Influence of bacterial components on the developmental programming of enteric neurons
Bibliographic record
Abstract
BACKGROUND: Intestinal bacteria have been increasingly shown to be involved in early postnatal development. Previous work has shown that intestinal bacteria are necessary for the structural development and intrinsic function of the enteric nervous system in early postnatal life. Furthermore, colonization with a limited number of bacteria appears to be sufficient for the formation of a normal enteric nervous system. We tested the hypothesis that common bacterial components could influence the programming of developing enteric neurons. METHODS: The developmental programming of enteric neurons was studied by isolating enteric neural crest-derived cells from the fetal gut of C57Bl/6 mice at embryonic day 15.5. After the establishment of the cell line, cultured enteric neuronal precursors were exposed to increasing concentrations of a panel of bacterial components including lipopolysaccharide, flagellin, and components of peptidoglycan. KEY RESULT: Exposure to bacterial components consistently affected proportions of enteric neuronal precursors that developed into nitrergic neurons. Furthermore, flagellin and D-gamma-Glu-mDAP were found to promote the development of serotonergic neurons. Proportions of dopaminergic neurons remained unchanged. Proliferation of neuronal precursor cells was significantly increased upon exposure to lipopolysaccharide and flagellin, while no significant changes were observed in the proportion of apoptotic neuronal precursors compared to baseline with exposure to any bacterial component. CONCLUSIONS AND INTERFACES: These findings suggest that bacterial components may influence the development of enteric neurons.
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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".