Use of a Multi-Omic Approach for Identifying Rumen Microbiome Mechanisms in CowsModulated by an Anti-Methanogenic Additive

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Karla Fabiola, Corral-Jara | Bénédict, Yanibada | Abdelhamid, Boudra | Martin, Cécile | Morgavi, Diego | Popova, Milka

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International audience. The rumen microbiome allows ruminants to feed on forages not adapted for monogastrics’ consumption.Nevertheless, ruminant production contributes to the greenhouse effect through the production of enteric methaneby rumen archaea. The aim of this work was to get insight into how rumen microbes adapt and function when ananti-methanogenic compound inhibits methane production in cows.The experimental setup consisted of 25 lactating Holstein cows fed a total mixed ration of corn silage, grass hay andconcentrate with (n= 12) or without (n= 13) a specific methane inhibitor. In week 5, rumen fluid samples werecollected before the morning feeding from each cow via stomach tubing and subjected to RNASeq (Illumina HiSeq)and metabolomics (RPLC-QToF/MS, HILIC-Orbitrap, LC-MS/MS and GC-FID) analysis. In week 6, cows weretransferred into respiration chambers for measuring methane emissions for 4 days. The MetaTrans pipeline was usedfor metatranscriptomic analysis and metabolomic data were processed using the web-based GalaxyWorkflow4Metabolomics. KEGGs (mapped mRNA), OTUs (based on rRNA), and metabolomic data were integrated viacausality relationships using Bayesian Networks.In the treated group, enteric methane emissions were reduced by 23%. Our initial analysis uncovered novelrelationships between OTUs, KEGGs, and metabolites. The treated group of cows had two OTUs and 57 KEGGsdifferentially expressed, together with 39 discriminant metabolites, in comparison with control. After integration, theanaerobic carbon-monoxide dehydrogenase catalytic subunit, upregulated in the treated group, was related with thegenera Methanosphaera, Butyrivibrio, Ruminococcus, and Methanobrevibacter, whose abundance did not differsignificantly between the groups. This enzyme is involved in the initial step of carbon fixation in methanogens andacetogens. Other associations will be performed using this multi-omic approach and microbes associated with thedecrease in methane emissions may be identified.

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