In order to have an effective treatment for tuberculosis (TB), there is an urgent need to modify the current treatment regime, which includes low efficacy, high toxicity and long duration drugs, and consumes significant resources. In 2015 alone, there have been 10.4 million TB infections and 1.8 million deaths, making TB one of the top 9 killers in the world, higher than HIV/AIDS. This unsatisfactory situation is mainly due to the development of drug resistant TB, which is immune to isoniazid and rifampicin. The three different types of resistance include multi-drug resistance (MDR), extensive drug-resistance (XDR) and totally drug-resistance (TDR), based on the exact strain of Mycobacterium tuberculosis responsible for the infection.
At the same time, there has been the emergence of “off-label” repurposed drugs such as oxazolidinones, carbapenems and clofazimine, which are being used to treat highly-resistant TB cases. The identification of the type of infection and proper diagnosis have been found to be the most important factor that determines the clinical outcome in such cases, even as drug-resistant strains are becoming a major global challenge. This challenge can only be addressed by genetic analysis to understand its complex biology and the mechanisms of drug-resistance.
In this respect, one of the most important technological advances is whole-genome sequencing (WGS), which was first reported by Cloe et al in 1998, sequencing the M. tuberculosis strain H37Rv. This has significantly improved our understanding of the bacterial strain about its physiology, metabolism, and pathogenicity.
The advent of NextGen Sequencing (NGS) technologies, combined with bioinformatics tools, has enabled the establishment of new platforms for better diagnosis and potential therapeutic approaches. Generation of large-scale genomic data in less time and at a lower cost (Metzker, 2010) has led to its use in various clinical scenarios, such as drug susceptibility, diagnosis and genetic diversity (Satta et al., 2017). Zhang et al (2013) analyzed 161 clinical isolates in China and Casali et al (2014) analyzed 1000 M. tuberculosis genomes using WGS technology, leading to the identification of 28 intergenic regions (IGRs) associated with drug resistance, 10 IGR single nucleotide polymorphisms (SNPs), 72 new genes and 11 non-synonymous SNPs. Several other uncharacterized genes, intergenic regions and SNPs were also reported to be associated with drug resistance phenotypes by other research groups (Anderson et al., 2014; Ali et al., 2015; Pankhurst et al., 2016). Further improvements, like the use of direct clinical samples or biospecimens, without the need for costly infrastructure has also changed the ease with which data can be generated. NGS can now be directly applied to clinical isolates using tabletop machines without the need for too much pre-processing and in very short times.
The quality and depth of the complete genome sequence provided by NGS has helped in the development of MDR/XDR-TB diagnostics. It has also helped in comparative genomic analysis to understand genomic diversity and the evolution of drug-resistant pathogens (Liu et al., 2014). Some of the recent NGS-based studies focusing on the evolution of XDR M. tuberculosis have shown that a single patient can have both a susceptible ancestor for fluoroquinolones as well as resistant bacteria (Zhang et al. 2016). One of the most important limiting steps yet in NGS is cumbersome bioinformatics and data inconsistencies about correlations between gene mutations and phenotypic drug susceptibility test (DST). However, the emergence of a new generation of sequencing, enabling long reads of up to 100kb (Lee et al 2016), has resulted in a reduction of fragment assemblies. Presently there are three third-generation technology platforms, namely Pacific Biosciences (PacBio) Single Molecule Real Time (SMRT) sequencing, Illumina Tru-seq Synthetic Long-Read technology and Oxford Nanopore Technologies’ MinION. These technologies are now making whole-genome sequencing much faster and simpler. In addition, recent publications have generated data on mythelomes that suggest that MTases activities, which are up-regulated, contribute to genome modifications and mutations leading to the evolution of drug resistance in MDR-TB clinical isolates (Leung et al., 2017).
Enabling faster detection
Some of the important challenges which need urgent attention include, DNA extraction, the need for culture for WGS, understanding the molecular mechanisms of drug resistance and large-scale data analysis. Starting with DNA extraction, there are many technologies in this arena, and an evaluation of these will help in selecting the best. For example, Nextera DNA Flex / Nextera XT / Nextera mate Pair work on an Illumina platform with as low as 1ng of DNA. The new concept of DNA isolation from saliva without culturing has gained a lot of momentum as it reduces the time required and gives faster results.
However, this also comes with its own difficulties in removing human DNA and other potential contaminants. Furthermore, this gets more complicated due to the use of nonspecific primers. Some of the new technologies which are picking up this challenge are differential lysis protocol followed by DNA extraction using a NucleoSpin tissue-kit or Illumina MiSeq sequencer with 87% success rate (Doughty et al 2014), SureSelectXT target enrichment system (Agilent Technologies) with 83% success (Brown et al., 2015). Further developments are required to evolve these technologies for lower cost and improved diagnosis. As we are still at a nascent stage of understanding the evolution of drug-resistant phenotypes using NGS, tools for automated pipeline, like Mykrobe Predictor TB, TB Profiler, CASTB, PhyResSE and KvarQ, are enabling faster detection of drug resistance and lineage-specific mutations from raw, whole genome sequence of M. tuberculosis.
The future of M. tuberculosis management will not only depend on NGS technologies but also in developing direct DNA isolation for sequencing, along with better bioinformatics. Such developments might help us finally realize the goals of the StopTB partnership and reduce disease burden.