Detection of Low-Level Mixed-Population Drug Resistance in Mycobacterium tuberculosis Using High Fidelity Amplicon Sequencing

Abstract

Undetected and untreated, low-levels of drug resistant (DR) subpopulations in clinical Mycobacterium tuberculosis (Mtb) infections may lead to development of DR-tuberculosis, potentially resulting in treatment failure. Current phenotypic DR susceptibility testing has a theoretical potential for 1% sensitivity, is not quantitative, and requires several weeks to complete. The use of "single molecule-overlapping reads" (SMOR) analysis with next generation DNA sequencing for determination of ultra-rare target alleles in complex mixtures provides increased sensitivity over standard DNA sequencing. Ligation free amplicon sequencing with SMOR analysis enables the detection of resistant allele subpopulations at ≥0.1% of the total Mtb population in near real-time analysis. We describe the method using standardized mixtures of DNA from resistant and susceptible Mtb isolates and the assay's performance for detecting ultra-rare DR subpopulations in DNA extracted directly from clinical sputum samples. SMOR analysis enables rapid near real-time detection and tracking of previously undetectable DR sub-populations in clinical samples allowing for the evaluation of the clinical relevance of low-level DR subpopulations. This will provide insights into interventions aimed at suppressing minor DR subpopulations before they become clinically significant.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Illustration of universal tail approach.

In a multiplex PCR, all M. tuberculosis gene specific targets are amplified with universal tailed primers. A second PCR extends the amplicon with a sample specific index that allows for several samples to be pooled together for sequencing. GS: gene specific primer; UT: universal tail sequence; Index A: Primer that includes the UT1 sequence and Illumina index sequence; Index. B: primer that includes the UT2 sequence and the Illumina sequence to bind to the flowcell.

Fig 2. Comparison of standard sequencing and SMOR analysis.

The percentage of base calls for the resistant allele compared to the erroneous alleles at six resistant SNP loci in five different genes, five replicates each, is shown. All 36 SNP loci were examined; however most SNP loci are consistent between the resistant and susceptible strains used in the mixtures. The seven different mixtures contain six known allelic differences in resistant conferring loci. Each circle represents the percent of calls for a particular allele for each replicate and the color represents the type of allele. The pure control was the pan susceptible isolate DNA 2–0112. A. Results from standard sequencing analysis, ignoring read pair information. B. Results from SMOR analysis. For the means and standard deviations see S6 Table.

Fig 3. SMOR minor subpopulation examination of a single replicate of 7 mixtures at six known differing resistance SNP loci.

Each circle represents the percent SMOR call, where color represents allele state, for a single sample at the six resistant SNP loci. All 36 SNP loci were examined; however the seven different mixtures contain six known allelic differences in resistant conferring SNP loci.

Fig 4. Variation of erroneous alleles.

Average frequency (five replicates) of the resistant allele compared to the erroneous alleles at six specific SNP loci from the 0.05:99.95 XDR:Pan-susceptible mixture. Each error bar is constructed using 1 standard deviation from the mean.

Fig 5. Minor subpopulation detection in two sputum samples from Moldova.

Resistant and erroneous allele frequencies from three resistance SNP loci in the inhA promoter are shown. Patient 21–0067 with 0.05% resistant allele and patient 22–0129 with 11.39% resistant allele at inhA -15, compared to erroneous and resistant alleles below 0.01% at the other two SNP positions.