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.

scholarly journals Transcriptional bursting explains the noise-versus-mean relationship in mRNA and protein levels

2016 ◽  
Author(s):  
Roy D. Dar ◽  
Sydney M. Schaffer ◽  
Siddarth S. Dey ◽  
Jonathan E. Foley ◽  
Abhyudai Singh ◽  
...  
Keyword(s):  
Conflict Of Interest ◽  
Burst Size ◽  
Inverse Correlation ◽  
Recent Analysis ◽  
Mrna Levels ◽  
Burst Frequency ◽  
Expression Variability ◽  
Protein Levels ◽  
Transcriptional Bursting ◽  
Cell Expression

Recent analysis (Dey et al, 2015), demonstrates that the HIV-1 Long Terminal Repeat (HIV LTR) promoter exhibits a range of possible transcriptional burst sizes and frequencies for any mean-expression level. However, these results have also been interpreted as demonstrating that cell-to-cell expression variability (noise) and mean are uncorrelated, a significant deviation from previous results. Here, we re-examine the available mRNA and protein abundance data for the HIV LTR and find that noise in mRNA and protein expression scales inversely with the mean along analytically predicted transcriptional burst-size manifolds. We then experimentally perturb transcriptional activity to test a prediction of the multiple burst-size model: that increasing burst frequency will cause mRNA noise to decrease along given burst-size lines as mRNA levels increase. The data show that mRNA and protein noise decrease as mean expression increases, supporting the canonical inverse correlation between noise and mean.Conflict of InterestThe authors declare that they have no conflict of interest.

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 Intrauterine growth restriction affects hippocampal dual specificity phosphatase 5 gene expression and epigenetic characteristics

2011 ◽  
Vol 43 (20) ◽  
pp. 1160-1169 ◽  
Author(s):  
Xingrao Ke ◽  
Robert A. McKnight ◽  
Diana Caprau ◽  
Shannon O'Grady ◽  
Qi Fu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cpg Methylation ◽  
Histone Code ◽  
Male Rat ◽  
Female Rat ◽  
Dual Specificity Phosphatase ◽  
Intrauterine Growth ◽  
Protein Levels ◽  
Dual Specificity ◽  
The Impact

Intrauterine growth retardation (IUGR) predisposes humans toward hippocampal morbidities, such as impaired learning and memory. Hippocampal dual specificity phosphatase 5 (DUSP5) may be involved in these morbidities because DUSP5 regulates extracellular signal-regulated kinase phosphorylation (Erk). In the rat, IUGR causes postnatal changes in hippocampal gene expression and epigenetic characteristics. However, the impact of IUGR upon hippocampal DUSP5 expression and epigenetic characteristics is not known. We therefore hypothesized that IUGR affects hippocampal 1) DUSP5 expression, DNA CpG methylation, and histone code, and 2) erk1/2 phosphorylation in a well-characterized rat model of IUGR. We found that IUGR significantly decreased DUSP5 expression in the day of life (DOL) 0 and 21 male rat, while decreasing only DUSP5 protein levels in the DOL21 female rat. Fluorescent in situ hybridization and immunohistochemistry analyses localized the changes in DUSP5 mRNA and protein, many of which occurred in the dentate gyrus. IUGR also caused sex-specific differences in DNA CpG methylation and histone code in two sites of the hippocampal DUSP5 gene, a 5′-flanking specificity protein-1 (SP1) site and exon 2. Finally, when IUGR decreased DUSP5 protein levels, Erk phosphorylation increased. We conclude that IUGR affects hippocampal DUSP5 expression and epigenetic characteristics in a sex-specific manner.

scholarly journals Promoter and transcription factor dynamics tune protein mean and noise strength in a quorum sensing-based feedback synthetic circuit

2017 ◽  
Author(s):  
Yadira Boada ◽  
Alejandro Vignoni ◽  
Jesús Picó
Keyword(s):  
Gene Expression ◽  
Quorum Sensing ◽  
Negative Feedback ◽  
In Silico ◽  
Stochastic Simulations ◽  
Expression Level ◽  
Gene Expression Noise ◽  
Expression Noise ◽  
Synthetic Circuit ◽  
The Mean

AbstractGene expression is a fundamental cellular process. Its stochastic fluctuations due to intrinsic and extrinsic sources, known generically as ‘gene expression noise’, trigger both beneficial and harmful consequences for the cell behavior.Controlling gene expression noise is of interest in many applications in biotechnology, biomedicine and others. Yet, control of the mean expression level is an equally desirable goal. Here, we analyze a gene synthetic network designed to reduce gene expression noise while achieving a desired mean expression level. The circuit combines a negative feedback loop over the gene of interest, and a cell-to-cell communication mechanism based on quorum sensing. We analyze the ability of the circuit to reduce noise as a function of parameters that can be tuned in the wet-lab, and the role quorum sensing plays. Intrinsic noise is generated by the inherent stochasticity of biochemical reactions. On the other hand, extrinsic noise is due to variability in the cell environment and the amounts of cellular components that affect gene expression. We develop a realistic model of the gene synthetic circuit over the population of cells using mass action kinetics and the stochastic Chemical Langevin Equation to include intrinsic noise, with parameters drawn from a distribution to account for extrinsic noise. Stochastic simulations allow us to quantify the mean expression level and noise strength of all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise inE. coli. Ourin silicoexperiments reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. Thesein silicoconclusions are validated by preliminary experimental results. This gene network could have important implications as a robust protein production system in industrial biotechnology.Author SummaryControlling gene expression level is of interest in many applications in biotechnology, biomedicine and others. Yet, the stochastic nature of biochemical reactions plays an important role in biological systems, and cannot be disregarded. Gene expression noise resulting from this stochasticity has been studied over the past years bothin vivo, andin silicousing mathematical models. Nowadays, synthetic biology approaches allow to design novel biological circuits, drawing on principles elucidated from biology and engineering, for the purpose of decoupled control of mean gene expression and its variance. We propose a gene synthetic circuit with these characteristics, using negative feedback and quorum sensing based cell-to-cell communication to induce population consensus. Ourin silicoanalysis using stochastic simulations with a realistic model reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. Preliminaryin vivoresults fully agree with the computational ones.

scholarly journals AICAR Protects against High Palmitate/High Insulin-Induced Intramyocellular Lipid Accumulation and Insulin Resistance in HL-1 Cardiac Cells by Inducing PPAR-Target Gene Expression

PPAR Research ◽  
2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ricardo Rodríguez-Calvo ◽  
Manuel Vázquez-Carrera ◽  
Luis Masana ◽  
Dietbert Neumann
Keyword(s):  
Gene Expression ◽  
Insulin Resistance ◽  
Cardiac Cells ◽  
Metabolic Adaptation ◽  
Mrna Levels ◽  
Intramyocellular Lipid ◽  
Protein Levels ◽  
Compound C ◽  
High Insulin ◽  
The Impact

Here we studied the impact of 5-aminoimidazole-4-carboxamide riboside (AICAR), a well-known AMPK activator, on cardiac metabolic adaptation. AMPK activation by AICAR was confirmed by increased phospho-Thr172-AMPK and phospho-Ser79-ACC protein levels in HL-1 cardiomyocytes. Then, cells were exposed to AICAR stimulation for 24 h in the presence or absence of the AMPK inhibitor Compound C, and the mRNA levels of the three PPARs were analyzed by real-time RT-PCR. Treatment with AICAR induced gene expression of all three PPARs, but only thePparaandPpargregulation were dependent on AMPK. Next, we exposed HL-1 cells to high palmitate/high insulin (HP/HI) conditions either in presence or in absence of AICAR, and we evaluated the expression of selected PPAR-targets genes. HP/HI induced insulin resistance and lipid storage was accompanied by increasedCd36,Acot1, andUcp3mRNA levels. AICAR treatment induced the expression ofAcadvlandGlut4, which correlated to prevention of the HP/HI-induced intramyocellular lipid build-up, and attenuation of the HP/HI-induced impairment of glucose uptake. These data support the hypothesis that AICAR contributes to cardiac metabolic adaptationviaregulation of transcriptional mechanisms.

scholarly journals Engineered CRISPR-Cas9 system enables noiseless, fine-tuned and multiplexed repression of bacterial genes

2017 ◽  
Author(s):  
Antoine Vigouroux ◽  
Enno Oldewurtel ◽  
Lun Cui ◽  
Sven van Teeffelen ◽  
David Bikard
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Genetic Regulation ◽  
Copy Number Variations ◽  
Control Of Gene Expression ◽  
Guide Rna ◽  
Cas9 Nuclease ◽  
Feedback Strength ◽  
Bacterial Genes ◽  
The Impact

AbstractOver the past few years, tools that make use of the Cas9 nuclease have led to many breakthroughs, including in the control of gene expression. The catalytically dead variant of Cas9 known as dCas9 can be guided by small RNAs to block transcription of target genes, in a strategy also known as CRISPRi. Here, we reveal that the level of complementarity between the guide RNA and the target controls the rate at which dCas9 successfully blocks the RNA polymerase. We use this mechanism to precisely and robustly reduce gene expression by defined relative amounts. We demonstrate broad applicability of this method to the study of genetic regulation and cellular physiology. First, we characterize feedback strength of a model auto-repressor. Second, we study the impact of copy-number variations of cell-wall synthesizing enzymes on cell morphology. Finally, we demonstrate that this system can be multiplexed to obtain any combination of fractional repression of two genes.

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.

131. SIRT3 IN OVARIAN CELLS IS ALTERED BY MATERNAL AGE AND OVARIAN RESERVE

2010 ◽  
Vol 22 (9) ◽  
pp. 49
Author(s):  
L. Pacella ◽  
D. Zander-Fox ◽  
T. Hussein ◽  
T. Fullston ◽  
M. Lane
Keyword(s):  
Gene Expression ◽  
Ovarian Reserve ◽  
Maternal Age ◽  
Cumulus Cells ◽  
Cell Types ◽  
Advanced Maternal Age ◽  
Cellular Functions ◽  
Protein Levels ◽  
Ovarian Cells ◽  
The Impact

Maternal age and reduced AMH levels affect the follicular environment and consequently oocyte viability. The Sirtuin family of protein deacetylases are able to regulate various cellular functions involved in the ageing process in other tissues. In particular, SIRT3 is related to longevity in several cell types and regulates mitochondrial function, however, its presence and role in ovarian cells remains unknown. This study therefore, investigated the presence of SIRT3 in granulosa and cumulus cells, from patients undergoing IVF, and determined the impact of maternal age and low AMH on SIRT3 levels. Granulosa and cumulus cells were collected from women (n = 36), after informed consent, and classified into 3 groups; A (<35 years, normal AMH), B (>40 years (advanced maternal age), normal AMH) and C (<35 years, low AMH). The presence of SIRT3 was determined by q-PCR (expressed as fold-change) or immunohistochemistry. SIRT3 was present in the ovarian cells of all patients analysed. SIRT3 gene expression was reduced in granulosa cells from women with low AMH (0.67 ± 0.17) compared to women with normal AMH (1.00 ± 0.23; P < 0.05). In cumulus cells, levels were reduced with advanced maternal age (0.81 ± 0.08) compared to women <35 years (1.00 ± 0.22; P < 0.05). SIRT3 protein co-localised with mitochondria in the ovarian cells, confirming previous findings for other cell types. In comparison to women <35 years with normal AMH, image analysis determined that SIRT3 protein levels were significantly reduced in the granulosa and cumulus cells from women of advanced maternal age by 21.4% and 31.8% and in women with low AMH by 34.1% and 47.2% respectively. This is the first study to demonstrate SIRT3 presence in human ovarian cells. The observation that SIRT3 levels are altered by advanced maternal age or low AMH (reduced ovarian reserve) implicate its role in ovarian ageing and plausibly in the decrease in oocyte viability observed in these women.

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.

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