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

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2024 · $705,150

## Abstract

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.

## Key facts

- **NIH application ID:** 10928887
- **Project number:** 5R01AI176498-02
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Maha Farhat
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $705,150
- **Award type:** 5
- **Project period:** 2023-09-13 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10928887

## Citation

> US National Institutes of Health, RePORTER application 10928887, An RNA Nanosensor for the Diagnosis of Antibiotic Resistance in M. Tuberculosis (5R01AI176498-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10928887. Licensed CC0.

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