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We hypothesized that the large global variations in the prevalence of macrolide resistance in Treponema pallidum are related to differences in population-level macrolide consumption. The hypothesis was tested by, at a country-level, regressing the peak prevalence of macrolide resistance against the national macrolide consumption in the year prior to this, controlling for the year of the resistance prevalence estimate. A strong association was found between the per capita consumption of macrolides and macrolide resistance (coefficient 0.7, 95 % confidence interval 0.2–0.12, P=0.009).

Plasmid prediction may be of great interest when studying bacteria of medical importance such as Enterobacteriaceae as well as Staphylococcus aureus or Enterococcus. Indeed, many resistance and virulence genes are located on such replicons with major impact in terms of pathogenicity and spreading capacities. Beyond strain outbreak, plasmid outbreaks have been reported in particular for some extended-spectrum beta-lactamase- or carbapenemase-producing Enterobacteriaceae. Several tools are now available to explore the ‘plasmidome’ from whole-genome sequences with various approaches, but none of them are able to combine high sensitivity and specificity. With this in mind, we developed PlaScope, a targeted approach to recover plasmidic sequences in genome assemblies at the species or genus level. Based on Centrifuge, a metagenomic classifier, and a custom database containing complete sequences of chromosomes and plasmids from various curated databases, PlaScope classifies contigs from an assembly according to their predicted location. Compared to other plasmid classifiers, PlasFlow and cBar, it achieves better recall (0.87), specificity (0.99), precision (0.96) and accuracy (0.98) on a dataset of 70 genomes of Escherichia coli containing plasmids. In a second part, we identified 20 of the 21 chromosomal integrations of the extended-spectrum beta-lactamase coding gene in a clinical dataset of E. coli strains. In addition, we predicted virulence gene and operon locations in agreement with the literature. We also built a database for Klebsiella and correctly assigned the location for the majority of resistance genes from a collection of 12 Klebsiella pneumoniae strains. Similar approaches could also be developed for other well-characterized bacteria.

Increasingly rich metadata are now being linked to samples that have been whole-genome sequenced. However, much of this information is ignored. This is because linking this metadata to genes, or regions of the genome, usually relies on knowing the gene sequence(s) responsible for the particular trait being measured and looking for its presence or absence in that genome. Examples of this would be the spread of antimicrobial resistance genes carried on mobile genetic elements (MGEs). However, although it is possible to routinely identify the resistance gene, identifying the unknown MGE upon which it is carried can be much more difficult if the starting point is short-read whole-genome sequence data. The reason for this is that MGEs are often full of repeats and so assemble poorly, leading to fragmented consensus sequences. Since mobile DNA, which can carry many clinically and ecologically important genes, has a different evolutionary history from the host, its distribution across the host population will, by definition, be independent of the host phylogeny. It is possible to use this phenomenon in a genome-wide association study to identify both the genes associated with the specific trait and also the DNA linked to that gene, for example the flanking sequence of the plasmid vector on which it is encoded, which follows the same patterns of distribution as the marker gene/sequence itself. We present PlasmidTron, which utilizes the phenotypic data normally available in bacterial population studies, such as antibiograms, virulence factors, or geographical information, to identify traits that are likely to be present on DNA that can randomly reassort across defined bacterial populations. It is also possible to use this methodology to associate unknown genes/sequences (e.g. plasmid backbones) with a specific molecular signature or marker (e.g. resistance gene presence or absence) using PlasmidTron. PlasmidTron uses a k-mer-based approach to identify reads associated with a phylogenetically unlinked phenotype. These reads are then assembled de novo to produce contigs in a fast and scalable-to-large manner. PlasmidTron is written in Python 3 and is available under the open source licence GNU GPL3 from https://github.com/sanger-pathogens/plasmidtron.

Enteroaggregative Escherichia coli (EAEC) is an important agent of diarrhoeal diseases worldwide. The role of EAEC virulence factors in the clinical outcome of infection is not completely defined. This case–control study investigated the prevalence of EAEC, its virulence genes and the antimicrobial resistance profile of adult patients with and without diarrhoea attending three different hospitals in Zanjan, Iran. A total of 550 individual stool specimens (350 from diarrhoeal patients and 200 from patients without diarrhoea) were collected. One hundred and forty-one EAEC isolates were identified by a HEp-2 cell assay and PCR. EAEC isolates were detected with slightly higher frequency in patients with (27.7 %) than in patients without (22 %) diarrhoea (P ≥ 0.05). The EAEC genes aggR, aap and pet were identified more frequently in case patients compared with controls (P ≤ 0.05). Many of the EAEC isolates from the diarrhoeal patients had two or more virulence genes compared with those without diarrhoea (P ≤ 0.05). EAEC isolates exhibited high-level resistance to amoxicillin (82.3 %), co-amoxiclav (78 %), aztreonam (73.8 %), tetracycline (66.6 %) and ceftazidime (63.8 %). In addition, 53.2 % of isolates were resistant to at least three different classes of antimicrobial agents and were considered to be multidrug resistant. These results indicate a high prevalence and heterogeneity of gene profiles of EAEC in diarrhoeal and control patients, and suggest that the presence of aggR, aap and pet, the number of genes present and the antimicrobial resistance profile may be markers for more-virulent EAEC isolates.