Gut microbiota-mediated Gene-Environment interaction in the TashT mouse model of Hirschsprung disease
Bibliographic record
Abstract
Abstract Based on the bilateral relationship between the gut microbiota and formation/function of the enteric nervous system (ENS), we sought to determine whether antibiotics-induced dysbiosis might impact the expressivity of genetically-induced ENS abnormalities. To address this, we took advantage of the TashT mouse model of Hirschsprung disease, in which colonic aganglionosis and hypoganglionosis are both much more severe in males. These defects result into two male-biased colon motility phenotypes: either megacolon that is lethal around weaning age or chronic constipation in adults, the latter being also associated with an increased proportion of nitrergic neurons in the distal ENS. Induction of dysbiosis using a cocktail of broad-spectrum antibiotics specifically impacted the colonic ENS of TashTTg/Tg mice in a stage-dependent manner. It further decreased the neuronal density at post-weaning age and differentially modulated the otherwise increased proportion of nitrergic neurons, which appeared normalized around weaning age and further increased at post-weaning age. These changes delayed the development of megacolon around weaning age but led to premature onset of severe constipation later on. Finally, local inhibition of nitric oxide signaling improved motility and prevented death by megacolon. We thus conclude that exposure to antibiotics can negatively influence the expressivity of a genetically-induced enteric neuropathy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".