Gene co-expression network analysis reveals common system-level properties of genes involved in tuberculosis across independent gene expression studies

Jha, Mohit; Malhotra, Anvita Gupta; Singh, Sudha; Pandey, Khushhali M.

doi:10.1007/s13721-016-0131-8

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…Recent computational methods of systems biology, including network analysis and machine learning, are suitable for analyzing omics techniques such as high throughput RNA-sequencing (RNA-seq) at the systemic level (18). Weighted gene coexpression network analysis (WGCNA) is a systems biology method to identify clusters (modules) of highly correlated genes, candidate biomarkers, and therapeutic targets (19); it has been used in various human infectious diseases, e.g., Influenza (20), Tuberculosis (21,22), Hepatitis B,C (23)(24)(25), HIV (26), and various cancers (27)(28)(29)(30). Additionally, in some studies using the module-trait relationships approach of WGCNA, modules related to clinical traits of COVID-19 were identified at various stages (31)(32)(33).…”

Section: Introductionmentioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

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BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

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Section: Introductionmentioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

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“…liu et al (16) also reported that the glucose-6-phosphate dehydrogenase and S100 calcium binding protein a7 genes may represent potential targets in coronary artery disease (16). in a further study, WGCNA identified seven modules that are notably linked with latent and active tuberculosis (17). Therefore, WGcna may be applied to analyze microarray data for colon cancer.…”

Section: Introductionmentioning

confidence: 95%

Identification of specific modules and significant genes associated with colon cancer by weighted gene co‑expression network analysis

Feng

et al. 2019

Mol Med Report

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colon cancer is one of the most commonly diagnosed malignancies and is a leading cause of cancer-associated mortality. The aim of the present study was to investigate the molecular mechanisms underlying colon cancer and identify potentially significant genes associated with the disease using weighted gene co-expression network analysis (WGcna). The test datasets used were downloaded from The cancer Genome atlas (TcGa) database. WGcna was applied to analyze microarray data obtained from colon adenocarcinoma samples to identify significant modules and highly associated genes. a gene co-expression network was constructed and different gene modules were selected. Functional and pathway enrichment analyses were performed to investigate the molecular mechanisms of colon cancer. in addition, highly connected hub genes associated with the most significant module were selected for further analysis. Nine specific modules associated with colon cancer were identified, of which the turquoise module was observed to exhibit the greatest association with the disease. Pathway enrichment analysis of the turquoise module suggested that genes in the turquoise module were associated with 'rna polymerase' and 'purine metabolism'. Furthermore, gene ontology enrichment analysis revealed the top 30 hub genes with a higher degree in the turquoise module, such as σ-non-opioid intracellular receptor 1, transmembrane protein 147 TMeM147) and carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase, were predominantly enriched in the biological processes 'translation' and 'gene expression'. experimental verification demonstrated that the expression of TMeM147 in colon cancer was significantly increased compared with the control.Therefore, the results suggested that genes associated with rna polymerase and the purine metabolic pathways may be substantially involved in the pathogenesis of colon cancer. Furthermore, TMeM147 may represent a biomarker for colon cancer.

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Analysis of terpenoid biosynthesis pathways in German chamomile (Matricaria recutita) and Roman chamomile (Chamaemelum nobile) based on co-expression networks

et al. 2020

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Gene co-expression network analysis reveals common system-level properties of genes involved in tuberculosis across independent gene expression studies

Abstract: Network-based approaches to human disease have diverse biological and clinical applications.

Cited by 3 publications

References 41 publications

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Identification of specific modules and significant genes associated with colon cancer by weighted gene co‑expression network analysis

Analysis of terpenoid biosynthesis pathways in German chamomile (Matricaria recutita) and Roman chamomile (Chamaemelum nobile) based on co-expression networks

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