<scp>ATP</scp> conditions intestinal epithelial cells to an inflammatory state that promotes components of <scp>DC</scp> maturation
Bibliographic record
Abstract
Intestinal epithelial cells (IECs) normally promote the development of gut resident tolerogenic dendritic cells (DCs) and regulatory T cells, but how this process is altered in inflammatory bowel disease is not well characterized. Recently, we published that the cell injury signal ATP modulates IEC chemokine responses to the TLR5 ligand flagellin and exacerbates colitis in the presence of flagellin. We hypothesized that ATP switches these IECs from tolerogenic to proinflammatory, enhancing DC activation and immune responses to commensal antigens. Here, we report that ATP enhanced murine IEC production of KC, IL-6, TGF-β, and thymic stromal lymphopoietin in response to TLR1/2 stimulation by Pam(3) CSK(4) (PAM). Moreover, supernatants from IECs stimulated with ATP+PAM enhanced expression of CD80 on bone marrow derived dendritic cells, and increased their production of IL-12, IL-6, IL-23, TGF-β, and aldh1a2, suggesting a Th1/Th17 polarizing environment. DCs conditioned by stressed IECs stimulated an enhanced recall response to flagellin and supported the expansion of IFN-γ(+) and IL-17(+) memory T cells. Lastly, colonic administration of nonhydrolysable ATP increased production of IL-6 and Cxcl1 (KC) by IECs. These findings indicate that ATP influences the response of IECs to TLR ligands and biases the maturation of DCs to become inflammatory.
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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) | 0.003 | 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".