Genomic analysis of glaucoma pathogenesis due to gmds mutation in zebrafish
Bibliographic record
Abstract
Glaucoma, a major cause of irreversible blindness, is characterized by optic nerve damage and loss of retinal ganglion cells (RGC). SNPs in the GDP-MANNOSE 4,6-DEHYDRATASE (GMDS) gene have been linked to primary open-angle glaucoma (POAG) and treatment responses. The GMDS gene plays a critical role in fucosylation, a process essential for modifying glycoproteins and glycolipids, yet no mechanism for its role in glaucoma pathology has been described. Our study investigates the effects of gmds haploinsufficiency using a CRISPR/Cas9 induced mutation in zebrafish. RNA sequencing (RNAseq) analysis shows significant downregulation of stress response genes including those of the crystallin family, and increased expression of cell death genes in gmds heterozygous mutant eyes. These gene expression changes correlate with phenotypic alterations, including RGC layer thinning, RGC loss, and reduced optic nerve head width in adult gmds heterozygotes relative to wild type siblings. Our findings provide new insights into the role of GMDS in regulating eye function and suggests that GMDS may influence glaucoma risk by regulating the response to stress. This study provides a layer of functional evidence supporting the predictions made by previous GWAS findings, enhancing our understanding of the genetic basis of glaucoma. It highlights the potential of GMDS as a therapeutic target for mitigating glaucoma-related vision loss, opening new avenues for glaucoma research and treatment 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".