits from both analyses are likely to be mechanistically connected and biologically meaningful . In summary , we have demonstrated that PI3K vs . MAPK pathway dependence varies across HER2+ cancer cells . This dependence varies by indication , and can be predicted using a set of three non-intuitive protein measurements . These results might help stratify HER2+ patients for treatment with targeted therapeutics . More generally , our findings reveal that oncogenic signaling can be context dependent . A single genetic transformation , in this case ERBB2 amplification , can have differing effects on cell signaling and growth , contingent upon on the molecular and cellular background . Together , we believe that our results and approach will enable the design of more effective cancer treatment strategies for HER2+ cancer patients .
AU565 , HCC1419 , NCI-H2170 , HCC202 , HCC1954 , NCI-N87 , ZR75-1 , SKOV3 , ZR75-30 , MDAMB175VII , CALU3 , MDAMB453 , MDAMB361 , JIMT1 , SKBR3 and HCC2218 cells were obtained from ATCC . OE19 and OE33 were obtained from ECCC; COLO-678 was obtained from DSMZ , and KYSE-410 from Sigma-Aldrich . BT-474-M3 cells ( hereafter simply referred to as BT-474 ) were obtained from Hermes biosciences . All cell lines were maintained in RPMI supplemented with 10% FBS , penicillin , and streptomycin . GSK-1120212 and MK-2206 were purchased from Selleckchem . Recombinant human HRG-β1 ( EGF domain ) was from R&D Systems . Cells were seeded at 600 cells per 384-well plate in 4% FBS cell growth medium , stimulated ( or not ) with 2 nM HRG-b1 for 4 hours , and then treated with individual or combinations of the AKT and MEK inhibitors . Treatments consisted of 5x6 dose combination matrices covering a 3-fold dilution series from 1 μM ( MK-2206 ) and 10μM ( GSK-1120212 ) . Cell confluency was then monitored over 5 days in culture by video microscopy ( IncuCyte , Essen BioScience ) , and data normalized to density measured at initiation of treatment ( S1 Data ) . Cell lines were seeded at 7 , 500 cells per well in 384-well culture plates in RPMI containing 4% FBS . 48-hour post plating , cells were stimulated ( or not ) with 2 nM HRG-β1 for four hours . At harvest , cells were placed on ice , and 70 μl RIPA lysis buffer ( Sigma-Aldrich ) supplemented protease inhibitor and phosphatase inhibitor tablets ( Roche ) was added to each well . The plates were stored at -80°C until analysis . On the first day of protein profiling , the lysates were thawed at 4°C and centrifuged at 4000 rpm for 10 minutes . The supernatant was used for further analysis with multiplex Luminex protein assays as described below . Twenty micrograms of antibodies was conjugated to 100 μl ( ~1 . 25×106 beads ) of MagPlex beads ( Luminex Corp . ) according to the manufacturer’s instructions . Conjugated beads were then mixed and diluted 1000-fold in phosphate buffered saline ( PBS ) –1% bovine serum albumin ( BSA ) ( Sigma ) . Diluted beads were transferred into 384-well assay plates ( Corning ) at 30 μl per well and then washed three times with PBS–1% BSA . Washed beads were incubated with 20 μl of total protein lysates overnight with shaking at 4°C . The beads were then washed with PBS–1% BSA . Detection antibodies ( see S3 Table ) were added and incubated at 4°C overnight with shaking . After washing with PBS–1% BSA , streptavidin-conjugated phycoerythrin ( Invitrogen ) was added at 2 μg/ml and incubated at room temperature for 30 min . Finally , the beads were washed with PBS–1% BSA , and data were acquired with a FlexMap3D instrument ( Luminex Corp . ) according to the manufacturer’s instructions . Raw signals were normalized by background subtraction to signals from control lysates prepared from non-human cells . Antibodies are listed in S3 Table , and background-subtracted data is provided in S1 Data . Observed changes in cell density over time are determined by the balance of cell proliferation vs . death within the culture . Both cell proliferation and survival are regulated by PI3K/AKT and MAPK/ERK signaling cascades , which assuming an exponential growth can be expressed as: dXdt=μMAX⋅f1 ( pAKT , pERK ) −δMAX⋅f2 ( pAKT , pERK ) Where X = number of cells ( assumed proportional to surface area ) , μMAX = maximum rate of proliferation , δMAX = maximal rate of cell death , and f1 and f2 are functions integrating pAKT and pERK signaling . We implemented a quantitative logic-based formalism [59] to describe changes in cell density as function of PI3K/AKT and MAPK/ERK pathway activation . AKT and MEK inhibitor concentrations ( μM ) were used as surrogates for pathway activities , assuming monotonic dose-response relationships . As the logic by which cells integrate and interpret these signals remains obscure , we initially assessed 9 alternate growth regulatory functions combining null ( K ) , OR , and AND-type logic gates as proliferation and survival functions ( f1 and f2 ) : K=1 OR= ( wakt⋅AKT+werk⋅ERK ) kτ+ ( wakt⋅AKT+werk⋅ERK ) k AND= ( AKTk_aktτakt+AKTk_akt ) ⋅ ( ERKk_erkτerk+ERKk_erk ) Parameters for each of the 9 models ( S1 Table ) were estimated for each cell line using a Particle Swarm Optimization algorithm [60] minimizing the mean squared error between experimental measurements ( fold cell expansion over 96 hours ) and model simulations . Relative model performance was assessed using the Akakie Information Criterion ( AIC ) : AIC=2⋅P+N⋅log10 ( MSE ) Where P = number of parameters ( 2–10 ) , N = number of experimental measurements ( 30 ) , and MSE = mean squared error . The fourth model structure assessed ( M4 ) , consisting of an OR-Gate regulating cell survival , was found to be optimal ( lowest AIC ) for the largest number of cell lines tested . The final formulation of the cell growth regulatory model used in subsequent analyses was thus: dXdt=μMAX−δMAX ( ( wakt⋅AKTi+werk⋅MEKi ) kτ+ ( wakt⋅AKTi+werk⋅MEKi ) k ) Pathway Bias was then defined as the normalize differential between the parameters wakt and werk: Bias= ( wakt−werk ) ( wakt+werk ) Based on our finding that PI3K/AKT dependence correlated with the cell death rate ( δMAX ) , and MAPK-dependence with proliferation ( μMAX ) , we created a tenth model ( M10 ) which separates the regulatory terms accordingly: dXdt=μMAX ( 1-MEKik_erkτerk+MEKik_erk ) −δMAX ( AKTik_aktτakt+AKTik_akt ) The raw cell growth data , model parameters associated with each of the ten models ( M1-M10 ) , goodness-of-fit metrics ( MSE and AIC ) and simulations are provided in S1 Data . Parameter estimates across alternate models are quite consistent , indicating our results are robust regardless of the model chosen . The Pathway Bias measurement for each cell was first discretized into MAPK vs . PI3K-dependence ( Bias = -1 vs . +1 ) , a reasonable simplification given the observed bimodal distribution of this metric . The probability of MAPK-dependence ( PMAPK ) vs . PI3K-dependence ( PPI3K = 1 –PMAPK ) was then modelled as a function of input features using a logistic regression equation: ln ( PMAPKPPI3K ) =β0+∑i=1Nβi⋅Xi Where N = number of features ( Xi ) and βi = regression coefficients . The βi parameters were estimated by maximum likelihood estimation , and predictive power of the model assessed using leave-one-out cross validation ( LOOCV ) procedure . Model-predicted Bias was then back-calculated using the probabilities as: Predicted Bias=−1⋅PMAPK+1⋅PPI3K Statistical significance of model predictions was assessed by computing LOOCV accuracy , Pearson correlations , and mean squared error ( MSE ) from 10 , 000 randomized permutations of the cellular properties: Bias mapping . RNAseq was downloaded from the GDAC Firehose portal in June , 2014 ( http://gdac . broadinstitute . org/ ) . HER2+/- classifications were based on ERBB2 expression . Using BRCA , LUAD , and OV samples as controls , setting ERBB2 RNAseq count thresholds at 14 , 000 resulted in HER2+ frequencies consistent with known ERBB2-amplification frequencies of 13% , 2 . 5% , and 1 . 5% . This threshold was then applied across all indications , though results were insensitive to the specific value chosen . mRNA expression data was downloaded from CCLE ( www . broadinstitute . org/ccle/home ) and gene knockdown sensitivity from Project Achilles ( www . broadinstitute . org/achilles ) . Signaling networks were defined in NCI-PID ( http://pid . nci . nih . gov/index . shtml ) and accessed via the Pathway Commons portal ( www . pathwaycommons . org ) , and visualized using Cytoscape ( www . cytoscape . org ) . All analysis and simulations were carried out in MATLAB R2013b . | Biomarkers capable of accurately predicting patient responses to alternate therapies are critical to realizing the vision of precision medicine . Identifying such biomarkers is , however , challenging due to the inherent complexity of biological networks . Here we sought to identify molecular features that predict how a genetically defined subset of cancers ( HER2+ ) differentially depend on two oncogenic signaling pathways , the PI3K/AKT and MAPK/ERK cascades . We find that combined measurement of three non-intuitive proteins ( EGFR , ERBB3 , and CDKN1B ) accurately predicts cellular dependence on these signaling pathways , and responsiveness to drugs targeting their constituents . Notably , this three-biomarker model outperformed both biological intuition ( phosho-AKT and phospho-ERK ) and current clinical practice ( PIK3CA mutations ) . More broadly , this exemplifies how the functional consequences of a single oncogenic driver ( HER2 ) can depend upon molecular and cellular context . Expression of these markers also varies by indication , such that breast cancers are biased toward PI3K-dependnece , while non-breast indications ( lung , ovarian , and gastric ) are particularly MAPK-dependent , and thus may respond differently to therapeutic strategies developed for breast cancer . Together , we believe that our results will aid the design of novel , stratified treatment strategies for HER2+ disease . |
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