Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets

Abstract

Previous shotgun metagenomic analyses of ruminal digesta identified some microbial information that might be useful as biomarkers to select cattle that emit less methane (CH4), which is a potent greenhouse gas. It is known that methane production (g/kgDMI) and to an extent the microbial community is heritable and therefore biomarkers can offer a method of selecting cattle for low methane emitting phenotypes. In this study a wider range of Bos Taurus cattle, varying in breed and diet, was investigated to determine microbial communities and genetic markers associated with high/low CH4 emissions. Digesta samples were taken from 50 beef cattle, comprising four cattle breeds, receiving two basal diets containing different proportions of concentrate and also including feed additives (nitrate or lipid), that may influence methane emissions. A combination of partial least square analysis and network analysis enabled the identification of the most significant and robust biomarkers of CH4 emissions (VIP > 0.8) across diets and breeds when comparing all potential biomarkers together. Genes associated with the hydrogenotrophic methanogenesis pathway converting carbon dioxide to methane, provided the dominant biomarkers of CH4 emissions and methanogens were the microbial populations most closely correlated with CH4 emissions and identified by metagenomics. Moreover, these genes grouped together as confirmed by network analysis for each independent experiment and when combined. Finally, the genes involved in the methane synthesis pathway explained a higher proportion of variation in CH4 emissions by PLS analysis compared to phylogenetic parameters or functional genes. These results confirmed the reproducibility of the analysis and the advantage to use these genes as robust biomarkers of CH4 emissions. Volatile fatty acid concentrations and ratios were significantly correlated with CH4, but these factors were not identified as robust enough for predictive purposes. Moreover, the methanotrophic Methylomonas genus was found to be negatively correlated with CH4. Finally, this study confirmed the importance of using robust and applicable biomarkers from the microbiome as a proxy of CH4 emissions across diverse production systems and environments.

Nuclear Magnetic Resonance to Detect Rumen Metabolites Associated with Enteric Methane Emissions from Beef Cattle

“…Overall, what can be noted from the literature is that when trying to predict CH 4 emissions with metabolite-based models, goodness of fit as measured by the R 2 coefficient is generally between 0.3-0.5. Therefore, comparing our current results with these studies, even if comparison based on the R 2 measure only is a notable simplification as it depends on technical details of the modeling used and particuliarities of the data in each study, we find that they are in line with previous work 11,14,44,45 , although ours were derived from solely utilising NMR metabolic profiles as predictors. It should also be noted that, similarly to the studies mentioned above, predictive abilities improved following addition of both animal and diet covariates (R 2 = 0.70), reinforcing the evidence that these factors are still the main influencers on CH 4 emissions.…”

“…Studies have been undertaken to try and model different variables which might be correlated to CH 4 emissions with varying results. Auffret et al 14 aimed to identify robust microbial biomarkers which could be used to predict CH 4 emissions. By applying a simple regression analysis they identified the archaea:bacteria ratio as a biomarker for methane emissions, and a PLS analysis (which included 56 genera, diet effects and breed types) explained 50% of variation in methane yield.…”

Identification of Complex Rumen Microbiome Interaction Within Diverse Functional Niches as Mechanisms Affecting the Variation of Methane Emissions in Bovine

“…Several authors have succeeded in using information about microbial communities or microbial genes to predict CH 4 emissions (Roehe et al, 2016;Shabat et al, 2016;Auffret et al, 2018;Difford et al, 2018) but restricted to archaea and bacteria communities. However, in order to develop efficient CH 4 mitigation strategies using microbiome information, we need improved knowledge about the rumen microbiome.…”

“…Coabundance patterns between microbials have been previously used as a prediction of microbial interactions (Faust and Raes, 2012). Network-based analytical approaches have helped disentangle complex polymicrobial and microbehost interactions in ruminants, humans and soil (Barberán et al, 2012;Roehe et al, 2016;Sung et al, 2017;Auffret et al, 2018) by identifying patterns of microbial interactions in ecosystems occupied by highly diverse microorganisms. Within a network, several clusters considered as a single biological unit may provide information about the local interaction patterns, the biological contribution of each cluster and therefore its function in the microbiome (reviewed in Faust and Raes, 2012).…”

The genomic and proteomic landscape of the rumen microbiome revealed by comprehensive genome-resolved metagenomics

“…This production represents a loss of energy and carbon for the animal, estimated to be between 2 and 12% 6 . Methane production has been directly linked to the abundance of methanogenic archaea in the rumen 7,8 , and is also under the control of rumen microbial metabolism thermodynamically dependent of hydrogen partial pressure 9 . Moreover, it is been shown to be under the influence of host genetics 10 , offering possibilities for mitigating this issue through selection or manipulation of the microbiome.…”