Beyond sialylation: Exploring the multifaceted role of GNE in GNE myopathy
Bibliographic record
Abstract
Defects in sialic acid metabolism disrupt the sialylation of glycoproteins and glycolipids, contributing to a spectrum of diseases, including GNE myopathy (GNEM). This rare disorder is caused by mutations in the GNE gene that encodes for a bifunctional enzyme required for sialic acid biosynthesis, resulting in progressive muscle atrophy and weakness. There is no approved treatment for GNEM, and the number of affected individuals is underestimated. Although hyposialylation is considered the hallmark of GNEM, evidence showed lack of consistent correlation with GNEM severity and unveiled additional roles of GNE that contribute to the onset and/or progression of GNEM. Recent findings indicate that these mechanisms extend beyond glycosylation, encompassing cytoskeletal dynamics, oxidative stress, and muscle regeneration pathways. Understanding how GNE mutations result in a cascade of cellular and molecular dysregulations is crucial for developing targeted therapies aimed at improving the quality of life of patients. This review comprehensively examines GNEM's pathophysiology, clinical presentation, and therapeutic strategies, highlighting key findings on non-canonical GNE functions that account to GNEM clinical outcomes and emerging therapeutic targets. We propose future research directions to explore alternative target pathways that can ultimately support clinical development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".