A bacterium called Yersina pestis is responsible for numerous human outbreaks of plague throughout history. It is carried by rats and other rodents and can spread to humans causing what we conventionally refer to as plague. The most notorious of these plague outbreaks – the Black Death – claimed millions of lives in Europe in the mid-14^th century. Several other plague outbreaks emerged in Europe over the next 400 years. Then, there was a large gap before the plague re-emerged as threat in the 19^th century and it continues to infect humans today, though on a smaller scale.

Scientists have extensively studied Y. pestis to understand its origin and how it evolved to become such a deadly threat. These studies led to the assumption that the plague outbreaks of the 14–18^th centuries likely originated in rodents in Asia and spread along trade routes to other parts of the world. However, it is not clear why the plague persisted in Europe for 400 years after the Black Death. Could the bacteria have gained a foothold in local rodents instead of being reintroduced from Asia each time? If it did, why did it then disappear for such a long period from the end of the 18^th century?

To help answer these questions, Bos, Herbig et al. sequenced the DNA of Y. pestis samples collected from the teeth of five individuals who died of plague during the last major European outbreak of plague in 1722 in Marseille, France. The DNA sequences of these bacterial samples were then compared with the DNA sequences of modern day Y. pestis and other historical samples of the bacteria. The results showed the bacteria in the Marseille outbreak likely evolved from the strain that caused the Black Death back in the 14^th century.

The comparisons showed that the strain isolated from the teeth is not found today, and may be extinct. This suggests that a historical reservoir for plague existed somewhere, perhaps in Asia, or perhaps in Europe itself, and was able to cause outbreaks up until the 18^th century.Bos, Herbig et al.’s findings may help researchers trying to control the current outbreaks of the plague in Madagascar and other places.

The bacterium Yersinia pestis is among the most virulent pathogens known to cause disease in humans. As the agent of plague it is an existing threat to public health as the cause of both emerging and re-emerging rodent-derived epidemics in many regions of the world (Duplantier et al., 2005; Vogler et al., 2011; Gage and Kosoy, 2005). This, and its confirmed involvement in three major historical pandemics, have made it the subject of intense study. The first pandemic, also known as the Justinian Plague, occurred from the 6th through the 8th centuries; the second pandemic spanned the 14th to the 18th centuries; and the third pandemic started in the 19th century and persists to the present day.

Attempts to date the evolutionary history of Y. pestis using molecular clocks have been compromised by extensive variation in nucleotide substitution rates among lineages (Cui et al., 2013; Wagner et al., 2014), such that there is considerable uncertainty over how long this pathogen has caused epidemic disease in human populations. In addition, there has been lively debate as to whether or not it was the principal cause of the three historical pandemics (Cohn, 2008; Scott and Duncan, 2005). It is well established that extant lineages of Y. pestis circulated during the third pandemic (Achtman et al., 2004; Morelli et al., 2010), and various ancient DNA studies have now unequivocally demonstrated its involvement in the early phase of the first pandemic in the 6th century (Harbeck et al., 2013; Wagner et al., 2014) and the Black Death (1347–1351), which marks the beginning of the second plague pandemic (Bos et al., 2011; Haensch et al., 2010; Schuenemann et al., 2011). All three pandemics likely arose from natural rodent foci in Asia and spread along trade routes to Europe and other parts of the world (Morelli et al., 2010; Wagner et al., 2014). The strains associated with the first and second pandemics represent independent emergence events from these rodent reservoirs in Asia. The on-going third pandemic also originated in Asia, although genetic evidence suggests that it may be derived from strains that descended from those associated with the second wave that spread back to Asia and became re-established in rodent populations there (Wagner et al., 2014).

The impact of the second pandemic was extraordinary. During this time period there were hundreds and perhaps thousands of local plague outbreaks in human populations throughout Europe (Schmid et al., 2015). It is very likely that some of these outbreaks were caused by the spread of plague via the maritime transport of humans and cargo, as was undoubtedly the case during the global spread of plague during the third pandemic (Morelli et al., 2010). However, the processes responsible for the potential long-term persistence of plague in Europe during the second pandemic are still subject to debate. It is possible that once introduced to Europe around the time of the Black Death, Y. pestis persisted there for centuries, cycling in and between rodent and human populations and being introduced or reintroduced to various regions throughout Europe (Carmichael, 2014). Another possibility is that plague did not persist long-term in European rodent populations, but rather was continually reintroduced from rodent plague foci in Asia (Schmid et al., 2015).

To address this key issue in Y. pestis evolution and epidemiology, we investigated plague-associated skeletal material from one of the last well-documented European epidemics, the Great Plague of Marseille (1720–1722), that occurred in the Provence region of France at the end of the second pandemic. In particular, we compared the evolutionary relationship of these historical Y. pestis strains to those sampled from other time periods, both modern and ancient. Our results demonstrate that the strains responsible for the Black Death left descendants that persisted for several centuries in an as yet unidentified host reservoir population, accumulated genetic variation, and eventually contributed to the Great Plague of Marseille in mid-eighteenth century France. Although these lineages no longer seem to be represented within the genetic diversity of extant sampled Y. pestis, they may have been involved in additional, earlier second-pandemics in the Mediterranean region and beyond.

Skeletal material used in this investigation was sampled from the Observance (OBS) collection housed in the osteoarcheological library of the Regional Department of Archaeology, French Ministry of Culture, medical faculty of Aix-Marseille Université. Historical records indicate that this collection represents a catastrophic burial for victims of the Great Plague of Marseille relapse in 1722 (Signoli et al., 2002). Rescue excavations carried out in 1994 unearthed the remains of 261 individuals, mostly consisting of adult males (Figure 1). Material from this collection was also used in the first PCR-based investigation of ancient Y. pestis DNA from archaeological tissue (Drancourt et al., 1998). In our study 20 in situ teeth were sampled and screened for Y. pestis DNA through a quantitative PCR assay. Amplification products were detectable for five of the 20 teeth (Table 1). Products were not sequenced. Negative controls were free of amplification products. The five putatively Y. pestis-positive samples were subject to high-throughput DNA sequencing after array-based enrichment for Y. pestis DNA. Mapping to the Y. pestis CO92 reference genome revealed a minimum of 12-fold average genomic coverage for each of the five genomes (Table 2 and Figure 2). Raw sequencing data as well as mapped reads have been deposited at the European Nucleotide Archive under accession PRJEB12163. Comparative analysis together with 133 previously published Y. pestis genomes resulted in 3109 core genome SNPs for phylogenetic analysis. No homoplasies were identified.

Figure 1

Download asset Open asset

Figure 2

Download asset Open asset

The phylogenetic position of the five OBS sequences on a unique branch of the Y. pestis phylogeny (lineages shown in red in Figure 3), yet seemingly derived from those strains associated with the Black Death, both confirms its authenticity and reveals that it is likely a direct descendent of strains that were present in Europe during the Black Death. Although the topological uncertainty (i.e. trichotomy) among the second pandemic strains (London and Observance) suggests that there was a radiation of Y. pestis lineages at the time, it is notable that the OBS sequences do not possess the additional derived position that is present in both the 6330 London strain and the SNP-typed individual from Bergen Op Zoom (defined as the 's12' SNP by Haensch et al., 2010). Indeed, the tree is striking in that there is clear phylogenetic support (88% bootstrap values, Figure 3A) for 6330 clustering with the Branch 1 strains, including those that gave rise to the third (modern) plague pandemic. Finally, although evolutionary rates in Y. pestis are notoriously variable (Cui et al., 2013; Wagner et al., 2014), the comparatively late time period from which our samples derive and the long branch leading to these OBS sequences suggests that this lineage co-existed for an extended period with the Branch 1 strains responsible for all later human plague outbreaks outside of China.

Figure 3

Download asset Open asset

The Observance lineage of Y. pestis identified by our analysis of material from the Great Plague of Marseille (1720–1722) is clearly phylogenetically distinct and has not been identified in any extant plague foci for which full genome data are available. That this lineage is seemingly confined to these ancient samples suggests that it is now extinct, or has yet to be sampled from extant reservoirs. Based on available data, the Y. pestis strains most closely related to the Observance lineage are those obtained from other ancient DNA studies of victims of the Black Death in London and other areas (Haensch et al., 2010; Bos et al., 2011) that occurred some 350 years earlier.

The most important conclusion from the current study is that Y. pestis persisted and diversified in at least two lineages throughout the second pandemic, one represented by our Observance strain, and the other by London St. Mary Graces individual 6330. The absence of currently sequenced modern Y. pestis genomes that share an immediate phylogenetic relationship with the Observance lineage suggests that it may no longer exist. The length of the branch leading to the Observance lineage is consistent with a long history of circulation. Of note, this new lineage does not share the single derived mutation that is common to the London St. Mary Graces genome and the Bergen op Zoom plague victim from the Netherlands (Haensch et al., 2010); hence, multiple variants of Y. pestis evidently circulated in London and elsewhere in the 14th century, and at least one of these variants followed a path that differs from the lineage that later gave rise to the 18th century plague epidemic in Provence.

Since the novel lineage of Y. pestis identified here was obtained from an active Mediterranean port city that served as a main commercial hub and entry point into Western Europe from various origins, the precise location of the disease’s source population cannot be easily determined. Several scenarios can explain these data. First, all Y. pestis diversity documented in the Black Death and later plague outbreaks could have stemmed from foci in Asia, where European epidemics were the result of successive introductions from this distant, albeit prolific, source population (Schmid et al., 2015). This model would not require the back migration of plague into Asia for the third pandemic proposed elsewhere (Wagner et al, 2014), since all preexisting diversity would already be present. A multiple wave theory has previously been proposed to explain the presence of a reportedly different 14th century lineage in the Low Countries (defined by their single 's12' SNP) compared to those circulating in England and France (Haensch et al., 2010). However, the presence of this branch 1 position in both the ancestral and subsequently the derived state in two temporally close London outbreaks (Bos et al., 2011; Figure 3B) suggests that it became fixed during the Black Death or its immediate aftermath, rather than having been introduced via a separate pulse from an Asian focus. To date, all of the Y. pestis material examined from the second pandemic has been assigned to Branch 1 in the Y. pestis phylogeny, indicating a close evolutionary relationship. As the large “'big bang' radiation of Y. pestis at the beginning of the second pandemic (Cui et al., 2013) established at least four new lineages, it seems unlikely that independent waves of plague from Asia into Europe during the Black Death would have involved strains that group on the same branch and differ by so few positions. As China harbours many branch 1 descendent lineages that also share this 's12'” SNP, our observation adds weight to the notion that the branch 1 lineage of Y. pestis spread east sometime during the second pandemic after it entered Europe, and subsequently became established in one or more East Asian reservoir species (Wagner et al., 2014). Here it remained before radiating to other locations in the late 19^th century, giving rise to the current worldwide 'third wave' Y. pestis pandemic.

In our view, a far more plausible option to account for the distinct position of the Observance lineage is that a Y. pestis strain responsible for the Black Death became established in a natural host reservoir population within Europe or western Asia. Once established, this Y. pestis lineage evolved locally for hundreds of years and contributed to repeated human epidemics in Europe. A similar scenario is possible for the lineage(s) identified in the London St. Mary Graces and Bergen op Zoom material: like Observance, both descend from the Black Death and together they represent at least one source of European plague distinct from that which later caused the Great Plague of Marseille. Plague’s temporary persistence somewhere on the European mainland is a compelling possibility (Carmichael, 2014). Alternatively, reservoir populations located in geographically adjacent regions, such as in western Asia or the Caucasus, may have acted as regular sources of disease through successive westerly pulses during the three century-long second pandemic (Schmid et al., 2015). Since our study used material exclusively from a highly active port with trade connections to many areas in the Mediterranean and beyond, it is impossible to identify a source population for the Great Plague of Marseille at our current resolution.

Our analysis reveals a previously uncharacterized plague source that could have fuelled the European epidemics from the time of the Black Death until the mid-eighteenth century. This parallels aspects of the third pandemic, that rapidly spread globally and established novel, long-lived endemic rodent foci that periodically emerge to cause human disease (Morelli et al., 2010). The current resolution of Y. pestis phylogeography suggests that the historical reservoir identified here may no longer exist. More extensive sampling of both modern rodent populations and ancient human, as well as rodent, skeletal remains from various regions of Asia, the Caucasus, and Europe may reveal additional clues regarding past ecological niches for plague. The reasons for the apparent disappearance of this historic natural plague focus are, however, unknown, and any single model proposed for plague’s disappearance is likely to be overly simplistic. Pre-industrial Europe shared many of the same conditions that are correlated with plague dissemination today including pronounced social inequality, and poor sanitation coupled with high population density in urban centers (Vogler et al., 2011; Barnes, 2014). This constellation of anthropogenic factors, along with the significant social and environmental changes that occurred during the Industrial Revolution must be considered alongside models of climate and vector-driven dynamics as contributing to the rapid decline in historical European plague outbreaks, where genomic data provide but one piece of critical information in this consilient approach.

For this study 20 in situ teeth were freshly harvested, horizontally sectioned at the cementoenamel junction in a dedicated ancient DNA facility, and drilled to remove approximately 50 mg of dental pulp/dentin from the inner surface of the tooth crown or the roots. DNA extractions were carried out following protocols described elsewhere (Schwarz et al., 2009; Rohland and Hofreiter, 2007; Dabney et al., 2013), and survival of Y. pestis DNA was evaluated through an established quantitative PCR assay for the pla gene (Schuenemann et al., 2011). DNA extracts for the five putatively Y. pestis-positive samples, each from a separate individual, were converted into double-indexed Illumina libraries (Meyer and Kircher, 2010; Kircher et al., 2012) following an initial uracil DNA glycosylase treatment step (Briggs et al., 2010) to remove deaminated cytosines, the most common form of ancient DNA damage. Libraries were amplified with AccuPrime Pfx (Life Technologies, Germany) to a total of 20 µg and serially captured in equimolar concentrations over two identical 1 million-feature Agilent (Santa Clara, CA) microarrays (Hodges et al., 2009). Arrays were designed with probes tiled every three base pairs matching the CO92 Y. pestis reference chromosome (NC_003143), supplemented with additional chromosomal regions from Y. pestis biovar Microtus str. 91,001 (NC_005810), Y. pseudotuberculosis IP 32,953 (NC_006155) and Y. pseudotuberculosis IP 31,758 (NC_009708) that are absent in CO92. Captured products, including those from negative controls, were sequenced on one lane of an Illumina HiSeq 2000.

Raw reads were trimmed, overlapping paired reads were merged as described elsewhere (Schuenemann et al., 2013), and merged reads were subsequently filtered for a minimal length of 30 bp. Preprocessed reads were mapped to the CO92 reference genome using the Burrows-Wheeler Aligner (BWA) (Li and Durbin, 2010) with increased specificity (-n 0.1) and a map quality filter of 37.

For maximum accuracy in SNP calling, reads were processed independently at three research centres, and the intersection was used as our final SNP table (Supplementary file 1). At the University of Tuebingen, reads were processed as described above and SNP calling was done according to a protocol described by Bos et al. (2014) using the UnifiedGenotyper of the Genome Analysis Toolkit (GATK) (DePristo et al., 2011). Data for 133 previously published Y. pestis strains were processed respectively (Supplementary file 2). A custom tool was used for the comparative processing of the results. SNPs were called with a minimal coverage of 5-fold and a minimal frequency of 90% for the SNP allele. Problematic sites as identified by Morelli et al. (2010), genomic non-core regions as defined by Cui et al. (2013) as well as annotated repeat regions, rRNAs, tRNAs and tmRNAs were excluded from the SNP analysis. SNPs were annotated using SnpEff (Cingolani et al., 2012) with default parameters. The upstream and downstream region size was set to 100 nt. At McMaster University, raw reads were trimmed and merged using SeqPrep (https://github.com/jstjohn/SeqPrep), requiring a minimum overlap of 11 base pairs to merge. Reads shorter than 24 base pairs were filtered out using SeqPrep. Merged reads and unmerged forward reads were concatenated and mapped to the CO92 reference sequence (NC_003143) using the Burrows-Wheeler Aligner (BWA) version 0.7.5 (Li and Durbin, 2009) with the parameters described in Wagner et al. (2014). Duplicates were collapsed using a custom script, which collapses only reads with identical 5' position, 3' position, and direction. Assemblies were imported into Geneious R6 and SNPs with at least 5-fold coverage and 90% variance were called. SNPs were visually inspected for quality. At NAU, reads were trimmed with Trimmomatic (Bolger et al., 2014) and aligned against the CO92 reference sequence using BWA-MEM. SNPs were called with the Unified Genotyper in GATK and were filtered by minimum coverage (5X) and allele frequency (90%). SNPs in genome assemblies were identified by a direct mapping against the reference sequence using NUCmer (Delcher et al., 2002). These methods were wrapped by the Northern Arizona SNP Pipeline (NASP) (tgennorth.github.io/NASP).

1. 1. Achtman M
   2. Morelli G
   3. Zhu P
   4. Wirth T
   5. Diehl I
   6. Kusecek B
   7. Vogler AJ
   8. Wagner DM
   9. Allender CJ
   10. Easterday WR
   11. Chenal-Francisque V
   12. Worsham P
   13. Thomson NR
   14. Parkhill J
   15. Lindler LE
   16. Carniel E
   17. Keim P

   (2004) Microevolution and history of the plague bacillus, Yersinia pestis

   Proceedings of the National Academy of Sciences 101:17837–17842.

   https://doi.org/10.1073/pnas.0408026101

   • Google Scholar
2. 1. Barnes KB

   (2014) Social vulnerability and pneumonic plague: revisiting the 1994 outbreak in surat, india

   Environmental Hazards 13:161–180.

   https://doi.org/10.1080/17477891.2014.892867

   • Google Scholar
3. 1. Bolger AM
   2. Lohse M
   3. Usadel B

   (2014) Trimmomatic: a flexible trimmer for illumina sequence data

   Bioinformatics 30:2114–2120.

   https://doi.org/10.1093/bioinformatics/btu170

   • Google Scholar
4. 1. Bos KI
   2. Harkins KM
   3. Herbig A
   4. Coscolla M
   5. Weber N
   6. Comas I
   7. Forrest SA
   8. Bryant JM
   9. Harris SR
   10. Schuenemann VJ
   11. Campbell TJ
   12. Majander K
   13. Wilbur AK
   14. Guichon RA
   15. Wolfe Steadman DL
   16. Cook DC
   17. Niemann S
   18. Behr MA
   19. Zumarraga M
   20. Bastida R
   21. Huson D
   22. Nieselt K
   23. Young D
   24. Parkhill J
   25. Buikstra JE
   26. Gagneux S
   27. Stone AC
   28. Krause J

   (2014) Pre-columbian mycobacterial genomes reveal seals as a source of new world human tuberculosis

   Nature 514:494–497.

   https://doi.org/10.1038/nature13591

   • Google Scholar
5. 1. Bos KI
   2. Schuenemann VJ
   3. Golding GB
   4. Burbano HA
   5. Waglechner N
   6. Coombes BK
   7. McPhee JB
   8. DeWitte SN
   9. Meyer M
   10. Schmedes S
   11. Wood J
   12. Earn DJD
   13. Herring DA
   14. Bauer P
   15. Poinar HN
   16. Krause J

   (2011) A draft genome of yersinia pestis from victims of the black death

   Nature 478:506–510.

   https://doi.org/10.1038/nature10549

   • Google Scholar
6. 1. Briggs AW
   2. Stenzel U
   3. Meyer M
   4. Krause J
   5. Kircher M
   6. Pääbo S

   (2010) Removal of deaminated cytosines and detection of in vivo methylation in ancient DNA

   Nucleic Acids Research 38:e87.

   https://doi.org/10.1093/nar/gkp1163

   • Google Scholar
7. 1. Carmichael AG

   (2014)

   Plague persistence in western Europe: a hypothesis

   Pandemic Disease in the Medieval World: Rethinking the Black Death. the Medieval Globe 1:157–191.

   • Google Scholar
8. 1. Cingolani P
   2. Platts A
   3. Wang le L
   4. Coon M
   5. Nguyen T
   6. Wang L
   7. Land SJ
   8. Lu X
   9. Ruden DM

   (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of drosophila melanogaster strain w1118; iso-2; iso-3

   Fly 6:80–92.

   https://doi.org/10.4161/fly.19695

   • Google Scholar
9. 1. Cohn SK

   (2008)

   Epidemiology of the black death and successive waves of plague

   Medical History. Supplement 27:74–100.

   • Google Scholar
10. 1. Cui Y
    2. Yu C
    3. Yan Y
    4. Li D
    5. Li Y
    6. Jombart T
    7. Weinert LA
    8. Wang Z
    9. Guo Z
    10. Xu L
    11. Zhang Y
    12. Zheng H
    13. Qin N
    14. Xiao X
    15. Wu M
    16. Wang X
    17. Zhou D
    18. Qi Z
    19. Du Z
    20. Wu H
    21. Yang X
    22. Cao H
    23. Wang H
    24. Wang J
    25. Yao S
    26. Rakin A
    27. Li Y
    28. Falush D
    29. Balloux F
    30. Achtman M
    31. Song Y
    32. Wang J
    33. Yang R

    (2013) Historical variations in mutation rate in an epidemic pathogen, yersinia pestis

    Proceedings of the National Academy of Sciences of the United States of America 110:577–582.

    https://doi.org/10.1073/pnas.1205750110

    • Google Scholar
11. 1. Dabney J
    2. Knapp M
    3. Glocke I
    4. Gansauge M-T
    5. Weihmann A
    6. Nickel B
    7. Valdiosera C
    8. Garcia N
    9. Paabo S
    10. Arsuaga J-L
    11. Meyer M

    (2013) Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments

    Proceedings of the National Academy of Sciences 110:15758–15763.

    https://doi.org/10.1073/pnas.1314445110

    • Google Scholar
12. 1. Delcher AL
    2. Phillippy A
    3. Carlton J
    4. Salzberg SL

    (2002) Fast algorithms for large-scale genome alignment and comparison

    Nucleic Acids Research 30:2478–2483.

    https://doi.org/10.1093/nar/30.11.2478

    • Google Scholar
13. 1. DePristo MA
    2. Banks E
    3. Poplin R
    4. Garimella KV
    5. Maguire JR
    6. Hartl C
    7. Philippakis AA
    8. del Angel G
    9. Rivas MA
    10. Hanna M
    11. McKenna A
    12. Fennell TJ
    13. Kernytsky AM
    14. Sivachenko AY
    15. Cibulskis K
    16. Gabriel SB
    17. Altshuler D
    18. Daly MJ

    (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data

    Nature Genetics 43:491–498.

    https://doi.org/10.1038/ng.806

    • Google Scholar
14. 1. Drancourt M
    2. Aboudharam G
    3. Signoli M
    4. Dutour O
    5. Raoult D

    (1998) Detection of 400-year-old yersinia pestis DNA in human dental pulp: an approach to the diagnosis of ancient septicemia

    Proceedings of the National Academy of Sciences of the United States of America 95:12637–12640.

    https://doi.org/10.1073/pnas.95.21.12637

    • Google Scholar
15. 1. Duplantier J-M
    2. Duchemin J-B
    3. Chanteau S
    4. Carniel E

    (2005) From the recent lessons of the malagasy foci towards a global understanding of the factors involved in plague reemergence

    Veterinary Research 36:437–453.

    https://doi.org/10.1051/vetres:2005007

    • Google Scholar
16. 1. Gage KL
    2. Kosoy MY

    (2005) Natural history of plague: perspectives from more than a century of research

    Annual Review of Entomology 50:505–528.

    https://doi.org/10.1146/annurev.ento.50.071803.130337

    • Google Scholar
17. 1. Guindon S
    2. Dufayard JF
    3. Lefort V
    4. Anisimova M
    5. Hordijk W
    6. Gascuel O

    (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0

    Systematic Biology 59:307–321.

    https://doi.org/10.1093/sysbio/syq010

    • Google Scholar
18. 1. Haensch S
    2. Bianucci R
    3. Signoli M
    4. Rajerison M
    5. Schultz M
    6. Kacki S
    7. Vermunt M
    8. Weston DA
    9. Hurst D
    10. Achtman M
    11. Carniel E
    12. Bramanti B

    (2010) Distinct clones of yersinia pestis caused the black death

    PLoS Pathogens 6:e1001134.

    https://doi.org/10.1371/journal.ppat.1001134

    • Google Scholar
19. 1. Harbeck M
    2. Seifert L
    3. Hänsch S
    4. Wagner DM
    5. Birdsell D
    6. Parise KL
    7. Wiechmann I
    8. Grupe G
    9. Thomas A
    10. Keim P
    11. Zöller L
    12. Bramanti B
    13. Riehm JM
    14. Scholz HC
    15. Wagner DM
    16. Bridsell D

    (2013) Yersinia pestis DNA from skeletal remains from the 6th century AD reveals insights into justinianic plague

    PLoS Pathogens 9:e1003349.

    https://doi.org/10.1371/journal.ppat.1003349

    • Google Scholar
20. 1. Hodges E
    2. Rooks M
    3. Xuan Z
    4. Bhattacharjee A
    5. Benjamin Gordon D
    6. Brizuela L
    7. Richard McCombie W
    8. Hannon GJ

    (2009) Hybrid selection of discrete genomic intervals on custom-designed microarrays for massively parallel sequencing

    Nature Protocols 4:960–974.

    https://doi.org/10.1038/nprot.2009.68

    • Google Scholar
21. 1. Keeling MJ
    2. Gilligan CA

    (2000) Metapopulation dynamics of bubonic plague

    Nature 407:903–906.

    https://doi.org/10.1038/35038073

    • Google Scholar
22. 1. Kircher M
    2. Sawyer S
    3. Meyer M

    (2012) Double indexing overcomes inaccuracies in multiplex sequencing on the illumina platform

    Nucleic Acids Research 40:e3.

    https://doi.org/10.1093/nar/gkr771

    • Google Scholar
23. 1. Li H
    2. Durbin R

    (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform

    Bioinformatics 26:589–595.

    https://doi.org/10.1093/bioinformatics/btp698

    • Google Scholar
24. 1. Meyer M
    2. Kircher M

    (2010) Illumina sequencing library preparation for highly multiplexed target capture and sequencing

    Cold Spring Harbor Protocols 2010:pdb.prot5448.

    https://doi.org/10.1101/pdb.prot5448

    • Google Scholar
25. 1. Morelli G
    2. Song Y
    3. Mazzoni CJ
    4. Eppinger M
    5. Roumagnac P
    6. Wagner DM
    7. Feldkamp M
    8. Kusecek B
    9. Vogler AJ
    10. Li Y
    11. Cui Y
    12. Thomson NR
    13. Jombart T
    14. Leblois R
    15. Lichtner P
    16. Rahalison L
    17. Petersen JM
    18. Balloux F
    19. Keim P
    20. Wirth T
    21. Ravel J
    22. Yang R
    23. Carniel E
    24. Achtman M

    (2010) Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity

    Nature Genetics 42:1140–1143.

    https://doi.org/10.1038/ng.705

    • Google Scholar
26. 1. Quinlan AR
    2. Hall IM

    (2010) BEDTools: a flexible suite of utilities for comparing genomic features

    Bioinformatics 26:841–842.

    https://doi.org/10.1093/bioinformatics/btq033

    • Google Scholar
27. 1. Rohland N
    2. Hofreiter M

    (2007) Ancient DNA extraction from bones and teeth

    Nature Protocols 2:1756–1762.

    https://doi.org/10.1038/nprot.2007.247

    • Google Scholar
28. 1. Schmid BV
    2. Büntgen U
    3. Easterday WR
    4. Ginzler C
    5. Walløe L
    6. Bramanti B
    7. Stenseth NC

    (2015) Climate-driven introduction of the black death and successive plague reintroductions into europe

    Proceedings of the National Academy of Sciences of the United States of America 112:3020–3025.

    https://doi.org/10.1073/pnas.1412887112

    • Google Scholar
29. 1. Schuenemann VJ
    2. Bos K
    3. DeWitte S
    4. Schmedes S
    5. Jamieson J
    6. Mittnik A
    7. Forrest S
    8. Coombes BK
    9. Wood JW
    10. Earn DJD
    11. White W
    12. Krause J
    13. Poinar HN
    14. Schmedes S
    15. Jamieson J
    16. Mittnik A

    (2011) Targeted enrichment of ancient pathogens yielding the pPCP1 plasmid of yersinia pestis from victims of the black death

    Proceedings of the National Academy of Sciences of the United States of America 108:E746.

    https://doi.org/10.1073/pnas.1105107108

    • Google Scholar
30. 1. Schuenemann VJ
    2. Singh P
    3. Mendum TA
    4. Krause-Kyora B
    5. Jäger G
    6. Bos KI
    7. Herbig A
    8. Economou C
    9. Benjak A
    10. Busso P
    11. Nebel A
    12. Boldsen JL
    13. Kjellström A
    14. Wu H
    15. Stewart GR
    16. Taylor GM
    17. Bauer P
    18. Lee OY
    19. Wu HH
    20. Minnikin DE
    21. Besra GS
    22. Tucker K
    23. Roffey S
    24. Sow SO
    25. Cole ST
    26. Nieselt K
    27. Krause J

    (2013) Genome-wide comparison of medieval and modern mycobacterium leprae

    Science 341:179–183.

    https://doi.org/10.1126/science.1238286

    • Google Scholar
31. 1. Schwarz C
    2. Debruyne R
    3. Kuch M
    4. McNally E
    5. Schwarcz H
    6. Aubrey AD
    7. Bada J
    8. Poinar H

    (2009) New insights from old bones: DNA preservation and degradation in permafrost preserved mammoth remains

    Nucleic Acids Research 37:3215–3229.

    https://doi.org/10.1093/nar/gkp159

    • Google Scholar
32. 1. Scott S
    2. Duncan CJ

    (2005)

    The Biology of Plagues: Evidence From Historical Populations

    The Biology of Plagues: Evidence From Historical Populations, Cambridge.

    • Google Scholar
33. 1. Signoli M
    2. Séguy I
    3. Biraben J-N
    4. Dutour O

    (2002) Paleodemography and historical demography in the context of an epidemic

    Population 57:829–854.

    https://doi.org/10.3917/pope.206.0829

    • Google Scholar
34. 1. Vogler AJ
    2. Chan F
    3. Wagner DM
    4. Roumagnac P
    5. Lee J
    6. Nera R
    7. Eppinger M
    8. Ravel J
    9. Rahalison L
    10. Rasoamanana BW
    11. Beckstrom-Sternberg SM
    12. Achtman M
    13. Chanteau S
    14. Keim P

    (2011) Phylogeography and molecular epidemiology of yersinia pestis in madagascar

    PLoS Neglected Tropical Diseases 5:e1319.

    https://doi.org/10.1371/journal.pntd.0001319

    • Google Scholar
35. 1. Wagner DM
    2. Klunk J
    3. Harbeck M
    4. Devault A
    5. Waglechner N
    6. Sahl JW
    7. Enk J
    8. Birdsell DN
    9. Kuch M
    10. Lumibao C
    11. Poinar D
    12. Pearson T
    13. Fourment M
    14. Golding B
    15. Riehm JM
    16. Earn DJD
    17. DeWitte S
    18. Rouillard J-M
    19. Grupe G
    20. Wiechmann I
    21. Bliska JB
    22. Keim PS
    23. Scholz HC
    24. Holmes EC
    25. Poinar H
    26. Harbeck M
    27. Devault A
    28. Waglechner N
    29. Sahl JW

    (2014) Yersinia pestis and the plague of justinian 541–543 AD: a genomic analysis

    The Lancet Infectious Diseases 14:319–326.

    https://doi.org/10.1016/S1473-3099(13)70323-2

    • Google Scholar

Author details

1. Kirsten I Bos

   1. Department of Archeological Sciences, University of Tübingen, Tübingen, Germany
   2. Max Planck Institute for the Science of Human History, Jena, Germany

   Contribution

   KIB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Alexander Herbig

   For correspondence

   bos@shh.mpg.de

   Competing interests

   The authors declare that no competing interests exist.

2. Alexander Herbig

   1. Department of Archeological Sciences, University of Tübingen, Tübingen, Germany
   2. Max Planck Institute for the Science of Human History, Jena, Germany

   Contribution

   AH, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Kirsten I Bos

   Competing interests

   The authors declare that no competing interests exist.

3. Jason Sahl

   Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, United States

   Contribution

   JS, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Nicholas Waglechner

   Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Canada

   Contribution

   NW, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Mathieu Fourment

   1. Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, Australia
   2. Charles Perkins Centre, The University of Sydney, Sydney, Australia
   3. School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia
   4. Sydney Medical School, The University of Sydney, Sydney, Australia

   Contribution

   MF, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Stephen A Forrest

   Department of Archeological Sciences, University of Tübingen, Tübingen, Germany

   Contribution

   SAF, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Jennifer Klunk

   1. McMaster Ancient DNA Centre, Department of Anthropology, McMaster University, Hamilton, Canada
   2. Department of Biology, McMaster University, Hamilton, Canada

   Contribution

   JKl, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Verena J Schuenemann

   Department of Archeological Sciences, University of Tübingen, Tübingen, Germany

   Contribution

   VJS, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Debi Poinar

   McMaster Ancient DNA Centre, Department of Anthropology, McMaster University, Hamilton, Canada

   Contribution

   DP, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Melanie Kuch

    McMaster Ancient DNA Centre, Department of Anthropology, McMaster University, Hamilton, Canada

    Contribution

    MK, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. G Brian Golding

    Department of Biology, McMaster University, Hamilton, Canada

    Contribution

    GBG, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Olivier Dutour

    Laboratoire d'anthropologie biologique Paul Broca, Ecole Pratique des Hautes Etudes, PACEA, Université Bordeaux, Bordeaux, France

    Contribution

    OD, Conception and design, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

13. Paul Keim

    Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, United States

    Contribution

    PK, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

14. David M Wagner

    Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, United States

    Contribution

    DMW, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

15. Edward C Holmes

    1. Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, Australia
    2. Charles Perkins Centre, The University of Sydney, Sydney, Australia
    3. School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia
    4. Sydney Medical School, The University of Sydney, Sydney, Australia

    Contribution

    ECH, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

16. Johannes Krause

    1. Department of Archeological Sciences, University of Tübingen, Tübingen, Germany
    2. Max Planck Institute for the Science of Human History, Jena, Germany

    Contribution

    JKr, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    johannes.krause@uni-tuebingen.de

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0001-9144-3920

17. Hendrik N Poinar

    1. Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Canada
    2. McMaster Ancient DNA Centre, Department of Anthropology, McMaster University, Hamilton, Canada
    3. Department of Biology, McMaster University, Hamilton, Canada
    4. Department of Biochemistry, McMaster University, Hamilton, Canada

    Contribution

    HNP, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    poinarh@mcmaster.ca

    Competing interests

    The authors declare that no competing interests exist.

• 8,209

  views

• 1,587

  downloads

• 136

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.