Listeria monocytogenes (Lm) is a major human foodborne pathogen. Numerous Lm outbreaks have been reported worldwide and associated with a high case fatality rate, reinforcing the need for strongly coordinated surveillance and outbreak control. We developed a universally applicable genome-wide strain genotyping approach and investigated the population diversity of Lm using 1,696 isolates from diverse sources and geographical locations. We define, with unprecedented precision, the population structure of Lm, demonstrate the occurrence of international circulation of strains and reveal the extent of heterogeneity in virulence and stress resistance genomic features among clinical and food isolates. Using historical isolates, we show that the evolutionary rate of Lm from lineage I and lineage II is low (∼2.5 × 10 substitutions per site per year, as inferred from the core genome) and that major sublineages (corresponding to so-called 'epidemic clones') are estimated to be at least 50-150 years old. This work demonstrates the urgent need to monitor Lm strains at the global level and provides the unified approach needed for global harmonization of Lm genome-based typing and population biology.

An atypically large outbreak of Elizabethkingia anophelis infections occurred in Wisconsin. Here we show that it was caused by a single strain with thirteen characteristic genomic regions. Strikingly, the outbreak isolates show an accelerated evolutionary rate and an atypical mutational spectrum. Six phylogenetic sub-clusters with distinctive temporal and geographic dynamics are revealed, and their last common ancestor existed approximately one year before the first recognized human infection. Unlike other E. anophelis, the outbreak strain had a disrupted DNA repair mutY gene caused by insertion of an integrative and conjugative element. This genomic change probably contributed to the high evolutionary rate of the outbreak strain and may have increased its adaptability, as many mutations in protein-coding genes occurred during the outbreak. This unique discovery of an outbreak caused by a naturally occurring mutator bacterial pathogen provides a dramatic example of the potential impact of pathogen evolutionary dynamics on infectious disease epidemiology.

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ObjectiveTo assess whether gut microbiota composition is associated with patient characteristics and may have predictive value on the response to TNF inhibitor (TNFi) treatment in axial spondyloarthritis (AxSpA).MethodsThe study involved 61 patients fulfilling the Assessment of SpondyloArthritis International Society classification criteria for AxSpA. All patients had active disease despite non-steroidal anti-inflammatory drugs intake and were eligible for treatment with a TNFi. At baseline, the mean Ankylosing Spondylitis Disease Activity Score was 2.9±1 and mean C reactive protein (CRP) level 9.7±11.4 mg/L. Bacterial 16S ribosomal RNA gene sequencing was performed on stool samples collected at baseline (month 0 (M0)) and 3 months after TNFi initiation (month 3 (M3)). Alpha and beta diversity metrics were calculated on the relative abundance of core operational taxonomic units (OTUs).ResultsThe HLA-B27 status affected at least in part the global composition of faecal microbiota at M0 as well as the abundance/prevalence of several anaerobic bacteria in the familiesOscillospiraceae,LachnospiraceaeandBifidobacteriaceae. In contrast, smoking affected the global composition of faecal microbiota at both M0 and M3. The prevalence/abundance of seven bacterial OTUs at M0 was associated with response to TNFi treatment. One of the candidates, present only in non-responders, is the genusSutterella, and the other six candidates are in the classClostridia.ConclusionsSeveral SpA patients’ characteristics modulate the composition of gut microbiota as did TNFi treatment. Moreover, the abundance/prevalence of seven OTUs at baseline may be used as a novel non-invasive index that predicts the response to TNFi with greater accuracy than HLA-B27 status, CRP level and measures of disease activity.

BackgroundInterest in genomic medicine for human health studies and clinical applications is rapidly increasing. Clinical applications require contamination-free samples to avoid misleading results and provide a sound basis for diagnosis.ResultsHere we present ContaTester, a tool which requires only allele balance information gathered from a VCF file to detect cross-contamination in germline human DNA samples. Based on a regression model of allele balance distribution, ContaTester allows fast checking of contamination levels for single samples or large cohorts (less than two minutes per sample). We demonstrate the efficiency of ContaTester using experimental validations: ContaTester shows similar results to methods requiring alignment data but with a significantly reduced storage footprint and less computation time. Additionally, for contamination levels above 5%, ContaTester can identify contaminants across a cohort, providing important clues for troubleshooting and quality assessment.ConclusionsContaTester estimates contamination levels from VCF files generated from whole genome sequencing normal sample and provides reliable contaminant identification for cohorts or experimental batches.

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