Selected commensal‐related bacteria and Toll‐like receptor 3 agonist combinatorial codes synergistically induce interleukin‐12 production by dendritic cells to trigger a T helper type 1 polarizing programme
Bibliographic record
Abstract
Enteric infections remain a major health problem causing millions of deaths in developing countries. The interplay among the host intestinal epithelium, the mucosa-associated immune system and microbiota performs an essential role in gut homeostasis and protection against infectious diseases. Dendritic cells (DCs) play a key role in orchestrating protective immunity and tolerance in the gut. The mechanisms by which DCs adapt their responses and discriminate between virulent microbes and trillions of innocuous bacteria remain ill-defined. Here we investigated the effect of cross-talk between commensal-related bacteria (CB) and Toll-like receptor (TLR) agonists on DC activation and the outcome of the in vitro T helper response. Human monocyte-derived DCs were exposed to eight different Gram-positive or Gram-negative CB strains prior to activation with five different TLR agonists. The key polarizing cytokines interleukin (IL)-12p70, IL-10, IL-1beta and IL-6 were quantified and the fate of naïve T-cell differentiation was evaluated. We identified a unique combination of Lactobacillus casei and TLR3 signals that acted in synergy to selectively increase IL-12p70 secretion. Exposure to poly(I:C) converted L. casei-treated DCs into potent promoters of T helper type 1 (Th1) responses. We propose that DCs can integrate harmless and dangerous non-self signals delivered by viral products, to mount robust Th1 responses. Thus, in vivo DC targeting with selective probiotics may improve strategies for the management of enteric diseases.
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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) | 0.001 | 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".