Computational design of CRISPR guide RNAs to enable strain-specific control of microbial consortia

Abstract

Microbes naturally coexist in complex, multistrain communities. However, extracting individual microbes from and specifically manipulating the composition of these consortia remain challenging. The sequence-specific nature of CRISPR guide RNAs can be leveraged to accurately differentiate microorganisms and facilitate the creation of tools that can achieve these tasks. We developed a computational program, ssCRISPR, which designs strain-specific CRISPR guide RNA sequences with user-specified target strains, protected strains, and guide RNA properties. We experimentally verify the accuracy of the strain specificity predictions in both Escherichia coli and Pseudomonas spp. and show that up to three nucleotide mismatches are often required to ensure perfect specificity. To demonstrate the functionality of ssCRISPR, we apply computationally designed CRISPR-Cas9 guide RNAs to two applications: the purification of specific microbes through one- and two-plasmid transformation workflows and the targeted removal of specific microbes using DNA-loaded liposomes. For strain purification, we utilize gRNAs designed to target and kill all microbes in a consortium except the specific microbe to be isolated. For strain elimination, we utilize gRNAs designed to target only the unwanted microbe while protecting all other strains in the community. ssCRISPR will be of use in diverse microbiota engineering applications.

Document Details

Document Type
Pub Defense Publication
Publication Date
Dec 27, 2022
Source ID
10.1073/pnas.2213154120

Entities

People

  • Austin G. Rottinghaus
  • Steven Vo
  • Tae Seok Moon

Organizations

  • National Institutes of Health
  • National Science Foundation
  • Office of Naval Research
  • United States Department of Agriculture
  • United States Environmental Protection Agency
  • Washington University in St. Louis

Tags

Fields of Study

  • Biology

Readers

  • Computational Modeling and Simulation
  • Microbial Pathology
  • Molecular Genetics

Technology Areas

  • Biotechnology