Histamine production by the gut microbiota induces visceral hyperalgesia through histamine 4 receptor signaling in mice
Bibliographic record
Abstract
The gut microbiota has been implicated in chronic pain disorders, including irritable bowel syndrome (IBS), yet specific pathophysiological mechanisms remain unclear. We showed that decreasing intake of fermentable carbohydrates improved abdominal pain in patients with IBS, and this was accompanied by changes in the gut microbiota and decreased urinary histamine concentrations. Here, we used germ-free mice colonized with fecal microbiota from patients with IBS to investigate the role of gut bacteria and the neuroactive mediator histamine in visceral hypersensitivity. Germ-free mice colonized with the fecal microbiota of patients with IBS who had high but not low urinary histamine developed visceral hyperalgesia and mast cell activation. When these mice were fed a diet with reduced fermentable carbohydrates, the animals showed a decrease in visceral hypersensitivity and mast cell accumulation in the colon. We observed that the fecal microbiota from patients with IBS with high but not low urinary histamine produced large amounts of histamine in vitro. We identified Klebsiella aerogenes , carrying a histidine decarboxylase gene variant, as a major producer of this histamine. This bacterial strain was highly abundant in the fecal microbiota of three independent cohorts of patients with IBS compared with healthy individuals. Pharmacological blockade of the histamine 4 receptor in vivo inhibited visceral hypersensitivity and decreased mast cell accumulation in the colon of germ-free mice colonized with the high histamine-producing IBS fecal microbiota. These results suggest that therapeutic strategies directed against bacterial histamine could help treat visceral hyperalgesia in a subset of patients with IBS with chronic abdominal pain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".