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The worldwide emergence and dissemination of carbapenem-resistant Gram-negative bacteria (CRGNB) is a challenging problem of antimicrobial resistance today. Outbreaks with CRGNB have severe consequences for both the affected healthcare settings as well as the patients with infection. Thus, bloodstream infections caused by metallo-ß-lactamase-producing Enterobacterales can often have clinical implications, resulting in high mortality rates due to delays in administering effective treatment and the limited availability of treatment options. The overall threat of CRGNB is substantial because carbapenems are used to treat infections caused by ESBL-producing Enterobacterales which also exist with high frequency within the same geographical regions. A genome-based surveillance of 589 CRGNB from 61 hospitals across the federal state Hesse in Germany was implemented using next-generation sequencing (NGS) technology to obtain a high-resolution landscape of carbapenem-resistant isolates over a three-year period (2017–2019). The study examined all reportable CRGNB isolates submitted by participating hospitals. This included isolates carrying known carbapenemases (435) together with carbapenem-resistant non-carbapenemase producers (154). Predominant carbapenemase producers included Klebsiella pneumoniae, Escherichia coli, Citrobacter freundii and Acinetobacter baumannii. Over 80% of 375 carbapenem-resistant determinants including KPC-, NDM-, VIM- and OXA-48-like ones detected in 520 Enterobacterales were plasmid-encoded, and half of these were dominated by a few incompatibility (Inc) types, viz., IncN, IncL/M, IncFII and IncF(K). Our results revealed that plasmids play an extraordinary role in the dissemination of carbapenem resistance in the heterogeneous CRGNB population. The plasmids were also associated with several multispecies dissemination events and local outbreaks throughout the study period, indicating the substantial role of horizontal gene transfer in carbapenemase spread. Furthermore, due to vertical and horizontal plasmid transfer, this can have an impact on implant-associated infections and is therefore important for antibiotic-loaded bone cement and drug-containing devices in orthopedic surgery. Future genomic surveillance projects should increase their focus on plasmid characterization. [ABSTRACT FROM AUTHOR]

To the Editor: Findlay et al. (1) reported a high prevalence (>25%) of New Delhi metallo-[beta]-lactamase (NDM) among carbapenemase-producing Enterobacterales (CPE) in Switzerland during 2019-2020. To provide context, we analyzed [...]

During the past 20 years, Escherichia coli sequence type (ST) 131 has emerged as a prevalent vehicle for extended-spectrum p-lactamases (ESBL) worldwide. Particularly prevalent are isolates of the clade ST131 [...]

To the Editor: Recently, Yi-Yun Liu et al. reported on the discovery of mcr-1, a plasmidborne resistance gene mediating resistance to colistin, in isolates obtained from humans and animals (1). [...]

The EPIC consortium brings together experts from a wide range of fields that include clinical, molecular and basic microbiology, infectious diseases, computational biology and chemistry, drug discovery and design, bioinformatics, biochemistry, biophysics, pharmacology, toxicology, veterinary sciences, environmental sciences, and epidemiology. The main question to be answered by the EPIC alliance is the following: "What is the best approach for data mining on carbapenemase inhibitors and how to translate this data into experiments?" From this forum, we propose that the scientific community think up new strategies to be followed for the discovery of new carbapenemase inhibitors, so that this process is efficient and capable of providing results in the shortest possible time and within acceptable time and economic costs. [ABSTRACT FROM AUTHOR]

Extended-spectrum beta-lactamase (ESBL)-producing Escherichia (E.) coli have been widely described as the cause of treatment failures in humans around the world. The origin of human infections with these microorganisms is discussed controversially and in most cases hard to identify. Since they pose a relevant risk to human health, it becomes crucial to understand their sources and the transmission pathways. In this study, we analyzed data from different studies in Germany and grouped ESBL-producing E. coli from different sources and human cases into subtypes based on their phenotypic and genotypic characteristics (ESBL-genotype, E. coli phylogenetic group and phenotypic antimicrobial resistance pattern). Then, a source attribution model was developed in order to attribute the human cases to the considered sources. The sources were from different animal species (cattle, pig, chicken, dog and horse) and also from patients with nosocomial infections. The human isolates were gathered from community cases which showed to be colonized with ESBL-producing E. coli. We used the attribution model first with only the animal sources (Approach A) and then additionally with the nosocomial infections (Approach B). We observed that all sources contributed to the human cases, nevertheless, isolates from nosocomial infections were more related to those from human cases than any of the other sources. We identified subtypes that were only detected in the considered animal species and others that were observed only in the human population. Some subtypes from the human cases could not be allocated to any of the sources from this study and were attributed to an unknown source. Our study emphasizes the importance of human-to-human transmission of ESBL-producing E. coli and the different role that pets, livestock and healthcare facilities may play in the transmission of these resistant bacteria. The developed source attribution model can be further used to monitor future trends. A One Health approach is necessary to develop source attribution models further to integrate also wildlife, environmental as well as food sources in addition to human and animal data. [ABSTRACT FROM AUTHOR]

Escherichia coli belonging to the enterohemorrhagic (EHEC), Shiga toxin-producing (STEC) and atypical enteropathogenic (aEPEC) pathotypes are significant foodborne zoonotic pathogens posing serious health risks, with healthy cattle as their main reservoir. A representative sampling of Hungarian cattle farms during 2017-2018 yielded a prevalence of 6.5 and 5.8% for STEC and aEPEC out of 309 samples. The draft genomes of twelve STEC (of them 9 EHEC) and four aEPEC of bovine origin were determined. For comparative purposes, we also included 3 EHEC and 2 aEPEC strains of human origin, as well four commensal isolates and one extraintestinal pathogenic E. coli (ExPEC) obtained from animals in a final set of 26 strains for a WGS-based analysis. Apart from key virulence genes, these isolates harbored several additional virulence genes with arrays characteristic for the site of isolation. The most frequent insertion site of Shiga toxin (stx) encoding prophages was yehVfor the Stx1 prophage and wrbA and sbcB for Stx2. For O157:H7 strains, the locus of enterocyte effacement pathogenicity island was present at the selC site, with integration at pheV for other serotypes, and pheU in the case of O26:H11 strains. Several LEE-negative STEC and aEPEC as well as commensal isolates carried additional prophages, with an average of ten prophage regions per isolate. Comparative phylogenomic analysis showed no clear separation between bovine and human lineages among the isolates characterized in the current study. Similarities in virulence gene arrays and close phylogenetic relations of bovine and human isolates underline the zoonotic potential of bovine aEPEC and STEC and emphasize the need for frequent monitoring of these pathogens in livestock. [ABSTRACT FROM AUTHOR]

Motivation Antimicrobial resistance (AMR) is one of the biggest global problems threatening human and animal health. Rapid and accurate AMR diagnostic methods are thus very urgently needed. However, traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and whole-genome sequencing data without previously known knowledge remains to be done. Results In this study, we evaluated logistic regression (LR), support vector machine (SVM), random forest (RF) and convolutional neural network (CNN) for the prediction of AMR for the antibiotics ciprofloxacin, cefotaxime, ceftazidime and gentamicin. We could demonstrate that these models can effectively predict AMR with label encoding, one-hot encoding and frequency matrix chaos game representation (FCGR encoding) on whole-genome sequencing data. We trained these models on a large AMR dataset and evaluated them on an independent public dataset. Generally, RFs and CNNs perform better than LR and SVM with AUCs up to 0.96. Furthermore, we were able to identify mutations that are associated with AMR for each antibiotic. Availability and implementation Source code in data preparation and model training are provided at GitHub website (https://github.com/YunxiaoRen/ML-iAMR). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Shigella dysenteriae are significant agents of bacillary dysentery, accounting for a considerable number of illnesses with high morbidity worldwide. The Shiga toxin (Stx) encoded by a defective prophage is the key virulence factor of S. dysenteriae type 1 (SD1) strains. Here we present the full genome sequence of an SD1 strain HNCMB 20080 isolated in 1954, compare it to other sequenced SD1 genomes, and assess the diversity of Stx-prophages harbored by previously sequenced SD1 strains. The genome of HNCMB 20080 consists of a chromosome sized 4,393,622 bp containing 5,183 CDSs, as well as two small plasmids. Comparative genomic analysis revealed a high degree of uniformity among SD1 genomes, including the structure of Stx prophage regions, which we found to form two subgroups termed PT-I and PT-II. All PT-I strains are members of the sequence type (ST) 146 or ST260, while the only PT-II harboring strain, Sd1617 proved to be ST untypeable. In accordance with data from previous reports, the Stx1 prophage could not be induced from HNCMB 20080. Our cumulative data do not support the notion that stx -harboring phages in STEC are derived from historical SD1 isolates. [ABSTRACT FROM AUTHOR]

Objectives: To analyse the rectal carriage rate and the molecular epidemiology of vancomycin-resistant Enterococcus faecium (VREfm) recovered from patients upon hospital admission.Methods: Adult patients were screened at six German university hospitals from five different federal states upon hospital admission for rectal colonization with VREfm between 2014 and 2018. Molecular characterization of VREfm was performed by WGS followed by MLST and core-genome MLST analysis.Results: Of 16350 patients recruited, 263 were colonized with VREfm, with increasing prevalence rates during the 5 year study period (from 0.8% to 2.6%). In total, 78.5% of the VREfm were vanB positive and 20.2% vanA positive, while 1.2% harboured both vanA and vanB. The predominant ST was ST117 (56.7%) followed by ST80 (15%), ST203 (10.9%), ST78 (5.7%) and ST17 (3.2%). ST117/vanB VREfm isolates formed a large cluster of 96 closely related isolates extending across all six study centres and four smaller clusters comprising 13, 5, 4 and 3 isolates each. In contrast, among the other STs inter-regional clonal relatedness was rarely observed.Conclusions: To our knowledge, this is the largest admission prevalence and molecular epidemiology study of VREfm. These data provide insight into the epidemiology of VREfm at six German university hospitals and demonstrate the remarkable inter-regional clonal expansion of the ST117/vanB VREfm clone. [ABSTRACT FROM AUTHOR]

Whole genome sequencing of bacteria has become daily routine in many fields. Advances in DNA sequencing technologies and continuously dropping costs have resulted in a tremendous increase in the amounts of available sequence data. However, comprehensive in-depth analysis of the resulting data remains an arduous and time-consuming task. In order to keep pace with these promising but challenging developments and to transform raw data into valuable information, standardized analyses and scalable software tools are needed. Here, we introduce ASA3P, a fully automatic, locally executable and scalable assembly, annotation and analysis pipeline for bacterial genomes. The pipeline automatically executes necessary data processing steps, i.e. quality clipping and assembly of raw sequencing reads, scaffolding of contigs and annotation of the resulting genome sequences. Furthermore, ASA3P conducts comprehensive genome characterizations and analyses, e.g. taxonomic classification, detection of antibiotic resistance genes and identification of virulence factors. All results are presented via an HTML5 user interface providing aggregated information, interactive visualizations and access to intermediate results in standard bioinformatics file formats. We distribute ASA3P in two versions: a locally executable Docker container for small-to-medium-scale projects and an OpenStack based cloud computing version able to automatically create and manage self-scaling compute clusters. Thus, automatic and standardized analysis of hundreds of bacterial genomes becomes feasible within hours. The software and further information is available at: asap.computational.bio. [ABSTRACT FROM AUTHOR]