DOI : 10 . 1126 / science . 1140324 , 1753 ( 2007 ) ; 316 Science et al . Chris Bakal , Signaling Networks Regulating Cell Morphology Quantitative Morphological Signatures Define Local www . sciencemag . org ( this information is current as of November 2 , 2007 ) : The following resources related to this article are available online at http : / / www . sciencemag . org / cgi / content / full / 316 / 5832 / 1753 version of this article at : including high - resolution figures , can be found in the online Updated information and services , http : / / www . sciencemag . org / cgi / content / full / 316 / 5832 / 1753 / DC1 can be found at : Supporting Online Material found at : can be related to this article A list of selected additional articles on the Science Web sites http : / / www . sciencemag . org / cgi / content / full / 316 / 5832 / 1753 # related - content http : / / www . sciencemag . org / cgi / content / full / 316 / 5832 / 1753 # otherarticles , 13 of which can be accessed for free : cites 24 articles This article 2 article ( s ) on the ISI Web of Science . cited by This article has been http : / / www . sciencemag . org / cgi / content / full / 316 / 5832 / 1753 # otherarticles 1 articles hosted by HighWire Press ; see : cited by This article has been http : / / www . sciencemag . org / cgi / collection / cell _ biol Cell Biology : subject collections This article appears in the following http : / / www . sciencemag . org / about / permissions . dtl in whole or in part can be found at : this article permission to reproduce of this article or about obtaining reprints Information about obtaining registered trademark of AAAS . is a Science 2007 by the American Association for the Advancement of Science ; all rights reserved . The title Copyright American Association for the Advancement of Science , 1200 New York Avenue NW , Washington , DC 20005 . ( print ISSN 0036 - 8075 ; online ISSN 1095 - 9203 ) is published weekly , except the last week in December , by the Science on N o v e m be r 2 , 2007 www . sc i en c e m ag . o r g D o w n l oaded f r o m Quantitative Morphological Signatures Define Local Signaling Networks Regulating Cell Morphology Chris Bakal , 1 , 2 , 3 * † John Aach , 1 * George Church , 1 Norbert Perrimon 1 , 2 Although classical genetic and biochemical approaches have identified hundreds of proteins that function in the dynamic remodeling of cell shape in response to upstream signals , there is currently little systems - level understanding of the organization and composition of signaling networks that regulate cell morphology . We have developed quantitative morphological profiling methods to systematically investigate the role of individual genes in the regulation of cell morphology in a fast , robust , and cost - efficient manner . We analyzed a compendium of quantitative morphological signatures and described the existence of local signaling networks that act to regulate cell protrusion , adhesion , and tension . M orphogenesis commonly relies on the spatial and temporal regulation of distinct groups of genes acting in local signaling networks . The morphology of a single cell also results from the spatio - temporally regulated activity of signaling proteins . For example , Rac - type guanosine triphosphatases ( GTPases ) promote the for - mation of protrusive lamellipodia at the lead - ing edge of motile cells , whereas Rho - type GTPases promote cortical tension and cell re - traction at the rear of the cell through the activation of the actomyosin machinery ( 1 ) . Both protrusive activity and cell body retrac - tion are tightly coupled to the assembly and disassembly of adhesive structures through Rho signaling ( 2 ) . Many signaling proteins must act both upstream and downstream of specific Rho GTPases in spatially distinct subcellular local networks to translate extra - cellular signals to changes in GTPase activa - tion and ultimately in cellular morphology . However , the components of these networks and the precise role they play in regulating cell shape remain largely unclear . We performed a genetic screen of 249 gene - overexpressionordouble - strandedRNA ( dsRNA ) treatment conditions ( TCs ) using the Drosophila BG - 2 cell line to determine the roles of genes actingin localnetworks to controldistinct aspects of cell morphology . BG - 2 cells are highly motile and exhibit many of the traits observed in mam - malian fibroblasts and epithelial cells , including the formation of integrin - based adhesions , polar - ized lamellipodia , and coordinated retraction of the cell body ( 3 , 4 ) ; but unlike many mammalian cell types , the growth of BG - 2 cells is not inhibited by contact with other cells . To analyze the morphologies of cells in each TC , we used protocol and image - processing techniques de - signed to detect clear and complete boundaries of individual cells and to quantitatively analyze the shapes of these boundaries along with the in - tensities and textures of their interiors . First , we stochastically labeled samples with green fluo - rescent protein ( GFP ) [ supporting online material ( SOM ) ] to enable individual cells to stand out in the crowded and overgrown samples , acquired images of these cells using conventional fluores - cence microscopy , and used software to identify the boundaries of individual cells ( Fig . 1A and SOM ) . Although these techniques make lower numbers of cells per sample available to sub - sequent analysis as compared to other published methods ( 5 , 6 ) , we preferred them because they yielded detailed , high - quality empirical bounda - ries instead of algorithmically determined approx - imations . For each individual cell , we computed 145 different quantitative features that reflected basic aspects of cell geometry , detailed aspects of cellular protrusions , or the distribution of GFP intensity within the cellular boundaries ( Fig . 1B and SOM ) . Altogether we analyzed 12 , 601 individual cells from our 249 TCs . To transform our 145 features into biologi - cally meaningful morphological indicators , we trained a set of neural networks ( NNs ) to use informative subsets of the features to discriminate cells from particular reference TCs from sets of other reference TCs ( Fig . 1D and SOM ) . We targeted seven TCs for NN training because they produced phenotypes that were qualitatively dis - tinctive and discernable from control cells ( SOM ) . For example , overexpression of an N - terminally truncated form of the Rho guanine nucleotide exchange factor ( RhoGEF ) SIF ( D N - SIF ) , the Drosophila ortholog of mammalian Tiam - 1 , stimulates extensive lamellipodia formation , cell spreading , and a general loss of tension , dem - onstrated by the flat and thin appearance of the D N - SIF cells ( Fig . 2 ) ( 7 ) . After training a D N - SIF NN to distinguish D N - SIF cells from the cells of our six other target TCs , we applied this NN to all 12 , 601 cells in our data set to score each of them for this distinctive morphology ( Fig . 1D ) . In ad - dition to D N - SIF , we trained NNs for TCs treated with overexpression constructs for RacV12 , RacF28L , RhoV14 , RhoF30L , CG3799 full - length , and D N - RhoGEF3 . The seven TCs selected for NN training included several for which the underlying mechanisms responsible for their phenotypes are not understood . Finally , for each NN and TC , we calculated a NN Z score ( NNZ ) , which is the variance - adjusted difference between the mean NN score of all cells in the TC and the mean NN score of all cells in our data set . Each NNZ is thus an index of the morphology of an entire TC ( SOM ) ( Fig . 1E ) . For example , for cells in which CG10188 ( a RhoGEF ) was targeted by dsRNA , the NNZ for the D N - SIF NN was 0 . 76 , which indicates that CG10188 dsRNA induces a mor - phology that is slightly more “ D N - SIF – like ” than an equal number of randomly chosen cells ( which would have a NNZ of 0 ) . In contrast , the NNZ for D N - SIF cells using the D N - SIF classi - fier is 26 . 77 . Together , the seven NNZs com - puted for each TC constituted a quantitative morphological signature ( QMS ) of the TC ( Fig . 1E ) . A QMS is thus a high - order represen - tation of the morphology of cells in a TC as a vector of seven specific quantitative similarities and dissimilarities with seven panels of refer - ence cells with distinctive phenotypes . Two - dimensional hierarchical clustering ( SOM ) of 249 QMSs revealed that TCs , and in particular RNA interference ( RNAi ) against indi - vidual genes , fell into several distinct clusters . QMSs with similar qualitative phenotypes clus - tered tightly together ( Fig . 2 ) . We define “ pheno - clusters ” as genes grouped at the highest node in the clustering for which the cluster distance metric ( an average of uncentered Pearson corre - lation coefficients ) was greater than 0 . 80 and term this a “ cluster distance cutoff ” ( CDC ) ( Fig . 2 ) ( 8 ) . A value of 0 . 80 was chosen as the CDC because smaller cutoffs resulted in groupings of visually diverse morphologies , whereas higher thresholds resulted in the segregation of visually similar morphologies into distinct clusters . A large phenocluster was composed of TCs that clustered because their QMSs have high D N - RhoGEF3 NNZs ( cluster 6 ) . All cells in this cluster were extremely roundand had very fewor no protrusions of any type ( Fig . 2 and fig . S18 ) . QMSs for p190RhoGAP , SCAR , slingshot , arma - dillo , ankyrin , Sop2 , and RhoGEF3 RNAi were clustered together . Moreover , we observed an enrichment in this cluster for RNAi against genes involved in Rap signaling ( three of six genes in the data set ) . The finding that depletion of either SCAR , the cofilin phosphatase Slingshot , or Sop2 resulted in defects in protrusion is consist - ent with the known function of these three pro - teins ( 4 , 9 – 14 ) . Gef26 and its downstream target Rap1 function in the formation of adherence junctions in Drosophila ( 15 – 17 ) and have recently been observed to be required for cell spreading and migration of Drosophila macrophages ( 18 ) . In mammalian systems , p190RhoGAP acts down - stream of integrins and adhesions to promote cell 1 Department of Genetics , Harvard Medical School , Boston , MA 02115 , USA . 2 Howard Hughes Medical Institute , Boston , MA 02215 , USA . 3 MIT Computer Science and Technology Laboratory , Cambridge , MA 02139 , USA . * These authors contributed equally to this work . † To whom correspondence should be addressed . www . sciencemag . org SCIENCE VOL 316 22 JUNE 2007 1753 REPORTS on N o v e m be r 2 , 2007 www . sc i en c e m ag . o r g D o w n l oaded f r o m spreading ( 19 ) . Thus , our method successfully identified two distinct but coupled signaling path - ways that regulate the formation of protrusions . We anticipate that Drosophila RhoGEF3 plays a critical role in the regulation of adhesion , because overexpression of a N - terminally deleted form or inhibition by dsRNA have similar QMSs . D N - RhoGEF3 is not likely to be constitutively activated ( 20 ) but may promote cell rounding by acting in a dominant - negative manner toward endogenous RhoGEF3 signaling . A second phenocluster contained a group of TCs that co - clustered with RhoF30L and had high RhoF30L NNZs ( cluster 8 ) . These cells dif - fered qualitatively from cells in the D N - RhoGEF3 phenocluster by virtue of the fact that cells in this cluster did have some visible , but poorly formed , lamellipodial protrusions ( Fig . 2 and fig . S18 ) . dsRNAs in this cluster may target genes that specifically promote the formation of lamellipo - dia , and RhoF30L ( an activated form of Rho that cycles between GTP - and GDP - bound states ) may inhibit this process . In support of this notion , dsRNAs targeting twinstar , capt , and ARC - p20 were part of this cluster , representing an enrich - ment in genes that have been identified in pre - vious screens for genes required for lamellipodia organization ( three of seven genes ) ( 10 ) . Given the fact that lamellipodia formation occurs after the formation of adhesion , protrusion , and actin - filament nucleation , this suggests that phenotypic profiling can not only simultaneously monitor the activity of coupled signaling pathways ( within phenoclusters ) but can also monitor the temporal hierarchical relationships that exist among local signaling networks . The largest phenocluster was a group of TCs that shared high D N - SIF , RacV12 , and RacF28L NNZs ( cluster 18 ) and that corresponded to large flat cells , typically with extensive lamellipodia ( Fig . 2 and fig . S18 ) . This phenotype is consistent with repeated observations of cells overexpress - ing activated Rac mutant proteins or activated RacGEFs such as SIF or Tiam - 1 ( 7 , 21 ) . QMSs for cenG1A , cenB1A , CG16728 , and CG13692 RNAi were members of this phenocluster , rep - resenting an enrichment in ArfGAPs ( four of six genes ) . Mammalian ArfGAPs such as GIT1 and Fig . 1 . Phenotypic pro - filing workflow . ( A ) Cul - tured Drosophila BG - 2 cellsweretransfectedwithplasmidsencodingGFPandeithercotransfectedwithplasmidsencodingredfluorescentprotein – tagged proteins or incu - bated in the presence of dsRNA for 4 days . Images of GFP - labeled cells were acquired by standard fluo - rescence microscopy , and individualcellimageswithclearandcompletebound - aries were selected with custom - developed software ( SOM ) . ( B ) Graphical rep - resentations of some of the features computed from each individual cell image ( SOM ) . ( C ) 145 different features relevant to cell morphology and GFP signal intensity were derived from individual cells and expressed as Z scores relative to their values over a subset of ∆ N - RhoGEF3 Classifier Phenotypes RhoF30L RacV12 RacF28L ∆ N - SIF RhoV14 CG3799 0 . 00 0 . 00 0 . 00 0 . 01 . . . 0 . 08 0 . 14 0 . 05 0 . 15 . . . 0 . 32 0 . 05 0 . 08 0 . 10 . . . 0 . 01 0 . 18 0 . 04 0 . 00 . . . 0 . 67 0 . 38 0 . 76 0 . 47 . . . 0 . 03 0 . 45 0 . 18 0 . 18 . . . 0 . 00 0 . 99 0 . 00 0 . 00 . . . QMS - 2 . 1 - 5 . 1 11 . 5 3 . 9 26 . 8 - 6 . 9 - 2 . 8 N o r m a li z ed f ea t u r e v a l ue s 145 features 1 4 3 2 S i ng l e - C e l l NN S c o r e NN Z - scores for TC 1 2 3 4 n cells Cellsin TC 1 Cell Culturing + GFP + / - dsRNA + / - Gene overexpression 2 Image Acquisition ( GFP ) 3 Cell Identification 4 Feature Extraction DNAGFPF - actin GFP Raw Image Half - Mass Boundary Process Area Drainage Area A B C D E GFP control cells which were transfected only with a construct coding for constitutive expression of GFP . ( D ) Individual cells were then scored with NNs trained to discriminate seven ref - erence TCs with distinctive morphologies ( SOM ) . ( E ) QMSs were computed for each TC as seven dimensional vectors containing the NNZs of the individual cells in the TC ( SOM ) . 22 JUNE 2007 VOL 316 SCIENCE www . sciencemag . org 1754 REPORTS on N o v e m be r 2 , 2007 www . sc i en c e m ag . o r g D o w n l oaded f r o m GIT2 promote the disassembly of integrin - based focal adhesions by binding the tyrosine - phosphorylated form of Paxillin , which in turn results in ArfGTPase activation and a concomi - tant down - regulation of Rac activity and adhesion turnover ( 22 – 24 ) . In addition to ArfGAPs , this phenocluster also contained QMSs for dsRNAs targeting paxillin ( fig . S18 ) and a - actinin , and was defined by high levels of Rac activity ( Fig . 2 ) . Based on this and sequence analysis , we suggest that CG16728 is the Drosophila ortholog of mammalian GITArfGAPs . Proteomic analysis has recently revealed that GIT1 is part of a supramolecular complex that , in addition to Paxillin , includes b 2 centaurin and gelsolin and directly binds moesin ( 25 ) . Drosophila cenB1A ( the Drosophila ortholog of b 2 centaurin ) , Gelsolin , and Moesin dsRNAwere also members of same phenocluster as CG16728 and paxillin , further demonstrating that our methodology is capable of identifying both functionally and physically coupled signaling components . Taken together , this suggests that the large , flat , and spread morphology of cells in this phenocluster was partially due to inhibiting the disassembly of adhesion and that QMSs can be used to functionally annotate genes . We have demonstrated that quantitative mor - phological profiling of single cells combined with RNAi - based genetic screening technology results in the identification of local signaling networks with spatially , temporally , and func - tionally defined characteristics that act in a hierarchical manner to regulate cell shape and migration . These methods can be used not only in the context of genetic screens but also in large - scale screens of small - molecule libraries or screens involving the overexpression of cDNAs . Because our approach is a fast and cost - effective way to query the activity of multiple signaling proteins and pathways , quantitative morpholog - ical profiling may also be useful as a diagnostic tool in the analysis of clinical samples . Further - more , akin to gene - expression data , we can now use morphological phenotypic data for computa - Rac1 cluster ( 4 ) Rac1 , Rac1 / Rac2 / MTL , GEF64C overex , L amellipodia formation ( 25 ) Arc - p20 , GEF64C , Trio , Twinstar , Kelch , Rab26 , Rab35 , Rab3 - GAP , Graf , RhoGAP68F , CSN1a , RhoF30L overex MT capture ( 3 ) APC , APC2 , Dia GFP / wild - type cluster ( 24 ) Cdc42 , Cdc42 Y32A overex , CG3799 , CG3799 overex , RhoV14 overex Rho1 cluster ( 19 ) Rho1 , RhoGAP1A , RhoGAP5A , RhoGAP93B , RhoGEF2 , RhoGEF4 1 4 6 7 8 17 18 20 242526272830 31 33 37 Cluster R a c V 12 R a c F 28L G F P C G 3799 R h o1 d s R N A R a c 1 d s RN A R ho F 30L R ho F 30L R a c F 28L R a c V 12 R ho V 14 C G 3799 Actin Polymerization Cortical Tension Tail Retraction Pebble , RacGAP50C Adhesion disassembly Twinstar , Capt , Arc - p20 SCAR , Sop2 Actin filament nucleation Adehsion complex formation RapGEFs , RapGAPs Rho1 , RhoGEF2 , RhoGEF4 Cytokinesis Fig . 2 . Identificationoflocalnetworksthatregulatedistinctaspectsofmorphology . Hierarchicalclusteringofthegenesinthedataset ( the y axis ) byhowcellsscoredon the D N - SIF , D N - RhoGEF3 , CG3799 , RacF28L , RacV12 , RhoF30L , and RhoV14 NNs ( the x axis ) isshown . WedefinephenoclustersofgenesasclusterswithaCDC > 0 . 80 , which results in 41 total clusters comprising 17 multigene clusters and 24 singletons . All multigene clusters are identified in brackets on the right - hand side of the clustergram . For some clusters , we describe prominent TCs , and the number of TCswithintheseclustersisindicatedinparentheses . Examplesofindividualcellsand theirpositionsintheclustergramareshownontheleft - handsideoftheclustergram . Based on their gene membership , a number of clusters were determined to have specialized roles in cell morphology . A complete listing of clusters is provided in table S8 . Scale bars , 10 m m . www . sciencemag . org SCIENCE VOL 316 22 JUNE 2007 1755 REPORTS on N o v e m be r 2 , 2007 www . sc i en c e m ag . o r g D o w n l oaded f r o m tional approaches that aim to model the dynamic nature of signaling networks , while the RNAi component pushes us closer to causal mechanis - tic linkages . References and Notes 1 . A . Hall , Biochem . Soc . Trans . 33 , 891 ( 2005 ) . 2 . K . A . DeMali , K . Wennerberg , K . Burridge , Curr . Opin . Cell Biol . 15 , 572 ( 2003 ) . 3 . Y . Takagi , K . Ui - Tei , T . Miyake , S . Hirohashi , Neurosci . Lett . 244 , 149 ( 1998 ) . 4 . A . Biyasheva , T . Svitkina , P . Kunda , B . Baum , G . Borisy , J . Cell Sci . 117 , 837 ( 2004 ) . 5 . Z . E . Perlman et al . , Science 306 , 1194 ( 2004 ) . 6 . A . E . Carpenter et al . , Genome Biol . 7 , R100 ( 2006 ) . 7 . M . Sone et al . , Science 275 , 543 ( 1997 ) . 8 . K . C . Gunsalus et al . , Nature 436 , 861 ( 2005 ) . 9 . J . A . Zallen et al . , J . 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A . van der Kammen , J . G . Collard , J . Cell Biol . 147 , 1009 ( 1999 ) . 22 . D . J . Webb et al . , Nat . Cell Biol . 6 , 154 ( 2004 ) . 23 . M . C . Brown , L . A . Cary , J . S . Jamieson , J . A . Cooper , C . E . Turner , Mol . Biol . Cell 16 , 4316 ( 2005 ) . 24 . N . Nishiya , W . B . Kiosses , J . Han , M . H . Ginsberg , Nat . Cell Biol . 7 , 343 ( 2005 ) . 25 . M . W . Mayhew et al . , J . Proteome Res . 5 , 2417 ( 2006 ) . 26 . We thank P . Bradley and A . Kiger for contributions to the stochastic labeling method , as well as B . Mathey - Prevot , A . Friedman , R . Griffin , B . Berger , S . Altschuler , the Drosophila RNAi Screening Center , and the Harvard Research Information Technology Group . This work is supported in part by grants from the National Human Genome Research Institute Centers of Excellence in Genomic Science . N . P . is an investigator of the Howard Hughes Medical Institute . C . B . is a fellow of the Leukemia and Lymphoma Society . Supporting Online Material www . sciencemag . org / cgi / content / full / 316 / 5832 / 1753 / DC1 Materials and Methods SOM Text Figs . S1 to S19 Tables S1 to S9 References 24 January 2007 ; accepted 15 May 2007 10 . 1126 / science . 1140324 Restriction of an Extinct Retrovirus by the Human TRIM5 a Antiviral Protein Shari M . Kaiser , 1 , 2 Harmit S . Malik , 3 Michael Emerman 2 , 3 * Primate genomes contain a large number of endogenous retroviruses and encode evolutionarily dynamic proteins that provide intrinsic immunity to retroviral infections . We report here the resurrection of the core protein of a 4 - million - year - old endogenous virus from the chimpanzee genome and show that the human variant of the intrinsic immune protein TRIM5 a can actively prevent infection by this virus . However , we suggest that selective changes that have occurred in the human lineage during the acquisition of resistance to this virus , and perhaps similar viruses , may have left our species more susceptible to infection by human immunodeficiency virus type 1 ( HIV - 1 ) . A large portion of primate genomes is composed of endogenous retroviruses that can be thought of as an archaeolog - ical record of past infections . Both chimpanzee and gorilla genomes harbor more than 100 copies of Pan troglodytes endogenous retrovirus ( PtERV1 ) , whereas it is absent from the human genome ( 1 ) . Comparison of individual PtERV1 proviruses in gorilla and chimpanzee genomes suggest that this virus was active 3 to 4 million years ago , after the separation of chimpanzee and human lineages ( 1 ) . This raises an evolutionary conundrum as to why sister species , but not humans , acquired germline copies of this retro - virus even though all three species cohabited when PtERV1 was an active exogenous virus ( 1 ) . One mechanism of active restriction from retroviral infections is conferred by the TRIM5 a protein , which binds directly to the incoming retroviral capsid ( CA ) core and targets its premature disassembly or destruction ( 2 , 3 ) . Each primate species encodes a TRIM5 a with a different antiviral specificity ( 4 ) . For example , TRIM5 a encoded by rhesus macaques renders them resistant to infection by HIV - 1 , but human TRIM5 a affords no such protection ( 5 ) . Indeed , the antiviral specificity of TRIM5 a has rapidly evolved by dramatic episodes of positive selec - tion during the past 30 million years of primate evolution ( 6 ) . The branch leading to the human lineage shows one of the strongest signatures of positive selection ( 6 ) , which suggests that at least one major pathogenic retroviral assailant has challenged the human lineage in the past 4 to 5 million years . Taken together , these findings suggest that TRIM5 a evolution was shaped by a species - specific history of ancestral retroviral challenges . Although human TRIM5 a has rela - tively poor activity against retroviruses compared with the gene from other primates , it potently blocks a g - retrovirus N - MLV , which is related to PtERV1 ( 7 , 8 ) . We therefore tested the hypothesis that TRIM5 a may have protected early humans from invasion by PtERV1 . All copies of PtERV1 in the chimpanzee genome have been inactivated by accumulated detrimental mutations ( 1 ) . However , the numer - ous proviral copies of PtERV1 present in the chimpanzee genome allow us to reconstruct the ancestral sequence of the gag gene of this an - cient , extinct retrovirus in silico ( see supporting online material ) . Analysis of the reconstructed PtERV1 ancestral sequence reveals about 50 % identity with murine leukemia virus ( MLV ) , and several characteristic conserved elements are in - tact ( Fig . 1A ) . Phylogenetic analysis of chim - panzee and gorilla PtERV sequences shows that a single - source virus likely infected both chimpan - zees and gorillas because viral sequences from both species form a monophyletic group ( Fig . 1B ) . We next used site - directed mutagenesis to reconstruct the ancestral p12 and CA coding regions ( ignoring synonymous changes ) starting from one chimpanzee PtERV1 provirus cloned from the genome . We focused on CA because it is the functional target of TRIM5 a and included p12 because of functional interactions that exist between p12 and CA in other g - retroviruses ( 9 ) . Because TRIM5 a interacts with the retroviral CA only in the multimeric structure characteristic of mature retroviral particles ( 10 ) , we generated the PtERV1 capsid core in the context of an infectious virus capable of only a single round of infection . This was achieved by constructing a chimeric virus between PtERV1 and MLV that encodes a gag / pol gene expressing the recon - structed p12 and CA proteins of PtERV1 with the remainder of the viral structural proteins and enzymes of MLV ( Fig . 1C ) . Our MLV / PtERV1 chimeric virus was indeed infectious ( Fig . 1D ) , which demonstrates that regions of a 3 - to 4 - million - year - old primate endogenous retrovirus can be successfully resurrected . We tested human TRIM5 a restriction of PtERV1 by infecting cells that express an exogenous copy of human TRIM5 a . A much younger humanendogenous retrovirus , HERV - K , was also recently resurrected ( 11 , 12 ) but was not restricted by human TRIM5 a ( 12 ) . In contrast , expression of human TRIM5 a in a heterologous cell type resulted in a dramatic reduction of infectivity of the MLV / PtERV1 chimera by a factor of more than 100 compared with cells that do not express TRIM5 a ( Fig . 2A ) . These data indicate that humans possess an intrinsic immu - nity gene capable of effectively neutralizing an extinct retrovirus that never successfully fixed into the human genome . Specificity of TRIM5 a for a particular retro - viral capsid is largely determined by amino acids within the C - terminal B30 . 2 domain . Within this 1 Molecular and Cellular Biology Program , University of Washington , Seattle , WA 98195 , USA . 2 Division of Human Biology , Fred Hutchinson Cancer Research Center , Seattle , WA 98109 , USA . 3 Division of Basic Sciences , Fred Hutchinson Cancer Research Center , Seattle , WA 98109 , USA . * To whom correspondence should be addressed . E - mail : memerman @ fhcrc . org 22 JUNE 2007 VOL 316 SCIENCE www . sciencemag . org 1756 REPORTS on N o v e m be r 2 , 2007 www . sc i en c e m ag . o r g D o w n l oaded f r o m