Oral Presentation BACPATH 2019

ARDaP: Antimicrobial Resistance Detection and Prediction from Whole-Genome Sequence Data (#45)

Derek S Sarovich 1 2 , Danielle E Madden 2 , Jessica R Webb 1 , Bart J Currie 1 3 , Erin P Price 1 2
  1. Menzies School of Health Research, Tiwi, NT, Australia
  2. GeneCology Research Centre, University of the Sunshine Coast, Sippy Downs, Queensland, Australia
  3. Departments of Infectious Diseases and Pathology, Royal Darwin Hospital, Tiwi, Northern Territory, Australia

Antibiotic resistance (AbR) is a major threat to human health worldwide1. Whole-genome sequencing (WGS) is rapidly changing the clinical microbiology landscape, with exciting potential for rapidly and accurately detecting AbR. Most work to date has focussed on developing software to detect gene presence2. Unfortunately, much less consideration has been given to identifying chromosomally-encoded AbR mechanisms.

We present software for Antibiotic Resistance Detection and Prediction (ARDaP) from WGS data. ARDaP was designed with two main aims: 1) to accurately identify all characterised AbR mechanisms and present the AbR profile in an easy-to-interpret report; and 2) to predict enigmatic mechanisms based on i) novel mutants in known genes, or ii) a microbial genome-wide association approach that correlates AbR phenotypes with genetic variants.

We demonstrate the application of ARDaP using Burkholderia pseudomallei as a model organism due to its exclusively chromosomally-encoded AbR mechanisms and high mortality rate3. Using a well-characterised collection of 1,042 clinical strains, we demonstrate that ARDaP accurately detects all known AbR mechanisms in B. pseudomallei (>40 mutations) with high rates of precision and recall. Furthermore, ARDaP predicted three novel loss-of-function mutations that decreased meropenem susceptibility in B. pseudomallei; this phenotype is associated with increased treatment failure and fatality rates3.

ARDaP is a comprehensive and accurate tool for identifying and predicting AbR mechanisms from WGS data. Its clinician-friendly report4, which summarises a given strain’s AbR profile, holds great promise for informing personalised treatment regimens and treatment shifts in response to the detection of precursor or AbR-conferring mutations.

  1. 1) Wellcome Trust and UK Government. Report: Review on Antimicrobial Resistance. “Antimicrobial resistance: tackling a crisis for the health and wealth of nations” (chaired by O’Neill, J). (2014).
  2. 2) Jia B, Raphenya AR, Alcock B, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res, 45, 566-573 (2017).
  3. 3) Sarovich DS, Webb JR, Pitman MC, et al. Raising the stakes: loss of efflux pump regulation decreases meropenem susceptibility in Burkholderia pseudomallei, Clin Infect Dis, 67, 243–250 (2018).
  4. 4) Crisan A, McKee G, Munzner T, & Gardy JL. Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory. PeerJ, 6 (2018).