Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36389792", "kind": "science-record", "revision": 4, "content_hash": "sha256:bfd3cdac89a71720810ed278fea8dbc61c6dbc61efc9a6b7f45fdc609f3683ef", "created_at": "2026-11-01T19:00:51Z", "updated_at": "2026-11-01T22:50:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.", "doi": "10.3389/fimmu.2022.1008653", "pmid": "36389792", "authors": ["Cheng Yan", "Yandie Niu", "Xuannian Wang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.\nMethods: COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the \"Deseq2\" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the \"limma\" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.\nResults: In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.\nConclusion: In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.", "claims": [{"id": "claim-method", "statement": "The “Deseq2” package in RStudio software (version 4.1.2) was performed to select DEGs with |Log2 Fold Change| > 0.585 and | adj.P.Val. | < 0.05 for COVID-19 datasets (GSE171110 and GSE152418). Meanwhile, the “limma” package was used to identify DEGs with |Log2 Fold Change| ≥ 0.1 and | adj.P.Val. | <0.001 for HIV dataset (GSE37250). Using “venn” package in R software, we obtained COVID-19 and HIV common DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36389792", "locator": "sections[4] \"Identification of common DEGs\" paragraph 2", "quote": "The “Deseq2” package in RStudio software (version 4.1.2) was performed to select DEGs with |Log2 Fold Change| > 0.585 and | adj.P.Val. | < 0.05 for COVID-19 datasets (GSE171110 and GSE152418). Meanwhile, the “limma” package was used to identify DEGs with |Log2 Fold Change| ≥ 0.1 and | adj.P.Val. | <0.001 for HIV dataset (GSE37250). Using “venn” package in R software, we obtained COVID-19 and HIV common DEGs."}], "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:36389792", "locator": "sections[21] \"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": "Herein, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36389792", "locator": "sections[1] \"Introduction\" paragraph 6", "quote": "Herein, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Herein, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets.", "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:36389792"], "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:36389792", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.1008653", "sha256": "sha256:c916f4fca9ea357e37b464c5e4dddbb607b0af75e7ab214fd3d51ed766420585", "bytes": 56263, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.1008653", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9650272", "source": "europe_pmc_fulltext", "coverage": "whole_body", "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 original contributions presented in the study are included in the article/ Supplementary Material . 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expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The “Deseq2” package in RStudio software (version 4.1.2) was performed to select DEGs with |Log2 Fold Change| > 0.585 and | adj.P.Val. | < 0.05 for COVID-19 datasets (GSE171110 and GSE152418). Meanwhile, the “limma” package was used to identify DEGs with |Log2 Fold Change| ≥ 0.1 and | adj.P.Val. | <0.001 for HIV dataset (GSE37250). Using “venn” package in R software, we obtained COVID-19 and HIV common DEGs."}}, {"id": "method-2", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.7, "band": "medium", "quote": "limma"}}, {"id": "method-3", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-4", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: deseq2."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE171110", "GSE152418", "GSE37250"], "resolved_accessions": ["GSE171110", "GSE152418", "GSE37250"], "statements": ["The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE171110, GSE152418, GSE37250 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:36389792", "data:geo:GSE152418"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE152418"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:15:58Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 16949, "completion_tokens": 1564, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36389792", "type": "paper", "label": "pmid:36389792"}, {"id": "source:pmid:36389792", "type": "manuscript", "label": "PMID 36389792 full text"}, {"id": "data:geo:GSE152418", "type": "dataset", "label": "GEO GSE152418"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36389792", "type": "paper", "label": "pmid:36389792"}, {"id": "source:pmid:36389792", "type": "manuscript", "label": "PMID 36389792 full text"}, {"id": "data:geo:GSE152418", "type": "dataset", "label": "GEO GSE152418"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36389792", "type": "extraction", "used": ["source:pmid:36389792"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE152418", "type": "accession_resolution", "used": ["data:geo:GSE152418"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36389792", "parent_hash": "sha256:6374d4a7b3f81987826c85a10665f32a3a3814c65c211fee72c766a714bbc2b8", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE152418 licence not read", "asset:data:geo:GSE152418 not downloaded", "asset:data:geo:GSE171110 licence not read", "asset:data:geo:GSE171110 not downloaded", "asset:data:geo:GSE37250 licence not read", "asset:data:geo:GSE37250 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The “Deseq2” package in RStudio software (version 4.1.2) was performed to select DEGs with |Log2 Fold Change| > 0.585 and | adj.P.Val. | < 0.05 for COVID-19 datasets (GSE171110 and GSE152418). Meanwhile, the “limma” package was used to identify DEGs with |Log2 Fold Change| ≥ 0.1 and | adj.P.Val. | <0.001 for HIV dataset (GSE37250). Using “venn” package in R software, we obtained COVID-19 and HIV common DEGs."], "order_basis": "analysis_only", "id": "assay:1"}]}