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Whole genome sequencing of Mycobacterium tuberculosis reveals slow growth and low mutation rates during latent infections in humans.

ABSTRACT: Very little is known about the growth and mutation rates of Mycobacterium tuberculosis during latent infection in humans. However, studies in rhesus macaques have suggested that latent infections have mutation rates that are higher than that observed during active tuberculosis disease. Elevated mutation rates are presumed risk factors for the development of drug resistance. Therefore, the investigation of mutation rates during human latency is of high importance. We performed whole genome mutation analysis of M. tuberculosis isolates from a multi-decade tuberculosis outbreak of the New Zealand Rangipo strain. We used epidemiological and phylogenetic analysis to identify four cases of tuberculosis acquired from the same index case. Two of the tuberculosis cases occurred within two years of exposure and were classified as recently transmitted tuberculosis. Two other cases occurred more than 20 years after exposure and were classified as reactivation of latent M. tuberculosis infections. Mutation rates were compared between the two recently transmitted pairs versus the two latent pairs. Mean mutation rates assuming 20 hour generation times were 5.5 X 10(-10) mutations/bp/generation for recently transmitted tuberculosis and 7.3 X 10(-11) mutations/bp/generation for latent tuberculosis. Generation time versus mutation rate curves were also significantly higher for recently transmitted tuberculosis across all replication rates (p = 0.006). Assuming identical replication and mutation rates among all isolates in the final two years before disease reactivation, the u 20 hr mutation rate attributable to the remaining latent period was 1.6 × 10(-11) mutations/bp/generation, or approximately 30 fold less than that calculated during the two years immediately before disease. Mutations attributable to oxidative stress as might be caused by bacterial exposure to the host immune system were not increased in latent infections. In conclusion, we did not find any evidence to suggest elevated mutation rates during tuberculosis latency in humans, unlike the situation in rhesus macaques.

SUBMITTER: Colangeli R

PROVIDER: S-EPMC3949705 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Whole genome sequencing of Mycobacterium tuberculosis reveals slow growth and low mutation rates during latent infections in humans.

Colangeli Roberto R Arcus Vic L VL Cursons Ray T RT Ruthe Ali A Karalus Noel N Coley Kathy K Manning Shannon D SD Kim Soyeon S Marchiano Emily E Alland David D

PloS one 20140311 3

Very little is known about the growth and mutation rates of Mycobacterium tuberculosis during latent infection in humans. However, studies in rhesus macaques have suggested that latent infections have mutation rates that are higher than that observed during active tuberculosis disease. Elevated mutation rates are presumed risk factors for the development of drug resistance. Therefore, the investigation of mutation rates during human latency is of high importance. We performed whole genome mutati ...[more]

PMID: 24618815

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Project description:BACKGROUND:Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment. Detection of mixed infections has been limited to molecular genotyping techniques, which lack the sensitivity and resolution to accurately estimate the multiplicity of TB infections. In contrast, whole genome sequencing offers sensitive views of the genetic differences between strains of M. tuberculosis within a sample. Although metagenomic tools exist to classify strains in a metagenomic sample, most tools have been developed for more divergent species, and therefore cannot provide the sensitivity required to disentangle strains within closely related bacterial species such as M. tuberculosis. Here we present QuantTB, a method to identify and quantify individual M. tuberculosis strains in whole genome sequencing data. QuantTB uses SNP markers to determine the combination of strains that best explain the allelic variation observed in a sample. QuantTB outputs a list of identified strains, their corresponding relative abundances, and a list of drugs for which resistance-conferring mutations (or heteroresistance) have been predicted within the sample. RESULTS:We show that QuantTB has a high degree of resolution and is capable of differentiating communities differing by less than 25 SNPs and identifying strains down to 1× coverage. Using simulated data, we found QuantTB outperformed other metagenomic strain identification tools at detecting strains and quantifying strain multiplicity. In a real-world scenario, using a dataset of 50 paired clinical isolates from a study of patients with either reinfections or relapses, we found that QuantTB could detect mixed infections and reinfections at rates concordant with a manually curated approach. CONCLUSION:QuantTB can determine infection multiplicity, identify hetero-resistance patterns, enable differentiation between relapse and re-infection, and clarify transmission events across seemingly unrelated patients - even in low-coverage (1×) samples. QuantTB outperforms existing tools and promises to serve as a valuable resource for both clinicians and researchers working with clinical TB samples.

Project description: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.

Project description:Mixed infections of Mycobacterium tuberculosis, defined as the coexistence of multiple genetically distinct strains within a single host, have been associated with unfavorable treatment outcomes. Different methods have been used to detect mixed infections, but their performances have not been carefully evaluated. To compare the sensitivity of whole-genome sequencing (WGS) and variable-number tandem repeats (VNTR) typing to detect mixed infections, we prepared 10 artificial samples composed of DNA mixtures from two strains in different proportions and retrospectively collected 1,084 clinical isolates. The limit of detection (LOD) for the presence of a minor strain was 5% for both WGS and VNTR typing. The overall clinical detection rate of mixed infections was 3.7% (40/1,084) for the two methods combined, WGS identified 37/1,084 (3.4%), and VNTR typing identified 14/1,084 (1.3%), including 11 also identified by WGS. Multivariate analysis demonstrated that retreatment patients had a 2.7 times (95% confidence interval [CI], 1.2 to 6.0) higher risk of mixed infections than new cases. Collectively, WGS is a more reliable tool to identify mixed infections than VNTR typing, and mixed infections are more common in retreated patients. IMPORTANCE Mixed infections of M. tuberculosis have the potential to render treatment regimens ineffective and affect the transmission dynamics of the disease. VNTR typing, currently the most widely used method for the detection of mixed infections, detects mixed infections only by interrogating a small fraction of the M. tuberculosis genome, which necessarily limits sensitivity. With the introduction of WGS, it became possible to study the entire genome, but no quantitative comparison has yet been undertaken. Our systematic comparison of the ability of WGS and VNTR typing to detect mixed infections, using both artificial samples and clinical isolates, revealed the superior performance of WGS at a high sequencing depth (~100×) and found that mixed infections are more common in patients being retreated for tuberculosis (TB) in the populations studied. This provides valuable information for the application of WGS in the detection of mixed infections and the implications of mixed infections for tuberculosis control.

Dataset Information

Whole genome sequencing of Mycobacterium tuberculosis reveals slow growth and low mutation rates during latent infections in humans.

Publications

Whole genome sequencing of Mycobacterium tuberculosis reveals slow growth and low mutation rates during latent infections in humans.

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