Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34975833", "kind": "science-record", "revision": 5, "content_hash": "sha256:e7276ba63bc8e097673ac83c3654942596fa8181f23ba70316e90bbff0ae163a", "created_at": "2026-11-01T17:36:41Z", "updated_at": "2026-11-01T22:48:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection.", "doi": "10.3389/fimmu.2021.724936", "pmid": "34975833", "authors": ["Chiranjib Chakraborty", "Ashish Ranjan Sharma", "Manojit Bhattacharya", "Hatem Zayed", "Sang-Soo Lee"], "journal": "Frontiers in immunology", "year": 2021, "abstract": "The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease's potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.", "claims": [{"id": "claim-method", "statement": "To analyze raw gene expression data, we used the statistical tool GEO2R, and this tool further uses the R/Bioconductor and limma package (34, 35).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34975833", "locator": "sections[5] \"Data Preprocessing, Identification, and Analysis of DEGs\" paragraph 2", "quote": "To analyze raw gene expression data, we used the statistical tool GEO2R, and this tool further uses the R/Bioconductor and limma package (34, 35)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34975833", "locator": "sections[29] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The protein interactome constructed from protein-coding genes among the 250 DEGs contained 2,901 interactions involving 453 proteins, with an average node degree of 12.53.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34975833", "locator": "sections[16] \"Construction of the Protein Interactome Map and Comparison With Other Studies\" paragraph 2", "quote": "We found that the number of interactions was 2901 and the number of proteins that participated in the interactions was 453, and the average node degree was 12.53."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The protein interactome constructed from protein-coding genes among the 250 DEGs contained 2,901 interactions involving 453 proteins, with an average node degree of 12.53.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34975833"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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