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Maximizing CRISPR/Cas9 phenotype penetrance applying predictive modeling of editing outcomes in Xenopus and zebrafish embryos.

Abstract：

:CRISPR/Cas9 genome editing has revolutionized functional genomics in vertebrates. However, CRISPR/Cas9 edited F0 animals too often demonstrate variable phenotypic penetrance due to the mosaic nature of editing outcomes after double strand break (DSB) repair. Even with high efficiency levels of genome editing, phenotypes may be obscured by proportional presence of in-frame mutations that still produce functional protein. Recently, studies in cell culture systems have shown that the nature of CRISPR/Cas9-mediated mutations can be dependent on local sequence context and can be predicted by computational methods. Here, we demonstrate that similar approaches can be used to forecast CRISPR/Cas9 gene editing outcomes in Xenopus tropicalis, Xenopus laevis, and zebrafish. We show that a publicly available neural network previously trained in mouse embryonic stem cell cultures (InDelphi-mESC) is able to accurately predict CRISPR/Cas9 gene editing outcomes in early vertebrate embryos. Our observations can have direct implications for experiment design, allowing the selection of guide RNAs with predicted repair outcome signatures enriched towards frameshift mutations, allowing maximization of CRISPR/Cas9 phenotype penetrance in the F0 generation.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Naert T,Tulkens D,Edwards NA,Carron M,Shaidani NI,Wlizla M,Boel A,Demuynck S,Horb ME,Coucke P,Willaert A,Zorn AM,Vleminckx K

doi

10.1038/s41598-020-71412-0

subject

Has Abstract

pub_date

2020-09-04 00:00:00

pages

14662

issue

1

issn

2045-2322

pii

10.1038/s41598-020-71412-0

journal_volume

10

pub_type

杂志文章

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