Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus

Journal name:
Nature
Volume:
544,
Pages:
357–361
Date published:
DOI:
doi:10.1038/nature21674
Received
Accepted
Published online

Recent genomic data have revealed multiple interactions between Neanderthals and modern humans1, but there is currently little genetic evidence regarding Neanderthal behaviour, diet, or disease. Here we describe the shotgun-sequencing of ancient DNA from five specimens of Neanderthal calcified dental plaque (calculus) and the characterization of regional differences in Neanderthal ecology. At Spy cave, Belgium, Neanderthal diet was heavily meat based and included woolly rhinoceros and wild sheep (mouflon), characteristic of a steppe environment. In contrast, no meat was detected in the diet of Neanderthals from El Sidrón cave, Spain, and dietary components of mushrooms, pine nuts, and moss reflected forest gathering2, 3. Differences in diet were also linked to an overall shift in the oral bacterial community (microbiota) and suggested that meat consumption contributed to substantial variation within Neanderthal microbiota. Evidence for self-medication was detected in an El Sidrón Neanderthal with a dental abscess4 and a chronic gastrointestinal pathogen (Enterocytozoon bieneusi). Metagenomic data from this individual also contained a nearly complete genome of the archaeal commensal Methanobrevibacter oralis (10.2× depth of coverage)—the oldest draft microbial genome generated to date, at around 48,000 years old. DNA preserved within dental calculus represents a notable source of information about the behaviour and health of ancient hominin specimens, as well as a unique system that is useful for the study of long-term microbial evolution.

At a glance

Figures

  1. Comparison of 16S amplicon and shotgun sequencing datasets obtained from ancient, historic, and modern dental calculus samples.
    Figure 1: Comparison of 16S amplicon and shotgun sequencing datasets obtained from ancient, historic, and modern dental calculus samples.

    Filtered and unfiltered 16S rRNA amplicon and shotgun sequencing datasets, as well as the 16S rRNA shotgun sequences identified using GraftM, were compared using UPGMA clustering of Bray–Curtis distances from a wild-caught chimpanzee (red), Neanderthals (El Sidrón 1 (dark green), El Sidrón 2 (light green), Spy I (grey), Spy II (blue), and a modern human (orange) (n = 6 total samples).

  2. Bacterial community composition at the phyla level of oral microbiota from chimpanzee, Neanderthal and modern human samples.
    Figure 2: Bacterial community composition at the phyla level of oral microbiota from chimpanzee, Neanderthal and modern human samples.

    a, Oral microbiota from shotgun sequencing datasets of a wild-caught chimpanzee (top), Neanderthals (n = 3; middle) and a modern human (bottom) are presented at the phyla level. Names of the phyla were simplified for clarity, and unidentified reads were excluded. Gram-positive (blue) and Gram-negative (red) phyla are differentiated by colour. b, UPGMA clustering of Bray–Curtis distances are displayed for 22 oral metagenomes, revealing a strong correlation with meat consumption. The scale bar represents differences in Bray–Curtis distances. The UPGMA clustering reveals four distinct groupings: Forager-Gatherers, Hunter-Gatherers, Ancient Agriculturalists, and the Modern human. Definitions for abbreviations can be found in the Supplementary Table 1: Spy and El Sidron, Neanderthals; Afr SF, African forager; LBK, Early European farmer; Afr PP, African Pastoralist period; Euro HG, European hunter–gatherer; Jewbury, UK Medieval; War, German (published German Medieval data sets from ref. 12) Medieval; and modern, modern human (C10).

  3. Draft genome and phylogeny of a 48,000-year-old archaeon, Methanobrevibacter oralis subsp. neandertalensis.
    Figure 3: Draft genome and phylogeny of a 48,000-year-old archaeon, Methanobrevibacter oralis subsp. neandertalensis.

    a, Ancient sequences mapping to Methanobrevibacter oralis JMR01 are displayed in a circos plot (black), alongside the depth of coverage obtained (red; scale, 0–2,757 sequences). The reference sequence is displayed (grey) with the GC content of the reference sequence calculated in 2,500 bp bins (green; 0–0.4852% GC). b, A Methanobrevibacter phylogeny was constructed from alignment of the whole genome in RAxML with 100 bootstrap replicates, with the per cent support shown in each node. The estimated dates were calculated from a whole genome phylogeny using a Bayesian methodology (in BEAST) assuming a strict clock model (see Supplementary Information).

  4. Proportions of bacterial phyla from filtered and unfiltered 16S amplicon and shotgun sequencing datasets.
    Extended Data Fig. 1: Proportions of bacterial phyla from filtered and unfiltered 16S amplicon and shotgun sequencing datasets.

    a, b, Proportions of bacterial phyla of El Sidrón 1(a) and the modern human oral microbiota were compared (b). Samples in blue are from shotgun sequencing datasets, whereas samples in red are from 16S amplicon datasets. The different shapes of each data point correspond to the microbial phyla, which are displayed next to each phyla grouping (for example, the cross represents Proteobacteria for both 16S and shotgun sequencing datasets).

  5. The presence/absence distance (Jaccard) calculated for each 16S OTU observed in the 99th percentile. OTUs from each sample were then clustered according to dissimilarity within each sample.
    Extended Data Fig. 2: The presence/absence distance (Jaccard) calculated for each 16S OTU observed in the 99th percentile. OTUs from each sample were then clustered according to dissimilarity within each sample.

    Clusters of unique operational taxonomic units (OTUs) are identified (dashed lines) and labelled according to cluster relationships (red, no-agriculture; green, agriculture; purple, 19th century; fuchsia, modern time). Calculations are consistent with ancient DNA metagenomic analysis.

  6. SourceTracker take-one-out analysis for all samples.
    Extended Data Fig. 3: SourceTracker take-one-out analysis for all samples.

    a, b, Samples were grouped into time periods, and the proportion of each taxa originating from each sample group was inferred. Other, summed proportions across non-oral microbial groups (non-oral human microbiome, air, and soil) and unknown classification. Groups have a minimum of two samples (the non-human primate group is removed in filtered analysis as filtering reduced the sample number to one) and are displayed for the raw (unfiltered) OTU (a) (n = 54) and filtered OTU (b) data (n = 42).

  7. MALTX analysis compared to 16S and alternative shotgun analysis methods.
    Extended Data Fig. 4: MALTX analysis compared to 16S and alternative shotgun analysis methods.

    a, Unfiltered prokaryotic phyla identified from 16S rRNA (QIIME) and shotgun sequencing results (MALTX) are compared. b, Raw shotgun sequences were analysed by MALTX and by MG-RAST, and bacterial phyla and kingdom level results are displayed.

  8. MALTX benchmarked using modern oral microbiota and simulated datasets.
    Extended Data Fig. 5: MALTX benchmarked using modern oral microbiota and simulated datasets.

    a, Phyla identified in simulated metagenomes (modern or ancient) are shown for four different analysis programs: MALTX, DIAMOND, MetaPhylAn, and MG-RAST. b, Simulated metagenomes (modern (circle) or ancient (square; damaged)) analysed using four different software (DIAMOND (green), MALT (red), MetaPhylAn (blue), MG-RAST (orange)) were UPGMA-clustered according to Bray–Curtis distances calculated from genera within samples. c, Phyla identified by MALTX analysis in shotgun and amplicon oral datasets obtained from this study and MG-RAST are displayed in stacked bar plots.

  9. The composition of DNA sequences within ancient dental calculus in contrast to laboratory and environmental controls.
    Extended Data Fig. 6: The composition of DNA sequences within ancient dental calculus in contrast to laboratory and environmental controls.

    a, Sequences identified by MALTX at the phyla level are displayed for dental calculus samples, extraction blank controls (EBCs), and environmental samples. Ancient dental calculus samples are ordered according to age, with the oldest specimens listed on the left. b, Identified reads from MALTX were filtered to remove reads corresponding to species identified in extraction blank controls from QG DNA extractions and environmental controls. c, Filtered data was summarized to analyse only archaea and bacterial phyla typically found in the modern oral cavity. Dental calculus samples are displayed in order of age.

  10. MapDamage analysis of oral bacterial species shared between Neanderthals and the modern human.
    Extended Data Fig. 7: MapDamage analysis of oral bacterial species shared between Neanderthals and the modern human.

    a, b, The per cent of C–T mutations (a) and read length (b) calculated from mapped reads from each sample are shown for ten conserved species.

  11. Alpha diversity from deeply sequenced unfiltered shotgun datasets.
    Extended Data Fig. 8: Alpha diversity from deeply sequenced unfiltered shotgun datasets.

    a, b, Calculations of rarefied data were carried out using Shannon–Weaver (a) and Simpson’s reciprocal (b) indexes.

  12. Neanderthal microbiota compared to other ancient and modern calculus specimens.
    Extended Data Fig. 9: Neanderthal microbiota compared to other ancient and modern calculus specimens.

    a, UPGMA clustering of Bray–Curtis values were calculated from filtered rarefied shotgun data. b, The groups in a split on the basis of their differences in proportion of Gram-positive and Gram-negative phyla in shotgun datasets and were plotted for each group (chimpanzee and modern human, n = 1; Neanderthals, n = 3). Error bars represent s.d.

  13. Phylogenetic analysis of unlikely bacterial pathogens observed in Neanderthal dental calculus.
    Extended Data Fig. 10: Phylogenetic analysis of unlikely bacterial pathogens observed in Neanderthal dental calculus.

    a, Reads from El Sidrón 2 were mapped onto shared Neisseria genes (that is, those gene regions shared between all of the species) and the resulting DNA fragments were aligned in MUGSY, compared to RAxML, and bootstrapped with 100 iterations. b, Phylogenetic analysis of whooping cough in Neanderthals was completed. Shared genomic regions within publically available Bordetella genomes were compared to ancient Bordetella reads from El Sidrón Neanderthals using RAxML with 1,000 iterations (bootstrap values).

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Author information

Affiliations

  1. Australian Centre for Ancient DNA, School of Biological Sciences and The Environment Institute, University of Adelaide, Adelaide, South Australia, Australia

    • Laura S. Weyrich,
    • Julien Soubrier,
    • Luis Arriola,
    • Bastien Llamas,
    • James Breen,
    • Andrew G. Farrer,
    • Wolfgang Haak &
    • Alan Cooper
  2. Marie Bashir Institute for Infectious Diseases and Biosecurity, Charles Perkins Centre, School of Life and Environmental Sciences and Sydney Medical School, University of Sydney, Sydney, Australia

    • Sebastian Duchene &
    • Edward C. Holmes
  3. Department of Human Biology, University of Cape Town, Cape Town, South Africa

    • Alan G. Morris
  4. Danube Private University, Krems, Austria

    • Kurt W. Alt
  5. State Office for Heritage Management and Archaeology, Saxony-Anhalt, Germany

    • Kurt W. Alt &
    • Veit Dresely
  6. Heritage Museum, Halle, Germany

    • Kurt W. Alt &
    • Veit Dresely
  7. Institute for Prehistory and Archaeological Science, Basel University, Switzerland

    • Kurt W. Alt
  8. Department of Biology, University of Florence, Florence, Italy

    • David Caramelli
  9. Human Origins and Palaeo Environments Group, Oxford Brookes University, Oxford, UK

    • Milly Farrell
  10. Paleoanthropology, Senckenberg Centre for Human Evolution and Paleoenvironments, Eberhard Karls University of Tübingen, Tübingen, Germany

    • Michael Francken &
    • Katerina Harvati
  11. School of Dentistry, The University of Adelaide, Adelaide, Australia

    • Neville Gully,
    • John Kaidonis &
    • Grant Townsend
  12. Catalan Institution for Research and Advanced Studies (ICREA), Pg Lluís Companys 23, 08010 Barcelona, Catalonia, Spain

    • Karen Hardy
  13. Departament de Prehistòria, Facultat de Filosofia i Lletres, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain

    • Karen Hardy
  14. Institute of Anthropology, University of Mainz, Mainz, Germany

    • Petra Held
  15. Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona, Spain

    • Carles Lalueza-Fox
  16. Área de Prehistoria, Departamento de Historia, Universidad de Oviedo, Oviedo, Spain

    • Marco de la Rasilla
  17. Paleoanthropology Group, Department of Paleobiology, Museo Nacional de Ciencias Naturales, CSIC, Madrid, Spain

    • Antonio Rosas
  18. Scientific Service Heritage, Royal Belgian Institute of Natural Sciences, Brussels, Belgium

    • Patrick Semal
  19. Department of Bioarchaeology, Institute of Archaeology, University of Warsaw, Warsaw, Poland

    • Arkadiusz Soltysiak
  20. Istituto Italiano per l’Africa e l’Oriente (IsIAO), Rome, Italy

    • Donatella Usai
  21. State Office for Cultural Heritage Management Baden-Württemberg, Esslingen, Germany

    • Joachim Wahl
  22. Department of Algorithms in Bioinformatics, University of Tübingen, Tübingen, Germany

    • Daniel H. Huson
  23. Department of Archaeology, Classics and Egyptology, School of Histories, Languages and Cultures, University of Liverpool, Liverpool, UK

    • Keith Dobney
  24. Department of Archaeology, University of Aberdeen, Aberdeen, UK

    • Keith Dobney
  25. Department of Archaeology, Simon Fraser University, Burnaby, British Columbia, Canada

    • Keith Dobney

Contributions

L.S.W., K.D. and A.C. designed the study; A.G.M., K.W.A., D.C., V.D., M.Fa., M.Fr., N.G., W.H., K.Hard., K.Harv., P.H., J.K., C.L.F., M.d.l.R., A.R., P.S., A.S., D.U. and J.W. provided samples and interpretations of associated archaeological goods; L.S.W. performed experiments; L.S.W., S.D., E.C.H., J.S., B.L., J.B., L.A., A.G.F. and A.C. performed bioinformatics analysis and interpretation of the data; D.H.H. developed bioinformatics tools; N.G., J.K., and G.T. analysed medical relevance of data; L.S.W. and A.C. wrote the paper; and all authors contributed to editing the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Reviewer Information Nature thanks P. Ungar and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Proportions of bacterial phyla from filtered and unfiltered 16S amplicon and shotgun sequencing datasets. (222 KB)

    a, b, Proportions of bacterial phyla of El Sidrón 1(a) and the modern human oral microbiota were compared (b). Samples in blue are from shotgun sequencing datasets, whereas samples in red are from 16S amplicon datasets. The different shapes of each data point correspond to the microbial phyla, which are displayed next to each phyla grouping (for example, the cross represents Proteobacteria for both 16S and shotgun sequencing datasets).

  2. Extended Data Figure 2: The presence/absence distance (Jaccard) calculated for each 16S OTU observed in the 99th percentile. OTUs from each sample were then clustered according to dissimilarity within each sample. (391 KB)

    Clusters of unique operational taxonomic units (OTUs) are identified (dashed lines) and labelled according to cluster relationships (red, no-agriculture; green, agriculture; purple, 19th century; fuchsia, modern time). Calculations are consistent with ancient DNA metagenomic analysis.

  3. Extended Data Figure 3: SourceTracker take-one-out analysis for all samples. (339 KB)

    a, b, Samples were grouped into time periods, and the proportion of each taxa originating from each sample group was inferred. Other, summed proportions across non-oral microbial groups (non-oral human microbiome, air, and soil) and unknown classification. Groups have a minimum of two samples (the non-human primate group is removed in filtered analysis as filtering reduced the sample number to one) and are displayed for the raw (unfiltered) OTU (a) (n = 54) and filtered OTU (b) data (n = 42).

  4. Extended Data Figure 4: MALTX analysis compared to 16S and alternative shotgun analysis methods. (266 KB)

    a, Unfiltered prokaryotic phyla identified from 16S rRNA (QIIME) and shotgun sequencing results (MALTX) are compared. b, Raw shotgun sequences were analysed by MALTX and by MG-RAST, and bacterial phyla and kingdom level results are displayed.

  5. Extended Data Figure 5: MALTX benchmarked using modern oral microbiota and simulated datasets. (639 KB)

    a, Phyla identified in simulated metagenomes (modern or ancient) are shown for four different analysis programs: MALTX, DIAMOND, MetaPhylAn, and MG-RAST. b, Simulated metagenomes (modern (circle) or ancient (square; damaged)) analysed using four different software (DIAMOND (green), MALT (red), MetaPhylAn (blue), MG-RAST (orange)) were UPGMA-clustered according to Bray–Curtis distances calculated from genera within samples. c, Phyla identified by MALTX analysis in shotgun and amplicon oral datasets obtained from this study and MG-RAST are displayed in stacked bar plots.

  6. Extended Data Figure 6: The composition of DNA sequences within ancient dental calculus in contrast to laboratory and environmental controls. (652 KB)

    a, Sequences identified by MALTX at the phyla level are displayed for dental calculus samples, extraction blank controls (EBCs), and environmental samples. Ancient dental calculus samples are ordered according to age, with the oldest specimens listed on the left. b, Identified reads from MALTX were filtered to remove reads corresponding to species identified in extraction blank controls from QG DNA extractions and environmental controls. c, Filtered data was summarized to analyse only archaea and bacterial phyla typically found in the modern oral cavity. Dental calculus samples are displayed in order of age.

  7. Extended Data Figure 7: MapDamage analysis of oral bacterial species shared between Neanderthals and the modern human. (384 KB)

    a, b, The per cent of C–T mutations (a) and read length (b) calculated from mapped reads from each sample are shown for ten conserved species.

  8. Extended Data Figure 8: Alpha diversity from deeply sequenced unfiltered shotgun datasets. (168 KB)

    a, b, Calculations of rarefied data were carried out using Shannon–Weaver (a) and Simpson’s reciprocal (b) indexes.

  9. Extended Data Figure 9: Neanderthal microbiota compared to other ancient and modern calculus specimens. (159 KB)

    a, UPGMA clustering of Bray–Curtis values were calculated from filtered rarefied shotgun data. b, The groups in a split on the basis of their differences in proportion of Gram-positive and Gram-negative phyla in shotgun datasets and were plotted for each group (chimpanzee and modern human, n = 1; Neanderthals, n = 3). Error bars represent s.d.

  10. Extended Data Figure 10: Phylogenetic analysis of unlikely bacterial pathogens observed in Neanderthal dental calculus. (396 KB)

    a, Reads from El Sidrón 2 were mapped onto shared Neisseria genes (that is, those gene regions shared between all of the species) and the resulting DNA fragments were aligned in MUGSY, compared to RAxML, and bootstrapped with 100 iterations. b, Phylogenetic analysis of whooping cough in Neanderthals was completed. Shared genomic regions within publically available Bordetella genomes were compared to ancient Bordetella reads from El Sidrón Neanderthals using RAxML with 1,000 iterations (bootstrap values).

Supplementary information

Additional data