Structural modeling of a novel <i><scp>SLC</scp>38A8</i> mutation that causes foveal hypoplasia
Bibliographic record
Abstract
Abstract Background Foveal hypoplasia ( FH ) in the absence of albinism, aniridia, microphthalmia, or achromatopsia is exceedingly rare, and the molecular basis for the disorder remains unknown. FH is characterized by the absence of both the retinal foveal pit and avascular zone, but with preserved retinal architecture. SLC 38A8 encodes a sodium‐coupled neutral amino acid transporter with a preference for glutamate as a substrate. SLC 38A8 has been linked to FH . Here, we describe a novel mutation to SLC 38A8 which causes FH , and report the novel use of OCT ‐angiography to improve the precision of FH diagnosis. More so, we used computational modeling to explore possible functional effects of known SLC 38A8 mutations. Methods Fundus autofluorescence, SD ‐ OCT , and OCT ‐angiography were used to make the clinical diagnosis. Whole‐exome sequencing led to the identification of a novel disease‐causing variant in SLC 38A8 . Computational modeling approaches were used to visualize known SLC 38A8 mutations, as well as to predict mutation effects on transporter structure and function. Results We identified a novel point mutation in SLC 38A8 that causes FH . A conclusive diagnosis was made using OCT ‐angiography, which more clearly revealed retinal vasculature penetrating into the foveal region. Structural modeling of the channel showed the mutation was near previously published mutations, clustered on an extracellular loop. Our modeling also predicted that the mutation destabilizes the protein by altering the electrostatic potential within the channel pore. Conclusion Our results demonstrate a novel use for OCT ‐angiography in confirming FH , and also uncover genotype–phenotype correlations of FH ‐linked SLC 38A8 mutations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".