Pubmed Record 32582125
Abstract Text
In this study, Bos Taurus cattle offered one high concentrate diet (92% concentrate-8% straw) during two independent trials allowed us to classify 72 animals comprising of two cattle breeds as "Low" or "High" feed efficiency groups. Digesta samples were taken from individual beef cattle at the abattoir. After metagenomic sequencing, the rumen microbiome composition and genes were determined. Applying a targeted approach based on current biological evidence, 27 genes associated with host-microbiome interaction activities were selected. Partial least square analysis enabled the identification of the most significant genes and genera of feed efficiency (VIP > 0.8) across years of the trial and breeds when comparing all potential genes or genera together. As a result, limited number of genes explained about 40% of the variability in both feed efficiency indicators. Combining information from rumen metagenome-assembled genomes and partial least square analysis results, microbial genera carrying these genes were determined and indicated that a limited number of important genera impacting on feed efficiency. In addition, potential mechanisms explaining significant difference between Low and High feed efficiency animals were analyzed considering, based on the literature, their gastrointestinal location of action. High feed efficiency animals were associated with microbial species including several Eubacterium having the genetic capacity to form biofilm or releasing metabolites like butyrate or propionate known to provide a greater contribution to cattle energy requirements compared to acetate. Populations associated with fucose sensing or hemolysin production, both mechanisms specifically described in the lower gut by activating the immune system to compete with pathogenic colonizers, were also identified to affect feed efficiency using rumen microbiome information. Microbial mechanisms associated with low feed efficiency animals involved potential pathogens within Proteobacteria and Spirochaetales, releasing less energetic substrates (e.g., acetate) or producing sialic acid to avoid the host immune system. Therefore, this study focusing on genes known to be involved in host-microbiome interaction improved the identification of rumen microbial genetic capacities and potential mechanisms significantly impacting on feed efficiency in beef cattle fed high concentrate diet.
Pubmed Record 32473013
Abstract Text
The rumen microbiota provides essential services to its host and, through its role in ruminant production, contributes to human nutrition and food security. A thorough knowledge of the genetic potential of rumen microbes will provide opportunities for improving the sustainability of ruminant production systems. The availability of gene reference catalogs from gut microbiomes has advanced the understanding of the role of the microbiota in health and disease in humans and other mammals. In this work, we established a catalog of reference prokaryote genes from the bovine rumen. Using deep metagenome sequencing we identified 13,825,880 non-redundant prokaryote genes from the bovine rumen. Compared to human, pig, and mouse gut metagenome catalogs, the rumen is larger and richer in functions and microbial species associated with the degradation of plant cell wall material and production of methane. Genes encoding enzymes catalyzing the breakdown of plant polysaccharides showed a particularly high richness that is otherwise impossible to infer from available genomes or shallow metagenomics sequencing. The catalog expands the dataset of carbohydrate-degrading enzymes described in the rumen. Using an independent dataset from a group of 77 cattle fed 4 common dietary regimes, we found that only <0.1% of genes were shared by all animals, which contrast with a large overlap for functions, i.e., 63% for KEGG functions. Different diets induced differences in the relative abundance rather than the presence or absence of genes, which explains the great adaptability of cattle to rapidly adjust to dietary changes. These data bring new insights into functions, carbohydrate-degrading enzymes, and microbes of the rumen to complement the available information on microbial genomes. The catalog is a significant biological resource enabling deeper understanding of phenotypes and biological processes and will be expanded as new data are made available.
Pubmed Record 32362882
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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 Gamma-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.
Pubmed Record 29375511
Abstract Text
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.
Pubmed Record 29228991
Abstract Text
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.
Pubmed Record 26494241
Abstract Text
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 2.5x 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.
