AI and CRISPR precisely control gene expression
RNA-based predictive models developed

Date:
July 3, 2023
Source:
Columbia University School of Engineering and Applied Science
Summary:
The study combines a deep learning model with CRISPR screens to
control the expression of human genes in different ways -- such as
flicking a light switch to shut them off completely or by using a
dimmer knob to partially turn down their activity. These precise
gene controls could be used to develop new CRISPR-based therapies.


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FULL STORY ========================================================================== Artificial intelligence can predict on- and off-target activity of CRISPR
tools that target RNA instead of DNA, according to new research published
in Nature Biotechnology.

The study by researchers at New York University, Columbia Engineering,
and the New York Genome Center, combines a deep learning model with CRISPR screens to control the expression of human genes in different ways --
such as flicking a light switch to shut them off completely or by using
a dimmer knob to partially turn down their activity. These precise gene controls could be used to develop new CRISPR-based therapies.

CRISPR is a gene editing technology with many uses in biomedicine and
beyond, from treating sickle cell anemia to engineering tastier mustard
greens. It often works by targeting DNA using an enzyme called Cas9. In
recent years, scientists discovered another type of CRISPR that instead
targets RNA using an enzyme called Cas13.

RNA-targeting CRISPRs can be used in a wide range of applications,
including RNA editing, knocking down RNA to block expression of a
particular gene, and high-throughput screening to determine promising drug candidates. Researchers at NYU and the New York Genome Center created a platform for RNA-targeting CRISPR screens using Cas13 to better understand
RNA regulation and to identify the function of non-coding RNAs. Because
RNA is the main genetic material in viruses including SARS-CoV-2 and flu, RNA-targeting CRISPRs also hold promise for developing new methods to
prevent or treat viral infections. Also, in human cells, when a gene is expressed, one of the first steps is the creation of RNA from the DNA
in the genome.

A key goal of the study is to maximize the activity of RNA-targeting
CRISPRs on the intended target RNA and minimize activity on other
RNAs which could have detrimental side effects for the cell. Off-target activity includes both mismatches between the guide and target RNA as well
as insertion and deletion mutations. Earlier studies of RNA-targeting
CRISPRs focused only on on-target activity and mismatches; predicting off-target activity, particularly insertion and deletion mutations, has
not been well-studied. In human populations, about one in five mutations
are insertions or deletions, so these are important types of potential off-targets to consider for CRISPR design.

"Similar to DNA-targeting CRISPRs such as Cas9, we anticipate that RNA- targeting CRISPRs such as Cas13 will have an outsized impact in molecular biology and biomedical applications in the coming years," said Neville
Sanjana, associate professor of biology at NYU, associate professor of neuroscience and physiology at NYU Grossman School of Medicine, a core
faculty member at New York Genome Center, and the study's co-senior
author. "Accurate guide prediction and off-target identification will
be of immense value for this newly developing field and therapeutics."
In their study inNature Biotechnology, Sanjana and his colleagues
performed a series of pooled RNA-targeting CRISPR screens 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 teamed up with the lab of machine learning expert David
Knowles to engineer a deep learning model they named TIGER (Targeted
Inhibition of Gene Expression via guide RNA design) that was trained on
the data from the CRISPR screens. Comparing the predictions generated
by the deep learning model and laboratory tests in human cells,
TIGER was able to predict both on-target and off-target activity,
outperforming previous models developed for Cas13 on- target guide
design and providing the first tool for predicting off-target activity
of RNA-targeting CRISPRs.

"Machine learning and deep learning are showing their strength in
genomics because they can take advantage of the huge datasets that can
now be generated by modern high-throughput experiments. Importantly, we
were also able to use "interpretable machine learning" to understand why
the model predicts that a specific guide will work well," said Knowles, assistant professor of computer science and systems biology at Columbia Engineering, a core faculty member at New York Genome Center, and the
study's co-senior author.

"Our earlier research demonstrated how to design Cas13 guides that can
knock down a particular RNA. With TIGER, we can now design Cas13 guides
that strike a balance between on-target knockdown and avoiding off-target activity," said Hans-Hermann (Harm) Wessels, the study's co-first author
and a senior scientist at the New York Genome Center, who was previously
a postdoctoral fellow in Sanjana's laboratory.

The researchers also demonstrated that TIGER's off-target predictions
can be used to precisely modulate gene dosage -- the amount of a
particular gene that is expressed -- by enabling partial inhibition
of gene expression in cells with mismatch guides. This may be useful
for diseases in which there are too many copies of a gene, such as Down syndrome, certain forms of schizophrenia, Charcot-Marie-Tooth disease (a hereditary nerve disorder), or in cancers where aberrant gene expression
can lead to uncontrolled tumor growth.

"Our deep learning model can tell us not only how to design a guide
RNA that knocks down a transcript completely, but can also 'tune' it --
for instance, having it produce only 70% of the transcript of a specific
gene," said Andrew Stirn, a PhD student at Columbia Engineering and the
New York Genome Center, and the study's co-first author.

By combining artificial intelligence with an RNA-targeting CRISPR screen,
the researchers envision that TIGER's predictions will help avoid
undesired off- target CRISPR activity and further spur development of
a new generation of RNA- targeting therapies.

"As we collect larger datasets from CRISPR screens, the opportunities
to apply sophisticated machine learning models are growing rapidly. We
are lucky to have David's lab next door to ours to facilitate this
wonderful, cross-disciplinary collaboration. And, with TIGER, we can
predict off-targets and precisely modulate gene dosage which enables many exciting new applications for RNA- targeting CRISPRs for biomedicine,"
said Sanjana.

Additional study authors include Alejandro Me'ndez-Mancilla and Sydney
K. Hart of NYU and the New York Genome Center, and Eric J. Kim of
Columbia University.

The research was supported by grants from the National Institutes of
Health (DP2HG010099, R01CA218668, R01GM138635), DARPA (D18AP00053),
the Cancer Research Institute, and the Simons Foundation for Autism
Research Initiative.

* RELATED_TOPICS
o Health_&_Medicine
# Human_Biology # Genes # Personalized_Medicine
o Plants_&_Animals
# CRISPR_Gene_Editing # Genetics # Biology
o Matter_&_Energy
# Organic_Chemistry # Biochemistry # Engineering
o Computers_&_Math
# Computer_Modeling # Neural_Interfaces #
Computational_Biology
* RELATED_TERMS
o Gene o Computational_genomics o BRCA2 o BRCA1 o Gene_therapy
o Bioluminescence o Soil_pH o DNA_microarray

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Story Source: Materials provided by Columbia_University_School_of_Engineering_and_Applied Science. Note:
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========================================================================== Journal Reference:
1. Hans-Hermann Wessels, Andrew Stirn, Alejandro Me'ndez-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 learning. Nature Biotechnology,
2023; DOI: 10.1038/s41587-023-01830-8 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/07/230703133058.htm

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