Micro RNA-based regulation of genomics and transcriptomics of inflammatory cytokines in COVID-19
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Abstract
Background
Coronavirus disease 2019 is characterized by the elevation of a wide spectrum of inflammatory mediators, which are associated with poor disease outcomes. We aimed at an in-silico analysis of regulatory microRNA and their transcription factors (TF) for these inflammatory genes that may help to devise potential therapeutic strategies in the future.
Methods
The cytokine regulating immune-expressed genes (CRIEG) was sorted from literature and the GEO microarray dataset. Their co-differentially expressed miRNA and transcription factors were predicted from publicly available databases. Enrichment analysis was done through mienturnet, MiEAA, Gene Ontology, and pathways predicted by KEGG and Reactome pathways. Finally, the functional and regulatory features were analyzed and visualized through Cytoscape.
Results
Sixteen CRIEG were observed to have a significant protein-protein interaction network. The ontological analysis revealed significantly enriched pathways for biological processes, molecular functions, and cellular components. The search performed in the MiRNA database yielded 10 (ten) miRNAs that are significantly involved in regulating these genes and their transcription factors.
Conclusion
An in-silico representation of a network involving miRNAs, CRIEGs, and TF which take part in the inflammatory response in COVID-19 has been elucidated. These regulatory factors may have potentially critical roles in the inflammatory response in COVID-19 and may be explored further to develop targeted therapeutic strategies and mechanistic validation.
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SciScore for 10.1101/2021.06.08.21258565: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 2.1 Identification of cytokine responsible for inflammation and cytokine storm in SARS-CoV-2: Several keywords including “Inflammation”, “Immunity”, “Immunogenetics”, “Cytokine storm”, “Acute respiratory distress syndrome”, “ARDS”, “COVID-19”, “cytokines”, “Coronavirus disease”, “SARS-CoV-2” and “Severe Acute Respiratory Syndrome” and “19990101 to 20200706” were searched in PubMed (Figure 1, Supplementary Tables 1 & Table-1A). PubMedsuggested: (PubMed, RRID:SCR_004846)We identified the common transcription factors of CRIEGs through five different databases-TRRUST, RegNetwork, ENCODE, … SciScore for 10.1101/2021.06.08.21258565: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 2.1 Identification of cytokine responsible for inflammation and cytokine storm in SARS-CoV-2: Several keywords including “Inflammation”, “Immunity”, “Immunogenetics”, “Cytokine storm”, “Acute respiratory distress syndrome”, “ARDS”, “COVID-19”, “cytokines”, “Coronavirus disease”, “SARS-CoV-2” and “Severe Acute Respiratory Syndrome” and “19990101 to 20200706” were searched in PubMed (Figure 1, Supplementary Tables 1 & Table-1A). PubMedsuggested: (PubMed, RRID:SCR_004846)We identified the common transcription factors of CRIEGs through five different databases-TRRUST, RegNetwork, ENCODE, JASPAR, and CHEA. CHEAsuggested: (ChEA, RRID:SCR_005403)A co-regulatory transcriptome network was created based on inter-correlation in Cytoscape software. Cytoscapesuggested: (Cytoscape, RRID:SCR_003032)miRBase, miRBasesuggested: (miRBase, RRID:SCR_003152)miRNet Version 2 and TargetScan (Chang et al. 2020). TargetScansuggested: (TargetScan, RRID:SCR_010845)(Tables 1 & 2) 2.6 Protein-protein interaction, functional enrichment and KEGG pathway analysis of CRIEGs and transcriptome of CRIEGs: The Search Tool for the Retrieval of Interacting Genes/Protein [STRING] (http://string-db.org/) was used to construct a protein-protein interaction (PPI) network using only overlapped DEGs and greater than 0.4 confidence score cut-off. STRINGsuggested: (STRING, RRID:SCR_005223)Analysis of the functional and regulatory features was carried out through gene ontology (GO), KEGG pathways through DAVID (the database for annotation, visualization and integrated discovery) and STRING (functional protein association networks) biological databases. ( KEGGsuggested: (KEGG, RRID:SCR_012773)DAVIDsuggested: (DAVID, RRID:SCR_001881)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The main limitation of this study is that the data is derived from the publicly available databases, and needs experimental substantiation to prove its clinical efficacy. Moreover, our study highlights the interaction and the pathways concerning the miRNA, immune-expressed and TFs. Such expression data of all these three entities together are not available in COVID-19 patients or in-vitro models, which can establish a better understanding of the mechanisms involved.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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