Role of <i>O</i>-GlcNAcylation in nutritional sensing, insulin resistance and in mediating the benefits of exercise
Bibliographic record
Abstract
The purpose of this review is to highlight the role of O-linked β-N-acetylglucosamine (O-GlcNAc) protein modification in metabolic disease states and to summarize current knowledge of how exercise affects this important post-translational signalling pathway. O-GlcNAc modification is an intracellular tool capable of integrating energy supply with demand. The accumulation of excess energy associated with obesity and insulin resistance is mediated, in part, by the hexosamine biosynthetic pathway (HBP), which results in the O-GlcNAcylation of a myriad of proteins, thereby affecting their respective function, stability, and localization. Insulin resistance is related to the excessive O-GlcNAcylation of key metabolic proteins causing a chronic blunting of insulin signalling pathways and precipitating the accompanying pathologies, such as heart and kidney disease. Lifestyle modifications such as diet and exercise also modify the pathway. Exercise is a front-line and cost-effective therapeutic approach for insulin resistance, and recent work shows that the intervention can alter O-GlcNAc gene expression, signalling, and protein modification. However, there is currently no consensus on the effect of frequency, intensity, type, and duration of exercise on O-GlcNAc modification, the HBP, and its related enzymes. On one end of the spectrum, mild, prolonged swim training reduces O-GlcNAcylation, while on the other end, higher intensity treadmill running increases cardiac protein O-GlcNAc modification. Clearly, a balance between acute and chronic stress of exercise is needed to reap the benefits of the intervention on O-GlcNAc signalling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".