Guide RNA (GRNA) And Software Trainings
Hello! This week, I completed the training I needed for this project and finished obtaining the template sequence and guide RNA (gRNA). Today, I will briefly introduce this training, followed by how my project has progressed so far.
Software Training and CRISPR Lectures
Throughout January and the first half of February, I worked on software training for my senior project as prescribed by my mentor. I learned how to obtain genetic and amino acid sequences through the BLAST designated for the nucleotide and protein systems. I learned then to understand BLAST graphical summaries and BLAST output descriptions. Here is the link to the training I used to learn about BLAST:
In addition to BLAST, I also had to learn how to utilize the UniProtKB database to my advantage. This was made possible through the training course offered by the European Bioinformatics Institute recommended by my mentor, which I completed. This course taught me how to get data from UniProt, where patient data comes from, and how to explore a UniProtKB entry. Here is the link to the training I used to learn about UniProtKB:
In the second half of February, I learned about the biological processes of the CRISPR-Cas9 system and how it has been manipulated into nickase forms to create single-strand breaks. I learned this through lecture PowerPoints provided by the Innovative Genomics Institute. I learned concepts like types of DNA repair, key takeaways, and protein specifics regarding Cas9 proteins.
Obtaining guide RNA (gRNA)
Now to the work that I have done this week. This week, I have applied what I learned from my training to make gRNAs, 20-nucleotide sequences used by Cas9 nickase to bind to the mutated DNA sequence. These 20-nucleotide gRNAs were screened using the BLAST tool to ensure that they would bind to human DNA sequences of MLH1 and MSH2 only. (To give some background, MLH1 and MSH2 were chosen as target genes due to their clinical significance and increased prevalence in HNPCC cases.) The 20-nucleotide gRNAs were screened using the CASOffFinder tool to simulate gRNA binding to all DNA sequences of the human genome to determine off-target effects and minimize them as much as possible.
In addition to this, I also identified the prevalence of genes most affected in HNPCC cases through the UniProtKB database (which turned out to be MLH1 and MSH2). I also identified the different clinical mutations in MLH1 and MSH2 and mapped them onto a structural domain diagram to find out which part of the gene should the gRNAs target.
In my results, CasOFFFinder showed the list of off-target effects present in the gRNA. I made sure that there were the least amount of off-target effects in the gRNA in addition to at least 80% GC content for gRNA stability when it enters the nucleus. I also made sure that these off-targets were at 2-3 base pair mismatches instead of at 0-1 base pairs. BLAST results showed that the four gRNAs did indeed bind to the human MLH1 and human MSH2 only.
Here is a link to the results of the CasOFFFinder work and the gRNAs as well.