The largest study of CRISPR-Cas9 mutations to date has developed a method to predict the exact mutations CRISPR-Cas9 gene editing can introduce to a cell.
The Wellcome Sanger Institute edited 40,000 different pieces of DNA and examined a thousand million resulting DNA sequences to discover the effects of the gene editing and develop a machine learning predictive tool of the outcomes. The research aims to assist those who are using CRISPR-Cas9 mutations to research disease mechanisms and drug targets and enable scientists to predict the best sequences to target to make CRISPR-Cas9 gene editing more reliable, cheaper and more efficient.
The gene editing tech that can cut DNA
CRISPR-Cas9 mutations is a form of gene editing technology that enables researchers to cut DNA at any position in the genome, therefore creating mutations and allowing to switch off specific genes. Being used worldwide by scientists to study which genes are important for various conditions, from cancer to rare diseases, CRISPR-Cas9 is now being trialled therapeutically to correct harmful mutations in people’s genes.
Details of the study
A specific guide RNA binds to an exact sequence of target DNA, guiding the Cas9 ‘scissors’ to cut the DNA at the right place. However, it is difficult to predict exactly what the final mutations will be, as further alterations often take place when the cell repairs the break, rejoining the two cut ends of the DNA.
To study this, the researchers created over 40,000 pairs of different target DNA and guide RNA, and carried out CRISPR-Cas9 gene editing. By deep sequencing of each pair in different cells, they were able to analyse in detail how the DNA was cut and rejoined. They found that the repair depended on the exact sequence of DNA and guide and discovered that it was reproducible within the same sequence.
Dr Luca Crepaldi, joint first author on the study from the Wellcome Sanger Institute, explains the success of the study: “We have carried out the largest, most comprehensive study on CRISPR-Cas9 action to date, and analysed more than a thousand million DNA sequences to allow us to study the process. We demonstrated that specific target sequences were repaired by the cell in the same way, proving that the action of the cell mechanisms is reproducible.”
Dr Felicity Allen, joint first author from the Wellcome Sanger Institute, adds: “The discovery of reproducible DNA repair after CRISPR-Cas9 editing, combined with the vast amount of sequence data we generated, meant that we could create a predictive tool using machine learning methods. Our resource can predict the exact mutations resulting from CRISPR-Cas9 gene editing, just from the sequence of the target DNA.
“It will save time and resources for future CRISPR-Cas9 applications, and is openly available for use by all researchers using gene editing to study health and disease.”
