Phylogenetic Analyses of Shigella and Enteroinvasive Escherichia coli for the Identification of Molecular Epidemiological Markers: Whole-Genome Comparative Analysis Does Not Support Distinct Genera Designation

Abstract

As a leading cause of bacterial dysentery, Shigella represents a significant threat to public health and food safety. Related, but often overlooked, enteroinvasive Escherichia coli (EIEC) can also cause dysentery. Current typing methods have limited ability to identify and differentiate between these pathogens despite the need for rapid and accurate identification of pathogens for clinical treatment and outbreak response. We present a comprehensive phylogeny of Shigella and EIEC using whole genome sequencing of 169 samples, constituting unparalleled strain diversity, and observe a lack of monophyly between Shigella and EIEC and among Shigella taxonomic groups. The evolutionary relationships in the phylogeny are supported by analyses of population structure and hierarchical clustering patterns of translated gene homolog abundance. Lastly, we identified a panel of 404 single nucleotide polymorphism (SNP) markers specific to each phylogenetic cluster for more accurate identification of Shigella and EIEC. Our findings show that Shigella and EIEC are not distinct evolutionary groups within the E. coli genus and, thus, EIEC as a group is not the ancestor to Shigella. The multiple analyses presented provide evidence for reconsidering the taxonomic placement of Shigella. The SNP markers offer more discriminatory power to molecular epidemiological typing methods involving these bacterial pathogens.

Keywords: Shigella; classification; enteroinvasive E. coli (EIEC); epidemiological markers; phylogeny; whole genome sequencing.

FIGURE 1

A maximum-likelihood (ML) phylogeny of Shigella, enteroinvasive Escherichia coli (EIEC) and non-invasive E. coli strains based on 7,062 core SNPs using kSNP (Gardner and Hall, 2013). The ML tree was generated using GARLI v. 2.0.1019 under the GTR + I + Γ model and other default settings (Zwickl, 2006). Trees were visualized with Figtree v. 1.3.1 (Rambaut and Drummond, 2009). The best tree was chosen from 1,000 runs of the data set and bootstrap values (1,000 iterations) are reported above each node. Bootstrap values <80% are not shown. A tree that includes the Salmonella outgroup can be found in Supplementary Figure S1.

FIGURE 2

DISTRUCT diagrams showing clustering of Shigella, EIEC and non-enteroinvasive E. coli genomes derived from the STRUCTURE analyses with the core SNP matrix: (A) six genetic groups were determined to be the best fitting number of groups by STRUCTURE HARVESTER program and (B) the 11 groups identified from the phylogeny. Each bar represents a single genome and the color represents the proportion of SNPs that represent a cluster. Color assignment was random and does not coordinate with phylogenetic clusters in Figure 1.

FIGURE 3

Hierarchical clustering and heat map illustrating the differences in predicted protein homologs between genomes. Manhattan distances were calculated from a pairwise abundance matrix of 3,777 predicted protein homologs that were identified using the default BLASTP bidirectional best hit approach (75% amino acid sequence coverage, 1e-05 E-value and 60% sequence identity) within the program GET_HOMOLOGUES (Contreras-Moreira and Vinuesa, 2013). Only genes shared by at least two samples were included. Blue cells on the heat map indicate that genomes share more similar genes. The dendrogram on y-axis indicates hierarchical clustering of the abundance matrix using the average linkage method and Manhattan distances with bootstrap probabilities (BP, only values of ≥80 shown in black) and approximately unbiased p-values (AU, only values of ≥95 shown in red) from 10,000 replicates. The phylogenetic group of each genome from Figure 1 is represented as a colored bar in between the dendrogram and the heat map.