Methods to extract and study the biological effects of murine gut microbiota using Caenorhabditis elegans as a screening host
Bibliographic record
Abstract
Gut microbiota has been established as a main regulator of health. However, how changes in gut microbiota are directly associated with physiological and cellular alterations has been difficult to tackle on a large-scale basis, notably because of the cost and labor-extensive resources required for rigorous experiments in mammals. In the present study, we used the nematode Caenorhabditis elegans as a model organism to elucidate microbiota-host interactions. We developed a method to extract gut microbiota (MCB) from murine feces, and tested its potential as food source for and its impact on C. elegans biology compared to the standard bacterial diet Escherichia coli OP50. Although less preferred than OP50, MCB was not avoided but had a lower energy density (triglycerides and glucose). Consistently, MCB-fed worms exhibited smaller body length and size, lower fertility, and lower fat content than OP50-fed worms, but had a longer mean lifespan, which resembles the effects of calorie restriction in mammals. However, these outcomes were altered when bacteria were inactivated, suggesting an important role of symbiosis of MCB beyond nutrient source. Taken together, our findings support the effectiveness of gut MCB processing to test its effects in C. elegans. More work comparing MCB of differently treated mice or humans is required to further validate relevance to mammals before large-scale screening assays.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".