Direct detection of drug-resistant Mycobacterium tuberculosis using targeted next generation sequencing

Abstract

Mycobacterium tuberculosis complex (MTBC) infections are treated with combinations of antibiotics; however, these regimens are not as efficacious against multidrug and extensively drug resistant MTBC. Phenotypic (growth-based) drug susceptibility testing on slow growing bacteria like MTBC requires many weeks to months to complete, whereas sequencing-based approaches can predict drug resistance (DR) with reduced turnaround time. We sought to develop a multiplexed, targeted next generation sequencing (tNGS) assay that can predict DR and can be performed directly on clinical respiratory specimens. A multiplex PCR was designed to amplify a group of thirteen full-length genes and promoter regions with mutations known to be involved in resistance to first- and second-line MTBC drugs. Long-read amplicon libraries were sequenced with Oxford Nanopore Technologies platforms and high-confidence resistance mutations were identified in real-time using an in-house developed bioinformatics pipeline. Sensitivity, specificity, reproducibility, and accuracy of the tNGS assay was assessed as part of a clinical validation study. In total, tNGS was performed on 72 primary specimens and 55 MTBC-positive cultures and results were compared to clinical whole genome sequencing (WGS) performed on paired patient cultures. Complete or partial susceptibility profiles were generated from 82% of smear positive primary specimens and the resistance mutations identified by tNGS were 100% concordant with WGS. In addition to performing tNGS on primary clinical samples, this assay can be used to sequence MTBC cultures mixed with other mycobacterial species that would not yield WGS results. The assay can be effectively implemented in a clinical/diagnostic laboratory with a two to three day turnaround time and, even if batched weekly, tNGS results are available on average 15 days earlier than culture-derived WGS results. This study demonstrates that tNGS can reliably predict MTBC drug resistance directly from clinical specimens or cultures and provide critical information in a timely manner for the appropriate treatment of patients with DR tuberculosis.

Keywords: drug susceptibility; mycobacterium; nanopore; resistance; targeted sequencing; tuberculosis.

Figure 1

Overview of library preparation steps and bioinformatic analyses for tNGS nanopore sequencing. (A) For library preparation, two multiplex PCR reactions for each sample were combined and processed with AMPure bead-based clean-up steps (green arrows, “C”), enzymatic reactions (black arrows), dsDNA quantification via Qubit and normalization (black rectangles), and heat inactivation steps (red asterisk). (B) Bioinformatic tools used to analyze sequencing data and identify high confidence resistance mutations in MTBC. Diagrams created with BioRender.com.

Figure 2

tNGS data completeness is arranged by AFB smear (A) and (B) tNGS data completeness is arranged by AFB smear and real-time PCR values for (C) a single-copy target RD9 and (D) multi-copy target IS6110 for MTBC. Profiles are defined as complete (all 13 targets pass QC), partial (≥10 targets pass QC), or not sequenced.

Figure 3

Turnaround times for MTBC molecular testing and sequencing. (A) MTBC testing algorithm at the Wadsworth Center. Processed specimens are used for mycobacterial culture. Heat killed aliquots are tested for MTBC DNA via real-time PCR and positive specimens are then referred to tNGS. When positive cultures are available, WGS is performed. Phenotypic drug-susceptibility testing (DST) is performed only if unknown/novel mutations or multidrug resistant strains are detected. (B) Timeline showing the average number of days required for MTBC DNA detection via real-time PCR, tNGS (from extraction to result), MTBC isolation, and WGS (from extraction to result) (n = 16). Note that tNGS and WGS assays are batched weekly and average turnaround times include non-business days (i.e., weekends). Estimated time for first-line DST results are indicated with a dashed arrow. (C) Turnaround time (TAT) improvements (in days) of direct tNGS compared to culture-derived WGS.

Figure 4

In silico SNP-based lineage classifications for MTBC. The SNP-based ID algorithm looks for unique SNPs in gyrB, oxyR-ahpC, katG, ethA, inhA, and pncA in the order shown below. Diagram created with BioRender.com.