Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets
Su, C.; Rousseau, S.; Emad, A.
Show abstract
Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, we combined a series of recently developed computational tools to identify transcriptional regulatory networks involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus. In addition, using network-guided analyses, we identified signaling pathways that are associated with these networks and kinases that may regulate them. The results identified classical antiviral response pathways including Interferon response factors (IRFs), interferons (IFNs), and JAK-STAT signaling as key elements upregulated by SARS-CoV-2 in comparison to mock-treated cells. In addition, comparing SARS-Cov-2 infection of airway epithelial cells to other respiratory viruses identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory networks identified herein reflect a combination of experimentally validated hits and novel pathways supporting the computational pipeline to quickly narrow down promising avenue of investigations when facing an emerging and novel disease such as COVID-19.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- HELP: A computational framework for labelling and predicting human common and context-specific essential genes 94%
- GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data 93%
- Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression 93%
Similar papers in this journal
Similar papers in this journal
- Single cell gene expression profiling of nasal ciliated cells reveals distinctive biological processes related to epigenetic mechanisms in patients with severe COVID-19 95%
- Enrichment analysis on regulatory subspaces: a novel direction for the superior description of cellular responses to SARS-CoV-2 95%
- Uncovering Co-regulatory Modules and Gene Regulatory Networks in the Heart through Machine Learning-based Analysis of Large-scale Epigenomic Data 93%
Similar papers in this journal
- Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells 95%
- Comparative transcriptome analyses reveal genes associated with SARS-CoV-2 infection of human lung epithelial cells 94%
- Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics 94%
Similar papers in this journal
- Master Regulator Analysis of the SARS-CoV-2/Human interactome 96%
- Integrative systems biology approach identified crucial genes and transcription factors associated with gallbladder cancer pathogenesis 92%
- Emergent properties of HNF4α-PPARγ network may drive consequent phenotypic plasticity in NAFLD 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.