Microbiota-derived acetylcholine can promote gut motility in <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
The gut microbiota is crucial for intestinal health, including gastrointestinal (GI) motility. How commensal bacterial species influence GI motility has not been fully elucidated. A major factor of GI motility is the gut contraction promoting the propulsive movement of orally ingested materials. Here, we developed a method to monitor and quantify gut contractions in living Drosophila melanogaster larvae. We found that the culture medium of an isolated strain Lactiplantibacillus plantarum Lsi promoted gut contraction in vivo , which was not observed in Leuconostoc sp. Leui nor Acetobacter persici Ai culture medium. To identify bacteria-derived metabolites, we performed metabolome analysis of the culture media by liquid chromatography-tandem mass spectrometry (LC–MS/MS). Of the 66 metabolites detected, we found that some metabolites changed in a species-specific manner. Among them, acetylcholine was specifically produced by L. plantarum. Feeding exogenous acetylcholine increased the frequency of gut contractions, which was blocked by D-tubocurarine, an inhibitor of nicotinic acetylcholine receptors. In this study, we propose a mechanism by which the gut microbiota influences Drosophila gut motility. This article is part of the theme issue ‘Sculpting the microbiome: how host factors determine and respond to microbial colonization’.
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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".