Altering the Gut Microbiome of Cattle: Considerations of Host-Microbiome Interactions for Persistent Microbiome Manipulation.

Brooke A Clemmons, Brynn H Voy, Phillip R Myer
Author Information
  1. Brooke A Clemmons: Department of Animal Science, University of Tennessee Institute of Agriculture, Knoxville, TN, USA.
  2. Brynn H Voy: Department of Animal Science, University of Tennessee Institute of Agriculture, Knoxville, TN, USA.
  3. Phillip R Myer: Department of Animal Science, University of Tennessee Institute of Agriculture, Knoxville, TN, USA. pmyer@utk.edu. ORCID

Abstract

The beef cattle industry represents a significant portion of the USA's agricultural sect, with beef cattle accounting for the most red meat consumed in the USA. Feed represents the largest input cost in the beef industry, accounting for approximately 70% of total input cost. Given that, novel methods need to be employed to optimize feed efficiency in cattle to reduce monetary cost as well as environmental cost associated with livestock industries, such as methane production and nitrogen release into the environment. The rumen microbiome contributes to feed efficiency by breaking down low-quality feedstuffs into energy substrates that can subsequently be utilized by the host animal. Attempts to manipulate the rumen microbiome have been met with mixed success, though persistent changes have not yet been achieved beyond changing diet. Recent technological advances have made analyzing host-wide effects of the rumen microbiome possible, as well as provided finer resolution of those effects. This manuscript reviews contributing factors to the rumen microbiome establishment or re-establishment following rumen microbiome perturbation, as well as host-microbiome interactions that may be responsible for possible host specificity of the rumen microbiome. Understanding and accounting for the variety of factors contributing to rumen microbiome establishment or re-establishment in cattle will ultimately lead to identification of biomarkers of feed efficiency that will result in improved selection criteria, as well as aid to determine methods for persistent microbiome manipulation to optimize production phenotypes.

Keywords

References

  1. Appl Environ Microbiol. 1999 Oct;65(10):4606-10 [PMID: 10508096]
  2. J Anim Sci. 2001 Feb;79(2):515-24 [PMID: 11219463]
  3. Mutat Res. 2002 Jan 29;499(1):13-25 [PMID: 11804602]
  4. Appl Environ Microbiol. 2002 Apr;68(4):1561-8 [PMID: 11916669]
  5. J Anim Sci. 2002 Jul;80(7):1977-85 [PMID: 12162668]
  6. Proc Natl Acad Sci U S A. 2004 Feb 24;101(8):2398-403 [PMID: 14983021]
  7. Appl Environ Microbiol. 2004 Mar;70(3):1263-70 [PMID: 15006742]
  8. J Dairy Sci. 2004 Jun;87(6):1832-9 [PMID: 15453499]
  9. Proc Natl Acad Sci U S A. 2005 Aug 2;102(31):11070-5 [PMID: 16033867]
  10. J Nutr. 2005 Nov;135(11):2694-7 [PMID: 16251632]
  11. Appl Environ Microbiol. 1979 Jul;38(1):148-58 [PMID: 16345408]
  12. J Anim Sci. 2006 Apr;84 Suppl:E25-33 [PMID: 16582090]
  13. J Anim Sci. 2006 Jun;84(6):1489-96 [PMID: 16699105]
  14. J Gen Microbiol. 1977 Mar;99(1):107-17 [PMID: 16983]
  15. Appl Microbiol Biotechnol. 2007 May;75(1):165-74 [PMID: 17235560]
  16. J Dairy Sci. 2007 Jun;90(6):2580-95 [PMID: 17517698]
  17. J Dairy Sci. 2007 Jun;90 Suppl 1:E17-38 [PMID: 17517750]
  18. J Dairy Sci. 1991 Dec;74(12):4326-36 [PMID: 1787201]
  19. Cell Host Microbe. 2008 Apr 17;3(4):213-23 [PMID: 18407065]
  20. Appl Environ Microbiol. 2009 Jan;75(2):374-80 [PMID: 19028912]
  21. Nature. 2009 Jan 22;457(7228):480-4 [PMID: 19043404]
  22. Appl Environ Microbiol. 2009 Oct;75(20):6524-33 [PMID: 19717632]
  23. Appl Environ Microbiol. 2009 Nov;75(22):7115-24 [PMID: 19783747]
  24. Bioresour Technol. 2010 Mar;101(6):1558-69 [PMID: 19962886]
  25. Microb Ecol. 2010 Apr;59(3):511-22 [PMID: 20037795]
  26. FEMS Microbiol Lett. 2010 Apr;305(1):49-57 [PMID: 20158525]
  27. FEMS Microbiol Ecol. 2010 May;72(2):272-8 [PMID: 20236326]
  28. Appl Environ Microbiol. 2010 Jun;76(12):3776-86 [PMID: 20418436]
  29. J Anim Sci. 2010 Dec;88(12):3977-83 [PMID: 20729286]
  30. Appl Environ Microbiol. 2010 Nov;76(22):7482-90 [PMID: 20851965]
  31. Proc Natl Acad Sci U S A. 2010 Nov 2;107(44):18933-8 [PMID: 20937875]
  32. J Dairy Sci. 2010 Dec;93(12):5902-12 [PMID: 21094763]
  33. J Dairy Sci. 1985 Jul;68(7):1712-21 [PMID: 21800456]
  34. Physiol Rev. 1990 Apr;70(2):567-90 [PMID: 2181501]
  35. Environ Microbiol. 2012 Jan;14(1):129-39 [PMID: 21906219]
  36. Nature. 2012 May 09;486(7402):222-7 [PMID: 22699611]
  37. Curr Med Chem. 2013;20(2):257-71 [PMID: 23210853]
  38. ISME J. 2013 Jun;7(6):1069-79 [PMID: 23426008]
  39. Curr Microbiol. 2013 Aug;67(2):130-7 [PMID: 23471692]
  40. J Dairy Sci. 2013 May;96(5):3189-200 [PMID: 23498024]
  41. PLoS One. 2013;8(3):e58461 [PMID: 23520513]
  42. Appl Environ Microbiol. 2013 Jun;79(12):3744-55 [PMID: 23584771]
  43. J Appl Microbiol. 2014 Feb;116(2):245-57 [PMID: 24279326]
  44. PLoS One. 2014 Jan 22;9(1):e85423 [PMID: 24465556]
  45. Lett Appl Microbiol. 2014 Jul;59(1):79-85 [PMID: 24617926]
  46. BMC Genomics. 2014 Apr 03;15:257 [PMID: 24694284]
  47. Genome Res. 2014 Sep;24(9):1517-25 [PMID: 24907284]
  48. Microb Biotechnol. 2014 Sep;7(5):467-79 [PMID: 24986151]
  49. Cell. 2014 Nov 6;159(4):789-99 [PMID: 25417156]
  50. Environ Microbiol. 2016 Feb;18(2):525-41 [PMID: 25471302]
  51. Appl Environ Microbiol. 2015 Feb;81(4):1327-37 [PMID: 25501481]
  52. J Proteome Res. 2015 Feb 6;14(2):1287-98 [PMID: 25599412]
  53. Front Microbiol. 2015 Apr 10;6:296 [PMID: 25914693]
  54. PLoS One. 2015 Jun 01;10(6):e0129174 [PMID: 26030887]
  55. Sci Rep. 2015 Oct 09;5:14567 [PMID: 26449758]
  56. BMC Genomics. 2015 Oct 23;16:839 [PMID: 26494241]
  57. Front Microbiol. 2015 Oct 12;6:1060 [PMID: 26528248]
  58. J Appl Microbiol. 2016 Mar;120(3):588-99 [PMID: 26726754]
  59. J Anim Sci. 2016 Apr;94(4):1438-45 [PMID: 27136003]
  60. ISME J. 2016 Dec;10(12):2958-2972 [PMID: 27152936]
  61. Cell Host Microbe. 2016 May 11;19(5):731-43 [PMID: 27173935]
  62. Front Microbiol. 2016 May 03;7:582 [PMID: 27199916]
  63. Front Microbiol. 2016 May 18;7:701 [PMID: 27242724]
  64. Sci Rep. 2017 Mar 15;7(1):198 [PMID: 28298634]
  65. Sci Rep. 2017 Apr 28;7(1):1276 [PMID: 28455495]
  66. Sci Rep. 2017 Jun 6;7(1):2864 [PMID: 28588266]
  67. J Dairy Sci. 2017 Sep;100(9):7165-7182 [PMID: 28690067]
  68. J Anim Sci. 2017 Jul;95(7):3215-3224 [PMID: 28727105]
  69. J Dairy Sci. 2018 Mar;101(3):2285-2292 [PMID: 29274973]
  70. J Appl Bacteriol. 1987 Sep;63(3):233-8 [PMID: 3429358]
  71. J Anim Sci. 1984 Jun;58(6):1518-27 [PMID: 6378867]
  72. J Anim Sci. 1994 Nov;72(11):2969-79 [PMID: 7730193]
  73. J Biol Chem. 1994 Mar 18;269(11):8022-8 [PMID: 8132524]
  74. J Anim Sci. 1993 Aug;71(8):2260-9 [PMID: 8397175]
  75. J Anim Sci. 1995 Aug;73(8):2483-92 [PMID: 8567486]
  76. J Anim Sci. 1996 Nov;74(11):2803-9 [PMID: 8923195]
  77. Int J Syst Bacteriol. 1997 Apr;47(2):284-8 [PMID: 9103611]

MeSH Term

Animal Feed
Animals
Bacteria
Cattle
Gastrointestinal Microbiome
Rumen

Word Cloud

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