Multi-omics revealed long term effect of ruminal keystone bacteria and microbial metabolome on the performance in adult ruminants
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».