Somatic STAT3 Gain-of-Function (GOF) syndrome underlying susceptibility to Parvovirus B19, Pseudomonas aeruginosa, and Histoplasma capsulatum infections
Bibliographic record
Abstract
• Severe infections in healthy hosts may signal an underlying immune deficiency. • Inborn errors of immunity (IEI) are monogenic defects, often germline mutations. • IEI can also arise from autoantibodies or somatic mutations. • Somatic STAT3 gain-of-function (GOF) mutations underlie autoimmunity and cancers. • Somatic STAT3 GOF mutation can present as immunodeficiency with severe infections. Infections in overtly immunocompromised persons (e.g. those living with advanced HIV, receiving malignancy-targeting chemotherapy, or recipients of transplants), may present with severe disease. In the absence of overt immunosuppression, unexplained severe disease due to prevalent microbes may reflect an underlying “inborn error of immunity” (IEI). IEI are monogenic penetrant defects of immunity: Classically, the genetic mutation is germline, that is, derived from germ cells, vertically transmitted to offspring in Mendelian fashion (autosomal dominant, autosomal recessive, or X-linked), and present in all cells of the affected individual. IEI may also be caused by naturally-occurring autoantibodies directed to immunologic components (such as cytokines) ( Casanova et al., 2024 ) or by somatic mutations ( Aluri and Cooper, 2023 ). Somatic mutations occur when a deleterious genetic variant occurs in the post-zygotic stage of development and can affect any body cell, other than germ cells; they are well-known causes of solid and hematologic malignancies. Somatic IEI syndromes are emerging, typically associated with lymphoproliferative diseases or autoinflammatory disorders. We report the case of a young woman with unexplained severe infections in whom a somatic gain-of-function (GOF) mutation in Signal transducer and activator of transcription 3 (STAT3) was found.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".