Human CD4+ T Cells Express TLR5 and Its Ligand Flagellin Enhances the Suppressive Capacity and Expression of FOXP3 in CD4+CD25+ T Regulatory Cells
Bibliographic record
Abstract
Germline encoded pattern recognition receptors, such as TLRs, provide a critical link between the innate and adaptive immune systems. There is also evidence to suggest that pathogen-associated molecular patterns may have the capacity to modulate immune responses via direct effects on CD4+ T cells. Given the key role of both CD4+CD25+ T regulatory (Treg) cells and the TLR5 ligand flagellin in regulating mucosal immune responses, we investigated whether TLR5 may directly influence T cell function. We found that both human CD4+CD25+ Treg and CD4+CD25- T cells express TLR5 at levels comparable to those on monocytes and dendritic cells. Costimulation of effector T cells with anti-CD3 and flagellin resulted in enhanced proliferation and production of IL-2, at levels equivalent to those achieved by costimulation with CD28. In contrast, costimulation with flagellin did not break the hyporesponsiveness of CD4+CD25+ Treg cells, but rather, potently increased their suppressive capacity and enhanced expression of FOXP3. These observations suggest that, in addition to their APC-mediated indirect effects, TLR ligands have the capacity to directly regulate T cell responses and modulate the suppressive activity of Treg cells.
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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) | 0.003 | 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".