New insights into the role of mitochondrial metabolic dysregulation and immune infiltration in septic cardiomyopathy by integrated bioinformatics analysis and experimental validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38291374", "kind": "science-record", "revision": 4, "content_hash": "sha256:661526e123864cfe867c7e1bd87960aa690ce755bfd7864d0a13122b4e7d961d", "created_at": "2026-11-01T19:20:13Z", "updated_at": "2026-11-01T22:53:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "New insights into the role of mitochondrial metabolic dysregulation and immune infiltration in septic cardiomyopathy by integrated bioinformatics analysis and experimental validation.", "doi": "10.1186/s11658-024-00536-2", "pmid": "38291374", "authors": ["Yukun Li", "Jiachi Yu", "Ruibing Li", "Hao Zhou", "Xing Chang"], "journal": "Cellular & molecular biology letters", "year": 2024, "abstract": "BACKGROUND: Septic cardiomyopathy (SCM), a common cardiovascular comorbidity of sepsis, has emerged among the leading causes of death in patients with sepsis. SCM's pathogenesis is strongly affected by mitochondrial metabolic dysregulation and immune infiltration disorder. However, the specific mechanisms and their intricate interactions in SCM remain unclear. This study employed bioinformatics analysis and drug discovery approaches to identify the regulatory molecules, distinct functions, and underlying interactions of mitochondrial metabolism and immune microenvironment, along with potential interventional strategies in SCM.\nMETHODS: GSE79962, GSE171546, and GSE167363 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and module genes were identified using Limma and Weighted Correlation Network Analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms, including support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) regression, and random forest, were used to screen mitochondria-related hub genes for early diagnosis of SCM. Subsequently, a nomogram was developed based on six hub genes. The immunological landscape was evaluated by single-sample gene set enrichment analysis (ssGSEA). We also explored the expression pattern of hub genes and distribution of mitochondria/inflammation-related pathways in UMAP plots of single-cell dataset. Potential drugs were explored using the Drug Signatures Database (DSigDB). In vivo and in vitro experiments were performed to validate the pathogenetic mechanism of SCM and the therapeutic efficacy of candidate drugs.\nRESULTS: Six hub mitochondria-related DEGs [MitoDEGs; translocase of inner mitochondrial membrane domain-containing 1 (TIMMDC1), mitochondrial ribosomal protein S31 (MRPS31), F-box only protein 7 (FBXO7), phosphatidylglycerophosphate synthase 1 (PGS1), LYR motif containing 7 (LYRM7), and mitochondrial chaperone BCS1 (BCS1L)] were identified. The diagnostic nomogram model based on the six hub genes demonstrated high reliability and validity in both the training and validation sets. The immunological microenvironment differed between SCM and control groups. The Spearman correlation analysis revealed that hub MitoDEGs were significantly associated with the infiltration of immune cells. Upregulated hub genes showed remarkably high expression in the naive/memory B cell, CD14+ monocyte, and plasma cell subgroup, evidenced by the feature plot. The distribution of mitochondria/inflammation-related pathways varied across subgroups among control and SCM individuals. Metformin was predicted to be the most promising drug with the highest combined score. Its efficacy in restoring mitochondrial function and suppressing inflammatory responses has also been validated.\nCONCLUSIONS: This study presents a comprehensive mitochondrial metabolism and immune infiltration landscape in SCM, providing a potential novel direction for the pathogenesis and medical intervention of SCM.", "claims": [{"id": "claim-method", "statement": "DEG analysis was conducted using the “limma” package (version 3.54.0), a widely accepted method for analyzing data on gene expression [25].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38291374", "locator": "sections[4] \"Acquisition of microarray data and identification of DEGs\" paragraph 2", "quote": "DEG analysis was conducted using the “limma” package (version 3.54.0), a widely accepted method for analyzing data on gene expression [25]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets supporting the conclusions of this article are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/). All codes are available on GitHub (GitHub, Inc., San Francisco, California) at https://github.com/Lawrence-Albert/SCM_bioinform.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38291374", "locator": "sections[41] \"notes\" paragraph 1", "quote": "Datasets supporting the conclusions of this article are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/). All codes are available on GitHub (GitHub, Inc., San Francisco, California) at https://github.com/Lawrence-Albert/SCM_bioinform."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set."}, "evidence": [{"asset": "source:pmid:38291374", "locator": "sections[24] \"Modeling and testing a diagnostic nomogram model for SCM\" paragraph 2", "quote": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC of the nomogram model was 0.96 (95% CI: 0.91–1.00) in the training set and 0.94 (95% CI, 0.76–1.00) in the GSE171546 validation set.", "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:38291374"], "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:GSE167363"], "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:GSE167363"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38291374", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s11658-024-00536-2", "sha256": "sha256:4a698944642a6fa214b13318ed3513fce6a391b82f11ebd1d4b57686f7f18803", "bytes": 71385, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s11658-024-00536-2", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10826082", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE167363", "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=GSE167363", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE167363", "version": null, "note": "Datasets supporting the conclusions of this article are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/). 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In vivo and in vitro experiments were performed to v", "as the controls. For metformin intervention, mice were administered a single dose of metformin (250", "sis. Before conducting echocardiography, the mice were sufficiently anesthetized. Echocardiography", "mg/kg, Sigma) was intraperitoneally injected in mice for 48 h as described in previous studies [1"], "order_basis": "paper_text_order:1727", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["immune cell interactions in SCM. Analysis of single-cell RNA-seq data We used the single-cell transcriptome d", "for refined gene set enrichment analysis in single-cell transcriptomics, focusing on batch effect minimization. Indi"], "order_basis": "paper_text_order:4373", "id": "assay:2"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["interactions in SCM. Analysis of single-cell RNA-seq data We used the single-cell transcriptome d", "le-cell RNA-seq data We used the single-cell transcriptome dataset (GSE167363) from the GEO database, c", "gene set enrichment analysis in single-cell transcriptomics, focusing on batch effect minimization. Indi"], "order_basis": "paper_text_order:4385", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["lection (ATCC, Manassas, VA). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) su"], "order_basis": "paper_text_order:8248", "id": "assay:4"}, {"role": "data_generation", "definition_id": "clinical_trial", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["n. To simulate SCM in vitro, HL-1 cells were treated with 10 mg/mL of LPS (Sigma) with or without 4 mM of metformin (Sigma) for 24 h, accordin"], "order_basis": "paper_text_order:8558", "id": "assay:5"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["h the BCS1L knockdown model, HL-1 cells were transfected with siRNA targeting BCS1L (siBCS1L, obtaine", "hed studies [14, 34]. To establish the BCS1L knockdown model, HL-1 cells were transfected with siRN", "(Fig. 12B–D). We further constructed a BCS1L-knockdown cell model using siRNA to validate the inter", "on mitochondria were attenuated in the BCS1L-knockdown condition (n = 8 in each group, Fig. 12E–K)."], "order_basis": "paper_text_order:8722", "id": "assay:6"}, {"role": "data_generation", "definition_id": "cell_imaging", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tes were stained with JC-1 dye and imaged by confocal microscopy. JC-1 fluorescence analysis revealed decreas", "stained with JC-1 dye and imaged by confocal microscopy. JC-1 fluorescence analysis revealed decreas"], "order_basis": "paper_text_order:14305", "id": "assay:7"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["reatment (n = 8 in each group, Fig. 11E, F). ELISA was performed to assess whether metformin af", "under various conditions were observed using ELISA. Compared with the SCM + metformin group, a"], "order_basis": "paper_text_order:14508", "id": "assay:8"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["(FBXO7, PGS1, BCS1L, MRPS31, and TIMMDC1) by qPCR in the control, SCM, and SCM + metformin gro"], "order_basis": "paper_text_order:16338", "id": "assay:9"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["wnregulated transcriptional levels of BCS1L. Western blot analysis confirmed that the interventional e", "levels of BCS1L and FBXO7 were determined by western blotting. E–K Analysis of HL-1 mitochondrial metaboli"], "order_basis": "paper_text_order:16588", "id": "assay:10"}, {"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": ["DEG analysis was conducted using the “limma” package (version 3.54.0), a widely accepted method for analyzing data on gene expression [25]."], "order_basis": "consumes_earlier_output", "id": "assay:11"}]}