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The Evolutionary History, Demography, and Spread of the Complex

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  • Authors: Maxime Barbier1, Thierry Wirth2
  • Editors: William R. Jacobs Jr.3, Helen McShane4, Valerie Mizrahi5, Ian M. Orme6
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Laboratoire Biologie Intégrative des Populations, Evolution Moléculaire; Institut de Systématique, Evolution, Biodiversité, UMR-CNRS 7205, Muséum National d’Histoire Naturelle, Univ. Pierre et Marie Curie, EPHE, Sorbonne Universités, 75231 Paris cedex 05, France; 2: Laboratoire Biologie Intégrative des Populations, Evolution Moléculaire; Institut de Systématique, Evolution, Biodiversité, UMR-CNRS 7205, Muséum National d’Histoire Naturelle, Univ. Pierre et Marie Curie, EPHE, Sorbonne Universités, 75231 Paris cedex 05, France; 3: Howard Hughes Medical Institute, Albert Einstein School of Medicine, Bronx, NY 10461; 4: University of Oxford, Oxford OX3 7DQ, United Kingdom; 5: University of Cape Town, Rondebosch 7701, South Africa; 6: Colorado State University, Fort Collins, CO 80523
  • Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016
  • Received 13 January 2016 Accepted 21 January 2016 Published 12 August 2016
  • Maxime Barbier, maxime.barbier@etu.ephe.fr
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  • Abstract:

    With the advent of next-generation sequencing technology, the genotyping of clinical strains went through a major breakup that dramatically improved the field of molecular epidemiology but also revolutionized our deep understanding of the complex evolutionary history. The intricate paths of the pathogen and its human host are reflected by a common geographical origin in Africa and strong biogeographical associations that largely reflect the past migration waves out of Africa. This long coevolutionary history is cardinal for our understanding of the host-pathogen dynamic, including past and ongoing demographic components, strains’ genetic background, as well as the immune system genetic architecture of the host. Coalescent- and Bayesian-based analyses allowed us to reconstruct population size changes of through time, to date the most recent common ancestor and the several phylogenetic lineages. This information will ultimately help us to understand the spread of the Beijing lineage, the rise of multidrug-resistant sublineages, or the fall of others in the light of socioeconomic events, antibiotic programs, or host population densities. If we leave the present and go through the looking glass, thanks to our ability to handle small degraded molecules combined with targeted capture, paleomicrobiology covering the Pleistocene era will possibly unravel lineage replacements, dig out extinct ones, and eventually ask for major revisions of the current model.

  • Citation: Barbier M, Wirth T. 2016. The Evolutionary History, Demography, and Spread of the Complex. Microbiol Spectrum 4(4):TBTB2-0008-2016. doi:10.1128/microbiolspec.TBTB2-0008-2016.

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/content/journal/microbiolspec/10.1128/microbiolspec.TBTB2-0008-2016
2016-08-12
2017-03-26

Abstract:

With the advent of next-generation sequencing technology, the genotyping of clinical strains went through a major breakup that dramatically improved the field of molecular epidemiology but also revolutionized our deep understanding of the complex evolutionary history. The intricate paths of the pathogen and its human host are reflected by a common geographical origin in Africa and strong biogeographical associations that largely reflect the past migration waves out of Africa. This long coevolutionary history is cardinal for our understanding of the host-pathogen dynamic, including past and ongoing demographic components, strains’ genetic background, as well as the immune system genetic architecture of the host. Coalescent- and Bayesian-based analyses allowed us to reconstruct population size changes of through time, to date the most recent common ancestor and the several phylogenetic lineages. This information will ultimately help us to understand the spread of the Beijing lineage, the rise of multidrug-resistant sublineages, or the fall of others in the light of socioeconomic events, antibiotic programs, or host population densities. If we leave the present and go through the looking glass, thanks to our ability to handle small degraded molecules combined with targeted capture, paleomicrobiology covering the Pleistocene era will possibly unravel lineage replacements, dig out extinct ones, and eventually ask for major revisions of the current model.

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Figures

Image of FIGURE 1
FIGURE 1

Diagram of the proposed evolutionary pathway of the tubercle bacilli illustrating successive losses of DNA in certain lineages (gray boxes). The diagram is based on the presence or absence of conserved deleted regions and on sequence polymorphisms in five selected genes. The distances between certain branches may not correspond to actual phylogenetic differences calculated by other methods. Blue arrows indicate that strains are characterized by 463. CTG (Leu), 95 ACC (Thr), typical for group 1 organisms. Green arrows indicate that strains belong to group 2 characterized by 463 CGG (Arg), 95 ACC (Thr). The red arrow indicates that strains belong to group 3, characterized by 463 CGG (Arg), 95 AGC (Ser), as defined by Sreevatsan et al. ( 31 ). Adapted from Brosch et al. ( 33 ).

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016
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Image of FIGURE 2
FIGURE 2

The genome-based phylogeny of MTBC mirrors that of human mitochondrial genomes. Comparison of the MTBC phylogeny and a phylogeny derived from 4,955 mitochondrial genomes (mtDNA) representative of the main human haplogroups . Color-coding highlights the similarities in tree topology and geographic distribution between MTBC strains and the main human mitochondrial macrohaplogroups (black, African clades: MTBC lineages 5 and 6, human mitochondrial macrohaplogroups L0 to L3; pink, Southeast Asian and Oceanian clades: MTBC lineage 1, human mitochondrial macrohaplogroup M; blue, Eurasian clades: MTBC lineage 2 to 4, human mitochondrial macrohaplogroup N). Scale bars indicate substitutions per site. Adapted from Comas et al. ( 49 ).

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016
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Image of FIGURE 3
FIGURE 3

Whole-genome phylogeny of 261 strains belonging to the MTBC. Animal and specific deletions are indicated, as well as mutations affecting the PhoPR virulence regulator. Adapted from Bos et al. ( 55 ) and Gonzalo-Asensio et al. ( 34 ).

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016
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Image of FIGURE 4
FIGURE 4

Biogeographical structure of the Beijing lineage. MStree based on 24 MIRU-VNTR markers delineating the clonal complexes (CCs) gathered from a worldwide collection (n = 4,987). Major nodes and associated multilocus variants were grouped into six CCs and a basal sublineage (BL). Genetic variability in the different Beijing lineage CCs and the BL calculated using a rarefaction procedure. Dots correspond to the mean allelic richness; boxes correspond to mean values ± standard error of the mean and error bars correspond to mean values ± standard deviation. Worldwide distribution of the Beijing CCs and BL. Each circle corresponds to a country, and circle sizes are proportional to the number of strains. Adapted from Merker et al. ( 72 ).

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016
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Image of FIGURE 5
FIGURE 5

Global phylogeny of 1,601 MTBC isolates inferred from a total of 91,648 SNPs spanning the whole genome. All seven main MTBC lineages are indicated in the inner area of the tree. The main sublineages are annotated at the outer arc along with lineage-specific RDs. Identified clades are color-coded. Adapted from Coll et al. ( 86 ).

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016
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FIGURE 6

Consistent with a general pattern for measurably evolving populations, the evolutionary rates of microbial pathogens decrease as a function of the time span over which they are estimated. Data shown are selected representative examples, including one group of RNA viruses and several bacterial pathogens. Adapted from Biek et al. ( 100 ).

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016
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Tables

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TABLE 1

Correspondence table of the MTBC human-adapted strains identified by main typing methods and including the latest nomenclature

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.TBTB2-0008-2016

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