Gut microbiota modulates visceral sensitivity through calcitonin gene-related peptide (CGRP) production
Bibliographic record
Abstract
Abdominal pain is common in patients with gastrointestinal disorders, but its pathophysiology is unclear, in part due to poor understanding of basic mechanisms underlying visceral sensitivity. Accumulating evidence suggests that gut microbiota is an important determinant of visceral sensitivity. Clinical and basic research studies also show that sex plays a role in pain perception, although the precise pathways are not elucidated. We investigated pain responses in germ-free and conventionally raised mice of both sexes, and assessed visceral sensitivity to colorectal distension, neuronal excitability of dorsal root ganglia (DRG) neurons and the production of substance P and calcitonin gene-related peptide (CGRP) in response to capsaicin or a mixture of G-protein coupled receptor (GPCR) agonists. Germ-free mice displayed greater in vivo responses to colonic distention than conventional mice, with no differences between males and females. Pretreatment with intracolonic capsaicin or GPCR agonists increased responses in conventional, but not in germ-free mice. In DRG neurons, gut microbiota and sex had no effect on neuronal activation by capsaicin or GPCR agonists. While stimulated production of substance P by DRG neurons was similar in germ-free and conventional mice, with no additional effect of sex, the CGRP production was higher in germ-free mice, mainly in females. Absence of gut microbiota increases visceral sensitivity to colorectal distention in both male and female mice. This is, at least in part, due to increased production of CGRP by DRG neurons, which is mainly evident in female mice. However, central mechanisms are also likely involved in this process.
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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".