Researchers at The Jackson Laboratory (JAX), the Broad Institute of MIT, Harvard, and Yale University have used AI to design thousands of new DNA switches that can control genes in different cell types. Their new approach opens the possibility of controlling when and where genes are expressed in the body for the benefit of human health and medical research in ways never before possible.

"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 allowed scientists 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 partly because of the ongoing challenge of understanding the DNA switches, called cis-regulatory elements (CREs), that control the expression and repression of genes.

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 separate, regulatory DNA sequences—often located near the genes they control.

Scientists know that thousands of different CREs exist 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.

Using a form of artificial intelligence (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 of any sequence from an 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 impacts how much RNA would be made—a proxy for how much a gene is activated.

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.

"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," said study co-first author Sager Gosai, Ph.D., a postdoctoral fellow in Sabeti's lab.

Testing the model

Tewhey and his colleagues tested the new, AI-designed synthetic CREs by adding them to cells and measuring how well they activated genes in the desired cell type and avoided gene expression in other cells. They discovered that the new CREs were even more cell-type-specific than naturally occurring CREs known to be associated with the cell types.

"It was a thrilling surprise to us just how good CODA was at designing these elements," said Castro.

Phys Org