Allelic richness ± standard deviation (SD) of LAM9C1 and C2 subpopulations according to country of isolation.

<p>Allelic richness is evaluated for 12-loci MIRU-VNTRs using a rarefaction procedure (when n>25 per lineage and per country); countries names are defined by ISO 3166–1 alpha-3 code.</p><p>Allelic richness ± standard deviation (SD) of LAM9C1 and C2 subpopulations according to country of isolation.</p

12-loci MIRU-VNTR based demographic and dating estimates of LAM9 sublineages inferred by a Bayesian approach on Msvar 1.3 algorithm.

<p>(A) 2D Kernel density plots producing a smooth estimate of the density of the marginal posterior distribution of N0 the current effective population size, and N1 the population size before expansion (in log scale) for LAM9C1 isolates. (B) Same figure for LAM9C2 isolates. t_a, time elapsed since last expansion began expressed in years (log scale); R = N0/N1 traduce median value of expansion ratio; μ, mutation rate per locus and per generation. All estimates correspond to median values, followed by 95% highest posterior densities indicated in parentheses.</p

Distribution of main <i>M</i>. <i>tuberculosis</i> lineages and sublineages in Americas according to SITVIT2 database (n = 21183 strains) based on spoligotyping.

<p>Distribution of main <i>M</i>. <i>tuberculosis</i> lineages and sublineages in Americas according to SITVIT2 database (n = 21183 strains) based on spoligotyping.</p

Allele copy number of MIRU-VNTR markers in LAM9C1 and LAM9C2 <i>M</i>. <i>tuberculosis</i> isolates.

<p>ND: Not done</p><p>Allele copy number of MIRU-VNTR markers in LAM9C1 and LAM9C2 <i>M</i>. <i>tuberculosis</i> isolates.</p

Evolutionary relationships of the LAM9 sublineage isolates (n = 450).

<p>(A) Geographical distribution and LAM9C1 and C2 isolates defined by Bayesian cluster analysis using STRUCTURE software run on 12-loci MIRU-VNTRs. Each of the strains is represented by a thin vertical line, partitioned into black or white segments that represent the strains estimated proportion of membership in clusters LAM9C1 and LAM9C2 respectively. (B) MST analysis on combined spoligotyping and MIRU-VNTR data for strains prelabeled as LAM9C1 (n = 226) and C2 (n = 208) based on previous STRUCTURE analysis (strains in intermediate position between C1 and C2 are indicated as LAM9 Int, n = 16). The complexity of the lines denotes the number of allele/spacer changes between two patterns while the size of the circle is proportional to the total number of isolates sharing same pattern. Country codes are shown as ISO 3166–1 alpha-3 code.</p

Geographic distribution of LAM sublineages in various countries of Americas (when n>36).

<p>Phylogenetic clade assignation using spoligotyping follows rules of SITVITWEB database. Country codes are shown as ISO 3166–1 alpha-3 code.</p

Boxplot of allelic richness of <i>M</i>. <i>tuberculosis</i> T sublineages T1 to T8 calculated by a rarefaction procedure implemented in HP-RARE 1.0 software.

<p>Significant differences calculated by the Dunn’s test at p-values<0.05 are indicated by blue line, and p-value<0.1 by black line. P-value in parenthesis. Boxes correspond to median values ± quartiles of allelic richness; adjacent lines show the minimum/maximum values; dots represent outlier values.</p

MST based on 24-loci MIRU-VNTR illustrating evolutionary relationships of the T sublineages isolates (n = 607) prelabeled as T1 to T8 based on previous STRUCTURE analysis.

<p>Strains in intermediate position between sublineages are indicated as Int. The complexity of the lines denotes the number of allele/spacer changes between two patterns while the size of the circle is proportional to the total number of isolates sharing same pattern.</p

MST based on 24-loci MIRU-VNTR combined with spoligotyping and illustrating evolutionary relationships of the T sublineages isolates (n = 607) prelabeled as T1 to T8 based on previous STRUCTURE analysis, and LAM, H, X and S isolates from SITVIT2 database.

<p>MST based on 24-loci MIRU-VNTR combined with spoligotyping and illustrating evolutionary relationships of the T sublineages isolates (n = 607) prelabeled as T1 to T8 based on previous STRUCTURE analysis, and LAM, H, X and S isolates from SITVIT2 database.</p

Bayesian population structure analysis reveals presence of phylogeographically specific sublineages within previously ill-defined T group of <i>Mycobacterium tuberculosis</i>

<div><p><i>Mycobacterium tuberculosis</i> genetic structure, and evolutionary history have been studied for years by several genotyping approaches, but delineation of a few sublineages remains controversial and needs better characterization. This is particularly the case of T group within lineage 4 (L4) which was first described using spoligotyping to pool together a number of strains with ill-defined signatures. Although T strains were not traditionally considered as a real phylogenetic group, they did contain a few phylogenetically meaningful sublineages as shown using SNPs. We therefore decided to investigate if this observation could be corroborated using other robust genetic markers. We consequently made a first assessment of genetic structure using 24-loci MIRU-VNTRs data extracted from the SITVIT2 database (n = 607 clinical isolates collected in Russia, Albania, Turkey, Iraq, Brazil and China). Combining Minimum Spanning Trees and Bayesian population structure analyses (using STRUCTURE and TESS softwares), we distinctly identified eight tentative phylogenetic groups (T1-T8) with a remarkable correlation with geographical origin. We further compared the present structure observed with other L4 sublineages (n = 416 clinical isolates belonging to LAM, Haarlem, X, S sublineages), and showed that 5 out of 8 T groups seemed phylogeographically well-defined as opposed to the remaining 3 groups that partially mixed with other L4 isolates. These results provide with novel evidence about phylogeographically specificity of a proportion of ill-defined T group of <i>M</i>. <i>tuberculosis</i>. The genetic structure observed will now be further validated on an enlarged worldwide dataset using Whole Genome Sequencing (WGS).</p></div