Dataset Information

Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding.

ABSTRACT: The versatility of CRISPR-Cas endonucleases as a tool for biomedical research has led to diverse applications in gene editing, programmable transcriptional control, and nucleic acid detection. Most CRISPR-Cas systems, however, suffer from off-target effects and unpredictable nonspecific binding that negatively impact their reliability and broader applicability. To better evaluate the impact of mismatches on DNA target recognition and binding, we develop a massively parallel CRISPR interference (CRISPRi) assay to measure the binding energy between tens of thousands of CRISPR RNA (crRNA) and target DNA sequences. By developing a general thermodynamic model of CRISPR-Cas binding dynamics, our results unravel a comprehensive map of the energetic landscape of nuclease-dead Cas12a (dCas12a) from Francisella novicida as it inspects and binds to its DNA target. Our results reveal concealed thermodynamic factors affecting dCas12a DNA binding, which should guide the design and optimization of crRNA that limits off-target effects, including the crucial role of an extended protospacer adjacent motif (PAM) sequence and the impact of the specific base composition of crRNA-DNA mismatches. Our generalizable approach should also provide a mechanistic understanding of target recognition and DNA binding when applied to other CRISPR-Cas systems.

SUBMITTER: Specht DA

PROVIDER: S-EPMC7260945 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding.

Specht David A DA Xu Yasu Y Lambert Guillaume G

Proceedings of the National Academy of Sciences of the United States of America 20200506 21

The versatility of CRISPR-Cas endonucleases as a tool for biomedical research has led to diverse applications in gene editing, programmable transcriptional control, and nucleic acid detection. Most CRISPR-Cas systems, however, suffer from off-target effects and unpredictable nonspecific binding that negatively impact their reliability and broader applicability. To better evaluate the impact of mismatches on DNA target recognition and binding, we develop a massively parallel CRISPR interference ( ...[more]

PMID: 32376630

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Project description:Motivation:The majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets. Results:We have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data, we found 602 SNPs with significant (false discovery rate 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high-throughput reporter assays. Availability and implementation:http://github.com/piquelab/QuASAR/tree/master/mpra. Contact:fluca@wayne.edu or rpique@wayne.edu. Supplementary information:Supplementary data are available online at Bioinformatics.

Dataset Information

Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding.

Publications

Massively parallel CRISPRi assays reveal concealed thermodynamic determinants of dCas12a binding.

Similar Datasets

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