The Role of Sphingolipid Metabolism and Neuron Death in Ischemic Stroke: A New Perspective from Bioinformatics
Bibliographic record
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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".