A 1000-cow study across four European countries was undertaken to understand to what extent ruminant microbiomes can be controlled by the host animal and to identify characteristics of the host rumen microbiome axis that determine productivity and methane emissions. A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified. A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency). These phenotypes can be predicted from the core microbiome using machine learning algorithms. The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.

Department of Animal Science Food and Nutrition DIANA Università Cattolica del Sacro Cuore 29122 Piacenza Italy

Department of Life Sciences and the National Institute for Biotechnology in the Negev Ben Gurion University of the Negev Be'er Sheva Israel

Departments of Computer Science Computational Medicine Human Genetics and Anesthesiology University of California Los Angeles Los Angeles CA 90095 USA

Institute of Animal Physiology and Genetics CAS v v i Vídeňská 1083 Prague 14220 Czech Republic

Institute of Microbiology Università Cattolica del Sacro Cuore 29122 Piacenza Italy

Laboratoire d'Ecologie Alpine Domaine Universitaire de St Martin d'Hères CNRS 38041 Grenoble France

Parco Tecnologico Padano Via Einstein 26900 Lodi Italy

Production Systems Natural Resources Institute Finland 31600 Jokioinen Finland

Swedish University of Agricultural Sciences Department of Agriculture for Northern Sweden S 90 183 Umeå Sweden

The Rowett Institute University of Aberdeen Ashgrove Road West Aberdeen AB25 2ZD UK

University of Nottingham School of Biosciences Sutton Bonington Campus Loughborough LE12 5RD UK

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H. Steinfeld, P. Gerber, T. Wassenaar, V. Caste, M. Rosales, C. de Haan, Livestock’s Long Shadow (FAO, 2006).

Myer P. R., Smith T. P., Wells J. E., Kuehn L. A., Freetly H. C. I., Rumen microbiome from steers differing in feed efficiency. PLOS ONE 10, e0129174 (2015). PubMed PMC

Shabat S. K., Sasson G., Doron-Faigenboim A., Durman T., Yaacoby S., Berg Miller M. E., White B. A., Shterzer N., Mizrahi I., Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10, 2958–2972 (2016). PubMed PMC

A. G. Williams, G. S. Coleman, The Rumen Microbial Ecosystem (Chapman & Hall, 1997).

I. Mizrahi, The Prokaryotes (Springer Berlin Heidelberg, 2013).

Henderson G., Cox F., Ganesh S., Jonker A., Young W.; Global Census Collaborators, Janssen P. H., Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5, 14567 (2015). PubMed PMC

Newbold C. J., de la Fuente G., Belanche A., Ramos-Morales E., McEwan N. R., The role of ciliate protozoa in the rumen. Front. Microbiol. 6, 1313 (2015). PubMed PMC

Gruninger R. J., Puniya A. K., Callaghan T. M., Edwards J. E., Youssef N., Dagar S. S., Fliegerová K., Griffith G. W., Forster R., Tsang A., McAllister T., Elshahed M. S., Anaerobic fungi (phylum Neocallimastigomycota): Advances in understanding their taxonomy, life cycle, ecology, role and biotechnological potential. FEMS Microbiol. Ecol. 90, 1–17 (2014). PubMed

Janssen P. H., Kirs M., Structure of the archaeal community of the rumen. Appl. Environ. Microbiol. 74, 3619–3625 (2008). PubMed PMC

Morgavi D. P., Rathahao-Paris E., Popova M., Boccard J., Nielsen K. F., Boudra H., Rumen microbial communities influence metabolic phenotypes in lambs. Front. Microbiol. 6, 1060 (2015). PubMed PMC

Hayes B. J., Donoghue K. A., Reich C. M., Mason B. A., Bird-Gardiner T., Herd R. M., Arthur P. F., Genomic heritabilities and genomic estimated breeding values for methane traits in Angus cattle. J. Anim. Sci. 94, 902–908 (2016). PubMed

Roehe R., Dewhurst R. J., Duthie C. A., Rooke J. A., McKain N., Ross D. W., Hyslop J. J., Waterhouse A., Freeman T. C., Watson M., Wallace R. J., 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. PLOS Genet. 12, e1005846 (2016). PubMed PMC

Rooke J. A., Wallace R. J., Duthie C. A., McKain N., de Souza S. M., Hyslop J. J., Ross D. W., Waterhouse T., Roehe R., Hydrogen and methane emissions from beef cattle and their rumen microbial community vary with diet, time after feeding and genotype. Br. J. Nutr. 112, 398–407 (2014). PubMed

Goodrich J. K., Di Rienzi S. C., Poole A. C., Koren O., Walters W. A., Caporaso J. G., Knight R., Ley R. E., Conducting a microbiome study. Cell 158, 250–262 (2014). PubMed PMC

Sasson G., Kruger Ben-Shabat S., Seroussi E., Doron-Faigenboim A., Shterzer N., Yaacoby S., Berg Miller M. E., White B. A., Halperin E., Mizrahi I., Heritable bovine rumen bacteria are phylogenetically related and correlated with the cow’s capacity to harvest energy from its feed. MBio 8, e00703-17 (2017). PubMed PMC

Martiny A. C., Treseder K., Pusch G., Phylogenetic conservatism of functional traits in microorganisms. ISME J. 7, 830–838 (2013). PubMed PMC

Edwards J. E., McEwan N. R., Travis A. J., Wallace R. J., 16S rDNA library-based analysis of ruminal bacterial diversity. Antonie Van Leeuwenhoek 86, 263–281 (2004). PubMed

Wallace R. J., Rooke J. A., McKain N., Duthie C. A., Hyslop J. J., Ross D. W., Waterhouse A., Watson M., Roehe R., The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics 16, 839 (2015). PubMed PMC

Marquardt D. W., Snee R. D., Ridge regression in practice. Am. Stat. 29, 3–20 (1975).

Friedman J., Hastie T., Tibshirani R., Regularization paths for generalized linear models via coordinate descent. J. Statist. Software 33, 1–22 (2010). PubMed PMC

Liaw A., Wiener M., Classification and regression by randomForest. R News 2, 18–22 (2002).

Breiman L., Random forests. Mach. Learn. 45, 5–32 (2001).

Yáñez-Ruiz D. R., Macías B., Pinloche E., Newbold C. J., The persistence of bacterial and methanogenic archaeal communities residing in the rumen of young lambs. FEMS Microbiol. Ecol. 72, 272–278 (2010). PubMed

Foditsch C., Pereira R. V., Ganda E. K., Gomez M. S., Marques E. C., Santin T., Bicalho R. C., Oral administration of Faecalibacterium prausnitzii decreased the incidence of severe diarrhea and related mortality rate and increased weight gain in preweaned dairy heifers. PLOS ONE 10, e0145485 (2015). PubMed PMC

Yáñez-Ruiz D. R., Abecia L., Newbold C. J., Manipulating rumen microbiome and fermentation through interventions during early life: A review. Front. Microbiol. 6, 1133 (2015). PubMed PMC

Garnsworthy P. C., Craigon J., Hernandez-Medrano J., Saunders N., On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 95, 3166–3180 (2012). PubMed

Unal Y., Garnsworthy P. C., Estimation of intake and digestibility of forage-based diets in group-fed dairy cows using alkanes as markers. J. Agric. Sci. 133, 419–425 (1999).

Bani P., Piccioli Cappelli F., Minuti A., Ficuciello V., Lopreiato V., Garnsworthy P. C., Trevisi E., Estimation of dry matter intake by n-alkanes in dairy cows fed TMR: Effect of dosing technique and faecal collection time. Anim. Prod. Sci. 54, 1747–1751 (2014).

Playne M. J., Determination of ethanol, volatile fatty acids, lactic and succinic acids in fermentation liquids by gas chromatography. J. Sci. Food Agric. 36, 638–644 (1985).

Yu Z., Morrison M., Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 36, 808–812 (2004). PubMed

Riaz T., Shehzad W., Viari A., Pompanon F., Taberlet P., Coissac E., ecoPrimers: Inference of new DNA barcode markers from whole genome sequence analysis. Nucleic Acids Res. 39, e145 (2011). PubMed PMC

Boyer F., Mercier C., Bonin A., Le B. Y., Taberlet P., Coissac E., obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016). PubMed

Garnsworthy P. C., Craigon J., Hernandez-Medrano J., Saunders N., Variation among individual dairy cows in methane measurements made on farm during milking. J. Dairy Sci. 95, 3181–3189 (2012). PubMed

Huhtanen P., Cabezas-Garcia E. H., Utsumi S., Zimmerman S., Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98, 3394–3409 (2015). PubMed

Negussie E., Lehtinen J., Mäntysaari P., Bayat A. R., Liinamo A. E., Mantysaari E. A., Lidauer M. H., Non-invasive individual methane measurement in dairy cows. Animal 11, 890–899 (2017). PubMed

Skinner J. G., Brown R. A., Roberts L., Bovine haptoglobin response in clinically defined field conditions. Vet. Rec. 128, 147–149 (1991). PubMed

Maeda H., Fujimoto C., Haruki Y., Maeda T., Kokeguchi S., Petelin M., Arai H., Tanimoto I., Nishimura F., Takashiba S., Quantitative real-time PCR using TaqMan and SYBR Green for Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, Prevotella intermedia, tetQ gene and total bacteria. FEMS Immunol. Med. Microbiol. 39, 81–86 (2003). PubMed

Fuller Z., Louis P., Mihajlovski A., Rungapamestry V., Ratcliffe B., Duncan A. J., Influence of cabbage processing methods and prebiotic manipulation of colonic microflora on glucosinolate breakdown in man. Br. J. Nutr. 98, 364–372 (2007). PubMed

Ramirez-Farias C., Slezak K., Fuller Z., Duncan A., Holtrop G., Louis P., Effect of inulin on the human gut microbiota: stimulation of Bifidobacterium adolescentis and Faecalibacterium prausnitzii. Br. J. Nutr. 101, 541–550 (2009). PubMed

Hook S. E., Northwood K. S., Wright A.-D. G., McBride B. W., Long-term monensin supplementation does not significantly affect the quantity or diversity of methanogens in the rumen of the lactating dairy cow. Appl. Environ. Microbiol. 75, 374–380 (2009). PubMed PMC

Sylvester J. T., Karnati S. K. R., Yu Z., Morrison M., Firkins J. L., Development of an assay to quantify rumen ciliate protozoal biomass in cows using real-time PCR. J. Nutr. 134, 3378–3384 (2004). PubMed

Caporaso J. G., Kuczynski J., Stombaugh J., Bittinger K., Bushman F. D., Costello E. K., Fierer N., Peña A. G., Goodrich J. K., Gordon J. I., Huttley G. A., Kelley S. T., Knights D., Koenig J. E., Ley R. E., Lozupone C. A., McDonald D., Muegge B. D., Pirrung M., Reeder J., Sevinsky J. R., Turnbaugh P. J., Walters W. A., Widmann J., Yatsunenko T., Zaneveld J., Knight R., QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010). PubMed PMC

Edgar R. C., Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010). PubMed

Cole J. R., Wang Q., Fish J. A., Chai B., McGarrell D. M., Sun Y., Brown C. T., Porras-Alfaro A., Kuske C. R., Tiedje J. M., Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014). PubMed PMC

DeSantis T. Z., Hugenholtz P., Larsen N., Rojas M., Brodie E. L., Keller K., Huber T., Dalevi D., Hu P., Andersen G. L., Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006). PubMed PMC

Quast C., Pruesse E., Yilmaz P., Gerken J., Schweer T., Yarza P., Peplies J., Glockner F. O., The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012). PubMed PMC

Koetschan C., Kittelmann S., Lu J., Al-Halbouni D., Jarvis G. N., Muller T., Wolf M., Janssen P. H., Internal transcribed spacer 1 secondary structure analysis reveals a common core throughout the anaerobic fungi (Neocallimastigomycota). PloS One 9, e91928 (2014). PubMed PMC

R Core Team R: A Language and Environment for Statistical Comput. Secur. (2015).

Benjamini Y., Hochberg Y., Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B 57, 289–300 (1995).

Zaykin D. V., Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis. J. Evol. Biol. 24, 1836–1841 (2011). PubMed PMC

Rosenthal R., Combining results of independent studies. Psychol. Bull. 85, 185–193 (1978).

M. Dewey, Metap: meta-analysis of significance values. R package version 1.0 (2018).

Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M. A. R., Bender D., Maller J., Sklar P., de Bakker P. I. W., Daly M. J., Sham P. C., PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007). PubMed PMC

X. Zheng, snpgdsGRM: Genetic Relationship Matrix (GRM) for SNP genotype data. In “SNPRelate: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data” Version 1.14.0

C. T. Butts, yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.1 (2018); https://CRAN.R-project.org/package=yacca

Yang J., Lee S. H., Goddard M. E., Visscher P. M., GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011). PubMed PMC

Yang J., Benyamin B., McEvoy B. P., Gordon S., Henders A. K., Nyholt D. R., Madden P. A., Heath A. C., Martin N. G., Montgomery G. W., Goddard M. E., Visscher P. M., Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010). PubMed PMC

Schweiger R., Fisher E., Rahmani E., Shenhav L., Rosset S., Halperin E., Using stochastic approximation techniques to efficiently construct confidence intervals for heritability. J. Comput. Biol. 25, 794–808 (2018). PubMed

Kang H. M., Sul J. H, Service S. K, Zaitlen N. A, Kong S.-y, Freimer N. B, Sabatti C, Eskin E, Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010). PubMed PMC

Lipka A. E., Tian F., Wang Q., Peiffer J., Li M., Bradbury P. J., Gore M. A., Buckler E. S., Zhang Z., . GAPIT: genome association and prediction integrated tool. Bioinformatics 28, 2397–2399 (2012). PubMed

Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt. (2018). caret: Classification and Regression Training. R package version 6.0-80. https://CRAN.R-project.org/package=caret

Kurtz Z. D., Muller C. L., Miraldi E. R., Littman D. R., Blaser M. J., Bonneau R. A., Sparse and compositionally robust inference of microbial ecological networks. PLOS Comput. Biol. 11, e1004226 (2015). PubMed PMC

Katoh K., Misawa K., Kuma K.-i., Miyata T., MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002). PubMed PMC

Katoh K., Standley D. M., MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013). PubMed PMC

Price M. N., Dehal P. S., Arkin A. P., FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009). PubMed PMC

Price M. N., Dehal P. S., Arkin A. P., FastTree 2–approximately maximum-likelihood trees for large alignments. PLOS ONE 5, e9490 (2010). PubMed PMC

Wallace R. J., McPherson C. A., Factors affecting the rate of breakdown of bacterial protein in rumen fluid. Br. J. Nutr. 58, 313–323 (1987). PubMed

Leng R. A., Nolan J. V., Nitrogen metabolism in the rumen. J. Dairy Sci. 67, 1072–1089 (1984). PubMed

Newbold C. J., Hillman K., The effect of ciliate protozoa on the turnover of bacterial and fungal protien in the rumen of sheep. Lett. Appl. Microbiol. 11, 100–102 (1990).

Tapio I., Snelling T. J., Strozzi F., Wallace R. J., The ruminal microbiome associated with methane emissions from ruminant livestock. J. Anim. Sci. Biotechnol. 8, 7 (2017). PubMed PMC

Host Species Affects Bacterial Evenness, but Not Diversity: Comparison of Fecal Bacteria of Cows and Goats Offered the Same Diet

Anaerobic Fungi: Past, Present, and Future