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Functional Analysis Using Whole-Genome Sequencing of a Drug-Sensitive Mycobacterium tuberculosis Strain from Peru.

ABSTRACT: We report the whole-genome sequence of a Latin American-Mediterranean (LAM) lineage drug-sensitive Mycobacterium tuberculosis strain from Peru, INS-SEN. The functional analysis revealed more mutations in secondary metabolite biosynthesis, transport, and catabolism (clusters of orthologous groups [COG] category Q) than for other LAM-sensitive strains. This study contributes to the understanding of the genomic diversity of drug-sensitive M. tuberculosis.

SUBMITTER: Tarazona D

PROVIDER: S-EPMC3937605 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Functional Analysis Using Whole-Genome Sequencing of a Drug-Sensitive Mycobacterium tuberculosis Strain from Peru.

Tarazona D D Borda V V Galarza M M Agapito J C JC Guio H H

Genome announcements 20140227 1

We report the whole-genome sequence of a Latin American-Mediterranean (LAM) lineage drug-sensitive Mycobacterium tuberculosis strain from Peru, INS-SEN. The functional analysis revealed more mutations in secondary metabolite biosynthesis, transport, and catabolism (clusters of orthologous groups [COG] category Q) than for other LAM-sensitive strains. This study contributes to the understanding of the genomic diversity of drug-sensitive M. tuberculosis. ...[more]

PMID: 24578269

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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:The Philippines has a high incidence of tuberculosis disease (TB), with an increasing prevalence of multidrug-resistant Mycobacterium tuberculosis (MDR-TB) strains making its control difficult. Although the M. tuberculosis "Manila" ancient lineage 1 strain-type is thought to be prevalent in the country, with evidence of export to others, little is known about the genetic diversity of circulating strains. By whole genome sequencing (WGS) 178 isolates from the Philippines National Drug Resistance Survey, we found the majority (143/178; 80.3%) belonged to the lineage 1 Manila clade, with the minority belonging to lineages 4 (European-American; n = 33) and 2 (East Asian; n = 2). A high proportion were found to be multidrug-resistant (34/178; 19.1%), established through highly concordant laboratory drug susceptibility testing and in silico prediction methods. Some MDR-TB isolates had near identical genomic variation, providing potential evidence of transmission. By placing the Philippine isolates within a phylogeny of global M. tuberculosis (n > 17,000), we established that they are genetically similar to those observed outside the country, including a clade of Manila-like strain-types in Thailand. An analysis of the phylogeny revealed a set of ~200 SNPs that are specific for the Manila strain-type, and a subset can be used within a molecular barcode. Sixty-eight mutations known to be associated with 10 anti-TB drug resistance were identified in the Philippine strains, and all have been observed in other populations. Whilst nine putative streptomycin resistance conferring markers in gid (8) and rrs (1) genes appear to be novel and with functional consequences. Overall, this study provides an important baseline characterisation of M. tuberculosis genetic diversity for the Philippines, and will fill a gap in global datasets and aid the development of a nation-wide database for epidemiological studies and clinical decision making. Further, by establishing a molecular barcode for detecting Manila strains it will assist with the design of diagnostic tools for disease control activities.

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.

Dataset Information

Functional Analysis Using Whole-Genome Sequencing of a Drug-Sensitive Mycobacterium tuberculosis Strain from Peru.

Publications

Functional Analysis Using Whole-Genome Sequencing of a Drug-Sensitive Mycobacterium tuberculosis Strain from Peru.

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