A novel strategy for clustering major depression individuals using whole-genome sequencing variant data

Yu, Chenglong; Baune, Bernhard T.; Licinio, Júlio; Wong, Ma Li

doi:10.1038/srep44389

Cited by 17 publications

(6 citation statements)

References 39 publications

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“…The emergence of high‐throughput sequencing technologies has become an effective method to identify pathogenic genes in a variety of diseases which help to explore pathogenesis and develop biomarkers (Wong et al, ; Yu, Arcos‐Burgos, Licinio, & Wong, ; Yu, Baune, Licinio, & Wong, , ). To the best our knowledge, multiple studies have used whole genome sequencing to explore pathogenesis of MDD (Yu, Baune, Wong, & Licinio, , ).…”

Section: Introductionmentioning

confidence: 99%

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database

Feng¹,

Zhou²,

Gao³

et al. 2019

Asia-Pacific Psychiatry

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Introduction: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. The aim of this study was to identify potential pathological genes in MDD. Methods: We searched and downloaded gene expression data from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) in MDD. Then, Kyoto Encyclopedia of Genes and Genomes pathway, Gene Ontology analysis, and protein-protein interaction (PPI) network were applied to investigate the biological function of identified DEGs. The quantitative real-time polymerase chain reaction and a published dataset were used to validate the result of bioinformatics analysis. Results: A total of 514 DEGs were identified in MDD. In the PPI network, some hub genes with high degrees were identified, such as EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1. The result of in vitro validation of RPL26L1, RSRC1, TOMM20L, RPLPO, PRPF8, AP2B1, STIP1, and C5orf45 was consistent with the bioinformatics analysis. Electronic validation of C5orf45, STIP1, PRPF8, AP2B1, and SLC35E1 was consistent with the bioinformatics analysis. Discussion: The deregulated genes could be used as potential pathological factors of MDD. In addition, EEF2, RPL26L1, RPLP0, PRPF8, LSM3, DHX9, RSRC1, and AP2B1 might be therapeutic targets for MDD. K E Y W O R D S drug target, gene expression, in vitro validation, major depressive disorder, protein-protein interaction network

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Section: Introductionmentioning

confidence: 99%

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database

Feng¹,

Zhou²,

Gao³

et al. 2019

Asia-Pacific Psychiatry

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“…36 Novel strategis were applied, and rare variants was identified to be associated with major depression. 38 Whole-genome sequencing also found rare variants and loci were associated with MDD. 1,29 Our data showed that all 4 SNPs and the rs13411714-rs3820926 haplotype were not associated with symptom severity.…”

Section: Discussionmentioning

confidence: 99%

<p>Variants in the Upstream Region of the Insulin Receptor Substrate-1 Gene Is Associated with Major Depressive Disorder in the Han Chinese Population</p>

Wang¹,

Yu²,

Zhou³

et al. 2020

NDT

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Introduction: Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders, although its underlying genetic mechanism remains unknown. Insulin receptor substrate-1 (IRS-1) is one of the critical downstream molecules in the insulin resistance signaling pathway, linking depression and diabetes. Therefore, we hypothesized that IRS-1 would be a susceptible gene for MDD, and we aimed to examine the genetic association between IRS-1 and MDD. Methods: This case-control study included 583 patients with MDD and 564 controls, and the genotypic and allelic distributions of the IRS-1 gene's four single nucleotide polymorphisms (SNPs) were detected by TaqMan SNP genotyping technology. Of the 583 patients, 191 underwent a further detailed interview about symptom severity and family history of mental illness. The chi-square or t test was used to analyze the data, and analyses were performed using SPSS19.0 software. Results: A haplotype in the 5ʹ-upstream region of IRS-1 consisting of rs13411764 and rs3820926 was a risk factor of MDD. Patients with a family history of mental illness were more likely to have a GG genotype in rs13411764 and a G-T haplotype containing rs13411714-rs3820926. Discussion: The findings imply that the haplotype consisting of rs13411764 and rs3820926 in the upstream of IRS-1 is a risk factor for MDD. This haplotype could affect IRS-1 expression levels, and it is mostly inherited from parents. Thus, the presence of variants in the upstream region of IRS-1 is a risk factor of MDD, and this study could serve as a convincing reference for further studies.

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“…RNA-seq provides three to four magnitudes more information than previous gene expression systems. Deep sequencing has been applied to many different genomic levels including genomic DNA sequencing (DNA-seq) ( Yu et al, 2017 ), non-coding RNA profiling (smRNA-seq) ( Choi et al, 2005 ; Xu et al, 2011 ; Zhou et al, 2021 ), DNA-protein interactions (ChiP-seq) ( Barski et al, 2007 ) and DNA structure (e.g., open chromatin) (ATAC-seq) ( Bryois et al, 2018 ). The actual process of RNA-seq is detailed elsewhere but we will briefly review here.…”

Section: Transcriptomic Methodsmentioning

confidence: 99%

Transcriptomics of the depressed and PTSD brain

Zhang

Kaye

Wang

et al. 2021

Neurobiology of Stress

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Stress is the response of an organism to demands for change, yet excessive or chronic stress contributes to nearly all psychiatric disorders. The advent of high-throughput transcriptomic methods such as single cell RNA sequencing poses new opportunities to understand the neurobiology of stress, yet substantial barriers to understanding stress remain. Stress adaptation is an organismal survival mechanism conserved across all organisms, yet there is an infinity of potential stressful experiences. Unraveling shared and separate transcriptional programs for adapting to stressful experience remains a challenge, despite methodological and analytic advances. Here we review the state of the field focusing on the technologies used to study the transcriptome for the stress neurobiologist, and also attempt to identify central questions about the heterogeneity of stress for those applying transcriptomic approaches. We further explore how postmortem transcriptome studies aided by preclinical animal models are converging on common molecular pathways for adaptation to aversive experience. Finally, we discuss approaches to integrate large genomic datasets with human neuroimaging and other datasets.

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A novel strategy for clustering major depression individuals using whole-genome sequencing variant data

Cited by 17 publications

References 39 publications

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database

Seeking for potential pathogenic genes of major depressive disorder in the Gene Expression Omnibus database

<p>Variants in the Upstream Region of the Insulin Receptor Substrate-1 Gene Is Associated with Major Depressive Disorder in the Han Chinese Population</p>

Transcriptomics of the depressed and PTSD brain

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