Tracing the evolutionary pathway of SARS-CoV-2 through RNA sequencing analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40615624", "kind": "science-record", "revision": 3, "content_hash": "sha256:ce607b5408aa1e4e440f0ea030e72cb2df79405f82ed8d5f357c7b1d4aff90ed", "created_at": "2026-11-01T20:30:42Z", "updated_at": "2026-11-01T22:14:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Tracing the evolutionary pathway of SARS-CoV-2 through RNA sequencing analysis.", "doi": "10.1038/s41598-025-09911-1", "pmid": "40615624", "authors": ["Mostafa Rezapour", "Sean V Murphy", "David A Ornelles", "Patrick M McNutt", "Anthony Atala"], "journal": "Scientific reports", "year": 2025, "abstract": "The COVID-19 pandemic, driven by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has underscored the need to understand the virus's evolution due to its global health impact. This study employed RNA sequencing (RNA-Seq) to analyze gene expression differences across multiple SARS-CoV-2 variants. We used publicly available datasets from the Gene Expression Omnibus (GEO) with IDs GSE157103, GSE171110, GSE189039, and GSE201530, which contain RNA-Seq data extracted from white blood cells, whole blood, or PBMCs of individuals infected with the Original Wuhan variant (both hospitalized and non-hospitalized), the French variant (hospitalized), the Beta variant (hospitalized), and the Omicron variant (moderate and mild cases), along with COVID-negative controls. Our first objective was to examine differences in gene expression dynamics using Generalized Linear Models with Quasi-Likelihood F-tests and the Magnitude-Altitude Scoring (GLMQL-MAS) technique, followed by Gene Ontology (GO) and pathway analyses. Our second objective was to employ Cross-MAS to identify a robust set of genes indicative of SARS-CoV-2 infection regardless of the variant and to assess their classification performance. GO and pathway analyses revealed a significant evolutionary shift in how SARS-CoV-2 interacts with the host. Early variants such as the Original Wuhan and French cases primarily affected pathways related to viral replication, including Eukaryotic Translation Elongation and Viral mRNA Translation. In contrast, later variants like Beta and Omicron showed a strategic shift toward modulating and evading the host immune response, engaging immune-related pathways such as Interferon Alpha/Beta signaling and Cytokine signaling in the immune system. To evaluate the classification potential of the identified genes, we tested them on held-out datasets GSE152418, PMC8202013, GSE161731, and GSE166190, which contain RNA-Seq data from whole blood or PBMCs of COVID-positive and healthy individuals. Using top-ranked genes such as IFI27, CDC20, RRM2, HJURP, and CDC45 in linear models including logistic regression and linear SVM, we achieved 97.31% accuracy, with precision and recall rates of 0.97 and 0.99, respectively. These signatures also achieved perfect classification (100% accuracy, precision, and recall) in two additional datasets: GSE294888, which includes blood-derived plasmacytoid dendritic cells (pDCs) and type 2 conventional dendritic cells (DC2s) stimulated with Delta or Omicron variants, and GSE239595, which features Omicron-infected nasopharyngeal tissue. These findings demonstrate the potential of transcriptomic signatures for variant-agnostic COVID-19 detection and provide a foundation for flexible diagnostic and therapeutic approaches in response to SARS-CoV-2 evolution.", "claims": [{"id": "claim-method", "statement": "For the analysis of RNA-Seq data, we used the Generalized Linear Models with Quasi-Likelihood F-tests and Magnitude-Altitude Score (GLMQL-MAS) technique30–33.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40615624", "locator": "sections[5] \"Methodology\" paragraph 2", "quote": "For the analysis of RNA-Seq data, we used the Generalized Linear Models with Quasi-Likelihood F-tests and Magnitude-Altitude Score (GLMQL-MAS) technique30–33."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data supporting the findings of this study are openly available in the Gene Expression Omnibus (GEO) repository, hosted by the National Center for Biotechnology Information (NCBI). This includes: GSE15710319, GSE17111020, GSE18903922, GSE20153023, GSE15241824, PMC820201325, GSE16173142, GSE16619026, GSE29488840, and GSE23959541. All analysis scripts and code used in this study are publicly available on GitHub at https://github.com/MostafaRezapour/Tracing-the-Evolutionary-Pathway-of-SARS-CoV-2-Through-RNA-Sequencing-Analysis-, and have been archived on Zenodo with the DOI: 10.5281/zenodo.15722617.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40615624", "locator": "sections[15] \"notes\" paragraph 1", "quote": "The data supporting the findings of this study are openly available in the Gene Expression Omnibus (GEO) repository, hosted by the National Center for Biotechnology Information (NCBI). This includes: GSE15710319, GSE17111020, GSE18903922, GSE20153023, GSE15241824, PMC820201325, GSE16173142, GSE16619026, GSE29488840, and GSE23959541. All analysis scripts and code used in this study are publicly available on GitHub at https://github.com/MostafaRezapour/Tracing-the-Evolutionary-Pathway-of-SARS-CoV-2-Through-RNA-Sequencing-Analysis-, and have been archived on Zenodo with the DOI: 10.5281/zenodo.15722617."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using top-ranked genes such as IFI27, CDC20, RRM2, HJURP, and CDC45 in linear models including logistic regression and linear SVM, we achieved 97.31% accuracy, with precision and recall rates of 0.97 and 0.99, respectively.", "kind": "quantitative", "target": {"value": 97.31, "unit": "per cent", "tolerance": null, "source": "Using top-ranked genes such as IFI27, CDC20, RRM2, HJURP, and CDC45 in linear models including logistic regression and linear SVM, we achieved 97.31% accuracy, with precision and recall rates of 0.97 and 0.99, respectively."}, "evidence": [{"asset": "source:pmid:40615624", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Using top-ranked genes such as IFI27, CDC20, RRM2, HJURP, and CDC45 in linear models including logistic regression and linear SVM, we achieved 97.31% accuracy, with precision and recall rates of 0.97 and 0.99, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using top-ranked genes such as IFI27, CDC20, RRM2, HJURP, and CDC45 in linear models including logistic regression and linear SVM, we achieved 97.31% accuracy, with precision and recall rates of 0.97 and 0.99, respectively.", "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:40615624"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE152418"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE152418"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40615624", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-09911-1", "sha256": "sha256:0be54cbe97c6bf47c2f931cf18fb0c1e74e1b9220a30f3f9999e9bff340c7d32", "bytes": 74131, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-09911-1", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12227573", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE152418", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152418", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE152418", "version": null, "note": "The data supporting the findings of this study are openly available in the Gene Expression Omnibus (GEO) repository, hosted by the National Center for Biotechnology Information (NCBI). 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