31
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A standardised method for interpreting the association between mutations and phenotypic drug resistance in Mycobacterium tuberculosis

      research-article
      1 , 2 , 1 , 3 , 4 , 5 , 6 , 7 , 6 , 8 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 4 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 6 , 1 , 6 , 36 , 11 , 37
      The European Respiratory Journal
      European Respiratory Society

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A clear understanding of the genetic basis of antibiotic resistance in Mycobacterium tuberculosis is required to accelerate the development of rapid drug susceptibility testing methods based on genetic sequence.

          Raw genotype–phenotype correlation data were extracted as part of a comprehensive systematic review to develop a standardised analytical approach for interpreting resistance associated mutations for rifampicin, isoniazid, ofloxacin/levofloxacin, moxifloxacin, amikacin, kanamycin, capreomycin, streptomycin, ethionamide/prothionamide and pyrazinamide. Mutation frequencies in resistant and susceptible isolates were calculated, together with novel statistical measures to classify mutations as high, moderate, minimal or indeterminate confidence for predicting resistance.

          We identified 286 confidence-graded mutations associated with resistance. Compared to phenotypic methods, sensitivity (95% CI) for rifampicin was 90.3% (89.6–90.9%), while for isoniazid it was 78.2% (77.4–79.0%) and their specificities were 96.3% (95.7–96.8%) and 94.4% (93.1–95.5%), respectively. For second-line drugs, sensitivity varied from 67.4% (64.1–70.6%) for capreomycin to 88.2% (85.1–90.9%) for moxifloxacin, with specificity ranging from 90.0% (87.1–92.5%) for moxifloxacin to 99.5% (99.0–99.8%) for amikacin.

          This study provides a standardised and comprehensive approach for the interpretation of mutations as predictors of M. tuberculosis drug-resistant phenotypes. These data have implications for the clinical interpretation of molecular diagnostics and next-generation sequencing as well as efficient individualised therapy for patients with drug-resistant tuberculosis.

          Abstract

          A comprehensive basis for interpreting mutations to predict antibiotic resistance in tuberculosis http://ow.ly/hhwJ30g9jCY

          Related collections

          Most cited references30

          • Record: found
          • Abstract: not found
          • Article: not found

          Diagnostic tests 2: Predictive values.

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Refining clinical diagnosis with likelihood ratios.

            Likelihood ratios can refine clinical diagnosis on the basis of signs and symptoms; however, they are underused for patients' care. A likelihood ratio is the percentage of ill people with a given test result divided by the percentage of well individuals with the same result. Ideally, abnormal test results should be much more typical in ill individuals than in those who are well (high likelihood ratio) and normal test results should be most frequent in well people than in sick people (low likelihood ratio). Likelihood ratios near unity have little effect on decision-making; by contrast, high or low ratios can greatly shift the clinician's estimate of the probability of disease. Likelihood ratios can be calculated not only for dichotomous (positive or negative) tests but also for tests with multiple levels of results, such as creatine kinase or ventilation-perfusion scans. When combined with an accurate clinical diagnosis, likelihood ratios from ancillary tests improve diagnostic accuracy in a synergistic manner.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Molecular detection of mutations associated with first- and second-line drug resistance compared with conventional drug susceptibility testing of Mycobacterium tuberculosis.

              The emergence of multi- and extensively drug-resistant tuberculosis is a significant impediment to the control of this disease because treatment becomes more complex and costly. Reliable and timely drug susceptibility testing is critical to ensure that patients receive effective treatment and become noninfectious. Molecular methods can provide accurate and rapid drug susceptibility results. We used DNA sequencing to detect resistance to the first-line antituberculosis drugs isoniazid (INH), rifampin (RIF), pyrazinamide (PZA), and ethambutol (EMB) and the second-line drugs amikacin (AMK), capreomycin (CAP), kanamycin (KAN), ciprofloxacin (CIP), and ofloxacin (OFX). Nine loci were sequenced: rpoB (for resistance to RIF), katG and inhA (INH), pncA (PZA), embB (EMB), gyrA (CIP and OFX), and rrs, eis, and tlyA (KAN, AMK, and CAP). A total of 314 clinical Mycobacterium tuberculosis complex isolates representing a variety of antibiotic resistance patterns, genotypes, and geographical origins were analyzed. The molecular data were compared to the phenotypic data and the accuracy values were calculated. Sensitivity and specificity values for the first-line drug loci were 97.1% and 93.6% for rpoB, 85.4% and 100% for katG, 16.5% and 100% for inhA, 90.6% and 100% for katG and inhA together, 84.6% and 85.8% for pncA, and 78.6% and 93.1% for embB. The values for the second-line drugs were also calculated. The size and scope of this study, in numbers of loci and isolates examined, and the phenotypic diversity of those isolates support the use of DNA sequencing to detect drug resistance in the M. tuberculosis complex. Further, the results can be used to design diagnostic tests utilizing other mutation detection technologies.
                Bookmark

                Author and article information

                Journal
                Eur Respir J
                Eur. Respir. J
                ERJ
                erj
                The European Respiratory Journal
                European Respiratory Society
                0903-1936
                1399-3003
                December 2017
                28 December 2017
                : 50
                : 6
                : 1701354
                Affiliations
                [1 ]Emerging Bacterial Pathogens Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
                [2 ]Department of Medical Microbiology, University of Gondar, Gondar, Ethiopia
                [3 ]School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
                [4 ]Division of Tuberculosis Elimination, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
                [5 ]Institute on Ethics & Policy for Innovation, Department of Philosophy, McMaster University, Hamilton, ON, Canada
                [6 ]Critical Path Institute, Tucson, AZ, USA
                [7 ]Office of AIDS Research, National Institutes of Health, Rockville, MD, USA
                [8 ]Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland
                [9 ]Molecular and Experimental Mycobacteriology, Priority Area Infections, Research Center Borstel, Borstel, Germany
                [10 ]German Center for Infection Research, Borstel, Germany
                [11 ]Foundation for Innovative New Diagnostics, Campus Biotech, Geneva, Switzerland
                [12 ]Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
                [13 ]DST/NRF Centre of Excellence for Biomedical Tuberculosis Research/SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
                [14 ]Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
                [15 ]National Infection Service, Public Health England, London, UK
                [16 ]Swiss Tropical and Public Health Institute, Basel, Switzerland
                [17 ]University of Basel, Basel, Switzerland
                [18 ]Microbiology, Tumour and Cell Biology, Karolinska Institute, Stockholm, Sweden
                [19 ]Public Health Agency of Sweden, Solna, Sweden
                [20 ]Hinduja Hospital, Veer Savarkar Marg, Mumbai, India
                [21 ]Tuberculosis Genomics Unit, Biomedicine Institute of Valencia (IBV-CSIC), Valencia, Spain
                [22 ]Foundation for the Promotion of Health and Biomedical Research in the Valencian Community (FISABIO), Valencia, Spain
                [23 ]CIBER (Centros de Investigación Biomédica en Red) in Epidemiology and Public Health, Madrid, Spain
                [24 ]Translational Genomics Research Institute, Flagstaff, AZ, USA
                [25 ]Harvard School of Public Health, Department of Epidemiology, Boston, MA, USA
                [26 ]Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, NJ, USA
                [27 ]Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium
                [28 ]Division of Clinical Infectious Diseases and German Center for Infection Research Tuberculosis Unit, Research Center Borstel, Borstel, Germany
                [29 ]International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
                [30 ]Department of Medicine, Karolinska Institute, Stockholm, Sweden
                [31 ]Department of Internal Medicine, University of Namibia School of Medicine, Windhoek, Namibia
                [32 ]Lung Infection and Immunity Unit, Department of Medicine, Division of Pulmonology and UCT Lung Institute, University of Cape Town, Groote Schuur Hospital, Cape Town, South Africa
                [33 ]Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan
                [34 ]National Institute for Research in Tuberculosis (ICMR), No 1, Chennai, India
                [35 ]Department of Medicine, Division of Pulmonology, University of Cape Town, Groote Schuur Hospital, Cape Town, South Africa
                [36 ]Department of Genetics, University of Cambridge, Cambridge, UK
                [37 ]Department of Medicine, University of California, San Diego, CA, USA
                Author notes
                Paolo Miotto, Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS Ospedale San Raffaele, Via Olgettina 58, 20132 Milan, Italy. E-mail: miotto.paolo@ 123456hsr.it
                Author information
                http://orcid.org/0000-0003-4610-2427
                Article
                ERJ-01354-2017
                10.1183/13993003.01354-2017
                5898944
                29284687
                5f122b1b-d5ac-4dfe-ac93-2dc5df5c4d5d
                Copyright ©ERS 2017

                This ERJ Open article is open access and distributed under the terms of the Creative Commons Attribution Licence 4.0.

                History
                : 06 July 2017
                : 13 October 2017
                Funding
                Funded by: Bill and Melinda Gates Foundation http://doi.org/10.13039/100000865
                Award ID: OPP1115209
                Categories
                Original Articles
                Tuberculosis
                4

                Respiratory medicine
                Respiratory medicine

                Comments

                Comment on this article