Multi-omics reveals the mechanism of rumen microbiome and its metabolome together with host metabolome participating in the regulation of milk production traits in dairy buffaloes

Recently, it has been discovered that certain dairy buffaloes can produce higher milk yield and milk fat yield under the same feeding management conditions, which is a potential new trait. It is unknown to what extent, the rumen microbiome and its metabolites, as well as the host metabolism, contribute to milk yield and milk fat yield. Therefore, we will analyze the rumen microbiome and host-level potential regulatory mechanisms on milk yield and milk fat yield through rumen metagenomics, rumen metabolomics, and serum metabolomics experiments. Microbial metagenomics analysis revealed a significantly higher abundance of several species in the rumen of high-yield dairy buffaloes, which mainly belonged to genera, such as Prevotella, Butyrivibrio, Barnesiella, Lachnospiraceae, Ruminococcus, and Bacteroides. These species contribute to the degradation of diets and improve functions related to fatty acid biosynthesis and lipid metabolism. Furthermore, the rumen of high-yield dairy buffaloes exhibited a lower abundance of methanogenic bacteria and functions, which may produce less methane. Rumen metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, including lipids, carbohydrates, and organic acids, as well as volatile fatty acids (VFAs), such as acetic acid and butyric acid. Meanwhile, several Prevotella, Butyrivibrio, Barnesiella, and Bacteroides species were significantly positively correlated with these metabolites. Serum metabolome analysis showed that high-yield dairy buffaloes had significantly higher concentrations of metabolites, mainly lipids and organic acids. Meanwhile, several Prevotella, Bacteroides, Barnesiella, Ruminococcus, and Butyrivibrio species were significantly positively correlated with these metabolites. The combined analysis showed that several species were present, including Prevotella.sp.CAG1031, Prevotella.sp.HUN102, Prevotella.sp.KHD1, Prevotella.phocaeensis, Butyrivibrio.sp.AE3009, Barnesiella.sp.An22, Bacteroides.sp.CAG927, and Bacteroidales.bacterium.52-46, which may play a crucial role in rumen and host lipid metabolism, contributing to milk yield and milk fat yield. The "omics-explainability" analysis revealed that the rumen microbial composition, functions, metabolites, and serum metabolites contributed 34.04, 47.13, 39.09, and 50.14%, respectively, to milk yield and milk fat yield. These findings demonstrate how the rumen microbiota and host jointly affect milk production traits in dairy buffaloes. This information is essential for developing targeted feeding management strategies to improve the quality and yield of buffalo milk.

Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance

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.

Assessment of rumen bacteria in dairy cows with varied milk protein yield

The present study was conducted to assess rumen bacteria in lactating cows with different milk protein yield, aiming to understand the role of rumen bacteria in this trait. Cows with high milk protein yield (high milk yield and high milk protein content, HH; n = 20) and low milk protein yield (low milk yield and low milk protein content, LL; n = 20) were selected from 374 mid-lactation Holstein dairy cows fed a high-grain diet. Measurement of the rumen fermentation products showed that the concentrations of ruminal total volatile fatty acids, propionate, butyrate, and valerate and the proportion of isobutyrate were higher in the HH cows than in the LL cows. Amplicon sequencing analysis of the rumen bacterial community revealed that the richness (Chao 1 index) of rumen microbiota was higher in the LL cows than in the HH cows. Among the 10 predominant bacterial phyla (relative abundance being >0.10%, present in >60% of animals within each group), the relative abundance of Proteobacteria was 1.36-fold higher in the HH cows than in the LL cows. At the genus level, the relative abundance of Succinivibrio was significantly higher and that of Clostridium tended to be higher in the LL cows than in the HH cows. Sharpea was 2.28-fold enriched in the HH cows compared with the LL cows. Different relationships between the relative abundances of rumen microbial taxa and volatile fatty acid concentrations were observed in the HH and the LL animals, respectively. Succinivibrio and Prevotella were positively correlated with acetate, propionate, and valerate in the LL cows, whereas Sharpea was positively correlated with propionate and valerate concentrations in the HH cows. Collectively, our results revealed that rumen bacterial richness and the relative abundances of several bacterial taxa significantly differed between dairy cows with high and low milk protein yields, suggesting the potential roles of rumen microbiota contributing to milk protein yield in dairy cows.

Investigating the genetic background of bovine digital dermatitis using improved definitions of clinical status

Bovine digital dermatitis (DD) is an increasing claw health problem in all cattle production systems worldwide. The objective of this study was to evaluate the use of an improved scoring of the clinical status for DD via M-scores accounting for the dynamics of the disease; that is, the transitions from one stage to another. The newly defined traits were then subjected to a genetic analysis to determine the genetic background for susceptibility to DD. Data consisted of 6,444 clinical observations from 729 Holstein heifers in a commercial dairy herd, collected applying the M-score system. The M-score system is a classification scheme for stages of DD that allows a macroscopic scoring based on clinical inspections of the bovine foot, thus it describes the stages of lesion development. The M-scores were used to define new DD trait definitions with different complexities. Linear mixed models and logistic models were used to identify fixed environmental effects and to estimate variance components. In total, 68% of all observations showed no DD status, whereas 11% were scored as infectious for and affected by DD, and 21% of all observations exhibited an affected but noninfectious status. For all traits, the probability of occurrence and clinical status were associated with age at observation and period of observation. Risk of becoming infected increased with age, and month of observation significantly affected all traits. Identification of the optimal month concerning DD herd status was consistent for all trait definitions; the last month of the trial was identified. In contrast, months exhibiting the highest least squares means of transformed scores differed depending on trait definition. In this respect, traits that can distinguish between healthy, infectious, and noninfectious stages of DD can account for the infectious potential of the herd and can serve as an alert tool. Estimates of heritabilities of traits studied ranged between 0.19 (±0.11) and 0.52 (±0.17), revealing a tendency for higher values for more complex trait definitions. In terms of genetic selection, all trait definitions identified the best (i.e., most resistant) animals, but only the new trait definitions were able to distinguish between animals with average and high predispositions for DD. Considering repeated measurements resulted in heritability estimates ranging between 0.13 (±0.05) and 0.29 (±0.10).