# A systematic method for Gain-of Function genetics in mycobacteria

> **NIH NIH R21** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2021 · $244,183

## Abstract

Project Summary
It is estimated that about one third of the world’s population is latently infected with
Mycobacterium tuberculosis (Mtb). The 10 million annual new cases of tuberculosis resulting in
about one million deaths further underline the global public health impact of this pathogen. Mtb
has developed a multitude of pathways to evade the host immune response. For the most part,
the molecular mechanisms of these host pathogen interactions are only beginning to be
understood. The premise of the current proposal is that gain-of-function (GOF) genetic screens
provide a currently underappreciate approach towards identification of Mtb virulence factors. In
the specific aims 1 and 2 we propose to generate and characterize a barcoded, arrayed cosmid
library of Mtb regions of about 50kbp expressed in the host mycobacterial strain M. kansasii.
Specific aim 3 will test this newly generated resource in ex vivo GOF screens for genes
inhibiting host cell death and for Mtb genes that mediate increased virulence in an in vivo GOF
screen in immunodeficient SCID mice. Overall, our work will provide a novel resource to the
research community that may have a major impact in the discovery of virulence genes of Mtb.

## Key facts

- **NIH application ID:** 10122902
- **Project number:** 5R21AI151973-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** VOLKER BRIKEN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $244,183
- **Award type:** 5
- **Project period:** 2020-03-06 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10122902

## Citation

> US National Institutes of Health, RePORTER application 10122902, A systematic method for Gain-of Function genetics in mycobacteria (5R21AI151973-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10122902. Licensed CC0.

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