Targeted protein glycosylation (O‐GlcNAc) of mitochondrial proteins in rats selected for low running capacity
Bibliographic record
Abstract
Excess flux through the hexosamine biosynthesis pathway results in enhanced protein modification by O‐linked‐β‐N‐acetylglucosamine (O‐GlcNAc), glucotoxicity and insulin resistance. Contributing factors are complex and may include environment as well as genetics. Aims were to examine differences in O‐GlcNAc in rats artificially selected for either low (LCR) or high (HCR) running capacity. Reduced fitness is known to impair mitochondrial function, perpetuate O‐GlcNAc modification and contribute to reduced insulin sensitivity. Insulin sensitivity was assessed by a hyperinsulinemic‐euglycemic clamp in conscious animals while 2‐[14C]deoxyglucose tracked glucose disposal. Immunoblots of heart tissue examined O‐GlcNAc levels and enzymes that control its regulation (OGT, OGA). Artificial selection of rats to generation 19 resulted in 6‐fold greater running capacity and 63% reduction in fat mass in HCR compared to LCR (p<0.05). LCR rats were insulin resistant disposing of 65% less glucose than HCR (p<0.05). Tissue analysis revealed enhanced O‐GlcNAcylation of Complex I, Complex IV, VDAC and SERCA in LCR compared to HCR (p<0.05). Levels of OGT and OGA were not different between groups. Thus, increased O‐GlcNAc in LCR animals may contribute to mitochondrial dysfunction and the pathogenesis of insulin resistance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".