Human-associated microbial communities comprise not only complex mixtures of bacterial species, but also mixtures of conspecific strains, the implications of which are mostly unknown since strain level dynamics are underexplored due to the difficulties of studying them. We introduce the Strain Genome Explorer (StrainGE) toolkit, which deconvolves strain mixtures and characterizes component strains at the nucleotide level from short-read metagenomic sequencing with higher sensitivity and resolution than other tools. StrainGE is able to identify strains at 0.1x coverage and detect variants for multiple conspecific strains within a sample from coverages as low as 0.5x.

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.

A more complete understanding of the genetic basis of drug resistance in Mycobacterium tuberculosis is critical for prompt diagnosis and optimal treatment, particularly for toxic second-line drugs such as D-cycloserine. Here we used the whole-genome sequences from 498 strains of M. tuberculosis to identify new resistance-conferring genotypes. By combining association and correlated evolution tests with strategies for amplifying signal from rare variants, we found that loss-of-function mutations in ald (Rv2780), encoding L-alanine dehydrogenase, were associated with unexplained drug resistance. Convergent evolution of this loss of function was observed exclusively among multidrug-resistant strains. Drug susceptibility testing established that ald loss of function conferred resistance to D-cycloserine, and susceptibility to the drug was partially restored by complementation of ald. Clinical strains with mutations in ald and alr exhibited increased resistance to D-cycloserine when cultured in vitro. Incorporation of D-cycloserine resistance in novel molecular diagnostics could allow for targeted use of this toxic drug among patients with susceptible infections.