An RNA Nanosensor for the Diagnosis of Antibiotic Resistance in M. Tuberculosis

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Project Summary: Mycobacterium tuberculosis (Mtb) has among the highest resistance rates of any pathogen globally, but Mtb resistance remains the most challenging to diagnose due to its very slow growth in in vitro culture and culture’s inordinate cost and biohazard. Rapid and accurate alternative diagnostic approaches are urgently needed. DNA based tests are available for detecting resistance to a few drugs but are limited by their need for extensive knowledge of DNA resistance determinants for interpretation, and concerns about limit of detection at the point- of-care. For novel antibiotics entering clinical use, genetic resistance determinants are particularly poorly understood and may be too dispersed across the genome to lend themselves to targeted sequencing and delays in understanding mechanisms may miss an opportunity to avoid more widespread resistance. A work around is the development of a functional or phenotypic assay that circumvents the need to target specific genetic mechanisms, however there is no such rapid assay currently commercially available largely because these assays have traditionally relied on observed bacterial growth in vitro under antibiotic pressure. Leveraging considerable preliminary data from our group and others, we propose the development of an innovative ‘functional’ RNA-based assay for resistance diagnosis in Mtb. We anticipate increasing sensitivity of resistance detection, expanding the numbers of drugs to which resistance can be detected, while significantly shortening time-to-result. Specifically, in this early phase development proposal we will identify key assay parameters that maximize the Mtb RNA susceptibility signal to five key agents in multi-drug resistant tuberculosis treatment (bedaquiline, pretomanid, linezolid, moxifloxacin and pyrazinamide) using RNA sequencing in a factorial design of experiments assessing drug exposure dose and exposure duration. In addition, we will study the effect of key clinical variables including genetic resistance mechanism, background lineage (Aim 1). In Aim 2 we will work with NanoString technologies to develop hybridization probe sets (that we call nanosensors for short) to target antibiotic responsive and control genes in two iterative phases. This will be followed by assay performance assessment in vitro, and on sputum from participants newly diagnosed with rifampicin resistant TB before and after incubation in culture media. This work will build a strong foundation for an innovative resistance diagnostic that addresses an unmet need.