Sequencing ancient calcified dental plaque shows changes in oral microbiota with dietary shifts of the Neolithic and Industrial revolutions

Journal name:
Nature Genetics
Year published:
Published online

The importance of commensal microbes for human health is increasingly recognized1, 2, 3, 4, 5, yet the impacts of evolutionary changes in human diet and culture on commensal microbiota remain almost unknown. Two of the greatest dietary shifts in human evolution involved the adoption of carbohydrate-rich Neolithic (farming) diets6, 7 (beginning ~10,000 years before the present6, 8) and the more recent advent of industrially processed flour and sugar (in ~1850)9. Here, we show that calcified dental plaque (dental calculus) on ancient teeth preserves a detailed genetic record throughout this period. Data from 34 early European skeletons indicate that the transition from hunter-gatherer to farming shifted the oral microbial community to a disease-associated configuration. The composition of oral microbiota remained unexpectedly constant between Neolithic and medieval times, after which (the now ubiquitous) cariogenic bacteria became dominant, apparently during the Industrial Revolution. Modern oral microbiotic ecosystems are markedly less diverse than historic populations, which might be contributing to chronic oral (and other) disease in postindustrial lifestyles.

At a glance


  1. Phylum-level microbial composition of ancient dental calculus deposits.
    Figure 1: Phylum-level microbial composition of ancient dental calculus deposits.

    The distribution is similar to that of modern oral samples and distinct from those of non-template controls, ancient human teeth and environmental samples. The phylum frequencies for the V3 region are presented for the ancient calculus samples (BB, Bell Beaker), modern oral samples, which included pyrosequenced (calculus, plaque and saliva31) and cloned (plaque1, 2, 21) data, non-template controls (or extraction blanks), ancient human teeth and environmental samples (freshwater, sediments and soils34, 35, 36, 37, 38, 39, 40) (Supplementary Table 1). Phylum frequencies from HOMD were generated from partial and full-length sequences of the 16S rRNA gene. The phyla with a frequency of <1% include ABY1_OD1, AD3, Armatimonadetes, BRC1, CCM11b, Chlamydiae, Chlorobi, Cyanobacteria, Elusimicrobia, Euryarchaeota, Fibrobacteres, GAL15, Gemmatimonadetes, GN02, GN04, GOUTA4, KSB1, Lentisphaerae, NC10, Nitrospirae, NKB19, OP11, OP3, OP9, PAUC34f, Planctomycetes, SBR1093, SC3, SC4, SM2F11, SPAM, Spirochaetes, SR1, Tenericutes, Thermi, TM6, Verrucomicrobia, WPS-2, WS3 and ZB2.

  2. Principal-components plot of [beta] diversity.
    Figure 2: Principal-components plot of β diversity.

    Principal-components analysis (PCA) shows a close phylogenetic relationship between ancient dental calculus and modern oral samples, both of which are distinct from the non-template controls and environmental samples. β diversity was calculated for all samples (Supplementary Note) using the UniFrac metric for the V3 region, and PCA was applied to the unweighted UniFrac distances. (a,b) Plots of the first and second components (PC1 and PC2) (a) and the second and third components (PC2 and PC3) (b) from PCA clustered the ancient dental calculus samples with the modern oral pyrosequenced data (calculus, plaque and saliva), which were separated from the environmental samples and extraction blanks. (c,d) Restricted PCA plots of PC1 and PC2 (c) and PC2 and PC3 (d) that only include ancient and modern oral pyrosequencing samples separated the hunter-gatherer (Mesolithic) samples from modern, medieval and Neolithic samples.

  3. Changes in the diversity and composition of oral microbiota.
    Figure 3: Changes in the diversity and composition of oral microbiota.

    (a) For the V3 region sequences, we estimated the phylogenetic diversity50 (Supplementary Note) of the archaeological dental calculus samples (n = 34) and compared them to modern calculus (n = 6) and plaque (n = 13). We estimated phylogenetic diversity from only classified, Gram-positive bacterial sequences to minimize the influence of taphonomic bias (Supplementary Note). Diversity was calculated at a depth of 34 sequences and bootstrapped to assess the robustness of the pattern. Error bars represent bootstrapped diversity values generated by sampling 255 replicates without replacement. BP, years before the present. (b) Specific primers were used to amplify sequences unique to the oral pathogens S. mutans and P. gingivalis. Error bars represent bootstrapped frequencies generated by sampling 255 replicates without replacement.

  4. Discriminant analysis of [beta] diversity.
    Figure 4: Discriminant analysis of β diversity.

    Discriminant analysis was applied to the principal coordinates generated from the unweighted UniFrac distances calculated from the V3 region sequences. Each individual is represented by a circle and colored according to archaeological grouping (HG, hunter-gatherer; LN/BA, late Neolithic/Bronze Age; StHW, St. Helen-on-the-Walls). The majority of phylogenetic variation (91.2%) was described by the first discriminant function, showing that individuals from the same archaeological groups cluster according to microbial composition.

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Author information


  1. Australian Centre for Ancient DNA, School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, South Australia, Australia.

    • Christina J Adler,
    • Laura S Weyrich,
    • Wolfgang Haak &
    • Alan Cooper
  2. Environment Institute, The University of Adelaide, Adelaide, South Australia, Australia.

    • Christina J Adler,
    • Laura S Weyrich,
    • Wolfgang Haak,
    • Corey J A Bradshaw &
    • Alan Cooper
  3. Institute of Dental Research, Westmead Millennium Institute, Faculty of Dentistry, University of Sydney, Sydney, New South Wales, Australia.

    • Christina J Adler
  4. Department of Archaeology, School of Geosciences, University of Aberdeen, Aberdeen, UK.

    • Keith Dobney
  5. School of Dentistry, The University of Adelaide, Adelaide, South Australia, Australia.

    • John Kaidonis &
    • Grant Townsend
  6. The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.

    • Alan W Walker &
    • Julian Parkhill
  7. School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, South Australia, Australia.

    • Corey J A Bradshaw
  8. South Australian Research and Development Institute, Henley Beach, South Australia, Australia.

    • Corey J A Bradshaw
  9. Department of Bioarchaeology, Institute of Archaeology, University of Warsaw, Warsaw, Poland.

    • Arkadiusz Sołtysiak
  10. Institute for Anthropology, Johannes Gutenberg University of Mainz, Mainz, Germany.

    • Kurt W Alt


C.J.A., A.C., K.D., A.W.W., J.P., K.W.A., G.T., J.K. and W.H. designed the study. C.J.A., K.D., K.W.A., A.S., W.H., A.C. and J.K. collected samples. C.J.A. and L.S.W. extracted and amplified DNA from dental calculus. C.J.A. and L.S.W. analyzed sequence data. A.W.W. performed 454 sequencing. C.J.A.B. performed α diversity bootstrapping analyses. C.J.A., A.C. and K.D. wrote the manuscript. All authors discussed the results and contributed to writing the manuscript.

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