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Shao Z, Tam KKG, Achalla VPK, Woon ECY, Mason AJ, Chow SF, Yam WC, Lam JKW. Synergistic combination of antimicrobial peptide and isoniazid as inhalable dry powder formulation against multi-drug resistant tuberculosis. Int J Pharm 2024; 654:123960. [PMID: 38447778 DOI: 10.1016/j.ijpharm.2024.123960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/24/2024] [Accepted: 02/28/2024] [Indexed: 03/08/2024]
Abstract
Multidrug-resistant tuberculosis (MDR-TB) has posed a serious threat to global public health, and antimicrobial peptides (AMPs) have emerged to be promising candidates to tackle this deadly infectious disease. Previous study has suggested that two AMPs, namely D-LAK120-A and D-LAK120-HP13, can potentiate the effect of isoniazid (INH) against mycobacteria. In this study, the strategy of combining INH and D-LAK peptide as a dry powder formulation for inhalation was explored. The antibacterial effect of INH and D-LAK combination was first evaluated on three MDR clinical isolates of Mycobacteria tuberculosis (Mtb). The minimum inhibitory concentrations (MICs) and fractional inhibitory concentration indexes (FICIs) were determined. The combination was synergistic against Mtb with FICIs ranged from 0.25 to 0.38. The INH and D-LAK peptide at 2:1 mole ratio (equivalent to 1: 10 mass ratio) was identified to be optimal. This ratio was adopted for the preparation of dry powder formulation for pulmonary delivery, with mannitol used as bulking excipient. Spherical particles with mass median aerodynamic diameter (MMAD) of around 5 µm were produced by spray drying. The aerosol performance of the spray dried powder was moderate, as evaluated by the Next Generation Impactor (NGI), with emitted fraction and fine particle fraction of above 70 % and 45 %, respectively. The circular dichroism spectra revealed that both D-LAK peptides retained their secondary structure after spray drying, and the antibacterial effect of the combination against the MDR Mtb clinical isolates was successfully preserved. The combination was found to be effective against MDR Mtb isolates with KatG or InhA mutations. Overall, the synergistic combination of INH with D-LAK peptide formulated as inhaled dry powder offers a new therapeutic approach against MDR-TB.
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Affiliation(s)
- Zitong Shao
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; UCL School of Pharmacy, University College London, United Kingdom
| | - Kingsley King-Gee Tam
- Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - V P K Achalla
- UCL School of Pharmacy, University College London, United Kingdom
| | - Esther C Y Woon
- UCL School of Pharmacy, University College London, United Kingdom
| | - A James Mason
- Institute of Pharmaceutical Science, School of Cancer & Pharmaceutical Sciences, King's College London, United Kingdom
| | - Shing Fung Chow
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong Special Administrative Region
| | - Wing Cheong Yam
- Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Jenny K W Lam
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region; UCL School of Pharmacy, University College London, United Kingdom; Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong Special Administrative Region.
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Tafess K, Ng TTL, Tam KKG, Leung KSS, Leung JSL, Lee LK, Lao HY, Chan CTM, Yam WC, Wong SSY, Lau TCK, Siu GKH. Genetic mechanisms of co-emergence of INH-resistant Mycobacterium tuberculosis strains during the standard course of antituberculosis therapy. Microbiol Spectr 2024; 12:e0213323. [PMID: 38466098 DOI: 10.1128/spectrum.02133-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/12/2024] Open
Abstract
The incidence of isoniazid (INH) resistant Mycobacterium tuberculosis is increasing globally. This study aimed to identify the molecular mechanisms behind the development of INH resistance in M. tuberculosis strains collected from the same patients during the standard course of treatment. Three M. tuberculosis strains were collected from a patient before and during antituberculosis (anti-TB) therapy. The strains were characterized using phenotypic drug susceptibility tests, Mycobacterial Interspersed Repeated Unit-Variable-Number Tandem Repeats (MIRU-VNTR), and whole-genome sequencing (WGS) to identify mutations associated with INH resistance. To validate the role of the novel mutations in INH resistance, the mutated katG genes were electroporated into a KatG-deleted M. tuberculosis strain (GA03). Three-dimensional structures of mutated KatG were modeled to predict their impact on INH binding. The pre-treatment strain was susceptible to INH. However, two INH-resistant strains were isolated from the patient after anti-TB therapy. MIRU-VNTR and WGS revealed that the three strains were clonally identical. A missense mutation (P232L) and a nonsense mutation (Q461Stop) were identified in the katG of the two post-treatment strains, respectively. Transformation experiments showed that katG of the pre-treatment strain restored INH susceptibility in GA03, whereas the mutated katG genes from the post-treatment strains rendered negative catalase activity and INH resistance. The protein model indicated that P232L reduced INH-KatG binding affinity while Q461Stop truncated gene transcription. Our results showed that the two katG mutations, P232L and Q461Stop, accounted for the co-emergence of INH-resistant clones during anti-TB therapy. The inclusion of these mutations in the design of molecular assays could increase the diagnostic performance.IMPORTANCEThe evolution of drug-resistant strains of Mycobacterium tuberculosis within the lung lesions of a patient has a detrimental impact on treatment outcomes. This is particularly concerning for isoniazid (INH), which is the most potent first-line antimycobacterial drug. However, the precise genetic factors responsible for drug resistance in patients have not been fully elucidated, with approximately 15% of INH-resistant strains harboring unknown genetic factors. This raises concerns about the emergence of drug-resistant clones within patients, further contributing to the global epidemic of resistance. In this study, we revealed the presence of two novel katG mutations, which emerged independently due to the stress exerted by antituberculosis (anti-TB) treatment on a parental strain. Importantly, we experimentally demonstrated the functional significance of both mutations in conferring resistance to INH. Overall, this research sheds light on the genetic mechanisms underlying the evolution of INH resistance within patients and provides valuable insights for improving diagnostic performance by targeting specific mutations.
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Affiliation(s)
- Ketema Tafess
- Department of Applied Biology, School of Applied Natural Sciences, Adama Science and Technology University, Adama, Ethiopia
- Institute of Pharmaceutical Sciences, Adama Science and Technology University, Adama, Ethiopia
| | - Timothy Ting-Leung Ng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Kingsley King-Gee Tam
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Kenneth Siu-Sing Leung
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jake Siu-Lun Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Lam-Kwong Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Hiu Yin Lao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Chloe Toi-Mei Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Wing-Cheong Yam
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Samson Sai Yin Wong
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Terrence Chi-Kwong Lau
- Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Ng TTL, Su J, Lao HY, Lui WW, Chan CTM, Leung AWS, Jim SHC, Lee LK, Shehzad S, Tam KKG, Leung KSS, Tang F, Yam WC, Luo R, Siu GKH. Long-Read Sequencing with Hierarchical Clustering for Antiretroviral Resistance Profiling of Mixed Human Immunodeficiency Virus Quasispecies. Clin Chem 2023; 69:1174-1185. [PMID: 37537871 DOI: 10.1093/clinchem/hvad108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND HIV infections often develop drug resistance mutations (DRMs), which can increase the risk of virological failure. However, it has been difficult to determine if minor mutations occur in the same genome or in different virions using Sanger sequencing and short-read sequencing methods. Oxford Nanopore Technologies (ONT) sequencing may improve antiretroviral resistance profiling by allowing for long-read clustering. METHODS A new ONT sequencing-based method for profiling DRMs in HIV quasispecies was developed and validated. The method used hierarchical clustering of long amplicons that cover regions associated with different types of antiretroviral drugs. A gradient series of an HIV plasmid and 2 plasma samples was prepared to validate the clustering performance. The ONT results were compared to those obtained with Sanger sequencing and Illumina sequencing in 77 HIV-positive plasma samples to evaluate the diagnostic performance. RESULTS In the validation study, the abundance of detected quasispecies was concordant with the predicted result with the R2 of > 0.99. During the diagnostic evaluation, 59/77 samples were successfully sequenced for DRMs. Among 18 failed samples, 17 were below the limit of detection of 303.9 copies/μL. Based on the receiver operating characteristic analysis, the ONT workflow achieved an F1 score of 0.96 with a cutoff of 0.4 variant allele frequency. Four cases were found to have quasispecies with DRMs, in which 2 harbored quasispecies with more than one class of DRMs. Treatment modifications were recommended for these cases. CONCLUSIONS Long-read sequencing coupled with hierarchical clustering could differentiate the quasispecies resistance profiles in HIV-infected samples, providing a clearer picture for medical care.
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Affiliation(s)
- Timothy Ting-Leung Ng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Junhao Su
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Hiu-Yin Lao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Wui-Wang Lui
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Chloe Toi-Mei Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Amy Wing-Sze Leung
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Stephanie Hoi-Ching Jim
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Lam-Kwong Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Sheeba Shehzad
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Kingsley King-Gee Tam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Kenneth Siu-Sing Leung
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Forrest Tang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Wing-Cheong Yam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Su J, Lui WW, Lee Y, Zheng Z, Siu GKH, Ng TTL, Zhang T, Lam TTY, Lao HY, Yam WC, Tam KKG, Leung KSS, Lam TW, Leung AWS, Luo R. Evaluation of Mycobacterium tuberculosis enrichment in metagenomic samples using ONT adaptive sequencing and amplicon sequencing for identification and variant calling. Sci Rep 2023; 13:5237. [PMID: 37002338 PMCID: PMC10066345 DOI: 10.1038/s41598-023-32378-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Sensitive detection of Mycobacterium tuberculosis (TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detection sensitivity. Oxford nanopore technologies (ONT) MinION sequencing allows rapid and simple sample preparation for sequencing. Its recently developed adaptive sequencing selects reads from targets while allowing real-time base-calling to achieve sequence enrichment or depletion during sequencing. Another common enrichment method is PCR amplification of the target TB genes. In this study, we compared both methods using ONT MinION sequencing for TB detection and variant calling in metagenomic samples using both simulation runs and those with synthetic and patient samples. We found that both methods effectively enrich TB reads from a high percentage of human (95%) and other microbial DNA. Adaptive sequencing with readfish and UNCALLDE achieved a 3.9-fold and 2.2-fold enrichment compared to the control run. We provide a simple automatic analysis framework to support the detection of TB for clinical use, openly available at https://github.com/HKU-BAL/ONT-TB-NF . Depending on the patient's medical condition and sample type, we recommend users evaluate and optimize their workflow for different clinical specimens to improve the detection limit.
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Affiliation(s)
- Junhao Su
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Wui Wang Lui
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - YanLam Lee
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Zhenxian Zheng
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China
| | - Timothy Ting-Leung Ng
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China
| | - Tong Zhang
- Department of Computer Science and Engineering, Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Tommy Tsan-Yuk Lam
- State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Pak Shek Kok, Hong Kong SAR, China
| | - Hiu-Yin Lao
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China
| | - Wing-Cheong Yam
- Department of Microbiology, Lee Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Kingsley King-Gee Tam
- Department of Microbiology, Lee Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Kenneth Siu-Sing Leung
- Department of Microbiology, Lee Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Tak-Wah Lam
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Amy Wing-Sze Leung
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.
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Leung KSS, Tam KKG, Ng TTL, Lao HY, Shek RCM, Ma OCK, Yu SH, Chen JX, Han Q, Siu GKH, Yam WC. Clinical utility of target amplicon sequencing test for rapid diagnosis of drug-resistant Mycobacterium tuberculosis from respiratory specimens. Front Microbiol 2022; 13:974428. [PMID: 36160212 PMCID: PMC9505518 DOI: 10.3389/fmicb.2022.974428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/02/2022] [Indexed: 12/02/2022] Open
Abstract
An in-house-developed target amplicon sequencing by next-generation sequencing technology (TB-NGS) enables simultaneous detection of resistance-related mutations in Mycobacterium tuberculosis (MTB) against 8 anti-tuberculosis drug classes. In this multi-center study, we investigated the clinical utility of incorporating TB-NGS for rapid drug-resistant MTB detection in high endemic regions in southeast China. From January 2018 to November 2019, 4,047 respiratory specimens were available from patients suffering lower respiratory tract infections in Hong Kong and Guangzhou, among which 501 were TB-positive as detected by in-house IS6110-qPCR assay with diagnostic sensitivity and specificity of 97.9 and 99.2%, respectively. Preliminary resistance screening by GenoType MTBDRplus and MTBDRsl identified 25 drug-resistant specimens including 10 multidrug-resistant TB. TB-NGS was performed using MiSeq on all drug-resistant specimens alongside 67 pan-susceptible specimens, and demonstrated 100% concordance to phenotypic drug susceptibility test. All phenotypically resistant specimens with dominating resistance-related mutations exhibited a mutation frequency of over 60%. Three quasispecies were identified with mutation frequency of less than 35% among phenotypically susceptible specimens. They were well distinguished from phenotypically resistant cases and thus would not complicate TB-NGS results interpretations. This is the first large-scale study that explored the use of laboratory-developed NGS platforms for rapid TB diagnosis. By incorporating TB-NGS with our proposed diagnostic algorithm, the workflow would provide a user-friendly, cost-effective routine diagnostic solution for complicated TB cases with an average turnaround time of 6 working days. This is critical for timely management of drug resistant TB patients and expediting public health control on the emergence of drug-resistant TB.
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Affiliation(s)
- Kenneth Siu-Sing Leung
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Kingsley King-Gee Tam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Timothy Ting-Leung Ng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Hiu-Yin Lao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Raymond Chiu-Man Shek
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | | | - Shi-Hui Yu
- Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, Guangzhou, China
| | | | - Qi Han
- Guangzhou KingMed Diagnostics Group, Guangzhou, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Wing-Cheong Yam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- *Correspondence: Wing-Cheong Yam,
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Cheng VCC, Fung KSC, Siu GKH, Wong SC, Cheng LSK, Wong MS, Lee LK, Chan WM, Chau KY, Leung JSL, Chu AWH, Chan WS, Lu KK, Tam KKG, Ip JD, Leung KSS, Lung DC, Tse H, To KKW, Yuen KY. Nosocomial outbreak of COVID-19 by possible airborne transmission leading to a superspreading event. Clin Infect Dis 2021; 73:e1356-e1364. [PMID: 33851214 PMCID: PMC8083289 DOI: 10.1093/cid/ciab313] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Indexed: 12/24/2022] Open
Abstract
Background Nosocomial outbreaks with superspreading of COVID-19 due to a possible airborne transmission has not been reported. Methods Epidemiological analysis, environmental samplings, and whole genome sequencing (WGS) were performed for a hospital outbreak. Results A superspreading event involving 12 patients and 9 healthcare workers (HCWs) occurred within 4 days in 3 of 6 cubicles at an old-fashioned general ward with no air exhaust built within the cubicles. The environmental contamination by SARS-CoV-2 RNA was significantly higher in air grilles (>2m from patients’ head and not reachable by hands) than high-touch clinical surfaces (36.4%, 8/22 vs 3.4%, 1/29, p=0.003). Six (66.7%) of 9 contaminated air exhaust grilles were located outside patient cubicle. The clinical attack rate of patients was significantly higher than HCWs (15.4%, 12/78 exposed-patients vs 4.6%, 9/195 exposed-HCWs, p=0.005). Moreover, clinical attack rate of ward-based HCWs was significantly higher than non-ward-based HCWs (8.1%, 7/68 vs 1.8%, 2/109, p=0.045). The episodes (mean ± S.D) of patient-care duty assignment in the cubicles was significantly higher among infected ward-based HCWs than non-infected ward-based HCWs (6.0±2.4 vs 3.0±2.9, p=0.012) during the outbreak period. The outbreak strains belong to SARS-CoV-2 lineage, B.1.36.27 (GISAID Clade GH) with the unique S-T470N mutation on WGS. Conclusion This nosocomial point source superspreading due to possible airborne transmission demonstrated the need for stringent SARS-CoV-2 screening at admission to healthcare facilities and better architectural design of the ventilation system to prevent such outbreaks. Portable high-efficiency particulate filters were installed in each cubicle to improve ventilation before resumption of clinical service.
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Affiliation(s)
- Vincent Chi-Chung Cheng
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong Special Administrative Region, China.,Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Kitty Sau-Chun Fung
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Shuk-Ching Wong
- Infection Control Team, Queen Mary Hospital, Hong Kong West Cluster, Hong Kong Special Administrative Region, China
| | - Lily Shui-Kuen Cheng
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Man-Sing Wong
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Lam-Kwong Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Wan-Mui Chan
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ka-Yee Chau
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Jake Siu-Lun Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Allen Wing-Ho Chu
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Wai-Shan Chan
- Department of Pathology and Infection Control Team, United Christian Hospital, Hong Kong Special Administrative Region, China
| | - Kelvin Keru Lu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Kingsley King-Gee Tam
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jonathan Daniel Ip
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kenneth Siu-Sing Leung
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - David Christopher Lung
- Department of Pathology, Hong Kong Children's Hospital / Queen Elizabeth Hospital, Hong Kong Special Administrative Region, China
| | - Herman Tse
- Department of Pathology, Hong Kong Children's Hospital, Hong Kong Special Administrative Region, China
| | - Kelvin Kai-Wang To
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kwok-Yung Yuen
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Leung KSS, Ng TTL, Wu AKL, Yau MCY, Lao HY, Choi MP, Tam KKG, Lee LK, Wong BKC, Man Ho AY, Yip KT, Lung KC, Liu RWT, Tso EYK, Leung WS, Chan MC, Ng YY, Sin KM, Fung KSC, Chau SKY, To WK, Que TL, Shum DHK, Yip SP, Yam WC, Siu GKH. Territorywide Study of Early Coronavirus Disease Outbreak, Hong Kong, China. Emerg Infect Dis 2021; 27:196-204. [PMID: 33350913 PMCID: PMC7774584 DOI: 10.3201/eid2701.201543] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Initial cases of coronavirus disease in Hong Kong were imported from mainland China. A dramatic increase in case numbers was seen in February 2020. Most case-patients had no recent travel history, suggesting the presence of transmission chains in the local community. We collected demographic, clinical, and epidemiologic data from 50 patients, who accounted for 53.8% of total reported case-patients as of February 28, 2020. We performed whole-genome sequencing to determine phylogenetic relationship and transmission dynamics of severe acute respiratory syndrome coronavirus 2 infections. By using phylogenetic analysis, we attributed the community outbreak to 2 lineages; 1 harbored a common mutation, Orf3a-G251V, and accounted for 88.0% of the cases in our study. The estimated time to the most recent common ancestor of local coronavirus disease outbreak was December 24, 2019, with an evolutionary rate of 3.04 × 10−3 substitutions/site/year. The reproduction number was 1.84, indicating ongoing community spread.
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Siu GKH, Lee LK, Leung KSS, Leung JSL, Ng TTL, Chan CTM, Tam KKG, Lao HY, Wu AKL, Yau MCY, Lai YWM, Fung KSC, Chau SKY, Wong BKC, To WK, Luk K, Ho AYM, Que TL, Yip KT, Yam WC, Shum DHK, Yip SP. Will a new clade of SARS-CoV-2 imported into the community spark a fourth wave of the COVID-19 outbreak in Hong Kong? Emerg Microbes Infect 2020; 9:2497-2500. [PMID: 33206025 PMCID: PMC7717696 DOI: 10.1080/22221751.2020.1851146] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
| | - Lam-Kwong Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
| | - Kenneth Siu-Sing Leung
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, People's Republic of China
| | - Jake Siu-Lun Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
| | - Timothy Ting-Leung Ng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
| | - Chloe Toi-Mei Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
| | - Kingsley King-Gee Tam
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, People's Republic of China
| | - Hiu-Yin Lao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
| | - Alan Ka-Lun Wu
- Department of Clinical Pathology, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong Special Administrative Region, People's Republic of China
| | - Miranda Chong-Yee Yau
- Department of Clinical Pathology, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong Special Administrative Region, People's Republic of China
| | - Yvette Wai-Man Lai
- Department of Clinical Pathology, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong Special Administrative Region, People's Republic of China
| | - Kitty Sau-Chun Fung
- Department of Clinical Pathology, United Christian Hospital, Kwun Tong, Hong Kong Special Administrative Region, People's Republic of China
| | - Sandy Ka-Yee Chau
- Department of Clinical Pathology, United Christian Hospital, Kwun Tong, Hong Kong Special Administrative Region, People's Republic of China
| | - Barry Kin-Chung Wong
- Department of Clinical Pathology, United Christian Hospital, Kwun Tong, Hong Kong Special Administrative Region, People's Republic of China
| | - Wing-Kin To
- Department of Pathology, Princess Margaret Hospital, Kwai Chung, Hong Kong Special Administrative Region, People's Republic of China
| | - Kristine Luk
- Department of Pathology, Princess Margaret Hospital, Kwai Chung, Hong Kong Special Administrative Region, People's Republic of China
| | - Alex Yat-Man Ho
- Department of Pathology, Princess Margaret Hospital, Kwai Chung, Hong Kong Special Administrative Region, People's Republic of China
| | - Tak-Lun Que
- Department of Clinical Pathology, Tuen Mun Hospital, Tuen Mun, Hong Kong Special Administrative Region, People's Republic of China
| | - Kam-Tong Yip
- Department of Clinical Pathology, Tuen Mun Hospital, Tuen Mun, Hong Kong Special Administrative Region, People's Republic of China
| | - Wing Cheong Yam
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, People's Republic of China
| | - David Ho-Keung Shum
- Faculty of Health and Social Science, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region, People's Republic of China
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Leung KSS, Siu GKH, Tam KKG, Ho PL, Wong SSY, Leung EKC, Yu SH, Ma OCK, Yam WC. Diagnostic evaluation of an in-house developed single-tube, duplex, nested IS6110 real-time PCR assay for rapid pulmonary tuberculosis diagnosis. Tuberculosis (Edinb) 2018; 112:120-125. [PMID: 30205964 DOI: 10.1016/j.tube.2018.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 08/17/2018] [Accepted: 08/20/2018] [Indexed: 12/30/2022]
Abstract
OBJECTIVE To perform a prospective evaluation on the diagnostic performance of an in-house developed, duplex nested IS6110 real-time Polymerase-Chain-Reaction (PCR) assay (IS6110-qPCR assay) for rapid pulmonary TB diagnosis. METHODS A total of 503 sputum specimens were prospectively collected from July 2016 to November 2016. Diagnostic accuracy and optimal cut-off Cycle-threshold (Ct) value for IS6110-qPCR assay was determined by Receiver Operating Characteristic (ROC) curve. Using the optimal cut-off Ct, diagnostic performance of IS6110-qPCR assay was assessed with reference to both bacteriological and clinical information. Meanwhile, limit of detection (LOD) was calculated using Mycobacterium tuberculosis H37Rv as reference strain. RESULT ROC curve analysis of IS6110-qPCR assay showed a high Area Under the Curve (AUC) value (0.948) with optimal Ct value at 24.140. Prospective analysis of IS6110-qPCR assay with cut-off Ct = 24.140 showed a high overall sensitivity and specificity of 97.2% and 99.7%, respectively. No cross reactivity was observed among all non-tuberculous mycobacteria specimens in this study. LOD analysis on MTB-spiked sputum showed an average detection limit of 5.0 CFU/mL at Ct = 23.18 (±SD, 0.57). CONCLUSION IS6110-qPCR assay is a highly accurate and cost-effective assay developed for primary screening of suspected TB cases, which is particularly suitable for regions with limited resources but high TB burden.
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Affiliation(s)
- Kenneth Siu-Sing Leung
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region
| | - Kingsley King-Gee Tam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Pak-Leung Ho
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Samson Sai-Yin Wong
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Eunice Ka-Chun Leung
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Shi Hui Yu
- KingMed Diagnostics, Science Park, Hong Kong Special Administrative Region
| | - Oliver Chiu-Kit Ma
- KingMed Diagnostics, Science Park, Hong Kong Special Administrative Region
| | - Wing-Cheong Yam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong Special Administrative Region.
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Leung KSS, Siu GKH, Tam KKG, To SWC, Rajwani R, Ho PL, Wong SSY, Zhao WW, Ma OCK, Yam WC. Comparative Genomic Analysis of Two Clonally Related Multidrug Resistant Mycobacterium tuberculosis by Single Molecule Real Time Sequencing. Front Cell Infect Microbiol 2017; 7:478. [PMID: 29188195 PMCID: PMC5694780 DOI: 10.3389/fcimb.2017.00478] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 10/31/2017] [Indexed: 12/02/2022] Open
Abstract
Background: Multidrug-resistant tuberculosis (MDR-TB) is posing a major threat to global TB control. In this study, we focused on two consecutive MDR-TB isolated from the same patient before and after the initiation of anti-TB treatment. To better understand the genomic characteristics of MDR-TB, Single Molecule Real-Time (SMRT) Sequencing and comparative genomic analyses was performed to identify mutations that contributed to the stepwise development of drug resistance and growth fitness in MDR-TB under in vivo challenge of anti-TB drugs. Result: Both pre-treatment and post-treatment strain demonstrated concordant phenotypic and genotypic susceptibility profiles toward rifampicin, pyrazinamide, streptomycin, fluoroquinolones, aminoglycosides, cycloserine, ethionamide, and para-aminosalicylic acid. However, although both strains carried identical missense mutations at rpoB S531L, inhA C-15T, and embB M306V, MYCOTB Sensititre assay showed that the post-treatment strain had 16-, 8-, and 4-fold elevation in the minimum inhibitory concentrations (MICs) toward rifabutin, isoniazid, and ethambutol respectively. The results have indicated the presence of additional resistant-related mutations governing the stepwise development of MDR-TB. Further comparative genomic analyses have identified three additional polymorphisms between the clinical isolates. These include a single nucleotide deletion at nucleotide position 360 of rv0888 in pre-treatment strain, and a missense mutation at rv3303c (lpdA) V44I and a 6-bp inframe deletion at codon 67-68 in rv2071c (cobM) in the post-treatment strain. Multiple sequence alignment showed that these mutations were occurring at highly conserved regions among pathogenic mycobacteria. Using structural-based and sequence-based algorithms, we further predicted that the mutations potentially have deleterious effect on protein function. Conclusion: This is the first study that compared the full genomes of two clonally-related MDR-TB clinical isolates during the course of anti-TB treatment. Our work has demonstrated the robustness of SMRT Sequencing in identifying mutations among MDR-TB clinical isolates. Comparative genome analysis also suggested novel mutations at rv0888, lpdA, and cobM that might explain the difference in antibiotic resistance and growth pattern between the two MDR-TB strains.
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Affiliation(s)
- Kenneth Siu-Sing Leung
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong
| | - Gilman Kit-Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Kingsley King-Gee Tam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong
| | - Sabrina Wai-Chi To
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong
| | - Rahim Rajwani
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Pak-Leung Ho
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong
| | - Samson Sai-Yin Wong
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong
| | - Wei W. Zhao
- KingMed Diagnostics, Science Park, Hong Kong, Hong Kong
| | | | - Wing-Cheong Yam
- Department of Microbiology, Queen Mary Hospital, The University of Hong Kong, Hong Kong, Hong Kong
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Tam KKG, Leung KSS, To SWC, Siu GKH, Lau TCK, Shek VCM, Tse CWS, Wong SSY, Ho PL, Yam WC. Direct detection of Mycobacterium tuberculosis and drug resistance in respiratory specimen using Abbott Realti m e MTB detection and RIF/INH resistance assay. Diagn Microbiol Infect Dis 2017; 89:118-124. [DOI: 10.1016/j.diagmicrobio.2017.06.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/19/2017] [Accepted: 06/20/2017] [Indexed: 10/19/2022]
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