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Farhat M, Cox H, Ghanem M, Denkinger CM, Rodrigues C, Abd El Aziz MS, Enkh-Amgalan H, Vambe D, Ugarte-Gil C, Furin J, Pai M. Drug-resistant tuberculosis: a persistent global health concern. Nat Rev Microbiol 2024; 22:617-635. [PMID: 38519618 DOI: 10.1038/s41579-024-01025-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/12/2024] [Indexed: 03/25/2024]
Abstract
Drug-resistant tuberculosis (TB) is estimated to cause 13% of all antimicrobial resistance-attributable deaths worldwide and is driven by both ongoing resistance acquisition and person-to-person transmission. Poor outcomes are exacerbated by late diagnosis and inadequate access to effective treatment. Advances in rapid molecular testing have recently improved the diagnosis of TB and drug resistance. Next-generation sequencing of Mycobacterium tuberculosis has increased our understanding of genetic resistance mechanisms and can now detect mutations associated with resistance phenotypes. All-oral, shorter drug regimens that can achieve high cure rates of drug-resistant TB within 6-9 months are now available and recommended but have yet to be scaled to global clinical use. Promising regimens for the prevention of drug-resistant TB among high-risk contacts are supported by early clinical trial data but final results are pending. A person-centred approach is crucial in managing drug-resistant TB to reduce the risk of poor treatment outcomes, side effects, stigma and mental health burden associated with the diagnosis. In this Review, we describe current surveillance of drug-resistant TB and the causes, risk factors and determinants of drug resistance as well as the stigma and mental health considerations associated with it. We discuss recent advances in diagnostics and drug-susceptibility testing and outline the progress in developing better treatment and preventive therapies.
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Affiliation(s)
- Maha Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Helen Cox
- Institute of Infectious Disease and Molecular Medicine, Wellcome Centre for Infectious Disease Research and Division of Medical Microbiology, University of Cape Town, Cape Town, South Africa
| | - Marwan Ghanem
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Claudia M Denkinger
- Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany
- German Center for Infection Research (DZIF), partner site Heidelberg University Hospital, Heidelberg, Germany
| | | | - Mirna S Abd El Aziz
- Division of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Debrah Vambe
- National TB Control Programme, Manzini, Eswatini
| | - Cesar Ugarte-Gil
- School of Public and Population Health, University of Texas Medical Branch, Galveston, TX, USA
| | - Jennifer Furin
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Madhukar Pai
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada.
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Ghodousi A, Omrani M, Torri S, Teymouri H, Russo G, Vismara C, Matteelli A, Codecasa LR, Cirillo DM. Evaluating the efficacy of whole genome sequencing in predicting susceptibility profiles for first-line antituberculosis drugs. Clin Microbiol Infect 2024:S1198-743X(24)00456-7. [PMID: 39341416 DOI: 10.1016/j.cmi.2024.09.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 09/16/2024] [Accepted: 09/23/2024] [Indexed: 10/01/2024]
Abstract
OBJECTIVES This study aimed to examine the efficacy of whole genome sequencing (WGS) in accurately predicting susceptibility profiles, potentially eliminating the need for conventional phenotypic drug susceptibility testing (pDST) for first-line antituberculosis drugs in routine tuberculosis (TB) diagnosis. METHODS Over the period of 2017-2020, 1114 Mycobacterium tuberculosis complex isolates were collected with DST conducted using the MGIT960 system and WGS performed for predicting drug resistance profiles. In addition, we implemented a new algorithm with an updated WGS workflow, omitting pan-susceptible strains from pDST. RESULTS Results showed that out of 1075 analysed isolates, WGS based genotypic sensitivity predictions for isoniazid, rifampicin, ethambutol, and pyrazinamide were 100% (95% CI: 99.6%-100%), 100% (95% CI: 99.62%-100%), 99.8% (95% CI: 99.26%-99.94%), and 100% (95% CI: 99.63%-100%), respectively. In contrast, the WGS based genotypic resistance prediction, was 98.85% (95% CI: 93.77%-99.79%) for isoniazid, 94.74% (95% CI: 82.71%-98.54%) for rifampicin, 86.96% (95% CI: 67.87%-95.46%) for ethambutol, and 75.7% (95% CI: 59.9%-86.63%) for pyrazinamide. Moreover, WGS enabled the implementation of a new testing algorithm, that made unnecessary to perform pDST in 954 of all 1075 samples (88.7%) and in 890 of 901 pan-susceptible samples (98.8%). CONCLUSION Integrating WGS into TB management offers significant potential to replace phenotypic drug susceptibility testing especially for problematic drugs like pyrazinamide and ethambutol, potentially improving treatment outcomes.
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Affiliation(s)
- Arash Ghodousi
- Vita-Salute San Raffaele University, Milan, Italy; Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maryam Omrani
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefania Torri
- Regional TB Reference Centre and Laboratory, Villa Marelli Institute/Niguarda Hospital, Milano, Italy; Microbiological Analysis Unit, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Hedyeh Teymouri
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giulia Russo
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Chiara Vismara
- Microbiological Analysis Unit, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Alberto Matteelli
- Dept of Infectious and Tropical Diseases, Collaborating Centre for TB Elimination, University of Brescia, Brescia, Italy
| | - Luigi Ruffo Codecasa
- Regional TB Reference Centre and Laboratory, Villa Marelli Institute/Niguarda Hospital, Milano, Italy
| | - Daniela Maria Cirillo
- Division of Immunology, Transplantation, and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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Pei S, Song Z, Yang W, He W, Ou X, Zhao B, He P, Zhou Y, Xia H, Wang S, Jia Z, Walker TM, Zhao Y. The catalogue of Mycobacterium tuberculosis mutations associated with drug resistance to 12 drugs in China from a nationwide survey: a genomic analysis. THE LANCET. MICROBE 2024:100899. [PMID: 39353459 DOI: 10.1016/s2666-5247(24)00131-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/05/2024] [Accepted: 05/10/2024] [Indexed: 10/04/2024]
Abstract
BACKGROUND WHO issued the first edition catalogue of Mycobacterium tuberculosis complex (MTBC) mutations associated with drug resistance in 2021. However, country-specific issues might lead to arising complex and additional drug-resistant mutations. We aimed to fully reflect the characteristics of drug resistance mutations in China. METHODS We analysed MTBC isolates from the nationwide drug-resistant tuberculosis surveillance with 70 counties in 31 provinces, municipalities, and autonomous regions in China. Three types of MYCOTB plates were used to perform drug susceptibility testing for 12 antibiotics (rifampicin, isoniazid, ethambutol, levofloxacin, moxifloxacin, amikacin, kanamycin, ethionamide, clofazimine, linezolid, delamanid, and bedaquiline). Mutations were divided into five groups according to their odds ratios, positive predictive values, false discovery rate-corrected p values, and 95% CIs: (1) associated with resistance; (2) associated with resistance-interim; (3) uncertain significance; (4) not associated with resistance-interim; and (5) not associated with resistance. The Wilcoxon rank-sum and Kruskal-Wallis tests were used to quantify the association between mutations and minimum inhibitory concentrations (MICs). Our dataset was compared with the first edition of the WHO catalogue. FINDINGS We analysed 10 146 MTBC isolates, of which 9071 (89·4%) isolates were included in the final analysis. 744 (8·2%) isolates were resistant to rifampicin and 1339 (14·8%) to isoniazid. 208 (1·9%) of 11 065 mutations were classified as associated with resistance or associated with resistance-interim. 33 (97·1%) of 34 mutations in group 1 and 92 (52·9%) of 174 in group 2 also appeared in groups 1 or 2 of the WHO catalogue. Of 81 indel mutations in group 2, 15 (18·5%) were in the WHO catalogue. The newly discovered mutation gyrA_Ala288Asp was associated with levofloxacin resistance. MIC values for rifampicin, isoniazid, moxifloxacin, and levofloxacin corresponding to resistance mutations in group 1 were significantly different (p<0·0001), and 12 high-level resistance mutations were detected. 61 mutations in group 3 occurred as solo in at least five phenotypically susceptible isolates, but with MIC values moderately higher than other susceptible isolates. Among 945 phenotypically resistant but genotypically susceptible isolates, 433 (45·8%) were mutated for at least one efflux pump gene. INTERPRETATION Our analysis reflects the complexity of drug resistance mutations in China and suggests that indel mutations, efflux pump genes, protein structure, and MICs should be fully considered in the WHO catalogue, especially in countries with a high tuberculosis burden. FUNDING National Key Research and Development Program of China and the Science and Technology Major Project of Tibetan Autonomous Region of China.
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Affiliation(s)
- Shaojun Pei
- Department of Global Health, School of Public Health, Peking University, Beijing, China; National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zexuan Song
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wei Yang
- National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China
| | - Wencong He
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xichao Ou
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bing Zhao
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ping He
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yang Zhou
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hui Xia
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shengfen Wang
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhongwei Jia
- Department of Global Health, School of Public Health, Peking University, Beijing, China.
| | - Timothy M Walker
- Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam; Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
| | - Yanlin Zhao
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China; National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
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Qadir M, Khan MT, Khan SA, Akram M, Canseco JO, Faryal R, Wei DQ, Tahseen S. Unveiling the complexity of rifampicin drug susceptibility testing in Mycobacterium tuberculosis: comparative analysis with next-generation sequencing. J Med Microbiol 2024; 73. [PMID: 39229883 DOI: 10.1099/jmm.0.001884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024] Open
Abstract
Introduction. The discordance between phenotypic and molecular methods of rifampicin (RIF) drug susceptibility testing (DST) in Mycobacterium tuberculosis poses a significant challenge, potentially resulting in misdiagnosis and inappropriate treatment.Hypothesis/gap statement. A comparison of RIF phenotypic and molecular methods for DST, including whole genome sequencing (WGS), may provide a better understanding of resistance mechanisms.Aim. This study aims to compare RIF DST in M. tuberculosis using two phenotypic and molecular methods including the GeneXpert RIF Assay (GX) and WGS for better understanding.Methodology. The study evaluated two phenotypic liquid medium methods [Lowenstein-Jensen (LJ) and Mycobacterium Growth Indicator Tube (MGIT)], one targeted molecular method (GX), and one WGS method. Moreover, mutational frequency in ponA1 and ponA2 was also screened in the current and previous RIF resistance M. tuberculosis genomic isolates to find their compensatory role.Results. A total of 25 RIF-resistant isolates, including nine from treatment failures and relapse cases with both discordant and concordant DST results on LJ, MGIT and GX, were subjected to WGS. The phenotypic DST results indicated that 11 isolates (44%) were susceptible on LJ and MGIT but resistant on GX. These isolates exhibited multiple mutations in rpoB, including Thr444>Ala, Leu430>Pro, Leu430>Arg, Asp435>Gly, His445>Asn and Asn438>Lys. Conversely, four isolates that were susceptible on GX and MGIT but resistant on LJ were wild type for rpoB in WGS. However, these isolates possessed several novel mutations in the PonA1 gene, including a 10 nt insertion and two nonsynonymous mutations (Ala394>Ser, Pro631>Ser), as well as one nonsynonymous mutation (Pro780>Arg) in PonA2. The discordance rate of RIF DST is higher on MGIT than on LJ and GX when compared to WGS. These discordances in the Delhi/CAS lineages were primarily associated with failure and relapse cases.Conclusion. The WGS of RIF resistance is relatively expensive, but it may be considered for isolates with discordant DST results on MGIT, LJ and GX to ensure accurate diagnosis and appropriate treatment options.
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Affiliation(s)
- Mehmood Qadir
- National TB Control Program, National TB Reference Laboratory, Islamabad 44000, Pakistan
- Department of Microbiology, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Muhammad Tahir Khan
- Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, Guangzhou Key Laboratory of Tuberculosis Research, Guangzhou Chest Hospital, Institute of Tuberculosis, Guangzhou Medical University, Guangzhou, PR China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan 473006, PR China
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore 58810, Pakistan
| | - Sajjad Ahmed Khan
- National TB Control Program, National TB Reference Laboratory, Islamabad 44000, Pakistan
| | - Muhammad Akram
- Department of Life Sciences, School of Science, University of Management and Technology, Lahore, Pakistan
| | - Julio Ortiz Canseco
- Host-Pathogen Interactions in Tuberculosis Laboratory, The Francis Crick Institute, London, UK
| | - Rani Faryal
- Department of Microbiology, Quaid-i-Azam University, Islamabad 44000, Pakistan
| | - Dong Qing Wei
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006, PR China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, PR China
- Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, PR China
| | - Sabira Tahseen
- National TB Control Program, National TB Reference Laboratory, Islamabad 44000, Pakistan
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Sanchini A, Lanni A, Giannoni F, Mustazzolu A. Exploring diagnostic methods for drug-resistant tuberculosis: A comprehensive overview. Tuberculosis (Edinb) 2024; 148:102522. [PMID: 38850839 DOI: 10.1016/j.tube.2024.102522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/14/2024] [Accepted: 05/30/2024] [Indexed: 06/10/2024]
Abstract
Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.
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Affiliation(s)
| | - Alessio Lanni
- Department of Infectious Diseases, Istituto Superiore di Sanità, 00161, Rome, Italy.
| | - Federico Giannoni
- Department of Infectious Diseases, Istituto Superiore di Sanità, 00161, Rome, Italy.
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Xu Y, Liu D, Han P, Wang H, Wang S, Gao J, Chen F, Zhou X, Deng K, Luo J, Zhou M, Kuang D, Yang F, Jiang Z, Xu S, Rao G, Wang Y, Qu J. Rapid inference of antibiotic resistance and susceptibility for Klebsiella pneumoniae by clinical shotgun metagenomic sequencing. Int J Antimicrob Agents 2024; 64:107252. [PMID: 38908534 DOI: 10.1016/j.ijantimicag.2024.107252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/14/2024] [Accepted: 06/07/2024] [Indexed: 06/24/2024]
Abstract
OBJECTIVES The study aimed to develop a genotypic antimicrobial resistance testing method for Klebsiella pneumoniae using metagenomic sequencing data. METHODS We utilized Lasso regression on assembled genomes to identify genetic resistance determinants for six antibiotics (Gentamicin, Tobramycin, Imipenem, Meropenem, Ceftazidime, Trimethoprim/Sulfamethoxazole). The genetic features were weighted, grouped into clusters to establish classifier models. Origin species of detected antibiotic resistant gene (ARG) was determined by novel strategy integrating "possible species," "gene copy number calculation" and "species-specific kmers." The performance of the method was evaluated on retrospective case studies. RESULTS Our study employed machine learning on 3928 K. pneumoniae isolates, yielding stable models with AUCs > 0.9 for various antibiotics. GenseqAMR, a read-based software, exhibited high accuracy (AUC 0.926-0.956) for short-read datasets. The integration of a species-specific kmer strategy significantly improved ARG-species attribution to an average accuracy of 96.67%. In a retrospective study of 191 K. pneumoniae-positive clinical specimens (0.68-93.39% genome coverage), GenseqAMR predicted 84.23% of AST results on average. It demonstrated 88.76-96.26% accuracy for resistance prediction, offering genotypic AST results with a shorter turnaround time (mean ± SD: 18.34 ± 0.87 hours) than traditional culture-based AST (60.15 ± 21.58 hours). Furthermore, a retrospective clinical case study involving 63 cases showed that GenseqAMR could lead to changes in clinical treatment for 24 (38.10%) cases, with 95.83% (23/24) of these changes deemed beneficial. CONCLUSIONS In conclusion, GenseqAMR is a promising tool for quick and accurate AMR prediction in Klebsiella pneumoniae, with the potential to improve patient outcomes through timely adjustments in antibiotic treatment.
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Affiliation(s)
- Yanping Xu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Donglai Liu
- National Institutes for Food and Drug Control, Beijing, China
| | - Peng Han
- Genskey Medical Technology Co., Ltd, Beijing, China
| | - Hao Wang
- National Institutes for Food and Drug Control, Beijing, China
| | - Shanmei Wang
- Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jianpeng Gao
- Genskey Medical Technology Co., Ltd, Beijing, China
| | | | - Xun Zhou
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, China; Key Laboratory of Clinical Pharmacology of Antibiotics, Ministry of Health, Shanghai, China
| | - Kun Deng
- Department of Laboratory Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiajie Luo
- Genskey Medical Technology Co., Ltd, Beijing, China
| | - Min Zhou
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Dai Kuang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China
| | - Fan Yang
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, China; Key Laboratory of Clinical Pharmacology of Antibiotics, Ministry of Health, Shanghai, China
| | - Zhi Jiang
- Genskey Medical Technology Co., Ltd, Beijing, China
| | - Sihong Xu
- National Institutes for Food and Drug Control, Beijing, China.
| | - Guanhua Rao
- Genskey Medical Technology Co., Ltd, Beijing, China.
| | - Youchun Wang
- National Institutes for Food and Drug Control, Beijing, China.
| | - Jieming Qu
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, China.
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de la Lastra JMP, Wardell SJT, Pal T, de la Fuente-Nunez C, Pletzer D. From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review. J Med Syst 2024; 48:71. [PMID: 39088151 PMCID: PMC11294375 DOI: 10.1007/s10916-024-02089-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/12/2024] [Indexed: 08/02/2024]
Abstract
The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.
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Affiliation(s)
- José M Pérez de la Lastra
- Biotechnology of Macromolecules, Instituto de Productos Naturales y Agrobiología, IPNA (CSIC), Avda. Astrofísico Francisco Sánchez, 3, 38206, San Cristóbal de la Laguna, (Santa Cruz de Tenerife), Spain.
| | - Samuel J T Wardell
- Department of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, 9054, Dunedin, New Zealand
| | - Tarun Pal
- School of Bioengineering and Food Technology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, 173229, Himachal Pradesh, India
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel Pletzer
- Department of Microbiology and Immunology, School of Biomedical Sciences, University of Otago, 9054, Dunedin, New Zealand.
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Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning. PLoS Comput Biol 2024; 20:e1012260. [PMID: 39102420 PMCID: PMC11326700 DOI: 10.1371/journal.pcbi.1012260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 08/15/2024] [Accepted: 06/19/2024] [Indexed: 08/07/2024] Open
Abstract
There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs.
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Bahk K, Sung J, Seki M, Kim K, Kim J, Choi H, Whang J, Mitarai S. Pan-lineage Mycobacterium tuberculosis reference genome for enhanced molecular diagnosis. DNA Res 2024; 31:dsae023. [PMID: 39127874 PMCID: PMC11339604 DOI: 10.1093/dnares/dsae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 07/22/2024] [Accepted: 08/09/2024] [Indexed: 08/12/2024] Open
Abstract
In Mycobacterium tuberculosis (MTB) control, whole genome sequencing-based molecular drug susceptibility testing (molDST-WGS) has emerged as a pivotal tool. However, the current reliance on a single-strain reference limits molDST-WGS's true potential. To address this, we introduce a new pan-lineage reference genome, 'MtbRf'. We assembled 'unmapped' reads from 3,614 MTB genomes (751 L1; 881 L2; 1,700 L3; and 282 L4) into 35 shared, annotated contigs (54 coding sequences [CDSs]). We constructed MtbRf through: (1) searching for contig homologues among genome database that precipitate results uniquely within Mycobacteria genus; (2) comparing genomes with H37Rv ('lift-over') to define 18 insertions; and (3) filling gaps in H37Rv with insertions. MtbRf adds 1.18% sequences to H37rv, salvaging >60% of previously unmapped reads. Transcriptomics confirmed gene expression of new CDSs. The new variants provided a moderate DST predictive value (AUROC 0.60-0.75). MtbRf thus unveils previously hidden genomic information and lays the foundation for lineage-specific molDST-WGS.
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Affiliation(s)
- Kunhyung Bahk
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, 1, Gwanak-ro, Seoul, 08826, Korea
| | - Joohon Sung
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, 1, Gwanak-ro, Seoul, 08826, Korea
- Genome and Health Big Data Laboratory, Graduate School of Public Health, Seoul National University, 1, Gwanak-ro, Seoul, 08826, Korea
- Institute of Health and Environment, Seoul National University, 1, Gwanak-ro, Seoul, 08826, Korea
- Genomic Medicine Institute, Seoul National University College of Medicine, 103, Daehak-ro, Seoul, 03080, Korea
| | - Mitsuko Seki
- Division of Pediatric Dentistry, Department of Human Development and Fostering, Meikai University School of Dentistry, 1-1, Keyakidai, Sakado, Saitama, 350-0283, Japan
- Division of Microbiology, Department of Pathology and Microbiology, Nihon University School of Medicine, 30-1, Oyaguchi Kami-Cho, Itabashi-Ku, Tokyo, 173-8610, Japan
| | - Kyungjong Kim
- Research and Development Center, The Korean Institute of Tuberculosis, 168-5, Osongsaengmyeong 4-ro, Osong, Cheongju-City, Chungcheongbuk-do, 28158, Korea
- DNA Analysis Division, National Forensic Service, Ministry of the Interior and Safety, 139, Jiyang-ro, Seoul, 08036, Korea
| | - Jina Kim
- Departments of Urology and Computational Biomedicine, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA
| | - Hongjo Choi
- Division of Health Policy and Management, Korea University, Seoul, 02841, Korea
| | - Jake Whang
- Research and Development Center, The Korean Institute of Tuberculosis, 168-5, Osongsaengmyeong 4-ro, Osong, Cheongju-City, Chungcheongbuk-do, 28158, Korea
| | - Satoshi Mitarai
- Department of Mycobacterium Reference and Research, Research Institute of Tuberculosis, Japan Anti-Tuberculosis Association, 3-1-24 Matsuyama, Kiyose, Tokyo, 204-8533Japan
- Department of Basic Mycobacteriology, Graduate School of Biomedical Science, Nagasaki University, Nagasaki, 852-8523Japan
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10
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Hlanze H, Mutshembele A, Reva ON. Universal Lineage-Independent Markers of Multidrug Resistance in Mycobacterium tuberculosis. Microorganisms 2024; 12:1340. [PMID: 39065108 PMCID: PMC11278869 DOI: 10.3390/microorganisms12071340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 06/23/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024] Open
Abstract
(1) Background: This study was aimed to identify universal genetic markers of multidrug resistance (MDR) in Mycobacterium tuberculosis (Mtb) and establish statistical associations among identified mutations to enhance understanding of MDR in Mtb and inform diagnostic and treatment development. (2) Methods: GWAS analysis and the statistical evaluation of identified polymorphic sites within protein-coding genes of Mtb were performed. Statistical associations between specific mutations and antibiotic resistance were established using attributable risk statistics. (3) Results: Sixty-four polymorphic sites were identified as universal markers of drug resistance, with forty-seven in PE/PPE regions and seventeen in functional genes. Mutations in genes such as cyp123, fadE36, gidB, and ethA showed significant associations with resistance to various antibiotics. Notably, mutations in cyp123 at codon position 279 were linked to resistance to ten antibiotics. The study highlighted the role of PE/PPE and PE_PGRS genes in Mtb's evolution towards a 'mutator phenotype'. The pathways of acquisition of mutations forming the epistatic landscape of MDR were discussed. (4) Conclusions: This research identifies marker mutations across the Mtb genome associated with MDR. The findings provide new insights into the molecular basis of MDR acquisition in Mtb, aiding in the development of more effective diagnostics and treatments targeting these mutations to combat MDR tuberculosis.
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Affiliation(s)
- Hleliwe Hlanze
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hillcrest, Lynnwood Rd, Pretoria 0002, South Africa;
| | - Awelani Mutshembele
- South African Medical Research Council, TB Platform, 1 Soutpansberg Road, Private Bag X385, Pretoria 0001, South Africa;
| | - Oleg N. Reva
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hillcrest, Lynnwood Rd, Pretoria 0002, South Africa;
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11
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Serajian M, Marini S, Alanko JN, Noyes NR, Prosperi M, Boucher C. Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis. Bioinformatics 2024; 40:i39-i47. [PMID: 38940175 PMCID: PMC11211809 DOI: 10.1093/bioinformatics/btae243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available. RESULTS We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions. AVAILABILITY The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.
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Affiliation(s)
- Mohammadali Serajian
- Department of Computer and Information Science and Engineering, University of Florida, 1889 Museum Road, Gainesville, Florida 32611, United States
| | - Simone Marini
- Department of Epidemiology, University of Florida, PO Box 100231, Gainesville, Florida 32601, United States
| | - Jarno N Alanko
- Department of Computer Science, University of Helsinki, P.O. Box 4, Helsinki 00014, Finland
| | - Noelle R Noyes
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Avenue, St. Paul, Minnesota 55108, United States
| | - Mattia Prosperi
- Department of Epidemiology, University of Florida, PO Box 100231, Gainesville, Florida 32601, United States
| | - Christina Boucher
- Department of Computer and Information Science and Engineering, University of Florida, 1889 Museum Road, Gainesville, Florida 32611, United States
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12
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Park M, Satta G, Haldar P. Heteroresistance in tuberculosis: are we missing drug-resistant bacteria hiding in plain sight? Thorax 2024; 79:599-600. [PMID: 38604663 DOI: 10.1136/thorax-2024-221409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2024] [Indexed: 04/13/2024]
Affiliation(s)
- Mirae Park
- Respiratory Medicine, Imperial College Healthcare NHS Trust, London, UK
| | - Giovanni Satta
- University College London Centre for Clinical Microbiology, London, UK
| | - Pranabashis Haldar
- Department of Respiratory Sciences, NIHR Respiratory BRC, Glenfield Hospital, University of Leicester, Leicester, UK
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13
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Sharma MK, Stobart M, Akochy PM, Adam H, Janella D, Rabb M, Alawa M, Sekirov I, Tyrrell GJ, Soualhine H. Evaluation of Whole Genome Sequencing-Based Predictions of Antimicrobial Resistance to TB First Line Agents: A Lesson from 5 Years of Data. Int J Mol Sci 2024; 25:6245. [PMID: 38892433 PMCID: PMC11172968 DOI: 10.3390/ijms25116245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 05/24/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024] Open
Abstract
Phenotypic susceptibility testing of the Mycobacterium tuberculosis complex (MTBC) isolate requires culture growth, which can delay rapid detection of resistant cases. Whole genome sequencing (WGS) and data analysis pipelines can assist in predicting resistance to antimicrobials used in the treatment of tuberculosis (TB). This study compared phenotypic susceptibility testing results and WGS-based predictions of antimicrobial resistance (AMR) to four first-line antimicrobials-isoniazid, rifampin, ethambutol, and pyrazinamide-for MTBC isolates tested between the years 2018-2022. For this 5-year retrospective analysis, the WGS sensitivity for predicting resistance for isoniazid, rifampin, ethambutol, and pyrazinamide using Mykrobe was 86.7%, 100.0%, 100.0%, and 47.8%, respectively, and the specificity was 99.4%, 99.5%, 98.7%, and 99.9%, respectively. The predictive values improved slightly using Mykrobe corrections applied using TB Profiler, i.e., the WGS sensitivity for isoniazid, rifampin, ethambutol, and pyrazinamide was 92.31%, 100%, 100%, and 57.78%, respectively, and the specificity was 99.63%. 99.45%, 98.93%, and 99.93%, respectively. The utilization of WGS-based testing addresses concerns regarding test turnaround time and enables analysis for MTBC member identification, antimicrobial resistance prediction, detection of mixed cultures, and strain genotyping, all through a single laboratory test. WGS enables rapid resistance detection compared to traditional phenotypic susceptibility testing methods using the WHO TB mutation catalog, providing an insight into lesser-known mutations, which should be added to prediction databases as high-confidence mutations are recognized. The WGS-based methods can support TB elimination efforts in Canada and globally by ensuring the early start of appropriate treatment, rapidly limiting the spread of TB outbreaks.
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Affiliation(s)
- Meenu Kaushal Sharma
- National Reference Centre for Mycobacteriology, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada (M.S.)
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada;
| | - Michael Stobart
- National Reference Centre for Mycobacteriology, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada (M.S.)
| | - Pierre-Marie Akochy
- Laboratoire de Santé Publique du Québec-Institut National de Santé Publique du Québec, Sainte-Anne-de-Bellevue, QC H9X 3R5, Canada
| | - Heather Adam
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada;
- Diagnostic Services, Shared Health, Winnipeg, MB R3C 3H8, Canada
| | - Debra Janella
- National Reference Centre for Mycobacteriology, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada (M.S.)
| | - Melissa Rabb
- National Reference Centre for Mycobacteriology, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada (M.S.)
| | - Mohey Alawa
- Regina Qu’Appelle Health Region, Regina, SK S4T 1A5, Canada;
| | - Inna Sekirov
- Public Health Laboratory, B.C. Centre for Disease Control, Vancouver, BC V5Z 4R4, Canada;
| | - Gregory J. Tyrrell
- Division of Diagnostic and Applied Microbiology, Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2J2, Canada
- Alberta Precision Laboratories Public Health, Edmonton, AB T6G 2J2, Canada
| | - Hafid Soualhine
- National Reference Centre for Mycobacteriology, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB R3E 3R2, Canada (M.S.)
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, MB R3E 0J9, Canada;
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14
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Tang C, Wu L, Li M, Dai J, Shi Y, Wang Q, Xu F, Zheng L, Xiao X, Cai J, Zhang Y, Yang Y, Zheng X, Xiang G. High-throughput nanopore targeted sequencing for efficient drug resistance assay of Mycobacterium tuberculosis. Front Microbiol 2024; 15:1331656. [PMID: 38841074 PMCID: PMC11152171 DOI: 10.3389/fmicb.2024.1331656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/06/2024] [Indexed: 06/07/2024] Open
Abstract
Drug-resistant tuberculosis (TB), especially multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB), is one of the urgent clinical problems and public health challenges. Culture-based phenotypic drug susceptibility testing (pDST) is time-consuming, and PCR-based assays are limited to hotspot mutations. In this study, we developed and validated a convenient and efficient approach based on high-throughput nanopore sequencing technology combined with multiplex PCR, namely nanopore targeted sequencing (NTS), to simultaneously sequence 18 genes associated with antibiotic resistance in Mycobacterium tuberculosis (MTB). The analytical performance of NTS was evaluated, and 99 clinical samples were collected to assess its clinical performance. The NTS results showed that MTB and its drug resistance were successfully identified in approximately 7.5 h. Furthermore, compared to the pDST and Xpert MTB/RIF assays, NTS provided much more drug resistance information, covering 14 anti-TB drugs, and it identified 20 clinical cases of drug-resistant MTB. The mutations underlying these drug-resistant cases were all verified using Sanger sequencing. Our approach for this TB drug resistance assay offers several advantages, including being culture-free, efficient, high-throughput, and highly accurate, which would be very helpful for clinical patient management and TB infection control.
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Affiliation(s)
- Chen Tang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Lianpeng Wu
- Department of Clinical Laboratory, Wenzhou Central Hospital, Wenzhou, Zhejiang, China
| | - Machao Li
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jianyi Dai
- Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, Zhejiang, China
| | - Ye Shi
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Qiongdan Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Feng Xu
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Laibao Zheng
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xingxing Xiao
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Junwen Cai
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yanjun Zhang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yuting Yang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiaoqun Zheng
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Guangxin Xiang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Laboratory Medicine, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
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15
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Tagami Y, Horita N, Kaneko M, Muraoka S, Fukuda N, Izawa A, Kaneko A, Somekawa K, Kamimaki C, Matsumoto H, Tanaka K, Murohashi K, Aoki A, Fujii H, Watanabe K, Hara Y, Kobayashi N, Kaneko T. Whole-Genome Sequencing Predicting Phenotypic Antitubercular Drug Resistance: Meta-analysis. J Infect Dis 2024; 229:1481-1492. [PMID: 37946558 DOI: 10.1093/infdis/jiad480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 10/06/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND For simultaneous prediction of phenotypic drug susceptibility test (pDST) for multiple antituberculosis drugs, the whole genome sequencing (WGS) data can be analyzed using either a catalog-based approach, wherein 1 causative mutation suggests resistance, (eg, World Health Organization catalog) or noncatalog-based approach using complicated algorithm (eg, TB-profiler, machine learning). The aim was to estimate the predictive ability of WGS-based tests with pDST as the reference, and to compare the 2 approaches. METHODS Following a systematic literature search, the diagnostic test accuracies for 14 drugs were pooled using a random-effect bivariate model. RESULTS Of 779 articles, 44 with 16 821 specimens for meta-analysis and 13 not for meta-analysis were included. The areas under summary receiver operating characteristic curve suggested test accuracy was excellent (0.97-1.00) for 2 drugs (isoniazid 0.975, rifampicin 0.975), very good (0.93-0.97) for 8 drugs (pyrazinamide 0.946, streptomycin 0.952, amikacin 0.968, kanamycin 0.963, capreomycin 0.965, para-aminosalicylic acid 0.959, levofloxacin 0.960, ofloxacin 0.958), and good (0.75-0.93) for 4 drugs (ethambutol 0.926, moxifloxacin 0.896, ethionamide 0.878, prothionamide 0.908). The noncatalog-based and catalog-based approaches had similar ability for all drugs. CONCLUSIONS WGS accurately identifies isoniazid and rifampicin resistance. For most drugs, positive WGS results reliably predict pDST positive. The 2 approaches had similar ability. CLINICAL TRIALS REGISTRATION UMIN-ID UMIN000049276.
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Affiliation(s)
- Yoichi Tagami
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Nobuyuki Horita
- Chemotherapy Center, Yokohama City University Hospital, Yokohama, Japan
| | - Megumi Kaneko
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Suguru Muraoka
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Nobuhiko Fukuda
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Ami Izawa
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Ayami Kaneko
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Kohei Somekawa
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Chisato Kamimaki
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Hiromi Matsumoto
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Katsushi Tanaka
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Kota Murohashi
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Ayako Aoki
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Hiroaki Fujii
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Keisuke Watanabe
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Yu Hara
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Nobuaki Kobayashi
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Takeshi Kaneko
- Department of Pulmonology, Yokohama City University Graduate School of Medicine, Yokohama, Japan
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16
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Poonawala H, Zhang Y, Kuchibhotla S, Green AG, Cirillo DM, Di Marco F, Spitlaeri A, Miotto P, Farhat MR. Transcriptomic responses to antibiotic exposure in Mycobacterium tuberculosis. Antimicrob Agents Chemother 2024; 68:e0118523. [PMID: 38587412 PMCID: PMC11064486 DOI: 10.1128/aac.01185-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Transcriptional responses in bacteria following antibiotic exposure offer insights into antibiotic mechanism of action, bacterial responses, and characterization of antimicrobial resistance. We aimed to define the transcriptional antibiotic response (TAR) in Mycobacterium tuberculosis (Mtb) isolates for clinically relevant drugs by pooling and analyzing Mtb microarray and RNA-seq data sets. We generated 99 antibiotic transcription profiles across 17 antibiotics, with 76% of profiles generated using 3-24 hours of antibiotic exposure and 49% within one doubling of the WHO antibiotic critical concentration. TAR genes were time-dependent, and largely specific to the antibiotic mechanism of action. TAR signatures performed well at predicting antibiotic exposure, with the area under the receiver operating curve (AUC) ranging from 0.84-1.00 (TAR <6 hours of antibiotic exposure) and 0.76-1.00 (>6 hours of antibiotic exposure) for upregulated genes and 0.57-0.90 and 0.87-1.00, respectfully, for downregulated genes. This work desmonstrates that transcriptomics allows for the assessment of antibiotic activity in Mtb within 6 hours of exposure.
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Affiliation(s)
- Husain Poonawala
- Department of Medicine and Department of Pathology and Laboratory Medicine, Tufts Medical Center, Boston, Massachusetts, USA
- Department of Medicine and Department of Anatomic and Clinical Pathology, Tufts University School of Medicine, Boston, Massachusetts, USA
- Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yu Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Anna G. Green
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Daniela Maria Cirillo
- Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federico Di Marco
- Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Spitlaeri
- Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy
| | - Paolo Miotto
- Emerging Bacterial Pathogens Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maha R. Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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17
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Zhang G, Sun X, Fleming J, Ran F, Luo J, Chen H, Ju H, Wang Z, Zhao H, Wang C, Zhang F, Dai X, Yang X, Li C, Liu Y, Wang Y, Zhang X, Jiang Y, Wu Z, Bi L, Zhang H. Genetic factors associated with acquired phenotypic drug resistance and its compensatory evolution during tuberculosis treatment. Clin Microbiol Infect 2024; 30:637-645. [PMID: 38286176 DOI: 10.1016/j.cmi.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/15/2024] [Accepted: 01/22/2024] [Indexed: 01/31/2024]
Abstract
OBJECTIVES We elucidated the factors, evolution, and compensation of antimicrobial resistance (AMR) in Mycobacterium tuberculosis (MTB) isolates under dual pressure from the intra-host environment and anti-tuberculosis (anti-TB) drugs. METHODS This retrospective case-control study included 337 patients with pulmonary tuberculosis from 15 clinics in Tianjin, China, with phenotypic drug susceptibility testing results available for at least two time points between January 1, 2009 and December 31, 2016. Patients in the case group exhibited acquired AMR to isoniazid (INH) or rifampicin (RIF), while those in the control group lacked acquired AMR. The whole-genome sequencing (WGS) was conducted on 149 serial longitudinal MTB isolates from 46 patients who acquired or reversed phenotypic INH/RIF-resistance during treatment. The genetic basis, associated factors, and intra-host evolution of acquired phenotypic INH/RIF-resistance were elucidated using a combined analysis. RESULTS Anti-TB interruption duration of ≥30 days showed association with acquired phenotypic INH/RIF resistance (aOR = 2·2, 95% CI, 1·0-5·1) and new rpoB mutations (p = 0·024). The MTB evolution was 1·2 (95% CI, 1·02-1·38) single nucleotide polymorphisms per genome per year under dual pressure from the intra-host environment and anti-TB drugs. AMR-associated mutations occurred before phenotypic AMR appearance in cases with acquired phenotypic INH (10 of 16) and RIF (9 of 22) resistances. DISCUSSION Compensatory evolution may promote the fixation of INH/RIF-resistance mutations and affect phenotypic AMR. The TB treatment should be adjusted based on gene sequencing results, especially in persistent culture positivity during treatment, which highlights the clinical importance of WGS in identifying reinfection and AMR acquisition before phenotypic drug susceptibility testing.
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Affiliation(s)
- Guoqin Zhang
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; Tianjin Center for Tuberculosis Control, Tianjin, China; University of Chinese Academy of Sciences, Beijing, China
| | - Xianhui Sun
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Joy Fleming
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Fanlei Ran
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Jianjun Luo
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Hong Chen
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Hanfang Ju
- Tianjin Center for Tuberculosis Control, Tianjin, China
| | - Zhirui Wang
- Tianjin Center for Tuberculosis Control, Tianjin, China
| | - Hui Zhao
- Tianjin Center for Tuberculosis Control, Tianjin, China
| | - Chunhua Wang
- Tianjin Center for Tuberculosis Control, Tianjin, China
| | - Fan Zhang
- Tianjin Center for Tuberculosis Control, Tianjin, China
| | - Xiaowei Dai
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Xinyu Yang
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Chuanyou Li
- Biobank of Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumour Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Yi Liu
- Biobank of Beijing Chest Hospital, Beijing Tuberculosis and Thoracic Tumour Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, China
| | | | - Xilin Zhang
- Foshan Fourth People's Hospital, Foshan, China
| | - Yuan Jiang
- Shanghai Municipal Center for Disease Prevention and Control, Beijing, China
| | - Zhilong Wu
- Foshan Fourth People's Hospital, Foshan, China
| | - Lijun Bi
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; Guangzhou National Laboratory, Guangzhou, China; University of Chinese Academy of Sciences, Beijing, China
| | - Hongtai Zhang
- Beijing Center for Disease Prevention and Control, Beijing, China.
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Dippenaar A, Ismail N, Heupink TH, Grobbelaar M, Loubser J, Van Rie A, Warren RM. Droplet based whole genome amplification for sequencing minute amounts of purified Mycobacterium tuberculosis DNA. Sci Rep 2024; 14:9931. [PMID: 38689002 PMCID: PMC11061190 DOI: 10.1038/s41598-024-60545-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 04/24/2024] [Indexed: 05/02/2024] Open
Abstract
Implementation of whole genome sequencing (WGS) for patient care is hindered by limited Mycobacterium tuberculosis (Mtb) in clinical specimens and slow Mtb growth. We evaluated droplet multiple displacement amplification (dMDA) for amplification of minute amounts of Mtb DNA to enable WGS as an alternative to other Mtb enrichment methods. Purified genomic Mtb-DNA (0.1, 0.5, 1, and 5 pg) was encapsulated and amplified using the Samplix Xdrop-instrument and sequenced alongside a control sample using standard Illumina protocols followed by MAGMA-analysis. The control and 5 pg input dMDA samples underwent nanopore sequencing followed by Nanoseq and TB-profiler analysis. dMDA generated 105-2400 ng DNA from the 0.1-5 pg input DNA, respectively. Followed by Illumina WGS, dMDA raised mean sequencing depth from 7 × for 0.1 pg input DNA to ≥ 60 × for 5 pg input and the control sample. Bioinformatic analysis revealed a high number of false positive and false negative variants when amplifying ≤ 0.5 pg input DNA. Nanopore sequencing of the 5 pg dMDA sample presented excellent coverage depth, breadth, and accurate strain characterization, albeit elevated false positive and false negative variants compared to Illumina-sequenced dMDA sample with identical Mtb DNA input. dMDA coupled with Illumina WGS for samples with ≥ 5 pg purified Mtb DNA, equating to approximately 1000 copies of the Mtb genome, offers precision for drug resistance, phylogeny, and transmission insights.
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Affiliation(s)
- Anzaan Dippenaar
- Tuberculosis Omics Research Consortium, Department of Family Medicine and Population Health, Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
| | - Nabila Ismail
- South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Tim H Heupink
- Tuberculosis Omics Research Consortium, Department of Family Medicine and Population Health, Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Melanie Grobbelaar
- South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Johannes Loubser
- South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Annelies Van Rie
- Tuberculosis Omics Research Consortium, Department of Family Medicine and Population Health, Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Robin M Warren
- South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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19
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Gan M, Zhang Y, Yan G, Wang Y, Lu G, Wu B, Chen W, Zhou W. Antimicrobial resistance prediction by clinical metagenomics in pediatric severe pneumonia patients. Ann Clin Microbiol Antimicrob 2024; 23:33. [PMID: 38622723 PMCID: PMC11020437 DOI: 10.1186/s12941-024-00690-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 03/26/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Antimicrobial resistance (AMR) is a major threat to children's health, particularly in respiratory infections. Accurate identification of pathogens and AMR is crucial for targeted antibiotic treatment. Metagenomic next-generation sequencing (mNGS) shows promise in directly detecting microorganisms and resistance genes in clinical samples. However, the accuracy of AMR prediction through mNGS testing needs further investigation for practical clinical decision-making. METHODS We aimed to evaluate the performance of mNGS in predicting AMR for severe pneumonia in pediatric patients. We conducted a retrospective analysis at a tertiary hospital from May 2022 to May 2023. Simultaneous mNGS and culture were performed on bronchoalveolar lavage fluid samples obtained from pediatric patients with severe pneumonia. By comparing the results of mNGS detection of microorganisms and antibiotic resistance genes with those of culture, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. RESULTS mNGS detected bacterial in 71.7% cases (86/120), significantly higher than culture (58/120, 48.3%). Compared to culture, mNGS demonstrated a sensitivity of 96.6% and a specificity of 51.6% in detecting pathogenic microorganisms. Phenotypic susceptibility testing (PST) of 19 antibiotics revealed significant variations in antibiotics resistance rates among different bacteria. Sensitivity prediction of mNGS for carbapenem resistance was higher than penicillins and cephalosporin (67.74% vs. 28.57%, 46.15%), while specificity showed no significant difference (85.71%, 75.00%, 75.00%). mNGS also showed a high sensitivity of 94.74% in predicting carbapenem resistance in Acinetobacter baumannii. CONCLUSIONS mNGS exhibits variable predictive performance among different pathogens and antibiotics, indicating its potential as a supplementary tool to conventional PST. However, mNGS currently cannot replace conventional PST.
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Affiliation(s)
- Mingyu Gan
- Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China
| | - Yanyan Zhang
- Department of Neonatology, Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Gangfeng Yan
- Department of Critical Care Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China
| | - Yixue Wang
- Department of Critical Care Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China
| | - Guoping Lu
- Department of Critical Care Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China
| | - Bingbing Wu
- Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China
| | - Weiming Chen
- Department of Critical Care Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China.
| | - Wenhao Zhou
- Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, 201102, People's Republic of China.
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510005, China.
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20
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Xiong XS, Zhang XD, Yan JW, Huang TT, Liu ZZ, Li ZK, Wang L, Li F. Identification of Mycobacterium tuberculosis Resistance to Common Antibiotics: An Overview of Current Methods and Techniques. Infect Drug Resist 2024; 17:1491-1506. [PMID: 38628245 PMCID: PMC11020249 DOI: 10.2147/idr.s457308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 03/26/2024] [Indexed: 04/19/2024] Open
Abstract
Multidrug-resistant tuberculosis (MDR-TB) is an essential cause of tuberculosis treatment failure and death of tuberculosis patients. The rapid and reliable profiling of Mycobacterium tuberculosis (MTB) drug resistance in the early stage is a critical research area for public health. Then, most traditional approaches for detecting MTB are time-consuming and costly, leading to the inappropriate therapeutic schedule resting on the ambiguous information of MTB drug resistance, increasing patient economic burden, morbidity, and mortality. Therefore, novel diagnosis methods are frequently required to meet the emerging challenges of MTB drug resistance distinguish. Considering the difficulty in treating MDR-TB, it is urgently required for the development of rapid and accurate methods in the identification of drug resistance profiles of MTB in clinical diagnosis. This review discussed recent advances in MTB drug resistance detection, focusing on developing emerging approaches and their applications in tangled clinical situations. In particular, a brief overview of antibiotic resistance to MTB was present, referred to as intrinsic bacterial resistance, consisting of cell wall barriers and efflux pumping action and acquired resistance caused by genetic mutations. Then, different drug susceptibility test (DST) methods were described, including phenotype DST, genotype DST and novel DST methods. The phenotype DST includes nitrate reductase assay, RocheTM solid ratio method, and liquid culture method and genotype DST includes fluorescent PCR, GeneXpert, PCR reverse dot hybridization, ddPCR, next-generation sequencing and gene chips. Then, novel DST methods were described, including metabolism testing, cell-free DNA probe, CRISPR assay, and spectral analysis technique. The limitations, challenges, and perspectives of different techniques for drug resistance are also discussed. These methods significantly improve the detection sensitivity and accuracy of multidrug-resistant tuberculosis (MRT) and can effectively curb the incidence of drug-resistant tuberculosis and accelerate the process of tuberculosis eradication.
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Affiliation(s)
- Xue-Song Xiong
- Department of Laboratory Medicine, The Affiliated Huai’an Hospital of Yangzhou University, Huai’an, Jiangsu Province, People’s Republic of China
- Department of Laboratory Medicine, The Fifth People’s Hospital of Huai’an, Huai’an, Jiangsu Province, People’s Republic of China
| | - Xue-Di Zhang
- Department of Laboratory Medicine, Xuzhou Infectious Diseases Hospital, Xuzhou, Jiangsu Province, People’s Republic of China
| | - Jia-Wei Yan
- Department of Laboratory Medicine, Xuzhou Infectious Diseases Hospital, Xuzhou, Jiangsu Province, People’s Republic of China
| | - Ting-Ting Huang
- Department of Laboratory Medicine, The Affiliated Huai’an Hospital of Yangzhou University, Huai’an, Jiangsu Province, People’s Republic of China
- Department of Laboratory Medicine, The Fifth People’s Hospital of Huai’an, Huai’an, Jiangsu Province, People’s Republic of China
| | - Zhan-Zhong Liu
- Department of Pharmacy, Xuzhou Infectious Diseases Hospital, Xuzhou, Jiangsu Province, People’s Republic of China
| | - Zheng-Kang Li
- Department of Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Liang Wang
- Department of Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, People’s Republic of China
| | - Fen Li
- Department of Laboratory Medicine, The Affiliated Huai’an Hospital of Yangzhou University, Huai’an, Jiangsu Province, People’s Republic of China
- Department of Laboratory Medicine, The Fifth People’s Hospital of Huai’an, Huai’an, Jiangsu Province, People’s Republic of China
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21
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Huang CK, Yu MC, Hung CS, Lin JC. Emerging insight of whole genome sequencing coupled with protein structure prediction into the pyrazinamide-resistance signature of Mycobacterium tuberculosis. Int J Antimicrob Agents 2024; 63:107053. [PMID: 38081550 DOI: 10.1016/j.ijantimicag.2023.107053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 11/11/2023] [Accepted: 12/04/2023] [Indexed: 02/25/2024]
Abstract
Pyrazinamide (PZA) is considered to be a pivotal drug to shorten the treatment of both drug-susceptible and drug-resistant tuberculosis, but its use is challenged by the reliability of drug-susceptibility testing (DST). PZA resistance in Mycobacterium tuberculosis (MTB) is relevant to the amino acid substitution of pyrazinamidase that is responsible for the conversion of PZA to active pyrazinoic acid (POA). The single nucleotide variants (SNVs) within ribosomal protein S1 (rpsA) or aspartate decarboxylase (panD), the binding targets of POA, has been reported to drive the PZA-resistance signature of MTB. In this study, whole genome sequencing (WGS) was used to identify SNVs within the pncA, rpsA and panD genes in 100 clinical MTB isolates associated with DST results for PZA. The potential influence of high-confidence, interim-confidence or emerging variants on the interplay between target genes and PZA or POA was simulated computationally, and predicted with a protein structure modelling approach. The DST results showed weak agreement with the identification of high-confidence variants within the pncA gene (Cohen's kappa coefficient=0.58), the analytic results of WGS coupled with protein structure modelling on pncA mutants (Cohen's kappa coefficient=0.524) or related genes (Cohen's kappa coefficient=0.504). Taken together, these results suggest the practicable application of a genotypic-coupled bioinformatic approach to manage PZA-containing regimens for patients with MTB.
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Affiliation(s)
- Chun-Kai Huang
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Laboratory Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Ming-Chih Yu
- Division of Pulmonary Medicine, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan; Pulmonary Research Centre, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Ching-Sheng Hung
- Department of Laboratory Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Jung-Chun Lin
- Ph.D. Program in Medical Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Pulmonary Research Centre, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
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22
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Carter JJ, Walker TM, Walker AS, Whitfield MG, Morlock GP, Lynch CI, Adlard D, Peto TEA, Posey JE, Crook DW, Fowler PW. Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches. JAC Antimicrob Resist 2024; 6:dlae037. [PMID: 38500518 PMCID: PMC10946228 DOI: 10.1093/jacamr/dlae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/19/2024] [Indexed: 03/20/2024] Open
Abstract
Background Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis; however, antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, encoding an enzyme that converts pyrazinamide into its active form. Methods We curated a dataset of 664 non-redundant, missense amino acid mutations in PncA with associated high-confidence phenotypes from published studies and then trained three different machine-learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features. Results The best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates. Conclusions This work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.
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Affiliation(s)
- Joshua J Carter
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- NIHR Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Michael G Whitfield
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, SAMRC Centre for Tuberculosis Research, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Tygerberg, South Africa
| | - Glenn P Morlock
- Division of Tuberculosis Elimination, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Charlotte I Lynch
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Dylan Adlard
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - Timothy E A Peto
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
| | - James E Posey
- Division of Tuberculosis Elimination, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- NIHR Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Philip W Fowler
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
- National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford OX3 9DU, UK
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23
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Dixit A, Freschi L, Vargas R, Gröschel MI, Nakhoul M, Tahseen S, Alam SMM, Kamal SMM, Skrahina A, Basilio RP, Lim DR, Ismail N, Farhat MR. Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning. BMJ Glob Health 2024; 9:e013532. [PMID: 38548342 PMCID: PMC10982777 DOI: 10.1136/bmjgh-2023-013532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction. METHODS We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa. RESULTS Mtb isolates from 29 countries (n=19 149) met sequence quality criteria. Global marginal genotypic resistance among mono-resistant TB estimates overlapped with the South African DR survey, except for isoniazid, ethionamide and second-line injectables, which were underestimated (n=3134). Among multidrug resistant (MDR) TB (n=268), estimates overlapped for the fluoroquinolones but overestimated other drugs. Globally pooled mono-resistance to isoniazid was 10.9% (95% CI: 10.2-11.7%, n=14 012). Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% (0.1-11%), n=111 and India 2.8% (0.08-9.4%), n=114). Given the recent interest in drugs enhancing ethionamide activity and their expected activity against isolates with resistance discordance between isoniazid and ethionamide, we measured this rate and found it to be high at 74.4% (IQR: 64.5-79.7%) of isoniazid-resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3964). CONCLUSIONS This is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics, but public WGS data demonstrates oversampling of isolates with higher resistance levels than MDR. Nevertheless, our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug-susceptible TB in South Asia and indicate underutilisation of ethionamide in MDR treatment.
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Affiliation(s)
- Avika Dixit
- Division of Infectious Diseases, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Luca Freschi
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Roger Vargas
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Center for Computational Biomedicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Matthias I Gröschel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Maria Nakhoul
- Informatics and Analytics Department, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Sabira Tahseen
- National Tuberculosis Control Programme, Islamabad, Pakistan
| | - S M Masud Alam
- Ministry of Health and Family Welfare, Kolkata, West Bengal, India
| | - S M Mostofa Kamal
- National Institute of Diseases of the Chest and Hospital, Dhaka, Bangladesh
| | - Alena Skrahina
- Republican Scientific and Practical Center for Pulmonology and Tuberculosis, Minsk, Belarus
| | - Ramon P Basilio
- Research Institute for Tropical Medicine, Muntinlupa City, Philippines
| | - Dodge R Lim
- Research Institute for Tropical Medicine, Muntinlupa City, Philippines
| | - Nazir Ismail
- Clinical Microbiology and Infectious Diseases, University of the Witwatersrand Johannesburg Faculty of Health Sciences, Johannesburg, South Africa
| | - Maha R Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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24
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Ang MLT, Chan SM, Cheng LTE, Cheong HY, Chew KL, Chlebicki PM, Hsu LY, Kaw GJL, Kee ACL, Ng MCW, Ong RTH, Ong CWM, Quah JL, Selvamani DB, Sng LH, Tan JBX, Tan CH, Tay JY, Teo LLS, Thoon KC, Yan GZ, Chen JIP, Hud BMH, Khoo BBJ, Lee DYX, Ng BXY, Park JY, Tan BYT, Yang Q. Singapore tuberculosis (TB) clinical management guidelines 2024: A modified Delphi adaptation of international guidelines for drug-susceptible TB infection and pulmonary disease. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2024; 53:170-186. [PMID: 38920244 DOI: 10.47102/annals-acadmedsg.2023391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Introduction Tuberculosis (TB) remains endemic in Singapore. Singapore's clinical practice guidelines for the management of tuberculosis were first published in 2016. Since then, there have been major new advances in the clinical management of TB, ranging from diagnostics to new drugs and treatment regimens. The National TB Programme convened a multidisciplinary panel to update guidelines for the clinical management of drug-susceptible TB infection and disease in Singapore, contextualising current evidence for local practice. Method Following the ADAPTE framework, the panel systematically reviewed, scored and synthesised English-language national and international TB clinical guidelines published from 2016, adapting recommendations for a prioritised list of clinical decisions. For questions related to more recent advances, an additional primary literature review was conducted via a targeted search approach. A 2-round modified Delphi process was implemented to achieve consensus for each recommendation, with a final round of edits after consultation with external stakeholders. Results Recommendations for 25 clinical questions spanning screening, diagnosis, selection of drug regimen, monitoring and follow-up of TB infection and disease were formulated. The availability of results from recent clinical trials led to the inclusion of shorter treatment regimens for TB infection and disease, as well as consensus positions on the role of newer technologies, such as computer-aided detection-artificial intelligence products for radiological screening of TB disease, next-generation sequencing for drug-susceptibility testing, and video observation of treatment. Conclusion The panel updated recommendations on the management of drug-susceptible TB infection and disease in Singapore.
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Affiliation(s)
- Michelle Lay Teng Ang
- National Public Health Laboratory, National Centre for Infectious Diseases, Singapore
| | - Si Min Chan
- Division of Paediatric Infectious Diseases, Khoo Teck Puat-National University Children's Medical Institute, National University Hospital, Singapore
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Hau Yiang Cheong
- Department of Infectious Diseases, Changi General Hospital, Singapore
| | - Ka Lip Chew
- Department of Laboratory Medicine, National University Hospital, Singapore
| | | | - Li Yang Hsu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Adrian Chin Leong Kee
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, National University Hospital, Singapore
| | | | - Rick Twee Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Catherine Wei Min Ong
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, National University Hospital, Singapore
| | - Jessica Lishan Quah
- Department of Respiratory & Critical Care Medicine, Changi General Hospital, Singapore
| | | | - Li Hwei Sng
- Department of Microbiology, Singapore General Hospital, Singapore
| | | | - Cher Heng Tan
- Diagnostic Radiology, Tan Tock Seng Hospital, Singapore
| | - Jun Yang Tay
- Department of Infectious Diseases, National Centre for Infectious Diseases, Singapore
| | - Lynette Li San Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore
- Diagnostic Radiology, Yong Loo Lin School of Medicine, Singapore
| | - Koh Cheng Thoon
- Division of Medicine, KK Women's & Children's Hospital, Singapore
| | | | - Jacinta I-Pei Chen
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | | | | | - Dawn Yi Xin Lee
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Bob Xian Yi Ng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Jia Ying Park
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | | | - Qian Yang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
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Dheda K, Mirzayev F, Cirillo DM, Udwadia Z, Dooley KE, Chang KC, Omar SV, Reuter A, Perumal T, Horsburgh CR, Murray M, Lange C. Multidrug-resistant tuberculosis. Nat Rev Dis Primers 2024; 10:22. [PMID: 38523140 DOI: 10.1038/s41572-024-00504-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/16/2024] [Indexed: 03/26/2024]
Abstract
Tuberculosis (TB) remains the foremost cause of death by an infectious disease globally. Multidrug-resistant or rifampicin-resistant TB (MDR/RR-TB; resistance to rifampicin and isoniazid, or rifampicin alone) is a burgeoning public health challenge in several parts of the world, and especially Eastern Europe, Russia, Asia and sub-Saharan Africa. Pre-extensively drug-resistant TB (pre-XDR-TB) refers to MDR/RR-TB that is also resistant to a fluoroquinolone, and extensively drug-resistant TB (XDR-TB) isolates are additionally resistant to other key drugs such as bedaquiline and/or linezolid. Collectively, these subgroups are referred to as drug-resistant TB (DR-TB). All forms of DR-TB can be as transmissible as rifampicin-susceptible TB; however, it is more difficult to diagnose, is associated with higher mortality and morbidity, and higher rates of post-TB lung damage. The various forms of DR-TB often consume >50% of national TB budgets despite comprising <5-10% of the total TB case-load. The past decade has seen a dramatic change in the DR-TB treatment landscape with the introduction of new diagnostics and therapeutic agents. However, there is limited guidance on understanding and managing various aspects of this complex entity, including the pathogenesis, transmission, diagnosis, management and prevention of MDR-TB and XDR-TB, especially at the primary care physician level.
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Affiliation(s)
- Keertan Dheda
- Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine and UCT Lung Institute & South African MRC/UCT Centre for the Study of Antimicrobial Resistance, University of Cape Town, Cape Town, South Africa.
- Faculty of Infectious and Tropical Diseases, Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK.
| | - Fuad Mirzayev
- Global Tuberculosis Programme, WHO, Geneva, Switzerland
| | - Daniela Maria Cirillo
- Emerging Bacterial Pathogens Unit, IRCCS San Raffaele Scientific Institute Milan, Milan, Italy
| | - Zarir Udwadia
- Department of Pulmonology, Hinduja Hospital & Research Center, Mumbai, India
| | - Kelly E Dooley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kwok-Chiu Chang
- Tuberculosis and Chest Service, Centre for Health Protection, Department of Health, Hong Kong, SAR, China
| | - Shaheed Vally Omar
- Centre for Tuberculosis, National & WHO Supranational TB Reference Laboratory, National Institute for Communicable Diseases, a division of the National Health Laboratory Service, Johannesburg, South Africa
- Department of Molecular Medicine & Haematology, School of Pathology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - Anja Reuter
- Sentinel Project on Paediatric Drug-Resistant Tuberculosis, Boston, MA, USA
| | - Tahlia Perumal
- Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine and UCT Lung Institute & South African MRC/UCT Centre for the Study of Antimicrobial Resistance, University of Cape Town, Cape Town, South Africa
- Faculty of Infectious and Tropical Diseases, Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK
| | - C Robert Horsburgh
- Department of Epidemiology, Boston University Schools of Public Health and Medicine, Boston, MA, USA
| | - Megan Murray
- Department of Epidemiology, Harvard Medical School, Boston, MA, USA
| | - Christoph Lange
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), TTU-TB, Borstel, Germany
- Respiratory Medicine & International Health, University of Lübeck, Lübeck, Germany
- Department of Paediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA
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26
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Feng Q, Zhang G, Chen L, Wu H, Yang Y, Gao Q, Asakawa T, Zhao Y, Lu S, Zhou L, Lu H. Roadmap for ending TB in China by 2035: The challenges and strategies. Biosci Trends 2024; 18:11-20. [PMID: 38325824 DOI: 10.5582/bst.2023.01325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Tuberculosis (TB) is one of the top ten causes of death worldwide, taking the lives of over a million people annually. In addition to being a serious health issue, TB is also closely linked to eradicating poverty according to the Sustainable Development Goals (SDGs) of the United Nations (UN). All UN members have committed to ending the TB epidemic by 2030. China has one of the highest TB loads worldwide, ranking third in the world on many TB burden indices. The national strategy for TB control is aimed at creating a collaborative network and integrating TB treatment into the medical system. According to the WHO's global TB report, China is expected to have 748,000 new cases of TB in 2022 and an incidence of 52 cases per 100,000 people. Ending TB remains a huge challenge and requires comprehensive control strategies in China. In this work, we have discussed the challenges of TB prevention and control in China and proposed specific measures to end TB.
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Affiliation(s)
- Qishun Feng
- National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Guoliang Zhang
- National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Liang Chen
- Guangdong Provincial Research Center for Public Health, Guangdong Provincial Center for Diseases Control and Prevention, Guangzhou, Guangdong, China
| | - Huizhong Wu
- Guangdong Provincial Center for Tuberculosis Control, Guangzhou, Guangdong, China
| | - Yingzhou Yang
- Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong, China
| | - Qian Gao
- National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
- School of Basic Medical Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tetsuya Asakawa
- National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yanlin Zhao
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shuihua Lu
- National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Lin Zhou
- National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
| | - Hongzhou Lu
- National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Tuberculosis, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
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27
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Prommi A, Wongjarit K, Petsong S, Somsukpiroh U, Faksri K, Kawkitinarong K, Payungporn S, Rotcheewaphan S. Co-resistance to isoniazid and second-line anti-tuberculosis drugs in isoniazid-resistant tuberculosis at a tertiary care hospital in Thailand. Microbiol Spectr 2024; 12:e0346223. [PMID: 38323824 PMCID: PMC10913473 DOI: 10.1128/spectrum.03462-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/02/2024] [Indexed: 02/08/2024] Open
Abstract
Isoniazid-resistant tuberculosis (Hr-TB) is an important drug-resistant tuberculosis (TB). In addition to rifampicin, resistance to other medications for Hr-TB can impact the course of treatment; however, there are currently limited data in the literature. In this study, the drug susceptibility profiles of Hr-TB treatment and resistance-conferring mutations were investigated for Hr-TB clinical isolates from Thailand. Phenotypic drug susceptibility testing (pDST) and genotypic drug susceptibility testing (gDST) were retrospectively and prospectively investigated using the Mycobacterium Growth Indicator Tube (MGIT), the broth microdilution (BMD) method, and whole-genome sequencing (WGS)-based gDST. The prevalence of Hr-TB cases was 11.2% among patients with TB. Most Hr-TB cases (89.5%) were newly diagnosed patients with TB. In the pDST analysis, approximately 55.6% (60/108) of the tested Hr-TB clinical isolates exhibited high-level isoniazid resistance. In addition, the Hr-TB clinical isolates presented co-resistance to ethambutol (3/161, 1.9%), levofloxacin (2/96, 2.1%), and pyrazinamide (24/118, 20.3%). In 56 Hr-TB clinical isolates, WGS-based gDST predicted resistance to isoniazid [katG S315T (48.2%) and fabG1 c-15t (26.8%)], rifampicin [rpoB L430P and rpoB L452P (5.4%)], and fluoroquinolones [gyrA D94G (1.8%)], but no mutation for ethambutol was detected. The categorical agreement for the detection of resistance to isoniazid, rifampicin, ethambutol, and levofloxacin between WGS-based gDST and the MGIT or the BMD method ranged from 80.4% to 98.2% or 82.1% to 100%, respectively. pDST and gDST demonstrated a low co-resistance rate between isoniazid and second-line TB drugs in Hr-TB clinical isolates. IMPORTANCE The prevalence of isoniazid-resistant tuberculosis (Hr-TB) is the highest among other types of drug-resistant tuberculosis. Currently, the World Health Organization (WHO) guidelines recommend the treatment of Hr-TB with rifampicin, ethambutol, pyrazinamide, and levofloxacin for 6 months. The susceptibility profiles of Hr-TB clinical isolates, especially when they are co-resistant to second-line drugs, are critical in the selection of the appropriate treatment regimen to prevent treatment failure. This study highlights the susceptibility profiles of the WHO-recommended treatment regimen in Hr-TB clinical isolates from a tertiary care hospital in Thailand and the concordance and importance of using the phenotypic drug susceptibility testing or genotypic drug susceptibility testing for accurate and comprehensive interpretation of results.
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Affiliation(s)
- Ajala Prommi
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Systems Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Kanphai Wongjarit
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Suthidee Petsong
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Ubonwan Somsukpiroh
- Department of Microbiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Kiatichai Faksri
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Research and Diagnostic Center for Emerging Infectious Diseases, Khon Kaen University, Khon Kaen, Thailand
| | - Kamon Kawkitinarong
- Center of Excellence in Tuberculosis, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sunchai Payungporn
- Center of Excellence in Systems Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Suwatchareeporn Rotcheewaphan
- Center of Excellence in Systems Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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28
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Liu A, Liu S, Lv K, Zhu Q, Wen J, Li J, Liang C, Huang X, Gong C, Sun Q, Gu H. Rapid detection of multidrug resistance in tuberculosis using nanopore-based targeted next-generation sequencing: a multicenter, double-blind study. Front Microbiol 2024; 15:1349715. [PMID: 38495513 PMCID: PMC10940340 DOI: 10.3389/fmicb.2024.1349715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/13/2024] [Indexed: 03/19/2024] Open
Abstract
Background Resistance to anti-tuberculous drugs is a major challenge in the treatment of tuberculosis (TB). We aimed to evaluate the clinical availability of nanopore-based targeted next-generation sequencing (NanoTNGS) for the diagnosis of drug-resistant tuberculosis (DR-TB). Methods This study enrolled 253 patients with suspected DR-TB from six hospitals. The diagnostic efficacy of NanoTNGS for detecting Mycobacterium tuberculosis and its susceptibility or resistance to first- and second-line anti-tuberculosis drugs was assessed by comparing conventional phenotypic drug susceptibility testing (pDST) and Xpert MTB/RIF assays. NanoTNGS can be performed within 12 hours from DNA extraction to the result delivery. Results NanoTNGS showed a remarkable concordance rate of 99.44% (179/180) with the culture assay for identifying the Mycobacterium tuberculosis complex. The sensitivity of NanoTNGS for detecting drug resistance was 93.53% for rifampicin, 89.72% for isoniazid, 85.45% for ethambutol, 74.00% for streptomycin, and 88.89% for fluoroquinolones. Specificities ranged from 83.33% to 100% for all drugs tested. Sensitivity for rifampicin-resistant tuberculosis using NanoTNGS increased by 9.73% compared to Xpert MTB/RIF. The most common mutations were S531L (codon in E. coli) in the rpoB gene, S315T in the katG gene, and M306V in the embB gene, conferring resistance to rifampicin, isoniazid, and ethambutol, respectively. In addition, mutations in the pncA gene, potentially contributing to pyrazinamide resistance, were detected in 32 patients. Other prevalent variants, including D94G in the gyrA gene and K43R in the rpsL gene, conferred resistance to fluoroquinolones and streptomycin, respectively. Furthermore, the rv0678 R94Q mutation was detected in one sample, indicating potential resistance to bedaquiline. Conclusion NanoTNGS rapidly and accurately identifies resistance or susceptibility to anti-TB drugs, outperforming traditional methods. Clinical implementation of the technique can recognize DR-TB in time and provide guidance for choosing appropriate antituberculosis agents.
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Affiliation(s)
- Aimei Liu
- Department of Tuberculosis, Guangxi Zhuang Autonomous Region Chest Hospital, Liuzhou, Guangxi, China
| | - Sang Liu
- Department of Tuberculosis, Guangxi Zhuang Autonomous Region Chest Hospital, Liuzhou, Guangxi, China
| | - Kangyan Lv
- Department of Tuberculosis, Guangxi Zhuang Autonomous Region Chest Hospital, Liuzhou, Guangxi, China
| | - Qingdong Zhu
- Department of Tuberculosis, The Fourth People's Hospital of Nanning, Nanning, Guangxi, China
| | - Jun Wen
- Department of Pulmonary Medicine, The Third People's Hospital of Guilin, Guilin, Guangxi, China
| | - Jianpeng Li
- Department of Pulmonary Medicine, The Third People's Hospital of Wuzhou, Wuzhou, Guangxi, China
| | - Chengyuan Liang
- Department of Infectious Diseases, The People's Hospital of Baise, Baise, Guangxi, China
| | - Xuegang Huang
- Department of Infectious Diseases, The First People's Hospital of Fangchenggang, Fangchenggang, Guangxi, China
| | - Chunming Gong
- Department of Tuberculosis, Guangxi Zhuang Autonomous Region Chest Hospital, Liuzhou, Guangxi, China
| | - Qingfeng Sun
- Department of Tuberculosis, Guangxi Zhuang Autonomous Region Chest Hospital, Liuzhou, Guangxi, China
| | - Hongcang Gu
- Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
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29
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Wang Y, Jiang Z, Liang P, Liu Z, Cai H, Sun Q. TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations. BMC Genomics 2024; 25:167. [PMID: 38347478 PMCID: PMC10860279 DOI: 10.1186/s12864-024-10066-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP ), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.
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Affiliation(s)
- Yu Wang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment of the Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Zhonghua Jiang
- Key Laboratory of Bio-Resources and Eco-Environment of the Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Pengkuan Liang
- Key Laboratory of Bio-Resources and Eco-Environment of the Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China
- Zhejiang Yangshengtang Institute of Natural Medication Co., Ltd, Hangzhou, China
| | - Zhuochong Liu
- Key Laboratory of Bio-Resources and Eco-Environment of the Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China
| | - Haoyang Cai
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resources and Eco-Environment of the Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China.
| | - Qun Sun
- Key Laboratory of Bio-Resources and Eco-Environment of the Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China.
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30
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Brunner VM, Fowler PW. Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis. Microb Genom 2024; 10:001187. [PMID: 38315172 PMCID: PMC10926696 DOI: 10.1099/mgen.0.001187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 01/11/2024] [Indexed: 02/07/2024] Open
Abstract
Mutations in Mycobacterium tuberculosis associated with resistance to antibiotics often come with a fitness cost for the bacteria. Resistance to the first-line drug rifampicin leads to lower competitive fitness of M. tuberculosis populations when compared to susceptible populations. This fitness cost, introduced by resistance mutations in the RNA polymerase, can be alleviated by compensatory mutations (CMs) in other regions of the affected protein. CMs are of particular interest clinically since they could lock in resistance mutations, encouraging the spread of resistant strains worldwide. Here, we report the statistical inference of a comprehensive set of CMs in the RNA polymerase of M. tuberculosis, using over 70 000 M. tuberculosis genomes that were collated as part of the CRyPTIC project. The unprecedented size of this data set gave the statistical tests more power to investigate the association of putative CMs with resistance-conferring mutations. Overall, we propose 51 high-confidence CMs by means of statistical association testing and suggest hypotheses for how they exert their compensatory mechanism by mapping them onto the protein structure. In addition, we were able to show an association of CMs with higher in vitro growth densities, and hence presumably with higher fitness, in resistant samples in the more virulent M. tuberculosis lineage 2. Our results suggest the association of CM presence with significantly higher in vitro growth than for wild-type samples, although this association is confounded with lineage and sub-lineage affiliation. Our findings emphasize the integral role of CMs and lineage affiliation in resistance spread and increases the urgency of antibiotic stewardship, which implies accurate, cheap and widely accessible diagnostics for M. tuberculosis infections to not only improve patient outcomes but also prevent the spread of resistant strains.
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Affiliation(s)
| | - Philip W. Fowler
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Headley Way, Oxford, UK
- Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
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31
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Coll F, Gouliouris T, Blane B, Yeats CA, Raven KE, Ludden C, Khokhar FA, Wilson HJ, Roberts LW, Harrison EM, Horner CS, Le TH, Nguyen TH, Nguyen VT, Brown NM, Holmes MA, Parkhill J, Estee Török M, Peacock SJ. Antibiotic resistance determination using Enterococcus faecium whole-genome sequences: a diagnostic accuracy study using genotypic and phenotypic data. THE LANCET. MICROBE 2024; 5:e151-e163. [PMID: 38219758 DOI: 10.1016/s2666-5247(23)00297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 09/04/2023] [Accepted: 09/14/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND DNA sequencing could become an alternative to in vitro antibiotic susceptibility testing (AST) methods for determining antibiotic resistance by detecting genetic determinants associated with decreased antibiotic susceptibility. Here, we aimed to assess and improve the accuracy of antibiotic resistance determination from Enterococcus faecium genomes for diagnosis and surveillance purposes. METHODS In this retrospective diagnostic accuracy study, we first conducted a literature search in PubMed on Jan 14, 2021, to compile a catalogue of genes and mutations predictive of antibiotic resistance in E faecium. We then evaluated the diagnostic accuracy of this database to determine susceptibility to 12 different, clinically relevant antibiotics using a diverse population of 4382 E faecium isolates with available whole-genome sequences and in vitro culture-based AST phenotypes. Isolates were obtained from various sources in 11 countries worldwide between 2000 and 2018. We included isolates tested with broth microdilution, Vitek 2, and disc diffusion, and antibiotics with at least 50 susceptible and 50 resistant isolates. Phenotypic resistance was derived from raw minimum inhibitory concentrations and measured inhibition diameters, and harmonised primarily using the breakpoints set by the European Committee on Antimicrobial Susceptibility Testing. A bioinformatics pipeline was developed to process raw sequencing reads, identify antibiotic resistance genetic determinants, and report genotypic resistance. We used our curated database, as well as ResFinder, AMRFinderPlus, and LRE-Finder, to assess the accuracy of genotypic predictions against phenotypic resistance. FINDINGS We curated a catalogue of 228 genetic markers involved in resistance to 12 antibiotics in E faecium. Very accurate genotypic predictions were obtained for ampicillin (sensitivity 99·7% [95% CI 99·5-99·9] and specificity 97·9% [95·8-99·0]), ciprofloxacin (98·0% [96·4-98·9] and 98·8% [95·9-99·7]), vancomycin (98·8% [98·3-99·2] and 98·8% [98·0-99·3]), and linezolid resistance (after re-testing false negatives: 100·0% [90·8-100·0] and 98·3% [97·8-98·7]). High sensitivity was obtained for tetracycline (99·5% [99·1-99·7]), teicoplanin (98·9% [98·4-99·3]), and high-level resistance to aminoglycosides (97·7% [96·6-98·4] for streptomycin and 96·8% [95·8-97·5] for gentamicin), although at lower specificity (60-90%). Sensitivity was expectedly low for daptomycin (73·6% [65·1-80·6]) and tigecycline (38·3% [27·1-51·0]), for which the genetic basis of resistance is not fully characterised. Compared with other antibiotic resistance databases and bioinformatic tools, our curated database was similarly accurate at detecting resistance to ciprofloxacin and linezolid and high-level resistance to streptomycin and gentamicin, but had better sensitivity for detecting resistance to ampicillin, tigecycline, daptomycin, and quinupristin-dalfopristin, and better specificity for ampicillin, vancomycin, teicoplanin, and tetracycline resistance. In a validation dataset of 382 isolates, similar or improved diagnostic accuracies were also achieved. INTERPRETATION To our knowledge, this work represents the largest published evaluation to date of the accuracy of antibiotic susceptibility predictions from E faecium genomes. The results and resources will facilitate the adoption of whole-genome sequencing as a tool for the diagnosis and surveillance of antimicrobial resistance in E faecium. A complete characterisation of the genetic basis of resistance to last-line antibiotics, and the mechanisms mediating antibiotic resistance silencing, are needed to close the remaining sensitivity and specificity gaps in genotypic predictions. FUNDING Wellcome Trust, UK Department of Health, British Society for Antimicrobial Chemotherapy, Academy of Medical Sciences and the Health Foundation, Medical Research Council Newton Fund, Vietnamese Ministry of Science and Technology, and European Society of Clinical Microbiology and Infectious Disease.
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Affiliation(s)
- Francesc Coll
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK; Parasites & Microbes Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
| | - Theodore Gouliouris
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK.
| | - Beth Blane
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Corin A Yeats
- Centre for Genomic Pathogen Surveillance, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kathy E Raven
- Department of Medicine, University of Cambridge, Cambridge, UK
| | | | - Fahad A Khokhar
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Hayley J Wilson
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Leah W Roberts
- Department of Medicine, University of Cambridge, Cambridge, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Ewan M Harrison
- Parasites & Microbes Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
| | | | - Thi Hoi Le
- National Hospital for Tropical Diseases, Hanoi, Viet Nam; Hanoi Medical University, Hanoi, Viet Nam
| | - Thi Hoa Nguyen
- National Hospital for Tropical Diseases, Hanoi, Viet Nam; Department of Microbiology and National Tuberculosis Reference Laboratory, National Lung Hospital, Hanoi, Viet Nam
| | - Vu Trung Nguyen
- National Hospital for Tropical Diseases, Hanoi, Viet Nam; Hanoi Medical University, Hanoi, Viet Nam
| | - Nicholas M Brown
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; British Society for Antimicrobial Chemotherapy, Birmingham, UK; UK Health Security Agency, Cambridge, UK
| | - Mark A Holmes
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Mili Estee Török
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Sharon J Peacock
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Medicine, University of Cambridge, Cambridge, UK
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Kuruwa S, Zade A, Shah S, Moidu R, Lad S, Chande C, Joshi A, Hirani N, Nikam C, Bhattacharya S, Poojary A, Kapoor M, Kondabagil K, Chatterjee A. An integrated method for targeted Oxford Nanopore sequencing and automated bioinformatics for the simultaneous detection of bacteria, fungi, and ARG. J Appl Microbiol 2024; 135:lxae037. [PMID: 38346849 DOI: 10.1093/jambio/lxae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/26/2024] [Accepted: 02/10/2024] [Indexed: 02/24/2024]
Abstract
AIMS The use of metagenomics for pathogen identification in clinical practice has been limited. Here we describe a workflow to encourage the clinical utility and potential of NGS for the screening of bacteria, fungi, and antimicrobial resistance genes (ARGs). METHODS AND RESULTS The method includes target enrichment, long-read sequencing, and automated bioinformatics. Evaluation of several tools and databases was undertaken across standard organisms (n = 12), clinical isolates (n = 114), and blood samples from patients with suspected bloodstream infections (n = 33). The strategy used could offset the presence of host background DNA, error rates of long-read sequencing, and provide accurate and reproducible detection of pathogens. Eleven targets could be successfully tested in a single assay. Organisms could be confidently identified considering ≥60% of best hits of a BLAST-based threshold of e-value 0.001 and a percent identity of >80%. For ARGs, reads with percent identity of >90% and >60% overlap of the complete gene could be confidently annotated. A kappa of 0.83 was observed compared to standard diagnostic methods. Thus, a workflow for the direct-from-sample, on-site sequencing combined with automated genomics was demonstrated to be reproducible. CONCLUSION NGS-based technologies overcome several limitations of current day diagnostics. Highly sensitive and comprehensive methods of pathogen screening are the need of the hour. We developed a framework for reliable, on-site, screening of pathogens.
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Affiliation(s)
- Sanjana Kuruwa
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Amrutraj Zade
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Sanchi Shah
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Rameez Moidu
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Shailesh Lad
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Chhaya Chande
- Department of Microbiology, Sir J. J. Group of Hospitals, Mumbai 400008, India
| | - Ameeta Joshi
- Department of Microbiology, Sir J. J. Group of Hospitals, Mumbai 400008, India
| | - Nilma Hirani
- Department of Microbiology, Sir J. J. Group of Hospitals, Mumbai 400008, India
| | - Chaitali Nikam
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
- Thyrocare Technologies Pvt. Ltd, Navi Mumbai 400703, India
| | - Sanjay Bhattacharya
- Department of Microbiology, Tata Medical Center, 14, MAR(E-W), DH Block (Newtown), Action Area I, Newtown, Kolkata, Chakpachuria 700160, India
| | - Aruna Poojary
- Department of Microbiology, Breach Candy Hospital and Research Center, Mumbai 400026, India
| | - Mahua Kapoor
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Kiran Kondabagil
- Department of Bioscience and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Anirvan Chatterjee
- HaystackAnalytics Pvt. Ltd, SINE, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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Song Z, He W, Cao X, Ma A, He P, Zhao B, Wang S, Liu C, Zhao Y. The Recent Transmission and Associated Risk Factor of Mycobacterium tuberculosis in Golmud City, China. Infect Drug Resist 2024; 17:417-425. [PMID: 38318210 PMCID: PMC10840525 DOI: 10.2147/idr.s437026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/05/2023] [Indexed: 02/07/2024] Open
Abstract
Background Tuberculosis (TB) remains a severe public health problem globally, and it is essential to comprehend the transmission pattern to control tuberculosis. Herein, we evaluated the drug-resistant characteristics, recent transmission, and associated risk factors of TB in Golmud, Qinghai, China. Methods In this study, we performed a population-based study of patients diagnosed with TB in Golmud from 2013 to 2018. Drug-susceptibility testing and whole-genome sequencing were performed on 133 Mycobacterium tuberculosis strains. The genomic clustering rate was calculated to evaluate the level of recent transmission. Risk factors were identified by logistic regression analysis. Results Our results showed that 46.97% (62/132) of strains were phylogenetically clustered and formed into 23 transmission clusters, suggesting a high recent transmission of TB in the area. 12.78% (17/133) strains were multidrug-resistant/rifampicin tuberculosis (MDR/RR-TB), with a high drug-resistant burden. Based on drug resistance gene analysis, we found 23 strains belonging to genotype MDR/RR-TB, where some strains may have borderline mutations. Among these strains, 65.2% (15/23) were found within putative transmission clusters. Additionally, risk factor analysis showed that recent transmission of TB happened more in patients with Tibetan nationality or older age. Conclusion Overall our study indicates that the recent transmissions of MTB strains, especially genotypic MDR/RR strains, drive the tuberculosis epidemic in Golmud, which could contribute to developing effective TB prevention and control strategies.
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Affiliation(s)
- Zexuan Song
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Wencong He
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Xiaolong Cao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Aijing Ma
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Ping He
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Bing Zhao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Shengfen Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Chunfa Liu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
- Animal Science and Technology College, Beijing University of Agriculture, Huilongguan, Changping, Beijing, 102206, People’s Republic of China
| | - Yanlin Zhao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
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Cannas A, Butera O, Mazzarelli A, Messina F, Vulcano A, Parracino MP, Gualano G, Palmieri F, Di Caro A, Nisii C, Fontana C, Girardi E. Implementation of Whole Genome Sequencing of Tuberculosis Isolates in a Referral Center in Rome: Six Years' Experience in Characterizing Drug-Resistant TB and Disease Transmission. Antibiotics (Basel) 2024; 13:134. [PMID: 38391520 PMCID: PMC10885968 DOI: 10.3390/antibiotics13020134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/16/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024] Open
Abstract
Over the past years, Tuberculosis (TB) control strategies have been effective in reducing drug-resistant (DR) TB globally; however, a wider implementation of new diagnostic strategies, such as Whole genome sequencing (WGS), would be critical for further improvement. The aim of this study, based on WGS of Mycobacterium tuberculosis (MTB) strains isolated in a TB referral center over 6 years, was to evaluate the efficacy of this methodology in improving therapy guidance for clinicians and in improving the understanding of the epidemiology of TB transmission. WGS was performed in addition to pDST on 1001 strains consecutively isolated between January 2016 and December 2021; the results allowed us to improve the quality of data on resistance and to identify possible clusters of transmission. Prediction of rifampicin-resistant (RR) or multi-drug-resistant TB strains (MDR-TB, defined as resistance to at least rifampicin and isoniazid) was obtained for 50 strains (5%). Mutations predictive of an MDR isolate were further characterized, and Ser450Leu and Ser315Thr were found to be the most frequent mutations in rpoB and katG genes, respectively. Discordances between WGS and phenotypic drug susceptibility testing (pDST) were found in few strains, and their impact on clinical decisions and outcome was addressed. The introduction of WGS in our Institute improved our diagnostic routine, allowing accurate patient management, and was a valid instrument for epidemiological investigations and infection control.
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Affiliation(s)
- Angela Cannas
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Ornella Butera
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Antonio Mazzarelli
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Francesco Messina
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Antonella Vulcano
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | | | - Gina Gualano
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Fabrizio Palmieri
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Antonino Di Caro
- Department of Medicine, UniCamillus International University, 00131 Rome, Italy
| | - Carla Nisii
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Carla Fontana
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
| | - Enrico Girardi
- National Institute for Infectious Diseases "Lazzaro Spallanzani"-IRCCS, 00149 Rome, Italy
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Barilar I, Battaglia S, Borroni E, Brandao AP, Brankin A, Cabibbe AM, Carter J, Chetty D, Cirillo DM, Claxton P, Clifton DA, Cohen T, Coronel J, Crook DW, Dreyer V, Earle SG, Escuyer V, Ferrazoli L, Fowler PW, Gao GF, Gardy J, Gharbia S, Ghisi KT, Ghodousi A, Gibertoni Cruz AL, Grandjean L, Grazian C, Groenheit R, Guthrie JL, He W, Hoffmann H, Hoosdally SJ, Hunt M, Iqbal Z, Ismail NA, Jarrett L, Joseph L, Jou R, Kambli P, Khot R, Knaggs J, Koch A, Kohlerschmidt D, Kouchaki S, Lachapelle AS, Lalvani A, Lapierre SG, Laurenson IF, Letcher B, Lin WH, Liu C, Liu D, Malone KM, Mandal A, Mansjö M, Calisto Matias DVL, Meintjes G, de Freitas Mendes F, Merker M, Mihalic M, Millard J, Miotto P, Mistry N, Moore D, Musser KA, Ngcamu D, Nhung HN, Niemann S, Nilgiriwala KS, Nimmo C, O’Donnell M, Okozi N, Oliveira RS, Omar SV, Paton N, Peto TEA, Pinhata JMW, Plesnik S, Puyen ZM, Rabodoarivelo MS, Rakotosamimanana N, Rancoita PMV, Rathod P, Robinson ER, Rodger G, Rodrigues C, Rodwell TC, Roohi A, Santos-Lazaro D, Shah S, Smith G, Kohl TA, Solano W, Spitaleri A, Steyn AJC, Supply P, Surve U, Tahseen S, Thuong NTT, Thwaites G, Todt K, Trovato A, Utpatel C, Van Rie A, Vijay S, Walker AS, Walker TM, Warren R, Werngren J, Wijkander M, Wilkinson RJ, Wilson DJ, Wintringer P, Xiao YX, Yang Y, Yanlin Z, Yao SY, Zhu B. Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach. Nat Commun 2024; 15:488. [PMID: 38216576 PMCID: PMC10786857 DOI: 10.1038/s41467-023-44325-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 12/08/2023] [Indexed: 01/14/2024] Open
Abstract
The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis. However, molecular diagnostics to date have focused largely on first-line drugs and predicting susceptibilities in a binary manner (classifying strains as either susceptible or resistant). Here, we used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration (MIC) in 15,211 Mycobacterium tuberculosis clinical isolates from 23 countries across five continents. We identified 492 unique MIC-elevating variants across 13 drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.
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Zhou M, Liu AM, Yang XB, Guan CP, Zhang YA, Wang MS, Chen YL. The efficacy and safety of high-dose isoniazid-containing therapy for multidrug-resistant tuberculosis: a systematic review and meta-analysis. Front Pharmacol 2024; 14:1331371. [PMID: 38259285 PMCID: PMC10800833 DOI: 10.3389/fphar.2023.1331371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Objectives: Accumulating evidence are available on the efficacy of high-dose isoniazid (INH) for multidrug-resistant tuberculosis (MDR-TB) treatment. We aimed to perform a systematic review and meta-analysis to compare clinical efficacy and safety outcomes of high-dose INH- containing therapy against other regimes. Methods: We searched the following databases PubMed, Embase, Scopus, Web of Science, CINAHL, the Cochrane Library, and ClinicalTrials.gov. We considered and included any studies comparing treatment success, treatment unsuccess, or adverse events in patients with MDR-TB treated with high-dose INH (>300 mg/day or >5 mg/kg/day). Results: Of a total of 3,749 citations screened, 19 studies were included, accounting for 5,103 subjects, the risk of bias was low in all studies. The pooled treatment success, death, and adverse events of high-dose INH-containing therapy was 76.5% (95% CI: 70.9%-81.8%; I2: 92.03%), 7.1% (95% CI: 5.3%-9.1%; I2: 73.75%), and 61.1% (95% CI: 43.0%-77.8%; I2: 98.23%), respectively. The high-dose INH administration is associated with significantly higher treatment success (RR: 1.13, 95% CI: 1.04-1.22; p < 0.01) and a lower risk of death (RR: 0.45, 95% CI: 0.32-0.63; p < 0.01). However, in terms of other outcomes (such as adverse events, and culture conversion rate), no difference was observed between high-dose INH and other treatment options (all p > 0.05). In addition, no publication bias was observed. Conclusion: In MDR-TB patients, high-dose INH administration is associated with a favorable outcome and acceptable adverse-event profile. Systematic review registration: identifier CRD42023438080.
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Affiliation(s)
- Ming Zhou
- Department of Laboratory Medicine, Chest Hospital of Guangxi Zhuang Autonomous Region, Liuzhou, Guangxi, China
| | - Ai-Mei Liu
- Department of Infectious Diseases, Chest Hospital of Guangxi Zhuang Autonomous Region, Liuzhou, Guangxi, China
| | - Xiao-Bing Yang
- Department of Laboratory Medicine, Chest Hospital of Guangxi Zhuang Autonomous Region, Liuzhou, Guangxi, China
| | - Cui-Ping Guan
- Department of Lab Medicine, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong, China
- Shandong Key Laboratory of Infectious Respiratory Disease, Jinan, Shandong, China
| | - Yan-An Zhang
- Shandong Key Laboratory of Infectious Respiratory Disease, Jinan, Shandong, China
- Department of Cardiovascular Surgery, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong, China
| | - Mao-Shui Wang
- Department of Lab Medicine, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong, China
- Shandong Key Laboratory of Infectious Respiratory Disease, Jinan, Shandong, China
| | - Ya-Li Chen
- Department of Lab Medicine, Shandong Public Health Clinical Center, Shandong University, Jinan, Shandong, China
- Shandong Key Laboratory of Infectious Respiratory Disease, Jinan, Shandong, China
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Ou X, Song Z, Zhao B, Pei S, Teng C, Zheng H, He W, Xing R, Wang Y, Wang S, Xia H, Zhou Y, He P, Zhao Y. Diagnostic efficacy of an optimized nucleotide MALDI-TOF-MS assay for anti-tuberculosis drug resistance detection. Eur J Clin Microbiol Infect Dis 2024; 43:105-114. [PMID: 37980301 DOI: 10.1007/s10096-023-04700-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/01/2023] [Indexed: 11/20/2023]
Abstract
PURPOSE We aimed at evaluating the diagnostic efficacy of a nucleotide matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) assay to detect drug resistance of Mycobacterium tuberculosis. METHODS Overall, 263 M. tuberculosis clinical isolates were selected to evaluate the performance of nucleic MALDI-TOF-MS for rifampin (RIF), isoniazid (INH), ethambutol (EMB), moxifloxacin (MXF), streptomycin (SM), and pyrazinamide (PZA) resistance detection. The results for RIF, INH, EMB, and MXF were compared with phenotypic microbroth dilution drug susceptibility testing (DST) and whole-genome sequencing (WGS), and the results for SM and PZA were compared with those obtained by WGS. RESULTS Using DST as the gold standard, the sensitivity, specificity, and kappa values of the MALDI-TOF-MS assay for the detection of resistance were 98.2%, 98.7%, and 0.97 for RIF; 92.8%, 99%, and 0.90 for INH; 82.4%, 98.0%, and 0.82 for EMB; and 92.6%, 99.5%, and 0.94 for MXF, respectively. Compared with WGS as the reference standard, the sensitivity, specificity, and kappa values of the MALDI-TOF-MS assay for the detection of resistance were 97.4%, 100.0%, and 0.98 for RIF; 98.7%, 92.9%, and 0.92 for INH; 96.3%, 100.0%, and 0.98 for EMB; 98.1%, 100.0%, and 0.99 for MXF; 98.0%, 100.0%, and 0.98 for SM; and 50.0%, 100.0%, and 0.65 for PZA. CONCLUSION The nucleotide MALDI-TOF-MS assay yielded highly consistent results compared to DST and WGS, suggesting that it is a promising tool for the rapid detection of sensitivity to RIF, INH, EMB, and MXF.
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Affiliation(s)
- Xichao Ou
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Zexuan Song
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Bing Zhao
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Shaojun Pei
- School of Public Health, Peking University, Beijing, 100191, China
| | - Chong Teng
- Department of Tuberculosis, Beijing Dongcheng District Center for Disease Control, Beijing, 100050, China
| | - Huiwen Zheng
- Laboratory of Respiratory Diseases, Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Beijing Children's Hospital, Capital Medical University, Beijing, 100045, China
| | - Wencong He
- Clinical Laboratory, Beijing Tong Ren Hospital, Capital Medical University, Beijing, 100730, China
| | - Ruida Xing
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Yiting Wang
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Shengfen Wang
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Hui Xia
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Yang Zhou
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Ping He
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China
| | - Yanlin Zhao
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, No. 155 Chang Bai Road, Changping District, Beijing, 102206, People's Republic of China.
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Dorji T, Horan K, Sherry NL, Tay EL, Globan M, Viberg L, Bond K, Denholm JT, Howden BP, Andersson P. Whole genome sequencing of drug-resistant Mycobacterium tuberculosis isolates in Victoria, Australia. Int J Infect Dis 2024; 138:46-53. [PMID: 37967715 DOI: 10.1016/j.ijid.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/17/2023] Open
Abstract
OBJECTIVES Whole genome sequencing (WGS) can identify clusters, transmission patterns, and drug resistance mutations. This is important in low-burden settings such as Australia, as it can assist in efficient contact tracing and surveillance. METHODS We conducted a retrospective cohort study using WGS from 155 genomically defined drug-resistant Mycobacterium tuberculosis (DR-TB) isolates collected between 2018-2021 in Victoria, Australia. Bioinformatic analysis was performed to identify resistance-conferring mutations, lineages, clusters and understand how local sequences compared with international context. RESULTS Of the 155 sequences, 42% were identified as lineage 2 and 35% as lineage 1; 65.8% (102/155) were isoniazid mono-resistant, 8.4% were multi-drug resistant TB and 5.8% were pre-extensively drug-resistant / extensively drug-resistant TB. The most common mutations were observed in katG and fabG1 genes, especially at Ser315Thr and fabG1 -15 C>T for first-line drugs. Ser450Leu was the most frequent mutation in rpoB gene. Phylogenetic analysis confirmed that Victorian DR-TB were associated with importation events. There was little evidence of local transmission with only five isolate pairs. CONCLUSION Isoniazid-resistant TB is the commonest DR-TB in Victoria, and the mutation profile is similar to global circulating DR-TB. Most cases are diagnosed among migrants with limited transmission. This study highlights the value of WGS in identification of clusters and resistance-conferring mutations. This information is crucial in supporting disease mitigation and treatment strategies.
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Affiliation(s)
- Thinley Dorji
- Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Kristy Horan
- Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Norelle L Sherry
- Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia; Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Ee Laine Tay
- Communicable Disease Epidemiology and Surveillance, Health Protection Branch, Public Health Division, Department of Health, Melbourne, Australia
| | - Maria Globan
- Mycobacterium Reference Laboratory, Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Linda Viberg
- Mycobacterium Reference Laboratory, Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Katherine Bond
- Mycobacterium Reference Laboratory, Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia; Royal Melbourne Hospital, Melbourne, Australia
| | - Justin T Denholm
- Royal Melbourne Hospital, Melbourne, Australia; Victorian Tuberculosis Program. Melbourne Health at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia; Department of Infectious Diseases at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Benjamin P Howden
- Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia; Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia; Centre for Pathogen Genomics, University of Melbourne, Australia.
| | - Patiyan Andersson
- Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia; Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology at The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
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García-Marín AM, Cancino-Muñoz I, Torres-Puente M, Villamayor LM, Borrás R, Borrás-Máñez M, Bosque M, Camarena JJ, Colomer-Roig E, Colomina J, Escribano I, Esparcia-Rodríguez O, Gil-Brusola A, Gimeno C, Gimeno-Gascón A, Gomila-Sard B, González-Granda D, Gonzalo-Jiménez N, Guna-Serrano MR, López-Hontangas JL, Martín-González C, Moreno-Muñoz R, Navarro D, Navarro M, Orta N, Pérez E, Prat J, Rodríguez JC, Ruiz-García MM, Vanaclocha H, González-Candelas F, Furió V, Comas I. Role of the first WHO mutation catalogue in the diagnosis of antibiotic resistance in Mycobacterium tuberculosis in the Valencia Region, Spain: a retrospective genomic analysis. THE LANCET. MICROBE 2024; 5:e43-e51. [PMID: 38061383 PMCID: PMC10790317 DOI: 10.1016/s2666-5247(23)00252-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 05/13/2023] [Accepted: 08/04/2023] [Indexed: 01/19/2024]
Abstract
BACKGROUND In June, 2021, WHO published the most complete catalogue to date of resistance-conferring mutations in Mycobacterium tuberculosis. Here, we aimed to assess the performance of genome-based antimicrobial resistance prediction using the catalogue and its potential for improving diagnostics in a real low-burden setting. METHODS In this retrospective population-based genomic study M tuberculosis isolates were collected from 25 clinical laboratories in the low-burden setting of the Valencia Region, Spain. Culture-positive tuberculosis cases reported by regional public health authorities between Jan 1, 2014, and Dec 31, 2016, were included. The drug resistance profiles of these isolates were predicted by the genomic identification, via whole-genome sequencing (WGS), of the high-confidence resistance-causing variants included in the catalogue and compared with the phenotype. We determined the minimum inhibitory concentration (MIC) of the isolates with discordant resistance profiles using the resazurin microtitre assay. FINDINGS WGS was performed on 785 M tuberculosis complex culture-positive isolates, and the WGS resistance prediction sensitivities were: 85·4% (95% CI 70·8-94·4) for isoniazid, 73·3% (44·9-92·2) for rifampicin, 50·0% (21·1-78·9) for ethambutol, and 57·1% (34·0-78·2) for pyrazinamide; all specificities were more than 99·6%. Sensitivity values were lower than previously reported, but the overall pan-susceptibility accuracy was 96·4%. Genotypic analysis revealed that four phenotypically susceptible isolates carried mutations (rpoB Leu430Pro and rpoB Ile491Phe for rifampicin and fabG1 Leu203Leu for isoniazid) known to give borderline resistance in standard phenotypic tests. Additionally, we identified three putative resistance-associated mutations (inhA Ser94Ala, katG Leu48Pro, and katG Gly273Arg for isoniazid) in samples with substantially higher MICs than those of susceptible isolates. Combining both genomic and phenotypic data, in accordance with the WHO diagnostic guidelines, we could detect two new multidrug-resistant cases. Additionally, we detected 11 (1·6%) of 706 isolates to be monoresistant to fluoroquinolone, which had been previously undetected. INTERPRETATION We showed that the WHO catalogue enables the detection of resistant cases missed in phenotypic testing in a low-burden region, thus allowing for better patient-tailored treatment. We also identified mutations not included in the catalogue, relevant at the local level. Evidence from this study, together with future updates of the catalogue, will probably lead in the future to the partial replacement of culture testing with WGS-based drug susceptibility testing in our setting. FUNDING European Research Council and the Spanish Ministerio de Ciencia.
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Affiliation(s)
- Ana María García-Marín
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Valencia, Spain; Joint Research Unit Infección y Salud Pública, FISABIO-University of Valencia, Institute for Integrative Systems Biology, Valencia, Spain
| | - Irving Cancino-Muñoz
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Valencia, Spain; Joint Research Unit Infección y Salud Pública, FISABIO-University of Valencia, Institute for Integrative Systems Biology, Valencia, Spain
| | | | | | - Rafael Borrás
- Microbiology Service, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - María Borrás-Máñez
- Microbiology and Parasitology Service, Hospital Universitario de La Ribera, Alzira, Spain
| | | | - Juan J Camarena
- Microbiology Service, Hospital Universitario Dr Peset, Valencia, Spain
| | - Ester Colomer-Roig
- FISABIO Public Health, Valencia, Spain; Microbiology Service, Hospital Universitario Dr Peset, Valencia, Spain
| | - Javier Colomina
- Microbiology Service, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Isabel Escribano
- Microbiology Laboratory, Hospital Virgen de los Lirios, Alcoy, Spain
| | | | - Ana Gil-Brusola
- Microbiology Service, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Concepción Gimeno
- Microbiology Service, Hospital General Universitario de Valencia, Valencia, Spain
| | | | - Bárbara Gomila-Sard
- Microbiology Service, Hospital General Universitario de Castellón, Castellón, Spain
| | | | | | | | | | - Coral Martín-González
- Microbiology Service, Hospital Universitario de San Juan de Alicante, Alicante, Spain
| | - Rosario Moreno-Muñoz
- Microbiology Service, Hospital General Universitario de Castellón, Castellón, Spain
| | - David Navarro
- Microbiology Service, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - María Navarro
- Microbiology Service, Hospital de la Vega Baixa, Orihuela, Spain
| | - Nieves Orta
- Microbiology Service, Hospital Francesc de Borja, Gandía, Spain
| | - Elvira Pérez
- Subdirección General de Epidemiología y Vigilancia de la Salud y Sanidad Ambiental de Valencia, Valencia, Spain
| | - Josep Prat
- Microbiology Service, Hospital de Sagunto, Sagunto, Spain
| | | | | | - Hermelinda Vanaclocha
- Subdirección General de Epidemiología y Vigilancia de la Salud y Sanidad Ambiental de Valencia, Valencia, Spain
| | - Fernando González-Candelas
- Joint Research Unit Infección y Salud Pública, FISABIO-University of Valencia, Institute for Integrative Systems Biology, Valencia, Spain; CIBER of Epidemiology and Public Health, Madrid, Spain
| | - Victoria Furió
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Valencia, Spain.
| | - Iñaki Comas
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia, Valencia, Spain; CIBER of Epidemiology and Public Health, Madrid, Spain
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Dreyer V, Sonnenkalb L, Diricks M, Utpatel C, Barilar I, Mohr V, Niemann S, Kohl TA, Merker M. Use of Whole Genome Sequencing for Mycobacterium tuberculosis Complex Antimicrobial Susceptibility Testing: From Sequence Data to Resistance Profiles. Methods Mol Biol 2024; 2833:195-210. [PMID: 38949712 DOI: 10.1007/978-1-0716-3981-8_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Whole genome sequencing of Mycobacterium tuberculosis complex (MTBC) isolates has been shown to provide accurate predictions for resistance and susceptibility for many first- and second-line anti-tuberculosis drugs. However, bioinformatic pipelines and mutation catalogs to predict antimicrobial resistances in MTBC isolates are often customized and detailed protocols are difficult to access. Here, we provide a step-by-step workflow for the processing and interpretation of short-read sequencing data and give an overview of available analysis pipelines.
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Affiliation(s)
- Viola Dreyer
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Lindsay Sonnenkalb
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Margo Diricks
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Christian Utpatel
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Ivan Barilar
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Vanessa Mohr
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Stefan Niemann
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Thomas A Kohl
- Molecular and Experimental Mycobacteriology, Research Center Borstel - Leibniz Lung Center, Borstel, Germany
| | - Matthias Merker
- Evolution of the Resistome, Research Center Borstel - Leibniz Lung Center, Borstel, Germany.
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Pronyk PM, de Alwis R, Rockett R, Basile K, Boucher YF, Pang V, Sessions O, Getchell M, Golubchik T, Lam C, Lin R, Mak TM, Marais B, Twee-Hee Ong R, Clapham HE, Wang L, Cahyorini Y, Polotan FGM, Rukminiati Y, Sim E, Suster C, Smith GJD, Sintchenko V. Advancing pathogen genomics in resource-limited settings. CELL GENOMICS 2023; 3:100443. [PMID: 38116115 PMCID: PMC10726422 DOI: 10.1016/j.xgen.2023.100443] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Genomic sequencing has emerged as a powerful tool to enhance early pathogen detection and characterization with implications for public health and clinical decision making. Although widely available in developed countries, the application of pathogen genomics among low-resource, high-disease burden settings remains at an early stage. In these contexts, tailored approaches for integrating pathogen genomics within infectious disease control programs will be essential to optimize cost efficiency and public health impact. We propose a framework for embedding pathogen genomics within national surveillance plans across a spectrum of surveillance and laboratory capacities. We adopt a public health approach to genomics and examine its application to high-priority diseases relevant in resource-limited settings. For each grouping, we assess the value proposition for genomics to inform public health and clinical decision-making, alongside its contribution toward research and development of novel diagnostics, therapeutics, and vaccines.
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Affiliation(s)
- Paul Michael Pronyk
- Centre for Outbreak Preparedness, Duke-NUS Medical School, Singapore 169857, Singapore.
| | - Ruklanthi de Alwis
- Centre for Outbreak Preparedness, Duke-NUS Medical School, Singapore 169857, Singapore; Emerging Infectious Diseases Programme, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Rebecca Rockett
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia; Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Kerri Basile
- Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
| | - Yann Felix Boucher
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore; Infectious Diseases Translational Research Programme, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 117549, Singapore; Singapore Centre for Environmental Life Sciences Engineering, National University of Singapore, Singapore 117549, Singapore; Nanyang Technological University, Singapore 639798, Singapore
| | - Vincent Pang
- Centre for Outbreak Preparedness, Duke-NUS Medical School, Singapore 169857, Singapore
| | - October Sessions
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Marya Getchell
- Centre for Outbreak Preparedness, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Tanya Golubchik
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia; Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia; Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK
| | - Connie Lam
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia; Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Raymond Lin
- National Public Health Laboratory, National Centre for Infectious Diseases, Singapore 308442, Singapore
| | - Tze-Minn Mak
- Bioinformatics Institute, Agency for Science, Technology and Research, Singapore 138671, Singapore
| | - Ben Marais
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Hannah Eleanor Clapham
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Linfa Wang
- Emerging Infectious Diseases Programme, Duke-NUS Medical School, Singapore 169857, Singapore; Programme for Research in Epidemic Preparedness and Response (PREPARE), Ministry of Health, Singapore 169854, Singapore
| | - Yorin Cahyorini
- Center for Health Resilience and Resource Policy, Ministry of Health, Jakarta 12950, Indonesia
| | - Francisco Gerardo M Polotan
- Molecular Biology Laboratory, Research Institute for Tropical Medicine, Muntinlupa 1781, Metro Manila, Philippines
| | - Yuni Rukminiati
- Center for Health Resilience and Resource Policy, Ministry of Health, Jakarta 12950, Indonesia
| | - Eby Sim
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia; Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Carl Suster
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia; Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Gavin J D Smith
- Emerging Infectious Diseases Programme, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Vitali Sintchenko
- Sydney Infectious Diseases Institute, The University of Sydney, Camperdown, NSW 2006, Australia; Centre for Infectious Diseases and Microbiology - Public Health, Westmead Hospital, Westmead, NSW 2145, Australia; Centre for Infectious Diseases and Microbiology Laboratory Services, NSW Health Pathology - Institute of Clinical Pathology and Medical Research, Westmead, NSW 2145, Australia
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Silcocks M, Chang X, Thuong Thuong NT, Qin Y, Minh Ha DT, Khac Thai PV, Vijay S, Anh Thu DD, Ngoc Ha VT, Ngoc Nhung H, Huu Lan N, Quynh Nhu NT, Edwards D, Nath A, Pham K, Duc Bang N, Hong Chau TT, Thwaites G, Heemskerk AD, Chuen Khor C, Teo YY, Inouye M, Ong RTH, Caws M, Holt KE, Dunstan SJ. Evolution and transmission of antibiotic resistance is driven by Beijing lineage Mycobacterium tuberculosis in Vietnam. Microbiol Spectr 2023; 11:e0256223. [PMID: 37971428 PMCID: PMC10714959 DOI: 10.1128/spectrum.02562-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/12/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Drug-resistant tuberculosis (TB) infection is a growing and potent concern, and combating it will be necessary to achieve the WHO's goal of a 95% reduction in TB deaths by 2035. While prior studies have explored the evolution and spread of drug resistance, we still lack a clear understanding of the fitness costs (if any) imposed by resistance-conferring mutations and the role that Mtb genetic lineage plays in determining the likelihood of resistance evolution. This study offers insight into these questions by assessing the dynamics of resistance evolution in a high-burden Southeast Asian setting with a diverse lineage composition. It demonstrates that there are clear lineage-specific differences in the dynamics of resistance acquisition and transmission and shows that different lineages evolve resistance via characteristic mutational pathways.
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Affiliation(s)
- Matthew Silcocks
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Xuling Chang
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, , Singapore
- Khoo Teck Puat–National University Children’s Medical Institute, National University Health System, Singapore
| | - Nguyen Thuy Thuong Thuong
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Youwen Qin
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Dang Thi Minh Ha
- Pham Ngoc Thach Hospital for TB and Lung Disease, District 5, Ho Chi Minh City, Vietnam
| | - Phan Vuong Khac Thai
- Pham Ngoc Thach Hospital for TB and Lung Disease, District 5, Ho Chi Minh City, Vietnam
| | - Srinivasan Vijay
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
- Theoretical Microbial Ecology, Friedrich Schiller University Jena, Jena, Germany
| | - Do Dang Anh Thu
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
| | - Vu Thi Ngoc Ha
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
| | - Hoang Ngoc Nhung
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
| | - Nguyen Huu Lan
- Pham Ngoc Thach Hospital for TB and Lung Disease, District 5, Ho Chi Minh City, Vietnam
| | - Nguyen Thi Quynh Nhu
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
| | - David Edwards
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Artika Nath
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Kym Pham
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Nguyen Duc Bang
- Pham Ngoc Thach Hospital for TB and Lung Disease, District 5, Ho Chi Minh City, Vietnam
| | - Tran Thi Hong Chau
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
| | - Guy Thwaites
- Oxford University Clinical Research Unit, Hospital for Tropical Diseases, District 5, Ho Chi Minh City, Vietnam
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - A. Dorothee Heemskerk
- Department of Medical Microbiology and Infection Prevention, Amsterdam University Medical Centre, Amsterdam, Netherlands
| | | | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Public Health and Primary Care, Cambridge Baker Systems Genomics Initiative, University of Cambridge, Cambridge, United Kingdom
| | - Rick Twee-Hee Ong
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Maxine Caws
- Liverpool School of Tropical Medicine, Liverpool, United Kingdom
- Birat Nepal Medical Trust, Kathmandu, Nepal
| | - Kathryn E. Holt
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Sarah J. Dunstan
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
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Zhang X, Martinez E, Lam C, Crighton T, Sim E, Gall M, Donnan EJ, Marais BJ, Sintchenko V. Exploring programmatic indicators of tuberculosis control that incorporate routine Mycobacterium tuberculosis sequencing in low incidence settings: a comprehensive (2017-2021) patient cohort analysis. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 41:100910. [PMID: 37808343 PMCID: PMC10550799 DOI: 10.1016/j.lanwpc.2023.100910] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/02/2023] [Accepted: 09/06/2023] [Indexed: 10/10/2023]
Abstract
Background Routine whole genome sequencing of Mycobacterium tuberculosis has been implemented with increasing frequency. However, its value for tuberculosis (TB) control programs beyond individual case management and enhanced drug resistance detection has not yet been explored. Methods We analysed routine sequencing data of culture-confirmed TB cases notified between 1st January 2017 and 31st December 2021 in New South Wales (NSW), Australia. Genomic surveillance included evidence of local TB transmission, defined by single nucleotide polymorphism (SNP) clustering over a variable (0-25) SNP threshold, and drug resistance conferring mutations. Findings M. tuberculosis sequences from 1831 patients were examined, representing 64.8% of all notified TB cases and 96.2% of culture-confirmed cases. Applying a traditional 5-SNP cluster threshold identified 62 transmission clusters with 183 clustered cases; 101/183 (55.2%) had 0 SNP differences. Cluster assessment over a 5-year period, using a 5-SNP threshold, provided a comprehensive overview of likely recent transmission within NSW, Australia, as an indicator of local TB control. Genotypic drug susceptibility testing (DST) was highly concordant with phenotypic DST and provided a 6.8% increase in antimycobacterial resistance detection. Importantly, it detected mutations missed by routine molecular tests. Lineage 2 strains were more likely to be drug resistant (p < 0.0001) and locally transmitted if drug resistant (p < 0.0001). Interpretation Performing routine prospective WGS in a low incidence country like Australia, provides genomically informed programmatic indicators of local TB control. A rolling 5-year cluster assessment reflects epidemic containment and progress towards 'zero TB transmission'. Genomic DST also provides valuable information for clinical care and drug resistance surveillance. Funding NHMRC Centre for Research Excellence in Tuberculosis (www.tbcre.org.au) and NSW Health Prevention Research Support Program.
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Affiliation(s)
- Xiaomei Zhang
- Centre for Research Excellence in Tuberculosis (TB-CRE), Centenary Institute, Sydney, New South Wales, Australia
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, New South Wales, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, New South Wales, Australia
| | - Elena Martinez
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, New South Wales, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, New South Wales, Australia
- NSW Mycobacterium Reference Laboratory, Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology - Western, Sydney, New South Wales, Australia
| | - Connie Lam
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, New South Wales, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, New South Wales, Australia
| | - Taryn Crighton
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, New South Wales, Australia
- NSW Mycobacterium Reference Laboratory, Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology - Western, Sydney, New South Wales, Australia
| | - Eby Sim
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, New South Wales, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, New South Wales, Australia
| | - Mailie Gall
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, New South Wales, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, New South Wales, Australia
| | - Ellen J. Donnan
- New South Wales Tuberculosis Program, Health Protection NSW, Sydney, New South Wales, Australia
| | - Ben J. Marais
- Centre for Research Excellence in Tuberculosis (TB-CRE), Centenary Institute, Sydney, New South Wales, Australia
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, New South Wales, Australia
| | - Vitali Sintchenko
- Centre for Research Excellence in Tuberculosis (TB-CRE), Centenary Institute, Sydney, New South Wales, Australia
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, New South Wales, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, New South Wales, Australia
- NSW Mycobacterium Reference Laboratory, Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology - Western, Sydney, New South Wales, Australia
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Rao M, Wollenberg K, Harris M, Kulavalli S, Thomas L, Chawla K, Shenoy VP, Varma M, Saravu K, Hande HM, Shanthigrama Vasudeva CS, Jeffrey B, Gabrielian A, Rosenthal A. Lineage classification and antitubercular drug resistance surveillance of Mycobacterium tuberculosis by whole-genome sequencing in Southern India. Microbiol Spectr 2023; 11:e0453122. [PMID: 37671895 PMCID: PMC10580826 DOI: 10.1128/spectrum.04531-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 07/03/2023] [Indexed: 09/07/2023] Open
Abstract
IMPORTANCE Studies mapping genetic heterogeneity of clinical isolates of M. tuberculosis for determining their strain lineage and drug resistance by whole-genome sequencing are limited in high tuberculosis burden settings. We carried out whole-genome sequencing of 242 M. tuberculosis isolates from drug-sensitive and drug-resistant tuberculosis patients, identified and collected as part of the TB Portals Program, to have a comprehensive insight into the genetic diversity of M. tuberculosis in Southern India. We report several genetic variations in M. tuberculosis that may confer resistance to antitubercular drugs. Further wide-scale efforts are required to fully characterize M. tuberculosis genetic diversity at a population level in high tuberculosis burden settings for providing precise tuberculosis treatment.
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Affiliation(s)
- Mahadev Rao
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Kurt Wollenberg
- Department of Health and Human Services, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Michael Harris
- Department of Health and Human Services, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Shrivathsa Kulavalli
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Levin Thomas
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Kiran Chawla
- Department of Microbiology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Vishnu Prasad Shenoy
- Department of Microbiology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Muralidhar Varma
- Department of Infectious Diseases, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Kavitha Saravu
- Department of Infectious Diseases, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - H. Manjunatha Hande
- Department of Medicine, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | | | - Brendan Jeffrey
- Department of Health and Human Services, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Andrei Gabrielian
- Department of Health and Human Services, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Alex Rosenthal
- Department of Health and Human Services, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
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Auganova D, Atavliyeva S, Amirgazin A, Akisheva A, Tsepke A, Tarlykov P. Genomic Characterization of Drug-Resistant Mycobacterium tuberculosis L2/Beijing Isolates from Astana, Kazakhstan. Antibiotics (Basel) 2023; 12:1523. [PMID: 37887224 PMCID: PMC10604462 DOI: 10.3390/antibiotics12101523] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 09/25/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023] Open
Abstract
Kazakhstan ranks among the countries with the highest number of MDR-TB patients per 100,000 population worldwide. The successful transmission of local MDR strains of Mycobacterium tuberculosis (Mtb) poses a significant threat to disease control. In this study, we employed whole-genome sequencing to examine drug resistance, compensatory mutations, population structure, and transmission patterns in a sample of 24 clinical isolates of L2/Beijing Mtb collected in Astana, Kazakhstan between 2021 and 2022. The genotypic prediction of Mtb susceptibility to anti-TB agents was consistent with the phenotypic susceptibility, except for bedaquiline. An analysis of resistance-associated genes characterized most of the isolates as pre-extensively drug-resistant tuberculosis (pre-XDR-TB) (n = 15; 62.5%). The phylogenetic analysis grouped the isolates into four transmission clusters; the dominant cluster was assigned to the "aggressive" Central Asia outbreak (CAO) clade of L2/Beijing (n = 15; 62.5%). Thirteen mutations with putative compensatory effects were observed exclusively in Mtb isolates containing the rpoB S450L mutation. The putative compensatory mutations had a stabilizing effect on RpoABC protein stability and dynamics. The high prevalence of the CAO clade in the population structure of Mtb may explain the rapid spread of MDR-TB in Kazakhstan.
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Affiliation(s)
- Dana Auganova
- National Center for Biotechnology, Astana 010000, Kazakhstan (A.A.)
| | | | | | - Akmaral Akisheva
- City Center for Phthisiopulmonology of the Akimat of Astana, Astana 010000, Kazakhstan
| | - Anna Tsepke
- City Center for Phthisiopulmonology of the Akimat of Astana, Astana 010000, Kazakhstan
| | - Pavel Tarlykov
- National Center for Biotechnology, Astana 010000, Kazakhstan (A.A.)
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Koleske BN, Jacobs WR, Bishai WR. The Mycobacterium tuberculosis genome at 25 years: lessons and lingering questions. J Clin Invest 2023; 133:e173156. [PMID: 37781921 PMCID: PMC10541200 DOI: 10.1172/jci173156] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023] Open
Abstract
First achieved in 1998 by Cole et al., the complete genome sequence of Mycobacterium tuberculosis continues to provide an invaluable resource to understand tuberculosis (TB), the leading cause of global infectious disease mortality. At the 25-year anniversary of this accomplishment, we describe how insights gleaned from the M. tuberculosis genome have led to vital tools for TB research, epidemiology, and clinical practice. The increasing accessibility of whole-genome sequencing across research and clinical settings has improved our ability to predict antibacterial susceptibility, to track epidemics at the level of individual outbreaks and wider historical trends, to query the efficacy of the bacille Calmette-Guérin (BCG) vaccine, and to uncover targets for novel antitubercular therapeutics. Likewise, we discuss several recent efforts to extract further discoveries from this powerful resource.
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Affiliation(s)
- Benjamin N. Koleske
- Center for Tuberculosis Research, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - William R. Jacobs
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - William R. Bishai
- Center for Tuberculosis Research, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Carter J. Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach. RESEARCH SQUARE 2023:rs.3.rs-3378915. [PMID: 37886522 PMCID: PMC10602118 DOI: 10.21203/rs.3.rs-3378915/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis; however, molecular diagnostics to date have focused largely on first-line drugs and predicting binary susceptibilities. We used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration in 15,211 Mycobacterium tuberculosis patient isolates from 23 countries across five continents. This identified 492 unique MIC-elevating variants across thirteen drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.
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Vīksna A, Sadovska D, Berge I, Bogdanova I, Vaivode A, Freimane L, Norvaiša I, Ozere I, Ranka R. Genotypic and phenotypic comparison of drug resistance profiles of clinical multidrug-resistant Mycobacterium tuberculosis isolates using whole genome sequencing in Latvia. BMC Infect Dis 2023; 23:638. [PMID: 37770850 PMCID: PMC10540372 DOI: 10.1186/s12879-023-08629-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/19/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Multidrug-resistant tuberculosis (MDR-TB) remains a major public health problem in many high tuberculosis (TB) burden countries. Phenotypic drug susceptibility testing (DST) take several weeks or months to result, but line probe assays and Xpert/Rif Ultra assay detect a limited number of resistance conferring gene mutations. Whole genome sequencing (WGS) is an advanced molecular testing method which theoretically can predict the resistance of M. tuberculosis (Mtb) isolates to all anti-TB agents through a single analysis. METHODS Here, we aimed to identify the level of concordance between the phenotypic and WGS-based genotypic drug susceptibility (DS) patterns of MDR-TB isolates. Overall, data for 12 anti-TB medications were analyzed. RESULTS In total, 63 MDR-TB Mtb isolates were included in the analysis, representing 27.4% of the total number of MDR-TB cases in Latvia in 2012-2014. Among them, five different sublineages were detected, and 2.2.1 (Beijing group) and 4.3.3 (Latin American-Mediterranean group) were the most abundant. There were 100% agreement between phenotypic and genotypic DS pattern for isoniazid, rifampicin, and linezolid. High concordance rate (> 90%) between phenotypic and genotypic DST results was detected for ofloxacin (93.7%), pyrazinamide (93.7%) and streptomycin (95.4%). Phenotypic and genotypic DS patterns were poorly correlated for ethionamide (agreement 56.4%), ethambutol (85.7%), amikacin (82.5%), capreomycin (81.0%), kanamycin (85.4%), and moxifloxacin (77.8%). For capreomycin, resistance conferring mutations were not identified in several phenotypically resistant isolates, and, in contrary, for ethionamide, ethambutol, amikacin, kanamycin, and moxifloxacin the resistance-related mutations were identified in several phenotypically sensitive isolates. CONCLUSIONS WGS is a valuable tool for rapid genotypic DST for all anti-TB agents. For isoniazid and rifampicin phenotypic DST potentially can be replaced by genotypic DST based on 100% agreement between the tests. However, discrepant results for other anti-TB agents limit their prescription based solely on WGS data. For clinical decision, at the current level of knowledge, there is a need for combination of genotypic DST with modern, validated phenotypic DST methodologies for those medications which did not showed 100% agreement between the methods.
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Affiliation(s)
- Anda Vīksna
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
- Rīga Stradiņš University, Riga, Latvia
| | - Darja Sadovska
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia
| | - Iveta Berge
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
| | - Ineta Bogdanova
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
| | - Annija Vaivode
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia
| | - Lauma Freimane
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia
| | - Inga Norvaiša
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
| | - Iveta Ozere
- Riga East Clinical University Hospital, Centre of Tuberculosis and Lung Diseases, Ropaži Municipality, Stopiņi Parish, Upeslejas, Latvia
- Rīga Stradiņš University, Riga, Latvia
| | - Renāte Ranka
- Latvian Biomedical Research and Study Centre, Ratsupites Str. 1, Riga, LV-1067, Latvia.
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Song Z, Liu C, He W, Pei S, Liu D, Cao X, Wang Y, He P, Zhao B, Ou X, Xia H, Wang S, Zhao Y. Insight into the drug-resistant characteristics and genetic diversity of multidrug-resistant Mycobacterium tuberculosis in China. Microbiol Spectr 2023; 11:e0132423. [PMID: 37732780 PMCID: PMC10581218 DOI: 10.1128/spectrum.01324-23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/16/2023] [Indexed: 09/22/2023] Open
Abstract
Multidrug-resistant tuberculosis (MDR-TB) has a severe impact on public health. To investigate the drug-resistant profile, compensatory mutations and genetic variations among MDR-TB isolates, a total of 546 MDR-TB isolates from China underwent drug-susceptibility testing and whole genome sequencing for further analysis. The results showed that our isolates have a high rate of fluoroquinolone resistance (45.60%, 249/546) and a low proportion of conferring resistance to bedaquiline, clofazimine, linezolid, and delamanid. The majority of MDR-TB isolates (77.66%, 424/546) belong to Lineage 2.2.1, followed by Lineage 4.5 (6.41%, 35/546), and the Lineage 2 isolates have a strong association with pre-XDR/XDR-TB (P < 0.05) in our study. Epidemic success analysis using time-scaled haplotypic density (THD) showed that clustered isolates outperformed non-clustered isolates. Compensatory mutations happened in rpoA, rpoC, and non-RRDR of rpoB genes, which were found more frequently in clusters and were associated with the increase of THD index, suggesting that increased bacterial fitness was associated with MDR-TB transmission. In addition, the variants in resistance associated genes in MDR isolates are mainly focused on single nucleotide polymorphism mutations, and only a few genes have indel variants, such as katG, ethA. We also found some genes underwent indel variation correlated with the lineage and sub-lineage of isolates, suggesting the selective evolution of different lineage isolates. Thus, this analysis of the characterization and genetic diversity of MDR isolates would be helpful in developing effective strategies for treatment regimens and tailoring public interventions. IMPORTANCE Multidrug-resistant tuberculosis (MDR-TB) is a serious obstacle to tuberculosis prevention and control in China. This study provides insight into the drug-resistant characteristics of MDR combined with phenotypic drug-susceptibility testing and whole genome sequencing. The compensatory mutations and epidemic success analysis were analyzed by time-scaled haplotypic density (THD) method, suggesting clustered isolates and compensatory mutations are associated with MDR-TB transmission. In addition, the insertion and deletion variants happened in some genes, which are associated with the lineage and sub-lineage of isolates, such as the mpt64 gene. This study offered a valuable reference and increased understanding of MDR-TB in China, which could be crucial for achieving the objective of precision medicine in the prevention and treatment of MDR-TB.
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Affiliation(s)
- Zexuan Song
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chunfa Liu
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wencong He
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shaojun Pei
- School of Public Health, Peking University, Beijing, China
| | - Dongxin Liu
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaolong Cao
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yiting Wang
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ping He
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bing Zhao
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xichao Ou
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hui Xia
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shengfen Wang
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanlin Zhao
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, China
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He CJ, Wan JL, Luo SF, Guo RJ, Paerhati P, Cheng X, Duan CH, Xu AM. Comparative Study on Tuberculosis Drug Resistance and Molecular Detection Methods Among Different Mycobacterium Tuberculosis Lineages. Infect Drug Resist 2023; 16:5941-5951. [PMID: 37700800 PMCID: PMC10494918 DOI: 10.2147/idr.s423390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Purpose This study aims to compare drug resistance and detection efficacy across different Mycobacterium tuberculosis lineages, offering insights for precise treatment and molecular diagnosis. Methods 161 strains of Mycobacterium tuberculosis (M.tb) were tested for drug resistance using Phenotypic Drug Susceptibility Testing (pDST), High-Resolution Melting analysis (HRM), and Whole Genome Sequencing (WGS) methods. The main focus was on evaluating the accuracy of different methods for detecting resistance to rifampicin (RIF), isoniazid (INH), and streptomycin (SM). Results Among the 161 strains of M.tb, 83.85% (135/161) were fully sensitive to RIF, INH, and SM according to pDST, and the rate of multidrug resistance was 4.35% (7/161). The drug resistance rates of lineage 2 M.tb to the three drugs (26/219, 11.87%) were significantly higher than those of non-lineage 2 M.tb (12/264, 4.45%) (P<0.05). Compared with pDST, WGS had a sensitivity of 100%, 94.12%, and 92.31% and a specificity of 100%, 99.31%, and 98.65% for RIF, INH, and SM, respectively, with no significant difference. The sensitivity of HRM for RIF, INH, and SM was 87.50%, 52.94%, and 76.92%, respectively, while the specificity was 96.08%, 99.31%, and 99.32%, respectively. The sensitivity of HRM for detecting INH resistance was significantly lower than that of pDST (P=0.039). Compared with HRM, WGS increased the sensitivity of RIF, INH, and SM by 12.50%, 41.18%, and 15.38%, respectively. Conclusion There are significant differences in drug resistance rates among different lineages of M.tb, with lineage 2 having higher rates of RIF, INH, and SM resistance than lineages 3 and 4. The sensitivity of HRM is far lower than that of pDST, and currently, the accuracy of HRM is not sufficient to replace pDST. WGS has no significant difference in detecting drug resistance compared with pDST but can identify new anti-tuberculosis drug-resistant mutations, providing effective guidance for clinical decision-making.
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Affiliation(s)
- Chuan-Jiang He
- Department of Laboratory Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People’s Republic of China
- Department of Laboratory Medicine, The First People’s Hospital of Kashgar, Kashgar, 844000, People’s Republic of China
| | - Jiang-Li Wan
- Department of Laboratory Medicine, The First People’s Hospital of Kashgar, Kashgar, 844000, People’s Republic of China
| | - Sheng-Fang Luo
- Department of Laboratory Medicine, The First People’s Hospital of Kashgar, Kashgar, 844000, People’s Republic of China
| | - Rui-Jie Guo
- Department of Laboratory Medicine, The First People’s Hospital of Kashgar, Kashgar, 844000, People’s Republic of China
| | - Pawuziye Paerhati
- Department of Laboratory Medicine, The First People’s Hospital of Kashgar, Kashgar, 844000, People’s Republic of China
| | - Xiang Cheng
- Department of Laboratory Medicine, The First People’s Hospital of Kashgar, Kashgar, 844000, People’s Republic of China
| | - Chao-Hui Duan
- Department of Laboratory Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, People’s Republic of China
| | - Ai-Min Xu
- Department of Laboratory Medicine, The First People’s Hospital of Kashgar, Kashgar, 844000, People’s Republic of China
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