Rapid Identification of Lineage and Drug Resistance in Clinical Samples of Mycobacterium tuberculosis

Background: Mycobacterium tuberculosis is a slow-growing bacterium, which could delay its diagnosis and, therefore, promote the spread of the disease. Whole-genome sequencing allows us to obtain the complete drug-resistance profile of the strain; however, bacterial cultivation of clinical samples, along with complex processing, is required. Methods: In this work, we explore AmpliSeq, an amplicon-based enrichment method for preparing libraries for targeted next-generation sequencing, to identify lineage and drug resistance directly from clinical samples. Results: In our study, 111 clinical samples were tested. The lineage was identified in 100% of the culture-derived samples (52/52), in 95% of the smear (BK)-positive clinical samples (38/40) and in 42.1% of the BK-negative clinical samples (8/19). The drug-resistance profile was accurately identified in all but 11 samples, in which some phenotypic and genotypic discrepancies were found. In this respect, our panels were not exact in the detection of streptomycin resistance for isolates derived from clinical samples, as an extremely high number of SNPs in the rrs and rrl genes were detected due to cross-contamination. Conclusion: This technique has demonstrated high sensitivity in obtaining the drug-resistance profile of the isolates, as even those samples with DNA concentrations below the detection limit of Qubit produced a result. AmpliSeq technology is cheaper than whole-genome sequencing, easy to perform by laboratory technicians and applicable to any microorganism using the Ion Torrent platform.

Automated summary

In this study, the AmpliSeq method was used to directly identify lineage and drug resistance from clinical samples. The results showed that the lineage was identified in 100% of culture-derived samples, 95% of smear-positive samples, and 42.1% of smear-negative samples. The drug-resistance profile was accurately identified in most samples, with a few discrepancies.