Aberrant transcriptomes and DNA methylomes define pathways that drive pathogenesis and loss of brain laterality/asymmetry in schizophrenia and bipolar disorder

Abstract

Although the loss of brain laterality is one of the most consistent modalities in schizophrenia (SCZ) and bipolar disorder (BD), its molecular basis remains elusive. Our limited previous studies indicated that epigenetic modifications are key to the asymmetric transcriptomes of brain hemispheres. We used whole-genome expression microarrays to profile postmortem brain samples from subjects with SCZ, psychotic BD [BD[+]] or non-psychotic BD [BD(-)], or matched controls (10/group) and performed whole-genome DNA methylation (DNAM) profiling of the same samples (3-4/group) to identify pathways associated with SCZ or BD[+] and genes/sites susceptible to epigenetic regulation. qRT-PCR and quantitative DNAM analysis were employed to validate findings in larger sample sets (35/group). Gene Set Enrichment Analysis (GSEA) demonstrated that BMP signaling and astrocyte and cerebral cortex development are significantly (FDR q < 0.25) coordinately upregulated in both SCZ and BD[+], and glutamate signaling and TGFβ signaling are significantly coordinately upregulated in SCZ. GSEA also indicated that collagens are downregulated in right versus left brain of controls, but not in SCZ or BD[+] patients. Ingenuity Pathway Analysis predicted that TGFB2 is an upstream regulator of these genes (p = .0012). While lateralized expression of TGFB2 in controls (p = .017) is associated with a corresponding change in DNAM (p ≤ .023), lateralized expression and DNAM of TGFB2 are absent in SCZ or BD. Loss of brain laterality in SCZ and BD corresponds to aberrant epigenetic regulation of TGFB2 and changes in TGFβ signaling, indicating potential avenues for disease prevention/treatment.

Keywords: NR2E1; TGFB2; DNA methylation; brain asymmetry; schizophrenia.

Figure 1:. Leading edges of gene sets in pathways coordinately upregulated by GSEA in SCZ and/or BD(+).

Linear modeling was performed to assign t statistics to each gene with respect to SCZ or BD(+), and GSEA was performed to identify Gene Ontology (GO) terms or BioCarta, KEGG or Reactome pathways with significant coordinate regulation in either or both disease states. Groups of significant gene sets representing several pathways or processes were selected, and the expression of the genes in any of the “leading edges” of each set (i.e., the genes that contributed the most to the significance of the result)is shown across all subjects (including non-psychotic BD, which was not included in the analysis). Rows and columns correspond to genes and samples, respectively. Expression values were scaled by z-normalizing expression values to a mean of zero and standard deviation of one across all samples in each row, with red and blue indicating z-scores ≥ 2 or ≤ −2, respectively. Genes are arranged in descending order from top to bottom by the magnitude of the t statistic used to rank genes for GSEA. Gene sets are grouped according to theme: (A) BMP signaling; (B) astrocyte differentiation; (C) glutamate metabolism and secretion; (D) cerebral cortex development; (E) TGFβ signaling. The membership of each gene in the leading edge of each gene set for each disease state (SCZ or BD(+)) in each analysis is denoted in a separate color (orange or yellow) for each gene set in each panel.

Figure 2:. Collagen gene sets are coordinately regulated downstream of TGF-beta with respect to brain laterality in a disease-dependent manner.

(A) Leading edges of collagen-related gene sets with significant interaction between SCZ or BD(+) with laterality. Linear modeling was performed to assign t statistics to each gene with respect to the interaction of SCZ or BD(+) with laterality, and GSEA was performed to identify Gene Ontology (GO) terms or BioCarta, KEGG or Reactome pathways with coordinate regulation in either or both disease states with respect to laterality that differs significantly from that in controls. The expression of the genes in any of the “leading edges” of each set (i.e., the genes that contributed the most to the significance of the result) is shown across all subjects (including non-psychotic BD, which was not included in the analysis). Rows and columns correspond to genes and samples, respectively. Expression values were scaled by z-normalizing expression values to a mean of zero and standard deviation of one across all samples in each row, with red and blue indicating z-scores ≥ 2 or ≤ −2, respectively. Genes are arranged in descending order from top to bottom by the magnitude of the t statistic used to rank genes for GSEA. The membership of each gene in the leading edge of each gene set for each disease state (SCZ or BD(+)) in each analysis is denoted in a separate color (orange or yellow) for each gene set. (B) Mechanistic network of TGFB2-driven regulation of genes with significant SCZ:laterality interaction. Ingenuity Pathway Analysis was used to predict upstream regulators of the 265 genes with SCZ:laterality p< 0.05 and called Present in ≥ 3 samples. TGFB2 and SMAD3 were predicted to be activated, and SMAD7 was predicted to be inhibited, leading to coordinate downregulation of genes such as collagens in the right brain of controls only.

Figure 3:. Validation of NR2E1 and TGFB2 gene expression by qPCR.

Relative expression of (A) NR2E1 (n=100) and (B) TGFB2 (n=102), normalized to beta-actin, divided by disease state and brain laterality. The mean of each group is indicated by a heavy gray line. Filled circles indicate samples that were profiled by gene expression microarray.

Figure 4:. Measurement of TGFB2 DNA methylation by qPCR.

(A) Location of two qPCR amplicons (site 1 and site 2) in the TGFB2 locus. (B, C) Measurement of 5-methylcytosine (5-mC) at each site (site 1: n=103 samples total; site 2: n=90 samples total), divided by disease state and brain laterality. The mean of each group is indicated by a heavy gray line. Filled circles indicate samples that were profiled by gene expression microarray.