Genome-Scale Transcriptional Regulatory Network Models of Psychiatric and Neurodegenerative Disorders

Abstract

Graphical AbstractHighlights d Network model predicts binding sites and target genes of 741 TFs in the human brain d Target genes of key regulator TFs were dysregulated in brain diseases d POU3F2 regulates a schizophrenia-and bipolar-disorderassociated gene network SUMMARY Transcriptional regulatory changes in the developing and adult brain are prominent features of brain diseases, but the involvement of specific transcription factors (TFs) remains poorly understood. We integrated brain-specific DNase footprinting and TFgene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain. We identified key regulator TFs whose predicted target genes were enriched for differentially expressed genes in the prefrontal cortex of individuals with psychiatric and neurodegenerative diseases. Many of these TFs were further implicated in the same diseases through disruption of their binding sites by disease-associated SNPs and associations of TF loci with disease risk. Using primary human neural stem cells, we validated network predictions that link the TF POU3F2 to schizophrenia and bipolar disorder via both cisand trans-acting mechanisms. Our models of brain-specific TF binding sites and target genes provide a resource for network analysis of brain diseases.

Divergent brain gene expression patterns associate with distinct cell-specific tau neuropathology traits in progressive supranuclear palsy

“…5 ]. We also performed transcription factor (TF) regulatory network analysis using TReNA [ 41 ] and identified TFs within the three most significant modules, some of which have nominally significant cis -eQTL [suppl. Table 16 (Online Resource 1)].…”

“…The differentially expressed gene set was defined as DEGs identified in the Mayo RNAseq cohort (PSP vs. Control) with an FDR adjusted q value < 0.1. To identify transcription factors (TFs) which reside within co-expression modules of interest, we utilized the transcriptional regulatory network analysis package TReNA [ 41 ] suppl.text (Online Resource 3). Bubble plots highlighting significant GO BP (Bonferroni adjusted p value < 0.05) for targeted modules were generated using REVIGO [ 53 ] as described under the “Gene expression, neuropathological latent trait correlations ” methods section.…”

Behavioral Sensitization Induced by Methamphetamine Causes Differential Alterations in Gene Expression and Histone Acetylation of the Prefrontal Cortex in Rats

“…The change in the patterns suggests that histone acetylation of POU3F2 is involved in the mechanism of neuroplasticity underlying the transition from recreational METH use to compulsive use. Moreover, POU3F2 is involved in schizophrenia [54,55], the syndrome and pathology of which are very similar to METH addiction. Therefore, we speculated that POU3F2 potentially represents one of the common genetic bases underlying schizophrenia and METH use.…”

Identification of FMR1-regulated molecular networks in human neurodevelopment

“…To identify plausible network paths connecting FMR1 CLIP-seq targets to the RNA-seq DEGs, first we tested DEGs for enrichment of binding sites of TFs (FDR < 0.05), using transcriptional regulatory networks inferred specifically for neuronal cell types (Chasman et al 2019;Pearl et al 2019). Next, we combined the transcriptional networks with the STRING network and identified all possible paths from CLIP targets to the TFs of DEGs allowing for 0, 1, or 2 intermediate nodes.…”