Host and rumen microbiome contributions to feed efficiency traits in Holstein cows.

It is now widely accepted that dairy cow performance is influenced by both the host genome and rumen microbiome composition. The contributions of the genome and the microbiome to the phenotypes of interest are quantified by heritability (h2) and microbiability (m2), respectively. However, if the genome and microbiome are included in the model, then the h2 reflects only the contribution of the direct genetic effects quantified as direct heritability (hd2), and the holobiont effect reflects the joint action of the genome and the microbiome, quantified as the holobiability (ho2). The objectives of this study were to estimate h2, hd2,m2, and ho2 for dry matter intake, milk energy, and residual feed intake; and to evaluate the predictive ability of different models, including genome, microbiome, and their interaction. Data consisted of feed efficiency records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. Three kernel models were fit to each trait: one with only the genomic effect (model G), one with the genomic and microbiome effects (model GM), and one with the genomic, microbiome, and interaction effects (model GMO). The model GMO, or holobiont model, showed the best goodness-of-fit. The hd2 estimates were always 10% to 15% lower than h2 estimates for all traits, suggesting a mediated genetic effect through the rumen microbiome, and m2 estimates were moderate for all traits, and up to 26% for milk energy. The ho2 was greater than the sum of hd2 and m2, suggesting that the genome-by-microbiome interaction had a sizable effect on feed efficiency. Kernel models fitting the rumen microbiome (i.e., models GM and GMO) showed larger predictive correlations and smaller prediction bias than the model G. These findings reveal a moderate contribution of the rumen microbiome to feed efficiency traits in lactating Holstein cows and strongly suggest that the rumen microbiome mediates part of the host genetic effect.

A large database linking the rumen bacterial composition and milk traits in Lacaune sheep.

Ruminants are able to produce food for human consumption from plants, thanks to rumen bacteria. Bacteria are able to transform feed to microbial proteins and to biohydrogenate unsaturated fatty acids, contributing directly to fine milk composition. The database consists of daily records of milk yield, somatic cell score and 17 milk components such as fatty acids and proteins from 795 Lacaune dairy ewes. Ruminal samples were extracted from ewes using a gastric tube and sequenced to determine the bacterial composition by metabarcoding 16S rRNA gene on a next-generation sequencing platform. From bioinformatics analysis, 9,536,442 sequences were retained and re-grouped into 2,059 affiliated OTUs, represented by 751 to 168,617 sequences. Overall, 2,059 OTUs from 795 samples were attributed to 11 phyla. The most representative phyla were Bacteroidota (50.6%) and Firmicutes (43.6%), and the most abundant families were Prevotellaceae (37.9%), Lachnospiraceae (18.1%), Ruminococcaceae (8.97%). Both shared datasets will be useful for researchers to study the link between rumen bacteria and milk traits and to propose solutions to improve animal production and health.

Host genetic control on rumen microbiota and its impact on dairy traits in sheep.

BACKGROUND: Milk yield and fine composition in sheep depend on the volatile and long-chain fatty acids, microbial proteins, vitamins produced through feedstuff digestion by the rumen microbiota. In cattle, the host genome has been shown to have a low to moderate genetic control on rumen microbiota abundance but a high control on dairy traits with heritabilities higher than 0.30. There is little information on the genetic correlations and quantitative trait loci (QTL) that simultaneously affect rumen microbiota abundance and dairy traits in ruminants, especially in sheep. Thus, our aim was to quantify the effect of the host genetics on rumen bacterial abundance and the genetic correlations between rumen bacterial abundance and several dairy traits, and to identify QTL that are associated with both rumen bacterial abundance and milk traits. RESULTS: Our results in Lacaune sheep show that the heritability of rumen bacterial abundance ranges from 0 to 0.29 and that the heritability of 306 operational taxonomic units (OTU) is significantly different from 0. Of these 306 OTU, 96 that belong mainly to the Prevotellaceae, Lachnospiraceae and Ruminococcaceae bacterial families show strong genetic correlations with milk fatty acids and proteins (absolute values ranging from 0.33 to 0.99). Genome-wide association studies revealed a QTL for alpha-lactalbumin concentration in milk on Ovis aries chromosome (OAR) 11, and six QTL for rumen bacterial abundances i.e., for two OTU belonging to the genera Prevotella (OAR3 and 5), Rikeneleaceae_RC9_gut_group (OAR5), Ruminococcus (OAR5), an unknown genus of order Clostridia UCG-014 (OAR10), and CAG-352 (OAR11). None of these detected regions are simultaneously associated with rumen bacterial abundance and dairy traits, but the bacterial families Prevotellaceae, Lachnospiraceae and F082 show colocalized signals on OAR3, 5, 15 and 26. CONCLUSIONS: In Lacaune dairy sheep, rumen microbiota abundance is partially controlled by the host genetics and is poorly genetically linked with milk protein and fatty acid compositions, and three main bacterial families, Prevotellaceae, Lachnospiraceae and F082, show specific associations with OAR3, 5, 15 and 26.

Variation in Rumen Bacteria of Lacaune Dairy Ewes From One Week to the Next.

Bacteria are the most abundant microorganisms in the rumen microbiota and play essential roles, mainly fermenting plant compounds that yield fatty acids. In this study, we aimed at assessing stability of both bacterial composition and of its associations with rumen and milk fatty acids phenotypes over a 1-week period. The study was performed using 118 Lacaune dairy ewes from the INRAE Experimental Unit of La Fage. Rumen and milk samples were obtained from the ewes twice, 1 week apart, and microbiota composition, volatile and long-chain fatty acid concentrations were analyzed. Bacterial composition was assessed using 16S rRNA gene sequencing, and microbiota and fatty acids were analyzed as compositional data. As we worked with relative abundances expressed in a constrained space, the centered log-ratio transformation enabled to transform data to work with multivariate analyses in the Euclidian space. Bacterial composition differed between the 2 weeks of sampling, characterized by different proportions of the two main phyla, Bacteroidetes and Firmicutes. The repeatability of the operational taxonomic units (OTUs) was low, although it varied significantly. However, 66 of them presented a repeatability of over 0.50 and were particularly associated with fatty acid phenotypes. Even though the OTUs from the same bacterial families presented similar correlations to fatty acids in both weeks, only a few OTUs were conserved over the 2 weeks. We proved with the help of sequencing data that there is significant change in microbial composition over a week in terms of abundance of different families of bacteria. Further studies are required to determine the impact of bacterial composition alterations over 1 week, and the specificities of the highly repeatable OTUs.

Apart From the Diet, the Ruminal Microbiota of Lambs Is Modified in Relation to Their Genetic Potential for Feed Efficiency or Feeding Behavior.

Using two successive types of diets (100% concentrate and 67% forage), this study explores the relationship between the ruminal microbiota of 78 Romane lambs and their feed efficiency (residual feed intake trait) or feeding behavior (feeding rate trait). Analysis was carried out phenotypically by correlating feed efficiency or feeding behavior traits with the relative abundance of bacteria at the phylum, family, and genus levels, and then genetically by comparing the microbiota of lambs selected for extreme breeding values for residual feed intake or feeding rate. Our results confirmed the major effect of diet on the ruminal microbiota composition. The microbiota of lambs consuming a forage-based diet was distinguished by higher microbial diversity and also by higher relative abundance of Firmicutes, whereas Bacteriodetes and Actinobacteria were relatively more abundant in the microbiota of lambs consuming a concentrate-based diet. Moreover, the comparison of lambs divergent for residual feed intake breeding values revealed that regardless of diet, more efficient lambs possessed a ruminal microbiota enriched in Coprococcus, Moryella, [Eubacterium] Brachy group, and [Eubacterium] hallii group, but depleted in Lachnospiraceae FD2005 and Shuttleworthia. The connection between microbiota composition and feeding rate was more tenuous, with no link between the abundance of particular genera and lambs genetically divergent for feeding rate.

Compositional analysis of ruminal bacteria from ewes selected for somatic cell score and milk persistency.

Ruminants are dependent on their rumen microbiota to obtain energy from plants. The composition of the microbiome was well-known to be associated with health status, and production traits, but published results are difficult to reproduce due to large sources of variation. The objectives of this study were to evaluate the associations of ruminal microbiota and its association with genetic lines selected by somatic cell score (SCS) or milk persistency (PERS), as well as milk production, somatic cell score, fat and protein contents, and fatty acids and proteins of milk, using the principles of compositional data. A large sample of 700 Lacaune dairy ewes from INRAE La Fage feeding the same diet and belonging to two divergent genetic lines selected for SCS or PERS was used. The ruminal bacterial metagenome was sequenced using the 16S rRNA gene, resulting in 2,059 operational taxonomic units affiliated with 112 genera. The abundance data were centred log-transformed after the replacement of zeros with the geometric Bayesian method. Discriminant analysis of the SCS showed differences between SCS+ and SCS- ewes, while for PERS no difference was obtained. Milk traits as fat content, protein content, saturated fatty acids and caseins of milk were negatively associated with Prevotella (R = [-0.08;-0.16]), Suttonella (R = [-0.09;-0.16]) and Ruminococcus (R = [-0.08;-0.16]), and positively associated with Lachnospiraceae (R = [0.09;0.16]) and Christensenellaceae (R = [0.09;0.16]). Our findings provide an understanding of the application of compositional data to microbiome analysis, and the potential association of Prevotella, Suttonella, Ruminococcaceae and Lachnospiraceae with milk production traits such as milk fatty acids and proteins in dairy sheep.

Reproductive performance of Angus, Hereford, Salers and Nellore crossbred females: Additive and non-additive effects.

Reproductive traits in breeding herds can be improved through crossbreeding, which results in breed differences, heterosis and breed complementarity. The aim of this study was to estimate group additive genetic and dominance effects for three reproductive traits; probability of artificial insemination (AIP); calving success (CS); and days to calving (DC) for Hereford (H), Angus (A), Nellore (N) and Salers (S) breeds under grazing conditions. Data were obtained from an experiment carried out during 1992-2002 by the Faculty of Agronomy, Universidad de la Republica (UdelaR), Uruguay and Caja Notarial de Seguridad Social. The data set contained reproductive information of 1,164 females from 11 different genetic groups (GG) consisting of crosses between H, N, S and A. AIP, CS and DC were examined in first-calf heifers, while CS and DC were examined in second-calf and 3- to 7-year-old cows. Least square means for each GG and group additive genetic and dominance effects were estimated for each trait. F1 crossbreed females performed better for artificial insemination probability than purebred females. Crossbred A/H heifers had the highest AIPs and CS rates, while crossbred N/H 3- to 7-year-old cows recorded the highest averages for CS and DC. Estimates of group additive genetic effects did not differ amongst A, S, N and H; however, dominance increased the AIP and CS of the heifers.