TLR2 stimulation drives human CD4+CD25+CD127negFOXP3+ Tregs into a Th17-like phenotype with reduced suppressive function. (50.4)
Bibliographic record
Abstract
Abstract Naturally occurring CD4+CD25hiFOXP3+ Tregs suppress the activity of pathogenic T cells and prevent development of autoimmunity. TLRs are involved in modulating Treg functions both directly and indirectly. Specifically, TLR2 stimulation can reduce the suppressive function of Tregs by mechanisms that are incompletely understood. The developmental pathways of Tregs and Th17 cells are considered divergent and mutually inhibitory, and IL-17 production has been associated with reduced Treg function. We examined the effect of different TLR2 ligands on the suppressive functions of human CD4+CD25hiCD127negFOXP3+ Tregs and found that activation of TLR1/2 heterodimers reduces the suppressive activity of Tregs on CD4+CD25- responder T cell (Tresp) proliferation while at the same time enhancing the expression of IL-17, increasing RORC, and decreasing FOXP3 expression. Neutralisation of either IL-17 or IL-6 abrogated Pam3Cys-mediated reduction of Treg suppressive function. We also found that TLR2 stimulation in combination with TCR activation drives naïve human T helper precursors to Th17 differentiation. We conclude that TLR2 stimulation can induce human Tregs to a Th17 phenotype skewing and identify this plasticity as a new mechanism of regulation of Treg function by TLRs, which could enhance microbial clearance by releasing T effector functions.
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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) | 0.005 | 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".