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Hall MB, Wick RR, Judd LM, Nguyen AN, Steinig EJ, Xie O, Davies M, Seemann T, Stinear TP, Coin L. Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data. eLife 2024; 13:RP98300. [PMID: 39388235 PMCID: PMC11466455 DOI: 10.7554/elife.98300] [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] [Indexed: 10/12/2024] Open
Abstract
Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT's super-high accuracy model. ONT's superior performance is attributed to its ability to overcome Illumina's errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT's super-high accuracy data mitigates ONT's traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings.
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Affiliation(s)
- Michael B Hall
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Ryan R Wick
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
- Centre for Pathogen Genomics, The University of MelbourneMelbourneAustralia
| | - Louise M Judd
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
- Centre for Pathogen Genomics, The University of MelbourneMelbourneAustralia
| | - An N Nguyen
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Eike J Steinig
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Ouli Xie
- Department of Infectious Diseases, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
- Monash Infectious Diseases, Monash HealthMelbourneAustralia
| | - Mark Davies
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
| | - Torsten Seemann
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
- Centre for Pathogen Genomics, The University of MelbourneMelbourneAustralia
| | - Timothy P Stinear
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
- Centre for Pathogen Genomics, The University of MelbourneMelbourneAustralia
| | - Lachlan Coin
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and ImmunityMelbourneAustralia
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Hershko Y, Slutzkin M, Barkan D, Adler A. Construction of core genome multi-locus sequence typing schemes for population structure analyses of Nocardia species. Res Microbiol 2024:104246. [PMID: 39393617 DOI: 10.1016/j.resmic.2024.104246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2024] [Revised: 10/08/2024] [Accepted: 10/08/2024] [Indexed: 10/13/2024]
Abstract
Nocardia, a member of the Actinobacteria phylum, populates diverse habitats globally, with certain species being the cause of various clinical infections in humans. There is paucity of data regarding the population structure of this genus and of established genomic-based phylogenetic methods. We examined the whole genome sequences of 193 isolates spanning five major pathogenic Nocardia species sourced from public databases, encompassing diverse geographic regions. Using the chewBBACA pipeline, a species-specific core genome multilocus sequence typing (cgMLST) schema was created for N. cyriacigeorgica, N. farcinica, N. brasiliensis, N. wallacei, and N. abscessus. Additional genomic features that were examined included virulence factor (VF) profile, total length and open-reading frame count, the core genome length and core gene count, and GC content. Our findings indicated that: (i) N. brasiliensis diverges significantly from the other four species, underscoring its distinct evolutionary trajectory; (ii) the population structures of all species were polyclonal, with phylogenetic clustering occurring in the minority of isolates; (iii) clonal complexes were largely restricted to specific geographical locations, rather than demonstrating a global distribution, and (iv) initial evidence suggests no direct common-source transmission amongst the studied strains. Our study establishes a comprehensive genome-based phylogenetic methodology for population structure of Nocardia species.
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Affiliation(s)
- Yizhak Hershko
- Koret School of Veterinary Medicine, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Israel; Clinical Microbiology Laboratory, Tel Aviv Sourasky Medical Center, Israel.
| | - Matan Slutzkin
- Clinical Microbiology Laboratory, Tel Aviv Sourasky Medical Center, Israel
| | - Daniel Barkan
- Koret School of Veterinary Medicine, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Israel
| | - Amos Adler
- Clinical Microbiology Laboratory, Tel Aviv Sourasky Medical Center, Israel; Department of Epidemiology and preventive Medicine, School of Public Health, Faculty of Medicine, Tel Aviv University, Israel
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3
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Xiao YX, Chan TH, Liu KH, Jou R. Define SNP thresholds for delineation of tuberculosis transmissions using whole-genome sequencing. Microbiol Spectr 2024; 12:e0041824. [PMID: 38916321 PMCID: PMC11302064 DOI: 10.1128/spectrum.00418-24] [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: 02/19/2024] [Accepted: 05/26/2024] [Indexed: 06/26/2024] Open
Abstract
For facilitating tuberculosis (TB) control, we used a whole-genome sequencing (WGS)-based approach to delineate transmission networks in a country with an intermediate burden of TB. A cluster was defined as Mycobacterium tuberculosis isolates with identical genotypes, and an outbreak was defined as clustered cases with epidemiological links (epi-links). To refine a cluster predefined using space oligonucleotide typing and mycobacterial interspersed repetitive unit variable tandem repeat typing, we analyzed one pansusceptible TB (C1) and three multidrug-resistant (MDR)-TB (C2-C4) clusters from different scenarios. Pansusceptible TB cluster (C1) consisting of 28 cases had ≤5 single nucleotide polymorphisms (SNPs) difference between their isolates. C1 was a definite outbreak, with cases attending the same junior high school in 2012. Three MDR-TB clusters (C2-C4) with distinct genotypes were identified, each consisting of 12-22 cases. Some of the cases had either ≤5 or ≤15 SNPs difference with clear or probable epi-links. Of note, even though WGS could effectively assist TB contact tracing, we still observed missing epi-links in some cases within the same cluster. Our results showed that thresholds of ≤5 and ≤15 SNPs difference between isolates were used to categorize definite and probable TB transmission, respectively. Furthermore, a higher SNP threshold might be required to define an MDR-TB outbreak. WGS still needs to be combined with classical epidemiological methods for improving outbreak investigations. Importantly, different SNP thresholds have to be applied to define outbreaks. IMPORTANCE TB is a chronic disease. Depending on host factors and TB burden, clusters of cases may continue to increase for several years. Conventional genotyping methods overestimate TB transmission, hampering precise detection of outbreaks and comprehensive surveillance. WGS can be used to obtain SNP information of M. tuberculosis to improve discriminative limitations of conventional methods and to strengthen delineation of transmission networks. It is important to define the country-specific SNP thresholds for investigation of transmission. This study demonstrated the use of thresholds of ≤5 and ≤15 SNPs difference between isolates to categorize definite and probable transmission, respectively. Different SNP thresholds should be applied while a higher cutoff was required to define an MDR-TB outbreak. The utilization of SNP thresholds proves to be crucial for guiding public health interventions, eliminating the need for unnecessary public health actions, and potentially uncovering undisclosed TB transmissions.
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Affiliation(s)
- Yu-Xin Xiao
- Tuberculosis Research Center, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
- Reference Laboratory of Mycobacteriology, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Tai-Hua Chan
- Tuberculosis Research Center, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
- Reference Laboratory of Mycobacteriology, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Kuang-Hung Liu
- Tuberculosis Research Center, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
- Reference Laboratory of Mycobacteriology, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Ruwen Jou
- Tuberculosis Research Center, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
- Reference Laboratory of Mycobacteriology, Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
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Tao L, Wang X, Yu Y, Ge T, Gong H, Yong W, Si J, He M, Ding J. Identifying SNP threshold from P2 sequences for investigating norovirus transmission. Virus Res 2024; 346:199408. [PMID: 38797342 PMCID: PMC11153907 DOI: 10.1016/j.virusres.2024.199408] [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: 02/02/2024] [Revised: 05/21/2024] [Accepted: 05/24/2024] [Indexed: 05/29/2024]
Abstract
Noroviruses are a group of non-enveloped single-stranded positive-sense RNA virus belonging to Caliciviridae family. They can be transmitted by the fecal-oral route from contaminated food and water and cause mainly acute gastroenteritis. Outbreaks of norovirus infections could be difficult to detect and investigate. In this study, we developed a simple threshold detection approach based on variations of the P2 domain of the capsid protein. We obtained sequences from the norovirus hypervariable P2 region using Sanger sequencing, including 582 pairs of epidemiologically-related strains from 35 norovirus outbreaks and 6402 pairs of epidemiologically-unrelated strains during the four epidemic seasons. Genetic distances were calculated and a threshold was performed by adopting ROC (Receiver Operating Characteristic) curve which identified transmission clusters in all tested outbreaks with 80 % sensitivity. In average, nucleotide diversity between outbreaks was 67.5 times greater than the diversity within outbreaks. Simple and accurate thresholds for detecting norovirus transmissions of three genotypes obtained here streamlines molecular investigation of norovirus outbreaks, thus enabling rapid and efficient responses for the control of norovirus.
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Affiliation(s)
- Luqiu Tao
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China; School of Public Health, Nanjing Medical University, 101 Longmian Avenue, 211166 Nanjing, Jiangsu, China
| | - Xuan Wang
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China
| | - Yan Yu
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China
| | - Teng Ge
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China
| | - Hongjin Gong
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China
| | - Wei Yong
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China
| | - Jiali Si
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China
| | - Min He
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China
| | - Jie Ding
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003 Nanjing, Jiangsu, China; School of Public Health, Nanjing Medical University, 101 Longmian Avenue, 211166 Nanjing, Jiangsu, China.
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Walsh KF, Lee MH, Chaguza C, Pamphile W, Royal G, Escuyer V, Pape JW, Fitzgerald D, Cohen T, Ocheretina O. Molecular Epidemiology of Isoniazid-resistant M tuberculosis in Port-au-Prince, Haiti. Open Forum Infect Dis 2024; 11:ofae421. [PMID: 39119477 PMCID: PMC11306977 DOI: 10.1093/ofid/ofae421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 07/17/2024] [Indexed: 08/10/2024] Open
Abstract
Background Isoniazid-resistant, rifampin-susceptible tuberculosis (Hr-TB) is associated with poor treatment outcomes and higher rates of acquisition of further drug resistance during treatment. Due to a lack of widespread diagnostics, Hr-TB is frequently undetected and its epidemiology is incompletely understood. Methods We studied the molecular epidemiology of Hr-TB among all patients diagnosed with culture-positive pulmonary tuberculosis between January 1 and June 30, 2017, at an urban referral tuberculosis clinic in Port-au-Prince, Haiti. Demographic and clinical data were extracted from the electronic medical record. Archived diagnostic Mycobacterium tuberculosis isolates were tested for genotypic and phenotypic isoniazid resistance using the Genotype MTBDRplus assay (Hain, Nehren, Germany) and culture-based testing, respectively. All isoniazid-resistant isolates and a randomly selected subset of isoniazid-susceptible isolates underwent whole-genome sequencing to confirm the presence of mutations associated with isoniazid resistance, to validate use of Genotype MTBDRplus in this population, and to identify potential transmission links between isoniazid-resistant isolates. Results and Conclusions Among 845 patients with culture-positive pulmonary tuberculosis in Haiti, 65 (7.7%) had Hr-TB based on the Genotype MTBDRplus molecular assay. Age < 20 years was significantly associated with Hr-TB (odds ratio, 2.39; 95% confidence interval, 1.14, 4.70; P = .015). Thirteen (20%) isoniazid-resistant isolates were found in 5 putative transmission clusters based on a single nucleotide polymorphism distance of ≤ 5. No patients in these transmission clusters were members of the same household. Adolescents are at higher risk for Hr-TB. Strains of isoniazid-resistant M tuberculosis are actively circulating in Haiti and transmission is likely occurring in community settings.
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Affiliation(s)
- Kathleen F Walsh
- General Internal Medicine, Weill Cornell Medicine, New York, New York, USA
- Center for Global Health, Weill Cornell Medicine, New York, New York, USA
| | - Myung Hee Lee
- Center for Global Health, Weill Cornell Medicine, New York, New York, USA
| | - Chrispin Chaguza
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, USA
| | - Widman Pamphile
- Groupe Haitian d'Etude du Sarcoma da Kaposi at des Infections Opportunistas (GHESKIO), Port-au-Prince, Haiti
| | - Gertrude Royal
- Groupe Haitian d'Etude du Sarcoma da Kaposi at des Infections Opportunistas (GHESKIO), Port-au-Prince, Haiti
| | - Vincent Escuyer
- Wadsworth Center, New York State Department of Health, Albany, New York, USA
| | - Jean W Pape
- Center for Global Health, Weill Cornell Medicine, New York, New York, USA
- Groupe Haitian d'Etude du Sarcoma da Kaposi at des Infections Opportunistas (GHESKIO), Port-au-Prince, Haiti
| | - Daniel Fitzgerald
- Center for Global Health, Weill Cornell Medicine, New York, New York, USA
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, Connecticut, USA
| | - Oksana Ocheretina
- Center for Global Health, Weill Cornell Medicine, New York, New York, USA
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6
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Zhang X, Lam C, Sim E, Martinez E, Crighton T, Marais BJ, Sintchenko V. Genomic characteristics of prospectively sequenced Mycobacterium tuberculosis from respiratory and non-respiratory sources. iScience 2024; 27:110327. [PMID: 39055934 PMCID: PMC11269812 DOI: 10.1016/j.isci.2024.110327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/23/2024] [Accepted: 06/18/2024] [Indexed: 07/28/2024] Open
Abstract
Understanding the differences between Mycobacterium tuberculosis strains isolated from respiratory and non-respiratory sources may inform clinical care and control strategies. We examined demographic and genomic characteristics of all culture-confirmed M. tuberculosis cultures isolated from respiratory and non-respiratory sources in New South Wales, Australia, from January 2017 to December 2021, using logistic regression models. M. tuberculosis strains from 1,831 patients were sequenced; 64.7% were from respiratory, 32.1% from non-respiratory, and 2.2% from both sources. Female patients had more frequent isolation from a non-respiratory source (p = 0.03), and older adults (≧65 years) from a respiratory source (p < 0.0001). Lineage 2 strains were relatively over-represented among respiratory isolates (p = 0.01). Among 39 cases with sequenced isolates from both sources, 43.6% had 1-10 single nucleotide polymorphism differences. The finding that older adults were more likely to have M. tuberculosis isolated from respiratory sources has relevance for TB control given the expected rise of TB among older adults.
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Affiliation(s)
- Xiaomei Zhang
- Centre for Research Excellence in Tuberculosis (TB-CRE), Centenary Institute, Sydney, NSW, Australia
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, NSW, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, NSW, Australia
| | - Connie Lam
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, NSW, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, NSW, Australia
| | - Eby Sim
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, NSW, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, NSW, Australia
| | - Elena Martinez
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, NSW, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, NSW, Australia
- NSW Mycobacterium Reference Laboratory, Centre for Infectious Diseases and Microbiology-Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Sydney, NSW, Australia
| | - Taryn Crighton
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, NSW, Australia
- NSW Mycobacterium Reference Laboratory, Centre for Infectious Diseases and Microbiology-Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Sydney, NSW, Australia
| | - Ben J. Marais
- Centre for Research Excellence in Tuberculosis (TB-CRE), Centenary Institute, Sydney, NSW, Australia
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, NSW, Australia
| | - Vitali Sintchenko
- Centre for Research Excellence in Tuberculosis (TB-CRE), Centenary Institute, Sydney, NSW, Australia
- Sydney Infectious Diseases Institute (Sydney ID), The University of Sydney, Sydney, NSW, Australia
- Centre for Infectious Diseases and Microbiology-Public Health, Westmead Hospital, Western Sydney Local Health District, Sydney, NSW, Australia
- NSW Mycobacterium Reference Laboratory, Centre for Infectious Diseases and Microbiology-Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Sydney, NSW, Australia
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Deb S, Basu J, Choudhary M. An overview of next generation sequencing strategies and genomics tools used for tuberculosis research. J Appl Microbiol 2024; 135:lxae174. [PMID: 39003248 DOI: 10.1093/jambio/lxae174] [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: 04/15/2024] [Revised: 06/07/2024] [Accepted: 07/10/2024] [Indexed: 07/15/2024]
Abstract
Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.
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Affiliation(s)
- Sushanta Deb
- Department of Veterinary Microbiology and Pathology, College of Veterinary Medicine, Washington State University, Pullman 99164, WA, United States
- All India Institute of Medical Sciences, New Delhi 110029, India
| | - Jhinuk Basu
- Department of Clinical Immunology and Rheumatology, Kalinga Institute of Medical Sciences (KIMS), KIIT University, Bhubaneswar 751024, India
| | - Megha Choudhary
- All India Institute of Medical Sciences, New Delhi 110029, India
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Hofstetter KS, Jacko NF, Shumaker MJ, Talbot BM, Petit RA, Read TD, David MZ. Strain Differences in Bloodstream and Skin Infection: Methicillin-Resistant Staphylococcus aureus Isolated in 2018-2021 in a Single Health System. Open Forum Infect Dis 2024; 11:ofae261. [PMID: 38854395 PMCID: PMC11160326 DOI: 10.1093/ofid/ofae261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 05/02/2024] [Indexed: 06/11/2024] Open
Abstract
Staphylococcus aureus is a common cause of skin and soft-tissue infections (SSTIs) and has become the most common cause of bloodstream infections (BSIs) in recent years, but whether the strains causing these two clinical syndromes overlap has not been studied adequately. USA300/500 (clonal complex [CC] 8-sequence type [ST] 8) and USA100 (CC5-ST5) have dominated among methicillin-resistant S aureus (MRSA) strains in the United States since the early 2000s. We compared the genomes of unselected MRSA isolates from 131 SSTIs with those from 145 BSIs at a single US center in overlapping periods in 2018-2021. CC8 MRSA was more common among SSTIs, and CC5 was more common among BSIs, consistent with prior literature. Based on clustering genomes with a threshold of 15 single-nucleotide polymorphisms, we identified clusters limited to patients with SSTI and separate clusters exclusively comprising patients with BSIs. However, we also identified eight clusters that included at least one SSTI and one BSI isolate. This suggests that virulent MRSA strains are transmitted from person to person locally in the healthcare setting or the community and that single lineages are often capable of causing both SSTIs and BSIs.
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Affiliation(s)
- Katrina S Hofstetter
- Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, Georgia, USA
| | - Natasia F Jacko
- Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Margot J Shumaker
- Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Brooke M Talbot
- Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, Georgia, USA
| | - Robert A Petit
- Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, Georgia, USA
| | - Timothy D Read
- Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, Georgia, USA
| | - Michael Z David
- Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Sadovska D, Ozere I, Pole I, Ķimsis J, Vaivode A, Vīksna A, Norvaiša I, Bogdanova I, Ulanova V, Čapligina V, Bandere D, Ranka R. Unraveling tuberculosis patient cluster transmission chains: integrating WGS-based network with clinical and epidemiological insights. Front Public Health 2024; 12:1378426. [PMID: 38832230 PMCID: PMC11144917 DOI: 10.3389/fpubh.2024.1378426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024] Open
Abstract
Background Tuberculosis remains a global health threat, and the World Health Organization reports a limited reduction in disease incidence rates, including both new and relapse cases. Therefore, studies targeting tuberculosis transmission chains and recurrent episodes are crucial for developing the most effective control measures. Herein, multiple tuberculosis clusters were retrospectively investigated by integrating patients' epidemiological and clinical information with median-joining networks recreated based on whole genome sequencing (WGS) data of Mycobacterium tuberculosis isolates. Methods Epidemiologically linked tuberculosis patient clusters were identified during the source case investigation for pediatric tuberculosis patients. Only M. tuberculosis isolate DNA samples with previously determined spoligotypes identical within clusters were subjected to WGS and further median-joining network recreation. Relevant clinical and epidemiological data were obtained from patient medical records. Results We investigated 18 clusters comprising 100 active tuberculosis patients 29 of whom were children at the time of diagnosis; nine patients experienced recurrent episodes. M. tuberculosis isolates of studied clusters belonged to Lineages 2 (sub-lineage 2.2.1) and 4 (sub-lineages 4.3.3, 4.1.2.1, 4.8, and 4.2.1), while sub-lineage 4.3.3 (LAM) was the most abundant. Isolates of six clusters were drug-resistant. Within clusters, the maximum genetic distance between closely related isolates was only 5-11 single nucleotide variants (SNVs). Recreated median-joining networks, integrated with patients' diagnoses, specimen collection dates, sputum smear microscopy, and epidemiological investigation results indicated transmission directions within clusters and long periods of latent infection. It also facilitated the identification of potential infection sources for pediatric patients and recurrent active tuberculosis episodes refuting the reactivation possibility despite the small genetic distance of ≤5 SNVs between isolates. However, unidentified active tuberculosis cases within the cluster, the variable mycobacterial mutation rate in dormant and active states, and low M. tuberculosis genetic variability inferred precise transmission chain delineation. In some cases, heterozygous SNVs with an allelic frequency of 10-73% proved valuable in identifying direct transmission events. Conclusion The complex approach of integrating tuberculosis cluster WGS-data-based median-joining networks with relevant epidemiological and clinical data proved valuable in delineating epidemiologically linked patient transmission chains and deciphering causes of recurrent tuberculosis episodes within clusters.
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Affiliation(s)
- Darja Sadovska
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Iveta Ozere
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
- Department of Infectology, Riga Stradiņš University, Riga, Latvia
| | - Ilva Pole
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
| | - Jānis Ķimsis
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Annija Vaivode
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Anda Vīksna
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
- Department of Infectology, Riga Stradiņš University, Riga, Latvia
| | - Inga Norvaiša
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
| | - Ineta Bogdanova
- Centre of Tuberculosis and Lung Diseases, Riga East University Hospital, Upeslejas, Latvia
| | - Viktorija Ulanova
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Valentīna Čapligina
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Dace Bandere
- Department of Pharmaceutical Chemistry, Riga Stradiņš University, Riga, Latvia
| | - Renāte Ranka
- Laboratory of Molecular Microbiology, Latvian Biomedical Research and Study Centre, Riga, Latvia
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Wang Q, Wang R, Wang S, Zhang A, Duan Q, Sun S, Jin L, Wang X, Zhang Y, Wang C, Kang H, Zhang Z, Liao K, Guo Y, Jin L, Liu Z, Yang C, Wang H. Expansion and transmission dynamics of high risk carbapenem-resistant Klebsiella pneumoniae subclones in China: An epidemiological, spatial, genomic analysis. Drug Resist Updat 2024; 74:101083. [PMID: 38593500 DOI: 10.1016/j.drup.2024.101083] [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: 02/25/2024] [Revised: 03/11/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024]
Abstract
AIMS Carbapenem-resistant Klebsiella pneumonia (CRKP) is a global threat that varies by region. The global distribution, evolution, and clinical implications of the ST11 CRKP clone remain obscure. METHODS We conducted a multicenter molecular epidemiological survey using isolates obtained from 28 provinces and municipalities across China between 2011 and 2021. We integrated sequences from public databases and performed genetic epidemiology analysis of ST11 CRKP. RESULTS Among ST11 CRKP, KL64 serotypes exhibited considerable expansion, increasing from 1.54% to 46.08% between 2011 and 2021. Combining our data with public databases, the phylogenetic and phylogeography analyses indicated that ST11 CRKP appeared in the Americas in 1996 and spread worldwide, with key clones progressing from China's southeastern coast to the inland by 2010. Global phylogenetic analysis showed that ST11 KL64 CRKP has evolved to a virulent, resistant clade with notable regional spread. Single-nucleotide polymorphism (SNP) analysis identified BMPPS (bmr3, mltC, pyrB, ppsC, and sdaC) as a key marker for this clade. The BMPPS SNP clade is associated with high mortality and has strong anti-phagocytic and competitive traits in vitro. CONCLUSIONS The high-risk ST11 KL64 CRKP subclone showed strong expansion potential and survival advantages, probably owing to genetic factors.
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Affiliation(s)
- Qi Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Ruobing Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Shuyi Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Anru Zhang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Qiaoyan Duan
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Shijun Sun
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Longyang Jin
- Laboratory of Clinical Microbiology and Infectious Diseases, China-Japan, Friendship Hospital, Beijing, China
| | - Xiaojuan Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Yawei Zhang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China
| | - Chunlei Wang
- Laboratory of Clinical Microbiology and Infectious Diseases, China-Japan, Friendship Hospital, Beijing, China
| | - Haiquan Kang
- Department of Laboratory Medicine, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Zhijie Zhang
- Department of Laboratory Medicine, Shengjing Hospital of China Medical University, Liaoning Clinical Research Center for Laboratory Medicine, Shenyang, China
| | - Kang Liao
- Department of Laboratory Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yinghui Guo
- Hebei Children's Hospital, Shijiazhuang, China
| | - Liang Jin
- Department of Clinical Laboratory, First Hospital of Qinhuangdao, Hebei, China
| | - Zhiwu Liu
- Department of Medical Laboratory Center, the First Hospital of Lanzhou University, Lanzhou, China
| | - Chunxia Yang
- Department of Infectious Diseases and Clinical Microbiology, Beijing Chaoyang Hospital Affiliated to the Capital University of Medical Sciences, Beijing, China
| | - Hui Wang
- Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
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11
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Brown TS, Tang L, Omar SV, Joseph L, Meintjes G, Maartens G, Wasserman S, Shah NS, Farhat MR, Gandhi NR, Ismail N, Brust JCM, Mathema B. Genotype-Phenotype Characterization of Serial Mycobacterium tuberculosis Isolates in Bedaquiline-Resistant Tuberculosis. Clin Infect Dis 2024; 78:269-276. [PMID: 37874928 DOI: 10.1093/cid/ciad596] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Emerging resistance to bedaquiline (BDQ) threatens to undermine advances in the treatment of drug-resistant tuberculosis (DRTB). Characterizing serial Mycobacterium tuberculosis (Mtb) isolates collected during BDQ-based treatment can provide insights into the etiologies of BDQ resistance in this important group of DRTB patients. METHODS We measured mycobacteria growth indicator tube (MGIT)-based BDQ minimum inhibitory concentrations (MICs) of Mtb isolates collected from 195 individuals with no prior BDQ exposure who were receiving BDQ-based treatment for DRTB. We conducted whole-genome sequencing on serial Mtb isolates from all participants who had any isolate with a BDQ MIC >1 collected before or after starting treatment (95 total Mtb isolates from 24 participants). RESULTS Sixteen of 24 participants had BDQ-resistant TB (MGIT MIC ≥4 µg/mL) and 8 had BDQ-intermediate infections (MGIT MIC = 2 µg/mL). Participants with pre-existing resistance outnumbered those with resistance acquired during treatment, and 8 of 24 participants had polyclonal infections. BDQ resistance was observed across multiple Mtb strain types and involved a diverse catalog of mmpR5 (Rv0678) mutations, but no mutations in atpE or pepQ. Nine pairs of participants shared genetically similar isolates separated by <5 single nucleotide polymorphisms, concerning for potential transmitted BDQ resistance. CONCLUSIONS BDQ-resistant TB can arise via multiple, overlapping processes, including transmission of strains with pre-existing resistance. Capturing the within-host diversity of these infections could potentially improve clinical diagnosis, population-level surveillance, and molecular diagnostic test development.
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Affiliation(s)
- Tyler S Brown
- Section of Infectious Diseases, Boston University School of Medicine, Boston, Massachusetts, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Linrui Tang
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Shaheed Vally Omar
- Centre for Tuberculosis, National Institute for Communicable Diseases, Johannesburg, South Africa
- Department of Molecular Medicine & Hematology, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Lavania Joseph
- Centre for Tuberculosis, National Institute for Communicable Diseases, Johannesburg, South Africa
| | - Graeme Meintjes
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, and Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Gary Maartens
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, and Department of Medicine, University of Cape Town, Cape Town, South Africa
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Sean Wasserman
- Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, and Department of Medicine, University of Cape Town, Cape Town, South Africa
- Division of Infectious Diseases and HIV Medicine, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - N Sarita Shah
- Departments of Epidemiology and Global Health and Medicine, Rollins School of Public Health and Emory School of Medicine, Atlanta, Georgia, USA
| | - Maha R Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Neel R Gandhi
- Departments of Epidemiology and Global Health and Medicine, Rollins School of Public Health and Emory School of Medicine, Atlanta, Georgia, USA
| | - Nazir Ismail
- Centre for Tuberculosis, National Institute for Communicable Diseases, Johannesburg, South Africa
| | - James C M Brust
- Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
| | - Barun Mathema
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, USA
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12
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Simon SJ, Sater M, Herriott I, Huntley M, Briars E, Hollenbeck BL. Staphylococcus epidermidis joint isolates: Whole-genome sequencing demonstrates evidence of hospital transmission and common antimicrobial resistance. Infect Control Hosp Epidemiol 2024; 45:150-156. [PMID: 38099465 DOI: 10.1017/ice.2023.253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
OBJECTIVE We investigated genetic, epidemiologic, and environmental factors contributing to positive Staphylococcus epidermidis joint cultures. DESIGN Retrospective cohort study with whole-genome sequencing (WGS). PATIENTS We identified S. epidermidis isolates from hip or knee cultures in patients with 1 or more prior corresponding intra-articular procedure at our hospital. METHODS WGS and single-nucleotide polymorphism-based clonality analyses were performed, including species identification, in silico multilocus sequence typing (MLST), phylogenomic analysis, and genotypic assessment of the prevalence of specific antibiotic resistance and virulence genes. Epidemiologic review was performed to compare cluster and noncluster cases. RESULTS In total, 60 phenotypically distinct S. epidermidis isolates were identified. After removal of duplicates and impure samples, 48 isolates were used for the phylogenomic analysis, and 45 (93.7%) isolates were included in the clonality analysis. Notably, 5 S. epidermidis strains (10.4%) showed phenotypic susceptibility to oxacillin yet harbored mecA, and 3 (6.2%) strains showed phenotypic resistance despite not having mecA. Smr was found in all isolates, and mupA positivity was not observed. We also identified 6 clonal clusters from the clonality analysis, which accounted for 14 (31.1%) of the 45 S. epidermidis isolates. Our epidemiologic investigation revealed ties to common aspirations or operative procedures, although no specific common source was identified. CONCLUSIONS Most S. epidermidis isolates from clinical joint samples are diverse in origin, but we identified an important subset of 31.1% that belonged to subclinical healthcare-associated clusters. Clusters appeared to resolve spontaneously over time, suggesting the benefit of routine hospital infection control and disinfection practices.
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Affiliation(s)
- Samantha J Simon
- Research Department, New England Baptist Hospital, Boston, Massachusetts
| | | | | | | | | | - Brian L Hollenbeck
- Research Department, New England Baptist Hospital, Boston, Massachusetts
- Infectious Diseases, New England Baptist Hospital, Boston, Massachusetts
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13
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Tao L, Zhang X, Wang X, Ding J. Using molecular methods to delineate norovirus outbreaks: a systematic review. Arch Virol 2024; 169:16. [PMID: 38172375 DOI: 10.1007/s00705-023-05953-w] [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: 07/22/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024]
Abstract
Noroviruses are among the major causative agents of human acute gastroenteritis, and the nature of norovirus outbreaks can differ considerably. The number of single-nucleotide polymorphisms (SNPs) between strains is used to assess their relationships. There is currently no universally accepted cutoff value for clustering strains that define an outbreak or linking the individuals involved. This study was conducted to estimate the threshold value of genomic variations among related strains within norovirus outbreaks. We carried out a literature search in the PubMed and Web of Science databases. SNP rates were defined as the number of SNPs/sequence length (bp) × 100%. The Mann-Whitney U-test was used in comparisons of the distribution of SNP rates for different sequence regions, genogroups (GI and GII), transmission routes, and sequencing methods. A total of 25 articles reporting on 108 norovirus outbreaks were included. In 99.1% of the outbreaks, the SNP rates were below 0.50%, and in 89.8%, the SNP rates were under 0.20%. Outbreak strains showed higher SNP rates when the P2 domain was used for sequence analysis (Z = -2.652, p = 0.008) and when an NGS method was used (Z = -3.686, p < 0.001). Outbreaks caused by different norovirus genotypes showed no significant difference in SNP rates. Compared with person-to-person outbreaks, SNP rates were lower in common-source outbreaks, but no significant difference was found when differences in sequencing methods were taken into consideraton. SNP rates under 0.20% and 0.50% could be considered as the rigorous and relaxed threshold, respectively, of strain similarity within a norovirus outbreak. More data are needed to evaluate differences within and between various norovirus outbreaks.
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Affiliation(s)
- Luqiu Tao
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003, Nanjing, Jiangsu, China
- School of Public Health, Nanjing Medical University, 101 Longmian Avenue, 211166, Nanjing, Jiangsu, China
| | - Xinyang Zhang
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003, Nanjing, Jiangsu, China
- School of Public Health, Nanjing Medical University, 101 Longmian Avenue, 211166, Nanjing, Jiangsu, China
| | - Xuan Wang
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003, Nanjing, Jiangsu, China
| | - Jie Ding
- Nanjing Municipal Center for Disease Control and Prevention affiliated to Nanjing Medical University, Zizhulin 2, 210003, Nanjing, Jiangsu, China.
- School of Public Health, Nanjing Medical University, 101 Longmian Avenue, 211166, Nanjing, Jiangsu, China.
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14
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Warren JL, Chitwood MH, Sobkowiak B, Colijn C, Cohen T. Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data. Biometrics 2023; 79:3650-3663. [PMID: 36745619 PMCID: PMC10404301 DOI: 10.1111/biom.13836] [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: 08/23/2022] [Accepted: 01/31/2023] [Indexed: 02/07/2023]
Abstract
Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic pathogen genetic relatedness data, in the form of patristic distances and transmission probabilities, that simultaneously address each of these complications. Using individual-level spatially correlated random effect parameters, we account for multiple sources of correlation between the outcomes as well as other important features of their distribution. Through simulation, we show the limitations of existing approaches in terms of estimating key associations of interest, and the ability of the new methodology to correct for these issues across datasets with different levels of correlation. All methods are applied to Mycobacterium tuberculosis data from the Republic of Moldova, where we identify previously unknown factors associated with disease transmission and, through analysis of the random effect parameters, key individuals, and areas with increased transmission activity. Model comparisons show the importance of the new methodology in this setting. The methods are implemented in the R package GenePair.
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Affiliation(s)
| | - Melanie H. Chitwood
- Department of Epidemiology of Microbial Diseases, Yale University, Connecticut, USA
| | | | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Ted Cohen
- Department of Epidemiology of Microbial Diseases, Yale University, Connecticut, USA
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15
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Kain DC, Isabel S, Abdulnoor M, Boissinot K, De Borja R, Filkin A, Lam B, Li J, Lungu I, McCreight L, McGeer A, Mazzulli T, Paterson A, Zuzarte P, Vincelli F, Bergwerff C, Fattouh R, Simpson JT, Johnstone J. Coronavirus disease 2019 (COVID-19) outbreak on an in-patient medical unit associated with unrecognized exposures in common areas-Epidemiological and whole-genome sequencing investigation. Infect Control Hosp Epidemiol 2023; 44:1829-1833. [PMID: 36912329 PMCID: PMC10665866 DOI: 10.1017/ice.2023.34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/31/2023] [Accepted: 02/04/2023] [Indexed: 03/14/2023]
Abstract
OBJECTIVE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hospital outbreaks have been common and devastating during the coronavirus disease 2019 (COVID-19) pandemic. Understanding SARS-CoV-2 transmission in these environments is critical for preventing and managing outbreaks. DESIGN Outbreak investigation through epidemiological mapping and whole-genome sequencing phylogeny. SETTING Hospital in-patient medical unit outbreak in Toronto, Canada, from November 2020 to January 2021. PARTICIPANTS The outbreak involved 8 patients and 10 staff and was associated with 3 patient deaths. RESULTS Patients being cared for in geriatric chairs at the nursing station were at high risk for both acquiring and transmitting SARS-CoV-2 to other patients and staff. Furthermore, given the informal nature of these transmissions, they were not initially recognized, which led to further transmission and missing the opportunity for preventative COVID-19 therapies. CONCLUSIONS During outbreak prevention and management, the risk of informal patient care settings, such as geriatric chairs, should be considered. During high-risk periods or during outbreaks, efforts should be made to care for patients in their rooms when possible.
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Affiliation(s)
- Dylan C. Kain
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sandra Isabel
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Mariana Abdulnoor
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Sick Kids Hospital, Department of Infectious Disease, Toronto, Ontario, Canada
| | - Karel Boissinot
- Department of Laboratory Medicine, St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | | | - Amanda Filkin
- Occupational Health, Sinai Health, Toronto, Ontario, Canada
| | - Bernard Lam
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Jason Li
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Ilinca Lungu
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Liz McCreight
- Infection Prevention and Control, Sinai Health, Toronto, Ontario, Canada
| | - Allison McGeer
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Microbiology, Sinai Health System/University Health Network, Toronto, Ontario, Canada
| | - Tony Mazzulli
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Microbiology, Sinai Health System/University Health Network, Toronto, Ontario, Canada
| | - Aimee Paterson
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Philip Zuzarte
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | | | - Ramzi Fattouh
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine, St. Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
| | - Jared T. Simpson
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Jennie Johnstone
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Infection Prevention and Control, Sinai Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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16
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Hare D, Dembicka KM, Brennan C, Campbell C, Sutton-Fitzpatrick U, Stapleton PJ, De Gascun CF, Dunne CP. Whole-genome sequencing to investigate transmission of SARS-CoV-2 in the acute healthcare setting: a systematic review. J Hosp Infect 2023; 140:139-155. [PMID: 37562592 DOI: 10.1016/j.jhin.2023.08.002] [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: 05/30/2023] [Revised: 07/03/2023] [Accepted: 08/04/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Whole-genome sequencing (WGS) has been used widely to elucidate transmission of SARS-CoV-2 in acute healthcare settings, and to guide infection, prevention, and control (IPC) responses. AIM To systematically appraise available literature, published between January 1st, 2020 and June 30th, 2022, describing the implementation of WGS in acute healthcare settings to characterize nosocomial SARS-CoV-2 transmission. METHODS Searches of the PubMed, Embase, Ovid MEDLINE, EBSCO MEDLINE, and Cochrane Library databases identified studies in English reporting the use of WGS to investigate SARS-CoV-2 transmission in acute healthcare environments. Publications involved data collected up to December 31st, 2021, and findings were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. FINDINGS In all, 3088 non-duplicate records were retrieved; 97 met inclusion criteria, involving 62 outbreak analyses and 35 genomic surveillance studies. No publications from low-income countries were identified. In 87/97 (90%), WGS supported hypotheses for nosocomial transmission, while in 46 out of 97 (47%) suspected transmission events were excluded. An IPC intervention was attributed to the use of WGS in 18 out of 97 (18%); however, only three (3%) studies reported turnaround times ≤7 days facilitating near real-time IPC action, and none reported an impact on the incidence of nosocomial COVID-19 attributable to WGS. CONCLUSION WGS can elucidate transmission of SARS-CoV-2 in acute healthcare settings to enhance epidemiological investigations. However, evidence was not identified to support sequencing as an intervention to reduce the incidence of SARS-CoV-2 in hospital or to alter the trajectory of active outbreaks.
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Affiliation(s)
- D Hare
- UCD National Virus Reference Laboratory, University College Dublin, Ireland; School of Medicine, University of Limerick, Limerick, Ireland.
| | - K M Dembicka
- School of Medicine, University of Limerick, Limerick, Ireland
| | - C Brennan
- UCD National Virus Reference Laboratory, University College Dublin, Ireland
| | - C Campbell
- UCD National Virus Reference Laboratory, University College Dublin, Ireland
| | | | | | - C F De Gascun
- UCD National Virus Reference Laboratory, University College Dublin, Ireland
| | - C P Dunne
- School of Medicine, University of Limerick, Limerick, Ireland; Centre for Interventions in Infection, Inflammation & Immunity (4i), University of Limerick, Limerick, Ireland
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17
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Batisti Biffignandi G, Bellinzona G, Petazzoni G, Sassera D, Zuccotti GV, Bandi C, Baldanti F, Comandatore F, Gaiarsa S. P-DOR, an easy-to-use pipeline to reconstruct bacterial outbreaks using genomics. Bioinformatics 2023; 39:btad571. [PMID: 37701995 PMCID: PMC10533420 DOI: 10.1093/bioinformatics/btad571] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/24/2023] [Accepted: 09/12/2023] [Indexed: 09/14/2023] Open
Abstract
SUMMARY Bacterial Healthcare-Associated Infections (HAIs) are a major threat worldwide, which can be counteracted by establishing effective infection control measures, guided by constant surveillance and timely epidemiological investigations. Genomics is crucial in modern epidemiology but lacks standard methods and user-friendly software, accessible to users without a strong bioinformatics proficiency. To overcome these issues we developed P-DOR, a novel tool for rapid bacterial outbreak characterization. P-DOR accepts genome assemblies as input, it automatically selects a background of publicly available genomes using k-mer distances and adds it to the analysis dataset before inferring a Single-Nucleotide Polymorphism (SNP)-based phylogeny. Epidemiological clusters are identified considering the phylogenetic tree topology and SNP distances. By analyzing the SNP-distance distribution, the user can gauge the correct threshold. Patient metadata can be inputted as well, to provide a spatio-temporal representation of the outbreak. The entire pipeline is fast and scalable and can be also run on low-end computers. AVAILABILITY AND IMPLEMENTATION P-DOR is implemented in Python3 and R and can be installed using conda environments. It is available from GitHub https://github.com/SteMIDIfactory/P-DOR under the GPL-3.0 license.
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Affiliation(s)
| | - Greta Bellinzona
- Department of Biology and Biotechnology, University of Pavia, Pavia, 27100, Italy
| | - Greta Petazzoni
- Department of Medical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
- Microbiology and Virology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
| | - Davide Sassera
- Department of Biology and Biotechnology, University of Pavia, Pavia, 27100, Italy
- Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
| | - Gian Vincenzo Zuccotti
- Department of Biomedical and Clinical Sciences, Pediatric Clinical Research Center Romeo ed Enrica Invernizzi, University of Milan, Milan, 20157, Italy
- Pediatric Department, Buzzi Children’s Hospital, Milan, 20154, Italy
| | - Claudio Bandi
- Department of Biosciences, Pediatric Clinical Research Center Romeo ed Enrica Invernizzi, University of Milan, Milan, 20133, Italy
| | - Fausto Baldanti
- Department of Medical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
- Microbiology and Virology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
| | - Francesco Comandatore
- Department of Biomedical and Clinical Sciences, Pediatric Clinical Research Center Romeo ed Enrica Invernizzi, University of Milan, Milan, 20157, Italy
| | - Stefano Gaiarsa
- Microbiology and Virology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
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18
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Sobkowiak B, Haghmaram P, Prystajecky N, Zlosnik JEA, Tyson J, Hoang LMN, Colijn C. The utility of SARS-CoV-2 genomic data for informative clustering under different epidemiological scenarios and sampling. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2023; 113:105484. [PMID: 37531976 DOI: 10.1016/j.meegid.2023.105484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/25/2023] [Accepted: 07/30/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES Clustering pathogen sequence data is a common practice in epidemiology to gain insights into the genetic diversity and evolutionary relationships among pathogens. We can find groups of cases with a shared transmission history and common origin, as well as identifying transmission hotspots. Motivated by the experience of clustering SARS-CoV-2 cases using whole genome sequence data during the COVID-19 pandemic to aid with public health investigation, we investigated how differences in epidemiology and sampling can influence the composition of clusters that are identified. METHODS We performed genomic clustering on simulated SARS-CoV-2 outbreaks produced with different transmission rates and levels of genomic diversity, along with varying the proportion of cases sampled. RESULTS In single outbreaks with a low transmission rate, decreasing the sampling fraction resulted in multiple, separate clusters being identified where intermediate cases in transmission chains are missed. Outbreaks simulated with a high transmission rate were more robust to changes in the sampling fraction and largely resulted in a single cluster that included all sampled outbreak cases. When considering multiple outbreaks in a sampled jurisdiction seeded by different introductions, low genomic diversity between introduced cases caused outbreaks to be merged into large clusters. If the transmission and sampling fraction, and diversity between introductions was low, a combination of the spurious break-up of outbreaks and the linking of closely related cases in different outbreaks resulted in clusters that may appear informative, but these did not reflect the true underlying population structure. Conversely, genomic clusters matched the true population structure when there was relatively high diversity between introductions and a high transmission rate. CONCLUSION Differences in epidemiology and sampling can impact our ability to identify genomic clusters that describe the underlying population structure. These findings can help to guide recommendations for the use of pathogen clustering in public health investigations.
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Affiliation(s)
| | - Pouya Haghmaram
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Natalie Prystajecky
- BC Centre for Disease Control Public Health Laboratory, BC Centre for Disease Control, Vancouver, Canada; Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Canada
| | - James E A Zlosnik
- BC Centre for Disease Control Public Health Laboratory, BC Centre for Disease Control, Vancouver, Canada
| | - John Tyson
- BC Centre for Disease Control Public Health Laboratory, BC Centre for Disease Control, Vancouver, Canada
| | - Linda M N Hoang
- BC Centre for Disease Control Public Health Laboratory, BC Centre for Disease Control, Vancouver, Canada; Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Canada
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
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19
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Wang J, Feng Y, Zong Z. Worldwide transmission of ST11-KL64 carbapenem-resistant Klebsiella pneumoniae: an analysis of publicly available genomes. mSphere 2023; 8:e0017323. [PMID: 37199964 PMCID: PMC10449508 DOI: 10.1128/msphere.00173-23] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 05/19/2023] Open
Abstract
ST11-KL64 is an internationally distributed lineage of carbapenem-resistant Klebsiella pneumoniae and is the most common type in China. The international and interprovincial (in China) transmission of ST11-KL64 CRKP remains to be elucidated. We used both static clusters defined based on a fixed cutoff of ≤21 pairwise single-nucleotide polymorphisms and dynamic groups defined by modeling the likelihood to be linked by a transmission threshold to investigate the transmission of ST11-KL64 strains based on genome sequences mining. We analyzed all publicly available genomes (n = 730) of ST11-KL64 strains, almost all of which had known carbapenemase genes with KPC-2 being dominant. We identified 4 clusters of international transmission and 14 clusters of interprovincial transmission across China of ST11-KL64 strains. We found that dynamic grouping could provide further resolution for determining clonal relatedness in addition to the widely adopted static clustering and therefore increases the confidence for inferring transmission.IMPORTANCECarbapenem-resistant Klebsiella pneumoniae (CRKP) is a serious challenge for clinical management and is prone to spread in and between healthcare settings. ST11-KL64 is the dominant CRKP type in China with a worldwide distribution. Here, we used two different methods, the widely used clustering based on a fixed single-nucleotide polymorphism (SNP) cutoff and the recently developed grouping by modeling transmission likelihood, to mine all 730 publicly available ST11-KL64 genomes. We identified international transmission of several strains and interprovincial transmission in China of a few, which warrants further investigations to uncover the mechanisms for their spread. We found that static clustering based on ≤21 fixed SNPs is sensitive to detect transmission and dynamic grouping has higher resolutions to provide complementary information. We suggest the use of the two methods in combination for analyzing transmission of bacterial strains. Our findings highlight the need of coordinated actions at both international and interprovincial levels for tackling multi-drug resistant organisms.
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Affiliation(s)
- Junna Wang
- Center of Infectious Diseases, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Feng
- Center for Pathogen Research, West China Hospital, Sichuan University, Chengdu, China
| | - Zhiyong Zong
- Center of Infectious Diseases, West China Hospital, Sichuan University, Chengdu, China
- Center for Pathogen Research, West China Hospital, Sichuan University, Chengdu, China
- Division of Infectious Diseases, State Key Laboratory of Biotherapy, Chengdu, China
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20
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Marinelli TM, Dolan L, Jenkins F, Lee A, Davis RJ, Crawford S, Nield B, Ronnachit A, Van Hal SJ. The role of real-time, on-site, whole-genome sequencing of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in guiding the management of hospital outbreaks of coronavirus disease 2019 (COVID-19). Infect Control Hosp Epidemiol 2023; 44:1116-1120. [PMID: 36082784 DOI: 10.1017/ice.2022.220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE We aimed to demonstrate the role of real-time, on-site, whole-genome sequencing (WGS) of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the management of hospital outbreaks of coronavirus disease 2019 (COVID-19). DESIGN This retrospective study was undertaken at our institutions in Sydney, New South Wales, Australia, between July 2021 and April 2022. We included SARS-CoV-2 outbreaks due to SARS-CoV-2 δ (delta) and ο (omicron) variants. All unexpected SARS-CoV-2-positive cases identified within the hospital were managed by the infection control team. An outbreak was defined as 2 or more cases acquired on a single ward. We included only outbreaks with 2 or more suspected transmission events in which WGS was utilized to assist with outbreak assessment and management. RESULTS We studied 8 outbreaks involving 266 patients and 486 staff, of whom 73 (27.4%) and 39 (8.0%), respectively, tested positive for SARS-CoV-2 during the outbreak management. WGS was used to evaluate the source of the outbreak, to establish transmission chains, to highlight deficiencies in infection control practices, and to delineate between community and healthcare acquired infection. CONCLUSIONS Real-time, on-site WGS combined with epidemiologic assessment is a useful tool to guide management of hospital SARS-CoV-2 outbreaks. WGS allowed us (1) to establish likely transmission events due to personal protective equipment (PPE) breaches; (2) to detect inadequacies in infection control infrastructure including ventilation; and (3) to confirm multiple viral introductions during periods of high community SARS-CoV-2 transmission. Insights gained from WGS-guides outbreak management directly influenced policy including modifying PPE requirements, instituting routine inpatient SARS-CoV-2 surveillance, and confirmatory SARS-CoV-2 testing prior to placing patients in a cohort setting.
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Affiliation(s)
- Tina M Marinelli
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Leanne Dolan
- Infection Prevention and Control Unit, Royal Prince Alfred Hospital, Sydney, Australia
| | - Frances Jenkins
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Andie Lee
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
- Department of Medicine, The University of Sydney, Sydney, Australia
| | - Rebecca J Davis
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
- Department of Medicine, The University of Sydney, Sydney, Australia
| | - Simeon Crawford
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Blake Nield
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Amrita Ronnachit
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
- Department of Medicine, The University of Sydney, Sydney, Australia
| | - Sebastiaan J Van Hal
- Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Sydney, Australia
- Department of Medicine, The University of Sydney, Sydney, Australia
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21
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Commins N, Sullivan MR, McGowen K, Koch EM, Rubin EJ, Farhat M. Mutation rates and adaptive variation among the clinically dominant clusters of Mycobacterium abscessus. Proc Natl Acad Sci U S A 2023; 120:e2302033120. [PMID: 37216535 PMCID: PMC10235944 DOI: 10.1073/pnas.2302033120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 04/13/2023] [Indexed: 05/24/2023] Open
Abstract
Mycobacterium abscessus (Mab) is a multidrug-resistant pathogen increasingly responsible for severe pulmonary infections. Analysis of whole-genome sequences (WGS) of Mab demonstrates dense genetic clustering of clinical isolates collected from disparate geographic locations. This has been interpreted as supporting patient-to-patient transmission, but epidemiological studies have contradicted this interpretation. Here, we present evidence for a slowing of the Mab molecular clock rate coincident with the emergence of phylogenetic clusters. We performed phylogenetic inference using publicly available WGS from 483 Mab patient isolates. We implement a subsampling approach in combination with coalescent analysis to estimate the molecular clock rate along the long internal branches of the tree, indicating a faster long-term molecular clock rate compared to branches within phylogenetic clusters. We used ancestry simulation to predict the effects of clock rate variation on phylogenetic clustering and found that the degree of clustering in the observed phylogeny is more easily explained by a clock rate slowdown than by transmission. We also find that phylogenetic clusters are enriched in mutations affecting DNA repair machinery and report that clustered isolates have lower spontaneous mutation rates in vitro. We propose that Mab adaptation to the host environment through variation in DNA repair genes affects the organism's mutation rate and that this manifests as phylogenetic clustering. These results challenge the model that phylogenetic clustering in Mab is explained by person-to-person transmission and inform our understanding of transmission inference in emerging, facultative pathogens.
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Affiliation(s)
- Nicoletta Commins
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA02115
| | - Mark R. Sullivan
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA02115
| | - Kerry McGowen
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA02115
| | - Evan M. Koch
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA02115
| | - Eric J. Rubin
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA02115
- Department of Microbiology, Harvard Medical School, Boston, MA02115
| | - Maha Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA02115
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA02114
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22
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Mukherjee S, Perveen S, Negi A, Sharma R. Evolution of tuberculosis diagnostics: From molecular strategies to nanodiagnostics. Tuberculosis (Edinb) 2023; 140:102340. [PMID: 37031646 PMCID: PMC10072981 DOI: 10.1016/j.tube.2023.102340] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/12/2023] [Accepted: 03/30/2023] [Indexed: 04/09/2023]
Abstract
Tuberculosis has remained a global concern for public health affecting the lives of people for ages. Approximately 10 million people are affected by the disease and 1.5 million succumb to the disease worldwide annually. The COVID-19 pandemic has highlighted the role of early diagnosis to win the battle against such infectious diseases. Thus, advancement in the diagnostic approaches to provide early detection forms the foundation to eradicate and manage contagious diseases like tuberculosis. The conventional diagnostic strategies include microscopic examination, chest X-ray and tuberculin skin test. The limitations associated with sensitivity and specificity of these tests demands for exploring new techniques like probe-based assays, CRISPR-Cas and microRNA detection. The aim of the current review is to envisage the correlation between both the conventional and the newer approaches to enhance the specificity and sensitivity. A significant emphasis has been placed upon nanodiagnostic approaches manipulating quantum dots, magnetic nanoparticles, and biosensors for accurate diagnosis of latent, active and drug-resistant TB. Additionally, we would like to ponder upon a reliable method that is cost-effective, reproducible, require minimal infrastructure and provide point-of-care to the patients.
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Affiliation(s)
| | - Summaya Perveen
- Infectious Diseases Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Anjali Negi
- Infectious Diseases Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Rashmi Sharma
- Infectious Diseases Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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23
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Jiang Q, Liu HC, Liu QY, Phelan JE, Tao FX, Zhao XQ, Wang J, Glynn JR, Takiff HE, Clark TG, Wan KL, Gao Q. The Evolution and Transmission Dynamics of Multidrug-Resistant Tuberculosis in an Isolated High-Plateau Population of Tibet, China. Microbiol Spectr 2023; 11:e0399122. [PMID: 36912683 PMCID: PMC10101056 DOI: 10.1128/spectrum.03991-22] [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: 10/06/2022] [Accepted: 02/15/2023] [Indexed: 03/14/2023] Open
Abstract
On the Tibetan Plateau, most tuberculosis is caused by indigenous Mycobacterium tuberculosis strains with a monophyletic structure and high-level drug resistance. This study investigated the emergence, evolution, and transmission dynamics of multidrug-resistant tuberculosis (MDR-TB) in Tibet. The whole-genome sequences of 576 clinical strains from Tibet were analyzed with the TB-profiler tool to identify drug-resistance mutations. The evolution of the drug resistance was then inferred based on maximum-likelihood phylogeny and dated trees that traced the serial acquisition of mutations conferring resistance to different drugs. Among the 576 clinical M. tuberculosis strains, 346 (60.1%) carried at least 1 resistance-conferring mutation and 231 (40.1%) were MDR-TB. Using a pairwise distance of 50 single nucleotide polymorphisms (SNPs), most strains (89.9%, 518/576) were phylogenetically separated into 50 long-term transmission clusters. Eleven large drug-resistant clusters contained 76.1% (176/231) of the local multidrug-resistant strains. A total of 85.2% of the isoniazid-resistant strains were highly transmitted with an average of 6.6 cases per cluster, of which most shared the mutation KatG Ser315Thr. A lower proportion (71.6%) of multidrug-resistant strains were transmitted, with an average cluster size of 2.9 cases. The isoniazid-resistant clusters appear to have undergone substantial bacterial population growth in the 1970s to 1990s and then subsequently accumulated multiple rifampicin-resistance mutations and caused the current local MDR-TB burden. These findings highlight the importance of detecting and curing isoniazid-resistant strains to prevent the emergence of endemic MDR-TB. IMPORTANCE Emerging isoniazid resistance in the 1970s allowed M. tuberculosis strains to spread and form into large multidrug-resistant tuberculosis clusters in the isolated plateau of Tibet, China. The epidemic was driven by the high risk of transmission as well as the potential of acquiring further drug resistance from isoniazid-resistant strains. Eleven large drug-resistant clusters consisted of the majority of local multidrug-resistant cases. Among the clusters, isoniazid resistance overwhelmingly evolved before all the other resistance types. A large bacterial population growth of isoniazid-resistant clusters occurred between 1970s and 1990s, which subsequently accumulated rifampicin-resistance-conferring mutations in parallel and accounted for the local multidrug-resistant tuberculosis burden. The results of our study indicate that it may be possible to restrict MDR-TB evolution and dissemination by prioritizing screening for isoniazid (INH)-resistant TB strains before they become MDR-TB and by adopting measures that can limit their transmission.
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Affiliation(s)
- Qi Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Hai-Can Liu
- State Key Laboratory for Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qing-Yun Liu
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jody E. Phelan
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Feng-Xi Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Xiu-Qin Zhao
- State Key Laboratory for Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jian Wang
- Tibet Center for Disease Control and Prevention, Lhasa, Tibet Autonomous Region, China
| | - Judith R. Glynn
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Howard E. Takiff
- Laboratorio de Genética Molecular, CMBC, IVIC, Caracas, Venezuela
| | - Taane G. Clark
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Kang-Lin Wan
- State Key Laboratory for Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qian Gao
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen, Shenzhen, Guangdong, China
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24
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Susvitasari K, Tupper PF, Cancino-Muños I, Lòpez MG, Comas I, Colijn C. Epidemiological cluster identification using multiple data sources: an approach using logistic regression. Microb Genom 2023; 9. [PMID: 36867086 PMCID: PMC10132077 DOI: 10.1099/mgen.0.000929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023] Open
Abstract
In the management of infectious disease outbreaks, grouping cases into clusters and understanding their underlying epidemiology are fundamental tasks. In genomic epidemiology, clusters are typically identified either using pathogen sequences alone or with sequences in combination with epidemiological data such as location and time of collection. However, it may not be feasible to culture and sequence all pathogen isolates, so sequence data may not be available for all cases. This presents challenges for identifying clusters and understanding epidemiology, because these cases may be important for transmission. Demographic, clinical and location data are likely to be available for unsequenced cases, and comprise partial information about their clustering. Here, we use statistical modelling to assign unsequenced cases to clusters already identified by genomic methods, assuming that a more direct method of linking individuals, such as contact tracing, is not available. We build our model on pairwise similarity between cases to predict whether cases cluster together, in contrast to using individual case data to predict the cases' clusters. We then develop methods that allow us to determine whether a pair of unsequenced cases are likely to cluster together, to group them into their most probable clusters, to identify which are most likely to be members of a specific (known) cluster, and to estimate the true size of a known cluster given a set of unsequenced cases. We apply our method to tuberculosis data from Valencia, Spain. Among other applications, we find that clustering can be predicted successfully using spatial distance between cases and whether nationality is the same. We can identify the correct cluster for an unsequenced case, among 38 possible clusters, with an accuracy of approximately 35 %, higher than both direct multinomial regression (17 %) and random selection (< 5 %).
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Affiliation(s)
| | - Paul F Tupper
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
| | - Irving Cancino-Muños
- I2SysBio, University of Valencia-CSIC, Valencia, Spain.,FISABIO Public Health, Valencia, Spain
| | - Mariana G Lòpez
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain
| | - Iñaki Comas
- Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), Valencia, Spain.,Ciber en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Caroline Colijn
- Department of Mathematics, Simon Fraser University, Burnaby, Canada
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25
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Duval A, Opatowski L, Brisse S. Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study. THE LANCET MICROBE 2023; 4:e349-e357. [PMID: 37003286 PMCID: PMC10156608 DOI: 10.1016/s2666-5247(22)00380-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 10/12/2022] [Accepted: 12/09/2022] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Epidemiological surveillance relies on microbial strain typing, which defines genomic relatedness among isolates to identify case clusters and their potential sources. Although predefined thresholds are often applied, known outbreak-specific features such as pathogen mutation rate and duration of source contamination are rarely considered. We aimed to develop a hypothesis-based model that estimates genetic distance thresholds and mutation rates for point-source single-strain food or environmental outbreaks. METHODS In this modelling study, we developed a forward model to simulate bacterial evolution at a specific mutation rate (μ) over a defined outbreak duration (D). From the distribution of genetic distances expected under the given outbreak parameters and sample isolation dates, we estimated a distance threshold beyond which isolates should not be considered as part of the outbreak. We embedded the model into a Markov Chain Monte Carlo inference framework to estimate the most probable mutation rate or time since source contamination, which are both often imprecisely documented. A simulation study validated the model over realistic durations and mutation rates. We then identified and analysed 16 published datasets of bacterial source-related outbreaks; datasets were included if they were from an identified foodborne outbreak and if whole-genome sequence data and collection dates for the described isolates were available. FINDINGS Analysis of simulated data validated the accuracy of our framework in both discriminating between outbreak and non-outbreak cases and estimating the parameters D and μ from outbreak data. Precision of estimation was much higher for high values of D and μ. Sensitivity of outbreak cases was always very high, and specificity in detecting non-outbreak cases was poor for low mutation rates. For 14 of the 16 outbreaks, the classification of isolates as being outbreak-related or sporadic is consistent with the original dataset. Four of these outbreaks included outliers, which were correctly classified as being beyond the threshold of exclusion estimated by our model, except for one isolate of outbreak 4. For two outbreaks, both foodborne Listeria monocytogenes, conclusions from our model were discordant with published results: in one outbreak two isolates were classified as outliers by our model and in another outbreak our algorithm separated food samples into one cluster and human samples into another, whereas the isolates were initially grouped together based on epidemiological and genetic evidence. Re-estimated values of the duration of outbreak or mutation rate were largely consistent with a priori defined values. However, in several cases the estimated values were higher and improved the fit with the observed genetic distance distribution, suggesting that early outbreak cases are sometimes missed. INTERPRETATION We propose here an evolutionary approach to the single-strain conundrum by estimating the genetic threshold and proposing the most probable cluster of cases for a given outbreak, as determined by its particular epidemiological and microbiological properties. This forward model, applicable to foodborne or environmental-source single point case clusters or outbreaks, is useful for epidemiological surveillance and may inform control measures. FUNDING European Union Horizon 2020 Research and Innovation Programme.
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Affiliation(s)
- Audrey Duval
- Epidemiology and Modelling of Bacterial Escape to Antimicrobials Laboratory, Institut Pasteur, Université Paris Cité, Paris, France; Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris Cité, Biodiversity and Epidemiology of Bacterial Pathogens, Paris, France
| | - Lulla Opatowski
- Epidemiology and Modelling of Bacterial Escape to Antimicrobials Laboratory, Institut Pasteur, Université Paris Cité, Paris, France; Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France
| | - Sylvain Brisse
- Institut Pasteur, Université Paris Cité, Biodiversity and Epidemiology of Bacterial Pathogens, Paris, France.
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26
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Jacobson D, Barratt J. Optimizing hierarchical tree dissection parameters using historic epidemiologic data as 'ground truth'. PLoS One 2023; 18:e0282154. [PMID: 36827266 PMCID: PMC9955612 DOI: 10.1371/journal.pone.0282154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/07/2023] [Indexed: 02/25/2023] Open
Abstract
Hierarchical clustering of pathogen genotypes is widely used to complement epidemiologic investigations of outbreaks. Investigators must dissect trees to obtain genetic partitions that provide epidemiologists with meaningful information. Statistical approaches to tree dissection often require a user-defined parameter to predict the optimal partition number and augmenting this parameter can drastically impact resultant partition memberships. Here, we demonstrate how to optimize a given tree dissection parameter to maximize accuracy irrespective of the tree dissection method used. We hierarchically clustered 1,873 genotypes of the foodborne pathogen Cyclospora spp., including 587 possessing links to historic outbreaks. We dissected the resulting tree using a statistical method requiring users to select the value of a 'stringency parameter' (s), with a recommended value of 95% to 99.5%. We dissected this hierarchical tree across s-values from 94% to 99.5% (at increments of 0.25%), to identify a value that maximized partitioning accuracy, defined as the degree to which genetic partitions conform to known epidemiologic groupings. We show that s-values of 96.5% and 96.75% yield the highest accuracy (> 99.9%) when clustering Cyclospora sp. isolates with known epidemiologic linkages. In practice, the optimized s-value will generate robust genetic partitions comprising isolates likely derived from a common food source, even when the epidemiologic grouping is not known prior to genetic clustering. While the s-value is specific to the tree dissection method used here, the optimization approach described could be applied to any parameter/method used to dissect hierarchical trees.
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Affiliation(s)
- David Jacobson
- Parasitic Diseases Branch, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
- Oak Ridge Institute of Science and Education, Oak Ridge, Tennessee, United States of America
- * E-mail:
| | - Joel Barratt
- Parasitic Diseases Branch, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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27
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Hall MB, Rabodoarivelo MS, Koch A, Dippenaar A, George S, Grobbelaar M, Warren R, Walker TM, Cox H, Gagneux S, Crook D, Peto T, Rakotosamimanana N, Grandjean Lapierre S, Iqbal Z. Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis. THE LANCET. MICROBE 2023; 4:e84-e92. [PMID: 36549315 PMCID: PMC9892011 DOI: 10.1016/s2666-5247(22)00301-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 09/07/2022] [Accepted: 10/10/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Mycobacterium tuberculosis whole-genome sequencing (WGS) has been widely used for genotypic drug susceptibility testing (DST) and outbreak investigation. For both applications, Illumina technology is used by most public health laboratories; however, Nanopore technology developed by Oxford Nanopore Technologies has not been thoroughly evaluated. The aim of this study was to determine whether Nanopore sequencing data can provide equivalent information to Illumina for transmission clustering and genotypic DST for M tuberculosis. METHODS In this genomic analysis, we analysed 151 M tuberculosis isolates from Madagascar, South Africa, and England, which were collected between 2011 and 2018, using phenotypic DST and matched Illumina and Nanopore data. Illumina sequencing was done with the MiSeq, HiSeq 2500, or NextSeq500 platforms and Nanopore sequencing was done on the MinION or GridION platforms. Using highly reliable PacBio sequencing assemblies and pairwise distance correlation between Nanopore and Illumina data, we optimise Nanopore variant filters for detecting single-nucleotide polymorphisms (SNPs; using BCFtools software). We then used those SNPs to compare transmission clusters identified by Nanopore with the currently used UK Health Security Agency Illumina pipeline (COMPASS). We compared Illumina and Nanopore WGS-based DST predictions using the Mykrobe software and mutation catalogue. FINDINGS The Nanopore BCFtools pipeline identified SNPs with a median precision of 99·3% (IQR 99·1-99·6) and recall of 90·2% (88·1-94·2) compared with a precision of 99·6% (99·4-99·7) and recall of 91·9% (87·6-98·6) using the Illumina COMPASS pipeline. Using a threshold of 12 SNPs for putative transmission clusters, Illumina identified 98 isolates as unrelated and 53 as belonging to 19 distinct clusters (size range 2-7). Nanopore reproduced 15 out of 19 clusters perfectly; two clusters were merged into one cluster, one cluster had a single sample missing, and one cluster had an additional sample adjoined. Illumina-based clusters were also closely replicated using a five SNP threshold and clustering accuracy was maintained using mixed Illumina and Nanopore datasets. Genotyping resistance variants with Nanopore was highly concordant with Illumina, having zero discordant SNPs across more than 3000 SNPs and four insertions or deletions (indels), across 60 000 indels. INTERPRETATION Illumina and Nanopore technologies can be used independently or together by public health laboratories performing M tuberculosis genotypic DST and outbreak investigations. As a result, clinical and public health institutions making decisions on which sequencing technology to adopt for tuberculosis can base the choice on cost (which varies by country), batching, and turnaround time. FUNDING Academy for Medical Sciences, Oxford Wellcome Institutional Strategic Support Fund, and the Swiss South Africa Joint Research Award (Swiss National Science Foundation and South African National Research Foundation).
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Affiliation(s)
- Michael B Hall
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Marie Sylvianne Rabodoarivelo
- Mycobacteriology Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar; Departamento de Microbiología, Medicina Preventiva y Salud Pública, Universidad de Zaragoza, Zaragoza, Spain
| | - Anastasia Koch
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit and DST-NRF Centre of Excellence for Biomedical TB Research, Department of Pathology, University of Cape Town, Cape Town, South Africa; Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Anzaan Dippenaar
- Department of Science and Innovation-National Research Foundation Centre for Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa; Tuberculosis Omics Research Consortium, Family Medicine and Population Health, Institute of Global Health, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Sophie George
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, UK
| | - Melanie Grobbelaar
- Department of Science and Innovation-National Research Foundation Centre for Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Robin Warren
- Department of Science and Innovation-National Research Foundation Centre for Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Timothy M Walker
- Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
| | - Helen Cox
- Division of Medical Microbiology, Department of Pathology, University of Cape Town, Cape Town, South Africa; Wellcome Centre for Infectious Disease Research in Africa, University of Cape Town, Cape Town, South Africa; Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Sebastien Gagneux
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Derrick Crook
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, UK
| | - Tim Peto
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, UK
| | | | - Simon Grandjean Lapierre
- Mycobacteriology Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar; Department of Microbiology, Infectious Diseases and Immunology, Université de Montréal, Montréal, QC, Canada; Immunopathology Axis, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, QC, Canada
| | - Zamin Iqbal
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
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Killough N, Patterson L, Peacock SJ, Bradley DT. How public health authorities can use pathogen genomics in health protection practice: a consensus-building Delphi study conducted in the United Kingdom. Microb Genom 2023; 9:mgen000912. [PMID: 36745548 PMCID: PMC9997744 DOI: 10.1099/mgen.0.000912] [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: 07/13/2022] [Accepted: 10/13/2022] [Indexed: 02/07/2023] Open
Abstract
Pathogen sequencing guided understanding of SARS-CoV-2 evolution during the COVID-19 pandemic. Many health systems developed pathogen genomics services to monitor SARS-CoV-2. There are no agreed guidelines about how pathogen genomic information should be used in public health practice. We undertook a modified Delphi study in three rounds to develop expert consensus statements about how genomic information should be used. Our aim was to inform health protection policy, planning and practice. Participants were from organisations that produced or used pathogen genomics information in the United Kingdom. The first round posed questions derived from a rapid literature review. Responses informed statements for the subsequent rounds. Consensus was accepted when 70 % or more of the responses were strongly agree/agree, or 70 % were disagree/strongly disagree on the five-point Likert scale. Consensus was achieved in 26 (96 %) of 27 statements. We grouped the statements into six categories: monitoring the emergence of new variants; understanding the epidemiological context of genomic data; using genomic data in outbreak risk assessment and risk management; prioritising the use of limited sequencing capacity; sequencing service performance; and sequencing service capability. The expert consensus statements will help guide public health authorities and policymakers to integrate pathogen genomics in health protection practice.
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Affiliation(s)
| | - Lynsey Patterson
- Public Health Agency, Belfast, UK
- Centre for Public Health, Queen’s University Belfast, Belfast, UK
| | | | | | - Declan T. Bradley
- Public Health Agency, Belfast, UK
- Centre for Public Health, Queen’s University Belfast, Belfast, UK
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Cook KF, Beckett AH, Glaysher S, Goudarzi S, Fearn C, Loveson KF, Elliott S, Wyllie S, Lloyd A, Bicknell K, Lumley S, Chauhan AJ, Robson SC. Multiple pathways of SARS-CoV-2 nosocomial transmission uncovered by integrated genomic and epidemiological analyses during the second wave of the COVID-19 pandemic in the UK. Front Cell Infect Microbiol 2023; 12:1066390. [PMID: 36741977 PMCID: PMC9895378 DOI: 10.3389/fcimb.2022.1066390] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/20/2022] [Indexed: 01/22/2023] Open
Abstract
Introduction Throughout the global COVID-19 pandemic, nosocomial transmission has represented a major concern for healthcare settings and has accounted for many infections diagnosed within hospitals. As restrictions ease and novel variants continue to spread, it is important to uncover the specific pathways by which nosocomial outbreaks occur to understand the most suitable transmission control strategies for the future. Methods In this investigation, SARS-CoV-2 genome sequences obtained from 694 healthcare workers and 1,181 patients were analyzed at a large acute NHS hospital in the UK between September 2020 and May 2021. These viral genomic data were combined with epidemiological data to uncover transmission routes within the hospital. We also investigated the effects of the introduction of the highly transmissible variant of concern (VOC), Alpha, over this period, as well as the effects of the national vaccination program on SARS-CoV-2 infection in the hospital. Results Our results show that infections of all variants within the hospital increased as community prevalence of Alpha increased, resulting in several outbreaks and super-spreader events. Nosocomial infections were enriched amongst older and more vulnerable patients more likely to be in hospital for longer periods but had no impact on disease severity. Infections appeared to be transmitted most regularly from patient to patient and from patients to HCWs. In contrast, infections from HCWs to patients appeared rare, highlighting the benefits of PPE in infection control. The introduction of the vaccine at this time also reduced infections amongst HCWs by over four-times. Discussion These analyses have highlighted the importance of control measures such as regular testing, rapid lateral flow testing alongside polymerase chain reaction (PCR) testing, isolation of positive patients in the emergency department (where possible), and physical distancing of patient beds on hospital wards to minimize nosocomial transmission of infectious diseases such as COVID-19.
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Affiliation(s)
- Kate F. Cook
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom
| | - Angela H. Beckett
- School of Biological Science, University of Portsmouth, Portsmouth, United Kingdom
- Centre for Enzyme Innovation, University of Portsmouth, Portsmouth, United Kingdom
| | - Sharon Glaysher
- Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Salman Goudarzi
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom
| | - Christopher Fearn
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom
| | - Katie F. Loveson
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom
| | - Scott Elliott
- Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Sarah Wyllie
- Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Allyson Lloyd
- Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Kelly Bicknell
- Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Sally Lumley
- Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Anoop J. Chauhan
- Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Samuel C. Robson
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, United Kingdom
- School of Biological Science, University of Portsmouth, Portsmouth, United Kingdom
- Centre for Enzyme Innovation, University of Portsmouth, Portsmouth, United Kingdom
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30
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Luterbach CL, Chen L, Komarow L, Ostrowsky B, Kaye KS, Hanson B, Arias CA, Desai S, Gallagher JC, Novick E, Pagkalinawan S, Lautenbach E, Wortmann G, Kalayjian RC, Eilertson B, Farrell JJ, McCarty T, Hill C, Fowler VG, Kreiswirth BN, Bonomo RA, van Duin D. Transmission of Carbapenem-Resistant Klebsiella pneumoniae in US Hospitals. Clin Infect Dis 2023; 76:229-237. [PMID: 36173830 PMCID: PMC10202433 DOI: 10.1093/cid/ciac791] [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: 05/04/2022] [Revised: 09/08/2022] [Accepted: 09/23/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Carbapenem-resistant Klebsiella pneumoniae (CRKp) is the most prevalent carbapenem-resistant Enterobacterales in the United States. We evaluated CRKp clustering in patients in US hospitals. METHODS From April 2016 to August 2017, 350 patients with clonal group 258 CRKp were enrolled in the Consortium on Resistance Against Carbapenems in Klebsiella and other Enterobacteriaceae, a prospective, multicenter, cohort study. A maximum likelihood tree was constructed using RAxML. Static clusters shared ≤21 single-nucleotide polymorphisms (SNP) and a most recent common ancestor. Dynamic clusters incorporated SNP distance, culture timing, and rates of SNP accumulation and transmission using the R program TransCluster. RESULTS Most patients were admitted from home (n = 150, 43%) or long-term care facilities (n = 115, 33%). Urine (n = 149, 43%) was the most common isolation site. Overall, 55 static and 47 dynamics clusters were identified involving 210 of 350 (60%) and 194 of 350 (55%) patients, respectively. Approximately half of static clusters were identical to dynamic clusters. Static clusters consisted of 33 (60%) intrasystem and 22 (40%) intersystem clusters. Dynamic clusters consisted of 32 (68%) intrasystem and 15 (32%) intersystem clusters and had fewer SNP differences than static clusters (8 vs 9; P = .045; 95% confidence interval [CI]: -4 to 0). Dynamic intersystem clusters contained more patients than dynamic intrasystem clusters (median [interquartile range], 4 [2, 7] vs 2 [2, 2]; P = .007; 95% CI: -3 to 0). CONCLUSIONS Widespread intrasystem and intersystem transmission of CRKp was identified in hospitalized US patients. Use of different methods for assessing genetic similarity resulted in only minor differences in interpretation.
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Affiliation(s)
- Courtney L Luterbach
- Division of Infectious Diseases, University of North Carolina, Chapel Hill, North Carolina, USA
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Liang Chen
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, New Jersey, USA
| | - Lauren Komarow
- Biostatistics Center, George Washington University, Rockville, Maryland, USA
| | - Belinda Ostrowsky
- Division of Infectious Diseases, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Keith S Kaye
- Division of Infectious Diseases, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Blake Hanson
- Division of Infectious Diseases and Center for Antimicrobial Resistance and Microbial Genomics, UTHealth, McGovern School of Medicine at Houston, Houston, Texas, USA
- Center for Infectious Diseases, UTHealth School of Public Health, Houston, Texas, USA
| | - Cesar A Arias
- Division of Infectious Diseases and Center for Antimicrobial Resistance and Microbial Genomics, UTHealth, McGovern School of Medicine at Houston, Houston, Texas, USA
- Center for Infectious Diseases, UTHealth School of Public Health, Houston, Texas, USA
- Molecular Genetics and Antimicrobial Resistance Unit–International Center for Microbial Genomics, Universidad El Bosque, Bogota, Columbia
| | - Samit Desai
- Division of Infectious Diseases, Hackensack University Medical Center, Hackensack, New Jersey, USA
| | - Jason C Gallagher
- Temple University School of Pharmacy, Philadelphia, Pennsylvania, USA
| | - Elizabeth Novick
- Temple University School of Pharmacy, Philadelphia, Pennsylvania, USA
| | | | - Ebbing Lautenbach
- Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Glenn Wortmann
- Section of Infectious Diseases, MedStar Washington Hospital Center, Washington, District of Columbia, USA
| | - Robert C Kalayjian
- Department of Medicine, MetroHealth Medical Center, Cleveland, Ohio, USA
| | - Brandon Eilertson
- Division of Infectious Diseases, Department of Medicine, State University of New York Downstate, Brooklyn, NY, USA
| | - John J Farrell
- Division of Infectious Disease, Department of Internal Medicine, OSF Saint Francis Medical Center, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA
| | - Todd McCarty
- Division of Infectious Disease, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Carol Hill
- Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - Vance G Fowler
- Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - Barry N Kreiswirth
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, New Jersey, USA
| | - Robert A Bonomo
- Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, Ohio, USA
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Departments of Pharmacology, Molecular Biology and Microbiology, Biochemistry, and Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Case Western Reserve University-Cleveland Veterans Affairs Medical Center for Antimicrobial Resistance and Epidemiology (Case VA CARES), Cleveland, Ohio, USA
| | - David van Duin
- Division of Infectious Diseases, University of North Carolina, Chapel Hill, North Carolina, USA
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Talbot BM, Jacko NF, Petit RA, Pegues DA, Shumaker MJ, Read TD, David MZ. Unsuspected Clonal Spread of Methicillin-Resistant Staphylococcus aureus Causing Bloodstream Infections in Hospitalized Adults Detected Using Whole Genome Sequencing. Clin Infect Dis 2022; 75:2104-2112. [PMID: 35510945 DOI: 10.1093/cid/ciac339] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 04/11/2022] [Accepted: 04/27/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Though detection of transmission clusters of methicillin-resistant Staphylococcus aureus (MRSA) infections is a priority for infection control personnel in hospitals, the transmission dynamics of MRSA among hospitalized patients with bloodstream infections (BSIs) has not been thoroughly studied. Whole genome sequencing (WGS) of MRSA isolates for surveillance is valuable for detecting outbreaks in hospitals, but the bioinformatic approaches used are diverse and difficult to compare. METHODS We combined short-read WGS with genotypic, phenotypic, and epidemiological characteristics of 106 MRSA BSI isolates collected for routine microbiological diagnosis from inpatients in 2 hospitals over 12 months. Clinical data and hospitalization history were abstracted from electronic medical records. We compared 3 genome sequence alignment strategies to assess similarity in cluster ascertainment. We conducted logistic regression to measure the probability of predicting prior hospital overlap between clustered patient isolates by the genetic distance of their isolates. RESULTS While the 3 alignment approaches detected similar results, they showed some variation. A gene family-based alignment pipeline was most consistent across MRSA clonal complexes. We identified 9 unique clusters of closely related BSI isolates. Most BSIs were healthcare associated and community onset. Our logistic model showed that with 13 single-nucleotide polymorphisms, the likelihood that any 2 patients in a cluster had overlapped in a hospital was 50%. CONCLUSIONS Multiple clusters of closely related MRSA isolates can be identified using WGS among strains cultured from BSI in 2 hospitals. Genomic clustering of these infections suggests that transmission resulted from a mix of community spread and healthcare exposures long before BSI diagnosis.
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Affiliation(s)
- Brooke M Talbot
- Graduate School of Biological and Biomedical Sciences, Emory University, Atlanta, Georgia, USA
| | - Natasia F Jacko
- Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert A Petit
- Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Pegues
- Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Margot J Shumaker
- Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Timothy D Read
- Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Michael Z David
- Division of Infectious Diseases, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Pan J, Li X, Zhang M, Lu Y, Zhu Y, Wu K, Wu Y, Wang W, Chen B, Liu Z, Wang X, Gao J. TransFlow: a Snakemake workflow for transmission analysis of Mycobacterium tuberculosis whole-genome sequencing data. Bioinformatics 2022; 39:6873737. [PMID: 36469333 PMCID: PMC9825751 DOI: 10.1093/bioinformatics/btac785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 10/26/2022] [Accepted: 12/02/2022] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION Whole-genome sequencing (WGS) is increasingly used to aid the understanding of Mycobacterium tuberculosis (MTB) transmission. The epidemiological analysis of tuberculosis based on the WGS technique requires a diverse collection of bioinformatics tools. Effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts. RESULTS Here, we present TransFlow (Transmission Workflow), a user-friendly, fast, efficient and comprehensive WGS-based transmission analysis pipeline. TransFlow combines some state-of-the-art tools to take transmission analysis from raw sequencing data, through quality control, sequence alignment and variant calling, into downstream transmission clustering, transmission network reconstruction and transmission risk factor inference, together with summary statistics and data visualization in a summary report. TransFlow relies on Snakemake and Conda to resolve dependencies among consecutive processing steps and can be easily adapted to any computation environment. AVAILABILITY AND IMPLEMENTATION TransFlow is free available at https://github.com/cvn001/transflow. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Mingwu Zhang
- The Institute of TB Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Yewei Lu
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, Zhejiang 310020, China
| | - Yelei Zhu
- The Institute of TB Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Kunyang Wu
- The Institute of TB Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Yiwen Wu
- Department of Medical Oncology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China
| | - Weixin Wang
- Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Hangzhou, Zhejiang 310020, China
| | - Bin Chen
- The Institute of TB Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang 310051, China
| | - Zhengwei Liu
- To whom correspondence should be addressed. or or
| | | | - Junshun Gao
- To whom correspondence should be addressed. or or
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Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. PLoS Comput Biol 2022; 18:e1010745. [PMID: 36449514 PMCID: PMC9744331 DOI: 10.1371/journal.pcbi.1010745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 12/12/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.
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Brown TS, Robinson DA, Buckee CO, Mathema B. Connecting the dots: understanding how human mobility shapes TB epidemics. Trends Microbiol 2022; 30:1036-1044. [PMID: 35597716 PMCID: PMC10068677 DOI: 10.1016/j.tim.2022.04.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 01/13/2023]
Abstract
Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.
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Affiliation(s)
- Tyler S Brown
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Infectious Diseases Division, Massachusetts General Hospital, Boston, MA, USA
| | - D Ashley Robinson
- Department of Microbiology and Immunology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Barun Mathema
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
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Shrestha S, Winglee K, Hill AN, Shaw T, Smith JP, Kammerer JS, Silk BJ, Marks SM, Dowdy D. Model-based Analysis of Tuberculosis Genotype Clusters in the United States Reveals High Degree of Heterogeneity in Transmission and State-level Differences Across California, Florida, New York, and Texas. Clin Infect Dis 2022; 75:1433-1441. [PMID: 35143641 PMCID: PMC9412192 DOI: 10.1093/cid/ciac121] [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: 10/01/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Reductions in tuberculosis (TB) transmission have been instrumental in lowering TB incidence in the United States. Sustaining and augmenting these reductions are key public health priorities. METHODS We fit mechanistic transmission models to distributions of genotype clusters of TB cases reported to the Centers for Disease Control and Prevention during 2012-2016 in the United States and separately in California, Florida, New York, and Texas. We estimated the mean number of secondary cases generated per infectious case (R0) and individual-level heterogeneity in R0 at state and national levels and assessed how different definitions of clustering affected these estimates. RESULTS In clusters of genotypically linked TB cases that occurred within a state over a 5-year period (reference scenario), the estimated R0 was 0.29 (95% confidence interval [CI], .28-.31) in the United States. Transmission was highly heterogeneous; 0.24% of simulated cases with individual R0 >10 generated 19% of all recent secondary transmissions. R0 estimate was 0.16 (95% CI, .15-.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R0s were 0.34 (95% CI, .3-.4) in California, 0.28 (95% CI, .24-.36) in Florida, 0.19 (95% CI, .15-.27) in New York, and 0.38 (95% CI, .33-.46) in Texas. CONCLUSIONS TB transmission in the United States is characterized by pronounced heterogeneity at the individual and state levels. Improving detection of transmission clusters through incorporation of whole-genome sequencing and identifying the drivers of this heterogeneity will be essential to reducing TB transmission.
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Affiliation(s)
- Sourya Shrestha
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Kathryn Winglee
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Andrew N Hill
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Tambi Shaw
- California Department of Public Health, Richmond, California, USA
| | - Jonathan P Smith
- Department of Policy and Administration, Yale University, New Haven, Connecticut, USA
| | - J Steve Kammerer
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Benjamin J Silk
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Suzanne M Marks
- Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - David Dowdy
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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36
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Suster CJE, Arnott A, Blackwell G, Gall M, Draper J, Martinez E, Drew AP, Rockett RJ, Chen SCA, Kok J, Dwyer DE, Sintchenko V. Guiding the design of SARS-CoV-2 genomic surveillance by estimating the resolution of outbreak detection. Front Public Health 2022; 10:1004201. [PMID: 36276383 PMCID: PMC9581317 DOI: 10.3389/fpubh.2022.1004201] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/16/2022] [Indexed: 01/27/2023] Open
Abstract
Genomic surveillance of SARS-CoV-2 has been essential to inform public health response to outbreaks. The high incidence of infection has resulted in a smaller proportion of cases undergoing whole genome sequencing due to finite resources. We present a framework for estimating the impact of reduced depths of genomic surveillance on the resolution of outbreaks, based on a clustering approach using pairwise genetic and temporal distances. We apply the framework to simulated outbreak data to show that outbreaks are detected less frequently when fewer cases are subjected to whole genome sequencing. The impact of sequencing fewer cases depends on the size of the outbreaks, and on the genetic and temporal similarity of the index cases of the outbreaks. We also apply the framework to an outbreak of the SARS-CoV-2 Delta variant in New South Wales, Australia. We find that the detection of clusters in the outbreak would have been delayed if fewer cases had been sequenced. Existing recommendations for genomic surveillance estimate the minimum number of cases to sequence in order to detect and monitor new virus variants, assuming representative sampling of cases. Our method instead measures the resolution of clustering, which is important for genomic epidemiology, and accommodates sampling biases.
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Affiliation(s)
- Carl J. E. Suster
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
| | - Alicia Arnott
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Grace Blackwell
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Mailie Gall
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Jenny Draper
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Elena Martinez
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Alexander P. Drew
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Rebecca J. Rockett
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
| | - Sharon C.-A. Chen
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Jen Kok
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Dominic E. Dwyer
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
| | - Vitali Sintchenko
- Centre for Infectious Diseases and Microbiology Public Health, Westmead Hospital, Westmead, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
- Centre for Infectious Diseases and Microbiology Laboratory Services, Institute of Clinical Pathology and Medical Research, NSW Health Pathology, Westmead, NSW, Australia
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Nguyen Thi Nguyen T, Parry CM, Campbell JI, Vinh PV, Kneen R, Baker S. Endemic erythromycin resistant Corynebacterium diphtheriae in Vietnam in the 1990s. Microb Genom 2022; 8:mgen000861. [PMID: 36259695 PMCID: PMC9676054 DOI: 10.1099/mgen.0.000861] [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: 12/23/2021] [Accepted: 06/10/2022] [Indexed: 11/18/2022] Open
Abstract
Diphtheria is a potentially fatal respiratory disease caused by toxigenic forms of the Gram-positive bacterium Corynebacterium diphtheriae. Despite the availability of treatments (antitoxin and antimicrobials) and effective vaccines, the disease still occurs sporadically in low-income countries and in higher income where use of diphtheria vaccine is inconsistent. Diphtheria was highly endemic in Vietnam in the 1990s; here, we aimed to provide some historical context to the circulation of erythromycin resistant organisms in Vietnam during this period. After recovering 54 C. diphtheriae isolated from clinical cases of diphtheria in Ho Chi Minh City between 1992 and 1998 we conducted whole genome sequencing and analysis. Our data outlined substantial genetic diversity among the isolates, illustrated by seven distinct Sequence Types (STs), but punctuated by the sustained circulation of ST67 and ST209. With the exception of one isolate, all sequences contained the tox gene, which was classically located on a corynebacteriophage. All erythromycin resistant isolates, accounting for 13 % of organisms in this study, harboured a novel 18 kb erm(X)-carrying plasmid, which exhibited limited sequence homology to previously described resistance plasmids in C. diphtheriae. Our study provides historic context for the circulation of antimicrobial resistant C. diphtheriae in Vietnam; these data provide a framework for the current trajectory in global antimicrobial resistance trends.
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Affiliation(s)
- To Nguyen Thi Nguyen
- The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Department of Microbiology, Monash University, Melbourne, Victoria, Australia
| | - Christopher M. Parry
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Pl, Liverpool L3 5QA, UK
- Alder Hey Children’s Hospital, NHS Foundation Trust, Liverpool, UK
| | - James I. Campbell
- The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Phat Voong Vinh
- The Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Rachel Kneen
- Alder Hey Children’s Hospital, NHS Foundation Trust, Liverpool, UK
- Institute of Infection and Global Health, University of Liverpool, Liverpool L69 3BX, UK
| | - Stephen Baker
- University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK
- Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK
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38
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Threshold-free genomic cluster detection to track transmission pathways in health-care settings: a genomic epidemiology analysis. THE LANCET MICROBE 2022; 3:e652-e662. [PMID: 35803292 PMCID: PMC9869340 DOI: 10.1016/s2666-5247(22)00115-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 03/31/2022] [Accepted: 04/19/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND A crucial barrier to the routine application of whole-genome sequencing (WGS) for infection prevention is the insufficient criteria for determining whether a genomic linkage is consistent with transmission within the facility. We evaluated the use of single-nucleotide variant (SNV) thresholds, as well as a novel threshold-free approach, for inferring transmission linkages in a high-transmission setting. METHODS We did a retrospective genomic epidemiology analysis of samples previously collected in the context of an intervention study at a long-term acute care hospital in the USA. We performed WGS on 435 isolates of Klebsiella pneumoniae harbouring the blaKPC carbapenemase (KPC-Kp) collected from 256 patients through admission and surveillance culturing (once every 2 weeks) of almost every patient who was admitted to hospital over a 1-year period. FINDINGS Our analysis showed that the standard approach of using an SNV threshold to define transmission would lead to false-positive and false-negative inferences. False-positive inferences were driven by the frequent importation of closely related strains, which were presumably linked via transmission at connected health-care facilities. False-negative inferences stemmed from the diversity of colonising populations that were spread among patients, with multiple examples of hypermutator strain emergence within patients and, as a result, putative transmission links separated by large genetic distances. Motivated by limitations of an SNV threshold, we implemented a novel threshold-free transmission cluster inference approach, in which each of the acquired KPC-Kp isolates were linked back to the imported KPC-Kp isolate with which it shared the most variants. This approach yielded clusters that varied in levels of genetic diversity but where 105 (81%) of 129 unique strain acquisition events were associated with epidemiological links in the hospital. Of 100 patients who acquired KPC-Kp isolates that were included in a cluster, 47 could be linked to a single patient who was positive for KPC-Kp at admission, compared with 31 and 25 using 10 SNV and 20 SNV thresholds, respectively. Holistic examination of clusters highlighted extensive variation in the magnitude of onward transmission stemming from more than 100 importation events and revealed patterns in cluster propagation that could inform improvements to infection prevention strategies. INTERPRETATION Our results show how the integration of culture surveillance data into genomic analyses can overcome limitations of cluster detection based on SNV-thresholds and improve the ability to track pathways of pathogen transmission in health-care settings. FUNDING US Center for Disease Control and Prevention and University of Michigan.
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Leavitt SV, Jenkins HE, Sebastiani P, Lee RS, Horsburgh CR, Tibbs AM, White LF. Estimation of the generation interval using pairwise relative transmission probabilities. Biostatistics 2022; 23:807-824. [PMID: 33527996 PMCID: PMC9291635 DOI: 10.1093/biostatistics/kxaa059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 11/13/2022] Open
Abstract
The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.
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Affiliation(s)
- Sarah V Leavitt
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; Epidemiology Division, University of Toronto Dalla Lana School of Public Health, 155 College St Room 500, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; and Massachusetts Department of Public Health, 250 Washington St, Boston, MA 02108
| | - Helen E Jenkins
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; Epidemiology Division, University of Toronto Dalla Lana School of Public Health, 155 College St Room 500, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; and Massachusetts Department of Public Health, 250 Washington St, Boston, MA 02108
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; Epidemiology Division, University of Toronto Dalla Lana School of Public Health, 155 College St Room 500, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; and Massachusetts Department of Public Health, 250 Washington St, Boston, MA 02108
| | - Robyn S Lee
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; Epidemiology Division, University of Toronto Dalla Lana School of Public Health, 155 College St Room 500, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; and Massachusetts Department of Public Health, 250 Washington St, Boston, MA 02108
| | - C Robert Horsburgh
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; Epidemiology Division, University of Toronto Dalla Lana School of Public Health, 155 College St Room 500, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; and Massachusetts Department of Public Health, 250 Washington St, Boston, MA 02108
| | - Andrew M Tibbs
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; Epidemiology Division, University of Toronto Dalla Lana School of Public Health, 155 College St Room 500, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; and Massachusetts Department of Public Health, 250 Washington St, Boston, MA 02108
| | - Laura F White
- Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; Epidemiology Division, University of Toronto Dalla Lana School of Public Health, 155 College St Room 500, Toronto, ON M5T 3M7, Canada; Department of Epidemiology, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118; and Massachusetts Department of Public Health, 250 Washington St, Boston, MA 02108
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Carroll LM, Pierneef R, Mathole A, Atanda A, Matle I. Genomic Sequencing of Bacillus cereus Sensu Lato Strains Isolated from Meat and Poultry Products in South Africa Enables Inter- and Intranational Surveillance and Source Tracking. Microbiol Spectr 2022; 10:e0070022. [PMID: 35475639 PMCID: PMC9241823 DOI: 10.1128/spectrum.00700-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/06/2022] [Indexed: 12/22/2022] Open
Abstract
Members of the Bacillus cereus sensu lato species complex, also known as the B. cereus group, vary in their ability to cause illness but are frequently isolated from foods, including meat products; however, food safety surveillance efforts that use whole-genome sequencing (WGS) often neglect these potential pathogens. Here, we evaluate the surveillance and source tracking potential of WGS as applied to B. cereus sensu lato by (i) using WGS to characterize B. cereus sensu lato strains isolated during routine surveillance of meat products across South Africa (n = 25) and (ii) comparing the genomes sequenced here to all publicly available, high-quality B. cereus sensu lato genomes (n = 2,887 total genomes). Strains sequenced here were collected from meat products obtained from (i) retail outlets, processing plants, and butcheries across six South African provinces (n = 23) and (ii) imports held at port of entry (n = 2). The 25 strains sequenced here were partitioned into 15 lineages via in silico seven-gene multilocus sequence typing (MLST). While none of the South African B. cereus sensu lato strains sequenced here were identical to publicly available genomes, six MLST lineages contained multiple strains sequenced in this study, which were identical or nearly identical at the whole-genome scale (≤3 core single nucleotide polymorphisms). Five MLST lineages contained (nearly) identical genomes collected from two or three South African provinces; one MLST lineage contained nearly identical genomes from two countries (South Africa and the Netherlands), indicating that B. cereus sensu lato can spread intra- and internationally via foodstuffs. IMPORTANCE Nationwide foodborne pathogen surveillance programs that use high-resolution genomic methods have been shown to provide vast public health and economic benefits. However, Bacillus cereus sensu lato is often overlooked during large-scale routine WGS efforts. Thus, to our knowledge, no studies to date have evaluated the potential utility of WGS for B. cereus sensu lato surveillance and source tracking in foodstuffs. In this preliminary proof-of-concept study, we applied WGS to B. cereus sensu lato strains collected via South Africa's national surveillance program of domestic and imported meat products, and we provide strong evidence that B. cereus sensu lato can be disseminated intra- and internationally via the agro-food supply chain. Our results showcase that WGS has the potential to be used for source tracking of B. cereus sensu lato in foods, although future WGS and metadata collection efforts are needed to ensure that B. cereus sensu lato surveillance initiatives are on par with those of other foodborne pathogens.
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Affiliation(s)
- Laura M. Carroll
- Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
| | - Rian Pierneef
- Biotechnology Platform, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort, South Africa
| | - Aletta Mathole
- Bacteriology Division, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort, South Africa
| | - Abimbola Atanda
- Bacteriology Division, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort, South Africa
| | - Itumeleng Matle
- Bacteriology Division, Agricultural Research Council, Onderstepoort Veterinary Research, Onderstepoort, South Africa
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Menardo F. Understanding drivers of phylogenetic clustering and terminal branch lengths distribution in epidemics of Mycobacterium tuberculosis. eLife 2022; 11:76780. [PMID: 35762734 PMCID: PMC9239681 DOI: 10.7554/elife.76780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Detecting factors associated with transmission is important to understand disease epidemics, and to design effective public health measures. Clustering and terminal branch lengths (TBL) analyses are commonly applied to genomic data sets of Mycobacterium tuberculosis (MTB) to identify sub-populations with increased transmission. Here, I used a simulation-based approach to investigate what epidemiological processes influence the results of clustering and TBL analyses, and whether differences in transmission can be detected with these methods. I simulated MTB epidemics with different dynamics (latency, infectious period, transmission rate, basic reproductive number R0, sampling proportion, sampling period, and molecular clock), and found that all considered factors, except for the length of the infectious period, affect the results of clustering and TBL distributions. I show that standard interpretations of this type of analyses ignore two main caveats: (1) clustering results and TBL depend on many factors that have nothing to do with transmission, (2) clustering results and TBL do not tell anything about whether the epidemic is stable, growing, or shrinking, unless all the additional parameters that influence these metrics are known, or assumed identical between sub-populations. An important consequence is that the optimal SNP threshold for clustering depends on the epidemiological conditions, and that sub-populations with different epidemiological characteristics should not be analyzed with the same threshold. Finally, these results suggest that different clustering rates and TBL distributions, that are found consistently between different MTB lineages, are probably due to intrinsic bacterial factors, and do not indicate necessarily differences in transmission or evolutionary success.
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Affiliation(s)
- Fabrizio Menardo
- Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland
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42
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Whole-genome sequencing of Mycobacterium tuberculosis from Cambodia. Sci Rep 2022; 12:7693. [PMID: 35562174 PMCID: PMC9095694 DOI: 10.1038/s41598-022-10964-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 04/04/2022] [Indexed: 01/07/2023] Open
Abstract
Cambodia has one of the highest tuberculosis (TB) incidence rates in the WHO Western Pacific region. Remarkably though, the prevalence of multidrug-resistant TB (MDR-TB) remains low. We explored the genetic diversity of Mycobacterium tuberculosis (MTB) circulating in this unique setting using whole-genome sequencing (WGS). From October 2017 until January 2018, we collected one hundred sputum specimens from consenting adults older than 21 years of age, newly diagnosed with bacteriologically confirmed TB in 3 districts of Phnom Penh and Takeo provinces of Cambodia before they commence on their TB treatment, where eighty MTB isolates were successfully cultured and sequenced. Majority of the isolates belonged to Lineage 1 (Indo-Oceanic) (69/80, 86.25%), followed by Lineage 2 (East Asian) (10/80, 12.5%) and Lineage 4 (Euro-American) (1/80, 1.25%). Phenotypic resistance to both streptomycin and isoniazid was found in 3 isolates (3/80, 3.75%), while mono-resistance to streptomycin and isoniazid was identical at 2.5% (N = 2 each). None of the isolates tested was resistant to either rifampicin or ethambutol. The specificities of genotypic prediction for resistance to all drugs tested were 100%, while the sensitivities of genotypic resistance predictions to isoniazid and streptomycin were lower at 40% (2/5) and 80% (4/5) respectively. We identified 8 clusters each comprising of two to five individuals all residing in the Takeo province, making up half (28/56, 50%) of all individuals sampled in the province, indicating the presence of multiple ongoing transmission events. All clustered isolates were of Lineage 1 and none are resistant to any of the drugs tested. This study while demonstrating the relevance and utility of WGS in predicting drug resistance and inference of disease transmission, highlights the need to increase the representation of genotype-phenotype TB data from low and middle income countries in Asia and Africa to improve the accuracies for prediction of drug resistance.
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43
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Ko KKK, Chng KR, Nagarajan N. Metagenomics-enabled microbial surveillance. Nat Microbiol 2022; 7:486-496. [PMID: 35365786 DOI: 10.1038/s41564-022-01089-w] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 02/22/2022] [Indexed: 12/13/2022]
Abstract
Lessons learnt from the COVID-19 pandemic include increased awareness of the potential for zoonoses and emerging infectious diseases that can adversely affect human health. Although emergent viruses are currently in the spotlight, we must not forget the ongoing toll of morbidity and mortality owing to antimicrobial resistance in bacterial pathogens and to vector-borne, foodborne and waterborne diseases. Population growth, planetary change, international travel and medical tourism all contribute to the increasing frequency of infectious disease outbreaks. Surveillance is therefore of crucial importance, but the diversity of microbial pathogens, coupled with resource-intensive methods, compromises our ability to scale-up such efforts. Innovative technologies that are both easy to use and able to simultaneously identify diverse microorganisms (viral, bacterial or fungal) with precision are necessary to enable informed public health decisions. Metagenomics-enabled surveillance methods offer the opportunity to improve detection of both known and yet-to-emerge pathogens.
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Affiliation(s)
- Karrie K K Ko
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore.,Department of Microbiology, Singapore General Hospital, Singapore, Singapore.,Department of Molecular Pathology, Singapore General Hospital, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore.,Yong Loo Lin School of Medicine, National Univerisity of Singapore, Singapore, Singapore
| | - Kern Rei Chng
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore.,National Centre for Food Science, Singapore Food Agency, Singapore, Singapore
| | - Niranjan Nagarajan
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore. .,Yong Loo Lin School of Medicine, National Univerisity of Singapore, Singapore, Singapore.
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Senghore M, Chaguza C, Bojang E, Tientcheu PE, Bancroft RE, Lo SW, Gladstone RA, McGee L, Worwui A, Foster-Nyarko E, Ceesay F, Okoi CB, Klugman KP, Breiman RF, Bentley SD, Adegbola R, Antonio M, Hanage WP, Kwambana-Adams BA. Widespread sharing of pneumococcal strains in a rural African setting: proximate villages are more likely to share similar strains that are carried at multiple timepoints. Microb Genom 2022; 8. [PMID: 35119356 PMCID: PMC8942022 DOI: 10.1099/mgen.0.000732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The transmission dynamics of Streptococcus pneumoniae in sub-Saharan Africa are poorly understood due to a lack of adequate epidemiological and genomic data. Here we leverage a longitudinal cohort from 21 neighbouring villages in rural Africa to study how closely related strains of S. pneumoniae are shared among infants. We analysed 1074 pneumococcal genomes isolated from 102 infants from 21 villages. Strains were designated for unique serotype and sequence-type combinations, and we arbitrarily defined strain sharing where the pairwise genetic distance between strains could be accounted for by the mean within host intra-strain diversity. We used non-parametric statistical tests to assess the role of spatial distance and prolonged carriage on strain sharing using a logistic regression model. We recorded 458 carriage episodes including 318 (69.4 %) where the carried strain was shared with at least one other infant. The odds of strain sharing varied significantly across villages (χ2=47.5, df=21, P-value <0.001). Infants in close proximity to each other were more likely to be involved in strain sharing, but we also show a considerable amount of strain sharing across longer distances. Close geographic proximity (<5 km) between shared strains was associated with a significantly lower pairwise SNP distance compared to strains shared over longer distances (P-value <0.005). Sustained carriage of a shared strain among the infants was significantly more likely to occur if they resided in villages within a 5 km radius of each other (P-value <0.005, OR 3.7). Conversely, where both infants were transiently colonized by the shared strain, they were more likely to reside in villages separated by over 15 km (P-value <0.05, OR 1.5). PCV7 serotypes were rare (13.5 %) and were significantly less likely to be shared (P-value <0.001, OR −1.07). Strain sharing was more likely to occur over short geographical distances, especially where accompanied by sustained colonization. Our results show that strain sharing is a useful proxy for studying transmission dynamics in an under-sampled population with limited genomic data. This article contains data hosted by Microreact.
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Affiliation(s)
- Madikay Senghore
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia.,Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Chrispin Chaguza
- Infection Genomics, Wellcome Sanger Institute, Hinxton, UK.,Darwin College, University of Cambridge, Silver Street, Cambridge, UK.,Department of Clinical Infection, Microbiology and Immunology, Institute of Infection and Global Health, University of Liverpool, Liverpool, UK
| | - Ebrima Bojang
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia
| | - Peggy-Estelle Tientcheu
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia
| | - Rowan E Bancroft
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia
| | - Stephanie W Lo
- Infection Genomics, Wellcome Sanger Institute, Hinxton, UK
| | | | - Lesley McGee
- Respiratory Diseases Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Archibald Worwui
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia
| | - Ebenezer Foster-Nyarko
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia
| | - Fatima Ceesay
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia
| | - Catherine Bi Okoi
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia
| | - Keith P Klugman
- Rollins School Public Health, Emory University, Atlanta, USA
| | - Robert F Breiman
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | | | - Richard Adegbola
- Immunisation and Global Health Consulting, RAMBICON, Lagos, Nigeria
| | - Martin Antonio
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia.,Microbiology and Infection Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | - William P Hanage
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
| | - Brenda A Kwambana-Adams
- WHO Regional Reference Laboratory (RRL), West Africa Strategy and Partnership, Medical Research Council Unit the Gambia at the London School of Hygiene and Tropical Medicine, Atlantic Road, Fajara, The Gambia.,NIHR Global Health Research Unit on Mucosal Pathogens, Division of Infection and Immunity, University College London, London, UK
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Leavitt SV, Horsburgh CR, Lee RS, Tibbs AM, White LF, Jenkins HE. What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission? Epidemiology 2022; 33:55-64. [PMID: 34847084 PMCID: PMC8638913 DOI: 10.1097/ede.0000000000001414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission pairs or explored how closely covariates associated with genetic relatedness mirror those associated with transmission. METHODS We simulated a TB-like outbreak with pathogen genetic data and estimated odds ratios (ORs) to correlate each covariate and genetic relatedness. We used a naive Bayes approach to modify the genetic links and nonlinks to resemble the true links and nonlinks more closely and estimated modified ORs with this approach. We compared these two sets of ORs with the true ORs for transmission. Finally, we applied this method to TB data in Hamburg, Germany, and Massachusetts, USA, to find pair-level covariates associated with transmission. RESULTS Using simulations, we found that associations between covariates and genetic relatedness had the same relative magnitudes and directions as the true associations with transmission, but biased absolute magnitudes. Modifying the genetic links and nonlinks reduced the bias and increased the confidence interval widths, more accurately capturing error. In Hamburg and Massachusetts, pairs were more likely to be probable transmission links if they lived in closer proximity, had a shorter time between observations, or had shared ethnicity, social risk factors, drug resistance, or genotypes. CONCLUSIONS We developed a method to improve the use of genetic relatedness as a proxy for transmission, and aid in understanding TB transmission dynamics in low-burden settings.
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Affiliation(s)
- Sarah V Leavitt
- From the Boston University School of Public Health, Department of Biostatistics, Boston, MA
| | - C Robert Horsburgh
- Boston University School of Public Health, Department of Epidemiology, Boston, MA
| | - Robyn S Lee
- University of Toronto, Dalla Lana School of Public Health, Epidemiology Division, Toronto, ON, Canada
| | | | - Laura F White
- From the Boston University School of Public Health, Department of Biostatistics, Boston, MA
| | - Helen E Jenkins
- From the Boston University School of Public Health, Department of Biostatistics, Boston, MA
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46
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Tonkin-Hill G, Ling C, Chaguza C, Salter SJ, Hinfonthong P, Nikolaou E, Tate N, Pastusiak A, Turner C, Chewapreecha C, Frost SDW, Corander J, Croucher NJ, Turner P, Bentley SD. Pneumococcal within-host diversity during colonization, transmission and treatment. Nat Microbiol 2022; 7:1791-1804. [PMID: 36216891 PMCID: PMC9613479 DOI: 10.1038/s41564-022-01238-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022]
Abstract
Characterizing the genetic diversity of pathogens within the host promises to greatly improve surveillance and reconstruction of transmission chains. For bacteria, it also informs our understanding of inter-strain competition and how this shapes the distribution of resistant and sensitive bacteria. Here we study the genetic diversity of Streptococcus pneumoniae within 468 infants and 145 of their mothers by deep sequencing whole pneumococcal populations from 3,761 longitudinal nasopharyngeal samples. We demonstrate that deep sequencing has unsurpassed sensitivity for detecting multiple colonization, doubling the rate at which highly invasive serotype 1 bacteria were detected in carriage compared with gold-standard methods. The greater resolution identified an elevated rate of transmission from mothers to their children in the first year of the child's life. Comprehensive treatment data demonstrated that infants were at an elevated risk of both the acquisition and persistent colonization of a multidrug-resistant bacterium following antimicrobial treatment. Some alleles were enriched after antimicrobial treatment, suggesting that they aided persistence, but generally purifying selection dominated within-host evolution. Rates of co-colonization imply that in the absence of treatment, susceptible lineages outcompeted resistant lineages within the host. These results demonstrate the many benefits of deep sequencing for the genomic surveillance of bacterial pathogens.
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Affiliation(s)
- Gerry Tonkin-Hill
- grid.10306.340000 0004 0606 5382Parasites and Microbes, Wellcome Sanger Institute, Cambridge, UK ,grid.5510.10000 0004 1936 8921Department of Biostatistics, University of Oslo, Blindern, Norway
| | - Clare Ling
- grid.10223.320000 0004 1937 0490Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chrispin Chaguza
- grid.10306.340000 0004 0606 5382Parasites and Microbes, Wellcome Sanger Institute, Cambridge, UK ,grid.47100.320000000419368710Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT USA
| | - Susannah J. Salter
- grid.5335.00000000121885934Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Pattaraporn Hinfonthong
- grid.10223.320000 0004 1937 0490Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - Elissavet Nikolaou
- grid.48004.380000 0004 1936 9764Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK ,grid.1058.c0000 0000 9442 535XInfection and Immunity, Murdoch Children’s Research Institute, Melbourne, Victoria Australia ,grid.1008.90000 0001 2179 088XDepartment of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria Australia
| | - Natalie Tate
- grid.48004.380000 0004 1936 9764Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | | | - Claudia Turner
- grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.459332.a0000 0004 0418 5364Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia
| | - Claire Chewapreecha
- grid.10306.340000 0004 0606 5382Parasites and Microbes, Wellcome Sanger Institute, Cambridge, UK ,grid.10223.320000 0004 1937 0490Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Simon D. W. Frost
- grid.419815.00000 0001 2181 3404Microsoft Research, Redmond, WA USA ,grid.8991.90000 0004 0425 469XLondon School of Hygiene and Tropical Medicine, London, UK
| | - Jukka Corander
- grid.10306.340000 0004 0606 5382Parasites and Microbes, Wellcome Sanger Institute, Cambridge, UK ,grid.5510.10000 0004 1936 8921Department of Biostatistics, University of Oslo, Blindern, Norway ,grid.7737.40000 0004 0410 2071Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Nicholas J. Croucher
- grid.7445.20000 0001 2113 8111MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Paul Turner
- grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK ,grid.459332.a0000 0004 0418 5364Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia
| | - Stephen D. Bentley
- grid.10306.340000 0004 0606 5382Parasites and Microbes, Wellcome Sanger Institute, Cambridge, UK
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Gallego-García P, Varela N, Estévez-Gómez N, De Chiara L, Fernández-Silva I, Valverde D, Sapoval N, Treangen TJ, Regueiro B, Cabrera-Alvargonzález JJ, del Campo V, Pérez S, Posada D. OUP accepted manuscript. Virus Evol 2022; 8:veac008. [PMID: 35242361 PMCID: PMC8889950 DOI: 10.1093/ve/veac008] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/21/2021] [Accepted: 02/04/2022] [Indexed: 11/23/2022] Open
Abstract
A detailed understanding of how and when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced forty-nine SARS-CoV-2 patient samples from ten local clusters in NW Spain for which partial epidemiological information was available and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be one to two viral particles for sample pairs whose donor–recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.
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Affiliation(s)
| | - Nair Varela
- CINBIO, Universidade de Vigo, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Nuria Estévez-Gómez
- CINBIO, Universidade de Vigo, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Loretta De Chiara
- CINBIO, Universidade de Vigo, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
| | - Iria Fernández-Silva
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo 36310, Spain
| | - Diana Valverde
- CINBIO, Universidade de Vigo, Vigo 36310, Spain
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Biochemistry, Genetics, and Immunology, Universidade de Vigo, Vigo 36310, Spain
| | | | | | - Benito Regueiro
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Microbiology, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo 36213, Spain
- Microbiology and Parasitology Department, Medicine and Odontology, Universidade de Santiago, Santiago de Compostela 15782, Spain
| | - Jorge Julio Cabrera-Alvargonzález
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Microbiology, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo 36213, Spain
| | - Víctor del Campo
- Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO
- Department of Preventive Medicine, Complexo Hospitalario Universitario de Vigo (CHUVI), Sergas, Vigo 36213, Spain
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48
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Hoffman S, Lapp Z, Wang J, Snitkin ES. regentrans: a framework and R package for using genomics to study regional pathogen transmission. Microb Genom 2022; 8:000747. [PMID: 35037617 PMCID: PMC8914358 DOI: 10.1099/mgen.0.000747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/22/2021] [Indexed: 11/20/2022] Open
Abstract
Increasing evidence of regional pathogen transmission networks highlights the importance of investigating the dissemination of multidrug-resistant organisms (MDROs) across a region to identify where transmission is occurring and how pathogens move across regions. We developed a framework for investigating MDRO regional transmission dynamics using whole-genome sequencing data and created regentrans, an easy-to-use, open source R package that implements these methods (https://github.com/Snitkin-Lab-Umich/regentrans). Using a dataset of over 400 carbapenem-resistant isolates of Klebsiella pneumoniae collected from patients in 21 long-term acute care hospitals over a one-year period, we demonstrate how to use our framework to gain insights into differences in inter- and intra-facility transmission across different facilities and over time. This framework and corresponding R package will allow investigators to better understand the origins and transmission patterns of MDROs, which is the first step in understanding how to stop transmission at the regional level.
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Affiliation(s)
- Sophie Hoffman
- Department of Computational Medicine and Bioinformatics, University of Michigan, 1150 W. Medical Center Dr., Ann Arbor, MI, 48109-5680, USA
| | - Zena Lapp
- Department of Computational Medicine and Bioinformatics, University of Michigan, 1150 W. Medical Center Dr., Ann Arbor, MI, 48109-5680, USA
| | - Joyce Wang
- Department of Microbiology and Immunology, University of Michigan, 1150 W. Medical Center Dr., Ann Arbor, MI, 48109-5680, USA
| | - Evan S. Snitkin
- Department of Microbiology and Immunology, University of Michigan, 1150 W. Medical Center Dr., Ann Arbor, MI, 48109-5680, USA
- Department of Medicine, Division of Infectious Diseases, University of Michigan, 1150 W. Medical Center Dr., Ann Arbor, MI, 48109-5680, USA
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49
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Coll F. Key variables affecting genetic distance calculations in genomic epidemiology. THE LANCET MICROBE 2021; 2:e486-e487. [DOI: 10.1016/s2666-5247(21)00183-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022] Open
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50
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Wenlock RD, Tausan M, Mann R, Garr W, Preston R, Arnold A, Hoban J, Webb L, Quick C, Beckett A, Loveson K, Glaysher S, Elliott S, Malone C, Cogger B, Easton L, Robson SC, Hassan-Ibrahim MO, Sargent C. Nosocomial or not? A combined epidemiological and genomic investigation to understand hospital-acquired COVID-19 infection on an elderly care ward. Infect Prev Pract 2021; 3:100165. [PMID: 34485893 PMCID: PMC8397489 DOI: 10.1016/j.infpip.2021.100165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/23/2021] [Indexed: 10/27/2022] Open
Abstract
Background COVID-19 has the potential to cause outbreaks in hospitals. Given the comorbid and elderly cohort of patients hospitalized, hospital-acquired COVID-19 infection is often fatal. Pathogen genome sequencing is becoming increasingly important in infection prevention and control (IPC). Aim To inform the understanding of in-hospital SARS-CoV-2 transmission in order to improve IPC practices and to inform the future development of virological testing for IPC. Methods Patients detected COVID-19 positive by polymerase chain reaction on Ward A in April and May 2020 were included with contact tracing to identify other potential cases. Genome sequencing was undertaken for a subgroup of cases. Epidemiological, genomic, and cluster analyses were performed to describe the epidemiology and to identify factors contributing to the outbreak. Findings Fourteen cases were identified on Ward A. Contact tracing identified 16 further patient cases; in addition, eight healthcare workers (HCWs) were identified as being COVID-19 positive through a round of asymptomatic testing. Genome sequencing of 16 of these cases identified viral genomes differing by two single nucleotide polymorphisms or fewer, with further cluster analysis identifying two groups of infection (a five-person group and a six-person group). Conclusion Despite the temporal relationship of cases, genome sequencing identified that not all cases shared transmission events. However, 11 samples were found to be closely related and these likely represented in-hospital transmission. This included three HCWs, thereby confirming transmission between patients and HCWs.
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Affiliation(s)
- R D Wenlock
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - M Tausan
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - R Mann
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - W Garr
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - R Preston
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - A Arnold
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - J Hoban
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - L Webb
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - C Quick
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - A Beckett
- Centre for Enzyme Innovation, University of Portsmouth, Portsmouth, UK
| | - K Loveson
- School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, UK
| | - S Glaysher
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - S Elliott
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - C Malone
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - B Cogger
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | - L Easton
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | | | - S C Robson
- Centre for Enzyme Innovation, University of Portsmouth, Portsmouth, UK.,School of Pharmacy and Biomedical Science, University of Portsmouth, Portsmouth, UK
| | | | - C Sargent
- Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
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