A team from the University of Minnesota Twin Cities has, for the first time, developed a new tool to predict and tailor the rate of a specific type of DNA editing called “site-specific recombination”. The research is paving the way for more personalized and effective gene and cell therapies for diseases such as diabetes and cancer.
The study is published in Communication Nature.
The process of site-specific recombination involves the use of enzymes that recognize and modify specific DNA sequences in living cells. It has important applications for the treatment of a myriad of diseases using cell therapies.
Immunotherapy, for example, involves extracting immune cells from a patient and genetically modifying them to fight a disease such as cancer. In these applications, it is important to precisely control the timing of gene expression to maximize treatment effects while minimizing adverse effects in the body.
Engineers at the University of Minnesota have developed a method that combines high-throughput experiments with a machine learning model to make the site-specific recombination process more efficient and predictable. The model allows researchers to program the rate at which DNA is edited. This means that they can control the rate at which a therapeutic cell reacts to its environment, thereby controlling how quickly or slowly it produces a therapeutic drug or protein.
“To our knowledge, this is the first example of using a model to predict how altering a DNA sequence can control the rate of site-specific recombination,” said Casim Sarkar, lead author of article and associate professor at the University of Minnesota. Twin Cities Department of Biomedical Engineering. “By applying engineering principles to this problem, we can dial in the rate at which DNA editing occurs and use this form of control to tailor therapeutic cellular responses. Our study also identified novel DNA sequences. DNA that are much more efficiently recombined than those found in nature, which can speed up cellular response times.”
Sarkar and his team first developed an experimental method to calculate the site-specific recombination rate and then used this information to train a machine learning algorithm. Ultimately, this allows researchers to simply enter a DNA sequence, and the model predicts how quickly that DNA sequence will recombine.
They also discovered that they could use modeling to predict and control the simultaneous production of multiple proteins in a cell. This could be used to program stem cells to produce new tissues or organs for regenerative medicine applications or to endow therapeutic cells with the ability to produce multiple drugs in predefined proportions.
“Different patients may require different doses or a faster or slower cellular response – not everyone is the same,” Sarkar explained. “By building genetic circuits inside cells that use multiple DNA sequences with different and defined recombination rates, we can now do things that were hard to do before, like protein production schedule ratios in therapeutic cells. Our rational approach enables personalized treatment for the patient.”
This research was funded by the National Institutes of Health.
In addition to Sarkar, the research team included researchers from the University of Minnesota’s Department of Chemical Engineering and Materials Science, Qiuge Zhang, a recent doctoral student, and Samira Azarin, an associate professor.
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