Biology subjects

Cormican, M.

Publications and source records attributed to Cormican, M..

2 recordsLinked to original sources

bioRxiv2019

Genomic surveillance of Escherichia coli ST131 identifies local expansion and serial replacement of subclones

Escherichia coli sequence type 131 (ST131) is a pandemic clone that is evolving rapidly with increasing levels of antimicrobial resistance. Here, we investigated an outbreak of E. coli ST131 producing extended spectrum {beta}-lactamases (ESBLs) in a long-term care facility (LTCF) in Ireland by combining data from this LTCF (n=69) with other Irish (n=35) and global (n=690) ST131 genomes to reconstruct the evolutionary history and understand changes in population structure and genome architecture over time. This required a combination of short and long-read genome sequencing, de novo assembly, read mapping, ESBL gene screening, plasmid alignment and temporal phylogenetics. We found that clade C was the most prevalent (686 out of 794 isolates, 86%) of the three major ST131 clades circulating worldwide (A, B, C), and was associated with the presence of different ESBL alleles, diverse plasmids and transposable elements. Clade C was estimated to have emerged in [~]1985 and subsequently acquired different ESBL gene variants (blaCTX-M-14 vs blaCTX-M-15). An ISEcp1- mediated transposition of the blaCTX-M-15 gene further increased the diversity within Clade C. We discovered a local clonal expansion of a rare C2 lineage (C2_8) with a chromosomal insertion of blaCTX-M-15 at the mppA gene. This was acquired from an IncFIA plasmid. The C2_8 lineage clonally expanded in the Irish LTCF from 2006, displacing the existing C1 strain (C1_10) and highlighting the potential for novel ESBL-producing ST131 with a distinct genetic profile to cause outbreaks strongly associated with specific healthcare environments.\n\nImportanceExtraintestinal pathogenic E. coli (ExPEC) ST131 is adapting in the context of antibiotic exposure, resulting in a pandemic with distinct genetic subtypes. Here, we track the evolution of antibiotic-resistance gene variants originally discovered in an ExPEC ST131 outbreak that was identified in a LTCF in Ireland. Analysis of 794 global ST131 genomes show that subclade C1 was associated with the initial infection outbreak, but that a new lineage from subclade C2 successfully displaced C1. This genetically distinct C2 subclade with a chromosomal insertion of a key antibiotic-resistance gene had clonally expanded within the LTCF. We provide new insights into the timing of genetic events driving the diversification of C2 subclades to show that that outbreak C2 strain likely evolved elsewhere before spreading to the LTCF. This study highlights the scope of antibiotic-resistance gene rearrangement within ST131, reinforcing the need to integrate genomic, epidemiological and microbiological approaches to understand ST131 transmission.

genomics↗

bioRxiv2019

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

BackgroundAntimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a one-stop test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants and identify problem cases and factors that lead to discordant results.\n\nMethodsWe produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ( participants) were provided these sequence data without any other contextual information. Each participant used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime.\n\nResultsIndividual participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment a different antibiotic would have been recommended for each isolate by at least one participant.\n\nConclusionsWe found that participants produced discordant predictions from identical WGS data. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases and standardisation in the comparisons between genotype and resistance phenotypes will be fundamental before AST prediction using WGS can be successfully implemented in standard clinical microbiology laboratories.

microbiology↗