Bibliographic record
Abstract
O‐linked N‐acetylglucosamine (O‐GlcNAc) is an abundant form of protein O‐glycosylation found in the nucleus, cytoplasm, and mitochondria of all multicellular eukaryotes. This modification is found on several hundred proteins but is surprisingly regulated by only two enzymes; the glycosyltransferase O‐GlcNAc transferase (OGT) and the glycoside hydrolase O‐GlcNAcase (OGA). Because the substrate of OGT is derived from glucose, the levels of O‐GlcNAc within cells reflect environmental glucose availability. O‐GlcNAc has also emerged as part of a coordinated response to cellular stresses and levels of this modification increase markedly on exposure to diverse stresses ranging from heat shock to oxidative stress. These observations have stimulated interest in the potential protective roles of increased O‐GlcNAc in a range of tissues including in brain, where impaired nutrient utilization has been linked to various neurodegenerative diseases. Remarkably, brain permeable small molecule inhibitors of OGA have been shown to be surprisingly well tolerated in a range of animal models. Moreover, pharmacological agents that increase O‐GlcNAc levels in the brains of various transgenic models of Alzheimer Disease (AD) strikingly reduce the progression of disease pathology and neurodegeneration. While these effects have been shown to be remarkably reproducible, and OGA inhibitors have now been found to be generally well tolerated in early clinical trials, the mechanisms by which O‐GlcNAc protect against these pathologies and neurodegeneration remain less clear. Here we will discuss new advances in the biochemistry of OGA inhibitors, efforts to create improved inhibitors, and the protective effects of OGA in transgenic models of neurodegenerative diseases. Finally, we will discuss recent progress on various possible protective mechanisms by which increased O‐GlcNAc levels confer protection against various neurodegenerative diseases ranging from blockade of aggregation of toxic proteins to the induction of autophagy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".