RNA-targeting CRISPR systems from metagenomic discovery to transcriptomic engineering (original) (raw)

irroring prior efforts with DNA, biologists have leveraged nature's molecular diversity to target RNA in living cells since the turn of the 21st century. In a breakthrough for RNA biology, studies showed that the MS2 bacteriophage viral coat protein (VCP) could be programmed along with its cognate RNA loop binding partner to image and stabilise mRNA in eukaryotic cells 1,2. Three years later researchers converted a geneexpression inhibition system, RNA interference (RNAi), into one of the most widely applicable tools in the field 3. For the next fifteen years, these two systems-VCP and RNAi-would come to define RNA targeting, even as other promising technologies rose from obscurity. One such technology, CRISPR-Cas (clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins) originates in prokaryotes, in which it acts as an adaptive immune system against phage invaders 4. Canonically, Cas proteins and CRISPR RNA (crRNA) form a complex to catalyse the interference of foreign nucleic acids by recognising protospacer sequences mapping to spacer sequences present on crRNA 5. CRISPR-Cas (often simply 'CRISPR') systems display enormous evolutionary diversity earned through postulated convergence, divergence, and horizontal gene transfer 6. For instance, class 1 systems require multiple subunits for nucleic acid interference, whereas in class 2 systems an efficient single effector suffices. With an evolving classification nomenclature, unique class 1 and class 2 CRISPR systems have been found to target double-stranded DNA (dsDNA), single-stranded DNA (ssDNA) and/or single-stranded RNA (ssRNA) 7. Because of their potential for RNA programmability, built-in enzymatic interface, and remarkable ease of use, CRISPR systems have matured into an essential toolkit for genome engineering. Soon after reported uses of DNA-targeting CRISPR-Cas (which we term 'DCas' , not to be confused with catalytically inactive Cas, 'dCas') in mammalian cells via Cas9 (ref. 8), biology researchers applied DCas to high-throughput genomic screens and isogenic background mutant cell line generation, among other transformative applications 9. Today the RCas field is seeing similar progress, driven by a bioinformatics race to discover and characterise CRISPR systems. Discovery, diversity, and parallel systems RCas identification. Beyond adapting DCas systems to target RNA 10 , RCas identification has been accomplished through bioinformatic discovery (Fig. 1a) 11. Whereas such computational analysis Diverse RCas platforms. Class 1 RNA-targeting CRISPR systems, namely the Cmr complex (type III-B and type III-C) 17 and Csm complex (type III-A and type III-D) 18,19 , have been well characterised. Due to their relative simplicity, however, the class 2 type II and VI loci embodied by Cas9 and Cas13, respectively, define the RCas transcriptomic engineering space (Fig. 1b).