RUMINANT NUTRITION SYMPOSIUM: Use of genomics and transcriptomics to identify strategies to lower ruminal methanogenesis1,2,3
Bibliographic record
Abstract
Globally, methane (CH4) emissions account for 40% to 45% of greenhouse gas emissions from ruminant livestock, with over 90% of these emissions arising from enteric fermentation. Reduction of carbon dioxide to CH4 is critical for efficient ruminal fermentation because it prevents the accumulation of reducing equivalents in the rumen. Methanogens exist in a symbiotic relationship with rumen protozoa and fungi and within biofilms associated with feed and the rumen wall. Genomics and transcriptomics are playing an increasingly important role in defining the ecology of ruminal methanogenesis and identifying avenues for its mitigation. Metagenomic approaches have provided information on changes in abundances as well as the species composition of the methanogen community among ruminants that vary naturally in their CH4 emissions, their feed efficiency, and their response to CH4 mitigators. Sequencing the genomes of rumen methanogens has provided insight into surface proteins that may prove useful in the development of vaccines and has allowed assembly of biochemical pathways for use in chemogenomic approaches to lowering ruminal CH4 emissions. Metagenomics and metatranscriptomic analysis of entire rumen microbial communities are providing new perspectives on how methanogens interact with other members of this ecosystem and how these relationships may be altered to reduce methanogenesis. Identification of community members that produce antimethanogen agents that either inhibit or kill methanogens could lead to the identification of new mitigation approaches. Discovery of a lytic archaeophage that specifically lyses methanogens is 1 such example. Efforts in using genomic data to alter methanogenesis have been hampered by a lack of sequence information that is specific to the microbial community of the rumen. Programs such as Hungate1000 and the Global Rumen Census are increasing the breadth and depth of our understanding of global ruminal microbial communities, steps that are key to using these tools to further define the science of ruminal methanogenesis.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".