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High-Throughput DNA Sequencing of the Ruminal Bacteria from Moose (Alces alces) in Vermont, Alaska, and Norway

Microbial Ecology volume 68, pages 185–195 (2014)Cite this article

Abstract

In the present study, the rumen bacteria of moose (Alces alces) from three distinct geographic locations were investigated. Moose are large, browsing ruminants in the deer family, which subsist on fibrous, woody browse, and aquatic plants. Subspecies exist which are distinguished by differing body and antler size, and these are somewhat geographically isolated. Seventeen rumen samples were collected from moose in Vermont, Alaska, and Norway, and bacterial 16S ribosomal RNA genes were sequenced using Roche 454 pyrosequencing with titanium chemistry. Overall, 109,643 sequences were generated from the 17 individual samples, revealing 33,622 unique sequences. Members of the phylum Bacteroidetes were dominant in samples from Alaska and Norway, but representatives of the phylum Firmicutes were dominant in samples from Vermont. Within the phylum Bacteroidetes, Prevotellaceae was the dominant family in all three sample locations, most of which belonged to the genus Prevotella. Within the phylum Firmicutes, the family Lachnospiraceae was the most prevalent in all three sample locations. The data set supporting the results of this article is available in the Sequence Read Archive (SRA), available through NCBI [study accession number SRP022590]. Samples clustered by geographic location and by weight and were heterogenous based on gender, location, and weight class (p < 0.05). Location was a stronger factor in determining the core microbiome than either age or weight, but gender did not appear to be a strong factor. There were no shared operational taxonomic units across all 17 samples, which indicates that these moose may have been isolated long enough to preclude a core microbiome among moose. Other potential factors discussed include differences in climate, food quality and availability, gender, and life cycle.

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References

  1. Belovsky GE (1981) Food plant selection by a generalist herbivore: the moose. Ecology 64:1020–1030

    Article  Google Scholar 

  2. Hjeljord O, Sundstøl F, Haagenrud H (1982) The nutritional value of browse to moose. J Wildl Manag 46:333–343

    Article  Google Scholar 

  3. Botkin DB, Jordan PA, Dominski AS, Lowendorf HS, Hutchinson GE (1973) Sodium dynamics in a northern ecosystem. Proc Natl Acad Sci U S A 70:2745–2748

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Syroechkovsky EE, Rogacheva EV, Renecker LA (1989) Moose husbandry. In Hudson RJ, Drew KR, Baskin LM (eds) Wildlife production systems: economic utilization of wild ungulate systems, Cambridge University Press, Cambridge, pp 367–386

  5. Dehority BA (1986) Microbes in the foregut of arctic ruminants. In: Milligan LP, Grovum WL, Dobson A (eds) Control of digestion and metabolism in ruminants: proceedings of the Sixth International Symposium on Ruminant Physiology. Prentice-Hall, Englewood Cliffs, pp 307–325

    Google Scholar 

  6. Ishaq SL, Wright A-DG (2012) Insight into the bacterial gut microbiome of the North American moose (Alces alces). BMC Microbiol 12:212

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Lane DJ (1991) 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M (eds) Nucleic acid techniques in bacterial systematics. Wiley, New York, pp 115–175

    Google Scholar 

  8. Ovreås L, Forney L, Daae FL, Torsvik V (1997) Distribution of bacterioplankton in meromictic Lake Saelenvannet, as determined by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl Env Microbiol 63:3367–73

    Google Scholar 

  9. Godoy-Vitorino F, Goldfarb KC, Brodie EL, Garcia-Amado MA, Michelangeli F, Domınguez-Bello MG (2010) Developmental microbial ecology of the crop of the folivorous hoatzin. ISME J 4:611–620

    Article  CAS  PubMed  Google Scholar 

  10. Li RW, Connor EE, Li C, Baldwin V, Ransom L, Sparks ME (2012) Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ Microbiol 14:129–139

    Article  PubMed  Google Scholar 

  11. Sundset MA, Praesteng KE, Cann IKO, Mathiesen SD, Mackie RI (2007) Novel rumen bacterial diversity in two geographically separated sub-species of reindeer. Microb Ecol 54:424–438

    Article  PubMed  Google Scholar 

  12. Henderson G, Cox F, Kittelmann S, Miri VH, Zethof M, Noel SJ, Waghorn GC, Janssen PH (2013) Effect of DNA extraction methods and sampling techniques on the apparent structure of cow and sheep rumen microbial communities. PLoS One 8:e74787

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. National Oceanic and Atmospheric Administration (2012) [http://www.noaa.gov]. Accessed 20 Sept 2013

  14. Dupigny-Giroux L-A, Hogan S (2010) Initial climate impacts summary. Montpelier

  15. Yu Z, Morrison M (2004) Improved extraction of PCR-quality community DNA from digesta and fecal samples. BioTech 36:808–812

    CAS  Google Scholar 

  16. Kim M, Morrison M, Yu Z (2010) Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes. J Microbiol Methods 84:81–87

    Article  PubMed  Google Scholar 

  17. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Quince C, Lanzén A, Curtis TP, Davenport RJ, Hall N, Head IM, Read LF, Sloan WT (2009) Accurate determination of microbial diversity from 454 pyrosequencing data. Nat Methods 6:639–641

    Article  CAS  PubMed  Google Scholar 

  19. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Molec Biol 48:443–453

    Article  CAS  PubMed  Google Scholar 

  20. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinforma (Oxford, England) 27:2194–2200

    Article  CAS  Google Scholar 

  21. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO (2007) SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35:7188–96

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl Acids Res 41:D590–D596

    Google Scholar 

  23. Chao A, Shen T-J (2003) Nonparametric estimation of Shannon’s index of diversity when there are unseen species in sample. Environ Ecolog Stat 10:429–443

    Article  Google Scholar 

  24. Chao A, Shen T-J (2010) Program SPADE (Species Prediction And Diversity Estimation). Program and user’s guide available online at http://chao.stat.nthu.edu.tw

  25. Good IJ (1953) On population frequencies of species and the estimation of population parameters. Biometrika 40:237–264

    Article  Google Scholar 

  26. Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Urbana

    Google Scholar 

  27. Hamady M, Lozupone C, Knight R (2010) Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J 4:17–27

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Nelson KE, Zinder SH, Hance I, Burr P, Odongo D, Wasawo D, Odenyo A, Bishop R (2003) Phylogenetic analysis of the microbial populations in the wild herbivore gastrointestinal tract: insights into an unexplored niche. Environ Microbiol 5:1212–1220

    Article  PubMed  Google Scholar 

  29. Samsudin AA, Evans PN, Wright A-DG, Al Jassim R (2011) Molecular diversity of the foregut bacteria community in the dromedary camel (Camelus dromedarius). Environ Microbiol 13:3024–3035

    Article  CAS  PubMed  Google Scholar 

  30. Pope PB, Mackenzie AK, Gregor I, Smith W, Sundset MA, McHardy AC, Morrison M, Eijsink VGH (2012) Metagenomics of the Svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. PloS One 7:e38571

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Orpin CG, Mathiesen SD, Greenwood Y, Blix AS (1985) Seasonal changes in the ruminal microflora of the high-arctic Svalbard reindeer (Rangifer tarandus platyrhynchus). Appl Environ Microbiol 50:144–151

    PubMed Central  CAS  PubMed  Google Scholar 

  32. Dehority BA, Orpin CG (1997) Bacterial species in wild ruminants. In: Hobson PN, Stewart CS (eds) The rumen microbial ecosystem. Blackie Academic and Professional, London, pp 232–233

    Google Scholar 

  33. Baldwin RL, McLeod KR (2000) Effects of diet forage:concentrate ratio and metabolizable energy intake on isolated rumen epithelial cell metabolism in vitro. J Anim Sci 78:771–783

    CAS  PubMed  Google Scholar 

  34. Pope PB, Denman SE, Jones M, Tringe SG, Barry K, Malfatti SA, McHardy AC, Cheng J-F, Hugenholtz P, McSweeney CS, Morrison M (2010) Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different from other herbivores. Proc Natl Acad Sci U S A 107:14793–14798

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Kittelmann S, Seedorf H, Walters WA, Clemente JC, Knight R (2013) Simultaneous amplicon sequencing to explore co-occurrence patterns of bacterial, archaeal and eukaryotic microorganisms in rumen microbial communities. PLoS One 8:e47879

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Fernando SC, Purvis HT, Najar FZ, Sukharnikov LO, Krehbiel CR, Nagaraja TG, Roe BA, Desilva U (2010) Rumen microbial population dynamics during adaptation to a high-grain diet. Appl Environ Microbiol 76:7482–7490

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  37. Carey HV, Walters WA, Knight R (2013) Seasonal restructuring of the ground squirrel gut microbiota over the annual hibernation cycle. Am J Physiol Regul Integr Comp Physiol 304:R33–R42

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  38. Gürsoy M, Haraldsson G, Hyvönen M, Sorsa T, Pajukanta R, Könönen E (2009) Does the frequency of Prevotella intermedia increase during pregnancy? Oral Microbiol Immunol 24:299–303

    Article  PubMed  Google Scholar 

  39. Bäckhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI (2004) The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A 101:15718–15723

    Article  PubMed Central  PubMed  Google Scholar 

  40. Koren O, Goodrich JK, Cullender TC, Spor A, Laitinen K, Bäckhed HK, Gonzalez A, Werner JJ, Angenent LT, Knight R, Bäckhed F, Isolauri E, Salminen S, Ley RE (2012) Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150:470–80

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Saether B-E (1985) Annual variation in carcass weight of Norwegian moose in relation to climate along a latititudinal gradient. J Wildl Manag 49:977–983

    Article  Google Scholar 

  42. Baldwin RL, Allison MJ (1983) Rumen metabolism. J Anim Sci 57:461–477

    CAS  PubMed  Google Scholar 

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Acknowledgments

The authors would like to acknowledge the Vermont Fish and Wildlife Department for sample collection logistics; Terry Clifford, Archie Foster, Lenny Gerardi, Ralph Loomis, Beth and John Mayer, and Rob Whitcomb for collection of Vermont moose samples; Dr. Even Jørgensen, University of Tromsø, and Dr. Helge K. Johnsen, University of Tromsø, for collection of Norwegian moose samples; Dr. Monica A. Sunset, University of Tromsø, for facilitating sample collection and storage, as well as for providing DNA extraction materials for the Norwegian samples; Dr. John Crouse and Dr. Kimberlee Beckmen, both of the Alaska Department of Fish & Game for collection of Alaskan moose samples; and Dr. Benoit St-Pierre, University of Vermont for assistance with MOTHUR and Perl programming.

Conflict of Interest

The authors declare no competing interests.

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Authors and Affiliations

  1. Department of Animal Science, College of Agriculture and Life Sciences, University of Vermont, 203 Terrill Building, 570 Main Street, Burlington, VT, 05405, USA

    Suzanne L. Ishaq & André-Denis Wright

  2. Department of Medicine, University of Vermont, 111 Colchester Ave., Burlington, VT, 05401, USA

    André-Denis Wright

Authors
  1. Suzanne L. Ishaq
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  2. André-Denis Wright
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Correspondence to Suzanne L. Ishaq.

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Supplemental Table 1

The data set supporting the results of this article is available in the Sequence Read Archive (SRA), available through NCBI [study accession number SRP022590]. (DOCX 24 kb)

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Ishaq, S.L., Wright, AD. High-Throughput DNA Sequencing of the Ruminal Bacteria from Moose (Alces alces) in Vermont, Alaska, and Norway. Microb Ecol 68, 185–195 (2014). https://doi.org/10.1007/s00248-014-0399-0

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Keywords

  • Proteobacteria
  • Unique Sequence
  • Firmicutes
  • Bacteroidetes
  • Weight Class