A heat diffusion multilayer network approach for the identification of functional biomarkers in rumen methane emissions

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. 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 are from the PLS model of diffusion subset. 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 VFA affecting methane emissions which represented by the functional genes.

Bovine host genome acts on specific metabolism, communication and genetic processes of rumen microbes host-genomically linked to methane emissions

“…Nitrogen fixation protein nifU carries out N 2 reduction into ammonia 101 , which can act as an alternative H 2 -consuming sink competing with ruminal methanogenesis. Further negative r gCH4 were obtained for microbial genes in thiamine metabolism (iscS, thiD, thiH and thiE with r gCH4 from -0.88 to -0.70, P 0 ≥0.91) 102 ; hydration of long-chain fatty acid oleate into anti-tumoral hydroxystearic acid 103,104 (ohyA, -0.81, P 0 =0.95), or import of methanogen-inhibitors long-chain fatty acids 40 (ABCB-BAC 105 , r gCH4 =-0.9, P 0 =0.99). Moreover, highly abundant bacteria genera with ruminal fatty acid biohydrogenation activity 106,107 , Eubacterium and Butyrivibrio where H 2 producing bacteria are active [108][109][110] .…”

Microbiome-metabolome interactions predict host phenotype

“…As such, one could assume the concentration of the metabolites would depend on a linear combination of their production and consumption by a variety of different microbes. This assumption may fail following non-linear experimental response curves for both microbes and metabolites [68,69,70] or non-linear consumption/production. Third, that the relation production and consumption rate are not affected by external factors or other bacteria [71,72].…”

Identifying Hub Nodes and Sub-networks from Cattle Rumen Microbiome Multilayer Networks

“…In this research, a framework for extracting key topological hubs based on the ruminal metabolome and microbiome multilayer network was developed. Dataset: The 30 beef cattle rumen fluid samples used in this study were carried out at Scotland's Rural College's Beef and Sheep Research Centre (SRUC) [6]. The SRUC Animal Experiment Committee permitted the experiment, which was conducted in compliance with the Animals (Scientific Procedures) Act of 1986 in the United Kingdom.…”

“…There have been some network studies of the rumen microbiome, including the identification of biomarkers of methane emission and diet digestibility based on the correlation network of microbial gene abundance [3][4][5]. Other studies have used microbial taxonomy abundance correlated network community niche exploration and analysis [6], metabolite-related diffusion analysis based on a multi-layer network [7] or network analysis related to hosting phenotype and genetic characteristics of rumen methane emission [8]. However, most of the current research is based on singlelayer networks.…”