Integrated liver and serum proteomics uncover sexual dimorphism and alteration of several immune response proteins in an aging Werner syndrome mouse model
Bibliographic record
Abstract
Werner syndrome (WS) is a progeroid disorder caused by mutations in a protein containing both a DNA exonuclease and DNA helicase domains. Previous studies indicated that males lacking the helicase domain of the Wrn protein orthologue exhibited hepatic transcriptomic and metabolic alterations. In this study, we used a label-free liquid chromatography-tandem mass spectrometry approach to uncover proteins abundance associated with specific biological processes that differed depending on the age (four or ten months) and/or the genotype (wild type or Wrn mutant) in the serum and liver of mice. Principal component analysis of the proteomic data from both serum and hepatic tissue revealed a sexual dimorphism regardless of the age and the genotype of the mice. Moreover, although all Wrn mutant mice exhibited fatty liver by the age of ten months, a significant age and genotype dependent enrichment of proteins involved in lipid and fatty acid metabolic processes were uncovered only in males. Also, a genotype dependent increase in serum oxidant detoxification processes was observed in the serum of Wrn mutant males. Despite these sexual differences, several aspects of the immune system were affected in both females and males. Finally, an increase of specific immunoglobulin molecules was common in the liver and serum of both older Wrn mutant females and males. Such results suggest that specific immunoglobulin variants maybe associated with fatty liver progression in WS.
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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".