Madhavi Bhatia, National Pharmaceutical Education and Research, Guwahati
Genome-editing technologies have made genetic studies transform from traditional interventions to precise manipulations of the DNA sequences thus resulting in both simplicity and robust outcomes. There are various genome-editing technologies, the clustered regularly interspaced short palindromic repeats (CRISPR) systems are widely used. The standard CRISPR-Cas9 method introduces imprecise edits containing indels that vary in size from a single nucleotide to hundreds of nucleotides through non-homologous end joining (NHEJ). While the base editors can generate transition point mutations with high efficiency and also accuracy without introducing any double-strand breaks. But base editors are not suitable for another type of point mutations. A newly invented genome-editing technology is Prime editing which involves all types of base transitions and transversions, insertions, and deletions. The most important component present in a prime editor (PE) is Cas9 nickase that is merged with reverse transcriptase and a prime editing guide RNA (pegRNA), it brings the PE machinery to targeted sites by standard single-guide RNA (sgRNA) sequence. Upon binding, the Cas9 nickase makes an incision to the target strand and the reverse transcriptase uses the primer binding site (PBS) of the pegRNA to initiate the reverse transcription. There 4 different types of PE systems- PE1, PE2, PE3, and PE3b. The major issue with PE systems is that pegRNA/ngRNA design, which is more complicated than the other design process that might be associated with other precise editing methods. There are several programs for eg- PegFinder, primeedit.nygenome, PnB designer, DeepPE that help in the simplified search for pegRNAs and ngRNAs for primer editing and it is followed by general design guidance proposed in the original primer editing paper.
Easy-Prime:
Easy-Prime is a knowledge-based and one-step solution that searches and optimizes the designs of PE automatically. It is an approach that weighs and combines the contributions of the different features of the sample to predict editing efficiency and also prioritize candidate sequences. Easy–Prime also helps us to explore hidden features such as RNA folding; this provides new insights into the mechanism of prime editing. It was observed that for prime editing spCas9 activity and PBS GC content are important in the PE2 system. In the PE3 system, the PAM distribution feature (is_dPAM) and the target mutation position (Target_pos) are the 2 most important features, followed by the RNA folding and the spCas9 activity. It was established that the disruption of the PAM sequence with introduced mutations can improve the efficiency of the PE. The mutation type generally does not affect the PE efficiency significantly. Thus, confirming that primer editing is a versatile tool for various types of genome editing.
Limitations of Easy-Prime:
Certain features coverage is sparse and uneven. Eg. The C-base at the first position in the RTT decreases the editing efficiency of PEs. This happens because the C-base can pair with G81 in the RNA scaffold and also disrupt the interaction between G81 and Y1356 in Cas9. This pairing between the RTT and the scaffolds might result in destabilization of the RNA secondary structure and thus decrease the activity of PEs. Therefore, 90% of the endogenous PE datasets should be designed in such a way that C-base in the first position should not be present in the datasets. But the cell-type features of PE like chromatin openness, epigenetic modifications, and off-target, by-product deletions introduced by pegRNA nick sites in the PE3 system.
Conclusion: Easy-Prime is a machine-learning-based program. It expresses both the known and novel features such as RNA folding structure and also optimizes the feature combinations of the datasets to improve the editing PE efficiency.
Also read: GATA3: Analyzing a transcription factor involved in ovarian cancer
Reference: Li, Y., Chen, J., Tsai, S. Q., & Cheng, Y. (2021). Easy-Prime: A machine learning–based prime editor design tool. Genome Biology, 22(1), 235. https://doi.org/10.1186/s13059-021-02458-0
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About the author: Madhavi Bhatia is currently pursuing a Master of Science in Pharmaceutical Biotechnology from NIPER, Guwahati. Her area of interest lies in understanding the role of gene mutation in the development of various diseases and developing a treatment for such diseases.
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