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Sci Rep 2020 Sep 04;101:14662. doi: 10.1038/s41598-020-71412-0.
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Maximizing CRISPR/Cas9 phenotype penetrance applying predictive modeling of editing outcomes in Xenopus and zebrafish embryos.

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 .

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.

PubMed ID: 32887910
PMC ID: PMC7473854
Article link: Sci Rep
Grant support: [+]

Species referenced: Xenopus tropicalis Xenopus laevis
Genes referenced: abca4 adam12 amer3 bmal1 ccn4 celsr2 dnm2 eya1 ezh2 fap fbxw7 fxr1 hmgcr hmmr itsn1 kdm6a mdk nf1 notch1 nuak1 parm1 pkd1 pkd2 prickle1 prph2 pten pycr1 rab3gap2 rspo2 slc16a2 smad6 sox2 stag2 suz12 tbx4 tpcn1 ush2a
gRNAs referenced: abca4 gRNA1 abca4 gRNA2 abca4 gRNA3 adam12 gRNA1 amer3 gRNA1 arntl gRNA1 ccn4 gRNA1 celsr2 gRNA1 celsr2 gRNA2 dnm2 gRNA1 dnm2 gRNA2 eya1 gRNA1 ezh2 gRNA1 ezh2 gRNA2 fap gRNA1 fbxw7 gRNA1 fxr1 gRNA1 hmgcr gRNA1 hmmr gRNA1 itsn1 gRNA1 itsn1 gRNA2 kdm6a gRNA1 mdk gRNA1 nf1 gRNA1 nf1 gRNA2 nog gRNA1 notch1 gRNA1 nuak1 gRNA1 parm1 gRNA1 pkd1 gRNA1 pkd2 gRNA1 prickle1 gRNA1 prph2 gRNA1 pten gRNA1 rab3gap2 gRNA1 rab3gap2 gRNA2 rspo2 gRNA1 slc16a2 gRNA1 smad6 gRNA1 smad6 gRNA2 sox2 gRNA1 stag2 gRNA1 stag2 gRNA2 suz12 gRNA1 tpcn1 gRNA1 tpcn1 gRNA2 tyr gRNA1 tyr gRNA3 tyr gRNA4 tyr gRNA5 tyr gRNA6 ush2a gRNA2

Phenotypes: Xtr + tyr sgRNA CRISPR (Fig. S6) [+]

Article Images: [+] show captions
References [+] :
Allen, Predicting the mutations generated by repair of Cas9-induced double-strand breaks. 2018, Pubmed