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pmid:41089542

Identification of Potential Therapeutic Targets for Sepsis Using Mendelian Randomization and Integrated eQTL/pQTL Analysis.

Haigui Yue · Jinping Tian · Wei Yin · Houyu Zhao

International journal of general medicine · 2025 doi:10.2147/IJGM.S535716

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Experiment

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Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:41089542
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2026-11-01T20:31:08Z
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paper object · 10 fields
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title
Identification of Potential Therapeutic Targets for Sepsis Using Mendelian Randomization and Integrated eQTL/pQTL Analysis.
doi
10.2147/IJGM.S535716
pmid
41089542
authors array · 4 items
[0]
Haigui Yue
[1]
Jinping Tian
[2]
Wei Yin
[3]
Houyu Zhao
journal
International journal of general medicine
year
2025
abstract
Background: Sepsis significantly contributes to global morbidity, yet effective treatments remain limited. Mendelian randomization (MR), integrated with genetic data, offers promise for uncovering novel therapeutic targets. Methods: We utilized eQTL (eQTLGen) and pQTL (DECODE) data as exposures, and GWAS summaries for sepsis (UK Biobank, FinnGen) as outcomes. GEO datasets (GSE57065, GSE95233) underwent batch correction via PCA clustering using the "sva" R package. Differentially expressed genes (DEGs, |log2FC|>1, adjusted P<0.05) intersected with druggable genes were identified. MR analyses were performed using TwoSampleMR and MR-PRESSO, followed by drug-target predictions using DGIdb. Key genes (BCL6, PTX3, IL7R, BTN3A2, LGALS1) were validated experimentally through qRT-PCR and Western blot in a mouse sepsis model induced by cecal ligation and puncture (CLP). Results: Intersection analyses yielded 398 therapeutic candidates. MR revealed 6 genes and 21 proteins significantly associated with sepsis risk, including protective (eg, HDC, IFI27) and harmful factors (eg, CTSO, BTN3A2). Furthermore, 13 druggable genes correlated with sepsis-related factors, such as BTN3A2 with diabetes, and IL7R, BCL6, PTX3, among others, linked to vitamin D deficiency and cancer. DGIdb identified 34 potential drugs targeting these hub genes, with KEGG and GO analyses highlighting immune regulation and FoxO signaling pathways. qRT-PCR and Western blot confirmed consistent downregulation (BCL6, PTX3, IL7R) and upregulation (BTN3A2, LGALS1) at both mRNA and protein levels in septic mice compared to controls, supporting MR-based predictions. Conclusion: We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis.
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We performed a two-sample MR analysis on sepsis patients, including 26,052 cases and 487,214 controls from the UK Biobank consortium. This study uses the inverse variance weighted method (IVW) as the primary causal effect estimate.
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sections[20] "MR Analysis Between Drug Available Genes and Sepsis" paragraph 2
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We performed a two-sample MR analysis on sepsis patients, including 26,052 cases and 487,214 controls from the UK Biobank consortium.
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sections[10] "Mendelian Randomization Analysis" paragraph 2
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This study uses the inverse variance weighted method (IVW) as the primary causal effect estimate.
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sections[28] "Data Sharing Statement" paragraph 2
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The datasets analyzed during the current study are available in public databases such as GEO (https://www.ncbi.nlm.nih.gov/geo/), DECODE database (https://www.decode.com/) and FinnGen research database (https://www.finngen.fi/en).
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We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis.
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We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis.
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We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis.
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Early and dynamic changes in gene expression in septic shock patients: a genome wide approach
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The datasets analyzed during the current study are available in public databases such as GEO (https://www.ncbi.nlm.nih.gov/geo/), DECODE database (https://www.decode.com/) and FinnGen research database (https://www.finngen.fi/en).
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Fractalkine receptor CX3CR1 and leukocyte Ig-like receptor B2 LILRB2 are prognostic biomarkers in septic shock
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We performed a two-sample MR analysis on sepsis patients, including 26,052 cases and 487,214 controls from the UK Biobank consortium. This study uses the inverse variance weighted method (IVW) as the primary causal effect estimate.
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validated experimentally through qRT-PCR and Western blot in a mouse sepsis model induced by cecal lig
[1]
ion and FoxO signaling pathways. qRT-PCR and Western blot confirmed consistent downregulation (BCL6, P
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paper_text_order:869
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assay:1
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ntally through qRT-PCR and Western blot in a mouse sepsis model induced by cecal ligation and p
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prising data on 16,989 genes measured across blood samples from 31,684 healthy individuals of European desce
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tions. DECODE performed comprehensive plasma proteomic profiling on 54,219 participants from the UK
[1]
d in the attachment eQTL_drug_exposure.txt). Proteomic quantitative trait loci (pQTL) data are from
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assay:4
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We performed a two-sample MR analysis on sepsis patients, including 26,052 cases and 487,214 controls from the UK Biobank consortium. This study uses the inverse variance weighted method (IVW) as the primary causal effect estimate.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41089542", "kind": "science-record", "revision": 3, "content_hash": "sha256:7d69018bf6d2067002daf0af6e4e29c95d1040b8b01b391f31aa484e636b6b9a", "created_at": "2026-11-01T20:31:08Z", "updated_at": "2026-11-01T22:14:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Potential Therapeutic Targets for Sepsis Using Mendelian Randomization and Integrated eQTL/pQTL Analysis.", "doi": "10.2147/IJGM.S535716", "pmid": "41089542", "authors": ["Haigui Yue", "Jinping Tian", "Wei Yin", "Houyu Zhao"], "journal": "International journal of general medicine", "year": 2025, "abstract": "Background: Sepsis significantly contributes to global morbidity, yet effective treatments remain limited. Mendelian randomization (MR), integrated with genetic data, offers promise for uncovering novel therapeutic targets.\nMethods: We utilized eQTL (eQTLGen) and pQTL (DECODE) data as exposures, and GWAS summaries for sepsis (UK Biobank, FinnGen) as outcomes. GEO datasets (GSE57065, GSE95233) underwent batch correction via PCA clustering using the \"sva\" R package. Differentially expressed genes (DEGs, |log2FC|>1, adjusted P<0.05) intersected with druggable genes were identified. MR analyses were performed using TwoSampleMR and MR-PRESSO, followed by drug-target predictions using DGIdb. Key genes (BCL6, PTX3, IL7R, BTN3A2, LGALS1) were validated experimentally through qRT-PCR and Western blot in a mouse sepsis model induced by cecal ligation and puncture (CLP).\nResults: Intersection analyses yielded 398 therapeutic candidates. MR revealed 6 genes and 21 proteins significantly associated with sepsis risk, including protective (eg, HDC, IFI27) and harmful factors (eg, CTSO, BTN3A2). Furthermore, 13 druggable genes correlated with sepsis-related factors, such as BTN3A2 with diabetes, and IL7R, BCL6, PTX3, among others, linked to vitamin D deficiency and cancer. DGIdb identified 34 potential drugs targeting these hub genes, with KEGG and GO analyses highlighting immune regulation and FoxO signaling pathways. qRT-PCR and Western blot confirmed consistent downregulation (BCL6, PTX3, IL7R) and upregulation (BTN3A2, LGALS1) at both mRNA and protein levels in septic mice compared to controls, supporting MR-based predictions.\nConclusion: We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis.", "claims": [{"id": "claim-method", "statement": "We performed a two-sample MR analysis on sepsis patients, including 26,052 cases and 487,214 controls from the UK Biobank consortium. This study uses the inverse variance weighted method (IVW) as the primary causal effect estimate.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41089542", "locator": "sections[20] \"MR Analysis Between Drug Available Genes and Sepsis\" paragraph 2", "quote": "We performed a two-sample MR analysis on sepsis patients, including 26,052 cases and 487,214 controls from the UK Biobank consortium."}, {"asset": "source:pmid:41089542", "locator": "sections[10] \"Mendelian Randomization Analysis\" paragraph 2", "quote": "This study uses the inverse variance weighted method (IVW) as the primary causal effect estimate."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in public databases such as GEO (https://www.ncbi.nlm.nih.gov/geo/), DECODE database (https://www.decode.com/) and FinnGen research database (https://www.finngen.fi/en).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41089542", "locator": "sections[28] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets analyzed during the current study are available in public databases such as GEO (https://www.ncbi.nlm.nih.gov/geo/), DECODE database (https://www.decode.com/) and FinnGen research database (https://www.finngen.fi/en)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41089542", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified and experimentally validated 6 sepsis-associated genes and 21 proteins, providing crucial insights into potential therapeutic targets and enhancing understanding of the molecular pathogenesis of sepsis.", "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:41089542"], "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:GSE57065"], "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:GSE57065"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41089542", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S535716", "sha256": 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