Diminished TLR2-TLR9 mediated CD4+ T cell responses are associated with increased inflammation in intraocular tuberculosis
Bibliographic record
Abstract
Intraocular tuberculosis (IOTB) is amongst the leading causes of uveitis in tropical countries. Despite reports on involvement of proinflammatory cytokines, studies on innate immune responses in disease pathogenesis are lacking. Reports from animal models and patients with pulmonary tuberculosis indicate that defects in toll like receptor (TLR)2 and TLR9 signalling predispose them to tuberculosis. In this context, we investigated the role of TLR2, TLR4 and TLR9 in generation of CD4+ T effector (Teff) cell responses during IOTB. Firstly, the cells in vitreous fluids showed lower expression of TLR2 and TLR9 in IOTB as compared to non-uveitis and non-TB uveitis groups. Next, peripheral CD4+ Teff cells of subjects with IOTB showed decreased proliferative responses and lower induction of Tregs following TLR2 and TLR9 stimulation. Further, TLR9 ligation resulted in increased IFN-γ and IL-17a but decreased expression of IL-10 and TGF-β. Lastly, lower expression of genes involved in TLR9 signalling after direct TLR9 ligation was observed in IOTB. Collectively, our results show that a subdued response to direct TLR2 and TLR9 stimulation in CD4+ T cells is associated with increased proinflammatory responses in IOTB. These findings reveal an important link between innate immune signalling and ensuing adaptive immune responses in IOTB with implications in other forms of extrapulmonary tuberculosis.
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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 | 0.001 | 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) | 0.003 | 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".