A novel heterozygous pathogenic <i>AIRE</i> variant causing autoimmunity but not infectious susceptibility
Bibliographic record
Abstract
Autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED) is characterized by the triad of hypoparathyroidism, Addison’s disease, and chronic mucocutaneous candidiasis due to biallelic deleterious variants in AIRE. However, emerging evidence has established that some monoallelic variants affecting specific functional domains may also drive autoimmunity by negative dominance. Here, we describe a novel heterozygous AIRE variant, c.1010G>T (p.Cys337Phe), in three individuals from a Taiwanese-Singaporean family presenting with hypoparathyroidism, vitiligo, anemia, and ectodermal abnormalities, but not candidiasis. Functional studies confirmed AIREC337F is both loss-of-function and dominant negative to wild-type AIRE. Detection of neutralizing autoantibodies against type I IFNs, but not Th17 cytokines, further supported an APECED-like immunological profile and potentially explained the lack of infections in affected individuals. Like other dominant negative AIRE variants, AIREC337F localizes to the highly conserved PHD1 domain. Thus, our findings identify a novel pathogenic heterozygous AIRE variant and broaden the phenotype of autosomal dominant APECED. We also highlight the importance of functional validation in interpreting variants of unknown significance, particularly when disease prevalence and variant profiles differ from typical cohorts.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".