Single nucleotide variants (SNVs) are prevalent genetic factors shaping individual trait profiles and disease susceptibility. The recent development and optimizations of base editors, rubber and pencil genome editing tools now promise to enable direct functional assessment of SNVs in model organisms. However, the lack of bioinformatic tools aiding target prediction limits the application of base editing in vivo. Here, we provide a framework for adenine and cytosine base editing in medaka (Oryzias latipes) and zebrafish (Danio rerio), ideal for scalable validation studies. We developed an online base editing tool ACEofBASEs (a careful evaluation of base-edits), to facilitate decision-making by streamlining sgRNA design and performing off-target evaluation. We used state-of-the-art adenine (ABE) and cytosine base editors (CBE) in medaka and zebrafish to edit eye pigmentation genes and transgenic GFP function with high efficiencies. Base editing in the genes encoding troponin T and the potassium channel ERG faithfully recreated known cardiac phenotypes. Deep-sequencing of alleles revealed the abundance of intended edits in comparison to low levels of insertion or deletion (indel) events for ABE8e and evoBE4max. We finally validated missense mutations in novel candidate genes of congenital heart disease (CHD) dapk3, ube2b, usp44, and ptpn11 in F0 and F1 for a subset of these target genes with genotype-phenotype correlation. This base editing framework applies to a wide range of SNV-susceptible traits accessible in fish, facilitating straight-forward candidate validation and prioritization for detailed mechanistic downstream studies.

Genome editing with the CRISPR–Cas9 system has enabled unprecedented efficacy for reverse genetics and gene correction approaches. While off-target effects have been successfully tackled, the effort to eliminate variability in sgRNA efficacies—which affect experimental sensitivity—is in its infancy. To address this issue, studies have analyzed the molecular features of highly active sgRNAs, but independent cross-validation is lacking. Utilizing fluorescent reporter knock-out assays with verification at selected endogenous loci, we experimentally quantified the target efficacies of 430 sgRNAs. Based on this dataset we tested the predictive value of five recently-established prediction algorithms. Our analysis revealed a moderate correlation (r = 0.04 to r = 0.20) between the predicted and measured activity of the sgRNAs, and modest concordance between the different algorithms. We uncovered a strong PAM-distal GC-content-dependent activity, which enabled the exclusion of inactive sgRNAs. By deriving nine additional predictive features we generated a linear model-based discrete system for the efficient selection (r = 0.4) of effective sgRNAs (CRISPRater). We proved our algorithms’ efficacy on small and large external datasets, and provide a versatile combined on- and off-target sgRNA scanning platform. Altogether, our study highlights current issues and efforts in sgRNA efficacy prediction, and provides an easily-applicable discrete system for selecting efficient sgRNAs.

DNA adenine methyltransferase identification (DamID) has emerged as an alternative method to profile protein-DNA interactions; however, critical issues limit its widespread applicability. Here, we present iDamIDseq, a protocol that improves specificity and sensitivity by inverting the steps DpnI-DpnII and adding steps that involve a phosphatase and exonuclease. To determine genome-wide protein-DNA interactions efficiently, we present the analysis tool iDEAR (iDamIDseq Enrichment Analysis with R). The combination of DamID and iDEAR permits the establishment of consistent profiles for transcription factors, even in transient assays, as we exemplify using the small teleost medaka (Oryzias latipes). We report that the bacterial Dam-coding sequence induces aberrant splicing when it is used with different promoters to drive tissue-specific expression. Here, we present an optimization of the sequence to avoid this problem. This and our other improvements will allow researchers to use DamID effectively in any organism, in a general or targeted manner.

The gene regulatory network (GRN) that supports neural stem cell (NS cell) self-renewal has so far been poorly characterized. Knowledge of the central transcription factors (TFs), the noncoding gene regulatory regions that they bind to, and the genes whose expression they modulate will be crucial in unlocking the full therapeutic potential of these cells. Here, we use DNase-seq in combination with analysis of histone modifications to identify multiple classes of epigenetically and functionally distinct cis-regulatory elements (CREs). Through motif analysis and ChIP-seq, we identify several of the crucial TF regulators of NS cells. At the core of the network are TFs of the basic helix-loop-helix (bHLH), nuclear factor I (NFI), SOX, and FOX families, with CREs often densely bound by several of these different TFs. We use machine learning to highlight several crucial regulatory features of the network that underpin NS cell self-renewal and multipotency. We validate our predictions by functional analysis of the bHLH TF OLIG2. This TF makes an important contribution to NS cell self-renewal by concurrently activating pro-proliferation genes and preventing the untimely activation of genes promoting neuronal differentiation and stem cell quiescence.