DNA interference is controlled by R-loop length in a type I-F1 CRISPR-Cas system

Abstract

Background: CRISPR-Cas systems, which provide adaptive immunity against foreign nucleic acids in prokaryotes, can serve as useful molecular tools for multiple applications in genome engineering. Diverse CRISPR-Cas systems originating from distinct prokaryotes function through a common mechanism involving the assembly of small crRNA molecules and Cas proteins into a ribonucleoprotein (RNP) effector complex, and formation of an R-loop structure upon binding to the target DNA. Extensive research on the I-E subtype established the prototypical mechanism of DNA interference in type I systems, where the coordinated action of a ribonucleoprotein Cascade complex and Cas3 protein destroys foreign DNA. However, diverse protein composition between type I subtypes suggests differences in the mechanism of DNA interference that could be exploited for novel practical applications that call for further exploration of these systems. Results: Here we examined the mechanism of DNA interference provided by the type I-F1 system from Aggregatibacter actinomycetemcomitans D7S-1 (Aa). We show that functional Aa-Cascade complexes can be assembled not only with WT spacer of 32 nt but also with shorter or longer (14-176 nt) spacers. All complexes guided by the spacer bind to the target DNA sequence (protospacer) forming an R-loop when a C or CT protospacer adjacent motif (PAM) is present immediately upstream the protospacer (at −1 or −2,−1 position, respectively). The range of spacer and protospacer complementarity predetermine the length of the R-loop; however, only R-loops of WT length or longer trigger the nuclease/helicase Cas2/ 3, which initiates ATP-dependent unidirectional degradation at the PAM-distal end of the WT R-loop. Meanwhile, truncation of the WT R-loop at the PAM-distal end abolishes Cas2/3 cleavage. Conclusions: We provide a comprehensive characterisation of the DNA interference mechanism in the type I-F1 CRISPR-Cas system, which is different from the type I-E in a few aspects. First, DNA cleavage initiation, which usually happens at the PAM-proximal end in type I-E, is shifted to the PAM-distal end of WT R-loop in the type I-F1. Second, the R-loop length controls on/off switch of DNA interference in the type I-F1, while cleavage initiation is less restricted in the type I-E. These results indicate that DNA interference in type I-F1 systems is governed through a checkpoint provided by the Cascade complex, which verifies the appropriate length for the R-loop.

Genomic and Phenotypic Analysis of Multidrug-Resistant Acinetobacter baumannii Clinical Isolates Carrying Different Types of CRISPR/Cas Systems

“…Specific compositions of this complex include 9 subtypes, namely, A, B, C, G, D, E, F1 (previously F), F2 (previously F variant), and F3 [29,30]. The most well-known CRISPR/Cas Type I systems are Type I-E systems, while Type I-F1 CRISPR/Cas system is their closest relative [35]. The Type I-F1 CRISPR/Cas system is characterized by a unique fusion of Cas2 to Cas3 (Cas2/3), which together with Cas1 mediate spacer integration into CRISPR locus [36].…”

Rapid cell-free characterization of multi-subunit CRISPR effectors and transposons

“…S2B), indicating functional expression of all Cascade subunits. PAM-DETECT further revealed PAM profiles that overlapped --but were not identical to --the I-C and I-F1 systems with even a moderately mapped PAM profile (Almendros et al, 2012;Leenay et al, 2016;Rao et al, 2017;Rollins et al, 2015;Tuminauskaite et al, 2020;Zheng et al, 2019). In particular, the I-C system from X. albilineans recognizes TTC followed by TTT and CTC, while the characterized I-C system from Bacillus halodurans recognizes TTC followed by CTC and then TCC (Leenay et al, 2016) and the I-C system from Legionella pneumophila recognized TTC followed by TTT and CTT (Rao et al, 2017).…”

A quantitative model for the dynamics of target recognition and off-target rejection by the CRISPR-Cas Cascade complex

“…The development of engineered effector variants (Amrani et al, 2018;Chen et al, 2017;Edraki et al, 2019;Gleditzsch et al, 2016;Kleinstiver et al, 2016;Lee et al, 2018;Luo et al, 2016;Ran et al, 2015;Slaymaker et al, 2016;Songailiene et al, 2019;Tuminauskaite et al, 2020;Wu et al, 2018) could recently reduce but not abolish off-targeting (Frock et al, 2015;Slaymaker et al, 2016). A frequently used complementary approach to prevent off-targets are in silico off-target predictors that promise to identify crRNAs with least promiscuity (Aprilyanto et al, 2021;Bae et al, 2014;Charlier et al, 2021;Haeussler et al, 2016;Lei et al, 2014;Lin and Wong, 2018;Minkenberg et al, 2019;Singh et al, 2015;Stemmer et al, 2015;Xu et al, 2017).…”