Profiling the organ membrane proteome dysregulation in the context of liver disease
Bibliographic record
Abstract
Alcohol consumption and high-fat diets often coincide in Western society, exerting negative synergistic effects on the liver. While many studies have demonstrated the impact of ALD and NAFLD on organ protein expression, none have offered a comprehensive view of the dysregulation at the level of the membrane proteome. In this study, we utilize peptidisc and solvent precipitation (SP4) methods to isolate and compare the membrane protein content of the liver with its unique biological functions. Using mice treated with a high-fat diet and ethanol in drinking water, we identified 1,563 liver proteins, with 46% predicted to have a transmembrane segment. Among these, 106 integral membrane proteins are dysregulated compared to the untreated sample. Gene ontology analysis reveals several dysregulated membrane processes associated with 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 steatosis of the liver model. Our peptidisc-based membrane proteome profiling thus emerges as an effective way to gain insights into the role of the transmembrane proteome in disease development, warranting further in-depth analysis of the individual effect of the identified dysregulated membrane proteins.
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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".