Chronic mucocutaneous candidiasis associated with a novel frameshift mutation in IL-17 receptor alpha
Bibliographic record
Abstract
Background: Chronic mucocutaneous candidiasis (CMCC) has traditionally encompassed endocrinopathy, autoimmunity, and infection of the skin, nails, oral and genital mucosa. It is typically caused by Candida albicans, an organism that is found to be commensal in healthy individuals. To date, most patients with CMCC have mutations in AIRE or STAT1. While chronic Candida spp. infection is a feature of multiple profound T cell deficiencies, it has also been identified in rare cases involving selective immune defects, including interleukin-17 receptor A (IL-17RA) deficiency. An association between Staphylococcus aureus infections and candidiasis due to IL-17RA deficiency has recently been proposed. Aim: We sought to identify the genetic defect in a patient presenting with recurrent oral thrush and S. aureus infections, but otherwise unremarkable immune workup. Methods: Whole exome sequencing and Sanger confirmation was performed, and protein expression analysis utilized to assess the impact of the genetic aberration. A comprehensive immune workup was completed to characterize any possible deficits in his immune system. Results: Next generation sequencing techniques identified a homozygous mutation in IL17RA, c.1696insAG, resulting in the frameshift mutation p.Q566fs. Western blot analysis confirmed the loss of IL-17RA expression. Conclusion: We describe here a novel frameshift mutation in IL17RA. Clinically, the patient was a diagnostic challenge as he did not present with a classic CMCC phenotype. This case emphasizes the importance of genetic analysis in patients presenting with recurrent infections. Statement of novelty: We identify a novel frameshift mutation in IL17RA in a patient presenting with recurrent bacterial and fungal mucocutaneous infections.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".