Metabolic and Phenotypic Differences between Mice Producing a Werner Syndrome Helicase Mutant Protein and Wrn Null Mice
Bibliographic record
Abstract
Werner syndrome (WS) is a premature aging disorder caused by mutations in a RecQ-family DNA helicase, WRN. Mice lacking part of the helicase domain of the WRN orthologue exhibit many phenotypic features of WS, including metabolic abnormalities and a shorter mean life span. In contrast, mice lacking the entire Wrn protein (i.e. Wrn null mice) do not exhibit a premature aging phenotype. In this study, we used a targeted mass spectrometry-based metabolomic approach to identify serum metabolites that are differentially altered in young Wrn helicase mutant and Wrn null mice. An antibody-based quantification of 43 serum cytokines and markers of cardiovascular disease risk complemented this study. We found that Wrn helicase mutants exhibited elevated and decreased levels, respectively, of the anti-inflammatory cytokine IL-10 and the pro-inflammatory cytokine IL-18. Wrn helicase mutants also exhibited an increase in serum hydroxyproline and plasminogen activator inhibitor-1, markers of extracellular matrix remodeling of the vascular system and inflammation in aging. We also observed an abnormal increase in the ratio of very long chain to short chain lysophosphatidylcholines in the Wrn helicase mutants underlying a peroxisome perturbation in these mice. Remarkably, the Wrn mutant helicase protein was mislocalized to the endoplasmic reticulum and the peroxisomal fractions in liver tissues. Additional analyses with mouse embryonic fibroblasts indicated a severe defect of the autophagy flux in cells derived from Wrn helicase mutants compared to wild type and Wrn null animals. These results indicate that the deleterious effects of the helicase-deficient Wrn protein are mediated by the dysfunction of several cellular organelles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".