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Genomic variant-identification methods may alter Mycobacterium tuberculosis transmission inferences.

ABSTRACT: 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.

SUBMITTER: Walter KS

PROVIDER: S-EPMC7641424 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Genomic variant-identification methods may alter <i>Mycobacterium tuberculosis</i> transmission inferences.

Walter Katharine S KS Colijn Caroline C Cohen Ted T Mathema Barun B Liu Qingyun Q Bowers Jolene J Engelthaler David M DM Narechania Apurva A Lemmer Darrin D Croda Julio J Andrews Jason R JR

Microbial genomics 20200731 8

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 <i>Mycobacterium tuberculosis,</i> we conducted an experiment in which five genomic epidemiology groups applied variant-identification pipelines to the same outbreak se ...[more]

PMID: 32735210

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Project description:BackgroundTuberculosis (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.MethodsWe 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.ResultsWGS 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.ConclusionsThis study demonstrated that WGS is necessary for epidemiological investigation of TB in Hawaii and the Pacific.

Project description:BackgroundDue to excessive antibiotic use, drug-resistant Mycobacterium tuberculosis has become a serious public health threat and a major obstacle to disease control in many countries. To better understand the evolution of drug-resistant M. tuberculosis strains, we performed whole genome sequencing for 7 M. tuberculosis clinical isolates with different antibiotic resistance profiles and conducted comparative genomic analysis of gene variations among them.ResultsWe observed that all 7 M. tuberculosis clinical isolates with different levels of drug resistance harbored similar numbers of SNPs, ranging from 1409-1464. The numbers of insertion/deletions (Indels) identified in the 7 isolates were also similar, ranging from 56 to 101. A total of 39 types of mutations were identified in drug resistance-associated loci, including 14 previously reported ones and 25 newly identified ones. Sixteen of the identified large Indels spanned PE-PPE-PGRS genes, which represents a major source of antigenic variability. Aside from SNPs and Indels, a CRISPR locus with varied spacers was observed in all 7 clinical isolates, suggesting that they might play an important role in plasticity of the M. tuberculosis genome. The nucleotide diversity (Л value) and selection intensity (dN/dS value) of the whole genome sequences of the 7 isolates were similar. The dN/dS values were less than 1 for all 7 isolates (range from 0.608885 to 0.637365), supporting the notion that M. tuberculosis genomes undergo purifying selection. The Л values and dN/dS values were comparable between drug-susceptible and drug-resistant strains.ConclusionsIn this study, we show that clinical M. tuberculosis isolates exhibit distinct variations in terms of the distribution of SNP, Indels, CRISPR-cas locus, as well as the nucleotide diversity and selection intensity, but there are no generalizable differences between drug-susceptible and drug-resistant isolates on the genomic scale. Our study provides evidence strengthening the notion that the evolution of drug resistance among clinical M. tuberculosis isolates is clearly a complex and diversified process.

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Genomic variant-identification methods may alter Mycobacterium tuberculosis transmission inferences.

Publications

Genomic variant-identification methods may alter <i>Mycobacterium tuberculosis</i> transmission inferences.

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