Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance

[1]  J. Zempleni,et al.  Biotin , 2021, Reactions Weekly.

[2]  A. Abbassian,et al.  Food Outlook – Biannual Report on Global Food Markets , 2021 .

[3]  E. Halperin,et al.  A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions , 2019, Science Advances.

[4]  G. Plastow,et al.  Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle , 2019, Microbiome.

[5]  L. Guan,et al.  Assessment of rumen bacteria in dairy cows with varied milk protein yield. , 2019, Journal of dairy science.

[6]  Qianqian Zhou,et al.  The gut microbiota is largely independent of host genetics in regulating fat deposition in chickens , 2019, The ISME Journal.

[7]  D. Veira,et al.  Rumen-protected B vitamin complex supplementation during the transition period and early lactation alters endometrium mRNA expression on day 14 of gestation in lactating dairy cows. , 2019, Journal of dairy science.

[8]  Le Luo Guan,et al.  Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle , 2019, Microbiome.

[9]  Henrik Bjørn Nielsen,et al.  Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows , 2018, PLoS genetics.

[10]  Jianxin Liu,et al.  Assessment of Rumen Microbiota from a Large Dairy Cattle Cohort Reveals the Pan and Core Bacteriomes Contributing to Varied Phenotypes , 2018, Applied and Environmental Microbiology.

[11]  Diming Wang,et al.  Serum metabolome profiling revealed potential biomarkers for milk protein yield in dairy cows. , 2018, Journal of proteomics.

[12]  David S. Wishart,et al.  MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis , 2018, Nucleic Acids Res..

[13]  A. Kurilshikov,et al.  Environment dominates over host genetics in shaping human gut microbiota , 2018, Nature.

[14]  D. Bickhart,et al.  Symposium review: Host-rumen microbe interactions may be leveraged to improve the productivity of dairy cows. , 2017, Journal of dairy science.

[15]  E. Halperin,et al.  Heritable Bovine Rumen Bacteria Are Phylogenetically Related and Correlated with the Cow’s Capacity To Harvest Energy from Its Feed , 2017, mBio.

[16]  R. Wellmann,et al.  Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs , 2017, Genetics.

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

[18]  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.

[19]  M. Barile,et al.  Riboflavin transport and metabolism in humans , 2016, Journal of Inherited Metabolic Disease.

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

[21]  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.

[22]  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.

[23]  Seoae Cho,et al.  Transcriptome profiling of differentially expressed genes in floral buds and flowers of male sterile and fertile lines in watermelon , 2015, BMC Genomics.

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

[25]  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.

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

[27]  Diming Wang,et al.  Metabolomics of four biofluids from dairy cows: potential biomarkers for milk production and quality. , 2015, Journal of proteome research.

[28]  E. Wolfson,et al.  Bacterial Flagella: Twist and Stick, or Dodge across the Kingdoms , 2015, PLoS pathogens.

[29]  Kunihiko Sadakane,et al.  MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph , 2014, Bioinform..

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

[31]  M. Allen,et al.  Carbohydrate nutrition: managing energy intake and partitioning through lactation. , 2014, The Veterinary clinics of North America. Food animal practice.

[32]  S. König,et al.  Short communication: Genetic relationships between claw disorders, protein yield, and somatic cell score by days in milk. , 2014, Journal of dairy science.

[33]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[34]  J. Madsen,et al.  Effects of management practices on yield and quality of milk from smallholder dairy units in urban and peri-urban Morogoro, Tanzania , 2014, Tropical Animal Health and Production.

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

[36]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[37]  S. LeBlanc,et al.  Associations between herd-level feeding management practices, feed sorting, and milk production in freestall dairy farms. , 2013, Journal of dairy science.

[38]  Zhengwei Zhu,et al.  CD-HIT: accelerated for clustering the next-generation sequencing data , 2012, Bioinform..

[39]  B. Chen,et al.  Effect of biotin on milk performance of dairy cattle: a meta-analysis. , 2011, Journal of dairy science.

[40]  C. Huttenhower,et al.  Metagenomic biomarker discovery and explanation , 2011, Genome Biology.

[41]  Joshua D. Knowles,et al.  Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry , 2011, Nature Protocols.

[42]  Robert C. Edgar,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2001 .

[43]  J. Kearney,et al.  Food consumption trends and drivers , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[44]  David S. Wishart,et al.  Bioinformatics Applications Note Systems Biology Metpa: a Web-based Metabolomics Tool for Pathway Analysis and Visualization , 2022 .

[45]  David S. Wishart,et al.  MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data , 2010, Nucleic Acids Res..

[46]  O. Fiehn,et al.  FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. , 2009, Analytical chemistry.

[47]  Siu-Ming Yiu,et al.  SOAP2: an improved ultrafast tool for short read alignment , 2009, Bioinform..

[48]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[49]  Cristina Legido-Quigley,et al.  Advances in separation science applied to metabonomics , 2008, Electrophoresis.

[50]  E. Bayer,et al.  Plant Cell Wall Breakdown by Anaerobic Microorganisms from the Mammalian Digestive Tract , 2008, Annals of the New York Academy of Sciences.

[51]  P. Weimer,et al.  Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR , 2007, Applied Microbiology and Biotechnology.

[52]  Matthew A. Zapala,et al.  Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables , 2006, Proceedings of the National Academy of Sciences.

[53]  T. Tatusova,et al.  NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins , 2006, Nucleic Acids Research.

[54]  T. Takagi,et al.  MetaGene: prokaryotic gene finding from environmental genome shotgun sequences , 2006, Nucleic acids research.

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

[56]  J. Russell,et al.  Factors That Alter Rumen Microbial Ecology , 2001, Science.

[57]  Brian H. McArdle,et al.  FITTING MULTIVARIATE MODELS TO COMMUNITY DATA: A COMMENT ON DISTANCE‐BASED REDUNDANCY ANALYSIS , 2001 .

[58]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[59]  R. Thauer.  Biochemistry of methanogenesis: a tribute to Marjory Stephenson. 1998 Marjory Stephenson Prize Lecture. , 1998, Microbiology.

[60]  M. Stephenson,et al.  Biochemistry of methanogenesis : a tribute to , 1998 .

[61]  Won O Song,et al.  Comparative nutrition of pantothenic acid , 1996 .

[62]  R. Macnab,et al.  The gradient-sensing mechanism in bacterial chemotaxis. , 1972, Proceedings of the National Academy of Sciences of the United States of America.

[63]  I. Robinson,et al.  Importance of the isovalerate carboxylation pathway of leucine biosynthesis in the rumen. , 1966, Applied microbiology.

[64]  B. Larson.  BIOSYNTHESIS OF THE MILK PROTEINS. , 1965, Journal of dairy science.

[65]  M. P. Bryant,et al.  BACTEROIDES RUMINICOLA N. SP. AND SUCCINIMONAS AMYLOLYTICA THE NEW GENUS AND SPECIES , 1958, Journal of bacteriology.