{"id":9114,"date":"2026-04-20T12:32:04","date_gmt":"2026-04-20T16:32:04","guid":{"rendered":"https:\/\/provost.ncsu.edu\/global-one-health-academy\/2026\/04\/20\/study-finds-each-protein-in-the-epigenome-produces-a-different-pattern-of-gene-expression\/"},"modified":"2026-05-30T15:54:42","modified_gmt":"2026-05-30T19:54:42","slug":"study-finds-each-protein-in-the-epigenome-produces-a-different-pattern-of-gene-expression","status":"publish","type":"post","link":"https:\/\/provost.ncsu.edu\/global-one-health-academy\/2026\/04\/20\/study-finds-each-protein-in-the-epigenome-produces-a-different-pattern-of-gene-expression\/","title":{"rendered":"GOHA Affiliate Member Albert Keung is co-Author to New Study on the Epigenome and its Unique Gene Expression"},"content":{"rendered":"\n\n\n\n\n

For Immediate Release<\/h2>\n
Albert Keung<\/span>ajkeung@ncsu.edu<\/a><\/div>\n\n\n\n
Matt Shipman<\/span>matt_shipman@ncsu.edu<\/a><\/div>\n<\/section>\n\n\n\n

A new study finds the proteins responsible for controlling which genes are expressed in a genome do more than simply turn a gene on or off. Essentially, each type of protein that interacts with a gene produces different behaviors \u2013 a finding with ramifications for everything from biomedical therapeutics to biological computing.<\/p>\n\n\n\n

At issue are \u201cepigenome regulators.\u201d Every organism\u2019s genome is made up of DNA. But that DNA is bound up with many different proteins into very compact structures. The proteins that are bound to the DNA are called the epigenome, and they control which parts of the DNA get expressed. Your blood cells, nerve cells and skin cells all have the same DNA, but perform very different functions. That\u2019s because different parts of the DNA sequence are being expressed in each cell \u2013 and that <\/em>is largely controlled by which proteins are bound to different parts of the DNA in each cell.<\/p>\n\n\n\n

\u201cWe already knew that the proteins in the epigenome control the way DNA is expressed,\u201d says Albert Keung, corresponding author of the study and an associate professor of chemical and biomolecular engineering at North Carolina State University. \u201cOur goal here was to look at a single gene and quantify the full range of ways that the gene could be expressed by different proteins.\u201d Keung is the Goodnight Distinguished Scholar in Innovation in Biotechnology and Biomolecular Engineering and director of biotechnology programs in NC State\u2019s Integrative Sciences Initiative.<\/p>\n\n\n\n

\u201cThe results were fascinating,\u201d says Leandra Caywood, co-first author of the study and a recent Ph.D. graduate from NC State. \u201cFor example, one protein may turn the gene on quickly; a second protein may take slightly longer to turn the gene on \u2013 but then keep it on for a long time; and a third protein might have a long time delay before turning the gene on, at which point it spikes up quickly and then turns off right away.\u201d<\/p>\n\n\n\n

For this study, the researchers focused on a single gene from a yeast organism. The research team exposed the DNA from that gene to 87 different proteins, which were selected as a representative subset of the hundreds of proteins found in that yeast\u2019s epigenome. Each protein-gene interaction was tested in approximately 100 yeast cells.<\/p>\n\n\n\n

The researchers used light to control the binding of each protein to the gene, and microscopy and analytical tools to measure the resultant gene expression in real time for 12 hours.<\/p>\n\n\n\n

\u201cWe designed this study in a way that allowed us to capture the dynamics of this entire process,\u201d says Jessica Lee, co-first author of the study and recent Ph.D. graduate from NC State. \u201cWe could control and measure how long the protein was exposed to the gene and we could observe and measure the dynamic behavior of the gene in response to the protein.\u201d<\/p>\n\n\n\n

\u201cThe big finding here was that each protein produced a uniquely patterned response of gene expression from the gene,\u201d says Keung. \u201cThe proteins are far more than an on\/off switch.<\/p>\n\n\n\n

\u201cWe also found that some proteins produced the same gene response across all of the yeast cells we tested \u2013 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 \u2013 there was a lot of noise in the signal they produced.\u201d<\/p>\n\n\n\n

In analyzing the gene expression patterns produced by each protein, the researchers found a strong association between what the literature already knows about the function of each protein and the gene expression pattern those proteins produce.<\/p>\n\n\n\n

\u201cFor example, proteins that are known to recruit polymerase tend to produce similar gene expression patterns,\u201d Keung says.<\/p>\n\n\n\n

The researchers then ran a wide variety of computational models to see whether any of them were able to account for all of their experimental data.<\/p>\n\n\n\n

\u201cIdeally, you want a model that helps you understand what is happening in terms of the gene\u2019s response to each of the proteins, not just some of the proteins,\u201d says Keung. \u201cWe initially thought this would be difficult, because there were so many different gene expression patterns. But it turns out that a relatively simple model \u2013 a three-state model with positive feedback \u2013 was able to capture all of the data.\u201d<\/p>\n\n\n\n

Altogether, the findings of this study hold significant promise for cellular engineering.<\/p>\n\n\n\n

\u201cFrom a cell biology standpoint, this work gives us a much deeper understanding of how genes are regulated and expressed,\u201d says Keung. \u201cFrom an engineering standpoint, our findings can be used to more dynamically control cellular behavior.<\/p>\n\n\n\n

\u201cFor example, if you are biomanufacturing proteins or cell therapies for the pharmaceutical or biomedical sectors, our work can be used to fine-tune activities related to protein production.<\/p>\n\n\n\n

\u201cBy the same token, even the proteins that produce random patterns of gene expression could be useful. For example, if you are trying to optimize a bioproduction pathway in a cell, there\u2019s real value in testing the full range of protein levels in the cell,\u201d says Keung. \u201cWhich ratio of proteins produces the best output? In that scenario, it would be helpful to know how to induce random gene expression, essentially creating a way to get cells to produce varying levels of proteins.<\/p>\n\n\n\n

\u201cAnd this is where the computational model is also valuable. By understanding not only what each protein does, but how it does it, you can make more informed decisions about how to accomplish your goals from an engineering standpoint.\u201d<\/p>\n\n\n\n

A paper on the study, \u201cEpigenome Regulators Imbue a Single Eukaryotic Promoter with Diverse Gene Expression Dynamics<\/a>,\u201d is published open access in the journal iScience<\/em>. The paper was co-authored by Riley Basinger, an NC State undergraduate; Lucas Abbott, a Ph.D. student at NC State; and Nicholas Levering, a former undergraduate at NC State.<\/p>\n\n\n\n

This work was done with support from the National Institutes for Health under grants 5T32GM133366 and 5F31CA268873; and from the National Science Foundation under grants 2144539 and 1830910.<\/p>\n\n\n\n

-shipman-<\/p>\n\n\n\n

Note to Editors:<\/strong> The study abstract follows.<\/p>\n\n\n\n

\u201cEpigenome Regulators Imbue a Single Eukaryotic Promoter with Diverse Gene Expression Dynamics\u201d<\/strong><\/p>\n\n\n\n

Authors<\/em>: Jessica B. Lee, Leandra M. Caywood, Riley Basinger, Lucas Abbott, Nicholas Levering and Albert J. Keung, North Carolina State University<\/p>\n\n\n\n

Published<\/em>: April 16, iScience<\/em><\/p>\n\n\n\n

DOI<\/em>: 10.1016\/j.isci.2026.115805<\/p>\n\n\n\n

Abstract:<\/strong> Biological information can be encoded in signaling dynamics, which have been implicated in many physiological processes; yet the diversity of dynamic expression profiles driven by a single gene remains unclear. To explore this, we screen 80 chromatin associated proteins (CAPs) for their potential to drive diverse dynamic gene expression profiles from the same genome-integrated reporter in yeast. Using locus-specific optogenetic recruitment and live-cell microscopy, we measure dynamic expression profiles within single cells. CAP recruitment elicits a range of responses varying in activation delay, strength, production rate, and noise. We find that promoter activity is characterized by graded, rather than switchlike, transitions. A kinetic model with three promoter states and a positive feedback loop successfully captures the key features of expression driven by each CAP. These results reveal the rich dynamic landscape possible from a single gene, offering insights into native cellular processes and enhancing gene expression control in synthetic biology.<\/p>\n

This post was originally published<\/a> in NC State News.<\/em><\/p>","protected":false,"raw":"\n\n\n\n\n

For Immediate Release<\/h2>\n
Albert Keung<\/span>ajkeung@ncsu.edu<\/a><\/div>\n\n\n\n
Matt Shipman<\/span>matt_shipman@ncsu.edu<\/a><\/div>\n<\/section>\n\n\n\n

A new study finds the proteins responsible for controlling which genes are expressed in a genome do more than simply turn a gene on or off. Essentially, each type of protein that interacts with a gene produces different behaviors \u2013 a finding with ramifications for everything from biomedical therapeutics to biological computing.<\/p>\n\n\n\n

At issue are \u201cepigenome regulators.\u201d Every organism\u2019s genome is made up of DNA. But that DNA is bound up with many different proteins into very compact structures. The proteins that are bound to the DNA are called the epigenome, and they control which parts of the DNA get expressed. Your blood cells, nerve cells and skin cells all have the same DNA, but perform very different functions. That\u2019s because different parts of the DNA sequence are being expressed in each cell \u2013 and that <\/em>is largely controlled by which proteins are bound to different parts of the DNA in each cell.<\/p>\n\n\n\n

\u201cWe already knew that the proteins in the epigenome control the way DNA is expressed,\u201d says Albert Keung, corresponding author of the study and an associate professor of chemical and biomolecular engineering at North Carolina State University. \u201cOur goal here was to look at a single gene and quantify the full range of ways that the gene could be expressed by different proteins.\u201d Keung is the Goodnight Distinguished Scholar in Innovation in Biotechnology and Biomolecular Engineering and director of biotechnology programs in NC State\u2019s Integrative Sciences Initiative.<\/p>\n\n\n\n

\u201cThe results were fascinating,\u201d says Leandra Caywood, co-first author of the study and a recent Ph.D. graduate from NC State. \u201cFor example, one protein may turn the gene on quickly; a second protein may take slightly longer to turn the gene on \u2013 but then keep it on for a long time; and a third protein might have a long time delay before turning the gene on, at which point it spikes up quickly and then turns off right away.\u201d<\/p>\n\n\n\n

For this study, the researchers focused on a single gene from a yeast organism. The research team exposed the DNA from that gene to 87 different proteins, which were selected as a representative subset of the hundreds of proteins found in that yeast\u2019s epigenome. Each protein-gene interaction was tested in approximately 100 yeast cells.<\/p>\n\n\n\n

The researchers used light to control the binding of each protein to the gene, and microscopy and analytical tools to measure the resultant gene expression in real time for 12 hours.<\/p>\n\n\n\n

\u201cWe designed this study in a way that allowed us to capture the dynamics of this entire process,\u201d says Jessica Lee, co-first author of the study and recent Ph.D. graduate from NC State. \u201cWe could control and measure how long the protein was exposed to the gene and we could observe and measure the dynamic behavior of the gene in response to the protein.\u201d<\/p>\n\n\n\n

\u201cThe big finding here was that each protein produced a uniquely patterned response of gene expression from the gene,\u201d says Keung. \u201cThe proteins are far more than an on\/off switch.<\/p>\n\n\n\n

\u201cWe also found that some proteins produced the same gene response across all of the yeast cells we tested \u2013 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 \u2013 there was a lot of noise in the signal they produced.\u201d<\/p>\n\n\n\n

In analyzing the gene expression patterns produced by each protein, the researchers found a strong association between what the literature already knows about the function of each protein and the gene expression pattern those proteins produce.<\/p>\n\n\n\n

\u201cFor example, proteins that are known to recruit polymerase tend to produce similar gene expression patterns,\u201d Keung says.<\/p>\n\n\n\n

The researchers then ran a wide variety of computational models to see whether any of them were able to account for all of their experimental data.<\/p>\n\n\n\n

\u201cIdeally, you want a model that helps you understand what is happening in terms of the gene\u2019s response to each of the proteins, not just some of the proteins,\u201d says Keung. \u201cWe initially thought this would be difficult, because there were so many different gene expression patterns. But it turns out that a relatively simple model \u2013 a three-state model with positive feedback \u2013 was able to capture all of the data.\u201d<\/p>\n\n\n\n

Altogether, the findings of this study hold significant promise for cellular engineering.<\/p>\n\n\n\n

\u201cFrom a cell biology standpoint, this work gives us a much deeper understanding of how genes are regulated and expressed,\u201d says Keung. \u201cFrom an engineering standpoint, our findings can be used to more dynamically control cellular behavior.<\/p>\n\n\n\n

\u201cFor example, if you are biomanufacturing proteins or cell therapies for the pharmaceutical or biomedical sectors, our work can be used to fine-tune activities related to protein production.<\/p>\n\n\n\n

\u201cBy the same token, even the proteins that produce random patterns of gene expression could be useful. For example, if you are trying to optimize a bioproduction pathway in a cell, there\u2019s real value in testing the full range of protein levels in the cell,\u201d says Keung. \u201cWhich ratio of proteins produces the best output? In that scenario, it would be helpful to know how to induce random gene expression, essentially creating a way to get cells to produce varying levels of proteins.<\/p>\n\n\n\n

\u201cAnd this is where the computational model is also valuable. By understanding not only what each protein does, but how it does it, you can make more informed decisions about how to accomplish your goals from an engineering standpoint.\u201d<\/p>\n\n\n\n

A paper on the study, \u201cEpigenome Regulators Imbue a Single Eukaryotic Promoter with Diverse Gene Expression Dynamics<\/a>,\u201d is published open access in the journal iScience<\/em>. The paper was co-authored by Riley Basinger, an NC State undergraduate; Lucas Abbott, a Ph.D. student at NC State; and Nicholas Levering, a former undergraduate at NC State.<\/p>\n\n\n\n

This work was done with support from the National Institutes for Health under grants 5T32GM133366 and 5F31CA268873; and from the National Science Foundation under grants 2144539 and 1830910.<\/p>\n\n\n\n

-shipman-<\/p>\n\n\n\n

Note to Editors:<\/strong> The study abstract follows.<\/p>\n\n\n\n

\u201cEpigenome Regulators Imbue a Single Eukaryotic Promoter with Diverse Gene Expression Dynamics\u201d<\/strong><\/p>\n\n\n\n

Authors<\/em>: Jessica B. Lee, Leandra M. Caywood, Riley Basinger, Lucas Abbott, Nicholas Levering and Albert J. Keung, North Carolina State University<\/p>\n\n\n\n

Published<\/em>: April 16, iScience<\/em><\/p>\n\n\n\n

DOI<\/em>: 10.1016\/j.isci.2026.115805<\/p>\n\n\n\n

Abstract:<\/strong> Biological information can be encoded in signaling dynamics, which have been implicated in many physiological processes; yet the diversity of dynamic expression profiles driven by a single gene remains unclear. To explore this, we screen 80 chromatin associated proteins (CAPs) for their potential to drive diverse dynamic gene expression profiles from the same genome-integrated reporter in yeast. Using locus-specific optogenetic recruitment and live-cell microscopy, we measure dynamic expression profiles within single cells. CAP recruitment elicits a range of responses varying in activation delay, strength, production rate, and noise. We find that promoter activity is characterized by graded, rather than switchlike, transitions. A kinetic model with three promoter states and a positive feedback loop successfully captures the key features of expression driven by each CAP. These results reveal the rich dynamic landscape possible from a single gene, offering insights into native cellular processes and enhancing gene expression control in synthetic biology.<\/p>\n"},"excerpt":{"rendered":"

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