Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression

Abstract

The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ∼2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.

Molecular circuits for dynamic noise filtering

“…In the former case, ZðtÞ does not necessarily have a physical interpretation but rather serves as a phenomenological proxy that reflects the multitude of noise sources affecting a circuit. The synthesis rate of a protein, for instance, depends on various factors (e.g., gene dosage and ribosomal abundance) but itself may be described reasonably well by a one-dimensional quantity that fluctuates over time (22). For the sake of illustration, we assume ZðtÞ is a one-dimensional birth-death model with parameters ρ and ϕ (Fig.…”

Tuning gene expression variability and multi-gene regulation by dynamic transcription factor control

“…Previous studies suggest that two major sources of extrinsic gene expression variability in S. cerevisiae are heterogeneity in TF expression and the cell cycle 10,11 . Due to the fact that we directly affect TF dynamics, we thought to introduce cell-to-cell variability in TF concentration to our model and analyze the resulting CV-mean relationship for AM and PWM.…”

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

“…2. Fit a smoothing spline to the selected measurements to eliminate high-frequency measurement noise (as discussed in 9 ). 3.…”

“…3. Automatically determine bud emergence and cell division times for each cell based on changes in the slope of the volume time series as discussed in 9 . The bud measurements will then be added to the mother's measurements between emergence and division each cycle for a contiguous whole cell time series.…”

“…Fluorescence time-lapse microscopy presents a means to obtain both single-cell measurements and their history. Various experimental and analytical techniques have been developed to obtain and quantify movies of fluorescent reporter expression, thus imparting insights into gene regulation features (see 1 for a review) such as cell-to-cell variation 2,3 , bacterial persister formation 4 , transcription initiation and elongation 5 , transcriptional bursting 6,7 , cell-cycle dependence 8,9 , and heritability 10 . However, obtaining quality single-cell fluorescence time series involves significant technical challenges in culturing a monolayer of cells in a controllable environment and in high-throughput quantification of the acquired fluorescence movies.…”