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Whole genome sequencing data of 1110 Mycobacterium tuberculosis isolates identifies insertions and deletions associated with drug resistance.

ABSTRACT: BACKGROUND:Drug resistance in Mycobacterium tuberculosis (MTB) is one of the major challenges in tuberculosis (TB) treatment. However, known mutations cannot explain all of the cases of resistance and little research has focused on the relationship between insertions / deletions (indels) and drug resistance. RESULTS:Here, we retrieved whole genome sequencing data of 743 drug-resistant MTB strains and 367 pan-susceptible strains from TB patients from the public domain to identify novel genomic markers of drug resistance. A total of 20 region markers containing genes and intergenic regions (IGRs) with significant statistical correlation with antibiotic resistance were revealed, four of which have been previously reported to be associated with drug resistance. In addition, 83 point markers containing frameshift (FS) mutations and IGR indels were also identified independently based on differences in their incidence rates between drug-sensitive and -resistant strains. Among the 83 point markers, eight indels were detected in known drug-associated genes or IGRs. Furthermore, the overlap between 20 region markers and 83 point markers further indicated their associations with drug resistance. The markers identified were involved in essential bacterial metabolic functions, including cell wall and transmembrane transporter functions. A strong correlation between FS mutations and mutations in DNA repair genes including I21V in alkA, R48G in mutT4 and P2R in nth was also found. CONCLUSIONS:This study identified a set of novel genetic markers with FS mutations and IGR indels associated with MTB drug resistance, which greatly broadens the pool of mutations related to MTB drug resistance. This insight may be important in identifying novel mechanisms of drug resistance in MTB.

SUBMITTER: Zeng X

PROVIDER: S-EPMC5956929 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Whole genome sequencing data of 1110 Mycobacterium tuberculosis isolates identifies insertions and deletions associated with drug resistance.

Zeng Xi X Kwok Jamie Sui-Lam JS Yang Kevin Yi KY Leung Kenneth Siu-Sing KS Shi Mai M Yang Zhiyuan Z Yam Wing-Cheong WC Tsui Stephen Kwok-Wing SK

BMC genomics 20180516 1

<h4>Background</h4>Drug resistance in Mycobacterium tuberculosis (MTB) is one of the major challenges in tuberculosis (TB) treatment. However, known mutations cannot explain all of the cases of resistance and little research has focused on the relationship between insertions / deletions (indels) and drug resistance.<h4>Results</h4>Here, we retrieved whole genome sequencing data of 743 drug-resistant MTB strains and 367 pan-susceptible strains from TB patients from the public domain to identify n ...[more]

PMID: 29769016

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Project description:Combating the spread of drug resistant tuberculosis is a global health priority. Whole genome association studies are being applied to identify genetic determinants of resistance to anti-tuberculosis drugs. Protein structure and interaction modelling are used to understand the functional effects of putative mutations and provide insight into the molecular mechanisms leading to resistance.To investigate the potential utility of these approaches, we analysed the genomes of 144 Mycobacterium tuberculosis clinical isolates from The Special Programme for Research and Training in Tropical Diseases (TDR) collection sourced from 20 countries in four continents. A genome-wide approach was applied to 127 isolates to identify polymorphisms associated with minimum inhibitory concentrations for first-line anti-tuberculosis drugs. In addition, the effect of identified candidate mutations on protein stability and interactions was assessed quantitatively with well-established computational methods.The analysis revealed that mutations in the genes rpoB (rifampicin), katG (isoniazid), inhA-promoter (isoniazid), rpsL (streptomycin) and embB (ethambutol) were responsible for the majority of resistance observed. A subset of the mutations identified in rpoB and katG were predicted to affect protein stability. Further, a strong direct correlation was observed between the minimum inhibitory concentration values and the distance of the mutated residues in the three-dimensional structures of rpoB and katG to their respective drugs binding sites.Using the TDR resource, we demonstrate the usefulness of whole genome association and convergent evolution approaches to detect known and potentially novel mutations associated with drug resistance. Further, protein structural modelling could provide a means of predicting the impact of polymorphisms on drug efficacy in the absence of phenotypic data. These approaches could ultimately lead to novel resistance mutations to improve the design of tuberculosis control measures, such as diagnostics, and inform patient management.

Project description:Improved molecular diagnostic methods for detection drug resistance in Mycobacterium tuberculosis (MTB) strains are required. Resistance to first- and second- line anti-tuberculous drugs has been associated with single nucleotide polymorphisms (SNPs) in particular genes. However, these SNPs can vary between MTB lineages therefore local data is required to describe different strain populations. We used whole genome sequencing (WGS) to characterize 37 extensively drug-resistant (XDR) MTB isolates from Pakistan and investigated 40 genes associated with drug resistance. Rifampicin resistance was attributable to SNPs in the rpoB hot-spot region. Isoniazid resistance was most commonly associated with the katG codon 315 (92%) mutation followed by inhA S94A (8%) however, one strain did not have SNPs in katG, inhA or oxyR-ahpC. All strains were pyrazimamide resistant but only 43% had pncA SNPs. Ethambutol resistant strains predominantly had embB codon 306 (62%) mutations, but additional SNPs at embB codons 406, 378 and 328 were also present. Fluoroquinolone resistance was associated with gyrA 91-94 codons in 81% of strains; four strains had only gyrB mutations, while others did not have SNPs in either gyrA or gyrB. Streptomycin resistant strains had mutations in ribosomal RNA genes; rpsL codon 43 (42%); rrs 500 region (16%), and gidB (34%) while six strains did not have mutations in any of these genes. Amikacin/kanamycin/capreomycin resistance was associated with SNPs in rrs at nt1401 (78%) and nt1484 (3%), except in seven (19%) strains. We estimate that if only the common hot-spot region targets of current commercial assays were used, the concordance between phenotypic and genotypic testing for these XDR strains would vary between rifampicin (100%), isoniazid (92%), flouroquinolones (81%), aminoglycoside (78%) and ethambutol (62%); while pncA sequencing would provide genotypic resistance in less than half the isolates. This work highlights the importance of expanded targets for drug resistance detection in MTB isolates.

Project description:Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without "co-occurrent resistance" markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation.

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Whole genome sequencing data of 1110 Mycobacterium tuberculosis isolates identifies insertions and deletions associated with drug resistance.

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Whole genome sequencing data of 1110 Mycobacterium tuberculosis isolates identifies insertions and deletions associated with drug resistance.

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