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Recent studies have reported that some rumen microbes are heritable. However, it is necessary to clarify the functions and specific contributions of the heritable rumen microbes to cattle phenotypes (microbiability) in comparison with those that are nonheritable. This study aimed to identify the distribution and predicted functions of heritable and nonheritable bacterial taxa at species level in the rumen of dairy cows and their respective contributions to energy-corrected milk yield, protein content and yield, and fat content and yield in milk. Thirty-two heritable and 674 nonheritable bacterial taxa were identified at species level, and the functional analysis revealed that predicted microbial functions for both groups were mainly enriched for energy, amino acid, and ribonucleotide metabolism. The mean microbiability (to reflect a single taxon's contribution) of heritable bacteria was found to range from 0.16% to 0.33% for the different milk traits, whereas the range for nonheritable bacteria was 0.03% to 0.06%. These findings suggest a strong contribution by host genetics in shaping the rumen microbiota, which contribute significantly to milk production traits. Therefore, there is an opportunity to further improve milk production traits through attention to host genetics and the interaction with the rumen microbiota.

Age is an important factor in shaping the gut microbiome. However, the age effect on the rumen microbial community for dairy buffaloes remains less explored. Using metagenomics, we examined the microbial composition and functions of rumen microbiota in dairy Murrah buffaloes of different ages: Y (1 year old), M (3–5 years old), E (6–8 years old), and O (>9 years old). We found that Bacteroidetes and Firmicutes were the predominant phyla, with Prevotella accounting for the highest abundance at the genus level. The proportion of Bacteroides and Methanobrevibacter significantly increased with age, while the abundance of genus Lactobacillus significantly decreased with age (LDA > 3, p < 0.05). Most differed COG and KEGG pathways were enriched in Y with carbohydrate metabolism, while older buffaloes enriched more functions of protein metabolism and the processing of replication and repair (LDA > 2, p < 0.05). Additionally, the functional contribution analysis revealed that the genera Prevotella and Lactobacillus of Y with more functions of CAZymes encoded genes of glycoside hydrolases and carbohydrate esterases for their roles of capable of metabolizing starch and sucrose-associated oligosaccharide enzyme, hemicellulase, and cellulase activities than the other three groups (LDA > 2, p < 0.05), thus affecting the 1-year-old dairy buffalo rumen carbohydrate metabolism. This study provides comprehensive dairy buffalo rumen metagenome data and assists in manipulating the rumen microbiome for improved dairy buffalo production.

Background As the global population continues to grow, competition for resources between humans and livestock has been intensifying. Increasing milk protein production and improving feed efficiency are becoming increasingly important to meet the demand for high-quality dairy protein. In a previous study, we found that milk protein yield in dairy cows was associated with the rumen microbiome. The objective of this study was to elucidate the potential microbial features that underpins feed efficiency in dairy cows using metagenomics, metatranscriptomics, and metabolomics. Results Comparison of metagenomic and metatranscriptomic data revealed that the latter was a better approach to uncover the associations between rumen microbial functions and host performance. Co-occurrence network analysis of the rumen microbiome revealed differential microbial interaction patterns between the animals with different feed efficiency, with high-efficiency animals having more and stronger associations than low-efficiency animals. In the rumen of high-efficiency animals, Selenomonas and members of the Succinivibrionaceae family positively interacted with each other, functioning as keystone members due to their essential ecological functions and active carbohydrate metabolic functions. At the metabolic level, analysis using random forest machine learning suggested that six ruminal metabolites (all derived from carbohydrates) could be used as metabolic markers that can potentially differentiate efficient and inefficient microbiomes, with an accuracy of prediction of 95.06%. Conclusions The results of the current study provided new insights into the new ruminal microbial features associated with feed efficiency in dairy cows, which may improve the ability to select animals for better performance in the dairy industry. The fundamental knowledge will also inform future interventions to improve feed efficiency in dairy cows.

Background Antimicrobial resistance poses super challenges in both human health and livestock production. Rumen microbiota is a large reservoir of antibiotic resistance genes (ARGs), which show significant varations in different host species and lifestyles. To compare the microbiome and resistome between dairy cows and dairy buffaloes, the microbial composition, functions and harbored ARGs of rumen microbiota were explored between 16 dairy cows (3.93 ± 1.34 years old) and 15 dairy buffaloes (4.80 ± 3.49 years old) using metagenomics. Results Dairy buffaloes showed significantly different bacterial species (LDA > 3.5 & P 2) in the rumen compared with dairy cows. Distinct resistive profiles were identified between dairy cows and dairy buffaloes. Among the total 505 ARGs discovered in the resistome of dairy cows and dairy buffaloes, 18 ARGs conferring resistance to 16 antibiotic classes were uniquely detected in dairy buffaloes. Gene tcmA (resistance to tetracenomycin C) presented high prevalence and age effect in dairy buffaloes, and was also highly positively correlated with 93 co-expressed ARGs in the rumen (R = 0.98 & P = 5E-11). In addition, 44 bacterial species under Lactobacillus genus were found to be associated with tcmA (R > 0.95 & P L. amylovorus and L. acidophilus showed greatest potential of harboring tcmA based on co-occurrence analysis and tcmA-containing contigs taxonomic alignment. Conclusions The current study revealed distinctive microbiome and unique ARGs in dairy buffaloes compared to dairy cattle. Our results provide novel understanding on the microbiome and resistome of dairy buffaloes, the unique ARGs and associated bacteria will help develop strategies to prevent the transmission of ARGs.

With the help of the bacteria in the rumen, ruminants can effectively convert human inedible plant fiber to edible food (meat and milk). However, the understanding of rumen bacteriome in dairy cows is still limited, especially in a large population under the same diet, breed, and milking period. Here we described the sequencing data of 16S rRNA gene of rumen bacteriome from 334 mid-lactation Holstein dairy cows generated using the Illumina HiSeq 2500 (PE250) platform. A total of 24,030,828 raw reads with an average of 71,946 ± 13,450 sequences per sample were obtained. The top ten genera with highest relative abundance accounted for 60.65% of total bacterial sequences. We observed 4,460 overall operational taxonomic units (1,827 ± 94 per sample) based on a 97% nucleotide sequence identity between reads. Totally 6,082 amplicon sequence variants (672 ± 131 per sample) were identified in 334 samples. The shareable datasets can be re-used by researchers to assess other rumen bacterial-related biological functions in dairy cows towards the improvement of animal production and health. Machine-accessible metadata file describing the reported data (ISA-Tab format)

Currently, knowledge on the extent to which rumen microbiota differ in a large population of cattle fed the same diet and whether such differences are associated with animal performance is limited. This study was conducted to characterize the rumen microbiota of a large cohort of lactating Holstein dairy cows (n = 334) that were fed the same diet and raised under the same environment, aiming to uncover linkages between core and pan rumen microbiomes and host phenotypes. Amplicon sequencing of the partial 16S rRNA gene identified 391 bacterial genera in the pan bacteriome and 33 genera in the core bacteriome. Interanimal variation existed in the pan and core bacteriomes, with the effect of lactation stage being more prominent than that of parity (the number of pregnancies, ranging from 2 to 7) and sire. Spearman's correlation network analysis revealed significant correlations among bacteria, rumen short-chain fatty acids, and lactation performance, with the core and noncore genera accounting for 53.9 and 46.2% of the network, respectively. These results suggest that the pan rumen bacteriome together with the core bacteriome potentially contributes to variations in milk production traits. Our findings provide an understanding of the potential functions of noncore rumen microbes, suggesting the possibility of enhancing bacterial fermentation using strategies to manipulate the core and noncore bacteriomes for improved cattle performance. IMPORTANCE This study revealed the rumen bacteriome from a large dairy cattle cohort (n = 334) raised under the same management and showed the linkages among the rumen core and pan bacteriomes, rumen short-chain fatty acids, and milk production phenotypes. The findings from this study suggest that the pan rumen bacteriome, together with the core bacteriome, potentially contributes to variations in host milk production traits. Fundamental knowledge on the rumen core and pan microbiomes and their roles in contributing to lactation performance provides novel insights into future strategies for manipulating rumen microbiota to enhance milk production in dairy cattle.

Milk yield (MY) and milk protein (MP) content are crucial milk performance traits of dairy cows that directly affect the dairy profits. This study first proposed milk protein yield (MPY) by considering MY and MP content together. Forty multiparous cows were selected from the 348 Holstein dairy cows, which fed the same diet under the same management condition, to investigate the serum metabolome profiles and to identify key metabolites associated with MPY. Among them, 20 cows with a higher MPY (MY 34.5 kg/d and MP 3.2%. i.e., MPY 1.11 kg/d) were defined as the HH group, and 20 cows with a lower MPY (MY 31 kg/d and MP 2.9%, i.e., MPY 0.87 kg/d) as the LL group. The GC-TOF/MS and the ultra HPLC-MS/MS platforms were used to identify metabolites and quantify biomarkers, respectively. Orthogonal partial least squares discriminant analysis of serum metabolomes revealed a clear separation between the 2 groups. Thirty-six significantly different metabolites were identified, which mainly involved in valine, leucine and isoleucine biosynthesis and glycine, serine and threonine metabolism. With biomarker analysis and validation, hippuric acid, nicotinamide and pelargonic acid may serve as key metabolites associated with MPY.This study reports the application of serum metabolomics to identify biomarkers related to MPY and to reveal the biological pathways affecting milk protein synthesis. Three novel serum biomarkers were discovered to reflect the MPY variation of dairy cows, which may be useful in quality control in dairy cow production and for optimizing industrial production of dairy products. This study confirms that individual physiological and metabolic differences contribute to the variations in MPY and provides directions for further improving the MPY of dairy cows.

Simple Summary Serum biochemical parameters and hormones play a role in directly reflecting the physiological state and modulating the milk performance of dairy cows. However, the variability of the serum biochemical parameters and hormones in multiparous mid-lactation cows have not drawn much attention due to their supposedly stable states, especially for those cows under the same nutrition and management condition. The aim of this study was to evaluate the effects of days-in-milk (DIM, within the mid-lactation) and parity (ranging from 2–6) on serum biochemical parameters and hormone profiles based on a large cohort of dairy cows. The results showed that DIM and parity contribute to the variations in serum biochemical parameters and hormones related to protein status, energy supply, liver and kidney function, and oxidative stress of mid-lactation dairy cows, with the effect of DIM being dominant over parity. Our result suggested that the DIM periods and parity should be taken into consideration to optimize nutritional strategies in order to improve the milk performance traits more precisely. Abstract It is well known that serum biochemical parameters and hormones contribute greatly to the physiological and metabolic status of dairy cows. However, few studies have focused on the variation of these serum parameters in multiparous mid-lactation cows without the interference of diet and management. A total of 287 Holstein dairy cows fed the same diet and maintained under the same management regime were selected from a commercial dairy farm to evaluate the effects of days-in-milk (DIM) and parity on serum biochemical parameters and hormone profiles. Milk yield and milk protein content were affected by DIM and parity (p < 0.05). Milk protein yield showed a numerically decreasing trend with parity, and it was relatively constant in cows with parities between 2 and 4 but lower in cows with parity 6 (p = 0.020). Ten and five serum biochemical parameters related to protein status, energy metabolism, liver and kidney function, and oxidative stress were affected by DIM and parity, respectively (p < 0.05). Glucagon, insulin-like growth factor 1 concentration, and the revised quantitative insulin sensitivity check index were significantly different (p < 0.05) among cows with different DIM. Parity had no effect on hormone concentrations. An interaction between DIM and parity effect was only detected for glucagon concentration (p = 0.015), which showed a significantly increasing trend with DIM and overall decreasing trend with parity. In summary, DIM and parity played an important role in affecting the serum biochemical parameters and/or hormones of dairy cows, with serum parameters affected more by DIM than parity.