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.

Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling.

The rumen microbiome is crucial for converting feed into absorbable nutrients used for milk synthesis, and the efficiency of this process directly impacts the profitability and sustainability of the dairy industry. Recent studies have found that the rumen microbial composition explains part of the variation in feed efficiency traits, including dry matter intake, milk energy, and residual feed intake. The main goal of this study was to reveal relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using structural equation models. Our specific objectives were to (i) infer the mediation effects of the rumen microbiome on feed efficiency traits, (ii) estimate the direct and total heritability of feed efficiency traits, and (iii) calculate the direct and total breeding values of feed efficiency traits. Data consisted of dry matter intake, milk energy, and residual feed intake records, SNP genotype data, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows from 2 research farms. We implemented structural equation models such that the host genome directly affects the phenotype (GP → P) and the rumen microbiome (GM → P), while the microbiome affects the phenotype (M → P), partially mediating the effect of the host genome on the phenotype (G → M → P). We found that 7 to 30% of microbes within the rumen microbial community had structural coefficients different from zero. We classified these microbes into 3 groups that could have different uses in dairy farming. Microbes with heritability <0.10 but significant causal effects on feed efficiency are attractive for external interventions. On the other hand, 2 groups of microbes with heritability ≥0.10, significant causal effects, and genetic covariances and causal effects with the same or opposite sign to feed efficiency are attractive for selective breeding, improving or decreasing the trait heritability and response to selection, respectively. In general, the inclusion of the different microbes in genomic models tends to decrease the trait heritability rather than increase it, ranging from -15% to +5%, depending on the microbial group and phenotypic trait. Our findings provide more understanding to target rumen microbes that can be manipulated, either through selection or management interventions, to improve feed efficiency traits.

Seminal cell-free DNA as a potential marker for in vitro fertility of Nellore bulls.

PURPOSE: This study aimed to identify a marker for freezability and in vitro fertility of sperm samples before freezing. METHODS: Semen was collected from nine Nelore bulls; half of the ejaculate was used for seminal plasma cell-free DNA (cfDNA) quantification, and the other half was cryopreserved. Evaluation of sperm movement using computer-assisted semen analysis and plasma membrane integrity and stability, acrosomal integrity, apoptosis, and mitochondrial potential using flow cytometry were performed on fresh and frozen/thawed semen at 0, 3, 6, and 12 h after thawing. Frozen/thawed sperm was also used for in vitro embryo production. cfDNA was extracted from each bull, and the total DNA and number of cell-free mitochondrial DNA (cfmtDNA) copies were quantified. Semen from each animal was used for IVF, and cleavage, blastocyst formation, and cell counts were evaluated. RESULTS: Two groups were formed and compared based on the concentrations of cfDNA and cfmDNA present: low-cfDNA and high-cfDNA and low-cfmtDNA and high-cfmtDNA. Up to 12 h post-thawing, there were no differences between the groups in the majority of the sperm parameters evaluated. Cleavage, day 6 and 7 blastocyst rates, and the number of cells were higher in the high cfDNA group than in the low cfDNA group. Similar results were observed for cfmtDNA, except for the number of cells, which was similar between the groups. CONCLUSION: The concentration of cfDNA and the relative number of copies of cfmtDNA in seminal plasma cannot predict the freezability of semen but can be used to predict in vitro embryo production.

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows.

Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

Seroprevalence estimate and associated risk factors for neosporosis in dairy cattle in the northwest region of Rio Grande do Sul State, Brazil / Estimativa da soroprevalência e fatores de risco para neosporose em rebanhos leiteiros na região Noroeste do estado do Rio Grande do Sul, Brasil

ABSTRACT: The aim of this study was to estimate neosporosis seroprevalence and its associated risk factors in milk herds (Bos taurus taurus) located in the northwestern region of Rio Grande do Sul State, Brazil. Three hundred twenty-two blood samples were collected from dairy cows on 18 farms in 17 cities of this region. An epidemiologic questionnaire was completed for each farm. It consisted of questions about the general characteristics of the herd, reproduction, and animal management. Serum samples were tested for Neospora caninum using a commercial enzyme-linked immunosorbent assay (ELISA) kit. Results indicated a seroprevalence of Neospora in 88.9% (16/18) of herds and 31.1% (100/322) of individuals. Risk factor analyses demonstrated that culling by reproductive disorder (OR = 0.6), flooding (OR = 0.5), and commercial sale (OR = 0.4) were associated with seroprevalence. Nevertheless, the purchase of replacement animals in the herd played an important role in disease occurrence (OR = 2.2). Results of this study suggested that Neospora caninum was present in the studied herds under investigation and that there are risk factors associated with its seroprevalence on the farms of the northwestern of Rio Grande do Sul.

RESUMO: O objetivo desse estudo foi estimar a soroprevalência da neosporose e os possíveis fatores de risco em rebanhos (Bos taurus taurus) localizados na mesorregião Noroeste do Rio Grande do Sul, Brasil. Foram coletadas 322 amostras de sangue de bovinos leiteiros, em 18 propriedades localizadas em 17 munícipios desta mesorregião. Um questionário epidemiológico foi aplicado em cada propriedade, contendo questões relacionadas às características gerais dos rebanhos, dados reprodutivos e manejo animal. As amostras de soro foram testadas através do teste de imunoensaio enzimático (ELISA) para Neospora caninum. Os resultados demonstraram uma soroprevalência de Neospora de 88,9% (16/18) entre os rebanhos e 31,1% (100/322) entre os indivíduos. Entre os fatores de risco analisados foi observado que descarte por problemas reprodutivos (OR=0,6), presença de áreas alagadiças (OR=0,5) e venda comercial (OR=0,4) estavam associados a soroprevalência. No entanto, a compra de animais substituídos no rebanho desempenhou um papel significativo na ocorrência da doença (OR=2,2). Os resultados desse estudo sugerem que o Neospora caninum esteve presente nos rebanhos estudados, bem como, existem fatores associados com a soroprevalência nas propriedades da mesorregião do Noroeste do Rio Grande do Sul.