Bread, beer, and wine might be the tastiest and most tangible contribution that yeast has provided the world, but its impact on advancing knowledge in the biological sciences is perhaps its most significant. Now, a team of investigators at Penn State University and Cornell are touting the tiny organism as a comprehensive model for the study of epigenomics. The research teams describe in a new study an extensive and high-resolution map of chromosome architecture and gene regulation in Saccharomyces cerevisiae—an effort they say is a major step toward improving understanding of development, evolution, and environmental responses in higher organisms.

Findings from the new study—published recently in Nature through an article titled, “A high-resolution protein architecture of the budding yeast genome”—mapped precise binding sites of more than 400 different chromosomal proteins in the yeast genome, most of which regulate the expression of genes.

Yeast cells provide a simple model system with 6,000 genes, most of which are found in other organisms, including humans, making them excellent candidates for studying fundamental genetics and complex biological pathways.

“It’s a vastly more complex proposition, but like the sequencing of the yeast genome preceded the sequencing of the human, I’m sure we will be able to see the regulatory architecture of the human genome,” noted senior study investigator B. Franklin Pugh, PhD, currently a professor of molecular biology and genetics in the College of Arts and Sciences at Cornell and a former professor at Pennsylvania State University, where he began this work.

In the current study, the team used a technique called ChIP-exo to map the binding locations of about 400 different proteins that interact with the yeast genome, some at a few locations and others at thousands of locations.

The team performed more than 1,200 individual ChIP-exo experiments, producing billions of individual data points. Analysis of so much data required the use of Penn State’s supercomputing clusters and the development of several novel bioinformatic tools to identify patterns and reveal the organization of regulatory proteins in the yeast genome. The analysis revealed a surprisingly small number of unique protein assemblages that are used repeatedly across the yeast genome.

“We used chromatin immunoprecipitation, exonuclease digestion, and DNA sequencing (ChIP–exo/seq) to define this architecture in Saccharomyces cerevisiae,” the authors wrote. “We identified 21 meta-assemblages consisting of roughly 400 different proteins that are related to DNA replication, centromeres, subtelomeres, transposons, and transcription by RNA polymerase (Pol) I, II, and III. Replication proteins engulf a nucleosome, centromeres lack a nucleosome, and repressive proteins encompass three nucleosomes at subtelomeric X-elements. We found that most promoters associated with Pol II evolved to lack a regulatory region, having only a core promoter. These constitutive promoters comprise a short nucleosome-free region (NFR) adjacent to a +1 nucleosome, which together bind the transcription-initiation factor TFIID to form a preinitiation complex.”

The study revealed two distinct gene regulatory architectures, expanding the traditional model of gene regulation. So-called constitutive genes—those that perform basic “housekeeping” functions and are nearly always active at low levels—required only a basic set of regulatory controls, whereas those activated by environmental signals, known as inducible genes, had a more specialized architecture.

The traditional model of gene regulation involves transcription factors, which bind to specific DNA sequences to control the expression of a nearby gene. However, the researchers found that “housekeeping” genes—which comprise the majority of genes in yeast—lacked a protein-DNA architecture that would allow specific transcription factors to bind, a hallmark of inducible genes.

“The resolution and completeness of the data allowed us to identify 21 protein assemblages and also to identify the absence of specific regulatory control signals at housekeeping genes,” concluded study co-author Shaun Mahony, PhD, an assistant professor of biochemistry and molecular biology at Penn State. “The computational methods that we’ve developed to analyze this data could serve as a jumping-off point for further development for gene regulatory studies in more complex organisms.”