Proteomic profiling reveals a higher presence of glycolytic enzymes in human atherosclerotic lesions with unfavourable histological characteristics
Bibliographic record
Abstract
AIMS: Molecular characterization of vulnerable atherosclerotic plaques often relies on transcriptomic data. However, RNA expression may not consistently align with protein expression. The proteomic landscape linked to plaque vulnerability is underexplored in human lesions. In this study, we analyzed a large mass spectrometry-based proteomics dataset from the plaque tissue of 320 patients to identify the molecular mechanisms associated with vulnerable plaques. Previous studies have shown significant differences in cell metabolism in murine atherosclerosis models, prompting an in-depth description of expression of key enzymes in glycolysis in human atherosclerotic plaques. METHODS AND RESULTS: Atherosclerotic lesions from 320 patients undergoing carotid endarterectomy surgery were collected (200 discovery set and 120 for the validation set) and underwent proteomic analyses. Plaque samples were digested, enriched for extracellular matrix proteins, and processed for untargeted proteomics analysis. The resulting protein levels were linked to pathological plaque characteristics, bulk and single cell transcriptomics, and clinical data. Proteomic analysis of 200 human atherosclerotic carotid lesions detected 1499 proteins with most showing poor correlation with RNA levels. We identified 240 proteins associated with plaque vulnerability index (FDR < 0.05), including key glycolysis enzymes: Hexokinase 3 (HK3) (P = 0.003, FDR = 0.03), PKM (P = 0.008, FDR = 0.05), and LDHA (P = 0.006, FDR = 0.04). The observed associations were mainly driven by macrophage content and fat content, reflected the severity of pre-operative symptoms, exhibited significant sex differences, and correlated with plaque haemorrhage biomarker BLVRB. Validation in 120 patients confirmed HK3 and PKM's association with plaque progression and clinical symptoms (all P < 0.001). CONCLUSION: Enzymes involved in the glycolysis process are more abundant in plaques with vulnerable histological characteristics and are significantly associated with plaque haemorrhage biomarker BLVRB. This implies that plaque destabilisation may be driven by higher glycolysis metabolism, which may contribute to plaque haemorrhage. This association was stronger in women, underlining the important role of energy metabolism in sex-specific mechanisms of atherosclerotic disease.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".