Invited review:Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: Implications for methane emissions in cattle

The rumen microbiome is responsible for methane emission in ruminants. The study of microbes in the rumen has attracted great interest in the last decade. High-throughput sequencing technologies have been key in expanding the knowledge of the microorganisms that populate the rumen through metagenomic studies. There is substantial evidence that the composition of the rumen microbiota is influenced by host genotype. Therefore, modulation of the microbiota poses an important tool for breeding for lower emissions in large and small ruminants. The main challenges of metagenomic studies are addressed and some solutions are proposed when available, including the incorporation of metagenomic information into statistical models regularly used in animal breeding. To incorporate microbiome information into breeding programs, the particularities of the rumen microbiome must be considered, from sampling to inclusion in selection indices. The latest advances in this area are discussed in this review.Universidad de Costa RicaUCR::Vicerrectoría de Docencia::Ciencias Agroalimentarias::Facultad de Ciencias Agroalimentarias::Escuela de ZootecniaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Agroalimentarias::Centro de Investigación en Nutrición Animal (CINA

Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle

14 Pág. Departamento de Mejora Genetica AnimalMitigating the effects of global warming has become the main challenge for humanity in recent decades. Livestock farming contributes to greenhouse gas emissions, with an important output of methane from enteric fermentation processes, mostly in ruminants. Because ruminal microbiota is directly involved in digestive fermentation processes and methane biosynthesis, understanding the ecological relationships between rumen microorganisms and their active metabolic pathways is essential for reducing emissions. This study analysed whole rumen metagenome using long reads and considering its compositional nature in order to disentangle the role of rumen microbes in methane emissions.This research was financed by RTA2015-00022-C03-02 (METALGEN) project from the National Plan of Research, Development and Innovation 2013–2020 and the Department of Economic Development and Competitiveness (Madrid, Spain). A.L.G. was funded by FPI-INIA grant with reference FPI-SGIT2016-06.Peer reviewe

Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle

Background: Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage. Results: By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits. Conclusions: Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle

Effect of alternative synchronisation treatments on the vaginal microbiota of ewes and their impact on pregnancy rate

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A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

17 Pág.  Departamento de ​Mejora Genética Animal (INIA)The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.This research was financed by the METALGEN project (RTA2015-00022-C03) from the national plan for research, development, and innovation 2013–2020 and the Department of Economic Development and Competitiveness (Madrid, Spain).Peer reviewe

Additive genetic and heterosis effects for milk fever in a population of Jersey, Holstein × Jersey, and Holstein cattle under grazing conditions

The authors acknowledge the Regional Informatics Center for Sustainable Animal Production (CRIPAS) of the Veterinary Medicine School, National University of Costa Rica for allowing us to use the data for this studyThe aim of this study was to estimate additive genetic and heterosis effects for milk fever (MF) in Costa Rican dairy cattle. A farm-based management information software was used to collect 223,783 parity records between years 1989 and 2016, from 64,008 cows, 2 breeds (Jersey, Holstein × Jersey crosses, and Holstein), and 134 herds. The pedigree file comprised 73,653 animals distributed across 10 generations. A total of 4,355 (1.95%) clinical cases of MF were reported within this population, affecting 3,469 (5.42%) cows. Data were analyzed using 2 animal models, both accounting for repeatability and assuming different distributions for MF event: normal (linear model) or binomial (threshold model). The models included parity as fixed effect, breed and heterosis as fixed regressions, and herd-year-season, additive genetic, and permanent environment as random effects. The models were fit using a generalized linear mixed model approach, as implemented in ASReml 4.0 software. We noted significant regression on the percentage of Holstein breed, depicting a −0.0086% [standard error (SE) = 0.0012] decrease in MF incidence for each 1-unit increase in percentage of Holstein breed. A favorable heterosis of 5.9% for MF was found, although this was not statistically significant. Heritability and repeatability were, respectively, 0.03 (SE = 0.002) and 0.05 (SE = 0.002) for the linear model, and 0.07 (SE = 0.007) and 0.07 (SE = 0.007) for the threshold model. The correlation between BLUP (all animals in pedigree) for linear and threshold models, was 0.89. The average accuracy of the estimated BLUP for all animals were 0.44 (standard deviation = 0.13) for the linear model and 0.29 (standard deviation = 0.14) for the threshold model. Heritability and repeatability for MF within this population was low, though significant.El objetivo de este estudio fue estimar los efectos genéticos aditivos y de heterosis para la fiebre de la leche (MF) en el ganado lechero de Costa Rica. Se utilizó un software de información de gestión basado en la granja para recopilar 223.783 registros de paridad entre los años 1989 y 2016, de 64.008 vacas, 2 razas (Jersey, cruces Holstein × Jersey y Holstein) y 134 rebaños. El archivo genealógico comprendía 73.653 animales distribuidos en 10 generaciones. En esta población se notificaron 4.355 (1,95%) casos clínicos de MF, que afectaron a 3.469 (5,42%) vacas. Los datos se analizaron mediante dos modelos animales, ambos teniendo en cuenta la repetibilidad y asumiendo diferentes distribuciones para el evento de MF: normal (modelo lineal) o binomial (modelo umbral). Los modelos incluían la paridad como efecto fijo, la raza y la heterosis como regresiones fijas, y el rebaño-estación, la genética aditiva y el entorno permanente como efectos aleatorios. Los modelos se ajustaron mediante un enfoque de modelo lineal mixto generalizado, implementado en el software ASReml 4.0. Se observó una regresión significativa sobre el porcentaje de raza Holstein, que representaba una disminución del -0,0086% [error estándar (SE) = 0,0012] en la incidencia de la MF por cada aumento de 1 unidad en el porcentaje de raza Holstein. Se encontró una heterosis favorable del 5,9% para la MF, aunque no fue estadísticamente significativa. La heredabilidad y la repetibilidad fueron, respectivamente, de 0,03 (SE = 0,002) y 0,05 (SE = 0,002) para el modelo lineal, y de 0,07 (SE = 0,007) y 0,07 (SE = 0,007) para el modelo de umbral. La correlación entre el BLUP (todos los animales del pedigrí) para los modelos lineal y umbral, fue de 0,89. La precisión media del BLUP estimado para todos los animales fue de 0,44 (desviación estándar = 0,13) para el modelo lineal y de 0,29 (desviación estándar = 0,14) para el modelo umbral. La heredabilidad y la repetibilidad para el MF dentro de esta población fueron bajas, aunque significativas.Universidad Nacional, Costa RicaEscuela de Medicina Veterinari

Rumen eukaryotes are the main phenotypic risk factors for larger methane emissions in dairy cattle

Mitigation of methane emissions from dairy cattle is a relevant strategy to reduce environmental impact from livestock as well as to increase farm profitability through improvement of energy usage. The objective of this study was to compare how microbiome composition determines methane concentration (MET) and methane intensity (MI, ppm CH4/kg Milk) with other traditional proxies (e.g. milk yield and conformation traits). A total of 1359 Holstein cows from 17 herds in 4 northern regions of Spain were included in this study. Microbiome data came from a subset of 437 cows from 14 herds. Cows were classified in quartiles for MET and MI, according to individual records of methane measurements during the cow's visit to the automatic milking system unit. A probit approach under a Markov chain Monte Carlo (McMC) Bayesian framework was used to determine risk factors for high MET and high MI. Reducing MET and MI genetic merit by unit of standard deviation (SD) reduced the probability of being classified in the upper quartile by 35.2% (33.9% to 36.4%) and 28.8% (27.6% to 29.6%), respectively. Increasing the relative abundance of most bacteria reduced the probability of a cow to be classified as high emitter (e.g., Firmicutes 9.9% (8.3 to 11.3) for MET and 7.1% (6.2 to 8.2) for MI, per unit of SD). An opposite effect was observed for the relative abundance of Eukaryotes. Larger abundance of most eukaryote caused larger risk for a cow to be classified as a high emitter animal (e.g., Oomycetes 14.2% (11.7% to 16.4%) for MET and 11.8% (9.4% to 14.0%) for MI, per unit of SD). One more unit of milk yield SD increased the probability of being classified in the upper quartile for MET by 3.7% (2.3% to 4.2%) and reduced the probability for MI by 12.6% (12.2% to 13.3%). Structure and capacity traits were not main drivers of being classified in the higher quartile of methane emission and intensity, with risk odds lower than 2% per unit of SD. Cow genetic merit for methane concentration and her microbiome composition (86 phylum and 1240 genus) were the main drivers for a cow to be classified as high MET or MI. This study suggests that mitigation of MET and MI could be addressed through animal breeding programs including genetic merits and strategies that modulate the microbiome.This research was financed by RTA2015-00022-C03 (METALGEN) project from the national plan of research, development, and innovation 2013-2020. The first author of this paper was granted a scholarship from Universidad de Costa Rica for course doctorate studies which partially conducted to the progress of this study.Peer reviewe