Quantifying fluorescent glycan uptake to elucidate strain-level variability in foraging behaviors of rumen bacteria
Bibliographic record
Abstract
Abstract Gut microbiomes have vast catabolic potential and are essential to host health and nutrition. An in-depth understanding of the metabolic pathways in these ecosystems will enable us to design treatments (i.e. prebiotics) that influence microbiome structure and enhance host physiology. Currently, the investigation of metabolic pathways relies on inferences derived from metagenomics or in vitro cultivations, however, novel approaches targeting specific cell physiologies can illuminate the functional potential encoded within microbial (meta)genomes to accurately assess metabolic abilities. Here, we present a multi-faceted study using complimentary next-generation physiology and ‘omics’ approaches to characterize the microbial adaptation to a prebiotic in the rumen ecosystem. Using fluorescently labeled polysaccharides, we identified bacteria that actively metabolize a glycan prebiotic in the rumen microbiome ex vivo . Subsequently, we characterized strain-level variability in carbohydrate utilization systems and predict metabolic strategies of isolated bovine-adapted strains of Bacteroides thetaiotaomicron using comparative whole genome sequencing, RNA-Seq, and carbohydrate-active enzyme fingerprinting.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".