The human Stat1 gain-of-function T385M mutation causes expansion of activated T-follicular helper/T-helper 1-like CD4 T cells and sex-biased autoimmunity in specific pathogen-free mice
Bibliographic record
Abstract
Introduction: Humans with gain-of-function (GOF) mutations in STAT1 (Signal Transducer and Activator of Transcription 1), a potent immune regulator, experience frequent infections. About one-third, especially those with DNA-binding domain (DBD) mutations such as T385M, also develop autoimmunity, sometimes accompanied by increases in T-helper 1 (Th1) and T-follicular helper (Tfh) CD4 effector T cells, resembling those that differentiate following infection-induced STAT1 signaling. However, environmental and molecular mechanisms contributing to autoimmunity in STAT1 GOF patients are not defined. Methods: We generated Stat1T385M/+ mutant mice to model the immune impacts of STAT1 DBD GOF under specific-pathogen free (SPF) conditions. Results: Stat1T385M/+ lymphocytes had more total Stat1 at baseline and also higher amounts of IFNg-induced pStat1. Young mutants exhibited expansion of Tfh-like cells, while older mutants developed autoimmunity accompanied by increased Tfh-like cells, B cell activation and germinal center (GC) formation. Mutant females exhibited these immune changes sooner and more robustly than males, identifying significant sex effects of Stat1T385M-induced immune dysregulation. Single cell RNA-Seq (scRNA-Seq) analysis revealed that Stat1T385M activated transcription of GC-associated programs in both B and T cells. However, it had the strongest transcriptional impact on T cells, promoting aberrant CD4 T cell activation and imparting both Tfh-like and Th1-like effector programs. Discussion: Collectively, these data demonstrate that in the absence of overt infection, Stat1T385M disrupted naïve CD4 T cell homeostasis and promoted expansion and differentiation of abnormal Tfh/Th1-like helper and GC-like B cells, eventually leading to sex-biased autoimmunity, suggesting a model for STAT1 GOF-induced immune dysregulation and autoimmune sequelae in humans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".