title: Quantitative evaluation of first, second, and third generation hairpin systems reveals the limit of mammalian vector-based RNAi.
abstract: Incorporating miRNA-like features into vector-based hairpin scaffolds has been shown to augment small RNA processing and RNAi efficiency. Therefore, defining an optimal, native hairpin context may obviate a need for hairpin-specific targeting design schemes, which confound the movement of functional siRNAs into shRNA/artificial miRNA backbones, or large-scale screens to identify efficacious sequences. Thus, we used quantitative cell-based assays to compare separate third generation artificial miRNA systems, miR-E (based on miR-30a) and miR-3G (based on miR-16-2 and first described in this study) to widely-adopted, first and second generation formats in both Pol-II and Pol-III expression vector contexts. Despite their unique structures and strandedness, and in contrast to first and second-generation RNAi triggers, the third generation formats operated with remarkable similarity to one another, and strong silencing was observed with a significant fraction of the evaluated target sequences within either promoter context. By pairing an established siRNA design algorithm with the third generation vectors we could readily identify targeting sequences that matched or exceeded the potency of those discovered through large-scale sensor-based assays. We find that third generation hairpin systems enable the maximal level of siRNA function, likely through enhanced processing and accumulation of precisely-defined guide RNAs. Therefore, we predict future gains in RNAi potency will come from improved hairpin expression and identification of optimal siRNA-intrinsic silencing properties rather than further modification of these scaffolds. Consequently, third generation systems should be the primary format for vector-based RNAi studies; miR-3G is advantageous due to its small expression cassette and simplified, cost-efficient cloning scheme.
authors.affiliation: a Departments of Bioinformatics and Computational Biology , South San Francisco , CA 94080.|c Genentech, Inc . South San Francisco , CA 94080 , USA.
authors.forename: Colin
authors.initials: C
authors.lastname: Watanabe
authors.affiliation: b Molecular Biology South San Francisco , CA 94080.|c Genentech, Inc . South San Francisco , CA 94080 , USA.
authors.forename: Trinna L
authors.initials: TL
authors.lastname: Cuellar
authors.affiliation: b Molecular Biology South San Francisco , CA 94080.|c Genentech, Inc . South San Francisco , CA 94080 , USA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haley
mesh_terms: D000465:Algorithms; D000818:Animals; D005822:Genetic Vectors / Q000235:genetics*; D057809:HEK293 Cells; D006801:Humans; D051379:Mice; D035683:MicroRNAs / Q000235:genetics*; D034622:RNA Interference; D012319:RNA Polymerase II / Q000378:metabolism; D012320:RNA Polymerase III / Q000378:metabolism; D012330:RNA, Double-Stranded; D034741:RNA, Small Interfering / Q000032:analysis* / Q000737:chemistry
chemical_list: D035683:MicroRNAs; D012330:RNA, Double-Stranded; D034741:RNA, Small Interfering; D012319:RNA Polymerase II; D012320:RNA Polymerase III
keywords: RNAi; amiRNA; miRNA; shRNA; siRNA