Dataset Information

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.

ABSTRACT: Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30?years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.

SUBMITTER: Galata V

PROVIDER: S-EPMC6624217 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.

Galata Valentina V Laczny Cédric C CC Backes Christina C Hemmrich-Stanisak Georg G Schmolke Susanne S Franke Andre A Meese Eckart E Herrmann Mathias M von Müller Lutz L Plum Achim A Müller Rolf R Stähler Cord C Posch Andreas E AE Keller Andreas A

Genomics, proteomics & bioinformatics 20190401 2

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the 11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main specie ...[more]

PMID: 31100356

Similar Datasets

Project description:Whole genome sequencing has revolutionised the interrogation of mycobacterial genomes. Recent studies have reported conflicting findings on the genomic stability of Mycobacterium tuberculosis during the evolution of drug resistance. In an age where whole genome sequencing is increasingly relied upon for defining the structure of bacterial genomes, it is important to investigate the reliability of next generation sequencing to identify clonal variants present in a minor percentage of the population. This study aimed to define a reliable cut-off for identification of low frequency sequence variants and to subsequently investigate genetic heterogeneity and the evolution of drug resistance in M. tuberculosis.Genomic DNA was isolated from single colonies from 14 rifampicin mono-resistant M. tuberculosis isolates, as well as the primary cultures and follow up MDR cultures from two of these patients. The whole genomes of the M. tuberculosis isolates were sequenced using either the Illumina MiSeq or Illumina HiSeq platforms. Sequences were analysed with an in-house pipeline.Using next-generation sequencing in combination with Sanger sequencing and statistical analysis we defined a read frequency cut-off of 30% to identify low frequency M. tuberculosis variants with high confidence. Using this cut-off we demonstrated a high rate of genetic diversity between single colonies isolated from one population, showing that by using the current sequencing technology, single colonies are not a true reflection of the genetic diversity within a whole population and vice versa. We further showed that numerous heterogeneous variants emerge and then disappear during the evolution of isoniazid resistance within individual patients. Our findings allowed us to formulate a model for the selective bottleneck which occurs during the course of infection, acting as a genomic purification event.Our study demonstrated true levels of genetic diversity within an M. tuberculosis population and showed that genetic diversity may be re-defined when a selective pressure, such as drug exposure, is imposed on M. tuberculosis populations during the course of infection. This suggests that the genome of M. tuberculosis is more dynamic than previously thought, suggesting preparedness to respond to a changing environment.

Project description:Antimicrobial 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 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. We 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 choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found 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. 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, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories.

Dataset Information

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.

Publications

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure