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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). [...]

Bacterial infections with the genus Enterobacter are notoriously difficult to treat and often associated with resistance to penicillin, aminoglycosides, fluoroquinolones, and third-generation cephalosporins. Also, Enterobacter species have emerged as the third most common hosts for carbapenemases worldwide, forcing the use of colistin as a "last-resort" antibiotic for the treatment. Studies on the population structure of the genus Enterobacter repeatedly detect E. xiangfangensis as a common clinical species present worldwide. Here, we report on the characteristics of an extreme drugresistant E. xiangfangensis isolate va18651 (ST88), obtained from a cervical swab of an expectant mother. The isolate was resistant to almost all the classes of antibiotics tested, including β-lactams (viz., penicillins, carbapenems, cephalosporin, monobactams, and their combinations), quinolone, aminoglycosides, and sulfonamide/dihydrofolate reductase inhibitor, and exhibited heteroresistance towards colistin. Analysis of its complete genome sequence revealed 37 antibiotic resistance genes (ARGs), including mcr-9.1, blaKPC-2, and blaOXA-48, encoded on three of the four different plasmids (cumulative plasmidome size 604,632 bp). An unusually high number of plasmid-based heavy metal resistance gene (HRG) clusters towards silver, arsenate, cadmium, copper, mercury, and tellurite were also detected. Virulence genes (VGs) for the lipopolysaccharide and capsular polysaccharide structures, iron acquisition (iroBCDEN, ent/fep/fes, sitABCD, iut, and fur), and a type VI secretion system, together with motility genes and Type IV pili, were encoded chromosomally. Thus, a unique combination of chromosomally encoded VGs, together with plasmid-encoded ARGs and HRGs, converged to result in an extreme drug-resistant, pathogenic isolate with survival potential in environmental settings. The use of a disinfectant, octenidine, led to its eradication; however, the existence of a highly antibiotic-resistant isolate with significant virulence potential is a matter of concern in public health settings and warrants further surveillance for extreme drug-resistant Enterobacter isolates. [ABSTRACT FROM AUTHOR]

Summary: Listeria monocytogenes, the causative agent of listeriosis, has been implicated in increasing foodborne outbreaks worldwide. The disease is manifested in various forms ranging from severe sepsis in immune‐compromised individuals, febrile gastroenteritis, still birth, abortions and meningoencephalitis. In India, data from studies on the detection and molecular epidemiological analysis of L. monocytogenes are only recently emerging. The presence of Listeria in different ecological niches has been recorded from India, including foods, soil, vegetables, mangrove swamps, seafood, freshwater fishes, clinical cases, and also insects. The organism has also been isolated from women with spontaneous abortions, miscarriage or recurrent obstetric history, aborted foetuses, animal clinical cases and wildlife samples. A novel species of Listeria has also been characterized. Listeria monocytogenes strains isolated from clinical, environmental, and foods showed biofilm‐forming abilities. Listeria monocytogenes serotype 4b isolates of ST328, a predominant and unique ST observed in India, was repeatedly isolated from different sources, times, and geographical locations. Here, we reviewed the occurrence of Listeria in different sources in India, its resistance to biocides, and provide epidemiological analysis on its genomic landscape. [ABSTRACT FROM AUTHOR]

Listeria monocytogenes is a pathogen of great concern to the food industry. The present study was aimed to explore the clonal relationships amongst L. monocytogenes strains isolated from foods of animal origin (milk, beef, chevon (goat meat), pork and chicken) and fish. Forty-seven L. monocytogenes strains were characterized by pulsed-field gel electrophoresis (PFGE). The PFGE analysis using ApaI and AscI enzymes revealed 37 pulsotypes, with Simpson's discriminatory index of 0.987. This study demonstrated the presence of a few similar L. monocytogenes pulsotypes in different foods of animal origin in different places and years of isolation and this indicates that some L. monocytogenes subtypes may be ubiquitous which are acclimatizing and persisting in different foods of animal origin. This also emphasizes the importance of cross-contamination in local wet markets. Thus, the understanding of genetic diversity will contribute to the development of rational and workable strategies to control this important zoonotic infection. [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]