Aiolos supports TFH cell differentiation by antagonizing the IL-2/STAT5 signaling pathway
Bibliographic record
Abstract
Abstract CD4+ T follicular helper (TFH) cells are critical for the generation of robust humoral immune responses, as they provide help to B cells to support the generation of both pathogen-neutralizing antibodies and long-lived plasma cell populations. To date, the mechanisms underlying TFH differentiation are incompletely understood. Here, we identify the transcription factor Aiolos as a key regulator of TFH responses. We find that Aiolos expression is increased in TFH cell populations generated during influenza infection and that Aiolos deficiency results in compromised TFH cell differentiation and B cell helper activity. Mechanistically, loss of Aiolos results in diminished expression of TFH genes, including those encoding the key transcription factors Bcl-6, TCF-1, and Tox. Conversely, expression of genes associated with IL-2/STAT5 signaling, a known antagonist of the TFH gene program, were significantly elevated in Aiolos-deficient settings. Consistent with these data, antigen-specific Aiolos-deficient effector CD4+ T cell populations generated in response to influenza infection exhibited significantly elevated IL-2Rα surface expression. Together, our findings suggest that repression of IL-2/STAT5 signaling represents a novel mechanism by which Aiolos regulates the TFH gene program. These findings are important, as they provide critical insight that may ultimately be leveraged for the development of novel TFH-focused immunotherapies and strategies to improve vaccination approaches. Supported by a grant from the NIAID (NIH; R01 AI134972), funds through The Ohio State University College of Medicine, and funds through The Ohio State University College of Medicine Advancing Research in Infection and Immunity Fellowship Program
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".