The Extended Clinical Phenotype of 26 Patients with Chronic Mucocutaneous Candidiasis due to Gain-of-Function Mutations in STAT1
Bibliographic record
Abstract
PURPOSE: Gain-of-function (GOF) mutations in the signal transducer and activator of transcription 1 (STAT1) result in unbalanced STAT signaling and cause immune dysregulation and immunodeficiency. The latter is often characterized by the susceptibility to recurrent Candida infections, resulting in the clinical picture of chronic mucocutaneous candidiasis (CMC). This study aims to assess the frequency of GOF STAT1 mutations in a large international cohort of CMC patients. METHODS: STAT1 was sequenced in genomic DNA from 57 CMC patients and 35 healthy family members. The functional relevance of nine different STAT1 variants was shown by flow cytometric analysis of STAT1 phosphorylation in patients' peripheral blood cells (PBMC) after stimulation with interferon (IFN)-α, IFN-γ or interleukin-27 respectively. Extended clinical data sets were collected and summarized for 26 patients. RESULTS: Heterozygous mutations within STAT1 were identified in 35 of 57 CMC patients (61%). Out of 39 familial cases from 11 families, 26 patients (67%) from 9 families and out of 18 sporadic cases, 9 patients (50%) were shown to have heterozygous mutations within STAT1. Thirteen distinct STAT1 mutations are reported in this paper. Eight of these mutations are known to cause CMC (p.M202V, p.A267V, p.R274W, p.R274Q, p.T385M, p.K388E, p.N397D, and p.F404Y). However, five STAT1 variants (p.F172L, p.Y287D, p.P293S, p.T385K and p.S466R) have not been reported before in CMC patients. CONCLUSION: STAT1 mutations are frequently observed in patients suffering from CMC. Thus, sequence analysis of STAT1 in CMC patients is advised. Measurement of IFN- or IL-induced STAT1 phosphorylation in PBMC provides a fast and reliable diagnostic tool and should be carried out in addition to genetic testing.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".