Of Man Or Machine? Why Not Both?
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How do you feel about the exponential growth of A.I? Are looking forward to it? Are you weary about it? Either way, the age of A.I is upon us and will continue to influence our everyday lives. If you'd like to live about your natural way of life in the modern-day, I present a compromise for you between man and machine. A.I can help us prevent disease and help us live overall better lives, how you may ask? Through the editing of our gene sequences, an all-natural "blueprint" that makes us who we are, and with A.I, can make us better.
During gene editing with CRISPR technology, the Cas9 scissors that cut DNA home in on the right spot to snip with the help of guide RNA. The way the genetic material is stitched back together afterward isn’t terribly precise, though; in fact, scientists have long thought that without a template, the process is random. However, “there’s been anecdotal evidence that cells don’t repair DNA randomly,” Sherwood wondered if artificial intelligence could predict these outcomes.
Following Cas9 cleavage, DNA repair without a donor template would be generally considered stochastic, heterogeneous, and impractical beyond gene disruption. In this scientific report, we see that template-free Cas9 editing is predictable and capable of precise repair to the intended genotype, enabling correction of disease-associated mutations in humans.
In this paper, Sherwood and her colleagues describe how they trained a machine-learning algorithm called inDelphi to predict repairs made to DNA snipped with Cas9, using experimental data from 1,872 target sequences cut and then restitched in mouse and human cell lines. The algorithm showed that 5–11 percent of the guide RNAs used induced a single, predictable repair genotype in the human genome in more than 50 percent of editing products. In other words, the edits aren’t random, the team reports.
However, a still-unpublished analysis of the algorithms results reveals that at times, it makes vastly different predictions for the same cuts in the same types of cells, suggesting that the algorithms’ accuracy needs improvement.
Accurate predictions of sequence repair could allow researchers to computationally predict the precise guide RNAs that will reproduce exact human patient mutations, leading to the development of better research models to study genetic disease. Sherwood and his colleagues also showed that their algorithm could predict which guide RNAs would be needed to—without a repair template—correct disease-causing mutations found in human patients, a clinical application of CRISPR that is still years, if not decades, from becoming a reality, meaning you still have time to decide. The predicted repairs worked on cell lines from patients with a rare genetic disease that causes a blood clotting deficiency and albinism, and another that includes growth failure and nervous system deterioration.
Next, Sherwood says, “we would want to test whether we can fix disease-causing mutations in animal models, with an eventual goal of doing so for human patients.”
