scholarly journals Selection at the pathway level drives the evolution of gene-specific transcriptional noise

2017 ◽  
Author(s):  
Gustavo Valadares Barroso ◽  
Natasa Puzovic ◽  
Julien Y Dutheil
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Networks ◽  
Phenotypic Variability ◽  
Published Data ◽  
Evolutionary Constraint ◽  
Stochastic Gene Expression ◽  
Transcriptional Noise ◽  
Evolutionary Forces ◽  
A Genome

ABSTRACTBiochemical reactions within individual cells result from the interactions of molecules, typically in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generate noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, in particular owing to the advent of single cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. We take advantage of two recently published data sets of the single-cell transcriptome of the domestic mouse Mus musculus in order to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We show that noise levels in the mRNA distributions (a.k.a. transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraint on the encoded protein and gene age. The position of the encoded protein in biological pathways, however, is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with mean expression level in functional and evolutionary studies of gene expression.

scholarly journals Estimating intrinsic and extrinsic noise from single-cell gene expression measurements

2016 ◽  
Vol 15 (6) ◽  
Author(s):  
Audrey Qiuyan Fu ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Hierarchical Model ◽  
Mean Squared Error ◽  
Single Cells ◽  
Geometric Interpretation ◽  
Published Data ◽  
Stochastic Gene Expression ◽  
Extrinsic Noise ◽  
Intrinsic And Extrinsic Noise

AbstractGene expression is stochastic and displays variation (“noise”) both within and between cells. Intracellular (intrinsic) variance can be distinguished from extracellular (extrinsic) variance by applying the law of total variance to data from two-reporter assays that probe expression of identically regulated gene pairs in single cells. We examine established formulas [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.] for the estimation of intrinsic and extrinsic noise and provide interpretations of them in terms of a hierarchical model. This allows us to derive alternative estimators that minimize bias or mean squared error. We provide a geometric interpretation of these results that clarifies the interpretation in [Elowitz, M. B., A. J. Levine, E. D. Siggia and P. S. Swain (2002): “Stochastic gene expression in a single cell,” Science, 297, 1183–1186.]. We also demonstrate through simulation and re-analysis of published data that the distribution assumptions underlying the hierarchical model have to be satisfied for the estimators to produce sensible results, which highlights the importance of normalization.

scholarly journals Cell-to-cell heterogeneity in Escherichia coli stress response originates from pulsatile expression and growth

2020 ◽  
Author(s):  
Nadia M. V. Sampaio ◽  
Caroline M. Blassick ◽  
Jean-Baptiste Lugagne ◽  
Mary J. Dunlop
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Stress Response ◽  
Single Cell ◽  
Phenotypic Variability ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Cell Heterogeneity ◽  
Temporal Expression ◽  
Functional Consequences

AbstractCell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is critical because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found widespread examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These pulsatile dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that pulsatile expression and growth dynamics are common in stress response networks and can have direct consequences for survival.

scholarly journals Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stuart P. Wilson ◽  
Sebastian S. James ◽  
Daniel J. Whiteley ◽  
Leah A. Krubitzer
Keyword(s):  
Gene Expression ◽  
Limit Cycles ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Specific Gene ◽  
Boolean Models ◽  
A Genome ◽  
Order Of Magnitude ◽  
Accelerate Evolution

AbstractDevelopmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.

scholarly journals Gene networks and transcriptional regulators associated with liver cancer development and progression

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Tatiana Meier ◽  
Max Timm ◽  
Matteo Montani ◽  
Ludwig Wilkens
Keyword(s):  
Gene Expression ◽  
Liver Cancer ◽  
Comparative Genomic Hybridization ◽  
Chromosomal Instability ◽  
Gene Networks ◽  
Transcriptional Regulators ◽  
Histological Evaluation ◽  
Comparative Genomic ◽  
Published Data ◽  
Genomic Hybridization

Abstract Background Treatment options for hepatocellular carcinoma (HCC) are limited, and overall survival is poor. Despite the high frequency of this malignoma, its basic disease mechanisms are poorly understood. Therefore, the aim of this study was to use different methodological approaches and combine the results to improve our knowledge on the development and progression of HCC. Methods Twenty-three HCC samples were characterized by histological, morphometric and cytogenetic analyses, as well as comparative genomic hybridization (aCGH) and genome-wide gene expression followed by a bioinformatic search for potential transcriptional regulators and master regulatory molecules of gene networks. Results Histological evaluation revealed low, intermediate and high-grade HCCs, and gene expression analysis split them into two main sets: GE1-HCC and GE2-HCC, with a low and high proliferation gene expression signature, respectively. Array-based comparative genomic hybridization demonstrated a high level of chromosomal instability, with recurrent chromosomal gains of 1q, 6p, 7q, 8q, 11q, 17q, 19p/q and 20q in both HCC groups and losses of 1p, 4q, 6q, 13q and 18q characteristic for GE2-HCC. Gene expression and bioinformatics analyses revealed that different genes and gene regulatory networks underlie the distinct biological features observed in GE1-HCC and GE2-HCC. Besides previously reported dysregulated genes, the current study identified new candidate genes with a putative role in liver cancer, e.g. C1orf35, PAFAH1B3, ZNF219 and others. Conclusion Analysis of our findings, in accordance with the available published data, argues in favour of the notion that the activated E2F1 signalling pathway, which can be responsible for both inappropriate cell proliferation and initial chromosomal instability, plays a pivotal role in HCC development and progression. A dedifferentiation switch that manifests in exaggerated gene expression changes might be due to turning on transcriptional co-regulators with broad impact on gene expression, e.g. POU2F1 (OCT1) and NFY, as a response to accumulating cell stress during malignant development. Our findings point towards the necessity of different approaches for the treatment of HCC forms with low and high proliferation signatures and provide new candidates for developing appropriate HCC therapies.

scholarly journals Power Analysis of Single Cell RNA-Sequencing Experiments

2016 ◽  
Author(s):  
Valentine Svensson ◽  
Kedar Nath Natarajan ◽  
Lam-Ha Ly ◽  
Ricardo J Miragaia ◽  
Charlotte Labalette ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Process ◽  
Cell Types ◽  
Rna Degradation ◽  
Published Data ◽  
Minute Amount ◽  
Trade Offs ◽  
Single Cell Rna Sequencing

AbstractHigh-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (https://github.com/vals/umis) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.

scholarly journals Identification of Novel Genes Associated with Chronic Obstructive Pulmonary Disease Using Different Penalized Logistics Regression with High-Dimensional Biological Data from Human Sputum Cells

2021 ◽  
Author(s):  
Kimiya Gohari ◽  
Anoshirvan Kazemnejad ◽  
Shayan Mostafaei ◽  
Ali Sheidaei ◽  
Maryam S Daneshpour ◽  
...  
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Gene Networks ◽  
Gene Expression Omnibus ◽  
Biological Data ◽  
Chronic Obstructive ◽  
Small Subset ◽  
Differential Analysis ◽  
Absolute Deviation ◽  
A Genome

Abstract Background: Comparison of LASSO, smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) logistic classifiers in order to reconnaissance of related genes with COPD disease and assessing the genes effects on the progression of the disease based on one of the main classes of cells involved in the disease, Sputum Cells. We used a genome-wide expression profiling to define gene networks relevant to the disease. The data retrieved from Gene Expression Omnibus (GEO) with accession numbers "GSE22148". From 143 samples in GOLD stage 2-4 COPD ex-smokers, 54,675 probes primary were assessed. After normalization, LASSO, SCAD and MCP logistic regressions were applied. K-fold cross-validation scheme was used to evaluate the performance of two methods. All of the computational processes were done using "ncvreg", "Affy," "Limma" and "SVA" R packages. Results: The results of LASSO (AUC=0.95, sensitivity= 0.91, specificity= 0.86) and SCAD (AUC=0.97, sensitivity= 0.95, specificity= 0.85) logistic regression were almost similar. There were 23 and 22 significantly associated genes for LASSO and SCAD, respectively. The only difference between these models is related to "stromal interaction molecule 2". Comparing to MCP approach, the most conservative method, we detected only 7 significant genes (AUC= 0.94, sensitivity= 0.94, specificity= 0.82). Conclusions: In the present study, the relative expressions of thousands of the genes were assessed and identified as associated genes with the progression of COPD. Differential analysis of gene expression data is able to reduce the number of genes but in a limited manner. In order to find an efficient and small subset of genes, we should use alternative approaches like logistic regression. Regularization solves the high dimensionality problem in using this kind of regression.

scholarly journals Drugs modulating stochastic gene expression affect the erythroid differentiation process

2018 ◽  
Author(s):  
Anissa Guillemin ◽  
Ronan Duchesne ◽  
Fabien Crauste ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Erythroid Differentiation ◽  
Causative Role ◽  
Stochastic Gene Expression ◽  
Differentiation Process ◽  
Modeling Tools ◽  
Erythroid Progenitor ◽  
Differentiated Cells

AbstractBackgroundTo understand how a metazoan cell makes the decision to differentiate, we assessed the role of stochastic gene expression (SGE) during the erythroid differentiation process. Our hypothesis is that stochastic gene expression has a role in single-cell decision-making. In agreement with this hypothesis, we and others recently showed that SGE significantly increased during differentiation. However, evidence for the causative role of SGE is still lacking. Such demonstration would require being able to experimentally manipulate SGE levels and analyze the resulting impact of these variations on cell differentiation.ResultWe identified three drugs that modulate SGE in primary erythroid progenitor cells. Artemisinin and Indomethacin simultaneously decreased SGE and reduced the amount of differentiated cells. Inversely, α-methylene-γ-butyrolactone-3 (MB-3) simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment.ConclusionUsing single-cell analysis and modeling tools, we provide experimental evidence that in a physiologically relevant cellular system, control of SGE can directly modify differentiation, supporting a causal link between the two.

scholarly journals Nanopore Long-Read RNAseq Reveals Widespread Transcriptional Variation Among the Surface Receptors of Individual B cells

2017 ◽  
Author(s):  
Ashley Byrne ◽  
Anna E. Beaudin ◽  
Hugh E. Olsen ◽  
Miten Jain ◽  
Charles Cole ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Surface Receptors ◽  
B1a Cells ◽  
A Genome ◽  
Long Read ◽  
Gene Expression Quantification ◽  
Expression Quantification

ABSTRACTUnderstanding gene regulation and function requires a genome-wide method capable of capturing both gene expression levels and isoform diversity at the single cell level. Short-read RNAseq, while the current standard for gene expression quantification, is limited in its ability to resolve complex isoforms because it fails to sequence full-length cDNA copies of RNA molecules. Here, we investigated whether RNAseq using the long-read single-molecule Oxford Nanopore MinION sequencing technology (ONT RNAseq) would be able to identify and quantify complex isoforms without sacrificing accurate gene expression quantification. After successfully benchmarking our experimental and computational approaches on a mixture of synthetic transcripts, we analyzed individual murine B1a cells using a new cellular indexing strategy. Using the Mandalorion analysis pipeline we developed, we identified thousands of unannotated transcription start and end sites, as well as hundreds of alternative splicing events in these B1a cells. We also identified hundreds of genes expressed across B1a cells that displayed multiple complex isoforms, including several B cell specific surface receptors and the antibody heavy chain (IGH) locus. Our results show that not only can we identify complex isoforms, but also quantify their expression, at the single cell level.

scholarly journals Constructing local cell-specific networks from single-cell data

2021 ◽  
Vol 118 (51) ◽  
pp. e2113178118
Author(s):  
Xuran Wang ◽  
David Choi ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Networks ◽  
Fetal Brain ◽  
Autism Spectrum ◽  
Cellular Level ◽  
Cellular Heterogeneity ◽  
Gene Coexpression ◽  
Downstream Analysis ◽  
Coexpression Networks

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

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