Loss of sirtuin 4 leads to elevated glucose‐ and leucine‐stimulated insulin levels and accelerated age‐induced insulin resistance in multiple murine genetic backgrounds
Bibliographic record
Abstract
Several inherited metabolic disorders are associated with an accumulation of reactive acyl-CoA metabolites that can non-enzymatically react with lysine residues to modify proteins. While the role of acetylation is well-studied, the pathophysiological relevance of more recently discovered acyl modifications, including those found in inherited metabolic disorders, warrants further investigation. We recently showed that sirtuin 4 (SIRT4) removes glutaryl, 3-hydroxy-3-methylglutaryl, 3-methylglutaryl, and 3-methylglutaconyl modifications from lysine residues. Thus, we used SIRT4 knockout mice, which can accumulate these novel post-translational modifications, as a model to investigate their physiological relevance. Since SIRT4 is localized to mitochondria and previous reports have shown SIRT4 influences metabolism, we thoroughly characterized glucose and lipid metabolism in male and female SIRT4KO mice across different genetic backgrounds. While only minor perturbations in overall lipid metabolism were observed, we found SIRT4KO mice consistently had elevated glucose- and leucine-stimulated insulin levels in vivo and developed accelerated age-induced insulin resistance. Importantly, elevated leucine-stimulated insulin levels in SIRT4KO mice were dependent upon genetic background since SIRT4KO mice on a C57BL/6NJ genetic background had elevated leucine-stimulated insulin levels but not SIRT4KO mice on the C57BL/6J background. Taken together, the data suggest that accumulation of acyl modifications on proteins in inherited metabolic disorders may contribute to the overall metabolic dysfunction seen in these patients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".