Identification of a novel RAG1 hypomorphic mutation in a child presenting with disseminated vaccine-strain varicella
Bibliographic record
Abstract
Background: Recombination-activating gene 1 (RAG1) and recombination-activating gene 2 (RAG2) encode unique lymphocyte endonuclease proteins that are crucial in T and B cell development through V(D)J recombination. RAG1 gene defects lead to variable phenotypes, ranging from immunocompetent to severe combined immunodeficiency (SCID). Curative therapy for severe manifestations can be achieved through hematopoietic stem cell transplantation (HSCT). Advances in genomic sequencing have led to the discovery of new variants and it is recognized that the level of recombinase activity correlates with disease severity. Aim: To report the clinical presentation, immunological work-up, decision process to undergo HSCT, and confirmatory genetic diagnosis in a patient who was well until her initial presentation with disseminated vaccine-strain varicella. Methods: Clinical data was gathered through retrospective chart review. Immunological investigations, targeted gene sequencing, and thymic biopsy results were reviewed. Further genetic analysis, including whole exome and whole genome sequencing was performed. Results: Whole exome sequencing identified a single missense mutation in RAG1, R474C (c.1420C>T), which would not account for the clinical presentation. Healthy individuals with only 1 mutation have been reported. Subsequently, whole genome sequencing revealed a novel second heterozygous missense variant, H945D (c.2833G>T) in the RAG1 gene. Conclusion: Hypomorphic RAG1 mutations with residual activity have a diverse phenotypic expression. Identifying and understanding the implications of these mutations is crucial for disease prognostication and tailoring management. Statement of novelty: We present a novel RAG1 missense variant, with likely complete or partial loss of function, in a patient with significant impairment in cellular immunity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".