scholarly journals Controlling E. coli gene expression noise

2015 ◽  
Author(s):  
Kyung Hyuk Kim ◽  
Kiri Choi ◽  
Bryan Bartley ◽  
Herbert M Sauro
Keyword(s):  
Gene Expression ◽  
Genetic Network ◽  
Biochemical Networks ◽  
Control Analysis ◽  
Gene Expression Noise ◽  
E Coli ◽  
Copy Numbers ◽  
Stochastic Fluctuations ◽  
Scaling Relationship ◽  
Cell To Cell Variability

Intracellular protein copy numbers show significant cell-to-cell variability within an isogenic population due to the random nature of biological reactions. Here we show how the variability in copy number can be controlled by perturbing gene expression. Depending on the genetic network and host, different perturbations can be applied to control variability. To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control. Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli. We show that (1) dual control of transcription and translation efficiencies provides the most efficient way of noise-vs.-mean control. (2) The expressed proteins follow the gamma distribution function as found in chromosomal proteins. (3) One of the major sources of noise, leading to the cell-to-cell variability in protein copy numbers, is related to bursty translation. (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.

scholarly journals SIR2 Expression Noise Can Generate Heterogeneity in Viability but Does Not Affect Cell-to-Cell Epigenetic Silencing of Subtelomeric URA3 in Yeast

2020 ◽  
Vol 10 (9) ◽  
pp. 3435-3443
Author(s):  
Jian Liu ◽  
Laureline Mosser ◽  
Catherine Botanch ◽  
Jean-Marie François ◽  
Jean-Pascal Capp
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Epigenetic Modification ◽  
Epigenetic Silencing ◽  
Cell Heterogeneity ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Cell Expression ◽  
Cell To Cell Variability ◽  
Epigenetic Processes

Abstract Chromatin structure clearly modulates gene expression noise, but the reverse influence has never been investigated, namely how the cell-to-cell expression heterogeneity of chromatin modifiers may generate variable rates of epigenetic modification. Sir2 is a well-characterized histone deacetylase of the Sirtuin family. It strongly influences chromatin silencing, especially at telomeres, subtelomeres and rDNA. This ability to influence epigenetic landscapes makes it a good model to study the largely unexplored interplay between gene expression noise and other epigenetic processes leading to phenotypic diversification. Here, we addressed this question by investigating whether noise in the expression of SIR2 was associated with cell-to-cell heterogeneity in the frequency of epigenetic silencing at subtelomeres in Saccharomyces cerevisiae. Using cell sorting to isolate subpopulations with various expression levels, we found that heterogeneity in the cellular concentration of Sir2 does not lead to heterogeneity in the epigenetic silencing of subtelomeric URA3 between these subpopulations. We also noticed that SIR2 expression noise can generate cell-to-cell variability in viability, with lower levels being associated with better viability. This work shows that SIR2 expression fluctuations are not sufficient to generate cell-to-cell heterogeneity in the epigenetic silencing of URA3 at subtelomeres in Saccharomyces cerevisiae but can strongly affect cellular viability.

scholarly journals Promoter architecture dictates cell-to-cell variability in gene expression

Science ◽  
2014 ◽  
Vol 346 (6216) ◽  
pp. 1533-1536 ◽  
Author(s):  
Daniel L. Jones ◽  
Robert C. Brewster ◽  
Rob Phillips
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Messenger Rna ◽  
Experimental Studies ◽  
Promoter Strength ◽  
Promoter Architecture ◽  
Copy Numbers ◽  
Regulatory Architecture ◽  
Genome Wide ◽  
Cell To Cell Variability

Variability in gene expression among genetically identical cells has emerged as a central preoccupation in the study of gene regulation; however, a divide exists between the predictions of molecular models of prokaryotic transcriptional regulation and genome-wide experimental studies suggesting that this variability is indifferent to the underlying regulatory architecture. We constructed a set of promoters in Escherichia coli in which promoter strength, transcription factor binding strength, and transcription factor copy numbers are systematically varied, and used messenger RNA (mRNA) fluorescence in situ hybridization to observe how these changes affected variability in gene expression. Our parameter-free models predicted the observed variability; hence, the molecular details of transcription dictate variability in mRNA expression, and transcriptional noise is specifically tunable and thus represents an evolutionarily accessible phenotypic parameter.

scholarly journals Subpopulations of sensorless bacteria drive fitness in fluctuating environments

2020 ◽  
Author(s):  
Thomas Julou ◽  
Ludovit Zweifel ◽  
Diana Blank ◽  
Athos Fiori ◽  
Erik van Nimwegen
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Population Level ◽  
General Mechanism ◽  
Gene Expression Noise ◽  
E Coli ◽  
Expression Noise ◽  
Basal Expression ◽  
Fluctuating Environments ◽  
Operon Expression

AbstractPopulations of bacteria often undergo a lag in growth when switching conditions. Because growth lags can be large compared to typical doubling times, variations in growth lag are an important but often overlooked component of bacterial fitness in fluctuating environments. We here explore how growth lag variation is determined for the archetypical switch from glucose to lactose as a carbon source in E. coli. First, we show that single-cell lags are bimodally distributed and controlled by a single-molecule trigger. That is, gene expression noise causes the population before the switch to divide cells with zero pre-existing into subpopulations with zero and nonzero lac operon expression. While ’sensorless’ lac expression at the switch have long lags because they are unable to sense the lactose signal, any nonzero lac operon expression suffices to ensure a short lag. Second, we show that the growth lag at the population level depends crucially on the fraction of sensorless cells, and that this fraction in turn depends sensitively on the growth condition before the switch. Consequently, even small changes in basal expression affecting the fraction of sensorless cells can significantly affect population lags and fitness under switching conditions, and may thus be subject to significant natural selection. Indeed, we show that condition-dependent population lags vary across wild E. coli isolates. Since many sensory genes are naturally low expressed in conditions where their inducer is not present, bimodal responses due to subpopulations of sensorless cells may be a general mechanism inducing phenotypic heterogeneity and controlling population lags in switching environments. This mechanism also illustrates how gene expression noise can turn even simple sensory gene circuits into a bet-hedging module, and underlines the profound role of gene expression noise in regulatory responses.

scholarly journals Independent control of mean and noise by convolution of gene expression distributions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Karl P. Gerhardt ◽  
Satyajit D. Rao ◽  
Evan J. Olson ◽  
Oleg A. Igoshin ◽  
Jeffrey J. Tabor
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Probability Theory ◽  
Biological Pathways ◽  
Cellular Heterogeneity ◽  
High Noise ◽  
Gene Expression Noise ◽  
Inducible Promoters ◽  
Expression Noise ◽  
Scaling Relationship

AbstractGene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

scholarly journals Controlling E. coli Gene Expression Noise

2015 ◽  
Vol 9 (4) ◽  
pp. 497-504 ◽  
Author(s):  
Kyung Hyuk Kim ◽  
Kiri Choi ◽  
Bryan Bartley ◽  
Herbert M. Sauro
Keyword(s):  
Gene Expression ◽  
Gene Expression Noise ◽  
E Coli ◽  
Expression Noise

scholarly journals Noise propagation shapes condition-dependent gene expression noise in Escherichia coli

2019 ◽  
Author(s):  
Arantxa Urchueguía ◽  
Luca Galbusera ◽  
Gwendoline Bellement ◽  
Thomas Julou ◽  
Erik van Nimwegen
Keyword(s):  
Gene Expression ◽  
Growth Conditions ◽  
Transcriptional Noise ◽  
Noise Propagation ◽  
Gene Expression Noise ◽  
E Coli ◽  
Expression Noise ◽  
Rate Dependent ◽  
Genome Wide ◽  
Principal Axes

AbstractAlthough it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the variation in transcriptional noise across growth conditions. Using flow cytometry we here quantify transcriptional noise in E. coli genome-wide across 8 growth conditions, and find that noise and gene regulation are intimately coupled. Apart from a growth-rate dependent lower bound on noise, we find that individual promoters show highly condition-dependent noise and that condition-dependent expression noise is shaped by noise propagation from regulators to their targets. A simple model of noise propagation identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. The overall correlation structure of sequence and expression properties of E. coli genes uncovers that genes are organized along two principal axes, with the first axis sorting genes by their mean expression and evolutionary rate of their coding regions, and the second axis sorting genes by their expression noise, the number of regulatory inputs in their promoter, and their expression plasticity.

Division time-based amplifiers for stochastic gene expression

2015 ◽  
Vol 11 (9) ◽  
pp. 2417-2428 ◽  
Author(s):  
Haohua Wang ◽  
Zhanjiang Yuan ◽  
Peijiang Liu ◽  
Tianshou Zhou
Keyword(s):  
Gene Expression ◽  
Cell Division ◽  
Stochastic Gene Expression ◽  
Noise Sources ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Stochastic Switching ◽  
Cell To Cell Variability ◽  
Birth Death

While cell-to-cell variability is a phenotypic consequence of gene expression noise, sources of this noise may be complex – apart from intrinsic sources such as the random birth/death of mRNA and stochastic switching between promoter states, there are also extrinsic sources of noise such as cell division where division times are either constant or random.

scholarly journals Expression noise facilitates the evolution of gene regulation

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Luise Wolf ◽  
Olin K Silander ◽  
Erik van Nimwegen
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
De Novo ◽  
Low Noise ◽  
Noise Levels ◽  
Gene Expression Noise ◽  
E Coli ◽  
Expression Noise ◽  
Evolution Of Gene Regulation ◽  
Rudimentary Form

Although it is often tacitly assumed that gene regulatory interactions are finely tuned, how accurate gene regulation could evolve from a state without regulation is unclear. Moreover, gene expression noise would seem to impede the evolution of accurate gene regulation, and previous investigations have provided circumstantial evidence that natural selection has acted to lower noise levels. By evolving synthetic Escherichia coli promoters de novo, we here show that, contrary to expectations, promoters exhibit low noise by default. Instead, selection must have acted to increase the noise levels of highly regulated E. coli promoters. We present a general theory of the interplay between gene expression noise and gene regulation that explains these observations. The theory shows that propagation of expression noise from regulators to their targets is not an unwanted side-effect of regulation, but rather acts as a rudimentary form of regulation that facilitates the evolution of more accurate regulation.

Characteristics of Promoter Architecture-Dependent Expression Noise and Bursting Kinetics

2016 ◽  
Vol 26 (01) ◽  
pp. 1650016
Author(s):  
Heli Tan ◽  
Zhanjiang Yuan ◽  
Jiajun Zhang ◽  
Tianshou Zhou
Keyword(s):  
Gene Expression ◽  
Transition Rates ◽  
Expression Noise ◽  
Promoter Architecture ◽  
Stochastic Fluctuations ◽  
The Common ◽  
Cell To Cell Variability ◽  
Two State Model ◽  
Influence Gene Expression ◽  
Insight Into

Genes are expressed often in a bursting manner, leading to stochastic fluctuations in mRNA and protein abundances, but how the binding and unbinding of transcriptional factors to DNA affect expression noise and bursting kinetics remains not fully understood. Here, we compare and analyze two representative cases: the (E) model and the (M) model, each assuming that a gene has three activity states (one active and two inactive states) with dual transitions between every two and that the inactive states are directly linked in the former but separated by the active state in the latter. We find that the activation and inactivation rates can significantly impact gene expression. First, these two rates can induce counterintuitive correlations of expression noise with bursting kinetics in contrast to the common two-state model. Second, the correlation in the (E) model is fundamentally different from that in (M) model, implying that the counterintuitive phenomena are regulator-specific. Our investigation primarily gives us insight into the design principles of promoter architecture (a major source of cell-to-cell variability in genetically identical cells) as well as into how transcription factors influence gene expression through regulating transition rates.

scholarly journals High cooperativity in negative feedback can amplify noisy gene expression

2017 ◽  
Author(s):  
Pavol Bokes ◽  
Yen Ting Lin ◽  
Abhyudai Singh
Keyword(s):  
Gene Expression ◽  
Negative Feedback ◽  
Coefficient Of Variation ◽  
Burst Size ◽  
Burst Frequency ◽  
Gene Expression Noise ◽  
Protein Levels ◽  
Feedback Strength ◽  
Cell To Cell Variability ◽  
The Impact

AbstractBurst-like synthesis of protein is a significant source of cell-to-cell variability in protein levels. Negative feedback is a common example of a regulatory mechanism by which such stochasticity can be controlled. Here we consider a specific kind of negative feedback, which makes bursts smaller in the excess of protein. Increasing the strength of the feedback may lead to dramatically different outcomes depending on a key parameter, the noise load, which is defined as the squared coefficient of variation the protein exhibits in the absence of feedback. Combining stochastic simulation with asymptotic analysis, we identify a critical value of noise load: for noise loads smaller than critical, the coefficient of variation remains bounded with increasing feedback strength; contrastingly, if the noise load is larger than critical, the coefficient of variation diverges to infinity in the limit of ever greater feedback strengths. Interestingly, high-cooperativity feedbacks have lower critical noise loads, implying that low-cooperativity feedbacks in burst size can be preferable for noisy proteins. Finally, we discuss our findings in the context of previous results on the impact of negative feedback in burst size and burst frequency on gene-expression noise.

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