Genetic Profiles Playing Opposite Roles of Pathogenesis in Schizophrenia and Glioma

Wen, Yujie; Xia, Zhiwei; Li, Dongjie; Cheng, Quan; Zhao, Qing; Cao, Hui

doi:10.1155/2020/3656841

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…As for SLC12A6, its corresponding variant (rs117799466) increases the binding affinity for many TFs with repressive capabilities. The three TFs with the greatest binding affinity gains at this locus – SP1, ZN281, and MAZ – all play a role in neuronal differentiation 58,64,65 , and both SP1 and ZN281 have also shown increased expression levels in SCZ as well 66,67 . Combined with their increased binding affinity incurred by rs117799466, the upregulation of these TFs may contribute to the repression of SLC12A6, an outcome that has been previously linked to the potassium uptake impairments that characterize SCZ glia 53 .…”

Section: Introductionmentioning

confidence: 99%

Unveiling causal regulatory mechanisms through cell-state parallax

Singh

Walsh

et al. 2023

Preprint

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Genome-wide association studies (GWAS) often report disease-linked genetic variation at noncoding genomic loci, but it is difficult to identify which genes these loci affect. Advances in single-cell multimodal assays that profile chromatin accessibility and gene expression in the same cell hold promise for addressing this challenge, as they can reveal causal locus-gene relationships. However, existing computational approaches for estimating such relationships from single-cell data are unable to account for what we refer to as “cell-state parallax,” the time lag between epigenetic and transcriptional modalities due to their cause-and-effect relationship. In dynamic biological processes, chromatin regulatory potential is a manifestation of this parallax which we hypothesized could be recovered from single-cell snapshots. Leveraging the econometric concept of predictive temporal causality, we newly identify tissue-specific locus– gene associations where accessibility of the locus predicts expression of the gene. Our algorithm, GrID-Net, is a neural network-based generalization of classical Granger causal inference that enables graph-structured analysis, instead of being limited to sequential orderings. We applied it to a single-cell study of human corticogenesis to predict neuronal cis-regulatory elements, enabling us to interpret genetic variants in schizophrenia (SCZ). We identified 132 genes linked to common noncoding SCZ variants, including the potassium transporters KCNG2 and SLC12A6, and we present a strategy for unveiling the regulatory mechanisms underlying gene dysregulation. Our work points to the transformative potential of single-cell multimodal assays for discovering novel gene regulatory mechanisms and provides a general framework for linking genetic variants to gene dysregulation in disease.

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Section: Introductionmentioning

confidence: 99%

Unveiling causal regulatory mechanisms through cell-state parallax

Singh

Walsh

et al. 2023

Preprint

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“…Through employment of bioinformatics tools like Weighted Gene Co-Expression Network Analysis (WGCNA) in several studies, interactions between risk co-expressed gene have been identi ed in patients with SCZ (20,21) in addition to normal and other mental diseases (22). For instance, Wen et al (23) identi ed 134 SCZ-speci c key genes using WGCNA and Radulescu et al (24) found 12 gene coexpression network modules in postmortem brain samples of SCZ patients. A number of other modules have been discovered using construction of WGCNA in other studies (25)(26)(27).…”

Section: Introductionmentioning

confidence: 99%

MiR-574-5P, miR-1827, And miR-4429 As Potential Biomarkers For Schizophrenia

Davarinejad

Najafi

Zhaleh

et al. 2021

Preprint

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Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Lack of a reliable mollecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, two mRNA expression arrays including GSE93987 and GSE38485, and one miRNA array, GSE54914, were downloaded from GEO, and meta-analysis was performed for mRNA expression arrays by employment of metaDE package. By WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we made protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array and dysregulated miRNAs (DEMs) were identified. Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulation of genes, expression values were evaluated by available datasets including GEO series GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate Schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, by performing Real-Time PCR, the expression level of genes and miRNAs were evaluated in 40 Schizophrenia patients compared with healthy controls. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients.

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