Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance
Bibliographic record
Abstract
BACKGROUND: Recently, we reported that some dairy cows could produce high amounts of milk with high amounts of protein (defined as milk protein yield [MPY]) when a population was raised under the same nutritional and management condition, a potential new trait that can be used to increase high-quality milk production. It is unknown to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to MPY. Here, analysis of rumen metagenomics and metabolomics, together with serum metabolomics was performed to identify potential regulatory mechanisms of MPY at both the rumen microbiome and host levels. RESULTS: Metagenomics analysis revealed that several Prevotella species were significantly more abundant in the rumen of high-MPY cows, contributing to improved functions related to branched-chain amino acid biosynthesis. In addition, the rumen microbiome of high-MPY cows had lower relative abundances of organisms with methanogen and methanogenesis functions, suggesting that these cows may produce less methane. Metabolomics analysis revealed that the relative concentrations of rumen microbial metabolites (mainly amino acids, carboxylic acids, and fatty acids) and the absolute concentrations of volatile fatty acids were higher in the high-MPY cows. By associating the rumen microbiome with the rumen metabolome, we found that specific microbial taxa (mainly Prevotella species) were positively correlated with ruminal microbial metabolites, including the amino acids and carbohydrates involved in glutathione, phenylalanine, starch, sucrose, and galactose metabolism. To detect the interactions between the rumen microbiome and host metabolism, we associated the rumen microbiome with the host serum metabolome and found that Prevotella species may affect the host's metabolism of amino acids (including glycine, serine, threonine, alanine, aspartate, glutamate, cysteine, and methionine). Further analysis using the linear mixed effect model estimated contributions to the variation in MPY based on different omics and revealed that the rumen microbial composition, functions, and metabolites, and the serum metabolites contributed 17.81, 21.56, 29.76, and 26.78%, respectively, to the host MPY. CONCLUSIONS: These findings provide a fundamental understanding of how the microbiome-dependent and host-dependent mechanisms contribute to varied individualized performance in the milk production quality of dairy cows under the same management condition. This fundamental information is vital for the development of potential manipulation strategies to improve milk quality and production through precision feeding. Video Abstract.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".