Experimental and Stochastic Model Analysis of the Influence of SIC1, CLN2 and CLB5 Transcriptional Noise on the Timing Regulation of G1/S Transition in S. Cerevisiae Cell-Cycle

Abstract

Gene expression results in significant fluctuations in transcriptional and translational abundance among cells of a genetically identical population. Remarkably, strong regulatory mechanisms in Saccharomyces cerevisiae cell-cycle enable its progression despite these fluctuations.Our work combining modeling and experimental approaches investigates the influence of stochasticity in gene expression, especially in transcription, for timing regulation of the G1/S transition. We focus on Sic1, a cyclin-dependent kinase inhibitor, and its interacting partners, the cyclins Cln2 and Clb5. The interplay of these cell-cycle regulators is critical for precise timing of the G1-phase exit.We quantify SIC1 transcripts level and transcriptional noise in a cell population via fluorescence microscopy by taking advantage of two highly sensitive single molecule detection methods, the MS2-CP and the Fluorescence in situ Hybridisation (FISH) techniques. We used these data to explore a stochastic model for the G1/S transition timing regulation focusing on the fluctuations of SIC1 transcripts level.The experimental investigation revealed a distribution between 0 and 10 SIC1 mRNAs per cell and a significant increase of transcripts in the G1-phase. Our model predicts that by producing only a few SIC1 mRNAs, as observed in our experiments, the cell ensures low noise level of Sic1 protein and an exact timing of the S-phase entrance.We conclude that timing properties in yeast cell-cycle are highly influenced on the molecular level by discrete abundance of transcription products. Essentially, transcription regulation is involved in maintaining robustness in cell-cycle progression.To obtain a comprehensive understanding of transcription regulation at an interacting-proteins-network level, we are now measuring CLN2 and CLB5 transcript levels to investigate the correlation of their transcript levels as well as that of SIC1.