bioRxiv2020
The role of genetic mutations in genes associated to phenotypic resistance to bedaquiline (BDQ) and delamanid (DLM) in Mycobacterium tuberculosis complex (MTBc) strains is poorly understood. However, a clear understanding of the role of each genetic variant is crucial to guide the development of molecular-based drug susceptibility testing (DST). In this work, we analysed all mutations in candidate genomic regions associated with BDQ- and DLM-resistant phenotypes using a whole genome sequencing (WGS) dataset from a collection of 4795 MTBc clinical isolates from six high-burden countries of tuberculosis (TB). From WGS analysis, we identified 61 and 158 unique mutations in genomic regions potentially involved in BDQ- and DLM-resistant phenotypes, respectively. Importantly, all strains were isolated from patients who likely have never been exposed to the medicines. In order to characterize the role of mutations, we performed an energetic in silico analysis to evaluate their effect in the available protein structures Ddn (DLM), Fgd1 (DLM) and Rv0678 (BDQ), and minimum inhibitory concentration (MIC) assays on a subset of MTBc strains carrying mutations to assess their phenotypic effect. The combination of structural protein information and phenotypic data allowed for cataloging the mutations clearly associated with resistance to BDQ (n= 4) and DLM (n= 35), as well as about a hundred genetic variants without any correlation with resistance. Importantly, these results show that both BDQ and DLM resistance-related mutations are diverse and distributed across the entire region of each gene target, which is of critical importance to the development of comprehensive molecular diagnostic tools. ImportancePhenotypic drug susceptibility tests (DST) are too slow to provide an early indication of drug susceptibility status at the time of treatment initiation and very demanding in terms of specimens handling and biosafety. The development of molecular assays to detect resistance to bedaquiline (BDQ) and delamanid (DLM) requires accurate categorization of genetic variants according to their association with phenotypic resistance. We have evaluated a large multi-country set of clinical isolates to identify mutations associated with increased minimum inhibitory concentrations (MICs) and used an in silico protein structure analysis to further unravel the potential role of these mutations in drug resistance mechanisms. The results of this study are an important source of information for the development of molecular diagnostic tests to improve the provision of appropriate treatment and care to TB patients.