AI-designed DNA switches flip genes on and off

Researchers at The Jackson Laboratory (JAX), Yale University, and the Broad Institute of MIT and Harvard 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.

Described in the journal Nature, their approach opens the possibility of controlling when and where genes are expressed in the body, in ways that were never before possible.

In recent years, gene-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 — 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. The researchers managed to design synthetic CREs that can successfully activate genes in brain, liver or blood cells without turning on those genes in other cell types.

“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, an associate professor at JAX and co-senior author on 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.”

Tissue- and time-specific instructions

Although every cell in an organism contains the same genes, not all the genes are needed in every cell, or at all times. CREs help ensure that genes needed in the brain are not used by skin cells, for instance, or that genes required during early development are not activated in adults. CREs themselves are not part of genes, but are separate, regulatory DNA sequences — often located near the genes they control.

Scientists know that there are thousands of different CREs in the human genome, each with slightly different roles. But according to JAX’s Dr Rodrigo Castro, co-first author on the new study, there are “no straightforward rules that control what each CRE does … [which] limits our ability to design gene therapies that only affect certain cell types in the human body”.

Steven Reilly, an assistant professor at Yale and senior author on the study, added, “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 analysing these predictions, the researchers discovered new patterns in the DNA, learning how the grammar of CRE sequences in the DNA impacts how much RNA would be made — a proxy for how much a gene is activated.

The team 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 co-first author Dr Sager Gosai, a postdoctoral fellow at the Broad Institute. “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.”

Pick and choose your organ

The colleagues tested their 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,” Gosai said. “We initially expected many of the sequences would misbehave inside living cells.”

“It was a thrilling surprise to us just how good CODA was at designing these elements,” Castro added.

Studying why the synthetic CREs were able to outperform naturally occurring CREs, the researchers discovered that their 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. They also tested several of their 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,” Tewhey concluded. “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.”

Image credit: iStock.com/DKosig