Article ; Online: Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic.
2021 Volume 12, Page(s) 789317
Abstract: Background: The 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 ... ...
| Abstract | Background: The 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. Methods: RNA-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. Results: Based 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 Conclusion: This 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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| MeSH term(s) | COVID-19/epidemiology ; COVID-19/genetics ; COVID-19/virology ; Cluster Analysis ; Gene Expression Profiling/methods ; Gene Ontology ; Gene Regulatory Networks ; Humans ; Immunity/genetics ; Models, Genetic ; Pandemics ; Protein Interaction Maps/genetics ; SARS-CoV-2/physiology ; Signal Transduction/genetics ; Transcription Factors/genetics ; Transcriptome/genetics |
| Chemical Substances | Transcription Factors |
| Language | English |
| Publishing date | 2021-12-15 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2606827-8 |
| ISSN | 1664-3224 ; 1664-3224 |
| ISSN (online) | 1664-3224 |
| ISSN | 1664-3224 |
| DOI | 10.3389/fimmu.2021.789317 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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