Effects of promoter leakage on dynamics of gene expression

Huang, Lifang; Yuan, Zhiwen; Liu, Yijie; Zhou, Tianshou

doi:10.1186/s12918-015-0157-z

Cited by 58 publications

(53 citation statements)

References 76 publications

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“…The leaky Telegraph model has been considered in a number of previous studies such as Refs. 11,18,19, and 25, and incorporates the well known phenomenon of promotor leakage and basal gene expression [41][42][43] . In this model, the gene is assumed to transition between two states, active (A) and inactive (I), with rates λ and µ, respectively.…”

Section: A the Leaky Telegraph Modelmentioning

confidence: 99%

Exactly solvable models of stochastic gene expression

Ham

Schnoerr

Brackston

et al. 2020

Preprint

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Stochastic models are key to understanding the intricate dynamics of gene expression. But the simplest models which only account for e.g. active and inactive states of a gene fail to capture common observations in both prokaryotic and eukaryotic organisms. Here we consider multistate models of gene expression which generalise the canonical Telegraph process, and are capable of capturing the joint effects of e.g. transcription factors, heterochromatin state and DNA accessibility (or, in prokaryotes, Sigma-factor activity) on transcript abundance. We propose two approaches for solving classes of these generalised systems. The first approach offers a fresh perspective on a general class of multistate models, and allows us to “decompose” more complicated systems into simpler processes, each of which can be solved analytically. This enables us to obtain a solution of any model from this class. We further show that these models cannot have a heavy-tailed distribution in the absence of extrinsic noise. Next, we develop an approximation method based on a power series expansion of the stationary distribution for an even broader class of multistate models of gene transcription. The combination of analytical and computational solutions for these realistic gene expression models also holds the potential to design synthetic systems, and control the behaviour of naturally evolved gene expression systems, e.g. in guiding cell-fate decisions.

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Section: A the Leaky Telegraph Modelmentioning

confidence: 99%

Exactly solvable models of stochastic gene expression

Ham

Schnoerr

Brackston

et al. 2020

Preprint

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“…Models of transcription kinetics distinguish between two discrete promoter states: active and inactive. Importantly, these models often consider transcription as occurring in both of these states, but with large differences in rates of transcription between them [20,21,26,27]. This implies that genes which are regulated to be inactive in a tissue or cell type will tend to produce expression levels much lower than genes regulated to be active in that tissue type, resulting in a bimodal distribution for the tissue transcriptome as a whole.…”

Section: Discussionmentioning

confidence: 99%

“…Simply put, a read count of zero for a given gene does not necessarily indicate that it is inactive, while a non-zero read count does not necessarily indicate that it is actively expressed. Random transcriptional noise can often yield reads that map to inactive genes; conversely, insufficient sampling (low sequencing depth) can cause active genes to be absent among mapped reads [17][18][19][20][21][22][23][24][25][26][27]. This produces an intrinsic ambiguity between background noise and active but low-abundance genes.…”

Section: Introductionmentioning

confidence: 99%

“…The relative expression levels of genes in transcriptomes typically fall into two overlapping distributions, of high-abundance and low-abundance genes [18,[32][33][34][35]. Genes in the lower expressed population of the bimodal transcriptome distribution tend to have inactive chromatin marks (e.g., H3K27me3), while genes in the higher expressed population have active chromatin marks, (e.g., H3K4me3) [18,21,35,36]. In addition, data from single-cell RNAseq also indicate that genes exhibit bimodal expression patterns among cells with the lower mode showing few or zero reads, leading researchers to suggest that these patterns indicate the difference between cells actively expressing the gene and cells producing trace amounts of expression due to background stochastic events [27,37].…”

Section: Introductionmentioning

confidence: 99%

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A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes

Thompson¹,

May²,

Moore³

et al. 2019

Preprint

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Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process, and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets-where we demonstrate its ability to correctly infer true (known) expression states-and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-seq datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and, remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely-available R package zigzag.detecting zero transcripts of a given gene in a given tissue does 53 not necessarily indicate that it is inactive. Third, even when 54 we detect transcripts of a given gene, its measured expression 55 level is likely to vary among libraries owing to both biological 56 factors (e.g., population-level variation) and technical factors 57 (i.e., the relative abundance of a given transcript in a given 58 library depends on the total transcript number of that library). 59Therefore, the rank order in expression level of two genes in 60 one library may differ from their rank order in a second library, 61 which complicates methods that infer the expression state of 62 genes based on fixed expression-level thresholds (17, 21). 63Here, we present a hierarchical Bayesian model that de-64 scribes the biological and technical processes that generate 65 transcriptomic data that-by explicitly accommodating the 66 factors described above-allows us to infer the expression state 67 of each gene from replicate RNA-seq libraries. We present anal-68 yses of simulated datasets that validate the implementation 69 and characterize the statistical behavior of our hierarchical 70 Bayesian model. We also apply our method to several em-71 pirical datasets, and demonstrate that the expression states 72 inferred using our method are consistent with expectations 73 based on independent information, such as epigenetic marks 74 and developmental-genetic studies. Finally, we demonstrate 75 our method with an empirical analysis of primate-brain tran-76 scriptomes that identi...

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“…The statistics and dynamics of stochastic gene expression are best characterized by the probability mass function, P (n; t), i.e., the probability that there are exactly n mRNA or protein molecules of a gene of interest at time t in a single cell. Previous studies have derived analytical gene product distributions in common two-state model of stochastic gene expression [19,28,31,34,42,43], or in similar gene models with linear feedback [14,15,23]. However, transcription factors regulate gene expression often in a nonlinear fashion.…”

Section: Introductionmentioning

confidence: 99%

Exact distributions for stochastic models of gene expression with arbitrary regulation

Wang

Zhang

2019

Sci. China Math.

Self Cite

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Stochasticity in gene expression can result in fluctuations in gene product levels. Recent experiments indicated that feedback regulation plays an important role in controlling the noise in gene expression. A quantitative understanding of the feedback effect on gene expression requires analysis of the corresponding stochastic model. However, for stochastic models of gene expression with general regulation functions, exact analytical results for gene product distributions have not been given so far. Here, we propose a technique to solve a generalized ON-OFF model of stochastic gene expression with arbitrary (positive or negative, linear or nonlinear) feedbacks including posttranscriptional or posttranslational regulation. The obtained results, which generalize results obtained previously, provide new insights into the role of feedback in regulating gene expression. The proposed analytical framework can easily be extended to analysis of more complex models of stochastic gene expression.

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Effects of promoter leakage on dynamics of gene expression

Cited by 58 publications

References 76 publications

Exactly solvable models of stochastic gene expression

Exactly solvable models of stochastic gene expression

A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes

Exact distributions for stochastic models of gene expression with arbitrary regulation

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