Abstract
BackgroundIt is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data.Here, we evaluate the performance of four popular tools (TBProfiler, MyKrobe, KvarQ, PhyResSE) with 6746 isolates compiled from publicly available databases, and subsequently identify highly probable phenotyping errors in the databases by genetically predicting the drug phenotypes using all four software.ResultsOur results show that these bioinformatics tools generally perform well in predicting the resistance status for two key first-line agents (isoniazid, rifampicin), but the accuracy is lower for second-line injectables and fluoroquinolones. The error rates in the databases are also non-trivial, reaching as high as 31.1% for prothionamide, and that phenotypes from ReSeqTB are more susceptible to errors.ConclusionsThe good performance of the automated software for drug resistance prediction from TB WGS data shown in this study further substantiates the usefulness and promise of utilising genetic data to accurately profile TB drug resistance, thereby reducing misdiagnoses arising from error-prone culture-based drug susceptibility testing.
Highlights
It is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites
Genome sequences and drug-susceptibility phenotypes for Mtb isolates We focused on two databases hosting Mycobacterium tuberculosis (Mtb) isolates with complete genomic sequences and drug-susceptibility phenotypes: (1) PATRIC – which hosts nearly 150,000 genomes belonging to more than 20 bacterial genera with drug resistance status for nearly 100 antibiotics [12, 13]; and (2) ReSeqTB – which is a specific database for driving the development of novel rapid diagnostic tests and personalised treatment of TB [14, 15]
Data from PATRIC was obtained by querying all Mtb contributions with resistance profiles for anti-TB drugs, and whose genome sequences were contributed to the Sequencing Reads Archive (SRA) and BioProject accessions
Summary
It is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. The World Health Organisation (WHO) has recommended that all TB patients should be tested for drug resistance [2], conventional drug susceptibility testing (DST) can take more than 6 weeks and requires a laboratory equipped for strict biosafety [3]. By performing whole genome sequencing (WGS) of infecting Mtb isolates which have undergone conventional DST, the mutations that confer drug resistance to specific antibiotics can be identified [5]. GeneXpert MTB/RIF is one of these rapid diagnostic tests that
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