Skip to main content

Advertisement

SpringerLink
Log in

Changes in Microbiota in Rumen Digesta and Feces Due to a Grain-Based Subacute Ruminal Acidosis (SARA) Challenge

  • Host Microbe Interactions
  • Published:
Microbial Ecology Aims and scope Submit manuscript

Abstract

The effects of a grain-based subacute ruminal acidosis (SARA) challenge on bacteria in the rumen and feces of lactating dairy cows were determined. Six lactating, rumen-cannulated Danish Holstein cows were used in a cross-over study with two periods. Periods included two cows on a control diet and two cows on a SARA challenge. The control diet was a total mixed ration containing 45.5% dry matter (DM), 43.8% DM neutral detergent fiber, and 19.6% DM starch. The SARA challenge was conducted by gradually substituting the control diet with pellets containing 50% wheat and 50% barley over 3 days to reach a diet containing 55.6% DM, 31.3% DM neutral detergent fiber, and 31.8% DM starch, which was fed for four more days. Rumen fluid samples were collected at day 7 and 10 of experimental periods. Feces samples were collected on days 8 and 10 of these periods. Extracted DNA from the rumen and feces samples was analyzed to assess their bacterial communities using MiSeq Illumina sequencing of the V4 region of the 16S rRNA gene. The induction of SARA reduced the richness, diversity, and stability of bacterial communities and resulted in distinctly different microbiota in the rumen and feces. Bacteroidetes and Firmicutes were the most abundant phyla and, combined, they represented 76.9 and 94.4% of the bacterial community in the rumen fluid and the feces, respectively. Only the relative abundance of Firmicutes in the rumen was increased by the SARA challenge. In rumen fluid and feces, the abundances of nine out of the 90 and 25 out of the 89 taxa, respectively, were affected by the challenge. Hence, SARA challenge altered the composition of the bacterial community at the lower taxonomical level in the feces and therefore also likely in the hindgut, as well as in the rumen. However, only reductions in the bacterial richness and diversity in the rumen fluid and feces were in agreement with those of other studies and had a biological basis. Although the composition of the bacterial community of the feces was affected by the SARA challenge, bacterial taxa in the feces that can be used for accurate and non-invasive diagnosis of SARA could not be identified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Plaizier JC, Khafipour E, Li S, Gozho GN, Krause DO (2012) Subacute ruminal acidosis (SARA), endotoxins and health consequences. Anim. Feed Sci. Technol. 172:9–21

    Article  CAS  Google Scholar 

  2. Kleen JL, Cannizzo C (2012) Incidence, prevalence and impact of SARA in dairy herds. Anim. Feed Sci. Technol. 172:4–8

    Article  Google Scholar 

  3. Plaizier JC, Krause DO, Gozho GN, McBride BW (2008) Subacute ruminal acidosis in dairy cows: the physiological causes, incidence and consequences. Vet J 176:21–31

    Article  CAS  PubMed  Google Scholar 

  4. Gozho GN, Plaizier JC, Krause DO, Kennedy AD, Wittenberg KM (2005) Subacute ruminal acidosis induces ruminal lipopolysaccharide endotoxin release and triggers an inflammatory response. J. Dairy Sci. 88:1399–1403

    Article  CAS  PubMed  Google Scholar 

  5. Enemark J (2008) The monitoring, prevention and treatment of sub-acute ruminal acidosis (SARA): a review. Vet J 176:32–43

    Article  PubMed  Google Scholar 

  6. Khafipour E, Krause DO, Plaizier JC (2009a) Alfalfa pellet-induced subacute ruminal acidosis in dairy cows increases bacterial endotoxin in the rumen without causing inflammation. J. Dairy Sci. 92:1712–1724

    Article  CAS  PubMed  Google Scholar 

  7. Khafipour E, Krause DO, Plaizier JC (2009b) A grain-based subacute ruminal acidosis challenge causes translocation of lipopolysaccharide and triggers inflammation. J. Dairy Sci. 92:1060–1070

    Article  CAS  PubMed  Google Scholar 

  8. Calsamiglia S, Blanch M, Ferret A, Moya D (2012) Is subacute ruminal acidosis a pH related problem? Causes and tools for its control. Anim. Feed Sci. Technol. 172:42–50

    Article  Google Scholar 

  9. Petri RM, Schwaiger T, Penner GB, Beauchemin KA, Forster RJ, McKinnon JJ, McAllister TA (2013) Characterization of the core rumen microbiome in cattle during transition from forage to concentrate as well as during and after an acidotic challenge. PLoS One 8:e83424

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mao SY, Zhang RY, Wang DS, Zhu WY (2013) Impact of subacute ruminal acidosis (SARA) adaptation on rumen microbiota in dairy cattle using pyrosequencing. Anaerobe 24:12–19

    Article  CAS  PubMed  Google Scholar 

  11. Khafipour E, Li S, Tun H, Derakhshani H, Moossavi S, Plaizier JC (2016) Effects of grain feeding on microbiota in the digestive tract of cattle. Anim Front 6:13–19

    Article  Google Scholar 

  12. Russell JB, Rychlik JL (2001) Factors that alter rumen microbial ecology. Science 292:1119–1122

    Article  CAS  PubMed  Google Scholar 

  13. Krause DO, Nagaraja TG, Wright AD, Callaway TR (2013) Board-invited review: rumen microbiology: leading the way in microbial ecology. J. Anim. Sci. 91:331–341

    Article  CAS  PubMed  Google Scholar 

  14. Weimer PJ (2015) Redundancy, resiliance, and host specificity of the rumen microbiota:implications for engineering improved ruminal fermentation. Front. Microbiol. 6:296. doi:10.3389/fmicb.2015.00296

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Petri R, Forster R, Yang W, McKinnon J, McAllister T (2012) Characterization of rumen bacterial diversity and fermentation parameters in concentrate fed cattle with and without forage. J. Appl. Microbiol. 112:1152–1162

    Article  CAS  PubMed  Google Scholar 

  17. Danscher AM, Li S, Andersen PH, Khafipour E, Kristensen NB, Plaizier JC (2015) Indicators of induced subacute ruminal acidosis (SARA) in Danish Holstein cows. Acta Vet. Scand. 57:39

    Article  PubMed  PubMed Central  Google Scholar 

  18. Derakhshani H, Tun HM, Khafipour E (2016) An extended single-index multiplexed 16S rRNA sequencing for microbial community analysis on MiSeq Illumina platforms. J Basic Microbiol 56:321–326

  19. Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformat 27:2957–2963

    Article  Google Scholar 

  20. Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Meth 10:996–998

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  22. Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R (2010) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatic 26:266–267

    Article  CAS  Google Scholar 

  23. Price MN, Dehal PS, Arkin AP (2009) FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Molec Biol Evol 26:1641–1650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Meth 7:335–336

    Article  CAS  Google Scholar 

  25. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73:5261–5267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. de Cárcer DA, Denman SE, McSweeney C, Morrison M (2011) Evaluation of subsampling-based normalization strategies for tagged high-throughput sequencing data sets from gut microbiomes. Appl. Environ. Microbiol. 77:8795–8798

    Article  PubMed  Google Scholar 

  28. Anderson MJ, Crist TO, Chase JM, Vellend M, Inouye BD, Freestone AL, Sanders NJ, Cornell HV, Comita LS, Davies KF (2011) Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14:19–28

    Article  PubMed  Google Scholar 

  29. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:550

    Article  PubMed  PubMed Central  Google Scholar 

  30. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Series B (Methodol) 57:289–300

    Google Scholar 

  31. Fisher RA, Corbet AS, Williams CB (1943) The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12:42–58

  32. Li S, Gozho GN, Gakhar N, Khafipour E, Krause DO, Plaizier JC (2012a) Evaluation of diagnostic measures for subacute ruminal acidosis in dairy cows. Can J Ani Sci 92:353–364

    Article  CAS  Google Scholar 

  33. Li S, Khafipour E, Krause DO, Kroeker A, Rodriguez-Lecompte JC, Gozho GN, Plaizier JC (2012b) Effects of subacute ruminal acidosis challenges on fermentation and endotoxins in the rumen and hindgut of dairy cows. J. Dairy Sci. 95:294–303

    Article  CAS  PubMed  Google Scholar 

  34. Saleem F, Ametaj BN, Bouatra S, Mandal R, Zebeli Q, Dunn SM, Wishart DS (2012) A metabolomics approach to uncover the effects of grain diets on rumen health in dairy cows. J. Dairy Sci. 95:6606–6623

    Article  CAS  PubMed  Google Scholar 

  35. Levine JM, D'Antonio CM (1999) Elton revisited: a review of evidence linking diversity and invasibility. Oikos 87:15–26

    Article  Google Scholar 

  36. Khafipour E, Li S, Plaizier JC, Krause DO (2009) Rumen microbiome composition determined using two nutritional models of subacute ruminal acidosis. Appl. Environ. Microbiol. 75:7115–7124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hook SE, Steele MA, Northwood KS, Dijkstra J, France J, Wright AD, McBride BW (2011) Impact of subacute ruminal acidosis (SARA) adaptation and recovery on the density and diversity of bacteria in the rumen of dairy cows. FEMS Microbiol. Ecol. 78:275–284

    Article  CAS  PubMed  Google Scholar 

  38. El Kaoutari A, Armougom F, Gordon JI, Raoult D, Henrissat B (2013) The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nature Rev Microbiol 11:497–504

    Article  CAS  Google Scholar 

  39. Henderson G, Cox F, Ganesh S, Jonker A, Young W (2015) Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep 5:14567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Plaizier JC, Li S, Tun HM, Krause DO, Khafipour E (2016) Effects of experimentally induced subacute ruminal acidosis (SARA) on the rumen and hindgut microbiome in dairy cows. Front Microbiol In press

  41. Russell JB (2002) Rumen microbiology and its role in ruminant nutrition: Cornell University

  42. Gressley TF, Hall MB, Armentano LE (2011) Ruminant nutrition symposium: productivity, digestion, and health responses to hindgut acidosis in ruminants. J. Anim. Sci. 89:1120–1130

    Article  CAS  PubMed  Google Scholar 

  43. Callaway TR, Dowd SE, Edrington TS, Anderson RC, Krueger N, Bauer N, Kononoff PJ, Nisbet DJ (2010) 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. 88:3977–3983

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was funded by the Danish Research Council for Technology and Production and the Manitoba Agricultural Development and Research Council. We would like to thank the students and staff at the research station Rørrendegård for practical assistance.

Author information

Authors and Affiliations

  1. Department of Animal Science, University of Manitoba, Winnipeg, MB, Canada

    Jan C. Plaizier, Shucong Li, Hooman Derakshani & Ehsan Khafipour

  2. Department of Large Animal Sciences, University of Copenhagen, Copenhagen, Denmark

    Anne Mette Danscher

  3. Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Pia H. Andersen

  4. Department of Medical Microbiology, University of Manitoba, Winnipeg, MB, Canada

    Ehsan Khafipour

Authors
  1. Jan C. Plaizier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shucong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anne Mette Danscher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hooman Derakshani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pia H. Andersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ehsan Khafipour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jan C. Plaizier or Ehsan Khafipour.

Ethics declarations

Approval for the project was obtained from the Danish Animal Experiments Inspectorate (file no. 2012-15-2934-00052).

Electronic supplementary material

ESM 1

(DOCX 74 kb)

ESM 2

(DOCX 120 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Plaizier, J.C., Li, S., Danscher, A.M. et al. Changes in Microbiota in Rumen Digesta and Feces Due to a Grain-Based Subacute Ruminal Acidosis (SARA) Challenge. Microb Ecol 74, 485–495 (2017). https://doi.org/10.1007/s00248-017-0940-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00248-017-0940-z

Keywords

Search

Navigation