A new study has found that the proteins that control which genes are expressed in the genome do more than just turn genes on or off. Essentially, each type of protein that interacts with a gene produces different behavior. This discovery has implications for everything from biomedical therapy to biological computing.
The problem is “epigenome regulatory factors.” The genome of all living things is made up of DNA. But that DNA is combined with many different proteins into a very compact structure. The proteins that bind to DNA are called the epigenome and control which parts of the DNA are expressed. Blood cells, nerve cells, and skin cells all have the same DNA, but perform completely different functions. That’s because different parts of the DNA sequence are expressed in each cell. that It is primarily controlled by which proteins bind to different parts of the DNA within each cell.
“We already knew that proteins in the epigenome control how DNA is expressed,” says Albert Kuhn, corresponding author of the study and associate professor of chemical and biomolecular engineering at North Carolina State University. “Our goal here was to look at a single gene and quantify all the ways that gene is expressed by different proteins.” Keung is a Goodnight Distinguished Scholar in Innovation in Biotechnology and Biomolecular Engineering and director of the biotechnology program at North Carolina State’s Integrative Science Initiative.
“The results were fascinating,” says study co-first author Leandra Caywood, a recent Ph.D. Graduated from North Carolina State University. “For example, one protein may turn on a gene quickly. A second protein may take a little while to turn on a gene, but then it continues to turn on the gene for a long time. And a third protein may take a long time to turn on the gene, at which point the gene turns on quickly and turns off quickly.”
In this study, the researchers focused on a single gene in the yeast organism. The research team exposed the gene’s DNA to 87 different proteins, which were selected as a representative subset of the hundreds of proteins in the yeast epigenome. Each protein-gene interaction was tested in approximately 100 yeast cells.
The researchers used light to control the binding of each protein to a gene and used a microscope and analytical tools to measure the resulting gene expression in real time for 12 hours.
“We designed this study in a way that allowed us to capture the dynamics of this entire process,” says study co-lead author Jessica Lee, who recently completed her Ph.D. Graduated from North Carolina State University. “We were able to control and measure the time that the protein was exposed to the gene, and we were able to observe and measure the dynamic behavior of the gene in response to the protein.”
“The big discovery here is that each protein generated a unique pattern of gene expression responses from that gene,” Keung says. “Proteins are much more than just on/off switches.
“We also found that some proteins produced the same genetic response in every yeast cell we tested. The pattern of gene expression they produced was very consistent. But other proteins produced a wide range of responses that varied from cell to cell. There was a lot of noise in the signals they produced.”
In analyzing the gene expression patterns produced by each protein, the researchers found a strong correlation between what is already known in the literature about each protein’s function and the gene expression patterns produced by those proteins.
“Proteins known to recruit polymerases, for example, tend to produce similar gene expression patterns,” Keung says.
The researchers then ran various computational models to see if they could account for all of the experimental data.
“Ideally, we want a model that helps us understand what’s going on in terms of the gene’s response to each protein, not just some proteins,” Keung says. “We initially thought this would be difficult because there are so many different gene expression patterns. But we found that we could capture all the data in a relatively simple model: a three-state model with positive feedback.”
Taken together, the results of this study have great potential for cell engineering.
“From a cell biology perspective, this study provides a deeper understanding of how genes are regulated and expressed,” Keung says. “From an engineering perspective, our findings can be used to more dynamically control cell behavior.
“For example, if you are biomanufacturing proteins or cell therapies in the pharmaceutical or biomedical fields, you can use our work to fine-tune activities related to protein production.
“Similarly, even proteins that generate random patterns of gene expression can be useful. For example, if you are trying to optimize bioproduction pathways within a cell, there is real value in testing the full range of protein levels within the cell,” Keung says. “What ratio of proteins will produce the best yield? In that scenario, knowing how to induce random gene expression would be helpful, essentially creating a way to force cells to produce different levels of protein.”
“And here, too, computational models are important. By understanding not just what each protein does, but how it functions, we can make more informed decisions about how to achieve our goals from an engineering perspective.”
A paper on this research, “Epigenome regulators imbue a single eukaryotic promoter with diverse gene expression dynamics,” will be published open access in the journal iscience. The paper was co-authored by Riley Basinger, an undergraduate student at North Carolina State University. Dr. Lucas Abbott is a student at North Carolina State University. Nicholas Levering is a former undergraduate student at North Carolina State University.
This research was supported by the National Institutes of Health under grants 5T32GM133366 and 5F31CA268873. and grants 2144539 and 1830910 from the National Science Foundation.
