A network analysis of methane and feed conversion genes in the rumen microbial community

Metagenomics involves the genetic analysis of microbial DNA extracted from communities in an environment sample. Advent and falling costs of next-generation sequencing technologies has accelerated metagenomics research providing an improved understanding of microbial communities. In this study we investigate if the traits methane production and feed conversion rates in the rumen microbial community overlap with top genes ranked by topological metrics in a co-abundance network. A co-abundance network was constructed from abundance values of 1570 microbial genes in rumen samples of 8 cattle identified in a metagenomics study at the Beef and Sheep Research Centre of Scotland's Rural College. We used 4 different topological measures: Degree Centrality, Betweenness Centrality, Bonacich Power Centrality and PageRank to the network. Using permutation testing, we discovered, methane production trait genes significantly overlapped with top ranked genes obtained using the metrics PageRank and Bonacich Power Centrality. Feed conversion trait genes overlapped with top ranked genes using Bonacich Power Centrality and Betweenness. Furthermore, we observed the top ranked genes from PageRank and Bonacich Power Centrality significantly overlapped with genes involved in the KEGG methane metabolism pathway and ranked highly key methanogenesis genes such as mcrA and fmdB. Identified functional clusters containing most methane and feed conversion genes were also analyzed in terms of overlap with top ranked genes from topological metrics.

[1]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[2]  Stefanie Widder,et al.  Deciphering microbial interactions and detecting keystone species with co-occurrence networks , 2014, Front. Microbiol..

[3]  S. Tringe,et al.  Metagenomic Discovery of Biomass-Degrading Genes and Genomes from Cow Rumen , 2011, Science.

[4]  Jens Möhring,et al.  Changes in Rumen Microbial Community Composition during Adaption to an In Vitro System and the Impact of Different Forages , 2016, PloS one.

[5]  J. Ferguson,et al.  Metagenomic assessment of the functional potential of the rumen microbiome in Holstein dairy cows. , 2016, Anaerobe.

[6]  T. Shinkai,et al.  Metagenomic analysis of the rumen microbial community following inhibition of methane formation by a halogenated methane analog , 2015, Front. Microbiol..

[7]  Mona Singh,et al.  Disentangling function from topology to infer the network properties of disease genes , 2013, BMC Systems Biology.

[8]  J. Handelsman,et al.  Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. , 1998, Chemistry & biology.

[9]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[10]  Jinghui Zhang,et al.  An unsupervised learning approach to find ovarian cancer genes through integration of biological data , 2015, BMC Genomics.

[11]  Falk Schreiber,et al.  Exploration of biological network centralities with CentiBiN , 2006, BMC Bioinformatics.

[13]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[14]  P J Kononoff,et al.  Evaluation of bacterial diversity in the rumen and feces of cattle fed different levels of dried distillers grains plus solubles using bacterial tag-encoded FLX amplicon pyrosequencing. , 2010, Journal of animal science.

[15]  Sharon I. Greenblum,et al.  Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease , 2011, Proceedings of the National Academy of Sciences.

[16]  Mehrdad Hajibabaei,et al.  Next‐generation sequencing technologies for environmental DNA research , 2012, Molecular ecology.

[17]  J. Banfield,et al.  Community structure and metabolism through reconstruction of microbial genomes from the environment , 2004, Nature.

[18]  Gunnar Rätsch,et al.  Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota , 2013, PLoS Comput. Biol..

[19]  F. Schreiber,et al.  Centrality Analysis Methods for Biological Networks and Their Application to Gene Regulatory Networks , 2008, Gene regulation and systems biology.

[20]  Eugene L. Madsen,et al.  Comparative Survey of Rumen Microbial Communities and Metabolites across One Caprine and Three Bovine Groups, Using Bar-Coded Pyrosequencing and 1H Nuclear Magnetic Resonance Spectroscopy , 2012, Applied and Environmental Microbiology.

[21]  Mark Gerstein,et al.  The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics , 2007, PLoS Comput. Biol..

[22]  J. Gilbert,et al.  Metagenomics - a guide from sampling to data analysis , 2012, Microbial Informatics and Experimentation.

[23]  S. Bergmann,et al.  Similarities and Differences in Genome-Wide Expression Data of Six Organisms , 2003, PLoS biology.

[24]  Mick Watson,et al.  The rumen microbial metagenome associated with high methane production in cattle , 2015, BMC Genomics.

[25]  William A. Walters,et al.  Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample , 2010, Proceedings of the National Academy of Sciences.

[26]  J. Hyslop,et al.  Archaeal abundance in post-mortem ruminal digesta may help predict methane emissions from beef cattle , 2014, Scientific Reports.

[27]  Olli Simell,et al.  Gut Microbiome Metagenomics Analysis Suggests a Functional Model for the Development of Autoimmunity for Type 1 Diabetes , 2011, PloS one.

[28]  G. Medley,et al.  Ruminating on complexity: macroparasites of wildlife and livestock. , 2004, Trends in ecology & evolution.

[29]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[30]  Hugues Bersini,et al.  Topology Analysis of Social Networks Extracted from Literature , 2015, PloS one.

[31]  J. Faith,et al.  Predicting a Human Gut Microbiota’s Response to Diet in Gnotobiotic Mice , 2011, Science.

[32]  Min Wang,et al.  Erratum: Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range , 2016, Scientific Reports.

[33]  M. Gerstein,et al.  Genomic analysis of essentiality within protein networks. , 2004, Trends in genetics : TIG.

[34]  T. Hackmann,et al.  Invited review: ruminant ecology and evolution: perspectives useful to ruminant livestock research and production. , 2010, Journal of dairy science.

[35]  Mick Watson,et al.  Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance , 2016, PLoS genetics.