Poster abstracts

Poster number 69 submitted by Alexander Krohannon

CASowary: a tool for systematic design and optimization of cas13 guide RNAs to facilitate disrupting mRNA function

Alexander M. Krohannon (Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, Indiana 46202), Simone Rauch (Department of Chemistry, The University of Chicago, Chicago, Illinois, USA), Bryan C. Dickinson (Department of Chemistry, The University of Chicago, Chicago, Illinois, USA), Sarath C. Janga (Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, Indiana 46202)

Abstract:
Recent discovery of the gene editing system - CRISPR associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems1,2,3. Cas13, a lesser studied cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The cas13 system utilizes base complementarity between a crRNA/sgRNA and a target RNA transcript, to preferentially bind to the target transcript. Unlike targeting the regulatory regions of genes on the genome, the transcriptome is significantly more redundant. Additionally, transcripts exhibit complex 3-dimensional structures and interact with an array of RBPs, both of which further limit the scope of effective sgRNAs. As a result, there currently exists no method to predict the efficacy of a specific sgRNA. Here we present a novel machine learning and computational tool, CASowary, to predict the efficacy of a sgRNA; using RNA knockdown data from cas13 characterization experiments4 for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information on RNA5. CASowary utilizes a Decision Tree architecture with a set of 112 features, to classify sgRNAs into 1 of 4 classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 90%; additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting cas system – CIRTS6, confirming the robustness and reproducibility of CASowary’s sgRNA predictions. In particular, several highly efficient sgRNA’s designed using CASowary against SMARCA4 gene exhibited strong agreement with experimental data. Applications of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of treatments for diseases linked with aberrations in RNA regulatory processes.

References:
1 Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 (Bethesda) 7, 2719–2727 (2017)

2 Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014).

3 Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

4 Abudayyeh O. O., et al. (2017). RNA targeting with CRISPR-Cas13. Nature. 550 280–284. 10.1038/nature24049.

5 Schueler M, et al. Differential protein occupancy profiling of the mRNA transcriptome. Genome Biol. 2014;15:R15. doi: 10.1186/gb-2014-15-1-r15.

6 Rauch, S. et al. Programmable RNA-guided RNA effector proteins built from human parts. Cell 178, 122–134 (2019).

Keywords: CRISPRCas13, mRNA regulation, machine learning