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

Abstract

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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Acknowledgements

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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The authors declare no competing financial interests.

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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