Sensitive Profiling of Mouse Liver Membrane Proteome Dysregulation Following a High‐Fat and Alcohol Diet Treatment
Bibliographic record
Abstract
Alcohol consumption and high-fat (HF) diets often coincide in Western society, resulting in synergistic negative effects on liver function. Although studies have analyzed the global protein expression in the context of alcoholic liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD), none has offered specific insights on liver dysregulation at the membrane proteome level. Membrane-specific profiling of metabolic and compensatory phenomena is usually overshadowed in conventional proteomic workflows. In this study, we use the Peptidisc method to isolate and compare the membrane protein (MP) content of the liver with its unique biological functions. From mice fed with an HF diet and ethanol in drinking water, we annotate over 1500 liver proteins with half predicted to have at least one transmembrane segment. Among them, we identify 106 integral MPs that are dysregulated compared to the untreated sample. Gene Ontology analysis reveals several dysregulated membrane-associated processes like lipid metabolism, cell adhesion, xenobiotic processing, and mitochondrial membrane formation. Pathways related to cholesterol and bile acid transport are also mutually affected, suggesting an adaptive mechanism to counter the upcoming steatosis of the liver model. Taken together, our Peptidisc-based profiling of the diet-dysregulated liver provides specific insights and hypotheses into the role of the transmembrane proteome in disease development, and flags desirable MPs for therapeutic and diagnostic targeting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".