scholarly journals Pathway dynamics can delineate the sources of transcriptional noise in gene expression

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Lucy Ham ◽  
Marcel Jackson ◽  
Michael Stumpf
Keyword(s):  
Intrinsic Noise ◽  
Stochastic Simulations ◽  
Transcriptional Noise ◽  
Extrinsic Noise ◽  
Cellular Processes ◽  
Dual Reporter ◽  
Extensive Cell ◽  
Noise In Gene Expression ◽  
Cell Expression ◽  
Cell To Cell Variability

Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells ('intrinsic noise') from variability across the population ('extrinsic noise'). Here we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that 'pathway-reporters' compare favourably to the well-known, but often difficult to implement, dual-reporter method.

scholarly journals Extrinsic noise acts to lower protein production at higher translation initiation rates

2020 ◽  
Author(s):  
Rati Sharma
Keyword(s):  
Translation Initiation ◽  
Protein Production ◽  
Translation Termination ◽  
Mrna Translation ◽  
Intrinsic Noise ◽  
Stochastic Simulations ◽  
Residence Times ◽  
Extrinsic Noise ◽  
Initiation Rate ◽  
The Individual

Any cellular process at the microscopic level is governed by both extrinsic and intrinsic noise. In this article, we incorporate extrinsic noise in a model of mRNA translation and carry out stochastic simulations of the same. We then evaluate various statistics related to the residence time of the ribosome on the mRNA and subsequent protein production. We also study the effect of slow codons. From our simulations, we show that noise in the translation initiation rate rather than the translation termination rate acts to significantly broaden the distribution of mRNA residence times near the membrane. Further, the presence of slow codons acts to increase the mean residence times. However, this increase also depends on the number and position of the slow codons on the lattice. We also show that the the slow codons act to mask any effect from the extrinsic noise themselves. Our results have implications towards a better understanding of the role the individual components play during the translation process.

scholarly journals Limits of noise for autoregulated gene expression

2017 ◽  
Author(s):  
Peter Czuppon ◽  
Peter Pfaffelhuber
Keyword(s):  
Gene Expression ◽  
Negative Feedback ◽  
Positive Feedback ◽  
Fano Factor ◽  
Intrinsic Noise ◽  
Stochastic Averaging ◽  
Jump Processes ◽  
Markov Jump ◽  
Extrinsic Noise ◽  
Cellular Processes

AbstractGene expression is influenced by extrinsic noise (involving a fluctuating environment of cellular processes) and intrinsic noise (referring to fluctuations within a cell under constant environment). We study the standard model of gene expression including an (in-)active gene, mRNA and protein. Gene expression is regulated in the sense that the protein feeds back and either represses (negative feedback) or enhances (positive feedback) its production at the stage of transcription. While it is well-known that negative (positive) feedback reduces (increases) intrinsic noise, we give a precise result on the resulting fluctuations in protein numbers. The technique we use is an extension of the Langevin approximation and is an application of a central limit theorem under stochastic averaging for Markov jump processes (Kang, Kurtz and Popovic, 2014). We find that (under our scaling and in equilibrium), negative feedback leads to a reduction in the Fano factor of at most 2, while the noise under positive feedback is potentially unbounded. The fit with simulations is very good and improves on known approximations.

scholarly journals PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models

2021 ◽  
Author(s):  
Sebastian Persson ◽  
Niek Welkenhuysen ◽  
Sviatlana Shashkova ◽  
Samuel Wiqvist ◽  
Patrick Reith ◽  
...  
Keyword(s):  
Single Cell ◽  
Intrinsic Noise ◽  
Time Lapse ◽  
Nutrient Sensing ◽  
Model Parameters ◽  
Stochastic Dynamic ◽  
Cell Models ◽  
Extrinsic Noise ◽  
Yeast Saccharomyces Cerevisiae ◽  
Cell To Cell Variability

Mathematical modelling is an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic and extrinsic noise. Here we present PEPSDI, a scalable and flexible framework for Bayesian inference in state-space mixed-effects stochastic dynamic single-cell models. Unlike previous frameworks, PEPSDI imposes a few modelling assumptions when inferring unknown model parameters from time-lapse data. Specifically, it can infer model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. This allowed us to identify hexokinase activity as a source of extrinsic noise, and to deduce that sugar availability dictates cell-to-cell variability in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway.

scholarly journals Pathway dynamics can delineate the sources of transcriptional noise in gene expression

2020 ◽  
Author(s):  
Lucy Ham ◽  
Marcel Jackson ◽  
Michael P.H. Stumpf
Keyword(s):  
Gene Expression ◽  
Formal Analysis ◽  
Single Gene ◽  
Population Data ◽  
Transcript Abundance ◽  
Population Variation ◽  
Stochastic Simulations ◽  
Transcriptional Level ◽  
Extrinsic Noise ◽  
Dual Reporter

Single-cell expression profiling is destructive, giving rise to only static snapshots of cellular states. This loss of temporal information presents significant challenges in inferring dynamics from population data. Here we provide a formal analysis of the extent to which dynamic variability from within individual systems (“intrinsic noise”) is distinguishable from variability across the population (“extrinsic noise”). Our results mathematically formalise observations that it is impossible to identify these sources of variability from the transcript abundance distribution alone. Notably, we find that systems subject to population variation invariably inflate the apparent degree of burstiness of the underlying process. Such identifiability problems can be remedied by the dual-reporter method, which separates the total gene expression noise into intrinsic and extrinsic contributions. This noise decomposition, however, requires strictly independent and identical gene reporters integrated into the same cell, which can be difficult to implement experimentally in many systems. Here we demonstrate mathematically that, in some cases, the same noise decomposition can be achieved at the transcriptional level with non-identical and not-necessarily independent reporters. We use our result to show that generic reporters lying in the same biochemical pathways (e.g. mRNA and protein) can replace dual reporters, enabling the noise decomposition to be obtained from only a single gene. Stochastic simulations are used to support our theory, and show that our “pathway-reporter” method compares favourably to the dual-reporter method.

scholarly journals Contribution of RNA Degradation to Intrinsic and Extrinsic Noise in Gene Expression

Cell Reports ◽  
2019 ◽  
Vol 26 (13) ◽  
pp. 3752-3761.e5 ◽  
Author(s):  
Antoine Baudrimont ◽  
Vincent Jaquet ◽  
Sandrine Wallerich ◽  
Sylvia Voegeli ◽  
Attila Becskei
Keyword(s):  
Gene Expression ◽  
Rna Degradation ◽  
Extrinsic Noise ◽  
Noise In Gene Expression ◽  
Intrinsic And Extrinsic Noise

scholarly journals Computation of Single-Cell Metabolite Distributions Using Mixture Models

2020 ◽  
Vol 8 ◽  
Author(s):  
Mona K. Tonn ◽  
Philipp Thomas ◽  
Mauricio Barahona ◽  
Diego A. Oyarzún
Keyword(s):  
Single Cell ◽  
Mixture Models ◽  
Single Cells ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Stochastic Simulations ◽  
Deterministic Models ◽  
Metabolic Heterogeneity ◽  
Cell Expression ◽  
The Impact

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

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 SVIP Is a Novel VCP/p97-interacting Protein Whose Expression Causes Cell Vacuolation

2003 ◽  
Vol 14 (1) ◽  
pp. 262-273 ◽  
Author(s):  
Masami Nagahama ◽  
Mie Suzuki ◽  
Yuko Hamada ◽  
Kiyotaka Hatsuzawa ◽  
Katsuko Tani ◽  
...  
Keyword(s):  
Endoplasmic Reticulum ◽  
Coiled Coil ◽  
Adaptor Proteins ◽  
Interacting Protein ◽  
Cellular Processes ◽  
Coiled Coil Region ◽  
Acid Protein ◽  
Extensive Cell ◽  
Dependent Protein ◽  
Amino Acid Protein

VCP/p97 is involved in a variety of cellular processes, including membrane fusion and ubiquitin-dependent protein degradation. It has been suggested that adaptor proteins such as p47 and Ufd1p confer functional versatility to VCP/p97. To identify novel adaptors, we searched for proteins that interact specifically with VCP/p97 by using the yeast two-hybrid system, and discovered a novel VCP/p97-interacting protein named smallVCP/p97-interactingprotein (SVIP). Rat SVIP is a 76-amino acid protein that contains two putative coiled-coil regions, and potential myristoylation and palmitoylation sites at the N terminus. Binding experiments revealed that the N-terminal coiled-coil region of SVIP, and the N-terminal and subsequent ATP-binding regions (ND1 domain) of VCP/p97, interact with each other. SVIP and previously identified adaptors p47 and ufd1p interact with VCP/p97 in a mutually exclusive manner. Overexpression of full-length SVIP or a truncated mutant did not markedly affect the structure of the Golgi apparatus, but caused extensive cell vacuolation reminiscent of that seen upon the expression of VCP/p97 mutants or polyglutamine proteins in neuronal cells. The vacuoles seemed to be derived from endoplasmic reticulum membranes. These results together suggest that SVIP is a novelVCP/p97 adaptor whose function is related to the integrity of the endoplasmic reticulum.

scholarly journals Pheromone-induced morphogenesis and gradient tracking are dependent on the MAPK Fus3 binding to Gα

2015 ◽  
Vol 26 (18) ◽  
pp. 3343-3358 ◽  
Author(s):  
Beverly Errede ◽  
Lior Vered ◽  
Eintou Ford ◽  
Matthew I. Pena ◽  
Timothy C. Elston
Keyword(s):  
G Protein ◽  
Negative Regulator ◽  
Mitogen Activated Protein Kinase ◽  
Yeast Saccharomyces Cerevisiae ◽  
Α Subunit ◽  
Cellular Processes ◽  
Mitogen Activated Protein ◽  
Catalytically Active ◽  
Differentiation And Proliferation ◽  
Cell To Cell Variability

Mitogen-activated protein kinase (MAPK) pathways control many cellular processes, including differentiation and proliferation. These pathways commonly activate MAPK isoforms that have redundant or overlapping function. However, recent studies have revealed circumstances in which MAPK isoforms have specialized, nonoverlapping roles in differentiation. The mechanisms that underlie this specialization are not well understood. To address this question, we sought to establish regulatory mechanisms that are unique to the MAPK Fus3 in pheromone-induced mating and chemotropic fate transitions of the budding yeast Saccharomyces cerevisiae. Our investigations reveal a previously unappreciated role for inactive Fus3 as a potent negative regulator of pheromone-induced chemotropism. We show that this inhibitory role is dependent on inactive Fus3 binding to the α-subunit of the heterotrimeric G-protein. Further analysis revealed that the binding of catalytically active Fus3 to the G-protein is required for gradient tracking and serves to suppress cell-to-cell variability between mating and chemotropic fates in a population of pheromone-responding cells.

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