Multi-drug resistant bacteria are a phenomenon which is on the increase around the world, particularly with the emergence of colistin-resistant Enterobacteriaceae and vancomycin-resistant enterococci strains. The recent discovery of a plasmid-mediated colistin resistance with the description of the transferable mcr-1 gene raised concerns about the need for an efficient detection method for these pathogens, to isolate infected patients as early as possible. The LBJMR medium was developed to screen for all polymyxin-resistant Gram-negative bacteria, including mcr-1 positive isolates, and vancomycin-resistant Gram-positive bacteria. The LBJMR medium was developed by adding colistin sulfate salt at a low concentration (4 μg/mL) and vancomycin (50 μg/mL), with glucose (7.5 g/L) as a fermentative substrate, to a Purple Agar Base (31 g/L). A total of 143 bacterial strains were used to evaluate this universal culture medium, and the sensitivity and specificity of detection were 100% for the growth of resistant strains. 68 stool samples were cultured on LBJMR, and both colistin-resistant Gram-negative and vancomycin-resistant Gram-positive strains were specifically detected. The LBJMR medium is a multipurpose selective medium which makes it possible to identify bacteria of interest from clinical samples and to isolate contaminated patients in hospital settings. This is a simple medium that could be easily used for screening in clinical microbiology laboratories.

Extrachromosomal circular DNA (eccDNA) of chromosomal origin is found in many eukaryotic species and cell types, including cancer, where eccDNAs with oncogenes drive tumorigenesis. Most studies of eccDNA employ short-read sequencing for their identification. However, short-read sequencing cannot resolve the complexity of genomic repeats, which can lead to missing eccDNA products. Long-read sequencing technologies provide an alternative to constructing complete eccDNA maps. We present a software suite, Construction-based Rolling-circle-amplification for eccDNA Sequence Identification and Location (CReSIL), to identify and characterize eccDNA from long-read sequences. CReSIL’s performance in identifying eccDNA, with a minimum F1 score of 0.98, is superior to the other bioinformatic tools based on simulated data. CReSIL provides many useful features for genomic annotation, which can be used to infer eccDNA function and Circos visualization for eccDNA architecture investigation. We demonstrated CReSIL’s capability in several long-read sequencing datasets, including datasets enriched for eccDNA and whole genome datasets from cells containing large eccDNA products. In conclusion, the CReSIL suite software is a versatile tool for investigating complex and simple eccDNA in eukaryotic cells.

Here, we report the description of a colistin-heteroresistant Klebsiella pneumoniae isolate fortuitously isolated from the stool sample of a patient with suspicion of tuberculosis in a public hospital of Marseille, France. In the colistin-resistant subpopulation, a mutation in the mgrB gene leading to a premature stop codon was found, and the hypermucoviscous phenotype was lost. Susceptibility to other antibiotics remained unchanged. To our knowledge, this is the first identification of such a colistin-heteroresistant Klebsiella pneumoniae isolate in France.

Interpreting the phenotypes of bla _SHV alleles in Klebsiella pneumoniae genomes is complex. Whilst all strains are expected to carry a chromosomal copy conferring resistance to ampicillin, they may also carry mutations in chromosomal bla _SHV alleles or additional plasmid-borne bla _SHV alleles that have extended-spectrum β-lactamase (ESBL) activity and/or β-lactamase inhibitor (BLI) resistance activity. In addition, the role of individual mutations/a changes is not completely documented or understood. This has led to confusion in the literature and in antimicrobial resistance (AMR) gene databases [e.g. the National Center for Biotechnology Information (NCBI) Reference Gene Catalog and the β-lactamase database (BLDB)] over the specific functionality of individual sulfhydryl variable (SHV) protein variants. Therefore, the identification of ESBL-producing strains from K. pneumoniae genome data is complicated. Here, we reviewed the experimental evidence for the expansion of SHV enzyme function associated with specific aa substitutions. We then systematically assigned SHV alleles to functional classes (WT, ESBL and BLI resistant) based on the presence of these mutations. This resulted in the re-classification of 37 SHV alleles compared with the current assignments in the NCBI’s Reference Gene Catalog and/or BLDB (21 to WT, 12 to ESBL and 4 to BLI resistant). Phylogenetic and comparative genomic analyses support that (i) SHV-1 (encoded by bla _SHV-1) is the ancestral chromosomal variant, (ii) ESBL- and BLI-resistant variants have evolved multiple times through parallel substitution mutations, (iii) ESBL variants are mostly mobilized to plasmids and (iv) BLI-resistant variants mostly result from mutations in chromosomal bla _SHV. We used matched genome–phenotype data from the KlebNET-GSP AMR Genotype-Phenotype Group to identify 3999 K. pneumoniae isolates carrying one or more bla _SHV alleles but no other acquired β-lactamases to assess genotype–phenotype relationships for bla _SHV. This collection includes human, animal and environmental isolates collected between 2001 and 2021 from 24 countries. Our analysis supports that mutations at Ambler sites 238 and 179 confer ESBL activity, whilst most omega-loop substitutions do not. Our data also provide support for the WT assignment of 67 protein variants, including 8 that were noted in public databases as ESBL. These eight variants were reclassified as WT because they lack ESBL-associated mutations, and our phenotype data support susceptibility to third-generation cephalosporins (SHV-27, SHV-38, SHV-40, SHV-41, SHV-42, SHV-65, SHV-164 and SHV-187). The approach and results outlined here have been implemented in Kleborate v2.4.1 (a software tool for genotyping K. pneumoniae), whereby known and novel bla _SHV alleles are classified based on causative mutations. Kleborate v2.4.1 was updated to include ten novel protein variants from the KlebNET-GSP dataset and all alleles in public databases as of November 2023. This study demonstrates the power of sharing AMR phenotypes alongside genome data to improve the understanding of resistance mechanisms.

The overall aim of the current study was to test the hypotheses that (i) antibiotic resistance in bacteria were more frequent in clinically health pigs in intensified company owned, medium-scale farms (MSFs) (100–500 sows) than in pigs in family-owned, small-scale farms (SSFs) (1–50 sows) and (ii) that farmers working at the MSFs were more prone to attain antibiotic resistant bacteria than farmers working at SSFs. The study was conducted in North-Eastern Thailand, comprising fecal Escherichia coli isolates from pigs, farmers working with the pigs (contact humans) and persons living in the same household as the farmer (non-contact humans) at 51 MSFs and 113 SSFs. Samples from all farms were also screened for methicillin-resistant staphylococcus aureus (MRSA), which was not detected in pig samples, but was found in one human sample. Susceptibility was tested by disc-diffusion for seven antibiotics commonly used in the study area. Resistance in pig isolates from MSFs were more frequent for chloramphenicol which (P < 0.001), trimethoprim/sulfamethoxazole (P < 0.001) and gentamicin (P < 0.05) compared with isolates from SSFs, whereas the opposite was true for tetracycline (P < 0.01). Resistance in the human isolates was lower than those in the isolates from pigs for tetracycline, trimethoprim/sulfamethoxazole and chloramphenicol (P < 0.001). The frequency of resistance in the contact human samples from SSFs and MSFs did not differ. There was no difference between isolates from contact and non-contact humans for any of the tested antibiotics. Multidrug resistance in isolates from pigs was 26%, significantly higher (P < 0.01) than the 13% from humans. The data indicate that (i) resistance to antibiotics, including those critical and highly important for human medicine, were more common in fecal E. coli from pigs at the MSFs than at the SSFs, whereas (ii) the resistance in fecal E. coli from pig farmers seemed not to be influenced by the level of intensification of the farm they were working at.

BACKGROUND: Ciprofloxacin resistant Klebsiella pneumoniae is common or emerging in many geographies, and knowledge of local resistance rates is important for empirical therapy. Whilst there are known K. pneumoniae ciprofloxacin resistance determinants, there is a lack of systematic data on the effect of determinants, alone and in combination, and there are no publicly accessible tools for predicting resistance from whole genome sequence data. METHODS: The KlebNET-GSP AMR Genotype-Phenotype Group aggregated a matched genotype-phenotype dataset of n=12,167 K. pneumoniae species complex ( Kp SC) isolates from 27 countries between 2001-2021. We developed a rules-based classifier to predict ciprofloxacin resistance by categorizing the number of quinolone resistance determining regions mutations in gyrA and parC , the number of plasmid-mediated quinolone resistance genes, and the presence/absence of aac(6ʹ)-Ib-cr (which can acetylate ciprofloxacin). Predictive performance was assessed using the discovery dataset, for which we re-phenotyped discrepant isolates; and validated using externally contributed datasets (n=7,030 Kp SC isolates). RESULTS: The rules-based classifier predicted R vs S/I with categorical agreement, sensitivity, and specificity >96%, and major/very major error rates <4%. Performance was similar across diverse Kp SC sources (human, animal, other), species, and intra-species lineages. External validation of the classifier yielded overall 93.12% categorical agreement [95% confidence interval (CI), 92.50-93.74%], 8.65% major errors [95% CI, 7.34-9.97%], and 6.20% very major errors [95% CI, 5.51-6.90%]. We implemented the classifier in Kleborate, a command-line tool that is integrated into the Pathogenwatch web platform. Using this to assess the global distribution of ciprofloxacin resistance determinants in Kp SC genomes available in Pathogenwatch (n=31,319, from 109 countries between years 2000-2023), we observed a significant positive association between national quinolone consumption rates and predicted ciprofloxacin resistance (R ² =0.20, p=0.004). CONCLUSIONS Ciprofloxacin resistance phenotypes can be reasonably predicted from genotypes, which is sufficient for informing surveillance. However, unexplained resistance remains and accuracy is insufficient for clinical applications. We demonstrate the value of aggregating genotype-phenotype data to explore resistance mechanisms and develop predictors, but highlight complexities in combining phenotype data from different assays and standards.