Activation domains can decouple the mean and noise of gene expression

Loell, Kaiser; Wu, Yawei; Staller, Max V.; Cohen, Barak A.

doi:10.1016/j.celrep.2022.111118

Cited by 3 publications

(6 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results also illustrate how the translation process can decouple mean protein expression and protein noise 27 , with a departure from the traditional inverse relationship between them. The nature and extent of translational decoupling is dictated by the translational efficiency of the coding sequence, as well as by the promoter and its mode of transcription.…”

Section: Discussionmentioning

confidence: 80%

“…Earlier studies have associated presence of TATA box motif 19,20 , occurrence of TFIID and SAGA co-activator complexes with TATA binding protein 21 , nucleosome occupancy, and histone modifications with expression noise [22][23][24][25] . In addition, transcription factors (TFs) and gene regulatory networks play important roles in determining noise [26][27][28] .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, transcription factors (TFs) and gene regulatory networks play important roles in determining noise [26][27][28] .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ribosome demand links transcriptional bursts to protein expression noise

Pal,

Ray,

Dhar

2023

Preprint

View full text Add to dashboard Cite

Stochastic variation in protein expression generates phenotypic heterogeneity in a cell population, and has important role in antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Studies investigating molecular origins of noise have predominantly focused on the transcription process. However, the noise generated during the transcription process is further modulated by the translation process, which eventually determines the expression noise at the protein level. Studies across different organisms have revealed a positive correlation between translational efficiency and protein noise. However, the molecular basis of this correlation has remained unknown. In this work, through stochastic modeling of translation in single mRNA molecules and empirical measurement of protein noise, we demonstrate that ribosome demand associated with high translational efficiency drives the correlation between translational efficiency and protein noise. We also show that this correlation is present only in genes with bursty transcription, where ribosome demand varies with stochastic fluctuations in mRNA numbers. Thus, our work reveals how transcriptional bursts are translated into protein noise, which has important implications for investigating protein noise and phenotypic heterogeneity across biological systems.

show abstract

Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ribosome demand links transcriptional bursts to protein expression noise

Pal,

Ray,

Dhar

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Not surprisingly, depending on the gene function and context, expression noise is subject to evolutionary pressures [5][6][7][8][9][10] and actively regulated through diverse mechanisms. For example, the promoter architecture/genomic environment [11][12][13], the kinetics of different gene expression steps [14][15][16], the inclusion of feedback/feedforward loops [17][18][19][20][21][22], and the ubiquitous binding of proteins to decoy sites [23][24][25] have been shown to both attenuate or amplify noise levels.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Zhang,

Zabaikina,

Nieto

et al. 2024

Preprint

View full text Add to dashboard Cite

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

show abstract

“…Several studies have now elucidated the key roles that the TFs play in noise regulation. The molecular mechanisms range from fluctuations in the expression levels of TFs, the number of binding sites in promoter and the binding process of TFs to the promoter [40][41][42][43][44][45][46][47][48][49].…”

mentioning

confidence: 99%

Living in a noisy world—origins of gene expression noise and its impact on cellular decision‐making

Pal,

Dhar

2024

FEBS Letters

View full text Add to dashboard Cite

The expression level of a gene can vary between genetically identical cells under the same environmental condition—a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision‐making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision‐making. As advances in single‐cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

show abstract

Activation domains can decouple the mean and noise of gene expression

Cited by 3 publications

References 84 publications

Ribosome demand links transcriptional bursts to protein expression noise

Ribosome demand links transcriptional bursts to protein expression noise

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Living in a noisy world—origins of gene expression noise and its impact on cellular decision‐making

Contact Info

Product

Resources

About