Understanding and exploiting the roles of O-GlcNAc in neurodegenerative diseases
Bibliographic record
Abstract
O-GlcNAc is a common modification found on nuclear and cytoplasmic proteins. Determining the catalytic mechanism of the enzyme O-GlcNAcase (OGA), which removes O-GlcNAc from proteins, enabled the creation of potent and selective inhibitors of this regulatory enzyme. Such inhibitors have served as important tools in helping to uncover the cellular and organismal physiological roles of this modification. In addition, OGA inhibitors have been important for defining the augmentation of O-GlcNAc as a promising disease-modifying approach to combat several neurodegenerative diseases including both Alzheimer's disease and Parkinson's disease. These studies have led to development and optimization of OGA inhibitors for clinical application. These compounds have been shown to be well tolerated in early clinical studies and are steadily advancing into the clinic. Despite these advances, the mechanisms by which O-GlcNAc protects against these various types of neurodegeneration are a topic of continuing interest since improved insight may enable the creation of more targeted strategies to modulate O-GlcNAc for therapeutic benefit. Relevant pathways on which O-GlcNAc has been found to exert beneficial effects include autophagy, necroptosis, and processing of the amyloid precursor protein. More recently, the development and application of chemical methods enabling the synthesis of homogenous proteins have clarified the biochemical effects of O-GlcNAc on protein aggregation and uncovered new roles for O-GlcNAc in heat shock response. Here, we discuss the features of O-GlcNAc in neurodegenerative diseases, the application of inhibitors to identify the roles of this modification, and the biochemical effects of O-GlcNAc on proteins and pathways associated with neurodegeneration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".