Proteomics analysis of liver pathological calcification suggests a role for the IQ motif containing GTPase activating protein 1 in myofibroblast function
Bibliographic record
Abstract
To date the cellular and molecular mechanisms by which liver pathological calcifications occur and are regulated are poorly investigated. To study the mechanisms linked to their appearance, we performed a proteomics analysis of calcified liver samples. To this end, human liver biopsies collected in noncalcified (N), precalcified (P), and calcified (C) areas of the liver were subjected to weak ion exchange chromatography, SDS-PAGE, and LC-ESI MS/MS analyses. As we previously demonstrated that alpha-smooth muscle actin (α-SMA) expressing myofibroblasts were involved in liver pathological calcification, we performed a targeted analysis of actin cytoskeleton remodeling-related proteins. This revealed dramatic changes in protein expression patterns in the periphery of the calcified areas. More particularly, we found that IQGAP1 and IQGAP2 proteins were subjected to major expression changes. We show that IQGAP1 expression within P and C areas of the liver correlates with the high abundance of myofibroblasts and that IQGAP1 is specifically expressed in these cells. In addition, we find that IQGAP1 is part of a protein complex including β-catenin and Rac1 mainly in P and C regions of the liver. These results suggest that IQGAP1 may play a critical role in the regulation of cytoskeleton remodeling in liver myofibroblasts in response to liver injury and consequently impact on their function.
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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 | 0.000 |
| 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) | 0.001 | 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".