Dynamic changes of yak (Bos grunniens) gut microbiota during growth revealed by polymerase chain reaction-denaturing gradient gel electrophoresis and metagenomics

  • Nie, Yuanyang (Key Laboratory of Biological Resources and Ecological Environment, College of Life Sciences, Sichuan University) ;
  • Zhou, Zhiwei (Key Laboratory of Biological Resources and Ecological Environment, College of Life Sciences, Sichuan University) ;
  • Guan, Jiuqiang (Sichuan Grassland Science Academy) ;
  • Xia, Baixue (Key Laboratory of Biological Resources and Ecological Environment, College of Life Sciences, Sichuan University) ;
  • Luo, Xiaolin (Sichuan Grassland Science Academy) ;
  • Yang, Yang (Key Laboratory of Biological Resources and Ecological Environment, College of Life Sciences, Sichuan University) ;
  • Fu, Yu (Key Laboratory of Biological Resources and Ecological Environment, College of Life Sciences, Sichuan University) ;
  • Sun, Qun (Key Laboratory of Biological Resources and Ecological Environment, College of Life Sciences, Sichuan University)
  • 투고 : 2016.10.26
  • 심사 : 2017.01.11
  • 발행 : 2017.07.01


Objective: To understand the dynamic structure, function, and influence on nutrient metabolism in hosts, it was crucial to assess the genetic potential of gut microbial community in yaks of different ages. Methods: The denaturing gradient gel electrophoresis (DGGE) profiles and Illumina-based metagenomic sequencing on colon contents of 15 semi-domestic yaks were investigated. Unweighted pairwise grouping method with mathematical averages (UPGMA) clustering and principal component analysis (PCA) were used to analyze the DGGE fingerprint. The Illumina sequences were assembled, predicted to genes and functionally annotated, and then classified by querying protein sequences of the genes against the Kyoto encyclopedia of genes and genomes (KEGG) database. Results: Metagenomic sequencing showed that more than 85% of ribosomal RNA (rRNA) gene sequences belonged to the phylum Firmicutes and Bacteroidetes, indicating that the family Ruminococcaceae (46.5%), Rikenellaceae (11.3%), Lachnospiraceae (10.0%), and Bacteroidaceae (6.3%) were dominant gut microbes. Over 50% of non-rRNA gene sequences represented the metabolic pathways of amino acids (14.4%), proteins (12.3%), sugars (11.9%), nucleotides (6.8%), lipids (1.7%), xenobiotics (1.4%), coenzymes, and vitamins (3.6%). Gene functional classification showed that most of enzyme-coding genes were related to cellulose digestion and amino acids metabolic pathways. Conclusion: Yaks' age had a substantial effect on gut microbial composition. Comparative metagenomics of gut microbiota in 0.5-, 1.5-, and 2.5-year-old yaks revealed that the abundance of the class Clostridia, Bacteroidia, and Lentisphaeria, as well as the phylum Firmicutes, Bacteroidetes, Lentisphaerae, Tenericutes, and Cyanobacteria, varied more greatly during yaks' growth, especially in young animals (0.5 and 1.5 years old). Gut microbes, including Bacteroides, Clostridium, and Lentisphaeria, make a contribution to the energy metabolism and synthesis of amino acid, which are essential to the normal growth of yaks.


Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis (PCR-DGGE);Metagenomic Sequencing;Metabolic Pathways;Gut Microbiota;Yak


  1. Qiu Q, Zhang GJ, Ma T, et al. The yak genome and adaptation to life at high altitude. Nat Genet 2012;44:946-9.
  2. Flint HJ, Scott KP, Duncan SH, Louis P, Forano E. Microbial degradation of complex carbohydrates in the gut. Gut microbes 2012;3: 289-306.
  3. Wei YQ, Long RJ, Hui Y, et al. Fiber degradation potential of natural co-cultures of Neocallimastix frontalis and Methanobrevibacter ruminantium isolated from yaks (Bos grunniens) grazing on the Qinghai Tibetan Plateau. Anaerobe 2016;39:158-64.
  4. Backhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Hostbacterial mutualism in the human intestine. Science 2005;307:1915-20.
  5. Hooper LV, Falk PG, Gordon JI. Analyzing the molecular foundations of commensalism in the mouse intestine. Curr Opin Microbiol 2000;3: 79-85.
  6. Zhu L, Wu Q, Dai J, Zhang S, Wei F. Evidence of cellulose metabolism by the giant panda gut microbiome. Proc Natl Acad Sci USA 2011; 108:17714-19.
  7. Eckburg PB, Bik EM, Bernstein CN, et al. Diversity of the human intestinal microbial flora. Science 2005;308:1635-8.
  8. Asano R, Otawa K, Ozutsumi Y, et al. Development and analysis of microbial characteristics of an acidulocomposting system for the treatment of garbage and cattle manure. J Biosci Bioeng 2010;110: 419-25.
  9. Qin JJ, Li RQ, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010;464:59-U70.
  10. Jung JY, Lee SH, Kim JM, et al. Metagenomic analysis of kimchi, a traditional Korean fermented food. Appl Environ Microbiol 2011;77: 2264-74.
  11. Patel V, Patel AK, Parmar NR, et al. Characterization of the rumen microbiome of Indian Kankrej cattle (Bos indicus) adapted to different forage diet. Appl Microbiol Biotechnol 2014;98:9749-61.
  12. Walter J, Tannock GW, Tilsala-Timisjarvi A, et al. Detection and identification of gastrointestinal Lactobacillus species by using denaturing gradient gel electrophoresis and species-specific PCR primers. Appl Environ Microbiol 2000;66:297-303.
  13. Muyzer G, de Waal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol 1993;59:695-700.
  14. Fromin N, Hamelin J, Tarnawski S, et al. Statistical analysis of denaturing gel electrophoresis (DGE) fingerprinting patterns. Environ Microbiol 2002;4:634-43.
  15. Li RQ, Zhu HM, Ruan J, et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res 2010;20: 265-72.
  16. Altschul SF, Madden TL, Schaffer AA, et al. Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389-402.
  17. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res 2007;17:377-86.
  18. Parks DH, Beiko RG. Identifying biologically relevant differences between metagenomic communities. Bioinformatics 2010;26:715-21.
  19. Wang X, Heazlewood SP, Krause DO, Florin THJ. Molecular characterization of the microbial species that colonize human ileal and colonic mucosa by using 16S rDNA sequence analysis. J Appl Microbiol 2003;95:508-20.
  20. Durso LM, Harhay GP, Smith TPL, et al. Animal-to-animal variation in fecal microbial diversity among beef cattle. Appl Environ Microb 2010;76:4858-62.
  21. DeLong EF, Preston CM, Mincer T, et al. Community genomics among stratified microbial assemblages in the ocean's interior. Science 2006; 311:496-503.
  22. Le Chatelier E, Nielsen T, Qin JJ, et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013;500:541-6.
  23. Qin JJ, Li YR, Cai ZM, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012;490:55-60.
  24. Zhao W, Wang Y, Liu S, et al. The dynamic distribution of porcine microbiota across different ages and gastrointestinal tract segments. PLoS ONE 2015;10:e0117441.
  25. Dowd SE, Callaway TR, Wolcott RD, et al. Evaluation of the bacterial diversity in the feces of cattle using 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). BMC Microbiol 2008;8: 208-13.
  26. Ozutsumi Y, Hayashi H, Sakamoto M, Itabashi H, Benno Y. Cultureindependent analysis of fecal microbiota in cattle. Biosci Biotechnol Biochem 2005;69:1793-97.
  27. Ziemer CJ. Newly cultured bacteria with broad diversity isolated from eight-week continuous culture enrichments of cow feces on complex polysaccharides. Appl Environ Microbiol 2014;80:574-85.
  28. Cotta MA, Whitehead TR, Zeltwanger RL. Isolation, characterization and comparison of bacteria from swine feces and manure storage pits. Environ Microbiol 2003;5:737-45.
  29. Wexler HM. Bacteroides: the good, the bad, and the nitty-gritty. Clin Microbiol Rev 2007;20:593-621.
  30. Li MJ, Zhou M, Adamowicz E, Basarab JA, Guan LL. Characterization of bovine ruminal epithelial bacterial communities using 16S rRNA sequencing, PCR-DGGE, and qRT-PCR analysis. Vet Microbiol 2012;155:72-80.
  31. Ley RE, Hamady M, Lozupone C, et al. Evolution of mammals and their gut microbes. Science 2008;320:1647-51.

피인용 문헌

  1. Changes in the ruminal fermentation and bacterial community structure by a sudden change to a high-concentrate diet in Korean domestic ruminants vol.32, pp.1, 2019,