Nanopore sequencing elucidates in vivo development of meropenem resistance by insertion of a mobile genetic element in the porin gene ompC in E. coli.

An ESBL-producing E. coli isolate recovered from a patient undergoing long-term treatment developed resistance to meropenem without acquiring carbapenem-hydrolysing enzymes. We performed Nanopore and Illumina sequencing and subsequent full hybrid genome assembly of this isolate and the meropenem-susceptible isolate recovered almost 8 weeks prior. Whole genome MLST patterns did not differ between isolates. However, we found the insertion of an IS5-like element in the sequence of the ompC gene and an increase in the number of copies of the CTX-M-15 gene in the resistant isolate. These results show that E. coli can develop meropenem resistance under antibiotic pressure by mutations in ompC genes and increasing the copy number of ESBL genes, and the value of next generation sequencing to reveal resistance mechanisms not detected by conventional PCR.

Concordance between culture, Molecular Culture and Illumina 16S rRNA gene amplicon sequencing of bone and ulcer bed biopsies in people with diabetic foot osteomyelitis.

BACKGROUND: In clinical practice the diagnosis of diabetic foot osteomyelitis (DFO) relies on cultures of bone or ulcer bed (UB) biopsies, of which bone biopsy is reference standard. The slow growth or fastidious nature of some bacteria, hamper expeditious detection and identification. Rapid molecular techniques may solve both issues, but their additional value for everyday practice is unknown. We investigated the concordance between conventional culture, the molecular techniques Molecular Culture (MC), and illumina 16S rRNA gene amplicon (16S) sequencing in people with DFO. METHODS: In the BeBoP trial, bone and UB biopsies were obtained from people with DFO who visited Amsterdam UMC. These biopsies were analysed using 1) conventional culture, 2)MC, a rapid broad range PCR analysing the 16S-23S ribosomal-interspace-region, and 3) 16S sequencing, and evaluated concordance among these techniques. RESULTS: We analysed 20 samples (11 bone and 9 UB) of 18 people. A total of 84 infectious agents were identified, 45 (54%) by all techniques, an additional 22 (26.5%, overall 80.5%) by both MC and 16S, and the remaining 16 species by culture and MC or 16S, or by a single method only. MC and 16S identified anaerobes not detected by culturing in 5 samples, and the presence of bacteria in 7 of 8 culture-negative (6 bone, 2 UB) samples. CONCLUSION: The high level of concordance between MC and 16S and the additional ability of molecular techniques to detect various bacteria not detected by culturing opens up prospects for routine use of fast molecular techniques, in clinical settings including DFO. TRIAL REGISTRATION: The BeBoP trial is retrospectively registered on 05-03-2019 in Netherlands Trial Register: NL 7582.

Genome-wide association reveals host-specific genomic traits in Escherichia coli.

BACKGROUND: Escherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood. RESULTS: We randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type. CONCLUSIONS: This study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli.

Presence of Burkholderia pseudomallei in Soil, Nigeria, 2019.

Melioidosis, caused by the soil-dwelling bacterium Burkholderia pseudomallei, is predicted to be endemic in Nigeria but is only occasionally reported. This report documents the systematic identification of the presence of B. pseudomallei and B. thailandensis in the soil across multiple states in Nigeria.

A comparison of five Illumina, Ion Torrent, and nanopore sequencing technology-based approaches for whole genome sequencing of SARS-CoV-2.

Rapid identification of the rise and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern remains critical for monitoring of the efficacy of diagnostics, therapeutics, vaccines, and control strategies. A wide range of SARS-CoV-2 next-generation sequencing (NGS) methods have been developed over the last years, but cross-sequence technology benchmarking studies have been scarce. In the current study, 26 clinical samples were sequenced using five protocols: AmpliSeq SARS-CoV-2 (Illumina), EasySeq RC-PCR SARS-CoV-2 (Illumina/NimaGen), Ion AmpliSeq SARS-CoV-2 (Thermo Fisher), custom primer sets (Oxford Nanopore Technologies (ONT)), and capture probe-based viral metagenomics (Roche/Illumina). Studied parameters included genome coverage, depth of coverage, amplicon distribution, and variant calling. The median SARS-CoV-2 genome coverage of samples with cycle threshold (Ct) values of 30 and lower ranged from 81.6 to 99.8% for, respectively, the ONT protocol and Illumina AmpliSeq protocol. Correlation of coverage with PCR Ct values varied per protocol. Amplicon distribution signatures differed across the methods, with peak differences of up to 4 log10 at disbalanced positions in samples with high viral loads (Ct values ≤ 23). Phylogenetic analyses of consensus sequences showed clustering independent of the workflow used. The proportion of SARS-CoV-2 reads in relation to background sequences, as a (cost-)efficiency metric, was the highest for the EasySeq protocol. The hands-on time was the lowest when using EasySeq and ONT protocols, with the latter additionally having the shortest sequence runtime. In conclusion, the studied protocols differed on a variety of the studied metrics. This study provides data that assist laboratories when selecting protocols for their specific setting.

Comparison of fast Fourier transform infrared spectroscopy biotyping with whole genome sequencing-based genotyping in common nosocomial pathogens.

Early detection of bacterial transmission and outbreaks in hospitals is important because nosocomial infections can result in health complications and longer hospitalization. Current practice to detect outbreaks uses genotyping methods amplified fragment length polymorphism (AFLP) and whole genome sequencing (WGS), which are not suitable methods for real-time transmission screening of both susceptible and resistant bacteria. The aim was to assess the typing technique Fourier transform infrared (FTIR) spectroscopy as real-time screening method to discriminate large amounts of susceptible and resistant bacteria at strain level when there is no evident outbreak in comparison with the WGS reference. Isolates of past hospital outbreak strains of Acinetobacter baumannii/calcoaceticus complex (n = 25), Escherichia coli (n = 31), Enterococcus faecium (n = 22), Staphylococcus aureus (n = 37) and Pseudomonas aeruginosa (n = 30) were used for validation of FTIR. Subsequently, Enterococcus faecalis (n = 106) and Enterococcus faecium (n = 104) isolates from weekly routine screening samples when no potential outbreak was present were analysed. FTIR showed reproducibility and congruence of cluster composition with WGS for A. baumannii/calcoaceticus complex and E. faecium outbreak isolates. The FTIR results of E. faecalis and E. faecium isolates from routine samples showed reproducibility, but the congruence of cluster composition with WGS was low. For A. baumannii/calcoaceticus complex and E. faecium outbreak isolates, FTIR appears to be a discriminatory typing tool. However, our study shows the discriminatory power is too low to screen real-time for transmission of E. faecium and E. faecalis at patient wards based on isolates acquired in routine surveillance cultures when there is no clear suspicion of an ongoing outbreak.

Metagenomic DNA sequencing for semi-quantitative pathogen detection from urine: a prospective, laboratory-based, proof-of-concept study.

BACKGROUND: Semi-quantitative bacterial culture is the reference standard to diagnose urinary tract infection, but culture is time-consuming and can be unreliable if patients are receiving antibiotics. Metagenomics could increase diagnostic accuracy and speed by sequencing the microbiota and resistome directly from urine. We aimed to compare metagenomics to culture for semi-quantitative pathogen and resistome detection from urine. METHODS: In this proof-of-concept study, we prospectively included consecutive urine samples from a clinical diagnostic laboratory in Amsterdam. Urine samples were screened by DNA concentration, followed by PCR-free metagenomic sequencing of randomly selected samples with a high concentration of DNA (culture positive and negative). A diagnostic index was calculated as the product of DNA concentration and fraction of pathogen reads. We compared results with semi-quantitative culture using area under the receiver operating characteristic curve (AUROC) analyses. We used ResFinder and PointFinder for resistance gene detection and compared results to phenotypic antimicrobial susceptibility testing for six antibiotics commonly used for urinary tract infection treatment: nitrofurantoin, ciprofloxacin, fosfomycin, cotrimoxazole, ceftazidime, and ceftriaxone. FINDINGS: We screened 529 urine samples of which 86 were sequenced (43 culture positive and 43 culture negative). The AUROC of the DNA concentration-based screening was 0·85 (95% CI 0·81-0·89). At a cutoff value of 6·0 ng/mL, culture positivity was ruled out with a negative predictive value of 91% (95% CI 87-93; 26 of 297 samples), reducing the number of samples requiring sequencing by 56% (297 of 529 samples). The AUROC of the diagnostic index was 0·87 (95% CI 0·79-0·95). A diagnostic index cutoff value of 17·2 yielded a positive predictive value of 93% (95% CI 85-97) and a negative predictive value of 69% (55-80), correcting for a culture-positive prevalence of 66%. Gram-positive pathogens explained eight (89%) of the nine false-negative metagenomic test results. Agreement of phenotypic and genotypic antimicrobial susceptibility testing varied between 71% (22 of 31 samples) and 100% (six of six samples), depending on the antibiotic tested. INTERPRETATION: This study provides proof-of-concept of metagenomic semi-quantitative pathogen and resistome detection for the diagnosis of urinary tract infection. The findings warrant prospective clinical validation of the value of this approach in informing patient management and care. FUNDING: EU Horizon 2020 Research and Innovation Programme.

Centre-specific bacterial pathogen typing affects infection-control decision making.

Whole-genome sequencing is becoming the de facto standard for bacterial outbreak surveillance and infection prevention. This is accompanied by a variety of bioinformatic tools and needs bioinformatics expertise for implementation. However, little is known about the concordance of reported outbreaks when using different bioinformatic workflows. In this multi-centre proficiency testing among 13 major Dutch healthcare-affiliated centres, bacterial whole-genome outbreak analysis was assessed. Centres who participated obtained two randomized bacterial datasets of Illumina sequences, a Klebsiella pneumoniae and a Vancomycin-resistant Enterococcus faecium, and were asked to apply their bioinformatic workflows. Centres reported back on antimicrobial resistance, multi-locus sequence typing (MLST), and outbreak clusters. The reported clusters were analysed using a method to compare landscapes of phylogenetic trees and calculating Kendall-Colijn distances. Furthermore, fasta files were analysed by state-of-the-art single nucleotide polymorphism (SNP) analysis to mitigate the differences introduced by each centre and determine standardized SNP cut-offs. Thirteen centres participated in this study. The reported outbreak clusters revealed discrepancies between centres, even when almost identical bioinformatic workflows were used. Due to stringent filtering, some centres failed to detect extended-spectrum beta-lactamase genes and MLST loci. Applying a standardized method to determine outbreak clusters on the reported de novo assemblies, did not result in uniformity of outbreak-cluster composition among centres.

Serologic Surveillance and Phylogenetic Analysis of SARS-CoV-2 Infection Among Hospital Health Care Workers.

Importance: It is unclear when, where, and by whom health care workers (HCWs) working in hospitals are infected with SARS-CoV-2. Objective: To determine how often and in what manner nosocomial SARS-CoV-2 infection occurs in HCW groups with varying exposure to patients with COVID-19. Design, Setting, and Participants: This cohort study comprised 4 weekly measurements of SARS-CoV-2-specific antibodies and collection of questionnaires from March 23 to June 25, 2020, combined with phylogenetic and epidemiologic transmission analyses at 2 university hospitals in the Netherlands. Included individuals were HCWs working in patient care for those with COVID-19, HCWs working in patient care for those without COVID-19, and HCWs not working in patient care. Data were analyzed from August through December 2020. Exposures: Varying work-related exposure to patients infected with SARS-CoV-2. Main Outcomes and Measures: The cumulative incidence of and time to SARS-CoV-2 infection, defined as the presence of SARS-CoV-2-specific antibodies in blood samples, were measured. Results: Among 801 HCWs, there were 439 HCWs working in patient care for those with COVID-19, 164 HCWs working in patient care for those without COVID-19, and 198 HCWs not working in patient care. There were 580 (72.4%) women, and the median (interquartile range) age was 36 (29-50) years. The incidence of SARS-CoV-2 was increased among HCWs working in patient care for those with COVID-19 (54 HCWs [13.2%; 95% CI, 9.9%-16.4%]) compared with HCWs working in patient care for those without COVID-19 (11 HCWs [6.7%; 95% CI, 2.8%-10.5%]; hazard ratio [HR], 2.25; 95% CI, 1.17-4.30) and HCWs not working in patient care (7 HCWs [3.6%; 95% CI, 0.9%-6.1%]; HR, 3.92; 95% CI, 1.79-8.62). Among HCWs caring for patients with COVID-19, SARS-CoV-2 cumulative incidence was increased among HCWs working on COVID-19 wards (32 of 134 HCWs [25.7%; 95% CI, 17.6%-33.1%]) compared with HCWs working on intensive care units (13 of 186 HCWs [7.1%; 95% CI, 3.3%-10.7%]; HR, 3.64; 95% CI, 1.91-6.94), and HCWs working in emergency departments (7 of 102 HCWs [8.0%; 95% CI, 2.5%-13.1%]; HR, 3.29; 95% CI, 1.52-7.14). Epidemiologic data combined with phylogenetic analyses on COVID-19 wards identified 3 potential HCW-to-HCW transmission clusters. No patient-to-HCW transmission clusters could be identified in transmission analyses. Conclusions and Relevance: This study found that HCWs working on COVID-19 wards were at increased risk for nosocomial SARS-CoV-2 infection with an important role for HCW-to-HCW transmission. These findings suggest that infection among HCWs deserves more consideration in infection prevention practice.

Recommendations for the introduction of metagenomic next-generation sequencing in clinical virology, part II: bioinformatic analysis and reporting.

Metagenomic next-generation sequencing (mNGS) is an untargeted technique for determination of microbial DNA/RNA sequences in a variety of sample types from patients with infectious syndromes. mNGS is still in its early stages of broader translation into clinical applications. To further support the development, implementation, optimization and standardization of mNGS procedures for virus diagnostics, the European Society for Clinical Virology (ESCV) Network on Next-Generation Sequencing (ENNGS) has been established. The aim of ENNGS is to bring together professionals involved in mNGS for viral diagnostics to share methodologies and experiences, and to develop application guidelines. Following the ENNGS publication Recommendations for the introduction of mNGS in clinical virology, part I: wet lab procedure in this journal, the current manuscript aims to provide practical recommendations for the bioinformatic analysis of mNGS data and reporting of results to clinicians.

Proficiency Testing of Metagenomics-Based Detection of Food-Borne Pathogens Using a Complex Artificial Sequencing Dataset.

Metagenomics-based high-throughput sequencing (HTS) enables comprehensive detection of all species comprised in a sample with a single assay and is becoming a standard method for outbreak investigation. However, unlike real-time PCR or serological assays, HTS datasets generated for pathogen detection do not easily provide yes/no answers. Rather, results of the taxonomic read assignment need to be assessed by trained personnel to gain information thereof. Proficiency tests are important instruments of validation, harmonization, and standardization. Within the European Union funded project COMPARE [COllaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks in Europe], we conducted a proficiency test to scrutinize the ability to assess diagnostic metagenomics data. An artificial dataset resembling shotgun sequencing of RNA from a sample of contaminated trout was provided to 12 participants with the request to provide a table with per-read taxonomic assignments at species level and a report with a summary and assessment of their findings, considering different categories like pathogen, background, or contaminations. Analysis of the read assignment tables showed that the software used reliably classified the reads taxonomically overall. However, usage of incomplete reference databases or inappropriate data pre-processing caused difficulties. From the combination of the participants' reports with their read assignments, we conclude that, although most species were detected, a number of important taxa were not or not correctly categorized. This implies that knowledge of and awareness for potentially dangerous species and contaminations need to be improved, hence, capacity building for the interpretation of diagnostic metagenomics datasets is necessary.

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples' MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.

Thermus thermophilus DNA can be used as internal control for process monitoring of clinical metagenomic next-generation sequencing of urine samples.

INTRODUCTION: Metagenomics is increasingly considered for clinical diagnostics. In order for this technology to become integrated in the clinical microbiology laboratory, process controls are required. Molecular diagnostic tests typically integrate an internal control (IC) to detect potential sources of variation and technical failure. However, few studies report on the integration of an IC in metagenomics. AIM: We aimed to develop an easy-to-use IC method for the process control of library preparation and sequencing applied to metagenomics in clinical microbiology diagnostics using Thermus thermophilus DNA. METHODOLOGY: DNA was extracted from urine samples and sequenced on the Ion Torrent Proton in the absence and presence of incremental concentrations (0.5-2-5%) of IC. Between aliquots of each sample, we compared the IC relative abundance (RA), and after in silico subtraction of IC reads, analysed microbial composition and the RA of pathogens. The optimal IC concentration was defined as the lowest concentration still detectable in all samples with the smallest impact on the microbial composition. RESULTS: The RA of IC correlated linearly with the spiked IC concentration (r2 = 0.99). IC added in a concentration of 0.5% of the total DNA concentration was detectable in all sample aliquots, regardless of human-bacterial DNA proportion, and after in silico removal gave the smallest difference in RA of pathogens compared to the sample aliquot sequenced in the absence of IC. The microbial composition in the presence and absence of IC was highly similar after in silico removal of IC reads (median BC-dissimilarity per sample: 0.059), provided samples had a mean of >10,000 bacterial reads. CONCLUSION: T. thermophilus DNA at a percentage of 0.5% of the total DNA concentration was successfully applied for the process control of metagenomics of urine samples. We demonstrated negligible alterations in sample microbial composition after in silico subtraction of IC reads. This approach contributes toward implementation of metagenomics in the clinical microbiology laboratory.

Accelerating surveillance and research of antimicrobial resistance - an online repository for sharing of antimicrobial susceptibility data associated with whole-genome sequences.

Antimicrobial resistance (AMR) is an emerging threat to modern medicine. Improved diagnostics and surveillance of resistant bacteria require the development of next-generation analysis tools and collaboration between international partners. Here, we present the 'AMR Data Hub', an online infrastructure for storage and sharing of structured phenotypic AMR data linked to bacterial whole-genome sequences. Leveraging infrastructure built by the European COMPARE Consortium and structured around the European Nucleotide Archive (ENA), the AMR Data Hub already provides an extensive data collection of more than 2500 isolates with linked genome and AMR data. Representing these data in standardized formats, we provide tools for the validation and submission of new data and services supporting search, browse and retrieval. The current collection was created through a collaboration by several partners from the European COMPARE Consortium, demonstrating the capacities and utility of the AMR Data Hub and its associated tools. We anticipate growth of content and offer the hub as a basis for future research into methods to explore and predict AMR.

Author Correction: Global phylogenetic analysis of Escherichia coli and plasmids carrying the mcr-1 gene indicates bacterial diversity but plasmid restriction.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Amplified fragment length polymorphism and whole genome sequencing: a comparison of methods in the investigation of a nosocomial outbreak with vancomycin resistant enterococci.

Background: Recognition of nosocomial outbreaks with antimicrobial resistant (AMR) pathogens and appropriate infection prevention measures are essential to limit the consequences of AMR pathogens to patients in hospitals. Because unrelated, but genetically similar AMR pathogens may circulate simultaneously, rapid high-resolution molecular typing methods are needed for outbreak management. We compared amplified fragment length polymorphism (AFLP) and whole genome sequencing (WGS) during a nosocomial outbreak of vancomycin-resistant Enterococcus faecium (VRE) that spanned 5 months. Methods: Hierarchical clustering of AFLP profiles was performed using unweighted pair-grouping and similarity coefficients were calculated with Pearson correlation. For WGS-analysis, core single nucleotide polymorphisms (SNPs) were used to calculate the pairwise distance between isolates, construct a maximum likelihood phylogeny and establish a cut-off for relatedness of epidemiologically linked VRE isolates. SNP-variations in the vanB gene cluster were compared to increase the comparative resolution. Technical replicates of 2 isolates were sequenced to determine the number of core-SNPs derived from random sequencing errors. Results: Of the 721 patients screened for VRE carriage, AFLP assigned isolates of 22 patients to the outbreak cluster. According to WGS, all 22 isolates belonged to ST117 but only 21 grouped in a tight phylogenetic cluster and carried vanB resistance gene clusters. Sequencing of technical replicates showed that 4-5 core-SNPs were derived by random sequencing errors. The cut-off for relatedness of epidemiologically linked VRE isolates was established at ≤7 core-SNPs. The discrepant isolate was separated from the index isolate by 61 core-SNPs and the vanB gene cluster was absent. In AFLP analysis this discrepant isolate was indistinguishable from the other outbreak isolates, forming a cluster with 92% similarity (cut-off for identical isolates ≥90%). The inclusion of the discrepant isolate in the outbreak resulted in the screening of 250 patients and quarantining of an entire ward. Conclusion: AFLP was a rapid and affordable screening tool for characterising hospital VRE outbreaks. For in-depth understanding of the outbreak WGS was needed. Compared to AFLP, WGS provided higher resolution typing of VRE isolates with implications for outbreak management.

Limited contribution of non-intensive chicken farming to ESBL-producing Escherichia coli colonization in humans in Vietnam: an epidemiological and genomic analysis.

OBJECTIVES: To investigate the risk of colonization with ESBL-producing Escherichia coli (ESBL-Ec) in humans in Vietnam associated with non-intensive chicken farming. METHODS: Faecal samples from 204 randomly selected farmers and their chickens, and from 306 age- and sex-matched community-based individuals who did not raise poultry were collected. Antimicrobial usage in chickens and humans was assessed by medicine cabinet surveys. WGS was employed to obtain a high-resolution genomic comparison between ESBL-Ec isolated from humans and chickens. RESULTS: The adjusted prevalence of ESBL-Ec colonization was 20.0% (95% CI 10.8%-29.1%) and 35.2% (95% CI 30.4%-40.1%) in chicken farms and humans in Vietnam, respectively. Colonization with ESBL-Ec in humans was associated with antimicrobial usage (OR = 2.52, 95% CI = 1.08-5.87) but not with involvement in chicken farming. blaCTX-M-55 was the most common ESBL-encoding gene in strains isolated from chickens (74.4%) compared with blaCTX-M-27 in human strains (47.0%). In 3 of 204 (1.5%) of the farms, identical ESBL genes were detected in ESBL-Ec isolated from farmers and their chickens. Genomic similarity indicating recent sharing of ESBL-Ec between chickens and farmers was found in only one of these farms. CONCLUSIONS: The integration of epidemiological and genomic data in this study has demonstrated a limited contribution of non-intensive chicken farming to ESBL-Ec colonization in humans in Vietnam and further emphasizes the importance of reducing antimicrobial usage in both human and animal host reservoirs.

Quantifying the contribution of four resistance mechanisms to ciprofloxacin MIC in Escherichia coli: a systematic review.

Background: Reviews assessing the genetic basis of ciprofloxacin resistance in Escherichia coli have mostly been qualitative. However, to predict resistance phenotypes based on genotypic characteristics, it is essential to quantify the contribution of genotypic determinants to resistance. Objectives: We performed a systematic review to assess the relative contribution of known genomic resistance determinants to the MIC of ciprofloxacin in E. coli. Methods: PubMed and Web of Science were searched for English language studies that assessed ciprofloxacin MIC and presence or introduction of genetic determinants of ciprofloxacin resistance in E. coli. We included experimental and observational studies without time restrictions. Medians and ranges of MIC fold changes were calculated for individual resistance determinants and combinations thereof. Results: We included 66 studies, describing 604 E. coli isolates that carried at least one genetic ciprofloxacin resistance determinant. Mutations in gyrA and parC, genes encoding targets of ciprofloxacin, contribute to the largest fold changes in ciprofloxacin resistance in E. coli compared with the WT. Efflux and physical blocking or enzymatic modifications confer smaller increases in ciprofloxacin MIC than mutations in gyrA and parC. However, the presence of these other resistance mechanisms in addition to target alteration mutations further increases ciprofloxacin MIC, thus resulting in ciprofloxacin MIC increases ranging from 250- to 4000-fold. Conclusions: This quantitative review of genomic determinants of ciprofloxacin resistance in E. coli demonstrates the complexity of resistance phenotype prediction from genomic data and serves as a reference point for studies aiming to predict ciprofloxacin MIC from E. coli genomes.

Global phylogenetic analysis of Escherichia coli and plasmids carrying the mcr-1 gene indicates bacterial diversity but plasmid restriction.

To understand the dynamics behind the worldwide spread of the mcr-1 gene, we determined the population structure of Escherichia coli and of mobile genetic elements (MGEs) carrying the mcr-1 gene. After a systematic review of the literature we included 65 E. coli whole genome sequences (WGS), adding 6 recently sequenced travel related isolates, and 312 MLST profiles. We included 219 MGEs described in 7 Enterobacteriaceae species isolated from human, animal and environmental samples. Despite a high overall diversity, 2 lineages were observed in the E. coli population that may function as reservoirs of the mcr-1 gene, the largest of which was linked to ST10, a sequence type known for its ubiquity in human faecal samples and in food samples. No genotypic clustering by geographical origin or isolation source was observed. Amongst a total of 13 plasmid incompatibility types, the IncI2, IncX4 and IncHI2 plasmids accounted for more than 90% of MGEs carrying the mcr-1 gene. We observed significant geographical clustering with regional spread of IncHI2 plasmids in Europe and IncI2 in Asia. These findings point towards promiscuous spread of the mcr-1 gene by efficient horizontal gene transfer dominated by a limited number of plasmid incompatibility types.

The oral cavity microbiota: between health, oral disease, and cancers of the aerodigestive tract.

Many studies show that the human microbiome plays a critical role in the chronic pathologies of obesity, inflammatory bowel diseases, and diabetes. More recently, the interaction between cancer and the microbiome has been highlighted. Most studies have focused on the gut microbiota because it represents the most extensive bacterial community, and the body of evidence correlating it with gut syndromes is increasing. However, in the strict sense, the gastrointestinal (GI) tract begins in the oral cavity, and special attention should be paid to the specific flora of this cavity. This study reviewed the current knowledge about the various microbial ecosystems of the upper part of the GI tract and discussed their potential link to carcinogenesis. The overall composition of the microbial communities, as well as the presence or absence of "key species", in relation to carcinogenesis is addressed. Alterations in the oral microbiota can potentially be used to predict the risk of cancer. Molecular advances and the further monitoring of the microbiota will increase our understanding of the role of the microbiota in carcinogenesis and open new perspectives for future therapeutic and prophylactic modalities.