# Mechanistic Studies of the Type I CRISPR-Cas system

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $376,401

## Abstract

Project Summary
Prokaryotes can acquire resistance to viruses and plasmids by integrating short fragments of
foreign DNA, called prespacers, into clusters of regularly interspaced short palindromic repeats
(CRISPR's). These repeats are then transcribed and processed into small guide RNA's that are
used to direct the destruction of foreign nucleic acid. This mechanism has many parallels with
eukaryotic RNA interference but the proteins that are associated with the CRISPR response are
evolutionarily unrelated to their eukaryotic counterparts. Our long-term goal is to understand the
biochemical and structural basis of CRISPR-mediated resistance in prokaryotes. The objectives
here are to understand how changes in the target sequence modulate the immune response
and provide insight into how the prespacers are incorporated into CRISPR arrays. Our
objectives will combine structural, biochemical and cell based experiments. Successful
completion of the proposed studies is significant because it will increase our understanding of
bacterial resistance to viruses and plasmids, both of which play important roles in the genetics
of pathogenic bacteria. It is also significant because these studies will help in the further
development of CRISPR-based tools.

## Key facts

- **NIH application ID:** 10436785
- **Project number:** 5R01GM097330-11
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Scott Bailey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $376,401
- **Award type:** 5
- **Project period:** 2011-08-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436785

## Citation

> US National Institutes of Health, RePORTER application 10436785, Mechanistic Studies of the Type I CRISPR-Cas system (5R01GM097330-11). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10436785. Licensed CC0.

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