Mechanistic insights into the digestion of complex dietary fibre by the rumen microbiota using combinatorial high-resolution glycomics and transcriptomic analyses
Bibliographic record
Abstract
There is a knowledge gap regarding the factors that impede the ruminal digestion of plant cell walls or if rumen microbiota possess the functional activities to overcome these constraints. Innovative experimental methods were adopted to provide a high-resolution understanding of plant cell wall chemistries, identify higher-order structures that resist microbial digestion, and determine how they interact with the functional activities of the rumen microbiota. We characterized the total tract indigestible residue (TTIR) from cattle fed a low-quality straw diet using two comparative glycomic approaches: ELISA-based glycome profiling and total cell wall glycosidic linkage analysis. We successfully detected numerous and diverse cell wall glycan epitopes in barley straw (BS) and TTIR and determined their relative abundance pre- and post-total tract digestion. Of these, xyloglucans and heteroxylans were of higher abundance in TTIR. To determine if the rumen microbiota can further saccharify the residual plant polysaccharides within TTIR, rumen microbiota from cattle fed a diet containing BS were incubated with BS and TTIR ex vivo in batch cultures. Transcripts coding for carbohydrate-active enzymes (CAZymes) were identified and characterized for their contribution to cell wall digestion based on glycomic analyses, comparative gene expression profiles, and associated CAZyme families. High-resolution phylogenetic fingerprinting of these sequences encoded CAZymes with activities predicted to cleave the primary linkages within heteroxylan and arabinan. This experimental platform provides unprecedented precision in the understanding of forage structure and digestibility, which can be extended to other feed-host systems and inform next-generation solutions to improve the performance of ruminants fed low-quality forages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".