Genome sequencing reveals diversification of virulence factor content and possible host adaptation in distinct subpopulations of Salmonella enterica

<p>Abstract</p> <p>Background</p> <p>Divergence of bacterial populations into distinct subpopulations is often the result of ecological isolation. While some studies have suggested the existence of <it>Salmonella enterica </it>subsp. <it>enterica </it>subclades, evidence for these subdivisions has been ambiguous. Here we used a comparative genomics approach to define the population structure of <it>Salmonella enterica </it>subsp. <it>enterica</it>, and identify clade-specific genes that may be the result of ecological specialization.</p> <p>Results</p> <p>Multi-locus sequence analysis (MLSA) and single nucleotide polymorphisms (SNPs) data for 16 newly sequenced and 30 publicly available genomes showed an unambiguous subdivision of <it>S. enterica </it>subsp. <it>enterica </it>into at least two subpopulations, which we refer to as clade A and clade B. Clade B strains contain several clade-specific genes or operons, including a β-glucuronidase operon, a S-fimbrial operon, and cell surface related genes, which strongly suggests niche specialization of this subpopulation. An additional set of 123 isolates was assigned to clades A and B by using qPCR assays targeting subpopulation-specific SNPs and genes of interest. Among 98 serovars examined, approximately 20% belonged to clade B. All clade B isolates contained two pathogenicity related genomic islands, SPI-18 and a cytolethal distending toxin islet; a combination of these two islands was previously thought to be exclusive to serovars Typhi and Paratyphi A. Presence of β-glucuronidase in clade B isolates specifically suggests an adaptation of this clade to the vertebrate gastrointestinal environment.</p> <p>Conclusions</p> <p><it>S. enterica </it>subsp. <it>enterica </it>consists of at least two subpopulations that differ specifically in genes involved in host and tissue tropism, utilization of host specific carbon and nitrogen sources and are therefore likely to differ in ecology and transmission characteristics.</p

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability