Microbial alginate foraging is conserved in geographically and taxonomically distinct ruminant microbiomes
Bibliographic record
Abstract
ABSTRACT Seaweed plays a crucial role in carbon cycling and is expected to be a valuable resource for sustainable biomass, with applications in biofuel production, human nutrition, and animal feed. Although seaweed has historically been used as a feed source for livestock grazing near coastlines, the process by which it is digested in the rumen remains unknown. Here, we show how the brown algae Saccharina latissima is catabolized in the rumen ecosystem of two different species using in vivo and in vitro experimental systems. We determined that the ruminal decomposition of alginate, a prominent component of the brown algae cell wall, requires microbial catabolic pathways complete with alginate lyases and transport proteins. Evidence of digestion was obtained through a combination of animal models, bacterial imaging, multilayered meta-omics, and enzyme biochemistry. The evolution of and implications for acquisition of ‘alginate utilization loci’ within geographically and taxonomically distinct ruminants are considered. Graphical abstract Saccharina latissima is a brown alga commonly found in the North Atlantic, Arctic and Pacific oceans. S. latissima was collected from the west coast and Canada and Norway for microbiome studies. Alginate constitutes a substantial portion of the cell wall of S. latissima (SL), and its digestion requires a specific set of enzymes, alginate lyases. We investigated if and how S. latissima is metabolized in geographically distinct rumen ecosystems through in vivo lamb feeding experiments (2.5 and 5% inclusion, DM basis) and in vitro cattle-based rumen simulation technique, RUSITEC, experiments (up to 50% inclusion). Evidence supporting ruminal degradation of alginate was explored using a combination of multilayered meta-omics, physiology (fluorescently labelled S. latissima hot water extracts (FLA-SLAT)) and biochemical characterization of PL6 alginate lyases.
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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 | 0.001 | 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".