The variability and reproducibility of whole genome sequencing technology for detecting resistance to anti-tuberculous drugs

Abstract

Background: The emergence of resistance to anti-tuberculosis drugs is a serious and growing threat to public health. Next-generation sequencing is rapidly gaining traction as a diagnostic tool for investigating drug resistance in Mycobacterium tuberculosis to aid treatment decisions. However, there are few little data regarding the precision of such sequencing for assigning resistance profiles.

Methods: We investigated two sequencing platforms (Illumina MiSeq, Ion Torrent PGM™) and two rapid analytic pipelines (TBProfiler, Mykrobe predictor) using a well characterised reference strain (H37Rv) and clinical isolates from patients with tuberculosis resistant to up to 13 drugs. Results were compared to phenotypic drug susceptibility testing. To assess analytical robustness individual DNA samples were subjected to repeated sequencing.

Results: The MiSeq and Ion PGM systems accurately predicted drug-resistance profiles and there was high reproducibility between biological and technical sample replicates. Estimated variant error rates were low (MiSeq 1 per 77 kbp, Ion PGM 1 per 41 kbp) and genomic coverage high (MiSeq 51-fold, Ion PGM 53-fold). MiSeq provided superior coverage in GC-rich regions, which translated into incremental detection of putative genotypic drug-specific resistance, including for resistance to para-aminosalicylic acid and pyrazinamide. The TBProfiler bioinformatics pipeline was concordant with reported phenotypic susceptibility for all drugs tested except pyrazinamide and para-aminosalicylic acid, with an overall concordance of 95.3%. When using the Mykrobe predictor concordance with phenotypic testing was 73.6%.

Conclusions: We have demonstrated high comparative reproducibility of two sequencing platforms, and high predictive ability of the TBProfiler mutation library and analytical pipeline, when profiling resistance to first- and second-line anti-tuberculosis drugs. However, platform-specific variability in coverage of some genome regions may have implications for predicting resistance to specific drugs. These findings may have implications for future clinical practice and thus deserve further scrutiny, set within larger studies and using updated mutation libraries.

Keywords: Diagnostics; Drug resistance; Drug-susceptibility testing; Next-generation sequencing; Tuberculosis; XDR-TB.

Fig. 1

The dependence of coverage on GC content. The coverage across regions of the genome with differing GC content compared using two different sequencing technologies; the Ion PGM and the Illumina MiSeq. The dashed blue line represents the cutoff used when calling variants. Any position which had a coverage <10 was marked as missing. The dashed red line shows at which GC% the median coverage across the window falls below the cutoff

Fig. 2

Coverage across drug-resistance genes. The coverage across the drug-resistance genes in POR1, 2 and 6 samples sequenced using both the a Ion PGM and b Illumina MiSeq. The dashed red line represents the cutoff used when calling variants. Any position with less than tenfold coverage was marked as missing. The low coverage in thyA is due to a deletion polymorphism

Fig. 3

Lack of genomic coverage in dfrA-thyA genes reveals deletions in the POR1A XDR isolate with PAS resistance. Uneven Ion PGM sequence coverage is due to high GC content