Researchers from New York University, Columbia University, and the New York Genome Center have developed TIGER (Targeted Inhibition of Gene Expression via guide RNA design), a deep learning model that combines CRISPR technology with artificial intelligence to precisely control gene expression. This innovative approach shows promise for the development of new CRISPR-based therapies.

CRISPR, a widely used gene editing tool, primarily utilizes the Cas9 enzyme to modify DNA. However, scientists have discovered a variation called Cas13 that targets RNA instead. RNA-targeting CRISPRs have diverse applications, including RNA editing, gene suppression, and drug screening, with the potential to advance our understanding of RNA regulation, non-coding RNAs, and combatting viral infections.

The researchers’ primary objective was to optimize the activity of RNA-targeting CRISPRs on the intended RNA targets while minimizing off-target effects caused by mismatches, insertions, and deletions. Previous studies focused primarily on on-target activity and mismatches, overlooking the impact of insertions and deletions, which contribute significantly to genetic variations in humans.

To address these challenges, the team performed RNA-targeting CRISPR screens on human cells, evaluating 200,000 guide RNAs that targeted essential genes, including both perfect matches and off-target variations. Collaborating with machine learning expert David Knowles’ lab, they developed the TIGER model, a deep learning framework trained on the CRISPR screen data. TIGER surpassed previous models by accurately predicting on-target and off-target activities of RNA-targeting CRISPRs.

The integration of artificial intelligence and RNA-targeting CRISPR screens offers exciting prospects for future advancements. TIGER’s predictions help avoid unintended off-target CRISPR activity, facilitating the development of RNA-targeting therapies. Moreover, TIGER enables precise modulation of gene expression by using mismatched guide RNAs to partially inhibit gene expression. This breakthrough has potential applications in conditions characterized by gene copy number imbalances, such as Down syndrome, specific forms of schizophrenia, Charcot-Marie-Tooth disease, and cancer.

The study’s combination of deep learning and RNA-targeting CRISPR screens introduces TIGER as a powerful tool for precise gene expression control. TIGER’s accurate predictions of on-target and off-target activities provide valuable insights for designing effective therapeutic interventions. The researchers anticipate that TIGER will drive significant advancements in biomedicine and promote further exploration of RNA-targeting CRISPR-based therapies.

Hans-Hermann Wessels, Andrew Stirn, Alejandro Méndez-Mancilla, Eric J. Kim, Sydney K. Hart, David A. Knowles, Neville E. Sanjana. Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learningNature Biotechnology, 2023; DOI: 10.1038/s41587-023-01830-8

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