Enhancement of gene expression noise from transcription factor binding to genomic decoy sites

Abstract

The genome contains several high-affinity non-functional binding sites for transcription factors (TFs) creating a hidden and unexplored layer of gene regulation. We investigate the role of such “decoy sites” in controlling noise (random fluctuations) in the level of a TF that is synthesized in stochastic bursts. Prior studies have assumed that decoy-bound TFs are protected from degradation, and in this case decoys function to buffer noise. Relaxing this assumption to consider arbitrary degradation rates for both bound/unbound TF states, we find rich noise behaviors. For low-affinity decoys, noise in the level of unbound TF always monotonically decreases to the Poisson limit with increasing decoy numbers. In contrast, for high-affinity decoys, noise levels first increase with increasing decoy numbers, before decreasing back to the Poisson limit. Interestingly, while protection of bound TFs from degradation slows the time-scale of fluctuations in the unbound TF levels, the decay of bound TFs leads to faster fluctuations and smaller noise propagation to downstream target proteins. In summary, our analysis reveals stochastic dynamics emerging from nonspecific binding of TFs and highlights the dual role of decoys as attenuators or amplifiers of gene expression noise depending on their binding affinity and stability of the bound TF.

Noise suppression in stochastic genetic circuits using PID controllers

“…Given that g ( z ) is a positively-valued monotonically decreasing function, using ( 21 ) and ( 22 ), the steady-state mean level of Y is the unique solution to the equation Having solved for the means, the burst frequency of Y can now be approximated using Taylor series as where is the log sensitivity of the function g evaluated at steady state. It is important to point out that this linearization of nonlinearities is a key element of the linear noise approximation, and is needed to obtain closed-form solutions to the noise levels [ 63 , 64 , 96 , 97 ]. Formulas obtained using this approximation are exact in the limit of small noise, and provide analytical insights into the regulation of noise levels.…”

“…is the log sensitivity of the function g evaluated at steady state. It is important to point out that this linearization of nonlinearities is a key element of the linear noise approximation, and is needed to obtain closed-form solutions to the noise levels [63,64,96,97]. Formulas obtained using this approximation are exact in the limit of small noise, and provide analytical insights into the regulation of noise levels.…”

Being noisy in a crowd: differential selective pressure on gene expression noise in model gene regulatory networks

“…These include epigenetic factors, such as chromatic dynamics (39) and presence of chromatin remodelling complexes (40). Other factors affect transcription directly and can, therefore, control expression noise: the promoter shape (33), presence of a TATA box (40), presence and number (4) of TF binding sites, TF binding dynamics (41), presence of TF decoy binding sites (42), and transcription rate. Factors affecting translation have also been shown to play a role in controlling noise: miRNA targetting (43), mRNA lifetime, translation rate, and post-translational modifications such as the protein degradation rate.…”

Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design

“…A related mechanism that has been investigated in relation to noise in the gene expression machinery is the competition for specific transcription factors between multiple promoter sites as well as decoy sites [48][49][50]. Although prokaryotic TFs can differentiate between regulatory and decoy binding sites rather easily due to the binding free energy of their targets [51,52], these sites can play a noise buffering role for the system [48][49][50]. In a model, the availability of such sites will have the same effect as a reduction in the binding rate of TF to the target promoter.…”