By Mary Pulgar
Designing and Selection class was lectured by George Church (Harvard). George introduced us to the world of Multiplex synthetic libraries & selections by establishing 3 main aspects: mutation, selection, and design both via libraries. The professor talked about different techniques, for instance: FACS, Phage display, Sensor-selectors, PhIP-Seq, PLATO, AAV, and many more. He mentions codons coding impacting protein expression. He also discusses recoding to confer multi-virus resistance, and finally, he engaged us in the edition of repeats (involved in senescence, neurogenesis, cancer, inflammation) in order to enable large-scale genome editing by reducing DNA nicking and off-target.
In order to design a genetic modification, I chose the MTHFR gene from George Church's list of potential human genome modifications.
MTHFR loci is related to high altitude resistance. It has been studied in Tibetan people and it has been found to be strongly associated with blood-related phenotypes, such as hemoglobin, homocysteine, and folate. "The folate-increasing allele of rs1801133 at the MTHFR locus has an increased frequency in Tibetans more than expected under a drift model, which is probably a consequence of adaptation to high UV radiation. These findings provide important insights into understanding the genomic consequences of high-altitude adaptation in Tibetans." (Yang et al, 2017)
The mutation reported is Alanine (A) is mutated to Valine (V) in position 222, or A222V.
The genomic sequence used is MTHFR (ENSG00000177000) from Homo sapiens that is located in Chromosome 1.
The information about this gene from NCBI reports that:
The sequence to edit (target region) is located at the non-coding region and the aminoacid to change in the codon is GCC to GTC. This change is located at 674 position. The part of the sequence is as follows:
5' -GCTCCTCGGGGGTGCGGGTCTCACGCTcaccgcagcagcagcaccacctgcagcagcagcacagAGACCCCCTGCACCCCGCCATCTTCCTCCTTTACTGTCACTCTGGACCCCTCTACCAACCCCCTCCCAGCCAGGATCTGCGCCTCACGTGACTGGCCCCGGGACGGTCACGTGGCCCTCTCGAGCTCTGGGACTGA - 3'
Using Benchling, it was imported the sequence with guide parameters as the default ones.
I chose CRISPR/Cas9-based editor.
Here, to find the guide sequences, I ordered first from the off-target score and then the on-target score, > 50.0; and it was selected the first one:
As it can be observed, this guide sequence (GCAGTAAAGGAGGAAGATGG), with PAM = CGG, has 20 bp of length, 50% of GC content and Tm= 52.0°C. However, the off-target score is 36.4.
After finding the guide sequence, I designed HR template following these steps:
