Greenberg, D. E. | bioRxiv Science

bioRxiv2019

β-lactamase Amplification and Porin Loss Drive Progressive β-lactam Resistance in Recurrent ESBL Enterobacteriaceae Bacteremia

BackgroundCarbapenem resistant Enterobacterales (CRE) remain urgent antimicrobial resistance threats. Approximately half of CRE clinical isolates lack carbapenem hydrolyzing enzymes and develop carbapenem resistance through alternative mechanisms. The purpose of this study was to elucidate the development of carbapenem resistance mechanisms from clonal, recurrent extended-spectrum {beta}-lactamase positive Enterobacterales (ESBL-E) bacteremia isolates in a vulnerable patient population. MethodsThis study investigated a historical, retrospective cohort of ESBL-E bacteremia cases in the University of Texas MD Anderson Cancer Center (MDACC) from January 2015 to July 2016. Phylogenetic and comparative genomic analyses were performed to identify clonal, recurrent ESBL-E isolates developing carbapenem resistance. Oxford Nanopore Technology (ONT) long-read and Illumina short-read sequencing data were used to generate consensus assemblies and to identify signatures of mobile genetic element mediated amplification and transposition of antimicrobial resistance genes. Serial passaging experiments were performed on a set of clinical ST131 ESBL-E isolates to recapitulate in vivo observations. qPCR and qRT-PCR were used to determine respective copy number and transcript levels of {beta}-lactamase genes. Results116 ESBL-E bacteremia cases were identified, 16 of which had documented recurrent infections. Four serial, recurrent isolates displayed a carbapenem resistant phenotype, three without the acquisition of a known carbapenemase. These three isolates had non-carbapenemase-producing CRE (non-CP-CRE) mechanisms driven by IS26- and ISEcp1-mediated amplification of respective translocatable units (TU) and transposition units (TPU) harboring both blaOXA-1 and blaCTX-M variants with concomitant outer membrane porin disruption. The TU and TPU structures inserted into the open reading frames of outer membrane porin genes in a subset of non-CP-CRE isolates. Serial passage of an index ST131 ESBL-E isolate under selective carbapenem exposure resulted in chromosomal amplification of modular, TUs harboring {beta}-lactamase genes with concomitant porin inactivation, recapitulating the in vivo carbapenem resistance progression. Long-read sequencing of two additional MDACC bacteremia strains identified similar non-CP-CRE mechanisms observed in the serial isolates. ConclusionsNon-CP-CRE de novo mechanisms were the primary driver of CRE development in recurrent bacteremia cases within this vulnerable patient population. The incorporation of long-read ONT data into AMR surveillance platforms is critical to identify high-risk CRE isolates that are difficult to identify with low-resolution phenotypic and molecular characterization methods.

genomics↗

bioRxiv2019

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in availability of high density genomic data for a diverse array of bacteria, development of algorithms that could utilize genomic information to predict phenotype could both be useful clinically and assist with discovery of heretofore unrecognized AMR pathways. To facilitate understanding of the connections between DNA variation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for variants; (2) interrogate these explainable gene-level variants for their known or novel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data. Following the Clinical & Laboratory Standards Institute (CLSI) guidelines, we curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species along with AMR phenotypes for 29 antibiotics. We detected 14,615 variant genotypes and built 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms, such as blaKPC and carbapenem resistance consistent with the accurate nature of our approach. The prediction models achieved high accuracies (mean accuracy of 91.1% for all antibiotic-pathogen combinations) internally through nested cross validation and were also validated using external clinical datasets. The VAMPr variant detection method, association and prediction models will be valuable tools for AMR research for basic scientists with potential for clinical applicability.

bioinformatics↗