ZNF804a Regulates Expression of the Schizophrenia-Associated Genes PRSS16, COMT, PDE4B, and DRD2

ZNF804a was identified by a genome-wide association study (GWAS) in which a single nucleotide polymorphism (SNP rs1344706) in ZNF804a reached genome-wide statistical significance for association with a combined diagnosis of schizophrenia (SZ) and bipolar disorder. Although the molecular function of ZNF804a is unknown, the amino acid sequence is predicted to contain a C2H2-type zinc-finger domain and suggests ZNF804a plays a role in DNA binding and transcription. Here, we confirm that ZNF804a directly contributes to transcriptional control by regulating the expression of several SZ associated genes and directly interacts with chromatin proximal to the promoter regions of PRSS16 and COMT, the two genes we find upregulated by ZNF804a. Using immunochemistry we establish that ZNF804a is localized to the nucleus of rat neural progenitor cells in culture and in vivo. We demonstrate that expression of ZNF804a results in a significant increase in transcript levels of PRSS16 and COMT, relative to GFP transfected controls, and a statistically significant decrease in transcript levels of PDE4B and DRD2. Furthermore, we show using chromatin immunoprecipitation assays (ChIP) that both epitope-tagged and endogenous ZNF804a directly interacts with the promoter regions of PRSS16 and COMT, suggesting a direct upregulation of transcription by ZNF804a on the expression of these genes. These results are the first to confirm that ZNF804a regulates transcription levels of four SZ associated genes, and binds to chromatin proximal to promoters of two SZ genes. These results suggest a model where ZNF804a may modulate a transcriptional network of SZ associated genes

Recombinant and endogenous ZNF804a is localized to the nucleus of neural progenitor cells.

<p><b>A</b>) pCAG-ZNF804A was transfected into rat neural progenitor cells for twenty-four hours and processed for immunocytochemistry with antibodies against an myc-tag fused to ZNF804a. Expression of ZNF804a co-localizes with the nuclear counter stain DAPI (Scale bar = 10 µm). <b>B</b>) Immunohistochemistry using anti-ZNF804a antibodies showing endogenous ZNF804a protein (left panel) co-localizes (right panel) with the nuclear counter stain TOPRO (middle panel) in E11 neural progenitor cells within the ventricle zone. <b>C</b>) Endogenous ZNF804a protein is devoid of the cytoplasmic fraction and observed in the nuclear fraction. As a control to demonstrate proper cellular fractionation, Nestin, a cytoplasmic marker of neural progenitor cells, is observed in the cytoplasmic fraction. Likewise, Histone H3, a chromatin marker, is observed in the nuclear fraction.</p

ZNF804a regulates the transcription of several Schizophrenia- associated genes.

<p><b>A</b>) Quantitative RT-PCR was performed on 37 SZ associated gene transcripts following ZNF804A transfection. PRSS16 and COMT (red) showed robust upregulation of transcription twenty-four hours after transfection (Bonferroni corrected p<0.05; n = 5). Conversely, PDE4B and DRD2 (green) transcript levels were downregulated following ZNF804a transfection (Bonferroni corrected p<0.05; n = 5).</p

Endogenous ZNF804a binds to the DNA regions directly upstream of the transcription start site of PRSS16 and COMT.

<p><b>A</b>) Tiling qRT-PCR of the promoter sequences of PRSS16 and COMT following ChIP using anti-ZNF804a reveals enrichment for ZNF804A. Enrichment of ZNF804A on the PRSS16 promoter appears 1.5 Kb 5′ upstream of the transcription start sites (TSS) while enrichment on the COMT promoter appears 1 Kb 5′ upstream of the TSS.</p

Recombinant ZNF804a binds to the DNA regions directly upstream of the transcription start site of PRSS16 and COMT.

<p><b>A</b>) Chromatin was immunoprecipitated with antibody directed against the myc-tag or IgG as control and then probed with anti-ZNF804a antibody. Recombinant ZNF804a was correctly identified by anti-ZNF804a antibody. <b>B–C</b>) Tiling qRT-PCR of the promoter sequences of PRSS16 and COMT following ChIP against the myc-tag reveals enrichment for ZNF804A. Enrichment of ZNF804A on the PRSS16 promoter appears 1.5 Kb 5′ upstream of the transcription start sites (TSS) while enrichment on the COMT promoter appears 1 Kb 5′ upstream of the TSS <b>D</b>) Tiling qRT-PCR of the promoter sequences of DRD2 and PDE4B following ChiP did not result in enrichment of ZNF804a (all figures *p<0.05, <i>Students</i> t-test, Error bars indicate ± SEM).</p

InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification

Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, time-consuming, and may report fuzzy insulator annotations with low resolution. Therefore, we propose a weakly supervised deep learning method, InsuLock, to address these challenges. Specifically, InsuLock first utilizes a Siamese neural network to predict the existence of insulators within a given region (up to 2000 bp). Then, it uses an object detection module for precise insulator boundary localization via gradient-weighted class activation mapping (~40 bp resolution). Finally, it quantifies variant impacts by comparing the insulator score differences between the wild-type and mutant alleles. We applied InsuLock on various bulk and single-cell datasets for performance testing and benchmarking. We showed that it outperformed existing methods with an AUROC of ~0.96 and condensed insulator annotations to ~2.5% of their original size while still demonstrating higher conservation scores and better motif enrichments. Finally, we utilized InsuLock to make cell-type-specific variant impacts from brain scATAC-seq data and identified a schizophrenia GWAS variant disrupting an insulator loop proximal to a known risk gene, indicating a possible new mechanism of action for the disease

InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification

Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, time-consuming, and may report fuzzy insulator annotations with low resolution. Therefore, we propose a weakly supervised deep learning method, InsuLock, to address these challenges. Specifically, InsuLock first utilizes a Siamese neural network to predict the existence of insulators within a given region (up to 2000 bp). Then, it uses an object detection module for precise insulator boundary localization via gradient-weighted class activation mapping (~40 bp resolution). Finally, it quantifies variant impacts by comparing the insulator score differences between the wild-type and mutant alleles. We applied InsuLock on various bulk and single-cell datasets for performance testing and benchmarking. We showed that it outperformed existing methods with an AUROC of ~0.96 and condensed insulator annotations to ~2.5% of their original size while still demonstrating higher conservation scores and better motif enrichments. Finally, we utilized InsuLock to make cell-type-specific variant impacts from brain scATAC-seq data and identified a schizophrenia GWAS variant disrupting an insulator loop proximal to a known risk gene, indicating a possible new mechanism of action for the disease

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings

Abstract Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework