Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation
- University of California, San Francisco, United States
- University of California, Berkeley, United States
Abstract
We recently found that nucleosomes directly block access of CRISPR/Cas9 to DNA (Horlbeck et al., 2016). Here, we build on this observation with a comprehensive algorithm that incorporates chromatin, position, and sequence features to accurately predict highly effective single guide RNAs (sgRNAs) for targeting nuclease-dead Cas9-mediated transcriptional repression (CRISPRi) and activation (CRISPRa). We use this algorithm to design next-generation genome-scale CRISPRi and CRISPRa libraries targeting human and mouse genomes. A CRISPRi screen for essential genes in K562 cells demonstrates that the large majority of sgRNAs are highly active. We also find CRISPRi does not exhibit any detectable non-specific toxicity recently observed with CRISPR nuclease approaches. Precision-recall analysis shows that we detect over 90% of essential genes with minimal false positives using a compact 5 sgRNA/gene library. Our results establish CRISPRi and CRISPRa as premier tools for loss- or gain-of-function studies and provide a general strategy for identifying Cas9 target sites.
Data availability
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A promoter-level mammalian expression atlasTSS_human.bed.gz; TSS_mouse.bed.gz;.
Article and author information
Author details
Jonathan S Weissman
Funding
Howard Hughes Medical Institute
- Max A Horlbeck
- Luke A Gilbert
- Jacqueline E Villalta
- Britt Adamson
- Yuwen Chen
- Alexander P Fields
National Institutes of Health (P50 GM102706, U01 CA168370, R01 DA036858)
- Max A Horlbeck
- Luke A Gilbert
- Jacqueline E Villalta
- Britt Adamson
- Yuwen Chen
- Alexander P Fields
Leukemia and Lymphoma Society
- Luke A Gilbert
Li Ka Shing Foundation
- Ryan A Pak
- Chong Y Park
- Jacob E Corn
National Institutes of Health (NIGMS DP2 GM119139)
- Martin Kampmann
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2016, Horlbeck et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation
eLife 5:e19760.
https://doi.org/10.7554/eLife.19760
Further reading
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- Chromosomes and Gene Expression
The prokaryotic CRISPR (clustered regularly interspaced palindromic repeats)-associated protein, Cas9, has been widely adopted as a tool for editing, imaging, and regulating eukaryotic genomes. However, our understanding of how to select single-guide RNAs (sgRNAs) that mediate efficient Cas9 activity is incomplete, as we lack insight into how chromatin impacts Cas9 targeting. To address this gap, we analyzed large-scale genetic screens performed in human cell lines using either nuclease-active or nuclease-dead Cas9 (dCas9). We observed that highly active sgRNAs for Cas9 and dCas9 were found almost exclusively in regions of low nucleosome occupancy. In vitro experiments demonstrated that nucleosomes in fact directly impede Cas9 binding and cleavage, while chromatin remodeling can restore Cas9 access. Our results reveal a critical role of eukaryotic chromatin in dictating the targeting specificity of this transplanted bacterial enzyme, and provide rules for selecting Cas9 target sites distinct from and complementary to those based on sequence properties.
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- Computational and Systems Biology
Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.
