Regulatory T cells control the dynamic and site-specific polarization of total CD4 T cells following <i>Salmonella</i> infection
Bibliographic record
Abstract
ABSTRACT FoxP3 + regulatory T cells (Tregs) control inflammation and maintain mucosal homeostasis, but their functions during infection are poorly understood. Th1, Th2 and Th17 cells can be identified by master transcription factors (TFs) T-bet, GATA3 and RORγT; Tregs also express these TFs. While T-bet + Tregs can selectively suppress Th1 cells, it is unclear whether distinct Treg populations can alter Th bias. To address this, we used Salmonella enterica serotype Typhimurium to induce non-lethal colitis. Following infection, we observed an early colonic Th17 response within total CD4 T cells, followed by a Th1 bias. The early Th17 response, which contains both Salmonella-specific and non- Salmonella -specific cells, parallels an increase in T-bet + Tregs. Later, Th1 cells and RORγT + Tregs dominate. This reciprocal dynamic may indicate that Tregs selectively suppress Th cells, shaping the immune response. Treg depletion 1-2 days post-infection shifted the early Th17 response to a Th1 bias; however, depletion 6-7 days post-infection abrogated the Th1 bias. Thus, Tregs are necessary for the early Th17 response, and for a maximal Th1 response later. These data show that Tregs shape the overall tissue CD4 T cell response and highlight the potential for subpopulations of Tregs to be used in targeted therapeutic approaches.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".