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


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.

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Figure 1: Comparison of 16S amplicon and shotgun sequencing datasets obtained from ancient, historic, and modern dental calculus samples.
Figure 2: Bacterial community composition at the phyla level of oral microbiota from chimpanzee, Neanderthal and modern human samples.
Figure 3: Draft genome and phylogeny of a 48,000-year-old archaeon, Methanobrevibacter oralis subsp. neandertalensis.


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We thank G. Manzi, the Odontological Collection of the Royal College of Surgeons, Royal Belgian Institute of Natural Sciences, Museo Nacional de Ciencias Naturales, and Adelaide University for access to dental calculus material. We thank A. Croxford for DNA sequencing and A. Walker, J. Krause and A. Herbig for feedback. The Australian Research Council supported this work through the Discovery Project and Fellowship schemes. We acknowledge the fundamental contribution of D. Brothwell (1933-2016) to this research by initiating the archaeological study of dental calculus.

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Authors and Affiliations



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.

Corresponding authors

Correspondence to Laura S. Weyrich or Alan Cooper.

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The authors declare no competing financial interests.

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

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Extended data figures and tables

Extended Data Figure 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).

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.

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.

Extended Data Figure 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).

Extended Data Figure 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.

Extended Data Figure 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.

Extended Data Figure 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.

Extended Data Figure 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.

Extended Data Figure 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.

Extended Data Figure 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.

Extended Data Figure 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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Weyrich, L., Duchene, S., Soubrier, J. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361 (2017).

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