Elena V Sheyda, Svyatoslav V Lebedev, Vitaly A Ryazanov, Victoria V Grechkina, Olga V Kwan, Shamil G Rakhmatullin

DOI: 10.33284/2658-3135-104-3-186

UDC 636.085:636.085.57

Acknowledgements:

Research was carried out according the plan of research scientific works on 2021-2023 yy. FSBSI FRC BST RAS (No 0761-2019-0005)

Changes in the taxonomic composition of the intestinal microbiome of cattle raised on a protein diet

Elena V Sheyda, Svyatoslav V Lebedev, Vitaly A Ryazanov, Victoria V Grechkina, Olga V Kwan, Shamil G Rakhmatullin

Federal Research Centre оf Biological Systems and Agrotechnologies of the Russian Academy of Sciences (Orenburg, Russia)

 Abstract. In this work, we studied the change in the taxonomic composition of the intestinal microbiome of cattle with the inclusion of protein components in the diet. The intestinal microflora was analyzed using MiSeq (Illumina, USA) according to a new generation sequencing method with the MiSeq® Reagent Kit v3 (600 cycle). With the additional introduction of protein components into the diet - sunflower cake, the dominant phyla also turned out to be Firmicutes (48.2% of the total number of individuals of all species), Bacteroidetes (36.8%), Verrucomicrobia (12.7%). The inclusion of sunflower oil cake contributed to a decrease in the number of microbiota by 23.7% relative to the control (P≤0.05), while the biomass of representatives of the Ruminococcaceae family relative to the control decreased by 24.1%, the number of unclassified Clostridiales in this sample was 19% higher than in control. Additional introduction of soybean meal reduced the number of bacterial sequences relative to the control by 36.6% (P≤0.05), which was 15211. The species composition was represented by 8 phyla, 15 classes, 33 families and 64 genera. A decrease in the α-diversity of the fecal microbiome in the experimental groups also had an effect on β-diversity, there was a partial coincidence of communities in the control and experimental groups, the indices of similarity of Jaccard and Sørensen microbiocenoses were equal to Kl = 0.5-0.67 and Kc = 0.6 -0.76.

Keywords: cattle, microbiome, fecal microflora, biodiversity, sunflower cake, soybean meal.

 References

  1. Elina EE. Biodiversity: Method. manual for bachelors of the training programme "Ecology and nature management". Orenburg: Express Printing Printing House; 2016:36 p.
  2. Sheyda EV, Lebedev SV, Miroshnikov SA, Grechkina VV, Ryazanov VA, Shoshina OV. Changes in the activity of digestive enzymes of pancreatic juice under the influence of ultrafine particles of CR2O3against the background of feeding with protein diets raising cattle. Animal Husbandry and Fodder Production. 2020a;103(4):26-36. doi: 10.33284/2658-3135-103-4-26
  3. Sheyda EV, Lebedev SV, Miroshnikov SA, Grechkina VV, Ryazanov VA. Assessment of influence of ultrafine particles of CR2Oon metabolic processes in the body of calves raised on protein diets. Animal Husbandry and Fodder Production. 2020b;103(4):14-25. doi: 10.33284/2658-3135-103-4-14
  4. Fitsev AI, Grigorev NG, Gaganov AP. Modern assessment of energy and protein nutritional value of plant feed. Feed Production. 2003;12:29-32.
  5. Dai X, Zhu YX, Luo YF, Song L, Liu D, Liu L et al. Metagenomic insights into the fibrolytic microbiome in yak rumen. Plos One. 2012;7(7): e40430. doi: https://doi.org/10.1371/journal.pone.0040430
  6. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M et al. Diversity of the human intestinal microbial flora. Science. 2005;308(5728): 1635-1638. doi: https://doi.org/10.1126/science.1110591
  7. Frey JC, Rothman JM, Pell AN, Nizeyi JB, Cranfield MR, Angert ER. Fecal bacterial diversity in a wild gorilla. Appl Environ Microbiol. 2006;72(5):3788-3792. doi: 10.1128/AEM.72.5.3788-3792.2006
  8. Golder HM, Denman SE, McSweeney CM, Celi P, Lean IJ. Ruminal bacterial community shifts in grain-, sugar-, and histidine-challenged dairy heifers. J Dairy Sci. 2014;97:5131-5150. doi: http://dx.doi.org/ 10.3168/jds.2014-8003
  9. Hess M, Sczyrba A, Egan R, Kim TW, Chokhawala H, Schroth G et al. Metagenomic discovery of bio-mass-degrading genes and genomes from cow rumen. Science. 2011;331(6016):463-467. doi: 10.1126/science.1200387
  10. Holman DB, Gzyl KE. A meta-analysis of the bovine gastrointestinal tract microbiota. FEMS Microbiol Ecol. 2019;95(6):fiz072. doi: 10.1093/femsec/fiz072
  11. Kim M, Wells JE. A meta-analysis of bacterial diversity in the feces of cattle. Curr Microbiol. 2016;72(2):145-151. doi: 10.1007/s00284-015-0931-6
  12. Lee C, Hristov AN, Heyler KS, Cassidy TW, Lapierre H, Varga GA, Parys C. Effects of metabolizable protein supply and amino acid supplementation on nitrogen utilization, milk production, and ammonia emissions from manure in dairy cows. Journal of Dairy Science. 2012;95(9):5253-5268. doi: 10.3168/jds.2012-5366
  13. Liu J, Taft DH, Maldonado-Gomez MX, Johnson D, Treiber ML, Lemay DG, DePeters EJ, Mills DA. The fecal resistome of dairy cattle is associated with diet during nursing. Nat Commun. 2019;10(1):4406. doi: 10.1038/s41467-019-12111-x
  14. Macfarlane GT, Allison C, Gibson SA, Cummings JH. Contribution of the microflora to proteolysis in the human large intestine. J. Appl. Bacteriol. 1988;64(1):37-46. doi: 10.1111/j.1365-2672.1988.tb02427.x
  15. Macfarlane GT, Cummings JH, Allison C. Protein degradation by human intestinal bacteria. J Gen Microbiology.1986;132(6):1647-1656. doi: 10.1099/00221287-132-6-1647
  16. Matthews C, Crispie F, Lewis E, Reid M, O'Toole PW, Cotter PD. The rumen microbiome: a crucial consideration when optimising milk and meat production and nitrogen utilisation efficiency. Gut Microbes. 2019;10(2):115-132. doi: 10.1080/19490976.2018.1505176
  17. Plaizier JC, Mesgaran MD, Derakhshani H, Golder H, Khafipour E, Kleen JL, Lean I, Loor J, Penner G, Zebeli Q. Review: Enhancing gastrointestinal health in dairy cows. Animal. 2018;12(s2):s399-s418. doi: 10.1017/S1751731118001921
  18. RDP Announcements [Internet] [cited 2021 March 15] Available from: http://rdp.cme.msu.edu
  19. SILVA. High Quality Ribosomal RNA databases [Internet] de.NBI. German network for bioinformatics infrastructure [cited 2021 March 15] Available from: https://www.arb-silva.de
  20. Singh KM, Ahir VB, Tripathi AK, Ramani UV, Sajnani M, Koringa PG et al. Metagenomic analysis of Surti buffalo (Bubalus bubalis) rumen: a preliminary study. Mol Biol Rep. 2012;39:4841-4848. doi: https://doi.org/10.1007/s11033-011-1278-0
  21. Thoetkiattikul H, Mhuantong W, Laothanachareon T, Tangphatsornruang S, Pattarajinda V, Eurwilaichitr L, Champreda V. Comparative analysis of microbial profiles in cow rumen fed with different dietary fiber by tagged 16S rRNA gene pyrosequencing. Curr. Microbiol. 2013;67(2):130-137. doi: 10.1007/s00284-013-0336-3
  22. Turnbaugh PJ, Hamady M, Yatsunenko T et al. A core gut microbiome in obese and lean twins. Nature. 2009;457(7228):480-484. doi: 10.1038/nature07540
  23. U.S. National Library of Medicine. National Center for Biotechnology Information [Internet] BLAST. Nucleotide BLAST [cited 2021 March 15] Available from: https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome
  24. U.S. National Library of Medicine. National Center for Biotechnology Information [Internet] BLAST. [cited 2021 March 15] Available from: http://blast.ncbi.nlm.nih.gov/Blast.cgi
  25. VAMPS Visualization and Analysis of Microbial Population Structures [Internet] The Josephine Bay Paul Center. [cited 2021 March 15] Available from: http://vamps.mbl.edu
  26. Weimer PJ. Redundancy, resilience, and  host specificity of the ruminal microbiota: implications  for  engineering  improved  ruminal  fermentations.  Front Microbiol. 2015;6:296. doi: 10.3389/fmicb.2015.00296
  27. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A fast and accurate Illumina Paired-End reAd merger. Bioinformatics. 2014;30(5):614-620. doi: 10.1093/bioinformatics/btt593

Sheyda Elena Vladimirovna, Cand. Sci (Biol.), Researcher, Laboratory for Biological Testing and Expertises, Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences, 460000, Orenburg, Russia, 29, 9 Yanvarya St., 8-922-862-64-02, e-mail: elena-shejjda@mail.ru

Lebedev Svyatoslav Valerevich, Dr. Sci. (Biol.), Leading Researcher, Laboratory for Biological Testing and Expertises, Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences, 460000, Orenburg, Russia, 29, 9 Yanvarya St.,  tel.: 8-912-345-87-38, e-mail: lsv74@list.ru

Ryazanov Vitaly Aleksandrovich, Cand. Sci. (Agr.), Researcher of Farm Animal Feeding and Feed Technology Department named after Leushin SG., Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences, 460000, Orenburg, Russia, 29, 9 Yanvarya St., tel.: 8(3532)30-81-79, e-mail: vita7456@yandex.ru

Grechkina Victoria Vladimirovna, Cand. Sci (Biol.), Acting Head of Laboratory for Biological Testing and Expertise, Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences, 460000, Orenburg, Russia, 29, 9 Yanvarya St., tel.: 8-922-877-14-97, e-mail: Viktoria1985too@mail.ru

Kwan Olga Vilorievna, Cand. Sci. (Biol.), Acting Head of the Department of Feeding Farm Animals and Feed Technology named after S.G. Leushin, Federal Research Center for Biological Systems and Agrotechnologies of the Russian Academy of Sciences, 460000, Orenburg, Russia, 29 9 Yanvarya St., tel.: 8-922-548-56-57,e-mail: kwan111@yandex.ru

Rakhmatullin Shamil Gafiullovich, Cand. Sci. (Biol.), Senior Researcher of the Department of Feeding Farm Animals and Feed Technology named after Leushin SG, Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences, 460000, Orenburg, Russia, 29 9 Yanvarya St., tel.: 8-922-815-72-25, е-mail: shahm2005@rambler.ru

Received: 25 August 2021; Accepted: 13 September 2021; Published: 30 September 2021

Download