Transcriptional regulatory network shapes the genome structure of Saccharomyces cerevisiae

Abstract

Among cellular processes gene transcription is central. More and more evidence is mounting that transcription is tightly connected with the spatial organization of the chromosomes. Spatial proximity of genes sharing transcriptional machinery is one of the consequences of this organization. Motivated by information on the physical relationship among genes identified via chromosomal conformation capture methods, we complement the spatial organization with the idea that genes under similar transcription factor control, but possible scattered throughout the genome, might be in physically proximity to facilitate the access of their commonly used transcription factors. Unlike the transcription factory model, “interacting” genes in our “Gene Proximity Model” are not necessarily immediate physical neighbors but are in spatial proximity. Considering the stochastic nature of TF-promoter binding, this local condensation mechanism could serve as a tie to recruit co-regulated genes to guarantee the swiftness of biological reactions. We tested this idea with a simple eukaryotic organism, Saccharomyces cerevisiae. Chromosomal interaction patterns and folding behavior generated by our model re-construct those obtained from experiments. We show that the transcriptional regulatory network has a close linkage with the genome organization in budding yeast, which is fundamental and instrumental to later studies on other more complex eukaryotes.

Keywords: :

• Saccharomyces cerevisiae
• gene proximity
• three-dimensional genome architecture
• transcription factor
• transcriptional regulatory network

Disclosure of Potential Conflicts of Interest

No potential conflict of interest was disclosed.

Acknowledgments

The authors appreciate here the assistance from Justin M O’Sullivan for kindly providing experimental data and the resources from the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGSMathComp). SL gratefully acknowledges funding from the Heinz-Goetze-Foundation and the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences.