Projects per year
Abstract
A better understanding of rumen microbial interactions is crucial for the study of rumen metabolism and methane emissions. Metagenomics-based methods can explore the relationship between microbial genes and metabolites to clarify the effect of microbial function on the host phenotype. This study investigated the rumen microbial mechanisms of methane metabolism in cattle by combining metagenomic data and network-based methods. Based on the relative abundance of 1461 rumen microbial genes and the main volatile fatty acids (VFAs), a multilayer heterogeneous network was constructed, and the functional modules associated with metabolite-microbial genes were obtained by heat diffusion algorithm. The PLS model by integrating data from VFAs and microbial genes explained 72.98% variation of methane emissions. Compared with single-layer networks, more previously reported biomarkers of methane prediction can be captured by the multilayer network. More biomarkers with the rank of top 20 topological centralities were from the PLS models of diffusion subsets.
The heat diffusion algorithm is different from the strategy used by the microbial metabolic system to understand methane phenotype. It inferred 24 novel biomarkers that were preferentially affected by changes in specific
VFAs. Results showed that the heat diffusion multilayer network approach improved the understanding of the microbial patterns of VFAs affecting methane emissions which represented by the functional microbial genes.
The heat diffusion algorithm is different from the strategy used by the microbial metabolic system to understand methane phenotype. It inferred 24 novel biomarkers that were preferentially affected by changes in specific
VFAs. Results showed that the heat diffusion multilayer network approach improved the understanding of the microbial patterns of VFAs affecting methane emissions which represented by the functional microbial genes.
Original language | English |
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Article number | 2020.09.014 |
Number of pages | 10 |
Journal | Methods |
Early online date | 15 Oct 2020 |
DOIs | |
Publication status | First published - 15 Oct 2020 |
Bibliographical note
Funding Information:This research is jointly supported by Ulster University and Scotland's Rural College, U.K.
Publisher Copyright:
© 2020 Elsevier Inc.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords
- Multilayer networks
- Methane emissions
- Metabolites
- Network diffusion algorithm
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Projects
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Elucidating bovine host genomic links with rumen microbial genes to improve sustainably feed conversion efficiency using unique selection criteria
Roehe, R., Dewhurst, R. & Auffret, M.
1/05/19 → 31/10/22
Project: Research