| Abstract: | The characterization of population structures plays a main role for understanding outbreaks and the dynamics of bacterial spreading. In Escherichia coli, the widely used combination of multiplex-PCR scheme together with goeBURST has some limitations. The purpose of this study is to show that the combination of different phylogenetic approaches based on concatenated sequences of MLST genes results in a more precise assignment of E. coli phylogenetic groups, complete understanding of population structure and reconstruction of ancestral clones. A collection of 80 Escherichia coli strains of different origins was analyzed following the Clermont and Doumith''s multiplex-PCR schemes. Doumith''s multiplex-PCR showed only 1.7% of misassignment, whereas Clermont''s-2000 protocol reached 14.0%, although the discrepancies reached 30% and 38.7% respectively when recombinant C, F and E phylogroups were considered. Therefore, correct phylogroup attribution is highly variable and depends on the clonal composition of the sample. As far as population structure of these E. coli strains, including 48 E. coli genomes from GenBank, goeBURST provides a quite dispersed population structure; whereas NeighborNet approach reveals a complex population structure. MLST-based eBURST can infer different founder genotypes, for instance ST23/ST88 could be detected as the founder genotypes for STC23; however, phylogenetic reconstructions might suggest ST410 as the ancestor clone and several evolutionary trajectories with different founders. To improve our routine understanding of E. coli molecular epidemiology, we propose a strategy based on three successive steps; first, to discriminate three main groups A/B1/C, D/F/E and B2 following Doumith''s protocol; second, visualization of population structure based on MLST genes according to goeBURST, using NeighborNet to establish more complex relationships among STs; and third, to perform, a cost-free characterization of evolutionary trajectories in variants emerging along the clonal expansion using parsimony methods of phylogenetic analysis. |
