Artificial intelligence can predict on- and off-target activity of CRISPR tools that target RNA, not DNA, according to new research published in nature biotechnology.
The study, by researchers at New York University, Columbia University, and the New York Genome Center, combines deep learning models with CRISPR screens to control human gene expression in different ways. For example, by flicking a light switch to turn it off completely, or using the dimmer knob to partially reduce activity. These precise gene controls could potentially be used to develop new CRISPR-based therapeutics.
CRISPR is a gene-editing technology that has many uses in biomedicine and beyond, from treating sickle cell anemia to making tastier mustard greens. They often work by targeting DNA using an enzyme called Cas9. In recent years, scientists have discovered another type of his CRISPR that instead uses an enzyme called Cas13 to target RNA.
RNA-targeted CRISPR can be used for a wide range of applications, including RNA editing, knockdown of RNA to block expression of specific genes, and high-throughput screening to determine potential drug candidates. Researchers at New York University and the New York Genome Center have created a platform for RNA-targeted CRISPR screening using Cas13 to better understand RNA regulation and identify the function of noncoding RNAs. Since RNA is the major genetic material of viruses such as SARS-CoV-2 and influenza, CRISPR targeting RNA is also expected to develop new methods to prevent or treat viral infections. Also, in human cells, one of the first steps in gene expression is the creation of RNA from the DNA in the genome.
An important goal of this study is to maximize the activity of RNA-targeting CRISPR on its intended target RNA and minimize its activity on other RNAs that may have deleterious side effects on the cell. Off-target activity includes both insertion and deletion mutations, as well as mismatches between guide and target RNA. Previous studies on CRISPR targeting RNA focused only on on-target activity and mismatches. Prediction of off-target activity, especially insertion and deletion mutations, is poorly studied. Approximately 1 in 5 mutations in the human population are insertions or deletions, so these are important types of potential off-targets to consider in CRISPR design.
“Like CRISPR targeting DNA, such as Cas9, CRISPR targeting RNA, such as Cas13, is expected to have a major impact on molecular biology and biomedical applications in the coming years,” said New York University. says Neville Sanjana, associate professor of biology at Professor of Neuroscience and Physiology at New York University’s Grossman School of Medicine, principal faculty member of the New York Genome Center, and co-senior author of this study. “Precise guide prediction and off-target identification will be of great value to this emerging field and therapy.”
in their research nature biotechnologySanjana et al. performed a series of pooled CRISPR screens targeting RNA in human cells. They measured the activity of 200,000 guide RNAs targeting essential genes in human cells, including both ‘perfect match’ guide RNAs and off-target mismatches, insertions and deletions.
Sanjana’s lab, in collaboration with machine learning expert David Knowles’ lab, dubbed TIGER (Targeted Inhibition of Gene Expression by Guide RNA Design) trained on data from CRISPR screens. I designed a deep learning model. Comparing predictions generated by deep learning models and laboratory tests on human cells, TIGER was able to predict both on-target and off-target activity, compared to previous models developed for Cas13 on-target guide design. It provided the first tool for outperforming models and predicting off-target. – Target activity of CRISPR targeting RNA.
“Machine learning and deep learning are showing their strength in the genomics field because they can take advantage of the huge datasets that can be generated by modern high-throughput experiments. , it also allowed us to understand why the model predicted that a particular guide would work well,” said Knowles, an assistant professor of computer science and systems biology in the core Columbia School of Engineering and Applied Sciences. increase. A faculty member at the New York Genome Center and co-senior author of this study.
“Our previous work demonstrated how to design Cas13 guides that can knockdown specific RNAs. Using TIGER, we balance on-target knockdown with avoidance of off-target activity We can now design a Cas13 guide,” said Hans Hermann (Harm) Wessels, co-first author of the study and senior fellow at the New York Genome Institute. Mr. Center previously said he was a postdoctoral fellow in Sanjana’s lab.
The researchers also used TIGER’s off-target prediction to precisely measure gene dosage (the amount of a particular gene expressed) by allowing partial inhibition of gene expression in cells with mismatched guides. I have proven that it is adjustable. This could be useful in diseases such as Down syndrome, certain schizophrenia, Charcot-Marie-Tooth disease (an inherited neurological disorder) where there are too many copies of a gene, and cancers caused by abnormal gene expression. There is a nature. Uncontrolled tumor growth.
“Our deep learning model not only tells us how to design a guide RNA that completely knocks down a transcript, but also allows us to ‘tune’ it. We can have it generate only 70% of the total,” said Andrew Stern, a Ph.D. student at Columbia Engineering and the New York Genome Center, and co-first author of the study.
The researchers envision that combining artificial intelligence with RNA-targeted CRISPR screening could help predict TIGER to avoid unwanted off-target CRISPR activity, further facilitating the development of a new generation of RNA-targeted therapeutics. .
“As we collect large datasets from CRISPR screens, opportunities to apply sophisticated machine learning models are increasing rapidly. Next to the Laboratory, we are facilitating this great cross-disciplinary collaboration, and TIGER allows us to predict off-targets and precisely modulate gene dosages, so many of RNA-targeting CRISPRs in biomedicine. Exciting new applications are possible,” Sanjana said.
This latest work builds on previous work by the New York University team to develop design rules for guide RNAs and target RNAs for diverse organisms, including viruses like SARS-CoV-2 and engineered proteins. , further advancing the broad applicability of RNA-targeted CRISPR to human genetics and drug discovery. and RNA therapeutics, exploiting single-cell biology to reveal synergistic drug combinations against leukemia.
Other study authors include Alejandro Mendes-Mancilla and Sidney K. Hart of New York University and the New York Genome Center, and Eric J. Kim of Columbia University. This work was supported by grants from the National Institutes of Health (DP2HG010099, R01CA218668, R01GM138635), DARPA (D18AP00053), Cancer Institute, and the Simons Autism Research Initiative.
