Researchers at the Jackson Laboratory (JAX), the Broad Institute of MIT and Harvard, and Yale University, have used artificial intelligence (AI) to design thousands of new DNA switches that can precisely control the expression of a gene in different cell types. Their new approach could revolutionize biotechnology by allowing precise activation or repression of genes in specific tissues.
The findings are published in Nature in an article titled, “Machine-guided design of cell-type-targeting cis-regulatory elements.”
“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, PhD, an associate professor at the Jackson Laboratory and co-senior author of the work. “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.”
In recent years, genetic editing technologies and other gene therapy approaches have given scientists the ability to alter the genes inside living cells. However, affecting genes only in selected cell types or tissues, rather than across an entire organism, has been difficult. That is in part because of the ongoing challenge of understanding the DNA switches, called cis-regulatory elements (CREs), that control the expression and repression of genes.
In the current study, the researchers designed new, never-before-seen synthetic CREs, and used the CREs to activate genes in brain, liver, or blood cells without turning on those genes in other cell types.
Scientists know that there are thousands of different CREs in the human genome, each with slightly different roles. But the grammar of CREs has been hard to figure out, “with no straightforward rules that control what each CRE does,” explained Rodrigo Castro, PhD, a computational scientist in the Tewhey lab at JAX and co-first author of the new paper. “This limits our ability to design gene therapies that only affect certain cell types in the human body.”
“This project essentially asks the question: ‘Can we learn to read and write the code of these regulatory elements?’” said Steven Reilly, PhD, assistant professor of genetics at Yale and one of the senior authors of the study. “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.”
Using a form of AI called deep learning, the group trained a model using hundreds of thousands of DNA sequences from the human genome that they measured in the laboratory for CRE activity in three types of cells: blood, liver, and brain. The AI model allowed the researchers to predict the activity for any sequence from the almost infinite number of possible combinations. By analyzing these predictions, the researchers discovered new patterns in the DNA, learning how the grammar of CRE sequences in the DNA impact how much RNA would be made.
The team, including Pardis Sabeti, MD, DPhil, co-senior author of the study and a core institute member at the Broad Institute and professor at Harvard, then developed a platform called CODA (Computational Optimization of DNA Activity), which used their AI model to efficiently design thousands of completely new CREs with requested characteristics, like activating a particular gene in human liver cells but not activating the same gene in human blood or brain cells. Through an iterative combination of “wet” and “dry” investigation, using experimental data to first build and then validate computational models, the researchers refined and improved the program’s ability to predict the biological impact of each CRE and enabled the design of specific CREs never before seen in nature.
“Natural CREs, while plentiful, represent a tiny fraction of possible genetic elements and are constrained in their function by natural selection,” said study co-first author Sager Gosai, PhD, a postdoctoral fellow in Sabeti’s lab. “These AI tools have immense potential for designing genetic switches that precisely tune gene expression for novel applications, such as biomanufacturing and therapeutics, that lie outside the scope of evolutionary pressures.”
Tewhey and his colleagues tested the new, AI-designed synthetic CREs by adding them into cells and measuring how well they activated genes in the desired cell type, as well as how good they were at avoiding gene expression in other cells. The new CREs, they discovered, were even more cell-type-specific than naturally occurring CREs known to be associated with the cell types.
“The synthetic CREs semantically diverged so far from natural elements that predictions for their effectiveness seemed implausible,” said Gosai. “We initially expected many of the sequences would misbehave inside living cells.”
Tewhey and his collaborators studied why the synthetic CREs were able to outperform naturally occurring CREs and discovered that the cell-specific synthetic CREs contained combinations of sequences responsible for expressing genes in the target cell types, as well as sequences that repressed or turned off the gene in the other cell types.
Finally, the group tested several of the synthetic CRE sequences in zebrafish and mice, with good results. One CRE, for instance, was able to activate a fluorescent protein in developing zebrafish livers but not in any other areas of the fish.
“This technology paves the way toward the writing of new regulatory elements with pre-defined functions,” said Tewhey. “Such tools will be valuable for basic research but also could have significant biomedical implications where you could use these elements to control gene expression in very specific cell types for therapeutic purposes.”
