Dietary carbohydrates alter immune-modulatory functionalities and DNA inversions in Bacteroides thetaiotaomicron
Bibliographic record
Abstract
The gut bacteria environment is highly dynamic. Environmental conditions were shown to affect microbial composition. Yet, their influences on bacterial functionality (e.g., immune-modulation activity) are mostly overlooked. Distinct strains of the same species, and even the same bacterial strain, may have different effects on the immune system depending on their growth environment. Therefore, studying the functionality of strains under different conditions is crucial. We analyzed functional alterations in the gut symbiont Bacteroides thetaiotaomicron (B. theta) under different dietary components consumption in humans, upon white sugar consumption in mice, and in response to 190 different carbon sources in vitro. Dietary alterations affected the orientation of phase variable regions in B. theta in humans, in vivo, and in vitro, and altered B. theta’s proteome and immune-modulatory functionality. Studying the effects of dietary components on the immune-modulatory functionalities of key members of the gut microbiota will allow for personalized dietary recommendations. Here, the authors characterize how different dietary components lead to functional alterations in the gut symbiont Bacteroides thetaiotaomicron, showing effects on the orientation of phase variable regions in humans, in vivo, and in vitro, and on modulating the bacterium´s proteome and immune-modulatory functionality.
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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".