Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism

Abstract

Methane emissions from ruminal fermentation contribute significantly to total anthropological greenhouse gas (GHG) emissions. New meta-omics technologies are beginning to revolutionise our understanding of the rumen microbial community structure, metabolic potential and metabolic activity. Here we explore these developments in relation to GHG emissions. Microbial rumen community analyses based on small subunit ribosomal RNA sequence analysis are not yet predictive of methane emissions from individual animals or treatments. Few metagenomics studies have been directly related to GHG emissions. In these studies, the main genes that differed in abundance between high and low methane emitters included archaeal genes involved in methanogenesis, with others that were not apparently related to methane metabolism. Unlike the taxonomic analysis up to now, the gene sets from metagenomes may have predictive value. Furthermore, metagenomic analysis predicts metabolic function better than only a taxonomic description, because different taxa share genes with the same function. Metatranscriptomics, the study of mRNA transcript abundance, should help to understand the dynamic of microbial activity rather than the gene abundance; to date, only one study has related the expression levels of methanogenic genes to methane emissions, where gene abundance failed to do so. Metaproteomics describes the proteins present in the ecosystem, and is therefore arguably a better indication of microbial metabolism. Both two-dimensional polyacrylamide gel electrophoresis and shotgun peptide sequencing methods have been used for ruminal analysis. In our unpublished studies, both methods showed an abundance of archaeal methanogenic enzymes, but neither was able to discriminate high and low emitters. Metabolomics can take several forms that appear to have predictive value for methane emissions; ruminal metabolites, milk fatty acid profiles, faecal long-chain alcohols and urinary metabolites have all shown promising results. Rumen microbial amino acid metabolism lies at the root of excessive nitrogen emissions from ruminants, yet only indirect inferences for nitrogen emissions can be drawn from meta-omics studies published so far. Annotation of meta-omics data depends on databases that are generally weak in rumen microbial entries. The Hungate 1000 project and Global Rumen Census initiatives are therefore essential to improve the interpretation of sequence/metabolic information.

Inter- and intra-domain functional redundancy in the rumen microbiome during plant biomass degradation

“…glycine betaine (from beet) and choline (from plant membranes), and methanol (from the hydrolysis of methanolic side-groups in plant polysaccharides) are well known [49], however the amount of these substrates might vary substantially with different diets. Previous, less temporally resolved work suggested that Methanobrevibacter was associated with high CH 4 emissions [14, 49]. However, a comparison of sheep rumen metagenomes and metatranscriptomes indicated that Methanomassiliicoccales are very active community members in both high and low CH 4 “emitters”, with around 5 times higher abundances in the metatranscriptomes compared to the metagenomes [16].…”

“…[13]). In addition, the usage of meta-omics techniques has paved the way for a better understanding of the rumen ecosystem and the microbial metabolic potential and activity in the rumen (reviewed by [14]). These studies have revealed differences in rumen microbiome structure between low and high CH 4 emitting cows (e.g.…”

“…Despite these major advances, a holistic understanding of the rumen microbiome is still lacking, including answers to rather simple questions such as “who is doing what and when during feed degradation?”. Such a fundamental understanding of the rumen ecosystem, as it was proposed by Hungate already in the early 1960s [11], can help to specifically manipulate the rumen microbiome, to lower CH 4 emissions, without hampering animal productivity, milk and meat quality or being harmful to the animal [14, 26].…”

Interação microbiana e metanogênese em ruminantes – uma revisão

“…As archaeas metanogênicas desenvolveram relação simbiótica com microrganismos do rúmen mais eficientes na produção de H2, fazendo com que alberguem ao seu redor muitas bactérias metanogênicas (Tapio et al, 2017;Wallace et al, 2017). Entretanto, observa-se que em muitos estudos apenas é avaliada a abundância de determinado grupo de microrganismos sob distintas condições, sem precisar quantificar a sua importância no ambiente ruminal (Wallace et al, 2017).…”

Symposium review: Understanding diet–microbe interactions to enhance productivity of dairy cows

“…In addition to the community composition of the rumen microbiota, analysis of microbial genes in the rumen (i.e., a gene-centric approach) using metagenomic sequencing allows characterization of microbial metabolic pathways (Brulc et al, 2009;Hess et al, 2011). These DNA-based NGS approaches have facilitated characterizing "who" is there but also allows prediction of their function in the rumen (Wallace et al, 2017). However, DNA-based methods do not give information on the metabolic activity of microbes in an environment because DNA can originate from inactive or dead microbial cells (Lettat and Benchaar, 2013).…”