Back to Search
Start Over
IL6-mediated HCoV-host interactome regulatory network and GO/Pathway enrichment analysis
- Source :
- PLoS Computational Biology, Vol 16, Iss 9, p e1008238 (2020), PLoS Computational Biology
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- During these days of global emergency for the COVID-19 disease outbreak, there is an urgency to share reliable information able to help worldwide life scientists to get better insights and make sense of the large amount of data currently available. In this study we used the results presented in [1] to perform two different Systems Biology analyses on the HCoV-host interactome. In the first one, we reconstructed the interactome of the HCoV-host proteins, integrating it with highly reliable miRNA and drug interactions information. We then added the IL-6 gene, identified in recent publications [2] as heavily involved in the COVID-19 progression and, interestingly, we identified several interactions with the reconstructed interactome. In the second analysis, we performed a Gene Ontology and a Pathways enrichment analysis on the full set of the HCoV-host interactome proteins and on the ones belonging to a significantly dense cluster of interacting proteins identified in the first analysis. Results of the two analyses provide a compact but comprehensive glance on some of the current state-of-the-art regulations, GO, and pathways involved in the HCoV-host interactome, and that could support all scientists currently focusing on SARS-CoV-2 research.<br />Author summary In this paper we provide data about the HCoV-host interactome that can be extracted from the integration of several public available databases. We used the initial interactome published by Zhou et al. and analyzed if there are already known and validated interactions. We also looked into possible known miRNAs and drugs interactions to suggest possible biomarker candidates and treatment options. We also performed a Gene Ontology and a Pathways enrichment analysis to understand which are the pathways most likely involved in the proteins targeted by SARS-CoV-2. This paper not only provides a set of validated and reliable data that could help researchers in their fight against the COVID-19 disease outbreak, but also demonstrates how Systems Biology can be effectively used to quickly gather preliminary but still significant data without resorting only to expensive lab experiments.
- Subjects :
- RNA viruses
Proteomics
0301 basic medicine
Science and Technology Workforce
Genes, Viral
Coronaviruses
Interaction Networks
Ontology (information science)
Betacoronavirus
Coronavirus Infections
Humans
Interleukin-6
Pandemics
Pneumonia, Viral
Viral Proteins
Gene Ontology
Host-Pathogen Interactions
Careers in Research
Biochemistry
Interactome
0302 clinical medicine
Medicine and Health Sciences
Drug Interactions
Viral
Biology (General)
Pathology and laboratory medicine
Ecology
Gene Ontologies
Genomics
Medical microbiology
Nucleic acids
Professions
Computational Theory and Mathematics
Modeling and Simulation
Viruses
Protein Interaction Networks
SARS CoV 2
Pathogens
Host (network)
Network Analysis
Research Article
Computer and Information Sciences
SARS coronavirus
Coronavirus disease 2019 (COVID-19)
Science Policy
QH301-705.5
Systems biology
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Computational biology
Biology
Research and Analysis Methods
Microbiology
03 medical and health sciences
Cellular and Molecular Neuroscience
Genetics
Molecular Biology Techniques
Non-coding RNA
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Life Scientists
Pharmacology
Natural antisense transcripts
SARS-CoV-2
Gene Mapping
Organisms
Viral pathogens
COVID-19
Biology and Life Sciences
Computational Biology
Pneumonia
Pathway enrichment
Genome Analysis
Gene regulation
Microbial pathogens
MicroRNAs
030104 developmental biology
Genes
People and Places
RNA
Scientists
Population Groupings
Gene expression
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....c8ab47d5ca000ce3c989493cae4c2f6f