The Role of Sphingolipid Metabolism and Neuron Death in Ischemic Stroke: A New Perspective from Bioinformatics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41474773", "kind": "science-record", "revision": 4, "content_hash": "sha256:2ab872281e44f0f960317aba7bcd5a96281069e9952e5d8b0c1d29565de61126", "created_at": "2026-11-01T20:31:27Z", "updated_at": "2026-11-01T22:57:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The Role of Sphingolipid Metabolism and Neuron Death in Ischemic Stroke: A New Perspective from Bioinformatics.", "doi": "10.1002/brb3.71172", "pmid": "41474773", "authors": ["Zhong-Can Chen", "Fang-Biao Xu", "Sen Hu", "Qiang Cui", "Hugo Andrade Barazarte", "Jing Zhang", "Li-Na Suo", "Jian-Jun Gu", "Jiang-Yu Xue"], "journal": "Brain and behavior", "year": 2026, "abstract": "BACKGROUND: Ischemic stroke (IS) is a leading cause of death and disability worldwide, but traditional risk factors do not fully explain its pathophysiology. Neuronal death in IS is influenced by multiple pathways, including sphingolipid metabolism, which plays a significant role in neuronal function and survival. Ceramides, key sphingolipid molecules, are involved in various neuronal processes, including cell death. This study aims to explore the relationship between sphingolipid metabolism and neuron death in IS using bulk and single-cell transcriptomics.\nMETHODS: We obtained sphingolipid metabolism gene sets from the GeneCard database and analyzed differential gene expression in IS datasets from the GEO database, including human peripheral blood bulk data (GSE16561) and MCAO mouse peripheral blood scRNA sequencing data (GSE225948). Gene set enrichment analysis (GSEA), immune infiltration analysis using CIBERSORT, and protein-protein interaction network construction were performed. Single-cell RNA sequencing (scRNA-seq) data were used to identify key genes and analyze cellular heterogeneity, differentiation, and cell interactions. In vivo validation of key gene expression was conducted in MCAO rats.\nRESULTS: GSEA revealed significant changes in the sphingolipid metabolism pathway in IS patients. Immune infiltration analysis showed altered immune cell profiles, with decreases in CD8 T cells and increases in monocytes and neutrophils. Enrichment analysis of sphingolipid metabolism-related genes highlighted pathways such as the sphingolipid signaling pathway and ceramide metabolism. Protein-protein interaction network analysis identified 19 key genes linked to sphingolipid metabolism and neuron death. scRNA-seq analysis revealed significant changes in sphingolipid metabolism in monocytes and neutrophils, with the App gene showing notable differential expression. Pseudotime analysis suggested diverse differentiation trajectories in monocytes, and cell interaction analysis indicated potential communication between monocytes and B cells. In vivo validation confirmed higher App gene expression in MCAO rats compared to sham controls.\nCONCLUSION: This study provides comprehensive insights into the role of sphingolipid metabolism in ischemic stroke, identifying key genes and cellular mechanisms involved in neuron death. The findings suggest that sphingolipid metabolism, particularly through the App gene, may be a potential therapeutic target for IS. Further exploration of the molecular mechanisms and cellular interactions involving sphingolipids could lead to novel therapeutic strategies for ischemic stroke.", "claims": [{"id": "claim-method", "statement": "Differential analysis of the chip data was performed using the limma‐trend method (Zhang et al. 2023).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41474773", "locator": "sections[3] \"Data Source and Differentially Expressed Genes\" paragraph 2", "quote": "Differential analysis of the chip data was performed using the limma‐trend method (Zhang et al. 2023)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets GSE16561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE16561) and GSE225948 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE225948), used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41474773", "locator": "sections[35] \"Data Availability Statement\" paragraph 2", "quote": "The datasets GSE16561 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE16561) and GSE225948 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE225948), used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:41474773"], "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. 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METHODS: We obtained sphingolipid metabolis", "raction network construction were performed. Single-cell RNA sequencing (scRNA-seq) data were used to identify key g", "were performed. Single-cell RNA sequencing (scRNA-seq) data were used to identify key genes and an", "to sphingolipid metabolism and neuron death. scRNA-seq analysis revealed significant changes in sph"], "order_basis": "paper_text_order:602", "id": "assay:1"}, {"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": ["ork construction were performed. Single-cell RNA sequencing (scRNA-seq) data were used to identify key g", "euron death in IS using bulk and single-cell transcriptomics. METHODS: We obtained sphingolipid metabolis"], "order_basis": "paper_text_order:614", "id": "assay:2"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ity, differentiation, and cell interactions. In vivo validation of key gene expression was conduc", "communication between monocytes and B cells. In vivo validation confirmed higher App gene express", "of key gene expression was conducted in MCAO rats. RESULTS: GSEA revealed significant changes", "confirmed higher App gene expression in MCAO rats compared to sham controls. CONCLUSION: This"], "order_basis": "paper_text_order:1218", "id": "assay:3"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["me fluorescent polymerase chain reaction (RT‐qPCR) involved setting up a reverse transcription", "verse transcription system, executing the RT‐qPCR reaction, and calculating target gene mRNA e"], "order_basis": "paper_text_order:3618", "id": "assay:4"}, {"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": ["Differential analysis of the chip data was performed using the limma‐trend method (Zhang et al. 2023)."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}