1932

Abstract

Ruminant production systems face significant challenges currently, driven by heightened awareness of their negative environmental impact and the rapidly rising global population. Recent findings have underscored how the composition and function of the rumen microbiome are associated with economically valuable traits, including feed efficiency and methane emission. Although omics-based technological advances in the last decade have revolutionized our understanding of host-associated microbial communities, there remains incongruence over the correct approach for analysis of large omic data sets. A global approach that examines host/microbiome interactions in both the rumen and the lower digestive tract is required to harness the full potential of the gastrointestinal microbiome for sustainable ruminant production. This review highlights how the ruminant animal production community may identify and exploit the causal relationships between the gut microbiome and host traits of interest for a practical application of omic data to animal health and production.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-animal-021419-083952
2020-02-15
2024-04-26
Loading full text...

Full text loading...

/deliver/fulltext/animal/8/1/annurev-animal-021419-083952.html?itemId=/content/journals/10.1146/annurev-animal-021419-083952&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Cammack KM, Austin KJ, Lamberson WR, Conant CG, Cunningham HC 2018. Tiny but mighty: the role of the rumen microbes in livestock production. J. Anim. Sci. 96:2752–70
    [Google Scholar]
  2. 2. 
    Malmuthuge N, Guan LL. 2017. Understanding host-microbial interactions in rumen: searching the best opportunity for microbiota manipulation. J. Anim. Sci. Biotechnol. 8:8
    [Google Scholar]
  3. 3. 
    Mackie RI. 2002. Mutualistic fermentative digestion in the gastrointestinal tract: diversity and evolution. Integr. Comp. Biol. 42:2319–26
    [Google Scholar]
  4. 4. 
    Gerber PJ, Hristov AN, Henderson B, Makkar H, Oh J et al. 2013. Technical options for the mitigation of direct methane and nitrous oxide emissions from livestock: a review. Animal 7:Suppl. 2220–34
    [Google Scholar]
  5. 5. 
    Lynch J, Pierrehumbert R. 2019. Climate impacts of cultured meat and beef cattle. Front. Sustain. Food Syst. 3:5 https://doi.org/10.3389/fsufs.2019.00005
    [Crossref] [Google Scholar]
  6. 6. 
    Intergov. Panel Clim. Change 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change RK Pachauri & LA Meyer Geneva: Intergov. Panel Clim. Change151
  7. 7. 
    Johnson KA, Johnson DE. 1995. Methane emissions from cattle. J. Anim. Sci. 73:82483–92
    [Google Scholar]
  8. 8. 
    Hungate RE. 1969. A roll tube method for cultivation of strict anaerobes. Methods Microbiol 3:B117–32
    [Google Scholar]
  9. 9. 
    Henderson G, Cox F, Ganesh S, Jonker A, Young W et al. 2015. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci. Rep. 5:14567
    [Google Scholar]
  10. 10. 
    Zhou M, Peng YJ, Chen Y, Klinger CM, Oba M et al. 2018. Assessment of microbiome changes after rumen transfaunation: implications on improving feed efficiency in beef cattle. Microbiome 6:62
    [Google Scholar]
  11. 11. 
    Deusch S, Camarinha-Silva A, Conrad J, Beifuss U, Rodehutscord M, Seifert J 2017. A structural and functional elucidation of the rumen microbiome influenced by various diets and microenvironments. Front. Microbiol. 8:1605
    [Google Scholar]
  12. 12. 
    Myer PR, Wells JE, Smith TPL, Kuehn LA, Freetly HC 2015. Microbial community profiles of the colon from steers differing in feed efficiency. SpringerPlus 4:454
    [Google Scholar]
  13. 13. 
    O'Hara E, Kelly A, McCabe MS, Kenny DA, Guan LL, Waters SM 2018. Effect of a butyrate-fortified milk replacer on gastrointestinal microbiota and products of fermentation in artificially reared dairy calves at weaning. Sci. Rep. 8:114901
    [Google Scholar]
  14. 14. 
    Li F, Neves ALA, Ghoshal B, Guan LL 2018. Symposium review: mining metagenomic and metatranscriptomic data for clues about microbial metabolic functions in ruminants. J. Dairy Sci. 101:65605–18
    [Google Scholar]
  15. 15. 
    Firkins JL, Yu Z. 2015. Ruminant nutrition symposium: how to use data on the rumen microbiome to improve our understanding of ruminant nutrition. J. Anim. Sci. 93:41450–70
    [Google Scholar]
  16. 16. 
    Bergman EN. 1990. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70:2567–90
    [Google Scholar]
  17. 17. 
    Bach A, Calsamiglia S, Stern MD 2005. Nitrogen metabolism in the rumen. J. Dairy Sci. 88:E9–E21
    [Google Scholar]
  18. 18. 
    Millen DD, De Beni Arrigoni M, Pacheco RDL, eds. 2016. Rumenology Cham, Switz: Springer Int.
  19. 19. 
    Wilkinson TJ, Huws SA, Edwards JE, Kingston-Smith AH, Siu-Ting K et al. 2018. CowPI: a rumen microbiome focused version of the PICRUSt functional inference software. Front. Microbiol. 9:1095
    [Google Scholar]
  20. 20. 
    Huws SA, Edwards JE, Creevey CJ, Rees Stevens P, Lin W et al. 2016. Temporal dynamics of the metabolically active rumen bacteria colonizing fresh perennial ryegrass. FEMS Microbiol. Ecol. 92:1fiv137
    [Google Scholar]
  21. 21. 
    Li F, Li C, Chen Y, Liu J, Zhang C et al. 2019. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome 7:92
    [Google Scholar]
  22. 22. 
    Jami E, Israel A, Kotser A, Mizrahi I 2013. Exploring the bovine rumen bacterial community from birth to adulthood. ISME J 7:61069–79
    [Google Scholar]
  23. 23. 
    Difford GF, Plitchta DR, Løvendahl P, Lassen J, Noel SJ et al. 2018. Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PLOS Genet 14:10e1007580
    [Google Scholar]
  24. 24. 
    Yáñez-Ruiz DR, Abecia L, Newbold CJ 2015. Manipulating rumen microbiome and fermentation through interventions during early life: a review. Front. Microbiol. 6:1133
    [Google Scholar]
  25. 25. 
    Huws SA, Creevey CJ, Oyama LB, Mizrahi I, Denman SE et al. 2018. Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future. Front. Microbiol. 9:2161
    [Google Scholar]
  26. 26. 
    Li F, Guan LL. 2017. Metatranscriptomic profiling reveals linkages between the active rumen microbiome and feed efficiency in beef cattle. Appl. Environ. Microbiol. 83:9e00061–17
    [Google Scholar]
  27. 27. 
    Kittelmann S, Pinares-Patiño CS, Seedorf H, Kirk MR, Ganesh S et al. 2014. Two different bacterial community types are linked with the low-methane emission trait in sheep. PLOS ONE 9:7e103171
    [Google Scholar]
  28. 28. 
    Silberberg M, Chaucheyras-Durand F, Commun L, Mialon MM, Monteils V et al. 2013. Repeated acidosis challenges and live yeast supplementation shape rumen microbiota and fermentations and modulate inflammatory status in sheep. Animal 7:121910–20
    [Google Scholar]
  29. 29. 
    Jami E, White BA, Mizrahi I 2014. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLOS ONE 9:1e85423
    [Google Scholar]
  30. 30. 
    Sasson G, Ben-Shabat SK, Seroussi E, Doron-Faigenboim A, Shterzer N et al. 2017. Heritable bovine rumen bacteria are phylogenetically related and correlated with the cow's capacity to harvest energy from its feed. mBio 8:4e00703–17
    [Google Scholar]
  31. 31. 
    Abecia L, Jiménez E, Martínez-Fernandez G, Martín-García AI, Ramos-Morales E et al. 2017. Natural and artificial feeding management before weaning promote different rumen microbial colonization but not differences in gene expression levels at the rumen epithelium of newborn goats. PLOS ONE 12:8e0182235
    [Google Scholar]
  32. 32. 
    Hristov AN, Oh J, Giallongo F, Frederick TW, Harper MT et al. 2015. An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production. PNAS 112:3410663–68
    [Google Scholar]
  33. 33. 
    Martínez-Fernández G, Abecia L, Arco A, Cantalapiedra-Hijar G, Martin-García AI et al. 2014. Effects of ethyl-3-nitrooxy propionate and 3-nitrooxypropanol on ruminal fermentation, microbial abundance, and methane emissions in sheep. J. Dairy Sci. 97:63790–99
    [Google Scholar]
  34. 34. 
    Romero-Perez A, Okine EK, McGinn SM, Guan LL, Oba M et al. 2015. Sustained reduction in methane production from long-term addition of 3-nitrooxypropanol to a beef cattle diet. J. Anim. Sci. 93:41780–91
    [Google Scholar]
  35. 35. 
    Berry DP, Crowley JJ. 2013. Cell biology symposium: genetics of feed efficiency in dairy and beef cattle. J. Anim. Sci. 91:41594–613
    [Google Scholar]
  36. 36. 
    Bach A. 2012. Ruminant nutrition symposium: optimizing performance of the offspring: nourishing and managing the dam and postnatal calf for optimal lactation, reproduction, and immunity. J. Anim. Sci. 90:61835–45
    [Google Scholar]
  37. 37. 
    Finneran E, Crosson P, O'Kiely P, Shalloo L, Forristal D, Wallace M 2011. Stochastic simulation of the cost of home-produced feeds for ruminant livestock systems. J. Agric. Sci. 150:1123–39
    [Google Scholar]
  38. 38. 
    Sherman EL, Nikrumah JD, Murdoch BM, Moore SS 2008. Identification of polymorphisms influencing feed intake and efficiency in beef cattle. Anim. Genet. 39:3225–31
    [Google Scholar]
  39. 39. 
    Sobrinho TL, Branco RH, Bonilha SFM, de Castilhos AM, de Figueiredo LA et al. 2011. Residual feed intake and relationships with performance of Nellore cattle selected for post weaning weight. Rev. Bras. Zootec. 40:929–37
    [Google Scholar]
  40. 40. 
    Koch RM, Swiger LA, Chambers D, Gregory KE 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:2486–94
    [Google Scholar]
  41. 41. 
    Richardson EC, Herd RM. 2004. Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection. Aust. J. Exp. Agric. 44:5431–40
    [Google Scholar]
  42. 42. 
    Guan LL, Nikrumah JD, Basarab JA, Moore SS 2008. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle's feed efficiency. FEMS Microbiol. Lett. 288:185–91
    [Google Scholar]
  43. 43. 
    Shabat SK, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S et al. 2016. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J 10:2958–72
    [Google Scholar]
  44. 44. 
    Roehe R, Dewhurst RJ, Duthie C-A, Rooke JA, McKain N et al. 2016. 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 12:2e1005846
    [Google Scholar]
  45. 45. 
    Hernandez-Sanabria E, Goonewardene LA, Wang Z, Durunna ON, Moore SS, Guan LL 2012. Impact of feed efficiency and diet on adaptive variations in the bacterial community in the rumen fluid of cattle. Appl. Environ. Microbiol. 78:41203–14
    [Google Scholar]
  46. 46. 
    Myer PR, Smith TPL, Wells JE, Kuehn LA, Freetly HC 2015. Rumen microbiome from steers differing in feed efficiency. PLOS ONE 10:6e0129174
    [Google Scholar]
  47. 47. 
    Carberry CA, Kenny DA, Kelly AK, Waters SM 2014. Quantitative analysis of ruminal methanogenic microbial populations in beef cattle divergent in phenotypic residual feed intake (RFI) offered contrasting diets. J. Anim. Sci. Biotechnol. 5:141
    [Google Scholar]
  48. 48. 
    Carberry CA, Waters SM, Kenny DA, Creevey CJ 2014. Rumen methanogenic genotypes differ in abundance according to host residual feed intake phenotype and diet type. Appl. Environ. Microbiol. 80:2586–94
    [Google Scholar]
  49. 49. 
    Ellison MJ, Conant GC, Lamberson WR, Cockrum RR, Austin KJ et al. 2017. Diet and feed efficiency status affect rumen microbial profiles of sheep. Small Rumin. Res. 156:12–19
    [Google Scholar]
  50. 50. 
    Durunna ON, Mujibi FD, Goonewardene L, Okine EK, Basarab JA et al. 2011. Feed efficiency differences and reranking in beef steers fed grower and finisher diets. J. Anim. Sci. 89:1158–67
    [Google Scholar]
  51. 51. 
    Carberry CA, Kenny DA, Han S, McCabe MS, Waters SM 2012. Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial community of beef cattle. Appl. Environ. Microbiol. 78:144949–58
    [Google Scholar]
  52. 52. 
    Basarab JA, Beauchemin KA, Baron VS, Ominski KH, Guan LL et al. 2013. Reducing GHG emissions through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production. Animal 7:s2303–15
    [Google Scholar]
  53. 53. 
    Krause DO, Nagaraja TG, Wright AD, Callaway TR 2013. Board-invited review: rumen microbiology: leading the way in microbial ecology. J. Anim. Sci. 91:1331–41
    [Google Scholar]
  54. 54. 
    Tajima K, Aminov RI, Nagamine T, Matsui H, Nakamura M, Benno Y 2001. Diet-dependent shifts in the bacterial population of the rumen revealed with real-time PCR. Appl. Environ. Microbiol. 67:62766–74
    [Google Scholar]
  55. 55. 
    Hoover WH. 1978. Digestion and absorption in the hindgut of ruminants. J. Anim. Sci. 46:61789–99
    [Google Scholar]
  56. 56. 
    Gressley TF, Hall MB, Armentano LE 2011. Ruminant nutrition symposium: productivity, digestion, and health responses to hindgut acidosis in ruminants. J. Anim. Sci. 89:41120–30
    [Google Scholar]
  57. 57. 
    Castro JJ, Gomez A, White B, Loften JR, Drackley JK 2016. Changes in the intestinal bacterial community, short-chain fatty acid profile, and intestinal development of preweaned Holstein calves. 2. Effects of gastrointestinal site and age. J. Dairy Sci. 99:129703–15
    [Google Scholar]
  58. 58. 
    Malmuthuge N, Griebel PJ, Guan LL 2014. Taxonomic identification of commensal bacteria associated with the mucosa and digesta throughout the gastrointestinal tracts of preweaned calves. Appl. Environ. Microbiol. 80:62021–28
    [Google Scholar]
  59. 59. 
    Mao S, Zhang M, Liu J, Zhu W 2015. Characterising the bacterial microbiota across the gastrointestinal tracts of dairy cattle: membership and potential function. Sci. Rep. 5:16116
    [Google Scholar]
  60. 60. 
    Myer PR, Wells JE, Smith TPL, Kuehn LA, Freetly HC 2016. Microbial community profiles of the jejunum from steers differing in feed efficiency. J. Anim. Sci. 94:1327–38
    [Google Scholar]
  61. 61. 
    Myer PR, Freetly HC, Wells JE, Smith TPL, Kuehn LA 2017. Analysis of the gut bacterial communities in beef cattle and their association with feed intake, growth, and efficiency. J. Anim. Sci. 95:73215–24
    [Google Scholar]
  62. 62. 
    Hooper LV, Littman DR, Macpherson AJ 2012. Interactions between the microbiota and the immune system. Science 336:60861268–73
    [Google Scholar]
  63. 63. 
    Mulder IE, Schmidt B, Lewis M, Delday M, Stokes CR et al. 2011. Restricting microbial exposure in early life negates the immune benefits associated with gut colonization in environments of high microbial diversity. PLOS ONE 6:12e28279
    [Google Scholar]
  64. 64. 
    Malmuthuge N, Li M, Goonewardene LA, Oba M, Guan LL 2013. Effect of calf starter feeding on gut microbial diversity and expression of genes involved in host immune responses and tight junctions in dairy calves during weaning transition. J. Dairy Sci. 96:53189–200
    [Google Scholar]
  65. 65. 
    Liang G, Malmathuge N, Bao H, Stothard P, Griebel PJ, Guan LL 2016. Transcriptome analysis reveals regional and temporal differences in mucosal immune system development in the small intestine of neonatal calves. BMC Genom 17:1602
    [Google Scholar]
  66. 66. 
    Liang G, Malmuthuge N, Guan LL, Griebel P 2015. Model systems to analyze the role of miRNAs and commensal microflora in bovine mucosal immune system development. Mol. Immunol. 66:157–67
    [Google Scholar]
  67. 67. 
    Malmuthuge N, Liang G, Griebel PJ, Guan LL 2019. Taxonomic and functional compositions of the small intestinal microbiome in neonatal calves provide a framework for understanding early life gut health. Appl. Environ. Microbiol. 85:6e02534–18
    [Google Scholar]
  68. 68. 
    Plaizier JC, Li S, Danscher AM, Derakshani H, Andersen PH, Khafipour E 2017. Changes in microbiota in rumen digesta and feces due to a grain-based subacute ruminal acidosis (SARA) challenge. Microb. Ecol. 74:2485–95
    [Google Scholar]
  69. 69. 
    Nagata R, Kim YH, Ohkubo A, Kushibiki S, Ichijo T, Sato S 2018. Effects of repeated subacute ruminal acidosis challenges on the adaptation of the rumen bacterial community in Holstein bulls. J. Dairy Sci. 101:54424–36
    [Google Scholar]
  70. 70. 
    Wolowczuk I, Verwaerde C, Viltart O, Delanoye A, Delacre M et al. 2008. Feeding our immune system: impact on metabolism. Clin. Dev. Immunol. 2008:639803
    [Google Scholar]
  71. 71. 
    Janssen PH, Kirs M. 2008. Structure of the archaeal community of the rumen. Appl. Environ. Microbiol. 74:123619–25
    [Google Scholar]
  72. 72. 
    Hungate RE. 1967. Hydrogen as an intermediate in the rumen fermentation. Arch. Mikrobiol. 59:1–3158–64
    [Google Scholar]
  73. 73. 
    Leahy SC, Kelly WJ, Ronimus RS, Wedlock N, Altermann E, Attwood GT 2013. Genome sequencing of rumen bacteria and archaea and its application to methane mitigation strategies. Animal 7:Suppl. 2235–43
    [Google Scholar]
  74. 74. 
    Hristov AN, Callaway TR, Lee C, Dowd SE 2012. Rumen bacterial, archaeal, and fungal diversity of dairy cows in response to ingestion of lauric or myristic acid. J. Anim. Sci. 90:124449–57
    [Google Scholar]
  75. 75. 
    Tapio I, Snelling TJ, Strozzi F, Wallace RJ 2017. The ruminal microbiome associated with methane emissions from ruminant livestock. J. Anim. Sci. Biotechnol. 8:7
    [Google Scholar]
  76. 76. 
    Zhou M, Chung YH, Beauchemin KA, Holtshausen L, Oba M et al. 2011. Relationship between rumen methanogens and methane production in dairy cows fed diets supplemented with a feed enzyme additive. J. Appl. Microbiol. 111:51148–58
    [Google Scholar]
  77. 77. 
    Danielsson R, Schnürer A, Arthurson V, Bertilsson J 2012. Methanogenic population and CH4 production in Swedish dairy cows fed different levels of forage. Appl. Environ. Microbiol. 78:176172–79
    [Google Scholar]
  78. 78. 
    Shi W, Moon CD, Leahy SC, Kang D, Froula J et al. 2014. Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Res 24:91517–25
    [Google Scholar]
  79. 79. 
    Wolin MJ. 1960. A theoretical rumen fermentation balance. J. Dairy Sci. 43:101452–59
    [Google Scholar]
  80. 80. 
    Plaizier JC, Krauze DO, Gozho GN, McBride BW 2008. Subacute ruminal acidosis in dairy cows: the physiological causes, incidence and consequences. Vet. J. 176:121–31
    [Google Scholar]
  81. 81. 
    Knapp JR, Laur GL, Vadas PA, Weiss WP, Tricarico JM 2014. Invited review: enteric methane in dairy cattle production: quantifying the opportunities and impact of reducing emissions. J. Dairy Sci. 97:63231–61
    [Google Scholar]
  82. 82. 
    Hristov AN, Oh J, Firkins JL, Dijkstra J, Kebreab E et al. 2013. Special topics—mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91:5045–69
    [Google Scholar]
  83. 83. 
    Qin WZ, Li CY, Kim JK, Ju JG, Song MK 2012. Effects of defaunation on fermentation characteristics and methane production by rumen microbes in vitro when incubated with starchy feed sources. Asian-Aust. J. Anim. Sci. 25:101381–88
    [Google Scholar]
  84. 84. 
    Newbold CJ, de la Fuente G, Belanche A, Ramos-Morales E, McEwan NR 2015. The role of ciliate protozoa in the rumen. Front. Microbiol. 6:1313
    [Google Scholar]
  85. 85. 
    Duval S, Kindermann M. 2012. Use of nitrooxy organic molecules in feed for reducing enteric methane emissions in ruminants, and/or to improve ruminant performance Patent No. WO2012085629A1
  86. 86. 
    Lopes JC, de Matos LF, Harper MT, Giallongo F, Oh J et al. 2016. Effect of 3-nitrooxypropanol on methane and hydrogen emissions, methane isotopic signature, and ruminal fermentation in dairy cows. J. Dairy Sci. 99:75335–44
    [Google Scholar]
  87. 87. 
    Immig I. 1996. The rumen and hindgut as source of ruminant methanogenesis. Environ. Monit. Assess. 42:1–257–72
    [Google Scholar]
  88. 88. 
    Weimer PJ. 2015. Redundancy, resilience, and host specificity of the ruminal microbiota: implications for engineering improved ruminal fermentations. Front. Microbiol. 6:296
    [Google Scholar]
  89. 89. 
    Li RW, Connor EE, Li C, Baldwin Vi RL, Sparks ME 2012. Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ. Microbiol. 14:1129–39
    [Google Scholar]
  90. 90. 
    Jiao J, Huang J, Zhou C, Tan Z 2015. Taxonomic identification of the ruminal epithelial bacterial diversity during rumen development in goats. Appl. Environ. Microbiol. 81:3502–9
    [Google Scholar]
  91. 91. 
    Yáñez-Ruiz DR, Macías B, Pinloche E, Newbold CJ 2010. The persistence of bacterial and methanogenic archaeal communities residing in the rumen of young lambs. FEMS Microbiol. Ecol. 72:2272–78
    [Google Scholar]
  92. 92. 
    Veneman JB, Muetzel S, Hart KJ, Faulkner CL, Moorby JM et al. 2015. Does dietary mitigation of enteric methane production affect rumen function and animal productivity in dairy cows?. PLOS ONE 10:10e0140282
    [Google Scholar]
  93. 93. 
    Krause DO, Denman SE, Mackie RI, Morrison M, Rae AL et al. 2003. Opportunities to improve fiber degradation in the rumen: microbiology, ecology, and genomics. FEMS Microbiol. Rev. 27:5663–93
    [Google Scholar]
  94. 93a. 
    O'Hara E, Kenny DA, McGovern E, Byrne CJ, McCabe MS, et al. 2020. Investigating temporal microbial dynamics in the rumen of beef calves raised on two farms during early life. FEMS Microbiol. Ecol 96:2fiz203
    [Google Scholar]
  95. 94. 
    Guzman CE, Bereza-Malcolm LT, De Groef B, Franks AE 2015. Presence of selected methanogens, fibrolytic bacteria, and proteobacteria in the gastrointestinal tract of neonatal dairy calves from birth to 72 hours. PLOS ONE 10:7e0133048
    [Google Scholar]
  96. 95. 
    Zhou M, Chen Y, Griebel PJ, Guan LL 2014. Methanogen prevalence throughout the gastrointestinal tract of pre-weaned dairy calves. Gut Microbes 5:5628–38
    [Google Scholar]
  97. 96. 
    Freetly HC, Lindholm-Perry AK, Hales KE, Brown-Brandl TM et al. 2015. Methane production and methanogen levels in steers that differ in residual gain. J. Anim. Sci. 93:52375–81
    [Google Scholar]
  98. 97. 
    Bartholomew B, Hill MJ. 1984. The pharmacology of dietary nitrate and the origin of urinary nitrate. Food Chem. Toxicol. 22:10789–95
    [Google Scholar]
  99. 98. 
    Stewart V. 1994. Regulation of nitrate and nitrite reductase synthesis in Enterobacteria. Antonie Van Leeuwenhoek 66:1–337–45
    [Google Scholar]
  100. 99. 
    Tiso M, Schechter AN. 2015. Nitrate reduction to nitrite, nitric oxide and ammonia by gut bacteria under physiological conditions. PLOS ONE 10:3e0119712
    [Google Scholar]
  101. 100. 
    Paz HA, Anderson CL, Muller MJ, Kononoff PJ, Fernando SC 2016. Rumen bacterial community composition in Holstein and Jersey cows is different under same dietary condition and is not affected by sampling method. Front. Microbiol. 7:1206
    [Google Scholar]
  102. 101. 
    Granja-Salcedo YT, Ramirez-Uscategui RAR, Machado EG, Messana JD, Kishi LT et al. 2017. Studies on bacterial community composition are affected by the time and storage method of the rumen content. PLOS ONE 12:4e0176701
    [Google Scholar]
  103. 102. 
    Henderson G, Cox F, Kittelmann S, Miri VH, Zethof M et al. 2013. Effect of DNA extraction methods and sampling techniques on the apparent structure of cow and sheep rumen microbial communities. PLOS ONE 8:9e74787
    [Google Scholar]
  104. 103. 
    Villegas-Rivera G, Vargas-Cabrera Y, González-Silva N, Aguilera-García F, Gutiérrez-Vázquez E et al. 2013. Evaluation of DNA extraction methods of rumen microbial populations. World J. Microbiol. Biotechnol. 29:2301–7
    [Google Scholar]
  105. 104. 
    Neves ALA, Li F, Ghoshal B, McAllister T, Guan LL 2017. Enhancing the resolution of rumen microbial classification from metatranscriptomic data using Kraken and Mothur. Front. Microbiol. 8:2445
    [Google Scholar]
  106. 105. 
    Aitchison J. 1982. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B Methodol. 44:2139–77
    [Google Scholar]
  107. 106. 
    Fernandes AD, Reid JNS, Macklaim JM, McMurrough TA, Edgell DR et al. 2014. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2:115
    [Google Scholar]
  108. 107. 
    Pearson K. 1897. Mathematical contributions to the theory of evolution.—On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proc. R. Soc. Lond. 60:359–367489–98
    [Google Scholar]
  109. 108. 
    Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bähler J 2015. Proportionality: a valid alternative to correlation for relative data. PLOS Comput. Biol. 11:31–12
    [Google Scholar]
  110. 109. 
    Gloor GB, Reid G. 2016. Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62:8692–703
    [Google Scholar]
  111. 110. 
    Fernandes AD, Maclaim JM, Linn TG, Reid G, Gloor GB 2013. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLOS ONE 8:7e67019
    [Google Scholar]
  112. 111. 
    Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD 2015. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26:27663
    [Google Scholar]
  113. 112. 
    Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ 2017. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8:2224
    [Google Scholar]
  114. 113. 
    McMurdie PJ, Holmes S. 2014. Waste not, want not: why rarefying microbiome data is inadmissible. PLOS Comput. Biol. 10:4e1003531
    [Google Scholar]
  115. 114. 
    Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K et al. 2017. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:27
    [Google Scholar]
  116. 115. 
    Love MI, Huber W, Anders S 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:12550
    [Google Scholar]
  117. 116. 
    Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biol 11:10R106
    [Google Scholar]
  118. 117. 
    Lê Cao KA, Costello ME, Lakis VA, Bartolo F, Chua XY et al. 2016. MixMC: a multivariate statistical framework to gain insight into microbial communities. PLOS ONE 11:8e0160169
    [Google Scholar]
  119. 118. 
    Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methodol. 57:1289–300
    [Google Scholar]
  120. 119. 
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet C et al. 2018. QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science. PeerJ Preprints 6:e27295v2
    [Google Scholar]
  121. 120. 
    Wood DE, Salzberg SL. 2014. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15:3R46
    [Google Scholar]
  122. 121. 
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M et al. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75:237537–41
    [Google Scholar]
  123. 122. 
    Rohart F, Gautier B, Singh A, Lê Cao K-A 2017. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLOS Comput. Biol. 13:111–19
    [Google Scholar]
  124. 123. 
    Westerhuis JA, van Velzen EJJ, Hoefsloot HCJ, Smilde AK 2010. Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics 6:1119–28
    [Google Scholar]
  125. 124. 
    Liquet B, Lê Cao KA, Hocini H, Thiébaut R 2012. A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinform 13:1325
    [Google Scholar]
  126. 125. 
    Kolios G, Drygiannakis I, Filidou E, Kandilogiannakis L, Arvanitidis K et al. 2018. Gut microbial signatures underline complicated Crohn's disease but vary between cohorts; an in silico approach. Inflamm. Bowel Dis. 25:2217–25
    [Google Scholar]
  127. 126. 
    San-Juan-Vergara H, Zurek E, Ajami NJ, Mogollon C, Peña M et al. 2018. A Lachnospiraceae-dominated bacterial signature in the fecal microbiota of HIV-infected individuals from Colombia, South America. Sci. Rep. 8:14479
    [Google Scholar]
  128. 127. 
    Broderick GA, Reynal SM. 2009. Effect of source of rumen-degraded protein on production and ruminal metabolism in lactating dairy cows. J. Dairy Sci. 92:62822–34
    [Google Scholar]
  129. 128. 
    Goodrich JK, Davenport ER, Clark AG, Ley RE 2017. The relationship between the human genome and microbiome comes into view. Annu. Rev. Genet. 51:1413–33
    [Google Scholar]
  130. 129. 
    Fan J, Han F, Liu H 2014. Challenges of big data analysis. Natl. Sci. Rev. 1:2293–314
    [Google Scholar]
  131. 130. 
    Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D 2015. Methods of integrating data to uncover genotype–phenotype interactions. Nat. Rev. Genet. 16:85–97
    [Google Scholar]
  132. 131. 
    Liu Y, Devescovi V, Chen S, Nardini C 2013. Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties. BMC Syst. Biol. 7:114
    [Google Scholar]
  133. 132. 
    Günther OP, Chen V, Freue GC, Balshaw RF, Tebbutt SJ et al. 2012. A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers. BMC Bioinform 13:1326
    [Google Scholar]
  134. 133. 
    Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G et al. 2012. Host-gut microbiota metabolic interactions. Science 336:60861262–67
    [Google Scholar]
  135. 134. 
    Francino MP. 2014. Early development of the gut microbiota and immune health. Pathogens 3:3769–90
    [Google Scholar]
  136. 135. 
    Fischbach MA. 2018. Microbiome: focus on causation and mechanism. Cell 174:4785–90
    [Google Scholar]
  137. 136. 
    Lebeer S, Spacova I. 2019. Exploring human host–microbiome interactions in health and disease—how to not get lost in translation. Genome Biol 20:156
    [Google Scholar]
  138. 137. 
    Huang S, Chaudhary K, Garmire LX 2017. More is better: recent progress in multi-omics data integration methods. Front. Genet. 8:84
    [Google Scholar]
  139. 138. 
    Bersanelli M, Mosca E, Remondini D, Giampieri E, Sala C et al. 2016. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinform 17:2S15
    [Google Scholar]
  140. 139. 
    Button KS, Ionnidis JPA, Mokrysz C, Nosek BA, Flint J et al. 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14:365–76
    [Google Scholar]
  141. 140. 
    Maurice CF, Haiser HJ, Turnbaugh PJ 2013. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152:1–239–50
    [Google Scholar]
  142. 141. 
    Oikonomou G, Teixeira AGV, Foditsch C, Bicalho ML, Machado VS, Bicalho RC 2013. Fecal microbial diversity in pre-weaned dairy calves as described by pyrosequencing of metagenomic 16S rDNA. Associations of Faecalibacterium species with health and growth. PLOS ONE 8:4e63157
    [Google Scholar]
  143. 142. 
    Klein-Jöbstl D, Schornsteiner E, Mann E, Wagner M, Drillich M, Schmitz-Esser S 2014. Pyrosequencing reveals diverse fecal microbiota in Simmental calves during early development. Front. Microbiol. 5:622
    [Google Scholar]
/content/journals/10.1146/annurev-animal-021419-083952
Loading
/content/journals/10.1146/annurev-animal-021419-083952
Loading

Data & Media loading...

Supplemental Material

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error