Targeted sequencing workflows for comprehensive drug resistance profiling of Mycobacterium tuberculosis cultures using Illumina MiSeq and Nanopore MinION: comparison of analytical and diagnostic performance, turnaround time and cost

The emergence of Mycobacterium tuberculosis strains with complex drug resistance profiles necessitates a rapid and extensive drug susceptibility test for comprehensive guidance of patient treatment. Here, we developed two targeted-sequencing workflows based on Illumina MiSeq and Nanopore MinION for the prediction of drug resistance in M. tuberculosis towards 12 anti-tuberculous agents. A total of 163 M. tuberculosis cultured isolates collected from Hong Kong and Ethiopia were subjected to a multiplex PCR for simultaneous amplification of 19 drug-resistance associated genetic regions. The amplicons were then barcoded and sequenced in parallel on MiSeq and MinION in respective batch sizes of 24 and 12 samples. Both platforms successfully sequenced all samples with average depths of coverage of 1,127x and 1,649x respectively. Utilizing a self-developed Web-based bioinformatics pipeline, Bacteriochek-TB, for variant analysis, we found that the MiSeq and MinION result could achieve 100% agreement if variants with an allele frequency of <40% reported by MinION were excluded. For drug resistance prediction, both workflows achieved an average sensitivity of 94.8% and specificity of 98.0% when compared with phenotypic drug susceptibility test. The turnaround times for the MiSeq and MinION workflows were 38 and 15 hours, facilitating the delivery of treatment guidance at least 17-18 days earlier than pDST respectively. The higher cost per sample on the MinION platform (US$71.56) versus the MiSeq platform (US$67.83) was attributed to differences in batching capabilities. Our study demonstrated the interchangeability of MiSeq and MinION sequencing workflows for generation of accurate and actionable results for the treatment of tuberculosis.

Authors: Ketema Tafess, Timothy Ting Leung Ng, Hiu Yin Lao, Kenneth Siu-Sing Leung, Kingsley King-Gee Tam, Rahim Rajwani, Sarah Tsz Yan Tam, Lily Pui Ki Ho, Corey Mang Kiu Chu, Dimitri Gonzalez, Chalom Sayada, Oliver Chiu-Kit Ma, Belete Haile Nega, Gobena Ameni, Wing-Cheong Yam, Gilman Kit Hang Siu