Fluorescence activated cell sorting and fermentation analysis to study rumen microbiome responses to administered live microbials and yeast cell wall derived prebiotics
Bibliographic record
Abstract
Rapid dietary changes, such as switching from high-forage to high-grain diets, can modify the rumen microbiome and initiate gastrointestinal distress, such as bloating. In such cases, feed additives, including prebiotics and live microbials, can be used to mitigate these negative consequences. Bio-Mos® is a carbohydrate-based prebiotic derived from yeast cells that is reported to increase livestock performance. Here, the responses of rumen bacterial cells to Bio-Mos® were quantified, sorted by flow cytometry using fluorescently-labeled yeast mannan, and taxonomically characterized using fluorescence in situ hybridization and 16S rRNA sequencing. Further, to evaluate the effects of bovine-adapted Bacteroides thetaiotaomicron administration as a live microbial with and without Bio-Mos® supplementation, we analyzed microbial fermentation products, changes to carbohydrate profiles, and shifts in microbial composition of an in vitro rumen community. Bio-Mos® was shown to be an effective prebiotic that significantly altered microbial diversity, composition, and fermentation; while addition of B. thetaiotaomicron had no effect on community composition and resulted in fewer significant changes to microbial fermentation. When combined with Bio-Mos®, there were notable, although not significant, changes to major bacterial taxa, along with increased significant changes in fermentation end products. These data suggest a synergistic effect is elicited by combining Bio-Mos® and B. thetaiotaomicron. This protocol provides a new in vitro methodology that could be extended to evaluate prebiotics and probiotics in more complex artificial rumen systems and live animals.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".