Identification of alternative splicing and RNA-binding proteins involved in myocardial ischemia–reperfusion injury
Bibliographic record
Abstract
Alternative splicing (AS) and RNA-binding proteins (RBPs) have been implicated in various cardiovascular diseases. Yet, a comprehensive understanding of their role in myocardial ischemia-reperfusion injury (MIRI) remains elusive. We aimed to identify potential therapeutic targets for MIRI by studying genome-wide changes in AS events and RBPs. We analyzed RNA-seq data from ischemia-reperfusion mouse models and the control group from the GSE130217 data set using Splicing Site Usage Variation Analysis software. We identified 28 regulated alternative splicing events (RASEs) and 47 differentially expressed RBP (DE-RBP) genes in MIRI. Most variable splicing events were involved in cassette exon, alternative 5' splice, alternative 3' splice, and retained intron types. Gene Ontology and Kyoto Encyclopedia of Genes (KOBAS 2.0 server) and Genomes pathway enrichment analyses showed that the differentially expressed variable splicing and RBP genes were mainly enriched in pathways related to myocardial function. The RBP-RASE network demonstrated a common variance relationship between DE-RBPs and RASEs, indicating that RBPs regulate variable shear events in MIRI. This study systematically identified important alterations in RASEs and RBPs in MIRI, expanding our understanding of the underlying pathogenesis of MIRI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".