Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis (original) (raw)

Accession codes

Primary accessions

Sequence Read Archive

Data deposits

Raw sequencing data have been deposited in the National Center for Biotechnology Information Sequence Read Archive under accession numbers SRP041177 for the ancient Peruvian samples and SRP041181 for the M. pinnipedii strains.

Change history

Minor changes were made to the author list, Fig. 3 and ED Figs 1 and 8.

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Acknowledgements

We thank the following people and institutions for assistance and/or permission for sampling: Museo Contisuyo, Centro Mallqui, S. Guillen, Instituto Nacional de Cultura, Peru, G. Cock, C. Gaither, M. Murphy, M. C. Lozada, S. Burgess, D. Blom, B. Owen, A. Oquiche Hernani, P. Palacios Filinich, S. Williams, B. Vargas, D. Rice, and H. Klaus, S. Pfieffer, the University of Toronto, the Upper Mississippi Valley Archaeological Research Foundation, L. Conrad, Indiana University, G. Milner, the Pennsylvania State University, the American Museum of Natural History, A. Stodder, B. Brier, I. Tattersall, K. Mowbray, the National Museum of Natural History (Smithsonian Institution), B. Billeck, the Smithsonian Institution, N. Tuross, L.-A. Pfister, Rochester Museum & Science Center, L. P. Saunders, C. Grivas, G. Housman, and M. Nieves-Colon. We thank H. Poinar for discussions about capture regions for M. tuberculosis screening. We thank B. Coombes and B. Krause-Kyora for providing modern tuberculosis for bait manufacture. The Huron Wendat Nation is aware of the sampling of Uxbridge bone and is a recipient of information from this study. We acknowledge the following sources of funding: European Research Council starting grant APGREID (to J.K.), the National Science Foundation (to A.C.S. and J.E.B.) for NSF BCS-1063939, NSF-REU BCS-0612222, and NSF BCS-0612222, the George E. Burch Fellow in Theoretic Medicine and Affiliated Sciences at the Smithsonian Institution (2003–2007, to J.E.B.), Social Sciences and Humanities Research Council of Canada postdoctoral fellowship grant 756-2011-501 (to K.I.B.), National Science Foundation Graduate Research Fellowship DGE-1311230 and Jacob K. Javits Fellowship (to K.M.H.), Ramón y Cajal Spanish research grant RYC-2012-10627 (to I.C.), Swiss National Science Foundation PP0033_119205 (to S.G.), National Institutes of Health AI090928 (to S.G.), European Research Council 309540 (to S.G.), PICT0575 Argentina (to R.A.G.), Wadsworth Fellowship from the Wenner-Gren Foundation (to T.J.C.), Wellcome Trust 098051 (to J.P., J.M.B. and S.R.H.), and funding from the Medical Research Council (to J.M.B.).

Author information

Author notes

  1. Kirsten I. Bos, Kelly M. Harkins, Alexander Herbig and Mireia Coscolla: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Archaeological Sciences, University of Tübingen, Ruemelinstraße 23, 72070 Tübingen, Germany,
    Kirsten I. Bos, Alexander Herbig, Stephen A. Forrest, Verena J. Schuenemann, Kerttu Majander & Johannes Krause
  2. School of Human Evolution and Social Change, Arizona State University, PO Box 872402, Tempe, Arizona 85287-2402, USA,
    Kelly M. Harkins, Alicia K. Wilbur, Jane E. Buikstra & Anne C. Stone
  3. Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany,
    Alexander Herbig, Nico Weber, Daniel Huson & Kay Nieselt
  4. Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Socinstrasse 57, 4002 Basel, Switzerland,
    Mireia Coscolla & Sebastien Gagneux
  5. University of Basel, Petersplatz 1, CH-4003 Basel, Switzerland,
    Mireia Coscolla & Sebastien Gagneux
  6. Genomics and Health Unit, FISABIO-Public Health, Avenida Cataluña 21, 46020 Valencia, Spain,
    Iñaki Comas
  7. CIBER (Centros de Investigación Biomédica en Red) in Epidemiology and Public Health, Instituto de Salud Carlos III, C/ Monforte de Lemos 3-5, Pabellón 11, Planta 0, 28029 Madrid, Spain,
    Iñaki Comas
  8. Pathogen Genomics, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK,
    Josephine M. Bryant, Simon R. Harris & Julian Parkhill
  9. Department of Archaeology, University of Cape Town, Private Bag X1, Rondebosch, 7701, South Africa,
    Tessa J. Campbell
  10. Departamento de Biología (FCEyN, CONICET, Laboratorio de Ecología Evolutiva Humana (FACSO, UNCPBA), UNMDP), Calle 508 No. 881 (7631), Quequen, Argentina,
    Ricardo A. Guichon
  11. Department of Anthropology, University of Tennessee, 250 South Stadium Hall, Knoxville, Tennessee 37996, USA,
    Dawnie L. Wolfe Steadman
  12. Department of Anthropology, Indiana University, 701 East Kirkwood Avenue, Bloomington, Indiana 47405-7100, USA,
    Della Collins Cook
  13. Molecular Mycobacteriology, Forschungszentrum Borstel, Parkallee 1, 23845 Borstel, Germany,
    Stefan Niemann
  14. German Center for Infection Research, Forschungszentrum Borstel, Parkallee 1, 23845 Borstel, Germany,
    Stefan Niemann
  15. McGill International TB Centre, McGill University, 1650 Cedar Avenue, Montreal H3G 1A4, Canada,
    Marcel A. Behr
  16. Biotechnology Institute, CICVyA-INTA Castelar, Dr. Nicolás Repetto y De Los Reseros S/N, (B1686IGC) Hurlingham, Buenos Aires, Argentina,
    Martin Zumarraga
  17. Instituto de Investigaciones Marinas y Costeras (CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, San Luis 1722, Mar del Plata 7600, Argentina,
    Ricardo Bastida
  18. Department of Medicine, Imperial College, London W2 1PG, UK
    Douglas Young
  19. Division of Mycobacterial Research, MRC National Institute for Medical Research, Mill Hill, London NW7 1AA, UK,
    Douglas Young
  20. Senckenberg Centre for Human Evolution and Palaeoenvironment, University of Tübingen, Tübingen 72070, Germany,
    Johannes Krause
  21. Max Planck Institute for Science and History, Khalaische Straße 10, 07745 Jena, Germany,
    Johannes Krause

Authors

  1. Kirsten I. Bos
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  2. Kelly M. Harkins
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  3. Alexander Herbig
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  4. Mireia Coscolla
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  5. Nico Weber
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  6. Iñaki Comas
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  7. Stephen A. Forrest
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  8. Josephine M. Bryant
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  9. Simon R. Harris
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  10. Verena J. Schuenemann
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  11. Tessa J. Campbell
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  12. Kerttu Majander
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  13. Alicia K. Wilbur
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  14. Ricardo A. Guichon
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  15. Dawnie L. Wolfe Steadman
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  16. Della Collins Cook
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  17. Stefan Niemann
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  18. Marcel A. Behr
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  19. Martin Zumarraga
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  20. Ricardo Bastida
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  21. Daniel Huson
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  22. Kay Nieselt
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  23. Douglas Young
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  24. Julian Parkhill
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  25. Jane E. Buikstra
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  26. Sebastien Gagneux
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  27. Anne C. Stone
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  28. Johannes Krause
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Contributions

A.C.S., J.E.B., J.K., K.I.B., and K.M.H. conceived the investigation. J.K., K.I.B., A.C.S., S.A.F., N.W., and A.K.W. designed experiments. J.P., R.A.G., D.L.W.S., D.C.C., S.N., M.A.B., M.Z., and R.B. provided samples for analysis. K.I.B., K.M.H., V.J.S., T.J.C., and A.K.W. performed laboratory work. A.H., J.K., S.G., M.C., N.W., K.I.B., I.C., D.Y., J.P., J.M.B., S.R.H., D.H., K.N., A.C.S., K.M.H., J.E.B., T.J.C., D.C.C., and D.L.W.S. performed analyses. K.I.B. wrote the manuscript with contributions from all co-authors.

Corresponding authors

Correspondence toKirsten I. Bos, Anne C. Stone or Johannes Krause.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Coverage and damage plots for the M. tuberculosis capture regions for samples 54, 58, and 64.

Extended Data Figure 2 Histograms of SNP allele frequency distributions for the ancient samples and the Hungarian mummy sample using standard mapping parameters.

The x axis denotes the frequency of reads covering a SNP position in which the SNP was detected. The y axis denotes the number of observed SNP calls with the respective frequency. All variants with a SNP allele frequency below 90% are shown.

Extended Data Figure 3 Histograms of SNP allele frequency distributions for the ancient samples, the Hungarian mummy sample, and two modern isolates using stricter mapping and filtering parameters.

The x axis denotes the frequency of reads covering a SNP position in which the SNP was detected. The y axis denotes the number of observed SNP calls with the respective frequency. All variants with a SNP allele frequency below 90% are shown.

Extended Data Figure 4 Maximum parsimony analysis.

a, Maximum parsimony tree of all 262 samples of the complete data set. Positions with missing data were excluded. b, Subtree of the full maximum parsimony tree showing the lineage 6 and animal strains. Positions with missing data were excluded. Branches are labelled with the absolute number of substitutions. Internal nodes are labelled with bootstrap statistics obtained from 1,000 replicates.

Extended Data Figure 5 Maximum likelihood analysis.

a, Maximum likelihood tree of all 262 samples of the complete data set. Positions with missing data were excluded. b, Maximum likelihood subtree showing the lineage 6 and animal strains. Positions with missing data were excluded. Internal nodes are labelled with bootstrap statistics obtained from 200 replicates.

Extended Data Figure 6 Neighbour joining analysis.

a, Neighbour joining tree of all 262 samples of the complete data set. Positions with missing data were excluded. b, Neighbour joining subtree showing the lineage 6 and animal strains. Positions with missing data were excluded. Internal nodes are labelled with bootstrap statistics obtained from 1,000 replicates.

Extended Data Figure 7 Maximum clade credibility tree of M. tuberculosis.

The tree was estimated using the uncorrelated log-normal relaxed clock model in BEAST 1.7.5 (ref. 31). The radiocarbon dates of the ancient Peruvian strains were used as temporal estimates to date the tree. Branch lengths are scaled to years. Branch colours indicate the estimated branch substitution rate on the logarithmic scale shown in the legend at the left.

Extended Data Figure 8

a, Posterior distributions of times to most recent common ancestor (TMRCA) for different MTBC branches, and exponential growth and constant size models. b, Bayesian skyline plot showing estimated effective population sizes for the human lineages. c, Bayesian skyline plot showing estimated effective population sizes for the animal lineages.

Extended Data Figure 9 Maximum likelihood phylogeny of L4 lineage including modern and ancient strains.

The mixed samples are separated out into Hungarian 1 and 2. SNPs were mapped back onto the phylogeny, and branches marked in red are those defined by variants found to be mixed in the Hungarian sample. This allowed us to determine the ancestral nodes and branches for each of the two strains on the tree. The dotted lines represent the unknown length of the terminal branches, with the stars representing the theoretical penultimate node for which age priors were determined.

Extended Data Figure 10 Maximum clade credibility tree produced using BEAST31.

Produced using TreeAnnotator from 9,000 trees. Branch lengths are scaled by age. The mean age (yr bp) of the MRCA plus 95% HPD, and the position of the separated Hungarian ancient strains, are marked on the phylogeny.

Supplementary information

Supplementary Information

This file contains Supplementary Methods and archaeological descriptions, Supplementary Tables 2-4, 7, 10-11 and Supplementary References. (PDF 1072 kb)

Supplementary Table 1

Summary table of all samples subject to screening. (XLSX 25 kb)

Supplementary Table 5

A list of all M. tuberculosis strains used for phylogeny and dating. (XLS 53 kb)

Supplementary Table 6

This table contains mapping statistics for the three Peruvian tuberculosis strains. (XLS 31 kb)

Supplementary Table 8

This table contains coverage statistics for all genomes used in analyses. (XLS 25 kb)

Supplementary Table 9

This table contains SNPs identified in the animal cluster. (XLS 95 kb)

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Bos, K., Harkins, K., Herbig, A. et al. Pre-Columbian mycobacterial genomes reveal seals as a source of New World human tuberculosis.Nature 514, 494–497 (2014). https://doi.org/10.1038/nature13591

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