Multi-omics revealed long term effect of ruminal keystone bacteria and microbial metabolome on the performance in adult ruminants
Bibliographic record
Abstract
Abstract Background Better growth and development of youth animals can lead to better lactation performance in adult goats, however, the effects of the ruminal microbiome on the growth of young goats, and the contribution of early life rumen microbiome to lifelong growth and lactation performance in goats has not yet been well defined. Hence, this study assessed the rumen microbiome in young goats with different average daily gain (ADG) and evaluated its contribution to the growth and lactation performance during the first lactation period. Results Based on monitoring of 99 goats cohort from youth to first lactation, 15 highest ADG (HADG) goats and 15 lowest ADG (LADG) goats were subject to rumen microbiome and metabolome profiling. The comparison of the rumen metagenome of HADG and LADG goats revealed that the ruminal carbohydrate metabolism and amino acids metabolism function were enhanced in HADG goats, suggesting the rumen microbiome of HADG goats have higher feed fermentation ability. Co-occurrence network and correlation analysis revealed that Streptococcus, Candidatus Saccharimonans, and Succinivibrionaceae UCG-001 were significantly positively correlated with young goats’ growth rates and some HADG-enriched carbohydrate and protein metabolites, such as propionate, butyrate, maltoriose, and amino acids; while several genera and species of Prevotella and Methanogens exhibited a negative relationship with young goats’ growth rates and also correlated with LADG-enriched metabolites, such as rumen acetate as well as methane. Additionally, some functional keystone bacterial taxa, such as Prevotella, in the rumen of young goats were significantly correlated with the same taxa in the rumen of adult lactation goats. Prevotella also enriched the rumen of LADG lactating goats, and has a negative effect on the rumen fermentation efficiency in lactating goats. Additional analysis using random forest machine learning showed that rumen microbiota and their metabolites of young goats, such as Prevotellaceae UCG-003, acetate to propionate ratio could be potential microbial markers that can potentially classify High or Low ADG goats with an accuracy of prediction of > 81.3%. Similarly, the abundance of Streptococcus in the rumen of young goats could be predictive for milk yield in adult goats with high accuracy (area under the curve 91.7%). Conclusions This study identified the keystone bacterial taxa that influence carbohydrate and amino acids metabolic functions and shape the rumen microbiota in the rumen of adult animals. The keystone bacteria and their effects on ruminal microbiota and metabolome composition during early life can lead to higher lactation performance in adult ruminants. These findings suggest that rumen microbiome together with their metabolites in young ruminants have long-term effect on feed efficiency and animal performance. The fundamental knowledge may allow us to develop advanced methods to manipulate the rumen microbiome and improve production efficiency of ruminants.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".