scholarly journals Plasmids for independently tunable, low-noise expression of two genes

2019 ◽  
Author(s):  
João P. N. Silva ◽  
Soraia Vidigal Lopes ◽  
Diogo J. Grilo ◽  
Zach Hensel
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Single Gene ◽  
Growth Conditions ◽  
Low Noise ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Compatible Plasmid ◽  
Noise Limit

AbstractSome microbiology experiments and biotechnology applications can be improved if it is possible to tune the expression of two different genes at the same time with cell-to-cell variation at or below the level of genes constitutively expressed from the chromosome (the “extrinsic noise limit”). This was recently achieved for a single gene by exploiting negative autoregulation by the tetracycline repressor (TetR) and bicistronic gene expression to reduce gene expression noise. We report new plasmids that use the same principles to achieve simultaneous, low-noise expression for two genes. The TetR system was moved to a compatible plasmid backbone, and a system based on the lac repressor (LacI) was found to also exhibit gene expression noise below the extrinsic noise limit. We characterize gene expression mean and noise across the range of induction levels for these plasmids, apply the LacI system to tune expression for single-molecule mRNA detection in two different growth conditions, and show that two plasmids can be co-transformed to independently tune expression of two different genes.

scholarly journals Plasmids for Independently Tunable, Low-Noise Expression of Two Genes

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
João P. N. Silva ◽  
Soraia Vidigal Lopes ◽  
Diogo J. Grilo ◽  
Zach Hensel
Keyword(s):  
Gene Expression ◽  
Protein Expression ◽  
Low Noise ◽  
Expression Systems ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Foreign Proteins ◽  
Noise Limit ◽  
Cell Variation

ABSTRACTSome microbiology experiments and biotechnology applications can be improved if it is possible to tune the expression of two different genes at the same time with cell-to-cell variation at or below the level of genes constitutively expressed from the chromosome (the “extrinsic noise limit”). This was recently achieved for a single gene by exploiting negative autoregulation by the tetracycline repressor (TetR) and bicistronic gene expression to reduce gene expression noise. We report new plasmids that use the same principles to achieve simultaneous, low-noise expression for two genes inEscherichia coli. The TetR system was moved to a compatible plasmid backbone, and a system based on thelacrepressor (LacI) was found to also exhibit gene expression noise below the extrinsic noise limit. We characterized gene expression mean and noise across the range of induction levels for these plasmids, applied the LacI system to tune expression for single-molecule mRNA detection under two different growth conditions, and showed that two plasmids can be cotransformed to independently tune expression of two different genes.IMPORTANCEMicrobiologists often express foreign proteins in bacteria in order study them or to use bacteria as a microbial factory. Usually, this requires controlling the number of foreign proteins expressed in each cell, but for many common protein expression systems, it is difficult to “tune” protein expression without large cell-to-cell variation in expression levels (called “noise” in protein expression). This work describes two protein expression systems that can be combined in the same cell, with tunable expression levels and very low protein expression noise. One new system was used to detect single mRNA molecules by fluorescence microscopy, and the two systems were shown to be independent of each other. These protein expression systems may be useful in any experiment or biotechnology application that can be improved with low protein expression noise.

Reduction in gene expression noise by targeted increase in accessibility at gene loci

2021 ◽  
Vol 118 (42) ◽  
pp. e2018640118
Author(s):  
LaTasha C. R. Fraser ◽  
Ryan J. Dikdan ◽  
Supravat Dey ◽  
Abhyudai Singh ◽  
Sanjay Tyagi
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Molecule ◽  
Single Cells ◽  
Global Changes ◽  
Messenger Rnas ◽  
Histone Acetyl Transferase ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Eukaryotic Genes

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this “noisy” transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable.

scholarly journals Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

2021 ◽  
Vol 18 (178) ◽  
pp. 20210274
Author(s):  
Philipp Thomas ◽  
Vahid Shahrezaei
Keyword(s):  
Gene Expression ◽  
Master Equation ◽  
Cell Size ◽  
Chemical Master Equation ◽  
Gillespie Algorithm ◽  
Agent Based Models ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Agent Based ◽  
Expression Noise

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis , a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

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 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.

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.

scholarly journals Subpopulations of sensorless bacteria drive fitness in fluctuating environments

PLoS Biology ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. e3000952
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 ◽  
Expression Noise ◽  
Basal Expression ◽  
Fluctuating Environments ◽  
Bacterial Fitness ◽  
Operon Expression

Populations 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 Escherichia 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 into subpopulations with zero and nonzero lac operon expression. While “sensorless” cells with zero preexisting 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 can significantly affect the fraction of sensorless cells, thereby 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 a simple sensory gene circuit into a bet hedging module and underlines the profound role of gene expression noise in regulatory responses.

scholarly journals Uncoupling gene expression noise along the central dogma using genome engineered human cell lines

2020 ◽  
Vol 48 (16) ◽  
pp. 9406-9413 ◽  
Author(s):  
Tyler Quarton ◽  
Taek Kang ◽  
Vasileios Papakis ◽  
Khai Nguyen ◽  
Chance Nowak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Reporter System ◽  
Extrinsic Noise ◽  
Cell Content ◽  
Gene Expression Noise ◽  
Central Dogma ◽  
Expression Noise ◽  
Global Correlation ◽  
Noise In Gene Expression

Abstract Eukaryotic protein synthesis is an inherently stochastic process. This stochasticity stems not only from variations in cell content between cells but also from thermodynamic fluctuations in a single cell. Ultimately, these inherently stochastic processes manifest as noise in gene expression, where even genetically identical cells in the same environment exhibit variation in their protein abundances. In order to elucidate the underlying sources that contribute to gene expression noise, we quantify the contribution of each step within the process of protein synthesis along the central dogma. We uncouple gene expression at the transcriptional, translational, and post-translational level using custom engineered circuits stably integrated in human cells using CRISPR. We provide a generalized framework to approximate intrinsic and extrinsic noise in a population of cells expressing an unbalanced two-reporter system. Our decomposition shows that the majority of intrinsic fluctuations stem from transcription and that coupling the two genes along the central dogma forces the fluctuations to propagate and accumulate along the same path, resulting in increased observed global correlation between the products.

scholarly journals Coordination of gene expression noise with cell size: extrinsic noise versus agent-based models of growing cell populations

2020 ◽  
Author(s):  
Philipp Thomas ◽  
Vahid Shahrezaei
Keyword(s):  
Gene Expression ◽  
Master Equation ◽  
Cell Size ◽  
Chemical Master Equation ◽  
Simulation Algorithm ◽  
Agent Based Models ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Agent Based ◽  
Expression Noise

The chemical master equation and the stochastic simulation algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled for through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise in a growing population. We find that the solution of the chemical master equation – including static extrinsic noise – exactly agrees with the one of the agent-based formulation when a simple condition on the network’s topology is met. We illustrate this result for a range of common gene expression networks. When these conditions are not met, we demonstrate using analytical solutions of the agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the effective master equation. Surprisingly, the latter distorts total noise in gene regulatory networks by at most 8% independently of network parameters. Our results highlight the accuracy of extrinsic noise modelling within the chemical master equation framework.

scholarly journals Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria

2020 ◽  
Vol 6 (41) ◽  
pp. eabc3478
Author(s):  
A. Deloupy ◽  
V. Sauveplane ◽  
J. Robert ◽  
S. Aymerich ◽  
M. Jules ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fluorescent Protein ◽  
Biological Engineering ◽  
Translation Control ◽  
Extrinsic Noise ◽  
Translation Rate ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Noise Optimization ◽  
Custom Made

It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

Sign in / Sign up

Export Citation Format

Share Document