DNA-guided CRISPR/Cas12 for RNA targeting
Orosco, C.; Rananaware, S. R.; Huang, B.; Hanna, M. P.; Ahmadimashhadi, M. R.; Lewis, J. G.; Baugh, M. P.; Bodin, A. P.; Flannery, S. J.; Lange, I. H.; Fang, Z. R.; Karalkar, V. N.; Meister, K. S.; Jain, P. K.
Show abstract
CRISPR-Cas nucleases are transforming genome editing, RNA editing, and diagnostics but have been limited to RNA-guided systems. We present {Psi}DNA, a DNA-based guide for Cas12 enzymes, engineered for specific and efficient RNA targeting. {Psi}DNA mimics a crRNA but with a reverse orientation, enabling stable Cas12-RNA assembly and activating trans-cleavage without RNA components. {Psi}DNAs are effective in sensing short and long RNAs and demonstrated 100% accuracy for detecting HCV RNA in clinical samples. We discovered that {Psi}DNAs can guide certain Cas12 enzymes for RNA targeting in cells, enhancing mRNA degradation via ribosome stalling and enabling multiplex knockdown of multiple RNA transcripts. This study establishes {Psi}DNA as a robust alternative to RNA guides, augmenting the potential of CRISPR-Cas12 for diagnostic applications and targeted RNA modulation in cellular environments.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- New design strategies for ultra-specific CRISPR-Cas13a-based RNA-diagnostic tools with single-nucleotide mismatch sensitivity 97%
- Precise and efficient C-to-U RNA Base Editing with SNAP-CDAR-S 96%
- Highly Specific Enrichment of Rare Nucleic Acids using Thermus Thermophilus Argonaute with Applications in Cancer Diagnostics 96%
Similar papers in this journal
Similar papers in this journal
- Characterization of nick binding and sealing by LIG1 Huntington's disease-asssociated K845N variant at biochemical, structural, and single-molecule levels 92%
- A compact regulatory RNA element in mouse Hsp70 mRNA 90%
- Targeted deletions in human mitochondrial DNA engineered by Type V CRISPR-Cas12a system 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.