Research ArticlePSYCHIATRIC DISEASE

The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders

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Science Translational Medicine  19 Dec 2018:
Vol. 10, Issue 472, eaat8178
DOI: 10.1126/scitranslmed.aat8178
  • Fig. 1 Conservation of daM genes in postmortem brain tissue from different sources.

    (A) Genes detected in the daM (red) for the Stanley postmortem brain samples were also clustered in the modules for FCTX postmortem brain samples (blue) and BrainGVEX postmortem brain samples (turquoise). The preservation Zsummary of the daM was 36.8 for FCTX samples (B) and 10.9 for BrainGVEX samples (C) (Zsummary > 10 indicates high preservation). miRNA and mRNA microarray expression data were obtained for 138 postmortem frontal cortex samples from healthy control individuals in the FCTX dataset. RNA-seq data were obtained for postmortem prefrontal cortex samples from the BrainGVEX dataset for 63 healthy control individuals, 70 patients with SCZ, and 48 patients with BD.

  • Fig. 2 Enrichment of common and rare genetic variants in the daM for SCZ or BD postmortem brain tissue.

    (A) Enrichment of genes in the daM with common and rare variants. For common variants, we applied MAGMA and INRICH to test the enrichment. Self-contained gene set analysis tested whether genes in a gene set showed joint association with SCZ, and competitive gene set analysis tested whether those genes showed differential association with SCZ compared with other genes in the rest of the genome. Thresholds of 1 × 10−5, top 0.1%, top 1%, and top 5% of all significant single-nucleotide polymorphisms (SNPs) were used as index SNPs in INRICH, and none of them detected significant enrichment. For rare variants, data from two exome sequencing studies and one database were used to test the enrichment. We applied a hypergeometric method to test the overlap with genes collected in the study of Purcell et al. (20), the NPdenovo database (19), and the combined gene sets from these two studies. We applied logistic regression to test the rare variant burden using disruptive and damaging ultra-rare variant counts for each gene from the study of Genovese et al. (21). (B) Number of genes containing de novo or rare mutations from three sources (1921) overlapping with genes in the daM.

  • Fig. 3 Transcription factors and their targets in the daM.

    All mRNAs, miRNAs, and their relationships in the daM were plotted. The colored lines indicate the pairwise correlation extracted from module testing with rw > 0.05. Six transcription factors (POU3F2, PAX6, EPAS1, ZNF423, SOX5, and SOX9), their binding targets, and names of five miRNAs were labeled in this figure, and other genes in the module were plotted as dots in this network. Transcription factors and their targets are shown in the six colored boxes. rw is the weighted correlation coefficient from the transformed pairwise correlation matrix, where rw > 0.05 is equivalent to the original r > 0.607.

  • Fig. 4 A causal relationship between POU3F2 and hsa-miR-320e.

    (A and B) Quantitative polymerase chain reaction (qPCR) results after knocking down POU3F2 (A) and after overexpression of POU3F2 (B) in SH-SY5Y neuroblastoma cells. (C and D) qPCR results after knocking down hsa-miR-320e (C) and after overexpression of hsa-miR-320e (D) in SH-SY5Y cells. Orange bars indicate expression of POU3F2, hsa-miR-320e, and negative controls after knocking down or overexpressing POU3F2 or hsa-miR-320e. The blue bars indicate gene expression in control groups before knocking down or overexpressing POU3F2 or hsa-miR-320e. ECM7 and PSMB4 were negative controls for POU3F2, and CHMP2A and VPS29 were negative controls for the miRNA hsa-miR-320e. Three biological replicates were used, and for each biological replicate, we designed three technical replicates.**P < 0.01 and ***P < 0.001. Data are means ± SEM.

  • Fig. 5 POU3F2 regulates proliferation and differentiation of NPCs.

    (A) Immunostaining of human NPCs for EdU (a marker of proliferating cells) after POU3F2 knockdown (KD). (B) Quantification of NPC proliferation. (C) Immunostaining of human NPCs for Tuj1 (a marker of immature neurons) and MAP2 (a marker of mature neurons) after POU3F2 knockdown. (D) Quantification of differentiation of NPCs into neurons. (E and F) qPCR data for putative targets of POU3F2 after knocking down (E) or overexpressing (OE) POU3F2 (F) in NPCs. Three biological replicates were used, and for each biological replicate, we designed three technical replicates. *P < 0.05, **P < 0.01, and ***P < 0.001. Data are means ± SEM. Scale bars, 150 μm (A) and 50 μm (C).

  • Table 1 Characteristics of miRNAs in daM and their predicted binding targets.

    ME, module eigengene.

    miRNAChrCorrelated with ME
    (P value)
    miRWalk2.0WGCNA
    Genes predicted as
    binding targets in
    daM (no. of total
    target genes)
    Significance of
    binding targets in
    module (enrichment P
    value)
    No. of correlated
    genes in module
    (rw* > 0.05)
    hsa-miR-320bChr13.70 × 10−5333 (10,126)<1 × 10−1014
    hsa-miR-320cChr181.31 × 10−5303 (9,480)<1 × 10−1034
    hsa-miR-320dChr131.09 × 10−5297 (8,904)<1 × 10−1044
    hsa-miR-320eChr194.79 × 10−6263 (7,887)<1 × 10−1068
    hsa-miR-585Chr51.30 × 10−5139 (7,742)0.0460

    *rw is the weighted correlation coefficient from the transformed pairwise correlation matrix, where rw > 0.05 is equivalent to the original r > 0.607.

    Supplementary Materials

    • www.sciencetranslationalmedicine.org/cgi/content/full/scitranslmed.aat8178/DC1

      Materials and Methods

      Fig. S1. Causal relationship between transcription factors and miRNAs.

      Fig. S2. Differences in cell type proportions between cases (BD and SCZ) and controls.

      Fig. S3. Immunofluorescence staining of human NPCs.

      Fig. S4. RT-qPCR analysis of POU3F2 knockdown efficiency.

      Fig. S5. Distribution of eigengene values in daM.

      Table S1. Demographic and clinical information for discovery SMRI data.

      Table S2. Demographic and clinical information for replicated BrainGVEX data.

      Table S3. Overlap of genes in the daM and genes with rare variants.

      Table S4. Transcription factor expression associated with disease in SMRI and BrainGVEX data.

      Table S5. Transcription factor expression correlations in BrainGVEX data.

      Table S6. OCA (LEO.NB.OCA) and CPA (LEO.NB.CPA) scores for six transcription factors and one miRNA.

      Table S7. Sequences of RT-qPCR primers and siRNA.

      Table S8. Top 50 connected genes in the daM, miRNAs, transcription factors, and their WGCNA parameters.

      Table S9. POU3F2 and its putative targets in daM.

      References (4664)

    • This PDF file includes:

      • Methods
      • Materials and Methods
      • Fig. S1. Causal relationship between transcription factors and miRNAs.
      • Fig. S2. Differences in cell type proportions between cases (BD and SCZ) and controls.
      • Fig. S3. Immunofluorescence staining of human NPCs.
      • Fig. S4. RT-qPCR analysis of POU3F2 knockdown efficiency.
      • Fig. S5. Distribution of eigengene values in daM.
      • Table S1. Demographic and clinical information for discovery SMRI data.
      • Table S2. Demographic and clinical information for replicated BrainGVEX data.
      • Table S3. Overlap of genes in the daM and genes with rare variants.
      • Table S4. Transcription factor expression associated with disease in SMRI and BrainGVEX data.
      • Table S5. Transcription factor expression correlations in BrainGVEX data.
      • Table S6. OCA (LEO.NB.OCA) and CPA (LEO.NB.CPA) scores for six transcription factors and one miRNA.
      • Table S7. Sequences of RT-qPCR primers and siRNA.
      • Table S8. Top 50 connected genes in the daM, miRNAs, transcription factors, and their WGCNA parameters.
      • Table S9. POU3F2 and its putative targets in daM.
      • References (4664)

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