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Emergence of human-adapted Salmonella enterica is linked to the Neolithization process

Nature Ecology & Evolution volume 4, pages 324–333 (2020)Cite this article

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Abstract

It has been hypothesized that the Neolithic transition towards an agricultural and pastoralist economy facilitated the emergence of human-adapted pathogens. Here, we recovered eight Salmonella enterica subsp. enterica genomes from human skeletons of transitional foragers, pastoralists and agropastoralists in western Eurasia that were up to 6,500 yr old. Despite the high genetic diversity of S. enterica, all ancient bacterial genomes clustered in a single previously uncharacterized branch that contains S. enterica adapted to multiple mammalian species. All ancient bacterial genomes from prehistoric (agro-)pastoralists fall within a part of this branch that also includes the human-specific S. enterica Paratyphi C, illustrating the evolution of a human pathogen over a period of 5,000 yr. Bacterial genomic comparisons suggest that the earlier ancient strains were not host specific, differed in pathogenic potential and experienced convergent pseudogenization that accompanied their downstream host adaptation. These observations support the concept that the emergence of human-adapted S. enterica is linked to human cultural transformations.

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Fig. 1: Geographic location and radiocarbon age of ancient human individuals infected with S. enterica.
Fig. 2: Phylogenetic relationships of reconstructed ancient and modern S. enterica core genomes.
Fig. 3: AESB topology, divergence times and gain–loss events.
Fig. 4: Pseudogenes and evolution of host adaptation across the AESB.

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Data availability

Raw metagenomic data used to reconstruct ancient S. enterica genomes, as well as unpublished ancient human DNA, are available from the European Nucleotide Archive (Accession no. PRJEB35216; see also Supplementary Data 2). The molecular dating archive containing files specifying the BEAST analysis of the two 50-taxon (argo-)pastoralist datasets, as well as the thinned posterior distributions of all parameters and the maximum clade consensus trees, are available at https://doi.org/10.6084/m9.figshare.10052084.v1.

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Acknowledgements

We thank the Pathogenomics group at MPI SHH and the Lieberman laboratory at MIT for critical discussions. We thank M. O’Reilly for support in graphical design. Funding for this study came from the Max Planck Society and the European Research Council (grant agreement no. 771234 PALEoRIDER). F.M.K. was supported by DFG (grant no. KE 2408/1-1) and a generous MPI EVA guest office. I.S. is supported by SNF (grant no. CR31I3L_157024). EnteroBase was supported by BBSRC (no. BB/L020319/1). N.-F.A., Z.Z. and M.A. were supported by the Wellcome Trust (grant no. 202792/Z/ 16/Z).

Author information

Authors and Affiliations

  1. Department of Archaeogenetics, Max Planck Institute for the Science of Human History, Jena, Germany

    Felix M. Key, Cosimo Posth, Ron Hübler, Maria A. Spyrou, Gunnar U. Neumann, Susanna Sabin, Marta Burri, Antje Wissgott, Aditya Kumar Lankapalli, Åshild J. Vågene, Rezeda Tukhbatova, Philipp W. Stockhammer, Kirsten Bos, Wolfgang Haak, Alexander Herbig & Johannes Krause

  2. Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Felix M. Key

  3. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Felix M. Key

  4. Transmission, Infection, Diversification & Evolution Group, Max Planck Institute for the Science of Human History, Jena, Germany

    Luis R. Esquivel-Gomez & Denise Kühnert

  5. Institute for Archaeological Sciences, Archaeo- and Palaeogenetics, University of Tuebingen, Tuebingen, Germany

    Anja Furtwängler

  6. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

    Matthias Meyer & Sarah Nagel

  7. Laboratory of Structural Biology, Kazan Federal University, Kazan, Russian Federation

    Rezeda Tukhbatova

  8. Samara State University of Social Sciences and Education, Samara, Russian Federation

    Aleksandr Khokhlov

  9. Institute of Archaeology named after A.Kh. Khalikov of the Academy of Sciences of the Republic of Tatarstan, Kazan, Russian Federation

    Andrey Chizhevsky

  10. Eurasia Department, German Archaeological Institute, Berlin, Germany

    Svend Hansen & Sabine Reinhold

  11. ‘Nasledie’ Cultural Heritage Unit, Stavropol, Russian Federation

    Andrey B. Belinsky & Alexey Kalmykov

  12. Department of Archaeology, Faculty of History, Lomonosov Moscow State University, Moscow, Russian Federation

    Anatoly R. Kantorovich

  13. Institute of Archaeology RAS, Moscow, Russian Federation

    Vladimir E. Maslov

  14. Institute for Pre- and Protohistoric Archaeology and Archaeology of the Roman Provinces, Ludwig Maximilian University Munich, Munich, Germany

    Philipp W. Stockhammer

  15. Department of Biology, University of Florence, Florence, Italy

    Stefania Vai, Alessandro Riga & David Caramelli

  16. Museum of Anthropology and Ethnology, Museum System of the University of Florence, Florence, Italy

    Monica Zavattaro

  17. Department of Archaeology, Durham University, Durham, UK

    Robin Skeates & Jessica Beckett

  18. School of Archaeology and Ancient History, Leicester University, Leicester, UK

    Maria Giuseppina Gradoli

  19. Institute of Archaeological Sciences and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Noah Steuri & Albert Hafner

  20. Archaeological Service of the Canton of Bern, Bern, Switzerland

    Marianne Ramstein

  21. Department of Physical Anthropology Institute of Forensic Medicine, University of Bern, Bern, Switzerland

    Inga Siebke & Sandra Lösch

  22. Department of Anthropology, Hacettepe University, Ankara, Turkey

    Yilmaz Selim Erdal

  23. Warwick Medical School, University of Warwick, Coventry, UK

    Nabil-Fareed Alikhan, Zhemin Zhou & Mark Achtman

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Contributions

F.M.K., A. Herbig and J.K. conceptualized the study. The original draft was written by F.M.K., which was then reviewed and edited by F.M.K., C.P., K.B., S.R., D.K., W.H., M.A., A. Herbig and J.K. F.M.K., C.P., L.R.E.-G. and D.K. carried out the formal analysis. F.M.K., C.P., M.A.S., G.U.N., A.F., S.S., M.B., A.W., M.M., S.N., R.T. and Z.Z. handled the investigation. F.M.K., R.H., Å.J.V., A.K.L., A. Khokhlov, A.C., S.H., A.B.B., A. Kalmykov, A.R.K., V.E.M., P.W.S., S.V., M.Z., A.R., D.C., R.S., J.B., M.G.G., N.S., A. Hafner, M.R., I.S., S.L., Y.S.E., N.-F.A., Z.Z., M.A., S.R. and W.H. contributed resources. A. Herbig and J.K. supervised.

Corresponding authors

Correspondence to Felix M. Key, Alexander Herbig or Johannes Krause.

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Extended data

Extended Data Fig. 1 Ancient human population genetic analysis.

a, PCA of newly reported ancient individuals with sufficient data (in red) and selected published ancient and modern individuals are projected onto principal components built with present-day West Eurasian populations (grey dots). b, ADMIXTURE analysis (K=10) of newly reported ancient individuals and relevant published ancient and modern individuals sorted by genetic clusters. Overview ancient human genetic data Supplementary Table 1 and further analysis Extended Data Fig. 2. EHG, Eastern hunter gatherer; E, Early; M, Middle; HG, hunter–gatherer; N, Neolithic; C, Caucasus; S, Scandinavian; W, Western; BA, Bronze Age.

Extended Data Fig. 2 Summary human genetic analysis.

a, ADMIXTURE analysis (K = 3 - 16) of newly reported ancient individuals (bold horizontal text) and published ancient and modern individuals sorted by genetic clusters and geographic origin (Europe, Near East and Caucasus, Asia, America, Africa). Each K was run five times and the replicate with the highest likelihood is reported. Ancient MK3001 shows Asian genetic ancestry components represented by Nganasan, Kankanaey, Atayal, and Ami. b, Box plot of five cross-validations (CV) values for every K calculated in ADMIXTURE. EHG, Eastern hunter gatherer; E, Early; M, Middle; HG, hunter–gatherer; N, Neolithic; C, Caucasus; S, Scandinavian; W, Western; BA, Bronze Age.

Extended Data Fig. 3 Maximum likelihood phylogeny of the AESB based on SNPs in positions present in 95% of strains.

Maximum likelihood tree of the AESB including the high coverage ancient genomes and 463 S. enterica genomes, considering all SNPs covered in at least 95% of strains (130,036 SNPs). New ancient genomes are shown in red, and previously reported ancient genomes (Tepos) in pink.

Extended Data Fig. 4 Recombination rate estimates for the AESB.

Estimated recombination rate is shown as recombination event per mutation event (r/m) and indicated on top of branch and by branch color. Recombination events have been inferred using all positions shared by 95% of strains from the AESB and are here reported for the SNPs shared by all strains on the AESB (correspond to maximum likelihood phylogeny shown in Fig. 2b). Maximum likelihood tree including all SNPs shared by at least 95% of strains from the AESB is shown in Extended Data Fig. 3.

Extended Data Fig. 5 Temporal signal analysis.

Results of the date randomization test for two subsets of the HC2600_1272 cluster. Circles represent mean substitution rate estimations with error bars representing 95% highest posterior density (HPD) intervals. For each subset 10 date randomizations were done. Significant temporal signal is indicated by non-overlapping HPD intervals between real data (red) and the randomizations (black), which is the case for both subsets.

Extended Data Fig. 6

Correlation between pseudogene frequency and time for all ancient genomes with mean genome-wide coverage above 5X.

Extended Data Fig. 7 Proportion of shared pseudogenes between strains across the AESB.

Proportion of pseudogene-sharing (0–100%) between strains on the AESB is shown in tones of red. Strains are ordered by phylogenetic branch and coloured accordingly.

Extended Data Fig. 8

Graphical abstract.

Extended Data Fig. 9 Mismatch distribution along positions at the 5′- and 3′- end of mapped sequencing reads.

C to T changes indicated in red and G to A changes in blue, all other substitutions in grey. IV3002 and MK3001 are UDG-half treated, which leads to observable damage only in the terminal positions. Plots generated with mapDamage2 (Jónsson H. et al, Bioinformatics 2013).

Extended Data Fig. 10 Photographs of archaeological specimens that harboured ancient S. enterica DNA.

(a) MUR009; (b) OBP001; (c) MUR019; (d) IKI003; (e) IV3002; (f) ETR001; (g) SUA004; (h) MK3001.

Supplementary information

Supplementary Information

Supplementary Information

Reporting Summary

Supplementary Data 1 and 2

Supplementary Data 1. Overview of pseudogenes across all genomes of the AESB, detailing branch association, number of pseudogenes, number of pan-genes present and cumulative size10. Supplementary Data 2. Overview of public data. ERR and ERS identifiers for all sequencing datasets generated.

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Key, F.M., Posth, C., Esquivel-Gomez, L.R. et al. Emergence of human-adapted Salmonella enterica is linked to the Neolithization process. Nat Ecol Evol 4, 324–333 (2020). https://doi.org/10.1038/s41559-020-1106-9

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