Molecular Characteristics of Extraintestinal Pathogenic E. coli (ExPEC), Uropathogenic E. coli (UPEC), and Multidrug Resistant E. coli Isolated from Healthy Dogs in Spain. Whole Genome Sequencing of Canine ST372 Isolates and Comparison with Human Isolates Causing Extraintestinal Infections

Under a one health perspective and the worldwide antimicrobial resistance concern, we investigated extraintestinal pathogenic Escherichia coli (ExPEC), uropathogenic E. coli (UPEC), and multidrug resistant (MDR) E. coli from 197 isolates recovered from healthy dogs in Spain between 2013 and 2017. A total of 91 (46.2%) isolates were molecularly classified as ExPEC and/or UPEC, including 50 clones, among which (i) four clones were dominant (B2-CH14-180-ST127, B2-CH52-14-ST141, B2-CH103-9-ST372 and F-CH4-58-ST648) and (ii) 15 had been identified among isolates causing extraintestinal infections in Spanish and French humans in 2015 and 2016. A total of 28 (14.2%) isolates were classified as MDR, associated with B1, D, and E phylogroups, and included 24 clones, of which eight had also been identified among the human clinical isolates. We selected 23 ST372 strains, 21 from healthy dogs, and two from human clinical isolates for whole genome sequencing and built an SNP-tree with these 23 genomes and 174 genomes (128 from canine strains and 46 from human strains) obtained from public databases. These 197 genomes were segregated into six clusters. Cluster 1 comprised 74.6% of the strain genomes, mostly composed of canine strain genomes (p < 0.00001). Clusters 4 and 6 also included canine strain genomes, while clusters 2, 3, and 5 were significantly associated with human strain genomes. Finding several common clones and clone-related serotypes in dogs and humans suggests a potentially bidirectional clone transfer that argues for the one health perspective

A comparison of Bayesian spatial models for cancer incidence at a small area level : Theory and performance

The increase in Bayesian models available for disease mapping at a small area level can pose challenges to the researcher: which one to use? Models may assume a smooth spatial surface (termed global smoothing), or allow for discontinuities between areas (termed local spatial smoothing). A range of global and local Bayesian spatial models suitable for disease mapping over small areas are examined, including the foundational and still most popular (global) Besag, York and Mollié (BYM) model through to more recent proposals such as the (local) Leroux scale mixture model. Models are applied to simulated data designed to represent the diagnosed cases of (1) a rare and (2) a common cancer using small-area geographical units in Australia. Key comparative criteria considered are convergence, plausibility of estimates, model goodness-of-fit and computational time. These simulations highlighted the dramatic impact of model choice on posterior estimates. The BYM, Leroux and some local smoothing models performed well in the sparse simulated dataset, while centroid-based smoothing models such as geostatistical or P-spline models were less effective, suggesting they are unlikely to succeed unless areas are of similar shape and size. Comparing results from several different models is recommended, especially when analysing very sparse data.</p