부산대학교 도서관

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence. Author summary: Author summary Expression levels of genes can vary even among genetically identical cells under identical environmental condition–a phenomenon termed expression noise. Gene expression noise has been experimentally measured in several cell populations and earlier studies have associated the presence of a specific sequence of bases such as the TATA box in the promoter region, the nucleosome occupancy levels and the histone modification patterns with high expression noise. However, how well these molecular features of a gene can let us predict its expression noise has not yet been assessed. In the current work, we test a large number of molecular features associated with gene expression for their ability to predict noise. We find that the number of transcription factors of a gene is a key predictor of expression noise. An increase in the number of transcription factors can change their binding process to the promoter region and can lead to more cooperation or competition. Through modeling and simulation of cooperative and competitive binding, we show that the transcription factor binding process primarily drives expression noise. Our work shows that the dynamics of gene expression regulation is the most important feature for predicting expression noise and uncovers a general mechanism of noise regulation. [ABSTRACT FROM AUTHOR]