Comparative analysis of macroalgae supplementation on the rumen microbial community: Asparagopsis taxiformis inhibits major ruminal methanogenic, fibrolytic, and volatile fatty acid-producing microbes in vitro
Bibliographic record
Abstract
Seaweeds have received a great deal of attention recently for their potential as methane-suppressing feed additives in ruminants. To date, Asparagopsis taxiformis has proven a potent enteric methane inhibitor, but it is a priority to identify local seaweed varieties that hold similar properties. It is essential that any methane inhibitor does not compromise the function of the rumen microbiome. In this study, we conducted an in vitro experiment using the RUSITEC system to evaluate the impact of three red seaweeds, A. taxiformis, Palmaria mollis, and Mazzaella japonica, on rumen prokaryotic communities. 16S rRNA sequencing showed that A. taxiformis had a profound effect on the microbiome, particularly on methanogens. Weighted Unifrac distances showed significant separation of A. taxiformis samples from the control and other seaweeds (p < 0.05). Neither P. mollis nor M. japonica had a substantial effect on the microbiome (p > 0.05). A. taxiformis reduced the abundance of all major archaeal species (p < 0.05), leading to an almost total disappearance of the methanogens. Prominent fiber-degrading and volatile fatty acid (VFA)-producing bacteria including Fibrobacter and Ruminococcus were also inhibited by A. taxiformis (p < 0.05), as were other genera involved in propionate production. The relative abundance of several other bacteria including Prevotella, Bifidobacterium, Succinivibrio, Ruminobacter, and unclassified Lachnospiraceae were increased by A. taxiformis suggesting that the rumen microbiome adapted to an initial perturbation. Our study provides baseline knowledge of microbial dynamics in response to seaweed feeding over an extended period and suggests that feeding A. taxiformis to cattle to reduce methane may directly, or indirectly, inhibit important fiber-degrading and VFA-producing bacteria.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".