A vanishing family of commensal bacteria and its impact on host health and disease
Bibliographic record
Abstract
Abstract The gut microbiome plays a critical role in intestinal structure, immune homeostasis, and inflammatory diseases. The rise of these diseases in industrialized countries has been correlated with the loss of microbial diversity in the gut. One bacterial family, Muribaculaceae (Mb), is becoming a lost member in industrialized human microbiomes and many studies have correlated low Mb abundance with different disease states. Our goal is to determine how Mb depletion may be connected to disease, if introducing Mb back can ameliorate the impact of disease, and uncover immune mechanisms necessary for colonization of Mb. To study this, we developed two mouse models with complex microbiomes; one containing Mb (Mb positive), and one without Mb (Mb naïve). We evaluated intestinal structure with confocal microscopy, measured the production of short-chain fatty acids (SCFAs), and compared these mice in a Citrobacter rodentium (C. rodentium) infection model. Mice without Mb lost colonic mucus striation and had a significant drop in the production of SCFAs. Additionally, Mb positive mice had reduced C. rodentium colonization compared to mice lacking Mb. Lastly, we introduced 8 characterized Mb isolates to the Mb naïve mice to quantify immune responses by flow cytometry. We found that Mb is capable of establishing in Mb naïve mice, resulting in robust germinal centre B cell responses, yet no subsequent plasmablast accumulation in the mLN or lamina propria. Determining the mechanisms involved in disease amelioration by Mb and its impact on host immune responses is a novel research direction that could increase our understanding of colonization dynamics and highlights the potential of this bacteria as a model for microbiota therapies. Supported by grants from The W. Garfield Weston Foundation
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".