Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle

BackgroundMicroorganisms are responsible for fermentation within the rumen and have been reported to contribute to the variation in feed efficiency of cattle. However, to what extent the breed affects the rumen microbiome and its association with host feed efficiency is unknown. Here, rumen microbiomes of beef cattle (n = 48) from three breeds (Angus, Charolais, Kinsella composite hybrid) with high and low feed efficiency were explored using metagenomics and metatranscriptomics, aiming to identify differences between functional potentials and activities of same rumen microbiomes and to evaluate the effects of host breed and feed efficiency on the rumen microbiome.ResultsRumen metagenomes were more closely clustered together and thus more conserved among individuals than metatranscriptomes, suggesting that inter-individual functional variations at the RNA level were higher than those at the DNA level. However, while mRNA enrichment significantly increased the sequencing depth of mRNA and generated similar functional profiles to total RNA-based metatranscriptomics, it led to biased abundance estimation of several transcripts. We observed divergent rumen microbial composition (metatranscriptomic level) and functional potentials (metagenomic level) among three breeds, but differences in functional activity (metatranscriptomic level) were less apparent. Differential rumen microbial features (e.g., taxa, diversity indices, functional categories, and genes) were detected between cattle with high and low feed efficiency, and most of them were breed-specific.ConclusionsMetatranscriptomes represent real-time functional activities of microbiomes and have the potential to better associate rumen microorganisms with host performances compared to metagenomics. As total RNA-based metatranscriptomics seem to avoid potential biases caused by mRNA enrichment and allow simultaneous use of rRNA for generation of compositional profiles, we suggest their use for linking the rumen microbiome with host phenotypes in future studies. However, if exploration of specific lowly expressed genes is desired, mRNA enrichment is recommended as it will enhance the resolution of mRNA. Finally, the differential microbial features observed between efficient and inefficient steers tended to be specific to breeds, suggesting that interactions between host breed genotype and the rumen microbiome contribute to the variations in feed efficiency observed. These breed-associated differences represent an opportunity to engineer specific rumen microbiomes through selective breeding of the hosts.

[1]  P. H. Robinson,et al.  Protein and fiber digestion, passage, and utilization in lactating cows. Microbial growth and flow as influenced by dietary manipulations. , 1987, Journal of dairy science.

[2]  B. White,et al.  Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants , 2016, The ISME Journal.

[3]  Karen A. Beauchemin,et al.  Characterization of the Core Rumen Microbiome in Cattle during Transition from Forage to Concentrate as Well as during and after an Acidotic Challenge , 2013, PloS one.

[4]  S. K. Ranjhan,et al.  The relationship between rumen bacterial growth, intake of dry matter, digestible organic matter and volatile fatty acid production in buffalo (Bos bubalis) calves , 1977, British Journal of Nutrition.

[5]  Christopher J. Creevey,et al.  Spherical: an iterative workflow for assembling metagenomic datasets , 2016, BMC Bioinformatics.

[6]  Tasia M. Taxis,et al.  The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity , 2015, Nucleic acids research.

[7]  A. Pathak Various factors affecting microbial protein synthesis in the rumen , 2008 .

[8]  M. Ranilla,et al.  Effects of dilution rate and retention time of concentrate on efficiency of microbial growth, methane production, and ruminal fermentation in Rusitec fermenters. , 2009, Journal of dairy science.

[9]  S. Moore,et al.  Correlation of Particular Bacterial PCR-Denaturing Gradient Gel Electrophoresis Patterns with Bovine Ruminal Fermentation Parameters and Feed Efficiency Traits , 2010, Applied and Environmental Microbiology.

[10]  D. Kenny,et al.  Effect of Phenotypic Residual Feed Intake and Dietary Forage Content on the Rumen Microbial Community of Beef Cattle , 2012, Applied and Environmental Microbiology.

[11]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[12]  M. Sogin,et al.  A single genus in the gut microbiome reflects host preference and specificity , 2014, The ISME Journal.

[13]  Y. Benno,et al.  Comprehensive analysis of the fecal microbiota of healthy Japanese adults reveals a new bacterial lineage associated with a phenotype characterized by a high frequency of bowel movements and a lean body type , 2016, BMC Microbiology.

[14]  P. Janssen,et al.  Taxonomic Assessment of Rumen Microbiota Using Total RNA and Targeted Amplicon Sequencing Approaches , 2016, Front. Microbiol..

[15]  Paul Theodor Pyl,et al.  HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.

[16]  P. V. Soest,et al.  Feed Intake, Apparent Diet Digestibility, and Rate of Particulate Passage in Dairy Cattle , 1982 .

[17]  Zhongtang Yu,et al.  Improved extraction of PCR-quality community DNA from digesta and fecal samples. , 2004, BioTechniques.

[18]  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.

[19]  L. Guan,et al.  Assessment of the Microbial Ecology of Ruminal Methanogens in Cattle with Different Feed Efficiencies , 2009, Applied and Environmental Microbiology.

[20]  Weiyun Zhu,et al.  Microbiome-metabolome analysis reveals unhealthy alterations in the composition and metabolism of ruminal microbiota with increasing dietary grain in a goat model. , 2016, Environmental microbiology.

[21]  H. Tun,et al.  Linking Peripartal Dynamics of Ruminal Microbiota to Dietary Changes and Production Parameters , 2017, Frontiers in microbiology.

[22]  Angela C. Poole,et al.  Human Genetics Shape the Gut Microbiome , 2014, Cell.

[23]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[24]  A. Ellington,et al.  Microbiota and Metatranscriptome Changes Accompanying the Onset of Gingivitis , 2018, mBio.

[25]  Cole Trapnell,et al.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions , 2013, Genome Biology.

[26]  Hélène Touzet,et al.  SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data , 2012, Bioinform..

[27]  Tom Misselbrook,et al.  Agriculture: Steps to sustainable livestock , 2014, Nature.

[28]  Vincent Lombard,et al.  Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection , 2018, Nature Biotechnology.

[29]  E. Rubin,et al.  Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation , 2016, Microbiome.

[30]  Chao Xie,et al.  Fast and sensitive protein alignment using DIAMOND , 2014, Nature Methods.

[31]  Martin Hartmann,et al.  Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities , 2009, Applied and Environmental Microbiology.

[32]  C. Huttenhower,et al.  Relating the metatranscriptome and metagenome of the human gut , 2014, Proceedings of the National Academy of Sciences.

[33]  J. Blanchard,et al.  Untangling the Genetic Basis of Fibrolytic Specialization by Lachnospiraceae and Ruminococcaceae in Diverse Gut Communities , 2013 .

[34]  S. Moore,et al.  Feed efficiency differences and reranking in beef steers fed grower and finisher diets. , 2011, Journal of animal science.

[35]  L. Rode,et al.  Short communication: salivary secretion during meals in lactating dairy cattle. , 2008, Journal of dairy science.

[36]  Residual feed intake adjusted for backfat thickness and feeding frequency is independent of fertility in beef heifers , 2011 .

[37]  G. Conant,et al.  Diet Alters Both the Structure and Taxonomy of the Ovine Gut Microbial Ecosystem , 2013, DNA research : an international journal for rapid publication of reports on genes and genomes.

[38]  S. Moore,et al.  Influence of Sire Breed on the Interplay among Rumen Microbial Populations Inhabiting the Rumen Liquid of the Progeny in Beef Cattle , 2013, PloS one.

[39]  P. Janssen Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics , 2010 .

[40]  A. Spang,et al.  Methylotrophic methanogenic Thermoplasmata implicated in reduced methane emissions from bovine rumen , 2013, Nature Communications.

[41]  W. V. Thayne,et al.  Fermentation of a High Concentrate Diet as Affected by Ruminal pH and Digesta Flow , 1986 .

[42]  R. Dewhurst,et al.  Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen , 2018, Nature Communications.

[43]  J. Tiedje,et al.  Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy , 2007, Applied and Environmental Microbiology.

[44]  E. Alm,et al.  Unraveling the processes shaping mammalian gut microbiomes over evolutionary time , 2017, Nature Communications.

[45]  Eric P. Nawrocki,et al.  An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea , 2011, The ISME Journal.

[46]  S. Moore,et al.  Genetic and phenotypic relationships of feeding behavior and temperament with performance, feed efficiency, ultrasound, and carcass merit of beef cattle. , 2007, Journal of animal science.

[47]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[48]  P. Myer,et al.  Rumen Microbiome from Steers Differing in Feed Efficiency , 2015, PloS one.

[49]  H. Barkema,et al.  The Features of Fecal and Ileal Mucosa-Associated Microbiota in Dairy Calves during Early Infection with Mycobacterium avium Subspecies paratuberculosis , 2016, Front. Microbiol..

[50]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

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

[52]  L. Guan,et al.  Characterization of Variation in Rumen Methanogenic Communities under Different Dietary and Host Feed Efficiency Conditions, as Determined by PCR-Denaturing Gradient Gel Electrophoresis Analysis , 2010, Applied and Environmental Microbiology.

[53]  J. Goopy,et al.  Cattle selected for lower residual feed intake have reduced daily methane production. , 2007, Journal of animal science.

[54]  Zhiyong Hu,et al.  High‐production dairy cattle exhibit different rumen and fecal bacterial community and rumen metabolite profile than low‐production cattle , 2018, MicrobiologyOpen.

[55]  J. Faith,et al.  Dissecting the in Vivo Metabolic Potential of Two Human Gut Acetogens , 2010, The Journal of Biological Chemistry.

[56]  P. B. Pope,et al.  Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range , 2015, Scientific Reports.

[57]  Eran Elinav,et al.  Use of Metatranscriptomics in Microbiome Research , 2016, Bioinformatics and biology insights.

[58]  Andreas Wilke,et al.  phylogenetic and functional analysis of metagenomes , 2022 .

[59]  S. Moore,et al.  Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. , 2006, Journal of animal science.

[60]  C. Joshi,et al.  Characterization of the rumen microbiome of Indian Kankrej cattle (Bos indicus) adapted to different forage diet , 2014, Applied Microbiology and Biotechnology.

[61]  O. Alzahal,et al.  Factors influencing ruminal bacterial community diversity and composition and microbial fibrolytic enzyme abundance in lactating dairy cows with a focus on the role of active dry yeast. , 2017, Journal of dairy science.

[62]  G. Suen,et al.  Ruminal Bacterial Community Composition in Dairy Cows Is Dynamic over the Course of Two Lactations and Correlates with Feed Efficiency , 2015, Applied and Environmental Microbiology.

[63]  V. H. Oddy,et al.  Low-methane yield sheep have smaller rumens and shorter rumen retention time , 2013, British Journal of Nutrition.

[64]  L. Guan,et al.  Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle , 2017, Applied and Environmental Microbiology.

[65]  Davide Heller,et al.  eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences , 2015, Nucleic Acids Res..

[66]  Andrew W. Brooks,et al.  Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History , 2016, PLoS biology.

[67]  P. Thornton Livestock production: recent trends, future prospects , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[68]  E. Birney,et al.  Velvet: algorithms for de novo short read assembly using de Bruijn graphs. , 2008, Genome research.

[69]  H. Seedorf,et al.  Distributed under Creative Commons Cc-by 4.0 Rim-db: a Taxonomic Framework for Community Structure Analysis of Methanogenic Archaea from the Rumen and Other Intestinal Environments , 2022 .

[70]  Gerhard G. Thallinger,et al.  Wx Scout Fashion Sneaker Splash Navy Women's Keds qAS4tR1wn4 for bawln.com , 2009 .

[71]  P. Janssen,et al.  Rumen microbial (meta)genomics and its application to ruminant production. , 2013, Animal : an international journal of animal bioscience.

[72]  P. Wincker,et al.  Comparison of library preparation methods reveals their impact on interpretation of metatranscriptomic data , 2014, BMC Genomics.

[73]  F. Dewhirst,et al.  Host-associated bacterial taxa from Chlorobi, Chloroflexi, GN02, Synergistetes, SR1, TM7, and WPS-2 Phyla/candidate divisions , 2014, Journal of oral microbiology.

[74]  S. Tringe,et al.  Validation of two ribosomal RNA removal methods for microbial metatranscriptomics , 2010, Nature Methods.

[75]  Bernard Henrissat,et al.  The Impact of a Consortium of Fermented Milk Strains on the Gut Microbiome of Gnotobiotic Mice and Monozygotic Twins , 2011, Science Translational Medicine.

[76]  Dongwan D. Kang,et al.  Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome , 2014, Genome research.

[77]  Katherine H. Huang,et al.  Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes , 2012, Genome Biology.

[78]  J. Firkins Maximizing microbial protein synthesis in the rumen. , 1996, The Journal of nutrition.

[79]  Zhongtang Yu,et al.  Status of the phylogenetic diversity census of ruminal microbiomes. , 2011, FEMS microbiology ecology.

[80]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[81]  Katherine H. Huang,et al.  Structure, Function and Diversity of the Healthy Human Microbiome , 2012, Nature.

[82]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[83]  L. Guan,et al.  Symposium review: Mining metagenomic and metatranscriptomic data for clues about microbial metabolic functions in ruminants. , 2017, Journal of dairy science.