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      Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges

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          Abstract

          Tuberculosis (TB) is a global infectious threat that is intensified by an increasing incidence of highly drug-resistant disease. Whole-genome sequencing (WGS) studies of Mycobacterium tuberculosis, the causative agent of TB, have greatly increased our understanding of this pathogen. Since the first M. tuberculosis genome was published in 1998, WGS has provided a more complete account of the genomic features that cause resistance in populations of M. tuberculosis, has helped to fill gaps in our knowledge of how both classical and new antitubercular drugs work, and has identified specific mutations that allow M. tuberculosis to escape the effects of these drugs. WGS studies have also revealed how resistance evolves both within an individual patient and within patient populations, including the important roles of de novo acquisition of resistance and clonal spread. These findings have informed decisions about which drug-resistance mutations should be included on extended diagnostic panels. From its origins as a basic science technique, WGS of M. tuberculosis is becoming part of the modern clinical microbiology laboratory, promising rapid and improved detection of drug resistance, and detailed and real-time epidemiology of TB outbreaks. We review the successes and highlight the challenges that remain in applying WGS to improve the control of drug-resistant TB through monitoring its evolution and spread, and to inform more rapid and effective diagnostic and therapeutic strategies.

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          The competitive cost of antibiotic resistance in Mycobacterium tuberculosis.

          Mathematical models predict that the future of the multidrug-resistant tuberculosis epidemic will depend on the fitness cost of drug resistance. We show that in laboratory-derived mutants of Mycobacterium tuberculosis, rifampin resistance is universally associated with a competitive fitness cost and that this cost is determined by the specific resistance mutation and strain genetic background. In contrast, we demonstrate that prolonged patient treatment can result in multidrug-resistant strains with no fitness defect and that strains with low- or no-cost resistance mutations are also the most frequent among clinical isolates.
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            Molecular genetic basis of antimicrobial agent resistance in Mycobacterium tuberculosis: 1998 update.

            Knowledge of the molecular genetic basis of resistance to antituberculous agents has advanced rapidly since we reviewed this topic 3 years ago. Virtually all isolates resistant to rifampin and related rifamycins have a mutation that alters the sequence of a 27-amino-acid region of the beta subunit of ribonucleic acid (RNA) polymerase. Resistance to isoniazid (INH) is more complex. Many resistant organisms have mutations in the katG gene encoding catalase-peroxidase that result in altered enzyme structure. These structural changes apparently result in decreased conversion of INH to a biologically active form. Some INH-resistant organisms also have mutations in the inhA locus or a recently characterized gene (kasA) encoding a beta-ketoacyl-acyl carrier protein synthase. Streptomycin resistance is due mainly to mutations in the 16S rRNA gene or the rpsL gene encoding ribosomal protein S12. Resistance to pyrazinamide in the great majority of organisms is caused by mutations in the gene (pncA) encoding pyrazinamidase that result in diminished enzyme activity. Ethambutol resistance in approximately 60% of organisms is due to amino acid replacements at position 306 of an arabinosyltransferase encoded by the embB gene. Amino acid changes in the A subunit of deoxyribonucleic acid gyrase cause fluoroquinolone resistance in most organisms. Kanamycin resistance is due to nucleotide substitutions in the rrs gene encoding 16S rRNA. Multidrug resistant strains arise by sequential accumulation of resistance mutations for individual drugs. Limited evidence exists indicating that some drug resistant strains with mutations that severely alter catalase-peroxidase activity are less virulent in animal models. A diverse array of strategies is available to assist in rapid detection of drug resistance-associated gene mutations. Although remarkable advances have been made, much remains to be learned about the molecular genetic basis of drug resistance in Mycobacterium tuberculosis. It is reasonable to believe that development of new therapeutics based on knowledge obtained from the study of the molecular mechanisms of resistance will occur.
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              DNA Sequencing Predicts 1st-Line Tuberculosis Drug Susceptibility Profiles

              Background The World Health Organization recommends universal drug susceptibility testing for Mycobacterium tuberculosis complex to guide treatment decisions and improve outcomes. We assessed whether DNA sequencing can accurately predict antibiotic susceptibility profiles for first-line anti-tuberculosis drugs. Methods Whole-genome sequences and associated phenotypes to isoniazid, rifampicin, ethambutol and pyrazinamide were obtained for isolates from 16 countries across six continents. For each isolate, mutations associated with drug-resistance and drug-susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These were predicted to be pan-susceptible if predicted susceptible to isoniazid and to other drugs, or contained mutations of unknown association in genes affecting these other drugs. We simulated how negative predictive value changed with drug-resistance prevalence. Results 10,209 isolates were analysed. The greatest proportion of phenotypes were predicted for rifampicin (9,660/10,130; (95.4%)) and the lowest for ethambutol (8,794/9,794; (89.8%)). Isoniazid, rifampicin, ethambutol and pyrazinamide resistance was correctly predicted with 97.1%, 97.5% 94.6% and 91.3% sensitivity, and susceptibility with 99.0%, 98.8%, 93.6% and 96.8% specificity, respectively. 5,250 (89.5%) drug profiles were correctly predicted for 5,865/7,516 (78.0%) isolates with complete phenotypic profiles. Among these, 3,952/4,037 (97.9%) predictions of pan-susceptibility were correct. The negative predictive value for 97.5% of simulated drug profiles exceeded 95% where the prevalence of drug-resistance was below 47.0%. Conclusions Phenotypic testing for first-line drugs can be phased down in favour of DNA sequencing to guide anti- tuberculosis drug therapy.
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                Author and article information

                Contributors
                kcohen8@jhmi.edu
                aearl@broadinstitute.org
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                25 July 2019
                25 July 2019
                2019
                : 11
                : 45
                Affiliations
                [1 ]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Division of Pulmonary and Critical Care Medicine, , Johns Hopkins University School of Medicine, ; Baltimore, MA 21205 USA
                [2 ]GRID grid.66859.34, Broad Institute of Harvard and Massachusetts Institute of Technology, ; 415 Main Street, Cambridge, MA 02142 USA
                [3 ]ISNI 0000 0001 2097 4740, GRID grid.5292.c, Delft Bioinformatics Lab, , Delft University of Technology, ; 2628 XE Delft, The Netherlands
                Author information
                http://orcid.org/0000-0001-7857-9145
                Article
                660
                10.1186/s13073-019-0660-8
                6657377
                31345251
                8232dcf2-ab77-4abc-99c1-52b7eecbb641
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Funding
                Funded by: National Institute of Allergy and Infectious Diseases (US)
                Award ID: U19AI110818
                Funded by: FundRef http://dx.doi.org/10.13039/100000861, Burroughs Wellcome Fund;
                Award ID: BWF CAMS
                Award Recipient :
                Funded by: National Heart, Lung, and Blood Institute (US)
                Award ID: K08 HL139994
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2019

                Molecular medicine
                Molecular medicine

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