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  • A Step Forward in Safer Gene Editing: Introduction of DANGER Analysis Tool

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    When off-target regions are affected by genome editing, unexpected changes in the mRNA quantity and sequence can emerge. The DANGER analysis is designed to analyze the effects of RNA-seq data at the Gene Ontology (GO) level. The DANGER analysis workflow includes: Initially, RNA-seq data from unedited samples is used to build a de novo transcriptome assembly, thereby identifying expressed sequences in the unedited samples. Based on the de novo transcriptome assembly and the expression profiles obtained in step 1, genomic regions with reduced expression in the edited sample's RNA-seq data are identified. The genomic regions where off-target effects are estimated from the expressed sequences, based on the de novo transcriptome assembly, are identified as potential off-target regions. The genomic regions detected in both steps 2 and 3 are inferred to be deleterious off-target regions. Gene ontology (GO) information on the deleterious off-target regions is retrieved from the public database. Considering both the number of deleterious off-target regions in each gene ontology and the sequence-specific features of these regions, their impact is quantified using an indicator called the Significant D-index, which estimates the risk to the phenotype. (Image credit: Kazuki Nakamae and Hidemasa Bono, Hiroshima University)​




    Hiroshima University's Genome Editing Innovation Center recently announced the development of a software tool, DANGER (Deleterious and ANticipatable Guides Evaluated by RNA-sequencing) analysis. This tool promises a safer approach to genome editing across various organisms. The development holds significant potential for applications in numerous domains, including medicine, agriculture, and biological research.

    Addressing the Challenges of CRISPR Technology
    For almost a decade, CRISPR technology has been at the forefront of genome editing—a technique that empowers scientists to modify an organism's genomic DNA. Using this technology, genetic material can be added, removed, or altered within the genome.

    While CRISPR-Cas9 is renowned for its accuracy, speed, and affordability compared to other gene-editing technologies, it's not without its challenges. One issue arises from unexpected CRISPR dynamics leading to phenotypic effects that aren't quantitatively monitored. Furthermore, CRISPR generally depends on fundamental genomic data, such as the reference genome. This genome acts as a blueprint, offering scientists overarching insights into an organism's genome. Nevertheless, unexpected sequence modifications can transpire, leading to off-target effects. As Kazuki Nakamae, an assistant professor at Hiroshima University, stated, "The design of genome editing requires a well-characterized genomic sequence. However, the genomic information of patients, cancers, and uncharacterized organisms is often incomplete.”

    DANGER Analysis: A Response to CRISPR's Challenges
    To address the above challenges, the research team crafted the DANGER analysis software. In their studies, they utilized gene-edited samples from human cells and zebrafish brains, performing risk assessments in RNA-sequencing data.

    The DANGER analysis software achieves multiple objectives:

    1. It spots potential DNA on- and off-target sites in the mRNA-transcribed region of the genome using RNA-sequencing data.

    2. By relying on changes in gene expression, it evaluates the phenotypic effects stemming from deleterious off-target sites.

    3. It assesses the phenotypic risk at the gene ontology term level, even without the need for a reference genome.

    Essentially, the DANGER analysis creates a genomic map based on de novo transcriptome assembly using RNA-sequencing data. This transcriptome captures all active gene readouts in a cell. With de novo assembly, the transcriptome gets pieced together without resorting to a reference genome. Following this, the software identifies deleterious off-targets, particularly those in the mRNA-transcribed regions that showcase a reduction in expression in edited samples versus their wild-type counterparts. In the end, the software gauges the phenotypic risk through the gene ontology of these deleterious off-targets.

    “Our DANGER analysis is a novel software that enables quantifying phenotypic effects caused by estimated off-target. Furthermore, our tool uses de novo transcriptome assembly whose sequences can be built from RNA-sequencing data of treated samples without a reference genome,” explained Hidemasa Bono, a professor at Hiroshima University.

    Future Avenues for DANGER Analysis
    The team envisions a bright future for DANGER analysis, anticipating its application across various genome-edited samples from patients and crops. They aim to discern the phenotypic impact and craft safer genome editing techniques.

    Importantly, DANGER analysis is open-source and can be modified as needed. This adaptability means it can be restructured for diverse genome editing systems, not just CRISPR-Cas9. Additionally, its specificity for CRISPR-Cas9 can be elevated by incorporating tailored off-target scoring algorithms. The team is optimistic that DANGER will broaden the horizons of genomic studies and real-world applications of genome editing.

    The researchers' findings and details of the DANGER tool can be found in the journal Bioinformatics Advances.




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