Biology subjects

Nakamae, K.

Publications and source records attributed to Nakamae, K..

3 recordsLinked to original sources

bioRxiv2023

DANGER analysis: Risk-averse on/off-target assessment for CRISPR editing without a reference genome

The CRISPR-Cas9 system has successfully achieved site-specific gene editing in organisms ranging from humans to bacteria. The technology efficiently generates mutants, allowing for phenotypic analysis of the on-target gene. However, some conventional studies did not investigate whether deleterious off-target effects partially affect the phenotype. Herein, we present a novel phenotypic assessment of CRISPR-mediated gene editing: Deleterious and ANticipatable Guides Evaluated by RNA-sequencing (DANGER) analysis. Using RNA-seq data, this bioinformatics pipeline can elucidate genomic on/off-target sites on mRNA-transcribed regions related to expression changes and then quantify phenotypic risk at the gene ontology (GO) term level. We demonstrated the risk-averse on/off-target assessment in RNA-seq data from gene-edited samples of human cells and zebrafish brains. Our DANGER analysis successfully detected off-target sites, and it quantitatively evaluated the potential contribution of deleterious off-targets to the transcriptome phenotypes of the edited mutants. Notably, DANGER analysis harnessed de novo transcriptome assembly to perform risk-averse on/off-target assessments without a reference genome. Thus, our resources would help assess genome editing in non-model organisms, individual human genomes, and atypical genomes from diseases and viruses. In conclusion, DANGER analysis facilitates the safer design of genome editing in all organisms with a transcriptome.

genomics↗

bioRxiv2022

Frame Editors for Precise, Template-Free Frameshifting

Efficiency and accuracy are paramount in genome editing. While CRISPR-Cas nucleases are efficient at editing target genes, their accuracy is limited because following DNA cleavage by Cas proteins, error-prone repair mechanisms introduce random mutations. Improving the accuracy of CRISPR-Cas by reducing random repairs using DNA- or RNA-based templates can compromise efficiency. To simultaneously improve both editing efficiency and accuracy, we created a frameshifting genome-editing technology by fusing Cas9 with DNA polymerases. These Frame Editors (FEs) introduce precise and controlled frameshifts into target loci via specific DNA repairs near Cas9-induced cleavage loci. We demonstrate two types of FEs: the insertion-inducing frame editor (iFE) and the deletion-inducing frame editor (dFE). For iFE, DNA polymerase beta (POLB) is fused with Cas9, which increases the frequency of 1-bp insertions. For dFE, T4 DNA polymerase (T4pol) is fused with Cas9, which increases the frequency of 1-bp deletions. Both types of FEs reduce the number of random mutations at target loci compared with Cas9. We show that off-target editing can be reduced by substituting Cas9 with high-fidelity variants, such as HiFi Cas9 or LZ3 Cas9. Thus, FEs can introduce frameshifts into target loci with much improved mutation profiles compared with Cas9 alone and without the requirement for template sequences, offering a new strategy for repairing pathogenic frameshifts.

bioengineering↗

bioRxiv2022

Detailed profiling with MaChIAto reveals various genomic and epigenomic features affecting the efficacy of knock-out, short homology-based knock-in and Prime Editing

Highly efficient gene knock-out and knock-in have been achieved by harnessing CRISPR-Cas9 and its advanced technologies such as Prime Editor. In addition, various bioinformatics resources have become available to quantify and qualify the efficiency and accuracy of CRISPR edits, which significantly increased the user-friendliness of the general next-generation sequencing (NGS) analysis in the context of genome editing. However, there is no specialized and integrated software for investigating the preference in the genomic context involved in the efficiency and accuracy of genome editing using CRISPR-Cas9 and beyond. Here, we address this issue by establishing a novel analysis platform of NGS data for profiling the outcome of template-free knock- out and short homology-based editing, named MaChIAto (Microhomology- associated Chromosomal Integration/editing Analysis tools) (https://github.com/KazukiNakamae/MaChIAto). MaChIAto accommodates the classification and profiling of the NGS reads to uncover the tendency of the corresponding method of genome editing. In the profiling function, MaChIAto can summarize the mutation patterns along with the editing efficiency, and > 70 kinds of feature analysis, e.g., correlation analysis with thermodynamics and secondary structure parameters, are available. Additionally, the classifying function of MaChIAto is based on, but much stricter than, that of the existing tool, which is achieved by implementing a novel method of parameter adaptation utilizing Bayesian optimization. To demonstrate the functionality of MaChIAto, we analyzed the NGS data of knock- out, short homology-based knock-in, and Prime Editing. We confirmed that some features of (epi-)genomic context affected the efficiency and accuracy. These results show that MaChIAto is a helpful tool for understanding the best design for CRISPR edits. More importantly, it is the first tool for discovering features in the short homology-based knock-in outcomes. MaChIAto would help researchers profile editing data and generate prediction models for CRISPR edits, further contributing to revealing a "black box" process to produce a variety of CRISPR and Prime Editing outcomes.

bioinformatics↗