Whole genome sequencing to predict clarithromycin susceptibility in Mycobacterium abscessus
PHE ePoster Library. Lipworth S. Apr 9, 2019; 259613; 15595
Dr. Sam Lipworth
Dr. Sam Lipworth
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Abstract
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Abstract Introduction

Mycobacterium abscessus is an important pathogen in chronic respiratory diseases, particularly cystic fibrosis. Recent British Thoracic Society guidelines advocate the use of a macrolide as part of combination antibiotic therapy in patients with phenotypically susceptible or inducibly resistant isolates.

Methods:

In September 2017, an initial set of 203 whole genome sequences (WGS) with paired in vitro drug susceptibility testing (DST, performed by the Sensititre RAPMYCOTM method) was acquired from the PHE Birmingham reference laboratory. Informative mutations identified in a systematic literature search were used to make in silico predictions of drug susceptibility which were compared to DST results. An heuristic algorithm was used to search for novel mutations. A further independent validation set of 238 isolates from the PHE Colindale laboratory was subsequently acquired in December 2018.

Results

For WGS predictions on the initial set (excluding intermediate phenotypes), sensitivity was 95/100 (95%, 89 - 98%) and specificity 52/79 (66%, 54 - 76%). Eight potential new resistance-conferring SNPs were identified, all in isolates predicted to be inducibly resistant. Performance was significantly better on the validation set; sensitivity 194/197 (98%, 96 - 100%) and specificity 61/62 (98%, 91-100%). In total, combining both datasets and including isolates with intermediate phenotypes, 29/80 (36%) of subspecies massiliense, 33/37 (89%) of subspecies bolletii and 253/345 (73%) of subspecies abscessus isolates were resistant by day 14.
Conclusion

Our algorithm makes sensitive and specific predictions regarding clarithromycin susceptibility in M. abscessus; in contrast subspecies has a poor predictive value. The improved performance of the algorithm on a more recent independent dataset likely demonstrates improved accuracy of phenotyping as laboratory staff become more experienced at the microdilution method. Funding Work supported by the NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England.
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