scholarly journals A quantitative evaluation of MIRU-VNTR typing against whole-genome sequencing for identifying Mycobacterium tuberculosis transmission: A prospective observational cohort study

2018 ◽  
Author(s):  
David Wyllie ◽  
Jennifer Davidson ◽  
Tim Walker ◽  
Preeti Rathod ◽  
Derrick Crook ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Mycobacterium Tuberculosis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Variable Number Tandem Repeat ◽  
Research Centre ◽  
Whole Genome ◽  
Prospective Comparison ◽  
Epidemiological Risk

SummaryBackgroundMycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR) typing is widely used in high-income countries for Mycobacterium tuberculosis typing. Whole-genome sequencing (WGS) is known to deliver greater specificity, but no quantitative prospective comparison has yet been undertaken.MethodsWe studied isolates from the English Midlands, sampled consecutively between 1 January 2012 and 31 December 2015. In addition to routinely performed MIRU-VNTR typing, DNA was extracted from liquid cultures and sequenced using Illumina technology. Demographic and epidemiological data were extracted from the Enhanced Tuberculosis Surveillance system maintained by Public Health England. Closely related samples, defined using a threshold of five single nucleotide variants (SNVs), were compared to samples with identical MIRU-VNTR profiles, with shared epidemiological risk factors, and to those with both characteristics.Findings1,999 patients were identified for whom at least one M. tuberculosis isolate had been MIRU-VNTR typed and sequenced. Comparing epidemiological risk factors with close genetic relatedness, only coresidence had a positive predictive value of over 5%. Excluding co-resident individuals, 18.6% of patients with identical MIRU-VNTR profiles were within 5 SNVs. Where patients also shared social risk factors and ethnic group, this rose to 48%. Only 8% of MIRU-VNTR linked pairs in lineage 1 were within 5 SNV, compared to 31% in lineage 4.InterpretationIn the setting studied, MIRU-VNTR typing and epidemiological risk factors are poorly predictive of close genomic relatedness, assessed by SNV. MIRU-VNTR performance varies markedly by lineage.FundingPublic Health England, National Institute of Health Research Oxford Biomedical Research Centre.

scholarly journals SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

2021 ◽  
Author(s):  
Einar Gabbasov ◽  
Miguel Moreno-Molina ◽  
Iñaki Comas ◽  
Maxwell Libbrecht ◽  
Leonid Chindelevitch
Keyword(s):  
Public Health ◽  
Mycobacterium Tuberculosis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Bacterial Pathogens ◽  
Superior Performance ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Multiple Strains

AbstractThe occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited.In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains.We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens, and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology.Author summaryWhen multiple strains of a pathogenic organism are present in a patient, it may be necessary to not only detect this, but also to identify the individual strains. However, this problem has not yet been solved for bacterial pathogens processed via whole-genome sequencing. In this paper, we propose the SplitStrains algorithm for detecting multiple strains in a sample, identifying their proportions, and inferring their sequences, in the case of Mycobacterium tuberculosis. We test it on both simulated and real data, with encouraging results. We believe that our work opens new horizons in public health microbiology by allowing a more precise detection, identification and quantification of multiple infecting strains within a sample.

scholarly journals Mycobacterium tuberculosis Next-Generation Whole Genome Sequencing: Opportunities and Challenges

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Thato Iketleng ◽  
Richard Lessells ◽  
Mlungisi Thabiso Dlamini ◽  
Tuelo Mogashoa ◽  
Lucy Mupfumi ◽  
...  
Keyword(s):  
Public Health ◽  
Drug Resistance ◽  
Mycobacterium Tuberculosis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Patient Management ◽  
Drug Susceptibility ◽  
Whole Genome ◽  
Next Generation ◽  
The Impact

Mycobacterium tuberculosis drug resistance is a threat to global tuberculosis (TB) control. Comprehensive and timely drug susceptibility determination is critical to inform appropriate treatment of drug-resistant tuberculosis (DR-TB). Phenotypic drug susceptibility testing (DST) is the gold standard for M. tuberculosis drug resistance determination. M. tuberculosis whole genome sequencing (WGS) has the potential to be a one-stop method for both comprehensive DST and epidemiological investigations. We discuss in this review the tremendous opportunities that next-generation WGS presents in terms of understanding the molecular epidemiology of tuberculosis and mechanisms of drug resistance. The potential clinical value and public health impact in the areas of DST for patient management and tracing of transmission chains for timely public health intervention are also discussed. We present the current challenges for the implementation of WGS in low and middle-income settings. WGS analysis has already been adapted routinely in laboratories to inform patient management and public health interventions in low burden high-income settings such as the United Kingdom. We predict that the technology will be adapted similarly in high burden settings where the impact on the epidemic will be greatest.

scholarly journals Expanding U.S. Laboratory Capacity for Neisseria gonorrhoeae Antimicrobial Susceptibility Testing and Whole-Genome Sequencing through the CDC's Antibiotic Resistance Laboratory Network

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Ellen N. Kersh ◽  
Cau D. Pham ◽  
John R. Papp ◽  
Robert Myers ◽  
Richard Steece ◽  
...  
Keyword(s):  
Public Health ◽  
Antibiotic Resistance ◽  
Neisseria Gonorrhoeae ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Antimicrobial Susceptibility ◽  
Susceptibility Testing ◽  
Whole Genome ◽  
Content Type ◽  
Laboratory Capacity

ABSTRACT U.S. gonorrhea rates are rising, and antibiotic-resistant Neisseria gonorrhoeae (AR-Ng) is an urgent public health threat. Since implementation of nucleic acid amplification tests for N. gonorrhoeae identification, the capacity for culturing N. gonorrhoeae in the United States has declined, along with the ability to perform culture-based antimicrobial susceptibility testing (AST). Yet AST is critical for detecting and monitoring AR-Ng. In 2016, the CDC established the Antibiotic Resistance Laboratory Network (AR Lab Network) to shore up the national capacity for detecting several resistance threats including N. gonorrhoeae. AR-Ng testing, a subactivity of the CDC’s AR Lab Network, is performed in a tiered network of approximately 35 local laboratories, four regional laboratories (state public health laboratories in Maryland, Tennessee, Texas, and Washington), and the CDC’s national reference laboratory. Local laboratories receive specimens from approximately 60 clinics associated with the Gonococcal Isolate Surveillance Project (GISP), enhanced GISP (eGISP), and the program Strengthening the U.S. Response to Resistant Gonorrhea (SURRG). They isolate and ship up to 20,000 isolates to regional laboratories for culture-based agar dilution AST with seven antibiotics and for whole-genome sequencing of up to 5,000 isolates. The CDC further examines concerning isolates and monitors genetic AR markers. During 2017 and 2018, the network tested 8,214 and 8,628 N. gonorrhoeae isolates, respectively, and the CDC received 531 and 646 concerning isolates and 605 and 3,159 sequences, respectively. In summary, the AR Lab Network supported the laboratory capacity for N. gonorrhoeae AST and associated genetic marker detection, expanding preexisting notification and analysis systems for resistance detection. Continued, robust AST and genomic capacity can help inform national public health monitoring and intervention.

scholarly journals Whole Genome Sequencing Refines Knowledge on the Population Structure of Mycobacterium bovis from a Multi-Host Tuberculosis System

2021 ◽  
Vol 9 (8) ◽  
pp. 1585
Author(s):  
Ana C. Reis ◽  
Liliana C. M. Salvador ◽  
Suelee Robbe-Austerman ◽  
Rogério Tenreiro ◽  
Ana Botelho ◽  
...  
Keyword(s):  
Population Structure ◽  
Whole Genome Sequencing ◽  
Wild Boar ◽  
Genome Sequencing ◽  
Mycobacterium Bovis ◽  
Red Deer ◽  
Variable Number Tandem Repeat ◽  
Variant Calling ◽  
Whole Genome ◽  
Network Analyses

Classical molecular analyses of Mycobacterium bovis based on spoligotyping and Variable Number Tandem Repeat (MIRU-VNTR) brought the first insights into the epidemiology of animal tuberculosis (TB) in Portugal, showing high genotypic diversity of circulating strains that mostly cluster within the European 2 clonal complex. Previous surveillance provided valuable information on the prevalence and spatial occurrence of TB and highlighted prevalent genotypes in areas where livestock and wild ungulates are sympatric. However, links at the wildlife–livestock interfaces were established mainly via classical genotype associations. Here, we apply whole genome sequencing (WGS) to cattle, red deer and wild boar isolates to reconstruct the M. bovis population structure in a multi-host, multi-region disease system and to explore links at a fine genomic scale between M. bovis from wildlife hosts and cattle. Whole genome sequences of 44 representative M. bovis isolates, obtained between 2003 and 2015 from three TB hotspots, were compared through single nucleotide polymorphism (SNP) variant calling analyses. Consistent with previous results combining classical genotyping with Bayesian population admixture modelling, SNP-based phylogenies support the branching of this M. bovis population into five genetic clades, three with apparent geographic specificities, as well as the establishment of an SNP catalogue specific to each clade, which may be explored in the future as phylogenetic markers. The core genome alignment of SNPs was integrated within a spatiotemporal metadata framework to further structure this M. bovis population by host species and TB hotspots, providing a baseline for network analyses in different epidemiological and disease control contexts. WGS of M. bovis isolates from Portugal is reported for the first time in this pilot study, refining the spatiotemporal context of TB at the wildlife–livestock interface and providing further support to the key role of red deer and wild boar on disease maintenance. The SNP diversity observed within this dataset supports the natural circulation of M. bovis for a long time period, as well as multiple introduction events of the pathogen in this Iberian multi-host system.

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