<i>GJA1</i>mutations, variants, and connexin 43 dysfunction as it relates to the oculodentodigital dysplasia phenotype
Bibliographic record
Abstract
The predominantly autosomal dominant disorder, oculodentodigital dysplasia (ODDD) has high penetrance with intra- and interfamilial phenotypic variability. Abnormalities observed in ODDD affect the eye, dentition, and digits of the hands and feet. Patients present with a characteristic facial appearance, narrow nose, and hypoplastic alae nasi. Neurological problems, including dysarthria, neurogenic bladder disturbances, spastic paraparesis, ataxia, anterior tibial muscle weakness, and seizures, are known to occur as well as conductive hearing loss, cardiac defects, and anomalies of the skin, hair, and nails. In 2003, our analysis of 17 ODDD families revealed that each had a different mutation within the human gap junction alpha 1 (GJA1) gene which encodes the protein connexin 43 (Cx43). Since then at least 17 publications have identified an additional 26 GJA1 mutations and in this study, we present 28 new cases with 18 novel GJA1 mutations. We include tables summarizing the 62 known GJA1 nucleotide changes leading to Cx43 protein alterations and the phenotypic information available on 177 affected individuals from 54 genotyped families. Mutations resulting in ODDD occur in each of the nine domains of the Cx43 protein, and we review our functional experiments and those in the literature, examining the effects of 13 different Cx43 mutations upon gap junction activity.
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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 | 0.001 | 0.001 |
| 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".