Conference poster
ARDaP: Antimicrobial Resistance Detection and Prediction from Whole-Genome Sequence Data
ASM Microbe, 2019 (San Francisco, United States, 20-Jun-2019–24-Jun-2019)
American Society for Microbiology
2019
Abstract
Antibiotic resistance (AbR) is a major threat to human health worldwide, with increasing instances of multi-drug resistant pathogens that are diminishing antibiotic effectiveness. Whole-genome sequencing (WGS) is rapidly changing the clinical microbiology landscape, with exciting potential for rapidly and accurately detecting AbR in diagnostic laboratories, a crucial factor in effective infection control and treatment. Most work to date has focussed on the development of software capable of detecting the presence of mobile genes conferring AbR from WGS data. However, less consideration has been given to the identification of chromosomally-encoded AbR mechanisms, such as single-nucleotide polymorphisms (SNPs), insertion-deletions (indels), copy number variants (CNV), and functional gene loss. We present an improved algorithm for Antibiotic Resistance Detection and Prediction (ARDaP) from WGS data for this purpose. ARDaP was designed with two main aims: 1) to accurately identify all characterised AbR genetic mechanisms (mobile gene gain, SNPs, indels, CNVs, and functional gene loss) and present the predicted AbR profile in an easy-to-interpret report; and 2) to predict enigmatic AbR mechanisms based on i) novel mutants in known AbR-conferring genes, or ii) a microbial genome-wide association approach that correlates AbR phenotypes with genetic variants to identify putative causative mutant/s. We demonstrate the applicability of ARDaP using the Tier 1 select agent and melioidosis pathogen, Burkholderia pseudomallei, as a model organism due to its exclusively chromosomally-encoded AbR mechanisms. Using an extensive, well-characterised collection of ~1,000 B. pseudomallei clinical strains, we demonstrate that ARDaP can accurately detect all known mechanisms of AbR in B. pseudomallei with high rates of precision and recall. This feature enables ARDaP to accurately predict AbR phenotype based on WGS data. Furthermore, ARDaP predicted novel mechanisms of AbR in B. pseudomallei by identifying novel missense and loss-of-function mutations in genomic loci important for conferring AbR. Finally, ARDaP generates an easily-interpretable report that summarises a given strain's AbR profile. This clinician-friendly report can then be used for personalised treatments and rapid treatment shifts in response to the detection of precursor or AbR-conferring mutations. Our findings show that ARDaP is a comprehensive and accurate tool for predicting AbR from WGS data.
Details
- Title
- ARDaP: Antimicrobial Resistance Detection and Prediction from Whole-Genome Sequence Data
- Authors
- Danielle Madden (Author) - University of the Sunshine CoastErin P Price (Author) - University of the Sunshine CoastEike J Steinig (Author) - James Cook UniversityJ Webb (Author) - Charles Darwin UniversityB Currie (Author) - Charles Darwin UniversityDerek S Sarovich (Author) - University of the Sunshine Coast
- Conference details
- ASM Microbe, 2019 (San Francisco, United States, 20-Jun-2019–24-Jun-2019)
- Publisher
- American Society for Microbiology
- Date published
- 2019
- Copyright note
- Copyright © 2019 The Authors. Reproduced with permission.
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99451129302621
- Output Type
- Conference poster
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