Regulatory T cells control the dynamic and site-specific polarization of total CD4 T cells following Salmonella infection
Bibliographic record
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 nonlethal 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, Treg 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) | 0.001 | 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".