Network analysis unraveled the complex interactions in the rumen microbiome associated with methane emission in dairy cattle

TY - ABST

T1 - Network analysis unraveled the complex interactions in the rumen microbiome associated with methane emission in dairy cattle

AU - Ye, Xiaoxing

AU - Sahana, Goutam

AU - Lund, Mogens Sandø

AU - Cai, Zexi

PY - 2024

Y1 - 2024

N2 - Methane emissions from ruminants, particularly dairy cattle, represent a significant source of greenhouse gas (GHG), contributing to climate changes. This study employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the complex interactions within the rumen microbiome that influence methane emissions. By integrating rumen microbiome sequencing data with methane emissions measurements from 750 Holstein cattle, we employed a quality-based bioinformatics pipeline to identify microbial communities and their associations with methane production. Meanwhile, our findings highlight the crucial role of both archaeal and bacterial communities in methane emissions, revealing specific microbial modules (MEblue, MEyellow, MEturquoise, and MEbrown), interactions (Clostridum-Clostridum-Prevotella-RF39-Clostridum) and different taxa (archaeal genera VadinCA11, Methanobrecivecter, and Methanosphaera; bacterial order Bacteroidales; bacterial family Ruminococcaceae; bacterial genera Prevotella, Treponema, Clostridum, Anaeropiasma, and CF231; bacterial species Succinogenes) significantly correlated with methane emissions. Methanobrecivecter, and Methanosphaera, archaeal phylum Euryarchaeota, are known for their methanogenic capabilities and predominant abundance for archaeal communities. In addition, bacteria taxa like Bacteroidales, Ruminococcaceae, Prevotella, and Clostridum can produce volatile fatty acids (VFAs) and hydrogen, which can be substrates for methanogenesis by archaeal communities. The application of network analysis provided a systematic understanding of the microbiome-methane emission relationship, providing its potential as an innovative approach to study microbiome-traits association in cattle.

AB - Methane emissions from ruminants, particularly dairy cattle, represent a significant source of greenhouse gas (GHG), contributing to climate changes. This study employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the complex interactions within the rumen microbiome that influence methane emissions. By integrating rumen microbiome sequencing data with methane emissions measurements from 750 Holstein cattle, we employed a quality-based bioinformatics pipeline to identify microbial communities and their associations with methane production. Meanwhile, our findings highlight the crucial role of both archaeal and bacterial communities in methane emissions, revealing specific microbial modules (MEblue, MEyellow, MEturquoise, and MEbrown), interactions (Clostridum-Clostridum-Prevotella-RF39-Clostridum) and different taxa (archaeal genera VadinCA11, Methanobrecivecter, and Methanosphaera; bacterial order Bacteroidales; bacterial family Ruminococcaceae; bacterial genera Prevotella, Treponema, Clostridum, Anaeropiasma, and CF231; bacterial species Succinogenes) significantly correlated with methane emissions. Methanobrecivecter, and Methanosphaera, archaeal phylum Euryarchaeota, are known for their methanogenic capabilities and predominant abundance for archaeal communities. In addition, bacteria taxa like Bacteroidales, Ruminococcaceae, Prevotella, and Clostridum can produce volatile fatty acids (VFAs) and hydrogen, which can be substrates for methanogenesis by archaeal communities. The application of network analysis provided a systematic understanding of the microbiome-methane emission relationship, providing its potential as an innovative approach to study microbiome-traits association in cattle.

KW - Rumen microbiome

KW - Cattle

KW - Methane emissions

KW - Network analysis

M3 - Conference abstract for conference

T2 - The 75th EAAP Annual Meeting

Y2 - 1 September 2024 through 5 September 2024

ER -