Methane represents 16 % of total anthropogenic greenhouse gas emissions. It has been estimated that ruminant livestock produce ca. 29 % of this methane. As individual animals produce consistently different quantities of methane, understanding the basis for these differences may lead to new opportunities for mitigating ruminal methane emissions. Metagenomics is a powerful new tool for understanding the composition and function of complex microbial communities. Here we have applied metagenomics to the rumen microbial community to identify differences in the microbiota and metagenome that lead to high- and low-methane-emitting cattle phenotypes. Four pairs of beef cattle were selected for extreme high and low methane emissions from 72 animals, matched for breed (Aberdeen-Angus or Limousin cross) and diet (high or medium concentrate). Community analysis was carried out by qPCR of 16S and 18S rRNA genes and by alignment of Illumina HiSeq reads to the GREENGENES database. Total genomic reads were aligned to the KEGG genes databasefor functional analysis. Deep sequencing produced on average 11.3 Gb per sample. 16S rRNA gene abundances indicated that archaea, predominantly Methanobrevibacter, were 2.5× more numerous (P = 0.026) in high emitters, whereas among bacteria Proteobacteria, predominantly Succinivibrionaceae, were 4-fold less abundant (2.7 vs. 11.2 %; P = 0.002). KEGG analysis revealed that archaeal genes leading directly or indirectly to methane production were 2.7-fold more abundant in high emitters. Genes less abundant in high emitters included acetate kinase, electron transport complex proteins RnfC and RnfD and glucose-6-phosphate isomerase. Sequence data were assembled de novo and over 1.5 million proteins were annotated on the subsequent metagenome scaffolds. Less than half of the predicted genes matched matched a domain within Pfam. Amongst 2774 identified proteins of the 20 KEGG orthologues that correlated with methane emissions, only 16 showed 100 % identity with a publicly available protein sequence. The abundance of archaeal genes in ruminal digesta correlated strongly with differing methane emissions from individual animals, a finding useful for genetic screening purposes. Lower emissions were accompanied by higher Succinovibrionaceae abundance and changes in acetate and hydrogen production leading to less methanogenesis, as similarly postulated for Australian macropods. Large numbers of predicted protein sequences differed between high- and low-methane-emitting cattle. Ninety-nine percent were unknown, indicating a fertile area for future exploitation.

Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions. The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed. We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets. Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency. Sire progeny groups differed significantly in their methane emissions measured in respiration chambers. Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet, suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly. In the metagenomic analysis of rumen contents, we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively. These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks. Methanogenesis genes (e.g. mcrA and fmdB) were associated with methane emissions, whilst host-microbiome cross talk genes (e.g. TSTA3 and FucI) were associated with feed conversion efficiency. These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts. Generally, the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e.g. human metabolism, health and behaviour, as well as to understand the genetic link between host and microbiome. Methane is a highly potent greenhouse gas and ruminants are the major source of methane emissions from anthropogenic activities. Here we show in an experiment with cattle that genetic selection of low-emitting animals is a viable option based on a newly developed selection criterion. The experimental data provided a comprehensive insight into the host additive genetic influence on the microbiome, the impact of nutrition on genetics and the microbiome, and the effect of metagenomic microbial genes on the analysed traits. We developed a selection criterion to change those traits by evaluation of hosts based on the relative abundance of microbial genes. This criterion is shown to be highly informative and it is therefore suggested to be used in studies analysing different traits and species. This study provides a proof of principle that there is an additive genetic influence of the host on its microbiome and that selection for the desired host can be based on the abundance of a suite of genes in the ruminal metagenome associated with the trait. The use of this criterion will allow genetic analysis on a large number of hosts, previously a significant barrier to determination of host genetic effects on such traits.

The emergence and spread of antimicrobial resistance is the most urgent current threat to human and animal health. An improved understanding of the abundance of antimicrobial resistance genes and genes associated with microbial colonisation and pathogenicity in the animal gut will have a major role in reducing the contribution of animal production to this problem. Here, the influence of diet on the ruminal resistome and abundance of pathogenicity genes was assessed in ruminal digesta samples taken from 50 antibiotic-free beef cattle, comprising four cattle breeds receiving two diets containing different proportions of concentrate. Two hundred and four genes associated with antimicrobial resistance (AMR), colonisation, communication or pathogenicity functions were identified from 4966 metagenomic genes using KEGG identification. Both the diversity and abundance of these genes were higher in concentrate-fed animals. Chloramphenicol and microcin resistance genes were dominant in samples from forage-fed animals (P < 0.001), while aminoglycoside and streptomycin resistances were enriched in concentrate-fed animals. The concentrate-based diet also increased the relative abundance of Proteobacteria, which includes many animal and zoonotic pathogens. A high ratio of Proteobacteria to (Firmicutes + Bacteroidetes) was confirmed as a good indicator for rumen dysbiosis, with eight cases all from concentrate-fed animals. Finally, network analysis demonstrated that the resistance/pathogenicity genes are potentially useful as biomarkers for health risk assessment of the ruminal microbiome. Diet has important effects on the complement of AMR genes in the rumen microbial community, with potential implications for human and animal health.

A network analysis including relative abundances of all ruminal microbial genera (archaea, bacteria, fungi, and protists) and their genes was performed to improve our understanding of how the interactions within the ruminal microbiome affects methane emissions (CH₄). Metagenomics and CH₄ data were available from 63 bovines of a two-breed rotational cross, offered two basal diets. Co-abundance network analysis revealed 10 clusters of functional niches. The most abundant hydrogenotrophic Methanobacteriales with key microbial genes involved in methanogenesis occupied a different functional niche (i.e., “methanogenesis” cluster) than methylotrophic Methanomassiliicoccales (Candidatus Methanomethylophylus) and acetogens (Blautia). Fungi and protists clustered together and other plant fiber degraders like Fibrobacter occupied a seperate cluster. A Partial Least Squares analysis approach to predict CH₄ variation in each cluster showed the methanogenesis cluster had the best prediction ability (57.3%). However, the most important explanatory variables in this cluster were genes involved in complex carbohydrate degradation, metabolism of sugars and amino acids and Candidatus Azobacteroides carrying nitrogen fixation genes, but not methanogenic archaea and their genes. The cluster containing Fibrobacter, isolated from other microorganisms, was positively associated with CH₄ and explained 49.8% of its variability, showing fermentative advantages compared to other bacteria and fungi in providing substrates (e.g., formate) for methanogenesis. In other clusters, genes with enhancing effect on CH₄ were related to lactate and butyrate (Butyrivibrio and Pseudobutyrivibrio) production and simple amino acids metabolism. In comparison, ruminal genes negatively related to CH₄ were involved in carbohydrate degradation via lactate and succinate and synthesis of more complex amino acids by γ-Proteobacteria. When analyzing low- and high-methane emitters data in separate networks, competition between methanogens in the methanogenesis cluster was uncovered by a broader diversity of methanogens involved in the three methanogenesis pathways and larger interactions within and between communities in low compared to high emitters. Generally, our results suggest that differences in CH₄ are mainly explained by other microbial communities and their activities rather than being only methanogens-driven. Our study provides insight into the interactions of the rumen microbial communities and their genes by uncovering functional niches affecting CH₄, which will benefit the development of efficient CH₄ mitigation strategies.

The aims of the present study were to quantify hydrogen (H₂) and methane (CH₄) emissions from beef cattle under different dietary conditions and to assess how cattle genotype and rumen microbial community affected these emissions. A total of thirty-six Aberdeen Angus-sired (AAx) and thirty-six Limousin-sired (LIMx) steers were fed two diets with forage:concentrate ratios (DM basis) of either 8:92 (concentrate) or 52:48 (mixed). Each diet was fed to eighteen animals of each genotype. Methane (CH₄) and H₂emissions were measured individually in indirect respiration chambers. H₂emissions (mmol/min) varied greatly throughout the day, being highest after feed consumption, and averaged about 0·10 mol H₂/mol CH₄. Higher H₂emissions (mol/kg DM intake) were recorded in steers fed the mixed diet. Higher CH₄emissions (mol/d and mol/kg DM intake) were recorded in steers fed the mixed diet (P< 0·001); the AAx steers produced more CH₄on a daily basis (mol/d,P< 0·05) but not on a DM intake basis (mol/kg DM intake). Archaea (P= 0·002) and protozoa (P< 0·001) were found to be more abundant and total bacteria (P< 0·001) less abundant (P< 0·001) on feeding the mixed diet. The relative abundance ofClostridiumcluster IV was found to be greater (P< 0·001) and that of cluster XIVa (P= 0·025) lower on feeding the mixed diet. The relative abundance ofBacteroidesplusPrevotellawas greater (P= 0·018) and that ofClostridiumcluster IV lower (P= 0·031) in the LIMx steers. There were no significant relationships between H₂emissions and microbial abundance. In conclusion, the rate of H₂production immediately after feeding may lead to transient overloading of methanogenic archaea capacity to use H₂, resulting in peaks in H₂emissions from beef cattle.