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Assessment of the gastrointestinal microbiota using 16S ribosomal RNA gene amplicon sequencing in ruminant nutrition

  • Minseok Kim (Division of Animal Science, Chonnam National University)
  • 투고 : 2022.10.04
  • 심사 : 2023.01.19
  • 발행 : 2023.02.01

초록

The gastrointestinal (GI) tract of ruminants contains diverse microbes that ferment various feeds ingested by animals to produce various fermentation products, such as volatile fatty acids. Fermentation products can affect animal performance, health, and well-being. Within the GI microbes, the ruminal microbes are highly diverse, greatly contribute to fermentation, and are the most important in ruminant nutrition. Although traditional cultivation methods provided knowledge of the metabolism of GI microbes, most of the GI microbes could not be cultured on standard culture media. By contrast, amplicon sequencing of 16S rRNA genes can be used to detect unculturable microbes. Using this approach, ruminant nutritionists and microbiologists have conducted a plethora of nutritional studies, many including dietary interventions, to improve fermentation efficiency and nutrient utilization, which has greatly expanded knowledge of the GI microbiota. This review addresses the GI content sampling method, 16S rRNA gene amplicon sequencing, and bioinformatics analysis and then discusses recent studies on the various factors, such as diet, breed, gender, animal performance, and heat stress, that influence the GI microbiota and thereby ruminant nutrition.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A2C1011188).

참고문헌

  1. Firkins JL, Yu Z. Ruminant nutrition symposium: How to use data on the rumen microbiome to improve our understanding of ruminant nutrition. J Anim Sci 2015;93:1450-70. https://doi.org/10.2527/jas.2014-8754
  2. Kim M, Morrison M, Yu Z. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol Ecol 2011;76:49-63. https://doi.org/10.1111/j.1574-6941.2010.01029.x
  3. Kastl AJ, Jr., Terry NA, Wu GD, Albenberg LG. The structure and function of the human small intestinal microbiota: current understanding and future directions. Cell Mol Gastroenterol Hepatol 2020;9:33-45. https://doi.org/10.1016/j.jcmgh.2019.07.006
  4. Liu Y, Liu C, Wu H, Meng Q, Zhou Z. Small intestine microbiome and metabolome of high and low residual feed intake angus heifers. Front Microbiol 2022;13:862151. https://doi.org/10.3389/fmicb.2022.862151
  5. Freetly HC, Dickey A, Lindholm-Perry AK, et al. Digestive tract microbiota of beef cattle that differed in feed efficiency. J Anim Sci 2020;98:skaa008. https://doi.org/10.1093/jas/skaa008
  6. Kim M, Park T, Yu Z. Metagenomic investigation of gastrointestinal microbiome in cattle. Asian-Australas J Anim Sci 2017;30:1515-28. https://doi.org/10.5713/ajas.17.0544
  7. Kim M, Wells JE. A meta-analysis of bacterial diversity in the feces of cattle. Curr Microbiol 2016;72:145-51. https://doi.org/10.1007/s00284-015-0931-6
  8. Laflin SL, Gnad DP. Rumen cannulation: Procedure and use of a cannulated bovine. Vet Clin North Am Food Anim Pract 2008;24:335-40. https://doi.org/10.1016/j.cvfa.2008.02.007
  9. Song J, Choi H, Jeong JY, et al. Effects of sampling techniques and sites on rumen microbiome and fermentation parameters in Hanwoo steers. J Microbiol Biotechnol 2018;28:1700-5. https://doi.org/10.4014/jmb.1803.03002
  10. Kim M, Kuehn LA, Bono JL, et al. The impact of the bovine faecal microbiome on Escherichia coli O157:H7 prevalence and enumeration in naturally infected cattle. J Appl Microbiol 2017;123:1027-42. https://doi.org/10.1111/jam.13545
  11. Yu ZT, Morrison M. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 2004;36:808-12. https://doi.org/10.2144/04365st04
  12. Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019;37:852-7. https://doi.org/10.1038/s41587-019-0209-9
  13. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007;73: 5261-7. https://doi.org/10.1128/AEM.00062-07
  14. Douglas GM, Maffei VJ, Zaneveld JR, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 2020;38: 685-8. https://doi.org/10.1038/s41587-020-0548-6
  15. Kim M, Morrison M, Yu Z. Phylogenetic diversity of bacterial communities in bovine rumen as affected by diets and microenvironments. Folia Microbiol 2011;56:453. https://doi.org/10.1007/s12223-011-0066-5
  16. Kim M, Yu Z. Quantitative comparisons of select cultured and uncultured microbial populations in the rumen of cattle fed different diets. J Anim Sci Biotechnol 2012;3:28. https://doi.org/10.1186/2049-1891-3-28
  17. Islam M, Kim SH, Ramos SC, et al. Holstein and Jersey steers differ in rumen microbiota and enteric methane emissions even fed the same total mixed ration. Front Microbiol 2021; 12:601061. https://doi.org/10.3389/fmicb.2021.601061
  18. Ramos-Morales E, Arco-Perez A, Martin-Garcia AI, YanezRuiz DR, Frutos P, Hervas G. Use of stomach tubing as an alternative to rumen cannulation to study ruminal fermentation and microbiota in sheep and goats. Anim Feed Sci Technol 2014;198:57-66. https://doi.org/10.1016/j.anifeedsci.2014.09.016
  19. Terre M, Castells L, Fabregas F, Bach A. Short communication: Comparison of pH, volatile fatty acids, and microbiome of rumen samples from preweaned calves obtained via cannula or stomach tube. J Dairy Sci 2013;96:5290-4. https://doi.org/10.3168/jds.2012-5921
  20. Paz HA, Anderson CL, Muller MJ, Kononoff PJ, Fernando SC. Rumen bacterial community composition in holstein and jersey cows is different under same dietary condition and is not affected by sampling method. Front Microbiol 2016;7:1206. https://doi.org/10.3389/fmicb.2016.01206
  21. Petri RM, Mickdam E, Klevenhusen F, Beyer B, Zebeli Q. Effects of the supplementation of plant-based formulations on microbial fermentation and predicted metabolic function in vitro. Anaerobe 2019;57:19-27. https://doi.org/10.1016/j.anaerobe.2019.03.001
  22. Zhang H, Tong J, Wang Z, Xiong B, Jiang L. Illumina MiSeq sequencing reveals the effects of grape seed procyanidin on rumen archaeal communities in vitro. Asian-Australas J Anim Sci 2020;33:61-8. https://doi.org/10.5713/ajas.19.0226
  23. Humer E, Aditya S, Kaltenegger A, Klevenhusen F, Petri RM, Zebeli Q. Graded substitution of grains with bakery byproducts modulates ruminal fermentation, nutrient degradation, and microbial community composition in vitro. J Dairy Sci 2018;101:3085-98. https://doi.org/10.3168/jds.2017-14051
  24. Cui K, Qi M, Wang S, Diao Q, Zhang N. Dietary energy and protein levels influenced the growth performance, ruminal morphology and fermentation and microbial diversity of lambs. Sci Rep 2019;9:16612. https://doi.org/10.1038/s41598-019-53279-y
  25. Li F, Hitch TCA, Chen Y, Creevey CJ, Guan LL. Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle. Microbiome 2019;7:6. https://doi.org/10.1186/s40168-019-0618-5
  26. Kim M, Park JK, Lee HG, Song J. Differences in rumen microbiomes of Korean Native Hanwoo and Jeju Black cattle under the same dietary condition. 47th Annual Meeting & International Symposium of Korean Society for Microbiology and Biotechnology; 2020 September 23-25; e-Conference.
  27. Li F, Li C, Chen Y, et al. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome 2019;7:92. https://doi.org/10.1186/s40168-019-0699-1
  28. Guo X, Sha Y, Lv W, et al. Sex differences in rumen fermentation and microbiota of Tibetan goat. Microb Cell Fact 2022; 21:55. https://doi.org/10.1186/s12934-022-01783-8
  29. Yurkovetskiy L, Burrows M, Khan AA, et al. Gender bias in autoimmunity is influenced by microbiota. Immunity 2013; 39:400-12. https://doi.org/10.1016/j.immuni.2013.08.013
  30. Kim YS, Unno T, Kim BY, Park MS. Sex differences in gut microbiota. World J Mens Health 2020;38:48-60. https://doi.org/10.5534/wjmh.190009
  31. Dominianni C, Sinha R, Goedert JJ, et al. Sex, body mass index, and dietary fiber intake influence the human gut microbiome. PLoS One 2015;10:e0124599. https://doi.org/10.1371/journal. pone.0124599
  32. Guan LL, Nkrumah JD, Basarab JA, Moore SS. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle's feed efficiency. FEMS Microbiol Lett 2008; 288:85-91. https://doi.org/10.1111/j.1574-6968.2008.01343.x
  33. Hernandez-Sanabria E, Guan LL, Goonewardene LA, et al. Correlation of particular bacterial PCR-denaturing gradient gel electrophoresis patterns with bovine ruminal fermentation parameters and feed efficiency traits. Appl Environ Microbiol 2010;76:6338-50. https://doi.org/10.1128/AEM.01052-10
  34. Myer PR, Smith TP, Wells JE, Kuehn LA, Freetly HC. Rumen microbiome from steers differing in feed efficiency. PLoS One 2015;10:e0129174. https://doi.org/10.1371/journal.pone.0129174
  35. Paz HA, Hales KE, Wells JE, et al. Rumen bacterial community structure impacts feed efficiency in beef cattle. J Anim Sci 2018;96:1045-58. https://doi.org/10.1093/jas/skx081
  36. McLoughlin S, Spillane C, Claffey N, et al. Rumen microbiome composition is altered in sheep divergent in feed efficiency. Front Microbiol 2020;11:1981. https://doi.org/10.3389/fmicb.2020.01981
  37. Zhang YK, Zhang XX, Li FD, et al. Characterization of the rumen microbiota and its relationship with residual feed intake in sheep. Animal 2021;15:100161. https://doi.org/10.1016/j.animal.2020.100161
  38. Park CJ, Lee HS, Yoon S, et al. Evaluation of rumen microbiome of early fattening Hanwoo steers with different feed efficiencies. The 19th AAAP Animal Science Congress; 2022 August 23-26; Jeju, Korea.
  39. Kim M, Park T, Jeong JY, Baek Y, Lee HJ. Association between rumen microbiota and marbling score in Korean native beef cattle. Animals-Basel 2020;10:712. https://doi.org/10.3390/ani10040712
  40. Correia Sales GF, Carvalho BF, Schwan RF, et al. Heat stress influence the microbiota and organic acids concentration in beef cattle rumen. J Therm Biol 2021;97:102897. https://doi.org/10.1016/j.jtherbio.2021.102897
  41. Zhao S, Min L, Zheng N, Wang J. Effect of heat stress on bacterial composition and metabolism in the rumen of lactating dairy cows. Animals (Basel) 2019;9:925. https://doi.org/10.3390/ani9110925
  42. Baek YC, Choi H, Jeong JY, et al. The impact of short-term acute heat stress on the rumen microbiome of Hanwoo steers. J Anim Sci Technol 2020;62:208-17. https://doi.org/10.5187/jast.2020.62.2.208
  43. Kim SH, Ramos SC, Valencia RA, Cho YI, Lee SS. Heat Stress: effects on rumen microbes and host physiology, and strategies to alleviate the negative impacts on lactating dairy cows. Front Microbiol 2022;13:804562. https://doi.org/10.3389/fmicb.2022.804562
  44. Park T, Ma L, Gao ST, Bu DP, Yu ZT. Heat stress impacts the multi-domain ruminal microbiota and some of the functional features independent of its effect on feed intake in lactating dairy cows. J Anim Sci Biotechnol 2022;13:71. https://doi.org/10.1186/s40104-022-00717-z
  45. Myer PR, Wells JE, Smith TP, Kuehn LA, Freetly HC. Microbial community profiles of the jejunum from steers differing in feed efficiency. J Anim Sci 2016;94:327-38. https://doi.org/10.2527/jas.2015-9839
  46. Myer PR, Wells JE, Smith TP, Kuehn LA, Freetly HC. Cecum microbial communities from steers differing in feed efficiency. J Anim Sci 2015;93:5327-40. https://doi.org/10.2527/jas.2015-9415
  47. Myer PR, Wells JE, Smith TP, Kuehn LA, Freetly HC. Microbial community profiles of the colon from steers differing in feed efficiency. Springerplus 2015;4:454. https://doi.org/10.1186/s40064-015-1201-6
  48. Wang K, Zhang H, Hu L, et al. Characterization of the microbial communities along the gastrointestinal tract in crossbred cattle. Animals (Basel) 2022;12:825. https://doi.org/10.3390/ani12070825
  49. Durso L, Wells JE, Kim MS. Diversity of microbiomes in beef cattle. In: Nelson KE, editor. Encyclopedia of metagenomics. New York, NY, USA: Springer; 2014. pp. 1-11. https://doi.org/10.1007/978-1-4614-6418-1_730-4
  50. Callaway TR, Dowd SE, Edrington TS, 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. J Anim Sci 2010;88:3977-83. https://doi.org/10.2527/jas.2010-2900
  51. Rice WC, Galyean ML, Cox SB, Dowd SE, Cole NA. Influence of wet distillers grains diets on beef cattle fecal bacterial community structure. BMC Microbiol 2012;12:25. https://doi.org/10.1186/1471-2180-12-25
  52. Kim M, Kim J, Kuehn LA, et al. Investigation of bacterial diversity in the feces of cattle fed different diets. J Anim Sci 2014;92:683-94. https://doi.org/10.2527/jas.2013-6841
  53. Sim S, Lee H, Yoon S, Seon H, Park C, Kim M. The impact of different diets and genders on fecal microbiota in Hanwoo cattle. J Anim Sci Technol 2022;64:897-910. https://doi.org/10.5187/jast.2022.e71
  54. Lourenco JM, Welch CB, Krause TR, et al. Fecal microbiome differences in angus steers with differing feed efficiencies during the feedlot-finishing phase. Microorganisms 2022;10:1128. https://doi.org/10.3390/microorganisms10061128
  55. Welch CB, Lourenco JM, Krause TR, et al. Evaluation of the fecal bacterial communities of angus steers with divergent feed efficiencies across the lifespan from weaning to slaughter. Front Vet Sci 2021;8:597405. https://doi.org/10.3389/fvets.2021.597405
  56. Seon HS, Lee HS, Yoon S, et al. Assessment of fecal microbiome of late fattening Hanwoo steers with different feed efficiencies. The 19th AAAP Animal Science Congress; 2022 August 23-26; Jeju, Korea.
  57. Wells JE, Kim M, Bono JL, Kuehn LA, Benson AK. Meat science and muscle biology symposium: Escherichia coli O157:H7, diet, and fecal microbiome in beef cattle. J Anim Sci 2014;92:1345-55. https://doi.org/10.2527/jas.2013-7282
  58. Berry ED, Wells JE, Varel VH, Hales KE, Kalchayanand N. Persistence of Escherichia coli O157:H7 and total Escherichia coli in feces and feedlot surface manure from cattle fed diets with and without corn or sorghum wet distillers grains with solubles. J Food Protect 2017;80:1317-27. https://doi.org/10.4315/0362-028x.Jfp-17-018
  59. Wells JE, Berry ED, Kim M, Shackelford SD, Hales KE. Evaluation of commercial beta-agonists, dietary protein, and shade on fecal shedding of Escherichia coli O157:H7 from feedlot cattle. Foodborne Pathog Dis 2017;14:649-55. https://doi.org/10.1089/fpd.2017.2313
  60. Pope PB, Smith W, Denman SE, et al. Isolation of Succinivibrionaceae implicated in low methane emissions from Tammar wallabies. Science 2011;333:646-8. https://doi.org/10.1126/science.1205760
  61. Myer PR. Bovine genome-microbiome interactions: metagenomic frontier for the selection of efficient productivity in cattle systems. mSystems 2019;4:e00103-19. https://doi.org/10.1128/mSystems.00103-19