Dataset Information

Genomic sequencing is required for identification of tuberculosis transmission in Hawaii.

ABSTRACT: BACKGROUND:Tuberculosis (TB) caused an estimated 1.4 million deaths and 10.4 million new cases globally in 2015. TB rates in the United States continue to steadily decline, yet rates in the State of Hawaii are perennially among the highest in the nation due to a continuous influx of immigrants from the Western Pacific and Asia. TB in Hawaii is composed of a unique distribution of genetic lineages, with the Beijing and Manila families of Mycobacterium tuberculosis (Mtb) comprising over two-thirds of TB cases. Standard fingerprinting methods (spoligotyping plus 24-loci Mycobacterial Interspersed Repetitive Units-Variable Number Tandem Repeats [MIRU-VNTR] fingerprinting) perform poorly when used to identify actual transmission clusters composed of isolates from these two families. Those typing methods typically group isolates from these families into large clusters of non-linked isolates with identical fingerprints. Next-generation whole-genome sequencing (WGS) provides a new tool for molecular epidemiology that can resolve clusters of isolates with identical spoligotyping and MIRU-VNTR fingerprints. METHODS:We performed WGS and SNP analysis and evaluated epidemiological data to investigate 19 apparent TB transmission clusters in Hawaii from 2003 to 2017 in order to assess WGS' ability to resolve putative Mtb clusters from the Beijing and Manila families. This project additionally investigated MIRU-VNTR allele prevalence to determine why standard Mtb fingerprinting fails to usefully distinguish actual transmission clusters from these two Mtb families. RESULTS:WGS excluded transmission events in seven of these putative clusters, confirmed transmission in eight, and identified both transmission-linked and non-linked isolates in four. For epidemiologically identified clusters, while the sensitivity of MIRU-VNTR fingerprinting for identifying actual transmission clusters was found to be 100%, its specificity was only 28.6% relative to WGS. We identified that the Beijing and Manila families' significantly lower Shannon evenness of MIRU-VNTR allele distributions than lineage 4 was the cause of standard fingerprinting's poor performance when identifying transmission in Beijing and Manila family clusters. CONCLUSIONS:This study demonstrated that WGS is necessary for epidemiological investigation of TB in Hawaii and the Pacific.

SUBMITTER: Koster KJ

PROVIDER: S-EPMC6276198 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Genomic sequencing is required for identification of tuberculosis transmission in Hawaii.

Koster Kent J KJ Largen Angela A Foster Jeffrey T JT Drees Kevin P KP Qian Lishi L Desmond Ed E Wan Xuehua X Hou Shaobin S Douglas James T JT

BMC infectious diseases 20181203 1

<h4>Background</h4>Tuberculosis (TB) caused an estimated 1.4 million deaths and 10.4 million new cases globally in 2015. TB rates in the United States continue to steadily decline, yet rates in the State of Hawaii are perennially among the highest in the nation due to a continuous influx of immigrants from the Western Pacific and Asia. TB in Hawaii is composed of a unique distribution of genetic lineages, with the Beijing and Manila families of Mycobacterium tuberculosis (Mtb) comprising over tw ...[more]

PMID: 30509214

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Project description:Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet, there is no current consensus on how to measure genomic variation. To test the effect of the variant-identification approach on transmission inferences for Mycobacterium tuberculosis, we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak sequence data. We compared the variants identified by each group in addition to transmission and phylogenetic inferences made with each variant set. To measure the performance of commonly used variant-identification tools, we simulated an outbreak. We compared the performance of three mapping algorithms, five variant callers and two variant filters in recovering true outbreak variants. Finally, we investigated the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant-calling approaches used by different groups do not recover consistent sets of variants, which can lead to conflicting transmission inferences. Further, performance in recovering true variation varied widely across approaches. While no single variant-identification approach outperforms others in both recovering true genome-wide and outbreak-level variation, variant-identification algorithms calibrated upon real sequence data or that incorporate local reassembly outperform others in recovering true pairwise differences between isolates. The choice of variant filters contributed to extensive differences across pipelines, and applying increasingly stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. Commonly used approaches to identify M. tuberculosis genomic variation have variable performance, particularly when predicting potential transmission links from pairwise genetic distances. Phylogenetic reconstruction may be improved by less stringent variant filtering. Approaches that improve variant identification in repetitive, hypervariable regions, such as long-read assemblies, may improve transmission inference.

Project description:BackgroundWhole genome sequencing (WGS) is becoming an important part of epidemiological investigations of infectious diseases due to greater resolution and cost reductions compared to traditional typing approaches. Many public health and clinical teams will increasingly use WGS to investigate clusters of potential pathogen transmission, making it crucial to understand the benefits and assumptions of the analytical methods for investigating the data. We aimed to understand how different approaches affect inferences of transmission dynamics and outline limitations of the methods.MethodsWe comprehensively searched electronic databases for studies that presented methods used to interpret WGS data for investigating tuberculosis (TB) transmission. Two authors independently selected studies for inclusion and extracted data. Due to considerable methodological heterogeneity between studies, we present summary data with accompanying narrative synthesis rather than pooled analyses.ResultsTwenty-five studies met our inclusion criteria. Despite the range of interpretation tools, the usefulness of WGS data in understanding TB transmission often depends on the amount of genetic diversity in the setting. Where diversity is small, distinguishing re-infections from relapses may be impossible; interpretation may be aided by the use of epidemiological data, examining minor variants and deep sequencing. Conversely, when within-host diversity is large, due to genetic hitchhiking or co-infection of two dissimilar strains, it is critical to understand how it arose. Greater understanding of microevolution and mixed infection will enhance interpretation of WGS data.ConclusionsAs sequencing studies have sampled more intensely and integrated multiple sources of information, the understanding of TB transmission and diversity has grown, but there is still much to be learnt about the origins of diversity that will affect inferences from these data. Public health teams and researchers should combine epidemiological, clinical and WGS data to strengthen investigations of transmission.

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Genomic sequencing is required for identification of tuberculosis transmission in Hawaii.

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Genomic sequencing is required for identification of tuberculosis transmission in Hawaii.

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