Abstract
BackgroundMixed 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.ResultsWe 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.ConclusionQuantTB 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.
Highlights
Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment
The six strains that comprise lineage 7 had a wide range of genetic diversity, suggesting the need for increased sequencing of less well-characterized lineages, Fig. 2 a Number of representatives from each lineage amongst all 5637 M. tuberculosis assemblies in our reference database. b Intra-lineage pairwise distance for each lineage as measured by the number of unique Single nucleotide polymorphism (SNP) between a pair
As genomes from the d50 database were used as test samples and tested against genomes in the d10small database, we evaluated the accuracy of strain predictions by assigning a true positive to each strain in a sample if QuantTB predicted the ‘correct’ relative genome in the d10small database
Summary
Mixed infections of Mycobacterium tuberculosis and antibiotic heteroresistance continue to complicate tuberculosis (TB) diagnosis and treatment. The World Health Organization’s End TB Strategy calls for a 95% reduction of TB deaths by 2035, a feat that will require more innovative and effective methods to treat, control and diagnose the disease [1]. For centuries it was assumed TB patients were infected with a single strain of Mycobacterium tuberculosis, the causative bacteria of TB. Mixed infections can complicate treatment and diagnosis through heteroresistance (presence of both drug susceptible and resistant patterns), which can cause false negatives in drug susceptibility tests and enable the spread of antibiotic resistance when left undetected [8,9,10]. Accurate detection of strains within a mixed infection, as well as their distinct resistance patterns, is important for decreasing the worldwide TB burden and slowing the spread of drug resistance
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