Functional and <i>in silico</i> analysis of ATP8A2 and other P4-ATPase variants associated with human genetic diseases
Bibliographic record
Abstract
P4-ATPases flip lipids from the exoplasmic to cytoplasmic leaflet of cell membranes, a property crucial for many biological processes. Mutations in P4-ATPases are associated with severe inherited and complex human disorders. We determined the expression, localization and ATPase activity of four variants of ATP8A2, the P4-ATPase associated with the neurodevelopmental disorder known as cerebellar ataxia, impaired intellectual development and disequilibrium syndrome 4 (CAMRQ4). Two variants, G447R and A772P, harboring mutations in catalytic domains, expressed at low levels and mislocalized in cells. In contrast, the E459Q variant in a flexible loop displayed wild-type expression levels, Golgi-endosome localization and ATPase activity. The R1147W variant expressed at 50% of wild-type levels but showed normal localization and activity. These results indicate that the G447R and A772P mutations cause CAMRQ4 through protein misfolding. The E459Q mutation is unlikely to be causative, whereas the R1147W may display a milder disease phenotype. Using various programs that predict protein stability, we show that there is a good correlation between the experimental expression of the variants and in silico stability assessments, suggesting that such analysis is useful in identifying protein misfolding disease-associated variants.
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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) | 0.000 | 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) | 0.001 | 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".