Aiolos restrains the acquisition of cytotoxic features by CD4+ T cells
Bibliographic record
Abstract
Abstract Classically, CD4+ T cells have been defined as “helper” type cells, providing aid to other immune cell populations via the secretion of cytokines and direct cell-cell interactions. More recently, a subset of CD4+ T cells with cytotoxic capabilities, termed CD4+ cytotoxic T lymphocytes (CD4-CTLs), have been observed in both mice and humans, performing protective functions in the settings of infection and cancer while conversely contributing to the pathogenesis of autoimmunity. Despite their well-documented importance in several disease contexts, the complete mechanisms that underlie their differentiation and function remain unknown. Here, we identify the Ikaros family member, Aiolos, as a novel regulator of CD4+ CTL differentiation and function. We find that Aiolos deficiency results in increased expression of key CD4+ CTL transcription factors and effector molecules both in vitro and in an in vivo murine model of influenza infection. Mechanistically, we find that Aiolos deficiency results in increased IL-2/STAT5 signaling, supporting a repressive role for Aiolos in CD4+ CTL differentiation via negative regulation of the IL-2/STAT5 pathway. Collectively, this work identifies Aiolos as a novel negative regulator of CD4+ cytotoxic gene programming, and thus may represent a therapeutic target for the treatment of autoimmune diseases and enhanced anti-tumor immunity. Sponsored by a grant from NIAID (NIH-RO1 AI134972) and funds through The Ohio State University College of Medicine
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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".