Abstract
Programmable transcriptional regulation is a powerful tool to study gene functions. Current methods to selectively regulate target genes are mainly based on promoter exchange or on overexpressing transcriptional activators. To expand the discovery toolbox, we designed a dCas9-based RNA-guided synthetic transcription activation system for Aspergillus nidulans that uses enzymatically disabled "dead" Cas9 fused to three consecutive activation domains (VPR-dCas9). The dCas9-encoding gene is under the control of an estrogen-responsive promoter to allow induction timing and to avoid possible negative effects by strong constitutive expression of the highly active VPR domains. Especially in silent genomic regions, facultative heterochromatin and strictly positioned nucleosomes can constitute a relevant obstacle to the transcriptional machinery. To avoid this negative impact and to facilitate optimal positioning of RNA-guided VPR-dCas9 to targeted promoters, we have created a genome-wide nucleosome map from actively growing cells and stationary cultures to identify the cognate nucleosome-free regions (NFRs). Based on these maps, different single-guide RNAs (sgRNAs) were designed and tested for their targeting and activation potential. Our results demonstrate that the system can be used to regulate several genes in parallel and, depending on the VPR-dCas9 positioning, expression can be pushed to very high levels. We have used the system to turn on individual genes within two different biosynthetic gene clusters (BGCs) which are silent under normal growth conditions. This method also opens opportunities to stepwise activate individual genes in a cluster to decipher the correlated biosynthetic pathway. Graphical abstract KEYPOINTS: • An inducible RNA-guided transcriptional regulator based on VPR-dCas9 was established in Aspergillus nidulans. • Genome-wide nucleosome positioning maps were created that facilitate sgRNA positioning. • The system was successfully applied to activate genes within two silent biosynthetic gene clusters.