Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting.

Journal: Cell systems
Published Date: Dec 12, 2023

Abstract

Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons. Our Cas13d guide efficiency model was successfully generalized to DjCas13d, illustrating the power of combining machine learning with ortholog discovery to advance RNA targeting in human cells.

Authors

  • Jingyi Wei
    Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Biochemistry, Stanford University, Stanford, CA, USA; Arc Institute, Palo Alto, CA, USA.
  • Peter Lotfy
    Laboratory of Molecular and Cell Biology, Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Kian Faizi
    Laboratory of Molecular and Cell Biology, Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Sara Baungaard
    Arc Institute, Palo Alto, CA, USA.
  • Emily Gibson
    Arc Institute, Palo Alto, CA, USA.
  • Eleanor Wang
    Laboratory of Molecular and Cell Biology, Salk Institute for Biological Studies, La Jolla, CA, USA; Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA.
  • Hannah Slabodkin
    Department of Biochemistry, Stanford University, Stanford, CA, USA; Arc Institute, Palo Alto, CA, USA.
  • Emily Kinnaman
    Department of Biochemistry, Stanford University, Stanford, CA, USA; Arc Institute, Palo Alto, CA, USA.
  • Sita Chandrasekaran
    Arc Institute, Palo Alto, CA, USA; Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA.
  • Hugo Kitano
    Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Matthew G Durrant
    Arc Institute, Palo Alto, CA, USA; Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA.
  • Connor V Duffy
    Arc Institute, Palo Alto, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA.
  • April Pawluk
    Arc Institute, Palo Alto, CA, USA.
  • Patrick D Hsu
    Arc Institute, Palo Alto, CA, USA; Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA. Electronic address: patrick@arcinstitute.org.
  • Silvana Konermann
    Department of Biochemistry, Stanford University, Stanford, CA, USA; Arc Institute, Palo Alto, CA, USA. Electronic address: silvana@arcinstitute.org.

Keywords

External Resources

Popular Topics
Recent Journals