Spacer2PAM: A computational framework for identification of functional PAM sequences for endogenous CRISPR systems

Abstract

RNA-guided nucleases from clustered regularly interspaced short palindromic repeats (CRISPR) systems expand opportunities for precise, targeted genome modification. Endogenous CRISPR systems in many bacteria and archaea are particularly attractive to circumvent expression, functionality, and unintended activity hurdles posed by heterologous CRISPR effectors. However, each CRISPR system recognizes a unique set of PAM sequences, which requires extensive screening of randomized DNA libraries. This challenge makes it difficult to develop endogenous CRISPR systems, especially in organisms that are slow-growing or have transformation idiosyncrasies. To address this limitation, we present Spacer2PAM, an easy-to-use, easy-to-interpret R package built to identify potential PAM sequences for any CRISPR system given its corresponding CRISPR array as input. Spacer2PAM can be used in “Quick” mode to generate a single PAM prediction that is likely to be functional or in “Comprehensive” mode to inform targeted, unpooled PAM libraries small enough to screen in difficult to transform organisms. We demonstrate Spacer2PAM by predicting PAM sequences for industrially relevant organisms and experimentally identifying seven PAM sequences that mediate interference from the Spacer2PAM-predicted PAM library for the type I-B CRISPR system from Clostridium autoethanogenum. We anticipate that Spacer2PAM will facilitate the use of endogenous CRISPR systems for industrial biotechnology and synthetic biology.

Endogenous CRISPR/Cas systems for genome engineering in the acetogens Acetobacterium woodii and Clostridium autoethanogenum

“…As CRISPR/Cas systems are prokaryotic defense mechanisms against invading elements (Makarova and Koonin, 2015), filtering of biologically relevant hits relies on their presence in mobile genetic elements (MGEs), including plasmids, phages, prophages, and any related genes. The similar performances between Spacer2PAM (Rybnicky et al, 2022) and our Python script, despite a more comprehensive filtering parameter selection for falsepositives in Spacer2PAM, could be explained by our refined selection for biologically relevant hit sequences.…”

“…In previously performed manual PAM prediction (Pyne et al, 2016), the array direction was incorrect, leading to the 3′ PAM being wrongly selected as the potential PAM. Spacer2PAM only predicted a TTNN PAM consensus, while a NYCN consensus was experimentally confirmed (Rybnicky et al, 2022). Lastly, Vink et al (2021).…”

“…In this study, a dedicated bioinformatics tool was successfully developed to allow fully automated predictions based on data from the CRISPRFinder database. In the meantime, two other tools to identify PAMs have been published (Vink et al, 2021;Rybnicky et al, 2022). In all three tools, spacers are run through BLAST, and the flanking sequences of the matches are retrieved to compile a prediction of the PAM.…”

“…The main advancement made over preceding tools is filtering of hits. False-positives are filtered based either on global mismatch (Vink et al, 2021) or a combination of parameters, including gap number, e-value, length of the hit, and starting position of the hit (Rybnicky et al, 2022). Our Python script filters on global mismatch but is more stringent in the seed region adjacent to the PAM, as interference relies on the recognition of a seed region (Semenova et al, 2011;Xue et al, 2015).…”

“…As C. autoethanogenum is an important industrial strain, its CRISPR/Cas system has been of interest in recent years (Pyne et al, 2016;Rybnicky et al, 2022) and can serve as a case study to compare the different methods of PAM elucidation. The output of our Python script was two putative 5′ PAMs, TCH and TTR, and TCA was confirmed in vivo by genome editing.…”