Molecular phenotype of SLC4A11 missense mutants: Setting the stage for personalized medicine in corneal dystrophies
Bibliographic record
Abstract
SLC4A11 mutations cause cases of congenital hereditary endothelial dystrophy (CHED), Harboyan syndrome (HS), and Fuchs endothelial corneal dystrophy (FECD). Defective water reabsorption from corneal stroma by corneal endothelial cells (CECs) leads to these corneal dystrophies. SLC4A11, in the CEC basolateral membrane, facilitates transmembrane movement of H2O, NH3, and H+-equivalents. Some SLC4A11 disease mutants have impaired folding, leading to a failure to move to the cell surface, which in some cases can be corrected by the drug, glafenine. To identify SLC4A11 mutants that are targets for folding-correction therapy, we examined 54 SLC4A11 missense mutants. Cell-surface trafficking was assessed on immunoblots, by the level of mature, high molecular weight, cell surface-associated form, and using a bioluminescence resonance energy transfer assay. Low level of cell surface trafficking was found in four out of 18 (20%) of FECD mutants, 19/ out of 31 (61%) of CHED mutants, and three out of five (60%) of HS mutants. Amongst ER-retained mutants, 16 showed increased plasma membrane trafficking when grown at 30°C, suggesting that their defect has potential for rescue. CHED-causing point mutations mostly resulted in folding defects, whereas the majority of FECD missense mutations did not affect trafficking, implying functional impairment. We identified mutations that make patients candidates for folding correction of their corneal dystrophy.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".