Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36107502", "kind": "science-record", "revision": 4, "content_hash": "sha256:0580594c853fb9a6e93b0c2bc47698a453561f50c4f372766660d05b88c038cd", "created_at": "2026-11-01T18:15:21Z", "updated_at": "2026-11-01T22:07:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Deciphering SARS CoV-2-associated pathways from RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in silico methods.", "doi": "10.1097/MD.0000000000029554", "pmid": "36107502", "authors": ["Peter Natesan Pushparaj", "Laila Abdullah Damiati", "Iuliana Denetiu", "Sherin Bakhashab", "Muhammad Asif", "Abrar Hussain", "Sagheer Ahmed", "Mohammad Hamid Hamdard", "Mahmood Rasool"], "journal": "Medicine", "year": 2022, "abstract": "BACKGROUND: Coronavirus (CoV) disease (COVID-19) identified in Wuhan, China, in 2019, is mainly characterized by atypical pneumonia and severe acute respiratory syndrome (SARS) and is caused by SARS CoV-2, which belongs to the Coronaviridae family. Determining the underlying disease mechanisms is central to the identification and development of COVID-19-specific drugs for effective treatment and prevention of human-to-human transmission, disease complications, and deaths.\nMETHODS: Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO) (GSE147507), and quality control (QC) was assessed before RNA Seq analysis using CLC Genomics Workbench 20.0. Differentially expressed genes (DEGs) were imported into BioJupies to decipher COVID-19 induced signaling pathways and small molecules derived from chemical synthesis or natural sources to mimic or reverse COVID -19 specific gene signatures. In addition, iPathwayGuide was used to identify COVID-19-specific signaling pathways, as well as drugs and natural products with anti-COVID-19 potential.\nRESULTS: Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3). COVID-19 infection activated key infectious disease-specific immune-related signaling pathways such as influenza A, viral protein interaction with cytokine and cytokine receptors, measles, Epstein-Barr virus infection, and IL-17 signaling pathway. Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy.\nCONCLUSIONS: In conclusion, we have used the in silico next-generation knowledge discovery (NGKD) methods to discover COVID-19-associated pathways and specific therapeutics that have the potential to ameliorate the disease pathologies associated with COVID-19.", "claims": [{"id": "claim-method", "statement": "Here, a multifactorial statistic based on a negative binomial generalized linear model (GLM) is used, and the differential expression in the two groups tool deals with one factor and two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36107502", "locator": "sections[6] \"2.4. COVID-19 RNA Seq data from A549 cells – differential gene and transcript expression analysis\" paragraph 2", "quote": "Here, a multifactorial statistic based on a negative binomial generalized linear model (GLM) is used, and the differential expression in the two groups tool deals with one factor and two groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Raw RNA Seq reads (single-end) (FASTQ format) in quadruplicate obtained with Illumina Next Seq 500 from A549 cells infected with SARS CoV-2 and mock-treated A549 cells were obtained from the Gene Expression Omnibus (GEO) (accession number: GSE147507)[16] and were used for subsequent downstream analysis with high-throughput NGKD platforms.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36107502", "locator": "sections[4] \"2.2. Next-Generation Sequencing (NGS) data source\" paragraph 2", "quote": "Raw RNA Seq reads (single-end) (FASTQ format) in quadruplicate obtained with Illumina Next Seq 500 from A549 cells infected with SARS CoV-2 and mock-treated A549 cells were obtained from the Gene Expression Omnibus (GEO) (accession number: GSE147507)[16] and were used for subsequent downstream analysis with high-throughput NGKD platforms."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified COVID-19-associated pathways, upstream regulators, and candidate synthetic drugs and natural products using in silico RNA-sequencing analyses.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36107502", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Here, we identified the potential activation of upstream regulators such as signal transducer and activator of transcription 2 (STAT2), interferon regulatory factor 9 (IRF9), and interferon beta (IFNβ), interleukin-1 beta (IL-1β), and interferon regulatory factor 3 (IRF3)."}, {"asset": "source:pmid:36107502", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Besides, we identified drugs such as prednisolone, methylprednisolone, diclofenac, compound JQ1, and natural products such as Withaferin-A and JinFuKang as candidates for further experimental validation of COVID-19 therapy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified COVID-19-associated pathways, upstream regulators, and candidate synthetic drugs and natural products using in silico RNA-sequencing analyses.", "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:36107502"], "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:GSE147507"], "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:GSE147507"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36107502", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000029554", "sha256": "sha256:04ccfa64d3a9730db0eda214dfe9a9f19ec2527a3ae4a8d6e4eec90590204ee0", "bytes": 42851, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1097/MD.0000000000029554", "version": null, "note": "Full article fetched from PMC; 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METHODS: Here, next-generation RNA sequencing (RNA Seq) data were obtained using Illumina", "rials and Methods 2.5. BioJupies analysis of RNASeq data BioJupies was used to analyze the DEGs", "-specific gene signatures.[15] In Biojupies, RNASeq datasets were compressed into an HDF5 data p"], "order_basis": "paper_text_order:3", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["rom RNA sequencing data of COVID-19-infected A549 cells and potential therapeutics using in si", "ARS CoV-infected A549 cells and mock-treated A549 cells from the Gene Expression Omnibus (GEO)", "Illumina Next Seq 500 from SARS CoV-infected A549 cells and mock-treated A549 cells from the G"], "order_basis": "paper_text_order:44", "id": "assay:2"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["principal component analysis"], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}