Article | Published:

Diversity-disease relationships and shared species analyses for human microbiome-associated diseases

The ISME Journal (2019) | Download Citation

Abstract

Diversity indices have been routinely computed in the study of human microbiome-associated diseases (MADs). However, it is still unclear whether there is a consistent diversity-disease relationship (DDR) for the human MADs, and whether there are consistent differences in the taxonomic composition of microbiomes sampled from healthy versus diseased individuals. Here we reanalyzed raw data and used a meta-analysis to compare the microbiome diversity and composition of healthy versus diseased individuals in 41 comparisons extracted from 27 previously published studies of human MADs. In the DDR analysis, the average effect size across studies did not differ from zero for a comparison of healthy versus diseased individuals. In 30 of 41 comparisons (73%) there was no significant difference in microbiome diversity of healthy versus diseased individuals, or of different disease classes. For the species composition analysis (shared species analysis), the effect sizes were significantly different from zero. In 33 of 41 comparisons (80%), there were fewer OTUs (operational taxonomic units) shared between healthy and diseased individuals than expected by chance, but with 49% (20 of 41 comparisons) statistically significant. These results imply that the taxonomic composition of disease-associated microbiomes is often distinct from that of healthy individuals. Because species composition changes with disease state, some microbiome OTUs may serve as potential diagnostic indicators of disease. However, the overall species diversity of human microbiomes is not a reliable indicator of disease.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

All datasets analyzed in this study are available in public domain and see Table S1 for the detailed access information for each of the 27 datasets.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Relman DA. The human microbiome: ecosystem resilience and health. Nutr Rev. 2012;70(Suppl. 1):S2–S9. https://doi.org/10.1111/j.1753-4887.2012.00489.x.

  2. 2.

    Theilade E. The non-specific theory in microbial etiology of inflammatory periodontal diseases. J Clin Periodontol. 1986;13:905–11.

  3. 3.

    Sobel JD. Is there a protective role for vaginal flora? Curr Infect Dis Rep. 1999;1:379–83.

  4. 4.

    Marsh PD. Microbial ecology of dental plaque and its significance in health and disease. Adv Dent Res. 1994;8:263.

  5. 5.

    Grenier D, Mayrand D. Adult periodontitis: an ecological perspective of mixed infections. Trends Microbiol. 1995;3:148.

  6. 6.

    HMP Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14. https://doi.org/10.1038/nature11234.

  7. 7.

    Li K, Bihan M, Yooseph S, Methe BA. Analyses of the microbial diversity across the human microbiome. PLoS ONE. 2012;7:e32118 https://doi.org/10.1371/journal.pone.0032118.

  8. 8.

    Ma B, Forney LJ, Ravel J. The vaginal microbiome: rethinking health and disease. Annu Rev Microbiol. 2012;66:371–89. https://doi.org/10.1146/annurev-micro-092611-150157.

  9. 9.

    Lozupone CA, Stombaugh JI, Gordon J, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012;489:220–30.

  10. 10.

    Lloyd-Price J, Abu-Ali G, Huttenhower C. The healthy human microbiome. Genome Med. 2016;8:51 https://doi.org/10.1186/s13073-016-0307-y.

  11. 11.

    Knight R, Callewaert C, Marotz C, Hyde ER, Debelius JW, McDonald D, et al. The microbiome and human biology. Annu Rev Genom Hum Genet. 2017;18:65–86.

  12. 12.

    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537.

  13. 13.

    Caporaso J, Kuczynski J, Stombaugh J. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

  14. 14.

    Logares R, Audic S, Bass D, Bittner L, Boutte C, Christen R, et al. Patterns of rare and abundant marine microbial eukaryotes. Curr Biol. 2014;24:813–21.

  15. 15.

    Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996.

  16. 16.

    Lundberg DS, Yourstone S, Mieczkowski P, Jones CD, Dangl JL. Practical innovations for high-throughput amplicon sequencing. Nat Methods. 2013;10:999.

  17. 17.

    Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S, Dröge J, et al. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat Methods. 2017;14:1063–71.

  18. 18.

    QIIME-2. 2018. https://qiime2.org/. Accessed 12 Sept 2018.

  19. 19.

    Dickson RP, Huffnagle GB. The Lung microbiome: new principles for respiratory bacteriology in health and disease. PLoS Pathog. 2015;11:e1004923. https://doi.org/10.1371/journal.ppat.1004923

  20. 20.

    Byrd AL, Belkaid Y, Segre JA. The human skin microbiome. Nat Rev Microbiol. 2018;16:143–55.

  21. 21.

    Verma D, Garg PK, Dubey AK. Insights into the oral microbiome. Arch Microbiol. 2018;200:525–40.

  22. 22.

    Hughes JB, Hellmann JJ, Ricketts TH, Bohannan BJM. Counting the uncountable: Statistical approaches to estimating microbial diversity. Appl Environ Microbiol. 2001;67:4399–406.

  23. 23.

    Gotelli NJ, Colwell RK. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 2001;4:379–91.

  24. 24.

    Colwell RK, Chao A, Gotelli NJ, Lin SY, Mao CX, Chazdon RL, et al. Models and estimators linking individual-based and sample-based rarefaction, extrapolation, and comparison of assemblages. J Plant Ecol. 2012;5:3–21.

  25. 25.

    Chao A, Chiu CH, Jost L. Unifying species diversity, phylogenetic diversity, functional diversity and related similarity and differentiation measures through Hill numbers. Annu Rev Ecol, Evol, Syst. 2014;45:297–324.

  26. 26.

    Gotelli NJ, Shimadzu H, Dornelas M, McGill B, Moyes F, Magurran AE. Community-level regulation of temporal trends in biodiversity. Sci Adv. 2017;3:e170031.

  27. 27.

    Anderson MJ, Crist TO, Chase JM, Vellend M, Inouye BD, Freestone AL, et al. Navigating the multiple meanings of beta-diversity: a roadmap for the practicing ecologist. Ecol Lett. 2011;14:19–28.

  28. 28.

    Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54:427–342.

  29. 29.

    Chao A, Chiu CH, Hsieh TC. Proposing a resolution to debates on diversity partitioning. Ecology. 2012;93:2037–51.

  30. 30.

    Chao A, Gotelli NG, Hsieh TC, Sander EL, Ma KH, Colwell RK, et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species biodiversity studies. Ecol Monogr. 2014;84:45–67. https://github.com/JohnsonHsieh/iNEXT).

  31. 31.

    Ehrlich SD, Consortium TM. MetaHIT: The European Union Project on Metagenomics of the Human Intestinal Tract. Metagenomics Human Body. 2011;25:968.

  32. 32.

    McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10:e1003531 https://doi.org/10.1371/journal.pcbi.1003531.

  33. 33.

    Colwell RK, Coddington JA. Estimating terrestrial biodiversity through extrapolation. Philos Trans R Soc Lond. 1994;345:101–18.

  34. 34.

    Chao A. Nonparametric estimation of the number of classes in a population. Scand J Stat. 1984;11:265–70.

  35. 35.

    Chao A, Colwell RK, Lin CW, Gotelli NJ. Sufficient sampling for asymptotic minimum species richness estimators. Ecology. 2009;90:1125–33.

  36. 36.

    Hsieh TC, Ma KH, Chao A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol Evol. 2016;7:1451–6.

  37. 37.

    Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd). Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.

  38. 38.

    Cooper H, Hedges LV, Valentine JC. The handbook of research synthesis and meta-analysis. 2nd ed. New York, NY, USA: Russell Sage Foundation; 1997. p. 592.

  39. 39.

    Ostfeld RS, Keesing F, Eviner VT (eds.). Infectious disease ecology: effects of ecosystems on disease and of disease on ecosystems. Princeton, NJ, USA: Princeton University Press; 2008.

  40. 40.

    Johnson PTJ, Preston DL, Hoverman JT, Richgels KLD. Biodiversity decreases disease through predictable changes in host community competence. Nature. 2013;494:230–4.

  41. 41.

    Johnson PTJ, Ostfeld RS, Keesing F. Frontiers in research on biodiversity and disease. Ecol Lett. 2015;18:1119–33. https://doi.org/10.1371/journal.pone.0041606.

  42. 42.

    Myers SS, Gaffikin L, Golden CD, Ostfeld RS, Redford KH, Ricketts TH, et al. Human health impacts of ecosystem alteration. Proc Natl Acad Sci USA. 2013;110:18753–60.

  43. 43.

    Elton CS. The ecology of invasions by animals and plants. Chicago, IL: University of Chicago Press; 1958.

  44. 44.

    Zadoks JC. Reflections on space, time, and diversity. Annu Rev Phytopathol. 1999;37:1–17.

  45. 45.

    Wan FH, Yang NW. Invasion and management of agricultural alien insects in China. Annu Rev Entomol. 2016;61:77–98.

Download references

Acknowledgements

This study received funding from the following sources: National Science Foundation of China (Grant No. 71473243), Cloud-Ridge Industry Technology Leader Grant, A China-US International Cooperation Project on Genomics/Metagenomics Big Data.

Author information

Affiliations

  1. Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China

    • Zhanshan (Sam) Ma
    •  & Lianwei Li
  2. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China

    • Zhanshan (Sam) Ma
  3. Department of Biology, University of Vermont Burlington, Burlington, VT, 05405, USA

    • Nicholas J. Gotelli

Authors

  1. Search for Zhanshan (Sam) Ma in:

  2. Search for Lianwei Li in:

  3. Search for Nicholas J. Gotelli in:

Contributions

ZSM and NJG defined the research objective. ZSM and LWL conducted data analysis and interpreted the results. ZSM wrote the manuscript and NJG revised the manuscript. All authors read and approved the final manuscript.

Conflict of interest

The authors declare that they have no conflict of interest.

Corresponding authors

Correspondence to Zhanshan (Sam) Ma or Nicholas J. Gotelli.

Supplementary information

About this article

Publication history

Received

Revised

Accepted

Published

DOI

https://doi.org/10.1038/s41396-019-0395-y