Serotonin Activates Dendritic Cell Function in the Context of Gut Inflammation
Bibliographic record
Abstract
Mucosal inflammation in the gut is characterized by infiltration of innate and adaptive immune cells and by an alteration in serotonin-producing enterochromaffin cells. We investigated the role of serotonin in the function of dendritic cells (DCs) and sequential T-cell activation in relation to generation of gut inflammation. DCs isolated from tryptophan hydroxylase-1-deficient (TPH1(-/-)) mice, which have reduced serotonin in the gut, and wild-type (TPH1(+/+)) mice with or without dextran sulfate sodium (DSS)-induced colitis were stimulated with lipopolysaccharide to assess interleukin-12 (IL-12) production. Isolated DCs from TPH1(+/+) and TPH1(-/-) mice were also cocultured with CD4(+) T cells of naive TPH1(+/+) mice to assess the role of serotonin in priming T cells. In addition, serotonin-pulsed DCs were transferred to TPH1(-/-) mice to assess the effect on DSS-induced colitis. Consistent with a reduced severity of colitis, DCs from DSS-induced TPH1(-/-) mice produced less IL-12 compared with the TPH1(+/+) mice. In vitro serotonin stimulation restored the cytokine production from TPH1(-/-) DCs and adoptive transfer of serotonin-pulsed DCs into TPH1(-/-) up-regulated colitis. Furthermore, CD4(+) T cells primed by TPH1(-/-) DCs produce reduced the levels of IL-17 and interferon-γ. This study provides novel information on serotonin-mediated immune signaling and promotion of interactions between innate and adaptive immune responses in the context of gut inflammation, which may ultimately lead to improved strategies to combat gut inflammatory disorders.
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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) | 0.001 | 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".