A tripartite transcription factor network regulates primordial germ cell specification in mice


Transitions in cell states are controlled by combinatorial actions of transcription factors. BLIMP1, the key regulator of primordial germ cell (PGC) specification, apparently acts together with PRDM14 and AP2γ. To investigate their individual and combinatorial functions, we first sought an in vitro system for transcriptional readouts and chromatin immunoprecipitation sequencing analysis. We then integrated this data with information from single-cell transcriptome analysis of normal and mutant PGCs. Here we show that BLIMP1 binds directly to repress somatic and cell proliferation genes. It also directly induces AP2γ, which together with PRDM14 initiates the PGC-specific fate. We determined the occupancy of critical genes by AP2γ—which, when computed altogether with those of BLIMP1 and PRDM14 (both individually and cooperatively), reveals a tripartite mutually interdependent transcriptional network for PGCs. We also demonstrate that, in principle, BLIMP1, AP2γ and PRDM14 are sufficient for PGC specification, and the unprecedented resetting of the epigenome towards a basal state.

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Figure 1: BLIMP1, AP2γ and PRDM14 repress somatic regulators and induce PGC genes in P19ECs.
Figure 2: BLIMP1 binding to gene promoters encoding transcription factors, and cell-cycle and developmental regulators.
Figure 3: RNA-seq analysis of PGCs, and BLIMP1 binding to differentially regulated genes.
Figure 4: BLIMP1 represses most of its direct targets.
Figure 5: AP2γ binds to germ cell genes and somatic regulators.
Figure 6: A transcription factor network for PGC specification.
Figure 7: BLIMP1 binds to targets of mESC self-renewal regulators and polycomb proteins.
Figure 8: Co-expression of BLIMP1, AP2γ and PRDM14 induces PGC-like cell fate in vitro.

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  1. 1

    Fuhrmann, G. et al. Mouse germline restriction of Oct4 expression by germ cell nuclear factor. Dev. Cell 1, 377–387 (2001).

  2. 2

    Seki, Y. et al. Extensive and orderly reprogramming of genome-wide chromatin modifications associated with specification and early development of germ cells in mice. Dev. Biol. 278, 440–458 (2005).

  3. 3

    Borgel, J. et al. Targets and dynamics of promoter DNA methylation during early mouse development. Nat. Genet. 42, 1093–1100 (2010).

  4. 4

    McLaren, A. & Lawson, K. A. How is the mouse germ-cell lineage established? Differentiation 73, 435–437 (2005).

  5. 5

    Saitou, M., Barton, S. C. & Surani, M. A. A molecular programme for the specification of germ cell fate in mice. Nature 418, 293–300 (2002).

  6. 6

    Yamaji, M. et al. Critical function of Prdm14 for the establishment of the germ cell lineage in mice. Nat. Genet. 40, 1016–1022 (2008).

  7. 7

    Kurimoto, K. et al. Complex genome-wide transcription dynamics orchestrated by Blimp1 for the specification of the germ cell lineage in mice. Genes Dev. 22, 1617–1635 (2008).

  8. 8

    Robertson, E. J. et al. Blimp1 regulates development of the posterior forelimb, caudal pharyngeal arches, heart and sensory vibrissae in mice. Development 134, 4335–4345 (2007).

  9. 9

    Ohinata, Y. et al. Blimp1 is a critical determinant of the germ cell lineage in mice. Nature 436, 207–213 (2005).

  10. 10

    Vincent, S. D. et al. The zinc finger transcriptional repressor Blimp1/Prdm1 is dispensable for early axis formation but is required for specification of primordial germ cells in the mouse. Development 132, 1315–1325 (2005).

  11. 11

    Saitou, M., Payer, B., O’Carroll, D., Ohinata, Y. & Surani, M. A. Blimp1 and the emergence of the germ line during development in the mouse. Cell Cycle 4, 1736–1740 (2005).

  12. 12

    Turner, C. A., Mack, D. H. & Davis, M. M. Blimp-1, a novel zinc finger-containing protein that can drive the maturation of B lymphocytes into immunoglobulin-secreting cells. Cell 77, 297–306 (1994).

  13. 13

    Keller, A. D. & Maniatis, T. Identification and characterization of a novel repressor of beta-interferon gene expression. Genes Dev. 5, 868–879 (1991).

  14. 14

    Morgan, M. a J. et al. Blimp-1/Prdm1 alternative promoter usage during mouse development and plasma cell differentiation. Mol. Cell. Biol. 29, 5813–5827 (2009).

  15. 15

    Martins, G. A. et al. Transcriptional repressor Blimp-1 regulates T cell homeostasis and function. Nat. Immunol. 7, 457–465 (2006).

  16. 16

    Kallies, A. et al. Transcriptional repressor Blimp-1 is essential for T cell homeostasis and self-tolerance. Nat. Immunol. 7, 466–474 (2006).

  17. 17

    Chan, Y-H. et al. Absence of the transcriptional repressor Blimp-1 in hematopoietic lineages reveals its role in dendritic cell homeostatic development and function. J. Immunol. 183, 7039–7046 (2009).

  18. 18

    Nishikawa, K. et al. Blimp1-mediated repression of negative regulators is required for osteoclast differentiation. Proc. Natl Acad. Sci. USA 107, 3117–3122 (2010).

  19. 19

    Chang, D. H., Angelin-Duclos, C. & Calame, K. BLIMP-1: Trigger for differentiation of myeloid lineage. Nat. Immunol. 1, 169–176 (2000).

  20. 20

    Kim, S. J., Zou, Y. R., Goldstein, J., Reizis, B. & Diamond, B. Tolerogenic function of Blimp-1 in dendritic cells. J. Exp. Med. 208, 2193–2199 (2011).

  21. 21

    Yu, J., Angelin-Duclos, C., Greenwood, J., Liao, J. & Calame, K. Transcriptional repression by Blimp-1 (PRDI-BF1) involves recruitment of histone deacetylase. Mol. Cell. Biol. 20, 2592–2603 (2000).

  22. 22

    Ren, B., Chee, K. J., Kim, T. H. & Maniatis, T. PRDI-BF1/Blimp-1 repression is mediated by corepressors of the Groucho family of proteins. Genes Dev. 13, 125–137 (1999).

  23. 23

    Gyory, I., Wu, J., Fejér, G., Seto, E. & Wright, K. L. PRDI-BF1 recruits the histone H3 methyltransferase G9a in transcriptional silencing. Nat. Immunol. 5, 299–308 (2004).

  24. 24

    Ancelin, K. et al. Blimp1 associates with Prmt5 and directs histone arginine methylation in mouse germ cells. Nat. Cell Biol. 8, 623–630 (2006).

  25. 25

    Su, S-T. et al. Involvement of histone demethylase LSD1 in Blimp-1-mediated gene repression during plasma cell differentiation. Mol. Cell. Biol. 29, 1421–1431 (2009).

  26. 26

    Cretney, E. et al. The transcription factors Blimp-1 and IRF4 jointly control the differentiation and function of effector regulatory T cells. Nat. Immunol. 12, 304–311 (2011).

  27. 27

    Weber, S. et al. Critical function of AP-2 gamma/TCFAP2C in mouse embryonic germ cell maintenance. Biol. Reprod. 82, 214–223 (2010).

  28. 28

    Hackett, J. a, Zylicz, J. J. & Surani, M. A. Parallel mechanisms of epigenetic reprogramming in the germline. Trends Genet. 28, 1–11 (2012).

  29. 29

    Mcburney, M. Isolation of male embryonal carcinoma cells and their chromosome replication patterns*1. Dev. Biol. 89, 503–508 (1982).

  30. 30

    Yeom, Y. I. et al. Germline regulatory element of Oct-4 specific for the totipotent cycle of embryonal cells. Development 122, 881–894 (1996).

  31. 31

    Chambers, I. et al. Functional expression cloning of nanog, a pluripotency sustaining factor in embryonic stem cells. Cell 113, 643–655 (2003).

  32. 32

    Yabuta, Y., Kurimoto, K., Ohinata, Y., Seki, Y. & Saitou, M. Gene expression dynamics during germline specification in mice identified by quantitative single-cell gene expression profiling. Biol. Reprod. 75, 705–716 (2006).

  33. 33

    Mitsui, K. et al. The homeoprotein Nanog is required for maintenance of pluripotency in mouse epiblast and ES cells. Cell 113, 631–642 (2003).

  34. 34

    Ma, Z., Swigut, T., Valouev, A., Rada-Iglesias, A. & Wysocka, J. Sequence-specific regulator Prdm14 safeguards mouse ESCs from entering extraembryonic endoderm fates. Nat. Struct. Mol. Biol. 18, 120–127 (2011).

  35. 35

    Kuo, T. C. & Calame, K. L. B lymphocyte-induced maturation protein (Blimp)-1, IFN regulatory factor (IRF)-1, and IRF-2 can bind to the same regulatory sites. J. Immunol. 173, 5556–5563 (2004).

  36. 36

    Hayashi, K., Ohta, H., Kurimoto, K., Aramaki, S. & Saitou, M. Reconstitution of the mouse germ cell specification pathway in culture by pluripotent stem cells. Cell 146, 519–532 (2011).

  37. 37

    Woodfield, G. W., Chen, Y., Bair, T. B., Domann, F. E. & Weigel, R. J. Identification of primary gene targets of TFAP2C in hormone responsive breast carcinoma cells. Genes Chromosomes Cancer 962, 948–962 (2010).

  38. 38

    Tan, S. K. et al. AP- 2γ regulates oestrogen receptor-mediated long-range chromatin interaction and gene transcription. EMBO J. 30, 2569–2581 (2011).

  39. 39

    Kidder, B. L. & Palmer, S. Examination of transcriptional networks reveals an important role for TCFAP2C, SMARCA4, and EOMES in trophoblast stem cell maintenance. Genome Res. 20, 458–472 (2010).

  40. 40

    Loh, Y. H. et al. The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells. Nat. Genet. 38, 431–440 (2006).

  41. 41

    Chen, X. et al. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133, 1106–1117 (2008).

  42. 42

    Martello, G et al. ESRRB is a pivotal target of the GSK3/Tcf3 axis regulating embryonic stem cell self-renewal. Cell Stem Cell 11, 491–504 (2012).

  43. 43

    Nakamura, T. et al. PGC7 binds histone H3K9me2 to protect against conversion of 5mC to 5hmC in early embryos. Nature 486, 415–419 (2012).

  44. 44

    Grabole, Nils et al. Prdm14 promotes germline fate and naïve pluripotency by modulating signalling and the epigenome. EMBO Rep. 14, 1–9 (2013).

  45. 45

    Hajkova, P. et al. Epigenetic reprogramming in mouse primordial germ cells. Mech. Dev. 117, 15–23 (2002).

  46. 46

    Hajkova, P. et al. Chromatin dynamics during epigenetic reprogramming in the mouse germ line. Nature 452, 877–881 (2008).

  47. 47

    Ma, Z., Swigut, T., Valouev, A., Rada-Iglesias, A. & Wysocka, J. Sequence-specific regulator Prdm14 safeguards mouse ESCs from entering extraembryonic endoderm fates. Nat. Struct. Mol. Biol. 18 (2011) 120–117.

  48. 48

    Kuckenberg, P. et al. The transcription factor TCFAP2C/AP-2gamma cooperates with CDX2 to maintain trophectoderm formation. Mol. Cell. Biol. 30, 3310–3320 (2010).

  49. 49

    Chia, N-Y. et al. A genome-wide RNAi screen reveals determinants of human embryonic stem cell identity. Nature 468, 316–320 (2010).

  50. 50

    Gillich, A. et al. Epiblast stem cell-based system reveals reprogramming synergy of germline factors. Cell Stem. Cell 10, 425–439 (2012).

  51. 51

    Magnúsdóttir, E. et al. Epidermal terminal differentiation depends on B lymphocyte-induced maturation protein-1. Proc. Natl Acad. Sci. USA 104, 14988–14993 (2007).

  52. 52

    Ng, J-H. et al. In vivo epigenomic profiling of germ cells reveals germ cell molecular signatures. Deve. Cell 24, 324–333 (2013).

  53. 53

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  54. 54

    Ji, H. et al. An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat. Biotech. 26, 1293–1300 (2008).

  55. 55

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  56. 56

    Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  57. 57

    Zhu, L. J. et al. ChIPpeakAnno: A Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 11, 237 (2010).

  58. 58

    Thomas, P. D. PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nuclic Acids Res. 31, 334–341 (2003).

  59. 59

    Machanick, P. & Bailey, T. L. MEME-ChIP: Motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).

  60. 60

    Zhang, Z., Chang, C. W., Goh, W. L., Sung, W-K. & Cheung, E. CENTDIST: Discovery of co-associated factors by motif distribution. Nucli. Acids Res. 39, W391–W399 (2011).

  61. 61

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  62. 62

    Huang, D. W., Sherman, B. T. & Lempicki, R. a Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4, 44–57 (2009).

  63. 63

    Tang, F. et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6, 468–478 (2010).

  64. 64

    De Hoon, M. J. L., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004).

  65. 65

    Ouyang, Z., Zhou, Q. & Wong, W. H. ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proc. Natl Acad. Sci. USA 106, 21521–21526 (2009).

  66. 66

    Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

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We thank N. Miller for flow cytometry, and M. Trotter, C. Bradshaw and G. Allen for bioinformatics analysis. We thank S. Kim for help with figures. We thank R. Sengupta and J. Hackett for critically reading the manuscript. This work was supported by grants from the Wellcome Trust and HFSP to M.A.S. and the ERC to E.M.

Author information

E.M. and M.A.S. conceived of and designed the study; E.M., K.M., U.G., F.T. and S.B. performed the experiments; E.M., S.D. and E.D. performed computational analysis; K.L. performed RNA sequencing; B.G., E.M. and M.A.S carried out critical assessment of the data; M.A.S. and E.M. wrote the manuscript.

Correspondence to M. Azim Surani.

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Integrated supplementary information

Supplementary Figure 1 Expression profiling of P19ECs with ectopic expression of BLIMP1.

(a). Flow cytometric plot showing the fluorescence intensity of EGFP and BLIMP1–EGFP transfected cells. Gates employed for cell sorting are indicated. (b) Heat map showing differentially regulated genes in P19ECs upon BLIMP1 expression. Each column represents a time point assayed in triplicate. The colours indicate the z-score for differential expression. (c) Gene ontology analysis of the top 10% of genes repressed and induced upon BLIMP1 expression in P19ECs.

Supplementary Figure 2 Functional categories of differentially expressed genes upon BLIMP1 expression in P19ECs.

(a-f). Gene ontology analysis of all genes with significant expression changes (FDR <0.005) in P19ECs upon BLIMP1 expression for both induced and repressed genes for each of the 3 comparisons performed.

Supplementary Figure 3 RNA-seq analysis during PGC specification; integrative analysis of PGC transcriptome, BLIMP1 induced changes in P19EC profiles, and BLIMP1targets.

(a and b). Gene ontology analysis showing functional categories of genes repressed and induced respectively, between E7.5 PGCs and somatic cells and the genes from the comparison that are bound by BLIMP1. (c) Relative enrichment of BLIMP1 binding regions and the scores associated with genes differentially expressed between E8.5 PGCs and E7.5 Prdm1 (encoding BLIMP1)-KO PGC–like cells. (d) Correlation analysis of differentially expressed genes during PGC specification and upon BLIMP1 expression in P19ECs. The Pearson correlation coefficients are indicated in the bottom half for each pair-wise comparison and each point on the plot indicates the differential expression of a gene in the comparisons indicated on the x and y axes respectively by the juxtaposition to the squares along the diagonal. (e) Relative enrichment of BLIMP1 binding regions associated with genes that are differentially expressed both upon BLIMP1 expression in P19ECs as well as during PGC specification. The x axis indicates the log2 (fold change) and the y axis indicates the log2 of the BLIMP1 target enrichment at each fold change-interval of differentially expressed genes over the average target frequency of the whole expression data set. Peaks: the enrichment of peaks associated with genes in each interval differential expression expression level interval Scores; the enrichment of binding scores calculated for genes in each interval. Intersect: The enrichment of peaks associated with genes differentially expressed in both comparisons, in each interval of differential expression. (f). ChIP sequencing tracks from the UCSC browser of genes showing example genomic loci of genes bound by BLIMP1 and repressed in both PGCs and P19ECs upon BLIMP1 expression.

Supplementary Figure 4 AP2γ motif analysis on BLIMP1 and PRDM14 binding regions.

(a). TRANSFAC motif scanning of the BLIMP1 binding regions. The enrichment scores and p-values for the enrichment of each motif are indicated. (b) The distribution of the BLIMP1, AP2 alpha- and the AP2γ motifs on the BLIMP1 binding regions. (c) TRANSFAC motif scanning of the PRDM14 binding regions. d). The distribution of the PRDM14 motif, AP2 alpha and AP2γ motifs around the centre of the binding regions. (e). Relative enrichment of BLIMP1, and PRDM14 targets on differentially expressed genes between E7.5 soma and PGCs filtered by the association of an AP2γ motif in the peak region and the combinatorial association of the peak regions to the differentially expressed genes.

Supplementary Figure 5 GO-Term clustering of genes differentially associated with BLIMP1, AP2γ and Prdm14 during PGC specification.

Clustering analysis of the GO-terms associated with genes that are repressed (a) or induced (b). in PGCs compared to neighbouring somatic cells at E7.5 and bound by different combinations of either BLIMP1, AP2γ or Prdm14. The dark blue colour indicates that fewer than 4 genes were mapped to the indicated GO-term.

Supplementary Figure 6 A transcription factor network for PGC specification.

BLIMP1, AP2γ and Prdm14 binding to differentially expressed genes at E7.5 between PGCs and soma. (a) Repressed genes. (b) Induced genes. The yellow nodes indicate the BLIMP1, PRDM14 and AP2γ peak and associations. The smaller nodes indicate genes bound by the factors, with the binding associations indicated by lines connected to the yellow nodes. The colours of the gene-nodes indicate functional categories as shown.

Supplementary Figure 7 A compendium of mESC transcription factor integrated profiles.

(a). A full hierarchical clustering analysis of the genome-wide BLIMP1 and AP2γ binding patterns together with binding patterns of transcriptional regulators from mESCs. The red lines above the heat-maps indicate the main clusters, showing the pluripotency cluster (Oct4 etc), polycomb-cluster (Ring1b etc), self-renewal/proliferation cluster (N-Myc etc), and a genome-architectural cluster (CTCF etc). The combinatorial binding pattern analysis was performed by generating a unified data matrix based on 165,607 unique peak regions, indicating for each factor whether it was bound or not. Subsequently the hierarchical clustering and Pearson’s correlation coefficients shown in the heat map were used to investigate global relationships. The colours indicate level of correlation for all pairwise comparisons as indicated on the figure. Note that BLIMP1 associates most strongly with the self-renewal cluster and has high correlation with polycomb factors. PRDM14 associates with the pluripotency cluster whereas AP2γ binding does not correlate highly with mESC transcriptional regulators.

Supplementary Figure 8 Statistical testing for binding overlap between BLIMP1, Ap2γ and PRDM14 and co-expression of BLIMP1, AP2γ and PRDM14 induces PGC-like cell fate in vitro.

(a). Number of observed and expected overlap in genomic binding sites of BLIMP1, AP2γ and PRDM14, and a scatterplot showing the observed against expected overlap in genomic binding sites of: 1. AP2γ and BLIMP1, 2. BLIMP1 and PRDM14, and 3. AP2γ and PRDM14. The P-value for the enrichment of overlap in binding sites is P<0.0001 for all comparisons. (b) A contingency table for the calculation of a chi-square p-value for the overlap of genes bound by different combinations of BLIMP1, AP2γ and PRDM14. The calculated p-value based on this table was p<1×10−299. The total number of genes (34274) represents the number of unique gene identifiers in Ensembl that were a basis for the gene annotation of the respective transcription factor binding sites. (c). RT–qPCR analysis of sorted fluorescent PGCLCs on Day2 and 4 of either cytokine or doxycycline induction, as well as EpiLCs. The experiment is the second of two experiments performed. The first experiment is shown in Fig. 8.

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Magnúsdóttir, E., Dietmann, S., Murakami, K. et al. A tripartite transcription factor network regulates primordial germ cell specification in mice. Nat Cell Biol 15, 905–915 (2013) doi:10.1038/ncb2798

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