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Food Microbiology and Biotechnology (FMB)  |  Probiotics and Foodborne Microorganisms

Deciphering Diversity Indices for a Better Understanding of Microbial Communities

Bo-Ra Kim 1, Jiwon Shin 1, Robin B. Guevarra 1, Jun Hyung Lee 1, Doo Wan Kim 2, Kuk-Hwan Seol 2, Ju-Hoon Lee 3, Hyeun Bum Kim 1* and Richard E. Isaacson 4

1Department of Animal Resources Science, Dankook University, Cheonan 31116, Republic of Korea, 2National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea, 3Department of Food Science and Biotechnology, Institute of Life Science and Resources, Kyung Hee University, Youngin 17104, Republic of Korea, 4Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, MN 55108, USA

Received: September 14, 2017; Accepted: October 12, 2017

J. Microbiol. Biotechnol. 2017; 27(12): 2089-2093

Published December 28, 2017 https://doi.org/10.4014/jmb.1709.09027

Copyright © The Korean Society for Microbiology and Biotechnology.

Abstract

The past decades have been a golden era during which great tasks were accomplished in the field of microbiology, including food microbiology. In the past, culture-dependent methods have been the primary choice to investigate bacterial diversity. However, using cultureindependent high-throughput sequencing of 16S rRNA genes has greatly facilitated studies exploring the microbial compositions and dynamics associated with health and diseases. These culture-independent DNA-based studies generate large-scale data sets that describe the microbial composition of a certain niche. Consequently, understanding microbial diversity becomes of greater importance when investigating the composition, function, and dynamics of the microbiota associated with health and diseases. Even though there is no general agreement on which diversity index is the best to use, diversity indices have been used to compare the diversity among samples and between treatments with controls. Tools such as the Shannon- Weaver index and Simpson index can be used to describe population diversity in samples. The purpose of this review is to explain the principles of diversity indices, such as Shannon- Weaver and Simpson, to aid general microbiologists in better understanding bacterial communities. In this review, important questions concerning microbial diversity are addressed. Information from this review should facilitate evidence-based strategies to explore microbial communities.

Keywords

Microbiota, microbial diversity, microbial ecology, diversity index

References

  1. Berg RD. 1996. The indigenous gastrointestinal microflora. Trends Microbiol. 4: 430-435.
    CrossRef
  2. Manson JM, Rauch M, Gilmore MS. 2008. The commensal microbiology of the gastrointestinal tract. Adv. Exp. Med. Biol. 635: 15-28.
    Pubmed CrossRef
  3. Xu J, Bjursell MK, Himrod J, Deng S, Carmichael LK, Chiang HC, et al. 2003. A genomic view of the humanBacteroides thetaiotaomicron symbiosis. Science 299: 2074-2076.
    Pubmed CrossRef
  4. Sonnenburg JL, Angenent LT, Gordon JI. 2004. Getting a grip on things: how do communities of bacterial symbionts become established in our intestine? Nat. Immun. 5: 569-573.
    Pubmed CrossRef
  5. Kim HB, Isaacson RE. 2015. The pig gut microbial diversity:understanding the pig gut microbial ecology through the next generation high throughput sequencing. Vet. Microbiol. 177: 242-251.
    Pubmed CrossRef
  6. Isaacson R, Kim HB. 2012. The intestinal microbiome of the pig. Anim. Health Res. Rev. 13: 100-109.
    Pubmed CrossRef
  7. Schmalenberger A, Schwieger F, Tebbe CC. 2001. Effect of primers hybridizing to different evolutionarily conserved regions of the small-subunit rRNA gene in PCR-based microbial community analyses and genetic profiling. Appl. Environ. Microbiol. 67: 3557-3563.
    Pubmed PMC CrossRef
  8. Chakravorty S, Helb D, Burday M, Connell N, Alland D. 2007. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 69: 330-339.
    Pubmed PMC CrossRef
  9. Sogin ML, Morrison HG, Huber JA, Mark Welch D, Huse SM, Neal PR, et al. 2 006. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. USA 103: 12115-12120.
    Pubmed PMC CrossRef
  10. Huber JA, Mark Welch DB, Morrison HG, Huse SM, Neal PR, Butterfield DA, et al. 2007. Microbial population structures in the deep marine biosphere. Science 318: 97-100.
    Pubmed CrossRef
  11. Highlander SK. 2012. High throughput sequencing methods for microbiome profiling: application to food animal systems. Anim. Health Res. Rev. 13: 40-53.
    Pubmed CrossRef
  12. Sanschagrin S, Yergeau E. 2014. Next-generation sequencing of 16S ribosomal RNA gene amplicons. J. Vis. Exp. DOI: 10.3791/51709.
    CrossRef
  13. Schloss PD, Handelsman J. 2005. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl. Environ. Microbiol. 71: 1501-1506.
    Pubmed PMC CrossRef
  14. Schloss PD, Handelsman J. 2006. Introducing SONS, a tool for operational taxonomic unit-based comparisons of microbial community memberships and structures. Appl. Environ. Microbiol. 72: 6773-6779.
    Pubmed PMC CrossRef
  15. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75: 7537-7541.
    Pubmed PMC CrossRef
  16. Chao A. 1984. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11: 265-270.
  17. Chao A, Bunge J. 2002. Estimating the number of species in a stochastic abundance model. Biometrics 58: 531-539.
    Pubmed CrossRef
  18. Chao A, Chazdon RL, Colwell RK, Shen TJ. 2006. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62: 361-371.
    Pubmed CrossRef
  19. Hughes JB, Bohannan BJM. 2004. Application of ecological diversity statistics in microbial ecology. Mol. Microb. Ecol. Manual 7.01: 1321-1344.
  20. Haegeman B, Hamelin J, Moriarty J, Neal P, Dushoff J, Weitz JS. 2013. Robust estimation of microbial diversity in theory and in practice. ISME J. 7: 1092-1101.
    Pubmed PMC CrossRef
  21. Lemos LN, Fulthorpe RR, Triplett EW, Roesch LF. 2011. Rethinking microbial diversity analysis in the high throughput sequencing era. J. Microbiol. Methods 86: 42-51.
    Pubmed CrossRef
  22. Magurran A. 2004. Measuring Biological Diversity. Blackwell Science Ltd, Oxford, United Kingdom.
  23. Simpson EH. 1949. Measurement of diversity. Nature 163: 688.
    CrossRef
  24. Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJ. 2001. Counting the uncountable: statistical approaches to estimating microbial diversity. Appl. Environ. Microbiol. 67: 4399-4406.
    Pubmed PMC CrossRef
  25. Sanders HL. 1969. Benthic marine diversity and the stability-time hypothesis. Brookhaven Symp. Biol. 22: 71-81.
    Pubmed
  26. Efron B, Tibshirani R. 1993. An Introduction to the Bootsrap. Chapman & Hall, New York.
    CrossRef
  27. Shen TJ, Chao A, Ling C. 2003. Predicting the number of new species in further taxonomic sampling. Ecology 84: 798-804.
    CrossRef
  28. Chao A, Lee S. 1992. Estimating the number of classes via sample coverage. J. Am. Stat. Assoc. 87: 210-217.
    CrossRef
  29. Chao A, Ma M, Yang M. 1993. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80: 193-201.
    CrossRef
  30. Sornplang P, Piyadeatsoontorn S. 2016. Probiotic isolates from unconventional sources: a review. J. Anim. Sci. Technol. 58: 26.
    Pubmed PMC CrossRef

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Article

Minireview

J. Microbiol. Biotechnol. 2017; 27(12): 2089-2093

Published online December 28, 2017 https://doi.org/10.4014/jmb.1709.09027

Copyright © The Korean Society for Microbiology and Biotechnology.

Deciphering Diversity Indices for a Better Understanding of Microbial Communities

Bo-Ra Kim 1, Jiwon Shin 1, Robin B. Guevarra 1, Jun Hyung Lee 1, Doo Wan Kim 2, Kuk-Hwan Seol 2, Ju-Hoon Lee 3, Hyeun Bum Kim 1* and Richard E. Isaacson 4

1Department of Animal Resources Science, Dankook University, Cheonan 31116, Republic of Korea, 2National Institute of Animal Science, Rural Development Administration, Wanju 55365, Republic of Korea, 3Department of Food Science and Biotechnology, Institute of Life Science and Resources, Kyung Hee University, Youngin 17104, Republic of Korea, 4Department of Veterinary and Biomedical Sciences, University of Minnesota, St. Paul, MN 55108, USA

Received: September 14, 2017; Accepted: October 12, 2017

Abstract

The past decades have been a golden era during which great tasks were accomplished in the
field of microbiology, including food microbiology. In the past, culture-dependent methods
have been the primary choice to investigate bacterial diversity. However, using cultureindependent
high-throughput sequencing of 16S rRNA genes has greatly facilitated studies
exploring the microbial compositions and dynamics associated with health and diseases.
These culture-independent DNA-based studies generate large-scale data sets that describe the
microbial composition of a certain niche. Consequently, understanding microbial diversity
becomes of greater importance when investigating the composition, function, and dynamics of
the microbiota associated with health and diseases. Even though there is no general agreement
on which diversity index is the best to use, diversity indices have been used to compare the
diversity among samples and between treatments with controls. Tools such as the Shannon-
Weaver index and Simpson index can be used to describe population diversity in samples. The
purpose of this review is to explain the principles of diversity indices, such as Shannon-
Weaver and Simpson, to aid general microbiologists in better understanding bacterial
communities. In this review, important questions concerning microbial diversity are
addressed. Information from this review should facilitate evidence-based strategies to explore
microbial communities.

Keywords: Microbiota, microbial diversity, microbial ecology, diversity index

References

  1. Berg RD. 1996. The indigenous gastrointestinal microflora. Trends Microbiol. 4: 430-435.
    CrossRef
  2. Manson JM, Rauch M, Gilmore MS. 2008. The commensal microbiology of the gastrointestinal tract. Adv. Exp. Med. Biol. 635: 15-28.
    Pubmed CrossRef
  3. Xu J, Bjursell MK, Himrod J, Deng S, Carmichael LK, Chiang HC, et al. 2003. A genomic view of the humanBacteroides thetaiotaomicron symbiosis. Science 299: 2074-2076.
    Pubmed CrossRef
  4. Sonnenburg JL, Angenent LT, Gordon JI. 2004. Getting a grip on things: how do communities of bacterial symbionts become established in our intestine? Nat. Immun. 5: 569-573.
    Pubmed CrossRef
  5. Kim HB, Isaacson RE. 2015. The pig gut microbial diversity:understanding the pig gut microbial ecology through the next generation high throughput sequencing. Vet. Microbiol. 177: 242-251.
    Pubmed CrossRef
  6. Isaacson R, Kim HB. 2012. The intestinal microbiome of the pig. Anim. Health Res. Rev. 13: 100-109.
    Pubmed CrossRef
  7. Schmalenberger A, Schwieger F, Tebbe CC. 2001. Effect of primers hybridizing to different evolutionarily conserved regions of the small-subunit rRNA gene in PCR-based microbial community analyses and genetic profiling. Appl. Environ. Microbiol. 67: 3557-3563.
    Pubmed KoreaMed CrossRef
  8. Chakravorty S, Helb D, Burday M, Connell N, Alland D. 2007. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 69: 330-339.
    Pubmed KoreaMed CrossRef
  9. Sogin ML, Morrison HG, Huber JA, Mark Welch D, Huse SM, Neal PR, et al. 2 006. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. USA 103: 12115-12120.
    Pubmed KoreaMed CrossRef
  10. Huber JA, Mark Welch DB, Morrison HG, Huse SM, Neal PR, Butterfield DA, et al. 2007. Microbial population structures in the deep marine biosphere. Science 318: 97-100.
    Pubmed CrossRef
  11. Highlander SK. 2012. High throughput sequencing methods for microbiome profiling: application to food animal systems. Anim. Health Res. Rev. 13: 40-53.
    Pubmed CrossRef
  12. Sanschagrin S, Yergeau E. 2014. Next-generation sequencing of 16S ribosomal RNA gene amplicons. J. Vis. Exp. DOI: 10.3791/51709.
    CrossRef
  13. Schloss PD, Handelsman J. 2005. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl. Environ. Microbiol. 71: 1501-1506.
    Pubmed KoreaMed CrossRef
  14. Schloss PD, Handelsman J. 2006. Introducing SONS, a tool for operational taxonomic unit-based comparisons of microbial community memberships and structures. Appl. Environ. Microbiol. 72: 6773-6779.
    Pubmed KoreaMed CrossRef
  15. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75: 7537-7541.
    Pubmed KoreaMed CrossRef
  16. Chao A. 1984. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11: 265-270.
  17. Chao A, Bunge J. 2002. Estimating the number of species in a stochastic abundance model. Biometrics 58: 531-539.
    Pubmed CrossRef
  18. Chao A, Chazdon RL, Colwell RK, Shen TJ. 2006. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62: 361-371.
    Pubmed CrossRef
  19. Hughes JB, Bohannan BJM. 2004. Application of ecological diversity statistics in microbial ecology. Mol. Microb. Ecol. Manual 7.01: 1321-1344.
  20. Haegeman B, Hamelin J, Moriarty J, Neal P, Dushoff J, Weitz JS. 2013. Robust estimation of microbial diversity in theory and in practice. ISME J. 7: 1092-1101.
    Pubmed KoreaMed CrossRef
  21. Lemos LN, Fulthorpe RR, Triplett EW, Roesch LF. 2011. Rethinking microbial diversity analysis in the high throughput sequencing era. J. Microbiol. Methods 86: 42-51.
    Pubmed CrossRef
  22. Magurran A. 2004. Measuring Biological Diversity. Blackwell Science Ltd, Oxford, United Kingdom.
  23. Simpson EH. 1949. Measurement of diversity. Nature 163: 688.
    CrossRef
  24. Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJ. 2001. Counting the uncountable: statistical approaches to estimating microbial diversity. Appl. Environ. Microbiol. 67: 4399-4406.
    Pubmed KoreaMed CrossRef
  25. Sanders HL. 1969. Benthic marine diversity and the stability-time hypothesis. Brookhaven Symp. Biol. 22: 71-81.
    Pubmed
  26. Efron B, Tibshirani R. 1993. An Introduction to the Bootsrap. Chapman & Hall, New York.
    CrossRef
  27. Shen TJ, Chao A, Ling C. 2003. Predicting the number of new species in further taxonomic sampling. Ecology 84: 798-804.
    CrossRef
  28. Chao A, Lee S. 1992. Estimating the number of classes via sample coverage. J. Am. Stat. Assoc. 87: 210-217.
    CrossRef
  29. Chao A, Ma M, Yang M. 1993. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80: 193-201.
    CrossRef
  30. Sornplang P, Piyadeatsoontorn S. 2016. Probiotic isolates from unconventional sources: a review. J. Anim. Sci. Technol. 58: 26.
    Pubmed KoreaMed CrossRef