Genomewide gain-of-function genetic screenidentifies functionally active genes in mouseembryonic stem cellsMoshe Pritsker*, Nicole R. Ford, Harry T. Jenq, and Ihor R. Lemischka†

Department of Molecular Biology, Princeton University, Princeton, NJ 08544

Edited by Rudolf Jaenisch, Whitehead Institute for Biomedical Research, Cambridge, MA, and approved March 23, 2006 (received for reviewNovember 11, 2005)

Embryonic stem (ES) cells hold great promise for the future ofmedicine. To elucidate the molecular mechanisms that control EScell self-renewal and differentiation, a comprehensive knowledgeof the molecules involved in these processes is required. Here wedescribe an effective approach for genomewide identification offunctionally active genes in ES cells. This approach combinesgenetic screens based on cDNA libraries with microarray detectionmethods to permit high-throughput functional analyses. We im-plement this strategy to identify genes whose overexpression canmaintain phenotypic properties of undifferentiated mouse ES cellsunder differentiation-inducing conditions, specifically in the ab-sence of leukemia inhibitory factor. The identified genes encode avariety of regulatory proteins whose function in ES cells waspreviously unknown. Moreover, our approach is capable of de-tecting genes whose overexpression promote differentiation orcell death. Overall, our studies establish a methodology for highlysensitive identification of genes that confer particular phenotypeson ES cells.

cDNA library � differentiation � microarray � phenotype � self-renewal

Mouse and human ES cells can be propagated extensively inculture as homogeneous self-renewing populations (1).

These cells are widely used for studies of developmental pro-cesses and thought to provide a system for design of noveltransplantation therapies (2, 3). In vitro, murine ES cells aremaintained in the presence of leukemia inhibitory factor (LIF),a growth factor activating the gp130�Stat3 signaling pathway (4).Undifferentiated ES cells rapidly proliferate, form tight colonieswith a characteristic morphology, possess high alkaline phos-phatase activity levels, and preserve their multilineage differen-tiation capacities. Upon removal of LIF, ES cells undergodifferentiation associated with decreased proliferation rates andmorphological changes. Previous studies uncovered various as-pects of ES self-renewal and differentiation (5–11). In addition,genomewide analyses have identified specific sets of transcriptsand splicing variants generated in ES and other stem cellpopulations (12–14). However, the current understanding ofmolecular mechanisms regulating self-renewal and differentia-tion is far from complete (15). For example, only two genes, thetranscription factors Nanog and Myc, have been shown to besufficient for maintaining undifferentiated ES cells in the ab-sence of LIF (5, 6, 16).

Gain-of-function genetic screens are a powerful method toidentify genes sufficient to confer a particular cellular phenotype(17). Such screens have a long history in studies of microorgan-isms. Gain-of-function screens generally begin with an introduc-tion of cDNA expression libraries into cells, followed by specificselective regimens. In mammalian cells, these screens usuallyrequire iterative rounds of selection and yield only one or fewfunctional gene products that are identified by clone sequencing.To facilitate the isolation of small numbers of gene productsamenable to direct sequencing, harsh selection conditions areused to minimize background noise. As a result, most gain-of-

function screens identify only the ‘‘most potent’’ phenotype-conferring genes. For example, the only currently describedgain-of-function screen in mouse ES cells identified a single geneencoding the transcription factor Nanog (5). Moreover, theclone sequencing method is incapable of negative detection,complicating the identification of gene products that promotedifferentiation or apoptosis (18). These limitations prevent thecomprehensive identification of functional genes in mammaliansystems.

In this study, we applied the microarray technology as amethod of large-scale parallel analysis to conduct comprehen-sive gain-of-function screens in ES cells (Fig. 1A). Our approachwas designed to simultaneously monitor the activity of all geneproducts in a cDNA library as they function to mediate a givenphenotype. We implemented this approach to identify geneswhose overexpression is sufficient to maintain undifferentiatedmouse ES cells in the face of differentiation-inducing conditions,specifically in the absence of LIF. We also show that ourapproach is capable of negative detection and can identify geneproducts that promote differentiation or cell death.

ResultsMicroarray-Assisted Gain-of-Function Screens. We introduced anepisomal cDNA library constructed from ES cells into theE14�T mouse ES cell line (Fig. 1 A; for complete experimentaldetails, see Materials and Methods; see also Supporting Materialsand Methods, which is published as supporting information onthe PNAS web site). This cell line constitutively expresses thepolyoma virus large T protein and can stably maintain episomalvectors carrying the polyoma origin of replication (4, 19).Transfected cells were divided into two pools and propagated for18 days in the presence or absence of LIF: permissive andselective conditions, respectively. The decreased proliferationrate associated with differentiation served as a selection crite-rion in our genetic screens. We reasoned that ES cells harboringgene products that maintain rapid proliferation rates wouldprogressively increase their relative representation in the cellpopulation propagated without LIF. Similarly, we expected thatcells harboring differentiation-inducing gene products wouldundergo progressive representational decreases during culture inthe presence of LIF. To simultaneously identify all gene productsthat confer these phenotypes, we used microarrays. Episomallibraries were isolated from transfected ES cells after selectionand used to prepare material for hybridization to microarrays.

Conflict of interest statement: No conflicts declared.

This paper was submitted directly (Track II) to the PNAS office.

Abbreviation: LIF, leukemia inhibitory factor.

*To whom correspondence may be sent at the present address: Harvard Medical School,Massachusetts General Hospital, 50 Blossom Street, EDR 410, Boston, MA 02114. E-mail:pritsker.moshe@mgh.harvard.edu.

†To whom correspondence may be addressed. E-mail: ilemischka@molbio.princeton.edu.

© 2006 by The National Academy of Sciences of the USA

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For analysis of obtained microarray data, we applied a 5-foldthreshold to ensure identification of genes that undergo signif-icant representational changes during selection. This threshold ishigher than the 2-fold value generally applied in the microarrayexperiments and considered biologically significant. It is alsojustified statistically: Only �1% of probes were identified as‘‘increased’’ by using this value. We identified �560 probes(representing 499 genes) whose intensity increased at least 5-foldon microarrays hybridized with material from the ‘‘�LIF’’conditions, relative to ‘‘�LIF’’ conditions (Table 2, which ispublished as supporting information on the PNAS web site).Probes representing Nanog and Oct4, whose overexpression isknown to promote self-renewal and differentiation (5, 7), re-spectively, showed expected changes in their signal intensity(Tables 3 and 4, which are published as supporting informationon the PNAS web site; microarray data). Intensity values forhousekeeping control genes such as Gapdh did not show signif-icant changes. Analysis of replicate experiments indicated re-producibility of screens conducted at the same conditions (Fig.4, which is published as supporting information on the PNASweb site). Correlation coefficients of all microarray valuesobtained at the same (both permissive) or different (permissiveand selective) conditions were 0.96 and 0.67, respectively. Weused PCR with gene-specific primers designed toward 3� ofcoding regions to confirm microarray-detected representationalchanges above the selected 5-fold threshold. All of the testedgenes were confirmed, and the examples are shown in Fig. 1B.

Identification of Genes that Rescue ES Cell Phenotype in the Absenceof LIF. From the set of probes whose intensity values increased inthe absence of LIF, we selected 25 genes for individual confir-

mation. No previous knowledge was used in selection of thesegenes. Full-length cDNAs derived from these genes were intro-duced individually into the episomal vector, and each was testedfor its ability to maintain growth of ES cells in the absence ofLIF. We further tested whether the identified genes can rescueother phenotypic properties of undifferentiated ES cells such ascolony formation at a clonal density, characteristic morphology,alkaline phosphatase activity, and the expression of pluripotencymarkers. Cells expressing Nanog and EGFP served as positiveand negative controls. In 11 of 25 tested cases (�40%), theidentified genes were sufficient to at least partially ‘‘rescue’’ EScell proliferation rates in the absence of LIF (Fig. 2 and Table1). Overexpression of these genes maintained at least a 10-foldhigher number of cells after 9 days without LIF, in comparisonwith the EGFP control. In comparison, these genes do notrender a significant growth advantage in the presence of LIF.The identified genes encode an array of regulatory proteins andshow different levels of rescue activity in the various assays. Forexample, the transcription factor Cited2 and signaling proteinPrune were sufficient to maintain all tested phenotypic proper-ties at levels observed with the positive Nanog control. On theother hand, the transcription regulator Chop10 maintainedrobust cell proliferation rates; however, a large fraction oftransfected cells displayed differentiated morphologies. Theweakly characterized Polycomb factor Nspc1 and intracellulartransport regulator Snx6 partially rescued cell growth; however,their overexpression was not sufficient to allow colony formationat a clonal density. The identified genes also were capable ofpromoting the continued expression of the ES cell pluripotencymarkers Rex1, Dppa4, and Zfp57 (20, 21). Expression levels ofthese markers decrease in the absence of LIF.

To compare the efficiency and sensitivity of our microarray-based approach with the traditional individual clone sequencingmethods, we randomly picked and sequenced 50 clones from thecDNA library isolated from ES cells cultured without LIF. Noneof the 11 functionally confirmed genes was found among thesesequenced clones. These observations clearly demonstrate thatour microarray-based approach can identify functional genes ata level of sensitivity not possible by using traditional clone-by-clone sequencing approaches.

To test the functional specificity of the identified genes in theES cell context, we analyzed the activities of Bmi1 and Cited1.These genes are closely related homologues of Nspc1 and Cited2,respectively. Neither Bmi1 nor Cited1 was capable of rescuingthe properties of undifferentiated ES cells in the absence of LIF.In fact, the overexpression of Cited1 and Bmi1 induced anopposite phenotypic effect, differentiation, both with and with-out LIF (Fig. 5, which is published as supporting information onthe PNAS web site). These data suggest that the observed ES cellfunctional activity is specific for individual members of multi-gene families.

Transcriptional Regulation of the Identified Genes. It is reasonableto expect that at least some of the genes that confer LIFindependence are regulated by LIF signaling at the transcrip-tional level. To test this hypothesis, we measured changes in theirexpression levels in response to withdrawal of LIF during a 5-daytime period (Fig. 6, which is published as supporting informationon the PNAS web site). At the used conditions, expression levelsof Socs-3, a known downstream target of the LIF�Stat3 signalingpathway, rapidly decreased (22). In comparison, the expressionlevels of the functionally identified genes remained unchanged.These data indicate that the identified genes are not activated byLIF at the transcriptional level. Transcription of Nanog also wasshown to be independent on LIF (5).

Identification of Genes that Induce Differentiation. The microarrayanalysis also has identified �3,000 probes (representing 2,953

Fig. 1. Microarray-assisted gain-of-function genetic screen in ES cells. (A)Schematic diagram for comprehensive functional analysis. The ES cells weretransfected with a cDNA library, selected and propagated at permissive orselective conditions in the presence or absence of LIF, respectively. The cDNAinsert populations before and after the screen were labeled and analyzed withmicroarrays. (B) Confirmation of microarray results. Changes in cDNA repre-sentation detected with microarrays were confirmed by using PCR with gene-specific primers and libraries before and after the screen as templates. GAPDHprimers were used as a control.

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genes) whose intensity decreased �5-fold in the library harbor-ing ES cells propagated at the permissive, LIF-containing con-ditions. From this set, we have selected 20 genes for functionalconfirmation. Our functional analyses found that nine of theselected genes induced the expected phenotypic effect, differ-entiation, when overexpressed in ES cells (Fig. 3 and Table 4).Cells transfected with these genes decreased their proliferationrate and underwent typical morphological changes associated

with differentiation. The identified genes encode various regu-latory proteins, including transcription factors, RNA-bindingfactors, and signaling molecules. Among the transcription fac-tors, we found the well known protooncogenes JunB and c-Fos,transcription repressor Nab1, and ETS family member SpiC. Wealso identified RNA-binding proteins, the ATP-dependent he-licase Ddx3, and translation regulator Pumilio 1. Other identi-fied genes included Catenin � 1, nucleosome-binding protein

Fig. 2. Confirmation of function for identified gene products. (A) Cumulative number of cells was determined for genes that rescue ES cell proliferation in theabsence of LIF. After transfection, ES cells were propagated in the absence of LIF, passaged, and counted every 3 days. Nanog and EGFP served as positive andnegative controls, respectively. Colored bars indicate the following: green, day 0; blue, day 3; yellow, day 6; red, day 9. (B) Fraction of stem cell colonies formedby transfected cells at a clonal density after 7 days in the absence of LIF. (C and D) Morphology (C) and alkaline phosphatase staining (D) of colonies formed ata clonal density after 7 days in the absence of LIF. (E) The functionally identified genes rescue expression of ES cell molecular markers after 4 days in the absenceof LIF, as determined by RT-PCR. Cells transfected with EGFP and Nanog served as a negative and positive control, respectively.

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Nsbp1, and ubiquitination regulator Fbxo 30. These resultsdemonstrate that our microarray-assisted approach also is ca-pable of negative detection and can identify genes that inducedifferentiation.

Sources of Noise. Although PCR analyses using gene-specificprimers confirmed microarray-detected changes (Fig. 1B), therate of functional confirmation for individual genes currentlyachieved in our experiments is �40% (Fig. 2). The followingobservations suggest that this experimental ‘‘noise’’ may be, atleast partially, due to differences between cDNAs representingidentified genes in the library used in the screen and cDNAswhose function was tested individually in our confirmationexperiments. For example, the microarray data showed a signif-icant increase in representation of the IFN Response Factor 1(IRF1) gene, which was further confirmed by PCR with specificprimers. A 5� truncated cDNA corresponding to an aminoterminus-deficient version of this protein was identified by

sequencing of clones isolated from cells after the screen. Sur-prisingly, overexpression of a full-length IRF1-encoding cDNAinduced cell death, opposite to the expected phenotype, whereascells carrying the 5� truncated construct proliferated normally.Similarly, truncated transcripts whose functions strongly differfrom the full-length genes were detected in gain-of-functionscreen studies previously published by other groups (23). Asanother example, constitutive expression of a Nanog cDNA witha truncated 3� UTR induced higher rates of proliferation thanthe full-length version. A possible explanation is the presence ofrepetitive B2 elements in the 3� UTR that can alter transcriptionfrom our vector (5).

Because of incomplete cDNA synthesis reactions, cDNAlibraries usually contain a significant number of clones repre-senting truncated transcripts. Approximately 50% of the clonesin the particular library used in our studies are full-length, anda similar percentage was detected among clones sequenced afterselection. Alternative splicing and alternative polyadenylationaffect �50% of mammalian genes, including those expressed inES cells (14, 24). Because microarray probes generally cover the3� portions of mRNAs and, therefore, cannot effectively distin-guish between full-length, truncated, or alternative isoforms,these phenomena may contribute to false positives observed inour studies. Additional possible sources of false positives alsomay include, for example, artificial f luctuations in the clonerepresentation during propagation of transfected cells in culture.However, to preserve the clone representation, the number oftransfected cells used in our studies was 100-fold higher than thenumber of independent recombinants present in the startingcDNA library.

We expect that employing full-length and completely charac-terized cDNA libraries will significantly improve the confirma-tion rate in our approach, so that each microarray-detectedchange in cDNA representation could be considered as asignificant indication of biological function. However, even intheir current format, our studies provide a significant advance-ment for genetic analysis in mammalian cells, as shown by a largenumber of functionally identified genes.

DiscussionThe development of large-scale robust methods for functionalanalyses is necessary to reduce the rapidly increasing gap be-tween the vast amount of gene expression data and the knowl-edge of biological function. Such methods have been widelyapplied in studies of microorganisms, yeast, and Escherichia coli(25–28). In these studies, we developed a combination of cDNA

Table 1. Genes that rescue ES cell phenotypic properties

Genes

Rescued ES properties

Rapidgrowth Morphology

Alkalinephosphatase

Akt1�PKB, Mm.6645, protein kinase � � �

Chop-10, Mm.110220, DNA damage-inducible transcript3 (Ddit3)

� � ���

Cited2, Mm.272321, Cbp�p300-interactingtransactivator 2

� � �

RhoJ, Mm.27467, Ras homolog gene family, member J � � �

NCE, Mm.337238, NEDD8-conjugating enzyme � � ���

Nspc1, Mm.12261, nervous system polycomb 1 � ��� ���

Prune, Mm.14155, Prune homolog (Drosophila) � � �

Asb6, Mm.27656, ankyrin repeat and SOCSbox-containing protein 6

��� ��� ���

Snx6, Mm.28240, sortin nexin 6 � ��� ���

Ssbp4, Mm.6667, single-stranded DNA-binding protein 4 � � �

Mm.41868, hypothetical protein ��� ��� ���

Fig. 3. Confirmation of function for genes promoting differentiation or celldeath. (A) Number of cells was determined after transfected ES cells werepropagated in the presence of LIF for 6 days. The EGFP construct served as acontrol. (B) Cell morphology of transfected cells.

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library screens and microarray analyses to identify functionalgenes in mouse ES cells. Although previously applied in E. coli(28), this methodology is used here in a mammalian system.Other large-scale gain-of-function techniques developed formammalian cells include transfections of arrayed cDNA collec-tions and transfected-cell microarrays (29, 30). Although valu-able and capable of providing genomewide functional informa-tion, these approaches require state-of-the-art procedures andspecial equipment such as robotic printers and analyzers. Incomparison, our approach is cost-effective and easily adaptablebecause it can be applied with any commercially availablemicroarrays and in any proliferating cell type after establishingmoderate selective conditions. We expect this approach willprovide information complementary to the previously describedlarge-scale loss-of-function approaches based on the RNA in-terference technology (31, 32).

Using the developed approach, we were able to identify anarray of genes that rescue the ES cell phenotypic properties inthe absence of LIF and whose function in ES cells was previouslyunknown. The identified genes encode a wide range of regula-tory proteins. We hypothesize that these genes inhibit differen-tiation processes induced at the withdrawal of LIF and they exerttheir activity through a variety of molecular pathways. Forexample, one of the identified genes encodes the Akt1 kinase,a known downstream component of the phosphatidylinositol3-kinase (PI3-K) pathway (33). Inhibition of this pathway wasshown to induce growth arrest and differentiation of ES cells inthe presence of LIF (34, 35). Then, it is reasonable to speculatethat the PI3-K pathway regulates genes that function down-stream of the LIF�Stat3 pathway. The rescue activity of anothergene, the Nedd8-conjugation enzyme NCE, suggests involve-ment of ubiquitin-mediated protein degradation in ES cellregulation. Previous studies have shown that NCE regulateslevels of the cell cycle inhibitor p27 (36). Another identifiedgene, Prune, was shown to undergo amplification in humansarcomas and possess a cAMP phosphodiesterase activity (37).It also was shown to interact with nm23-H1, a tumor suppressorgene encoding a nucleoside diphosphate kinase. An additionalgene with the rescue activity encodes the transcription factorCited2 that was shown previously to enhance proliferation ofembryonic fibroblasts (38). Interestingly, its close homologue,Cited1, induced an opposite effect, differentiation, which impliesspecificity of the identified functional activity across multigenefamilies. We also monitored changes in transcriptional levels ofthe genes with the rescue activity to test whether any of them isdirectly regulated by LIF signaling. We did not detect significantchanges in their expression as a result of LIF withdrawal. Theseresults indicate that the identified genes are not downstreamtranscriptional targets of the LIF�Stat3 signaling pathway. Asimilar lack of correlation between transcriptional regulationand functional activity was observed in genomewide studies ofyeast deletion mutants (27).

The set of genes inducing differentiation in ES cells includedtwo well known protooncogenes, JunB and c-Fos, that are knownto enhance proliferation and inhibit differentiation in other celltypes (39). In this set, we also identified SpiC, a member of theETS domain family of transcription factors activated in eryth-roleukemias (40). These data indicate that the functional activityof the identified genes depends on a specific context, which is anoverall set of molecules available in a particular type of cells.Therefore, gene functional roles must be experimentally deter-mined in each cell type of interest that further underlines therequirement for large-scale functional analyses such as thosepresented in these studies.

What is the role of the identified genes in vivo? Previousstudies indicate that a deficiency in either the Cited2 orAkt1gene is not detrimental to ES cell precursors in the mouseepiblast (41, 42). A possible explanation lies in the fundamental

difference between gain-of-function and loss-of-function analy-ses. Gain-of-function analysis identifies genes that are sufficientto confer a specific phenotype, whereas loss-of-function analysisidentifies genes that are necessary. The former type of analysiscannot determine whether genes are essential, whereas the latteris affected by functional redundancy in a studied system. Forexample, cyclin genes (e.g., cyclin E), whose functional roleswere firmly established in biochemical and gain-of-functionexperiments, were shown to be dispensable for mouse develop-ment (43). An additional possible explanation is that regulatorymechanisms in cultured ES cells do not accurately reflectregulatory pathways present in the early mouse embryo. Forexample, although LIF is required to maintain undifferentiatedES cells in vitro, mouse mutants that lack the LIF gene are viable(44). Further loss-of-function experiments are required to testwhether the genes identified in our studies are essential formaintenance of the ES cell phenotype.

Ultimately, a complete list of genetic perturbations, gain andloss of function, and their phenotypic consequences will benecessary to understand complex molecular mechanisms regu-lating ES cell self-renewal and differentiation. Our studiesestablish an efficient approach to comprehensively determinegain-of-function genetic modifications that confer particularfates on ES cells. Using lineage-specific reporter lines, thisapproach also can be used to identify gene products inducingdifferentiation toward a particular cell type and, thus, is poten-tially useful for development of stem cell therapies.

Materials and MethodsES Cell Culture. The E14�T ES cell line (gift of Austin Smith,University of Edinburgh) was used for most of the experiments.This cell line constitutively expresses the polyoma virus large Tantigen and, therefore, can maintain plasmid vectors (episomes)carrying the polyoma virus origin of replication such as pPyCA-GIP (4, 19). Cells were grown without feeder cells as describedelsewhere, in the presence of 15% FBS and LIF (4).

cDNA Library. The cDNA library in pPyCAGIP (gift of AustinSmith) prepared from undifferentiated ES cells cultured onembryonic fibroblasts has been described in ref. 5. We measuredthe fraction of full-length cDNAs. Sequences of 11 of 20randomly chosen clones included known initiation codons ascompared with annotations at the National Center for Biotech-nology Information database.

Gain-of-Function Screens in ES Cells. For the library screen, 5 � 107

E14�T cells were electroporated with 50 �g of the library DNA.The transfected cells were selected with puromycin (2 �g�ml) for6 days in regular growth conditions. After selection, the cellswere divided into two pools that were separately propagated inthe presence or absence of LIF for 18 days. At the end of thescreen, the library cDNA was extracted from cells, and cDNAinserts were amplified by using PCR with primers complimen-tary to the regions just outside of the cloning site; the reverseprimer was attached to the T7 promoter sequence. The productsof the reaction were used for in vitro biotinylation transcriptionreaction, and the biotinylated cRNA was analyzed by usingMouse430 microarrays (Affymetrix), according to the standardprocedure.

Analysis of Data from Genomewide Genetic Screens. To identifygenes that increase their representation in the total cDNApopulation in the absence of LIF, we compared microarrayprobe intensity values at �LIF versus �LIF conditions. Weidentified 560 probes that were recognized by the Affymetrixsystem as ‘‘present’’ and ‘‘increased’’ �5-fold (arbitrary thresh-old) at the �LIF conditions. To identify these genes that inducedifferentiation or cell death and, therefore, decrease their

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representation, we compared microarray probe intensity valuesbetween the library propagated at �LIF conditions and theinitial library. We identified 3,524 probes that were present in theinitial library and ‘‘decreased’’ �5-fold at the �LIF conditions.We applied PCR with gene-specific primers to confirm themicroarray data. The primers were designed to be in the 3� endsof coding regions corresponding to the design of microarrayprobes that are selected from 600 bases most proximal to the 3�end of the mRNA (Affymetrix) according to the manufacturer’sinstructions.

Experimental Confirmation of Gene Function. For experimentalconfirmation, we selected genes whose full-length cDNA cloneswere available at the Mammalian Gene Collection (Open Bio-systems, Huntsville, AL). The cDNA fragments were clonedbetween the XhoI and NotI sites of the pPyCAGIP vector. Theresulting constructs were individually tested in the E14�T cells,under conditions similar to those used in screens. To monitorproliferation, transfected cells were counted every 3 days. Todetermine an ability of identified genes to support stem cellcolony formation at clonal density, transfected cells were seeded1,000 per cm2 in the absence of LIF and presence of puromycin.The fraction of stem cell colonies was determined in 7 days byusing an alkaline phosphatase staining kit (Sigma).

Analysis of Gene Expression After LIF Withdrawal. ES cells werecultured in growth medium without LIF: 2 � 106, 1 � 106, 7 �

105, 5 � 105, and 3 � 105 cells per 10-cm plate for 1, 2, 3, 4, and5 days without replating. At these conditions, initial low-celldensities were used to avoid culture overgrowth. Total RNA wascollected and isolated with TRIzol (Invitrogen) and analyzed byusing Mouse430 microarrays (Affymetrix), according to thestandard procedure. PCR for 20–25 cycles with gene-specificprimers and AmpliTaq-Gold enzyme (Applied Biosystems) wasused to confirm expression profiles.

Transcriptional Analysis of ES Cell Molecular Markers. Transfectedcells were selected in the presence of LIF and puromycin andthen plated in the absence of LIF and presence of puromycin for4 days. Total RNA was collected, and gene expression wasanalyzed as described above.

Supporting Information. An extensive description of materials andmethods is included in Supporting Materials and Methods.

We thank Natalia Ivanova (Princeton University), Max Gassmann(University of Zurich), and Austin Smith and Ian Chambers (Universityof Edinburgh) for providing materials for this work. This work wassupported by funds from the National Institutes of Health, NationalInstitute of Diabetes and Digestive and Kidney Diseases. M.P. wassupported by the Burroughs Wellcome Fund fellowship in BiologicalDynamics.

1. Smith, A. G. (2001) Annu. Rev. Cell Dev. Biol. 17, 435–462.2. Hochedlinger, K. & Jaenisch, R. (2003) N. Engl. J. Med. 349, 275–286.3. Lengerke, C. & Daley, G. Q. (2005) Exp. Hematol. 33, 971–979.4. Niwa, H., Burdon, T., Chambers, I. & Smith, A. (1998) Genes Dev. 12,

2048–2060.5. Chambers, I., Colby, D., Robertson, M., Nichols, J., Lee, S., Tweedie, S. &

Smith, A. (2003) Cell 113, 643–655.6. Mitsui, K., Tokuzawa, Y., Itoh, H., Segawa, K., Murakami, M., Takahashi, K.,

Maruyama, M., Maeda, M. & Yamanaka, S. (2003) Cell 113, 631–642.7. Niwa, H., Miyazaki, J. & Smith, A. G. (2000) Nat. Genet. 24, 372–376.8. Sato, N., Meijer, L., Skaltsounis, L., Greengard, P. & Brivanlou, A. H. (2004)

Nat. Med. 10, 55–63.9. Boyer, L. A., Lee, T. I., Cole, M. F., Johnstone, S. E., Levine, S. S., Zucker, J. P.,

Guenther, M. G., Kumar, R. M., Murray, H. L., Jenner, R. G., et al. (2005) Cell122, 947–956.

10. Yamane, T., Dylla, S. J., Muijtjens, M. & Weissman, I. L. (2005) Proc. Natl.Acad. Sci. USA 102, 3312–3317.

11. Anneren, C., Cowan, C. A. & Melton, D. A. (2004) J. Biol. Chem. 279,31590–31598.

12. Ivanova, N. B., Dimos, J. T., Schaniel, C., Hackney, J. A., Moore, K. A. &Lemischka, I. R. (2002) Science 298, 601–604.

13. Ramalho-Santos, M., Yoon, S., Matsuzaki, Y., Mulligan, R. C. & Melton, D. A.(2002) Science 298, 597–600.

14. Pritsker, M., Doniger, T. T., Kramer, L. C., Westcot, S. E. & Lemischka, I. R.(2005) Proc. Natl. Acad. Sci. USA 102, 14290–14295.

15. Boiani, M. & Scholer, H. R. (2005) Nat. Rev. Mol. Cell. Biol. 6, 872–884.16. Cartwright, P., McLean, C., Sheppard, A., Rivett, D., Jones, K. & Dalton, S.

(2005) Development (Cambridge, U.K.) 132, 885–896.17. Stark, G. R. & Gudkov, A. V. (1999) Hum. Mol. Genet. 8, 1925–1938.18. Singhi, A. D., Kondratov, R. V., Neznanov, N., Chernov, M. V. & Gudkov,

A. V. (2004) Proc. Natl. Acad. Sci. USA 101, 9327–9332.19. Gassmann, M., Donoho, G. & Berg, P. (1995) Proc. Natl. Acad. Sci. USA 92,

1292–1296.20. Bortvin, A., Eggan, K., Skaletsky, H., Akutsu, H., Berry, D. L., Yanagimachi,

R., Page, D. C. & Jaenisch, R. (2003) Development (Cambridge, U.K.) 130,1673–1680.

21. Akagi, T., Usuda, M., Matsuda, T., Ko, M. S., Niwa, H., Asano, M., Koide, H.& Yokota, T. (2005) Biochem. Biophys. Res. Commun. 331, 23–30.

22. Duval, D., Reinhardt, B., Kedinger, C. & Boeuf, H. (2000) FASEB J. 14,1577–1584.

23. Erez, N., Milyavsky, M., Goldfinger, N., Peles, E., Gudkov, A. V. & Rotter, V.(2002) Oncogene 21, 6713–6721.

24. Modrek, B. & Lee, C. (2002) Nat. Genet. 30, 13–19.25. Smith, V., Botstein, D. & Brown, P. O. (1995) Proc. Natl. Acad. Sci. USA 92,

6479–6483.

26. Badarinarayana, V., Estep, P. W., 3rd, Shendure, J., Edwards, J., Tavazoie, S.,Lam, F. & Church, G. M. (2001) Nat. Biotechnol. 19, 1060–1065.

27. Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., Veronneau, S., Dow,S., Lucau-Danila, A., Anderson, K., Andre, B., et al. (2002) Nature 418,387–391.

28. Gill, R. T., Wildt, S., Yang, Y. T., Ziesman, S. & Stephanopoulos, G. (2002)Proc. Natl. Acad. Sci. USA 99, 7033–7038.

29. Ziauddin, J. & Sabatini, D. M. (2001) Nature 411, 107–110.30. Huang, Q., Raya, A., DeJesus, P., Chao, S.-H., Quon, K. C., Caldwell, J. S.,

Chanda, S. K., Izpisua-Belmonte, J. C. & Schultz, P. G. (2004) Proc. Natl. Acad.Sci. USA 101, 3456–3461.

31. Paddison, P. J., Silva, J. M., Conklin, D. S., Schlabach, M., Li, M., Aruleba, S.,Balija, V., O’Shaughnessy, A., Gnoj, L., Scobie, K., et al. (2004) Nature 428,427–431.

32. Berns, K., Hijmans, E. M., Mullenders, J., Brummelkamp, T. R., Velds, A.,Heimerikx, M., Kerkhoven, R. M., Madiredjo, M., Nijkamp, W., Weigelt, B.,et al. (2004) Nature 428, 431–437.

33. Watanabe, S., Umehara, H., Murayama, K., Okabe, M., Kimura, T. & Nakano,T. (January 9, 2006) Oncogene, 10.1038�sj.onc.1209307.

34. Jirmanova, L., Afanassieff, M., Gobert-Gosse, S., Markossian, S. & Savatier,P. (2002) Oncogene 21, 5515–5528.

35. Paling, N. R., Wheadon, H., Bone, H. K. & Welham, M. J. (2004) J. Biol. Chem.279, 48063–48070.

36. Podust, V. N., Brownell, J. E., Gladysheva, T. B., Luo, R.-S., Wang, C., Coggins,M. B., Pierce, J. W., Lightcap, E. S. & Chau, V. (2000) Proc. Natl. Acad. Sci.USA 97, 4579–4584.

37. D’Angelo, A., Garzia, L., Andre, A., Carotenuto, P., Aglio, V., Guardiola, O.,Arrigoni, G., Cossu, A., Palmieri, G., Aravind, L., et al. (2004) Cancer Cell 5,137–149.

38. Kranc, K. R., Bamforth, S. D., Braganca, J., Norbury, C., van Lohuizen, M. &Bhattacharya, S. (2003) Mol. Cell. Biol. 23, 7658–7666.

39. Wang, Z. Q., Liang, J., Schellander, K., Wagner, E. F. & Grigoriadis, A. E.(1995) Cancer Res. 55, 6244–6251.

40. Bemark, M., Martensson, A., Liberg, D. & Leanderson, T. (1999) J. Biol. Chem.274, 10259–10267.

41. Bamforth, S. D., Braganca, J., Eloranta, J. J., Murdoch, J. N., Marques, F. I.,Kranc, K. R., Farza, H., Henderson, D. J., Hurst, H. C. & Bhattacharya, S.(2001) Nat. Genet. 29, 469–474.

42. Cho, H., Thorvaldsen, J. L., Chu, Q., Feng, F. & Birnbaum, M. J. (2001) J. Biol.Chem. 276, 38349–38352.

43. Geng, Y., Yu, Q., Sicinska, E., Das, M., Schneider, J. E., Bhattacharya, S.,Rideout, W. M., Bronson, R. T., Gardner, H. & Sicinski, P. (2003) Cell 114,431–443.

44. Stewart, C. L., Kaspar, P., Brunet, L. J., Bhatt, H., Gadi, I., Kontgen, F. &Abbondanzo, S. J. (1992) Nature 359, 76–79.

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