Researchers from The Jackson Laboratory, Broad Institute, and Yale University have developed an innovative approach using artificial intelligence to create synthetic DNA switches that can precisely control gene expression in specific cell types. This breakthrough, published in Nature, offers new possibilities for targeted gene therapies and medical research.
The team trained a deep learning model on hundreds of thousands of DNA sequences to predict the activity of cis-regulatory elements (CREs) in blood, liver, and brain cells. Using this model, they created a platform called CODA (Computational Optimization of DNA Activity) to design novel CREs with unprecedented specificity.
"What is special about these synthetically designed elements is that they show remarkable specificity to the target cell type they were designed for," said Ryan Tewhey, co-senior author of the study. "This creates the opportunity for us to turn the expression of a gene up or down in just one tissue without affecting the rest of the body."
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The researchers successfully tested these AI-designed CREs in cells, zebrafish, and mice, demonstrating their ability to activate genes in specific cell types without affecting others. Surprisingly, the synthetic CREs outperformed naturally occurring ones in terms of cell-type specificity.
Steven Reilly, another senior author, explained the project's goal: "Can we learn to read and write the code of these regulatory elements? If we think about it in terms of language, the grammar and syntax of these elements is poorly understood. And so, we tried to build machine learning methods that could learn a more complex code than we could do on our own."
This technology opens up new avenues for controlling gene expression in targeted tissues, potentially revolutionizing gene therapy approaches and biomedical research. The ability to design CREs with such precision could lead to more effective and safer treatments for various genetic disorders and diseases.