Transcript counting in single cells reveals dynamics of rDNA transcription

Non-coding antisense transcripts: fine regulation of gene expression in cancer

Natural antisense transcripts (NATs) are coding or non-coding RNA sequences transcribed on the opposite direction from the same genomic locus. NATs are widely distributed throughout the human genome and seem to play crucial roles in physiological and pathological processes, through newly described and targeted mechanisms. NATs represent the intricate complexity of the genome organization and constitute another layer of potential targets in disease. Here, we focus on the interesting and unique role of non-coding NATs in cancer, paying particular attention to those acting as miRNA sponges.

Transcriptional timing and noise of yeast cell cycle regulators-a single cell and single molecule approach

Gene expression is a stochastic process and its appropriate regulation is critical for cell cycle progression. Cellular stress response necessitates expression reprogramming and cell cycle arrest. While previous studies are mostly based on bulk experiments influenced by synchronization effects or lack temporal distribution, time-resolved methods on single cells are needed to understand eukaryotic cell cycle in context of noisy gene expression and external perturbations. Using smFISH, microscopy and morphological markers, we monitored mRNA abundances over cell cycle phases and calculated transcriptional noise for SIC1, CLN2, and CLB5, the main G1/S transition regulators in budding yeast. We employed mathematical modeling for in silico synchronization and for derivation of time-courses from single cell data. This approach disclosed detailed quantitative insights into transcriptional regulation with and without stress, not available from bulk experiments before. First, besides the main peak in G1 we found an upshift of CLN2 and CLB5 expression in late mitosis. Second, all three genes showed basal expression throughout cell cycle enlightening that transcription is not divided in on and off but rather in high and low phases. Finally, exposing cells to osmotic stress revealed different periods of transcriptional inhibition for CLN2 and CLB5 and the impact of stress on cell cycle phase duration. Combining experimental and computational approaches allowed us to precisely assess cell cycle progression timing, as well as gene expression dynamics.

Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation

Background

Gene expression is known to be an intrinsically stochastic process which can involve single-digit numbers of mRNA molecules in a cell at any given time. The modelling of such processes calls for the use of exact stochastic simulation methods, most notably the Gillespie algorithm. However, this stochasticity, also termed “intrinsic noise”, does not account for all the variability between genetically identical cells growing in a homogeneous environment.

Despite substantial experimental efforts, determining appropriate model parameters continues to be a challenge. Methods based on approximate Bayesian computation can be used to obtain posterior parameter distributions given the observed data. However, such inference procedures require large numbers of simulations of the model and exact stochastic simulation is computationally costly.

In this work we focus on the specific case of trying to infer model parameters describing reaction rates and extrinsic noise on the basis of measurements of molecule numbers in individual cells at a given time point.

Results

To make the problem computationally tractable we develop an exact, model-specific, stochastic simulation algorithm for the commonly used two-state model of gene expression. This algorithm relies on certain assumptions and favourable properties of the model to forgo the simulation of the whole temporal trajectory of protein numbers in the system, instead returning only the number of protein and mRNA molecules present in the system at a specified time point. The computational gain is proportional to the number of protein molecules created in the system and becomes significant for systems involving hundreds or thousands of protein molecules.

Conclusions

We employ this simulation algorithm with approximate Bayesian computation to jointly infer the model’s rate and noise parameters from published gene expression data. Our analysis indicates that for most genes the extrinsic contributions to noise will be small to moderate but certainly are non-negligible.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0324-x) contains supplementary material, which is available to authorized users.

Viral genome imaging of hepatitis C virus to probe heterogeneous viral infection and responses to antiviral therapies

Hepatitis C virus (HCV) is a positive single-stranded RNA virus of enormous global health importance, with direct-acting antiviral therapies replacing an immunostimulatory interferon-based regimen. The dynamics of HCV positive and negative-strand viral RNAs (vRNAs) under antiviral perturbations have not been studied at the single-cell level, leaving a gap in our understanding of antiviral kinetics and host-virus interactions. Here, we demonstrate quantitative imaging of HCV genomes in multiple infection models, and multiplexing of positive and negative strand vRNAs and host antiviral RNAs. We capture the varying kinetics with which antiviral drugs with different mechanisms of action clear HCV infection, finding the NS5A inhibitor daclatasvir to induce a rapid decline in negative-strand viral RNAs. We also find that the induction of host antiviral genes upon interferon treatment is positively correlated with viral load in single cells. This study adds smFISH to the toolbox available for analyzing the treatment of RNA virus infections.

From analog to digital models of gene regulation

Recently, major progress has been made to develop computational models to predict and explain the mechanisms and behaviors of gene regulation. Here, we review progress on how these mechanisms and behaviors have been interpreted with analog models, where cell properties continuously modulate transcription, and digital models, where gene modulation involves discrete activation and inactivation events. We introduce recent experimental approaches, which measure these gene regulatory behaviors at single-cell and single-molecule resolution, and we discuss the integration of these approaches with computational models to reveal biophysical insight. By analyzing simple toy models in the context of existing experimental capabilities, we discuss the interplay between different experiments and different models to measure and interpret gene regulatory behaviors. Finally, we review recent successes in the development of predictive computational models for the control of gene regulation behaviors.

Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics

The production and degradation of RNA transcripts is inherently subject to biological noise that arises from small gene copy numbers in individual cells. As a result, cellular RNA levels can exhibit large fluctuations over time and from one cell to the next. This article presents a range of precise single-molecule experimental techniques, based upon RNA fluorescence in situ hybridization, which can be used to measure the fluctuations of RNA at the single-cell level. A class of models for gene activation and deactivation is postulated in order to capture complex stochastic effects of chromatin modifications or transcription factor interactions. A computational tool, known as the finite state projection approach, is introduced to accurately and efficiently analyze these models in order to predict how probability distributions of RNA change over time in response to changing environmental conditions. These single-molecule experiments, discrete stochastic models, and computational analyses are systematically integrated to identify models of gene regulation dynamics. To illustrate the power and generality of our integrated experimental and computational approach, we explore cases that include different models for three different RNA types (sRNA, mRNA and nascent RNA), three different experimental techniques and three different biological species (bacteria, yeast and human cells).

Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images

Background

Recent techniques for tagging and visualizing single molecules in fixed or living organisms and cell lines have been revolutionizing our understanding of the spatial and temporal dynamics of fundamental biological processes. However, fluorescence microscopy images are often noisy, and it can be difficult to distinguish a fluorescently labeled single molecule from background speckle.

Results

We present a computational pipeline to distinguish the true signal of fluorescently labeled molecules from background fluorescence and noise. We test our technique using the challenging case of wide-field, epifluorescence microscope image stacks from single molecule fluorescence in situ experiments on nematode embryos where there can be substantial out-of-focus light and structured noise. The software recognizes and classifies individual mRNA spots by measuring several features of local intensity maxima and classifying them with a supervised random forest classifier. A key innovation of this software is that, by estimating the probability that each local maximum is a true spot in a statistically principled way, it makes it possible to estimate the error introduced by image classification. This can be used to assess the quality of the data and to estimate a confidence interval for the molecule count estimate, all of which are important for quantitative interpretations of the results of single-molecule experiments.

Conclusions

The software classifies spots in these images well, with >95% AUROC on realistic artificial data and outperforms other commonly used techniques on challenging real data. Its interval estimates provide a unique measure of the quality of an image and confidence in the classification.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0534-z) contains supplementary material, which is available to authorized users.

Whole-mount single molecule FISH method for zebrafish embryo

Noise in gene expression renders cells more adaptable to changing environment by imposing phenotypic and functional heterogeneity on genetically identical individual cells. Hence, quantitative measurement of noise in gene expression is essential for the study of biological processes in cells. Currently, there are two complementary methods for quantitatively measuring noise in gene expression at the single cell level: single molecule FISH (smFISH) and single cell qRT-PCR (or single cell RNA-seq). While smFISH has been developed for culture cells, tissue sections and whole-mount invertebrate organisms, the method has not been reported for whole-mount vertebrate organisms. Here, we report an smFISH method that is suitable for whole-mount zebrafish embryo, a popular vertebrate model organism for the studies of development, physiology and disease. We show the detection of individual transcripts for several cell-type specific and ubiquitously expressed genes at the single cell level in whole-mount zebrafish embryo. We also demonstrate that the method can be adapted to detect two different genes in individual cells simultaneously. The whole-mount smFISH method described in this report is expected to facilitate the study of noise in gene expression and its role in zebrafish, a vertebrate animal model relevant to human biology.

Analytic approaches to stochastic gene expression in multicellular systems

Deterministic thermodynamic models of the complex systems, which control gene expression in metazoa, are helping researchers identify fundamental themes in the regulation of transcription. However, quantitative single cell studies are increasingly identifying regulatory mechanisms that control variability in expression. Such behaviors cannot be captured by deterministic models and are poorly suited to contemporary stochastic approaches that rely on continuum approximations, such as Langevin methods. Fortunately, theoretical advances in the modeling of transcription have assembled some general results that can be readily applied to systems being explored only through a deterministic approach. Here, I review some of the recent experimental evidence for the importance of genetically regulating stochastic effects during embryonic development and discuss key results from Markov theory that can be used to model this regulation. I then discuss several pairs of regulatory mechanisms recently investigated through a Markov approach. In each case, a deterministic treatment predicts no difference between the mechanisms, but the statistical treatment reveals the potential for substantially different distributions of transcriptional activity. In this light, features of gene regulation that seemed needlessly complex evolutionary baggage may be appreciated for their key contributions to reliability and precision of gene expression.

Silencing is noisy: population and cell level noise in telomere-adjacent genes is dependent on telomere position and sir2

Cell-to-cell gene expression noise is thought to be an important mechanism for generating phenotypic diversity. Furthermore, telomeric regions are major sites for gene amplification, which is thought to drive genetic diversity. Here we found that individual subtelomeric TLO genes exhibit increased variation in transcript and protein levels at both the cell-to-cell level as well as at the population-level. The cell-to-cell variation, termed Telomere-Adjacent Gene Expression Noise (TAGEN) was largely intrinsic noise and was dependent upon genome position: noise was reduced when a TLO gene was expressed at an ectopic internal locus and noise was elevated when a non-telomeric gene was expressed at a telomere-adjacent locus. This position-dependent TAGEN also was dependent on Sir2p, an NAD+-dependent histone deacetylase. Finally, we found that telomere silencing and TAGEN are tightly linked and regulated in cis: selection for either silencing or activation of a TLO-adjacent URA3 gene resulted in reduced noise at the neighboring TLO but not at other TLO genes. This provides experimental support to computational predictions that the ability to shift between silent and active chromatin states has a major effect on cell-to-cell noise. Furthermore, it demonstrates that these shifts affect the degree of expression variation at each telomere individually.

Modeling bi-modality improves characterization of cell cycle on gene expression in single cells

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%–17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

Recent technological advances have enabled the measurement of gene expression in individual cells, revealing that there is substantial variability in expression, even within a homogeneous cell population. In this paper, we develop new analytical methods that account for the intrinsic, stochastic nature of single cell expression in order to characterize the effect of cell cycle on gene expression at the single-cell level. Applying these methods to populations of asynchronously cycling cells, we are able to identify large numbers of genes with cell cycle-associated expression patterns. By measuring and adjusting for cellular-level factors, we are able to derive estimates of co-expressing gene networks that more closely reflect cellular-level processes as opposed to sample-level processes. We find that cell cycle phase only accounts for a modest amount of the overall variability of gene expression within an individual cell. The analytical methods demonstrated in this paper are universally applicable to single cell expression data and represent a promising tool to the scientific community.

The cohesin acetyltransferase Eco1 coordinates rDNA replication and transcription

Eco1 is the acetyltransferase that establishes sister-chromatid cohesion during DNA replication. A budding yeast strain with an eco1 mutation that genocopies Roberts syndrome has reduced ribosomal DNA (rDNA) transcription and a transcriptional signature of starvation. We show that deleting FOB1--a gene that encodes a replication fork-blocking protein specific for the rDNA region--rescues rRNA production and partially rescues transcription genome-wide. Further studies show that deletion of FOB1 corrects the genome-wide replication defects, nucleolar structure, and rDNA segregation that occur in the eco1 mutant. Our study highlights that the presence of cohesin at the rDNA locus has a central role in controlling global DNA replication and gene expression.

Cohesion promotes nucleolar structure and function

The cohesin complex contributes to ribosome function, although the molecular mechanisms involved are unclear. Compromised cohesin function is associated with a class of diseases known as cohesinopathies. One cohesinopathy, Roberts syndrome (RBS), occurs when a mutation reduces acetylation of the cohesin Smc3 subunit. Mutation of the cohesin acetyltransferase is associated with impaired rRNA production, ribosome biogenesis, and protein synthesis in yeast and human cells. Cohesin binding to the ribosomal DNA (rDNA) is evolutionarily conserved from bacteria to human cells. We report that the RBS mutation in yeast (eco1-W216G) exhibits a disorganized nucleolus and reduced looping at the rDNA. RNA polymerase I occupancy of the genes remains normal, suggesting that recruitment is not impaired. Impaired rRNA production in the RBS mutant coincides with slower rRNA cleavage. In addition to the RBS mutation, mutations in any subunit of the cohesin ring are associated with defects in ribosome biogenesis. Depletion or artificial destruction of cohesion in a single cell cycle is associated with loss of nucleolar integrity, demonstrating that the defects at the rDNA can be directly attributed to loss of cohesion. Our results strongly suggest that organization of the rDNA provided by cohesion is critical for formation and function of the nucleolus.

Ribosome biogenesis in the yeast Saccharomyces cerevisiae

Ribosomes are highly conserved ribonucleoprotein nanomachines that translate information in the genome to create the proteome in all cells. In yeast these complex particles contain four RNAs (>5400 nucleotides) and 79 different proteins. During the past 25 years, studies in yeast have led the way to understanding how these molecules are assembled into ribosomes in vivo. Assembly begins with transcription of ribosomal RNA in the nucleolus, where the RNA then undergoes complex pathways of folding, coupled with nucleotide modification, removal of spacer sequences, and binding to ribosomal proteins. More than 200 assembly factors and 76 small nucleolar RNAs transiently associate with assembling ribosomes, to enable their accurate and efficient construction. Following export of preribosomes from the nucleus to the cytoplasm, they undergo final stages of maturation before entering the pool of functioning ribosomes. Elaborate mechanisms exist to monitor the formation of correct structural and functional neighborhoods within ribosomes and to destroy preribosomes that fail to assemble properly. Studies of yeast ribosome biogenesis provide useful models for ribosomopathies, diseases in humans that result from failure to properly assemble ribosomes.

Patch-clamp recordings and calcium imaging followed by single-cell PCR reveal the developmental profile of 13 genes in iPSC-derived human neurons

Molecular genetic studies are typically performed on homogenized biological samples, resulting in contamination from non-neuronal cells. To improve expression profiling of neurons we combined patch recordings with single-cell PCR. Two iPSC lines (healthy subject and 22q11.2 deletion) were differentiated into neurons. Patch electrode recordings were performed on 229 human cells from Day-13 to Day-88, followed by capture and single-cell PCR for 13 genes: ACTB, HPRT, vGLUT1, βTUBIII, COMT, DISC1, GAD1, PAX6, DTNBP1, ERBB4, FOXP1, FOXP2, and GIRK2. Neurons derived from both iPSC lines expressed βTUBIII, fired action potentials, and experienced spontaneous depolarizations (UP states) ~2 weeks before vGLUT1, GAD1 and GIRK2 appeared. Multisite calcium imaging revealed that these UP states were not synchronized among hESC-H9-derived neurons. The expression of FOXP1, FOXP2 and vGLUT1 was lost after 50 days in culture, in contrast to other continuously expressed genes. When gene expression was combined with electrophysiology, two subsets of genes were apparent; those irrelevant to spontaneous depolarizations (including vGLUT1, GIRK2, FOXP2 and DISC1) and those associated with spontaneous depolarizations (GAD1 and ERBB4). The results demonstrate that in the earliest stages of neuron development, it is useful to combine genetic analysis with physiological characterizations, on a cell-to-cell basis.

Cell-cycle dependence of transcription dominates noise in gene expression

The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ∼2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.

There is an astonishing amount of variation in the number of mRNA and protein molecules generated from particular genes between genetically identical single cells grown in the same environment. Particularly for mRNA, the large variation seen from these “noisy” genes is consistent with the idea of transcriptional bursting where transcription occurs in random, intermittent periods of high activity. There is considerable experimental support for transcriptional bursting, and it is a primary feature of stochastic models of gene expression that account for variation. Still, it has long been recognized that variation, especially in protein levels, can occur because of global differences between genetically identical cells. We show that in budding yeast, mRNA variation is driven to a large extent by differences in the transcriptional activity of a noisy gene between different phases of the cell cycle. These differences are not because of specific cell-cycle regulation, and in some cases transcription appears restricted to certain phases, leading to pulses of mRNA production. These results raise new questions about the origins of transcriptional bursting and how the statistics of gene expression are regulated in a global way by the cell cycle.

Are gene loops the cause of transcriptional noise?

Expression levels of the same mRNA or protein vary significantly among the cells of an otherwise identical population. Such biological noise has great functional implications and is largely due to transcriptional bursting, the episodic production of mRNAs in short, intense bursts, interspersed by periods of transcriptional inactivity. Bursting has been demonstrated in a wide range of pro- and eukaryotic species, attesting to its universal importance. However, the mechanistic origins of bursting remain elusive. A different type of phenomenon, which has also been suggested to be widespread, is the physical interaction between the promoter and 3' end of a gene. Several functional roles have been proposed for such gene loops, including the facilitation of transcriptional reinitiation. Here, I discuss the most recent findings related to these subjects and argue that gene loops are a likely cause of transcriptional bursting and, thus, biological noise.

Visualising single molecules of HIV-1 and miRNA nucleic acids

BACKGROUND

The scarcity of certain nucleic acid species and the small size of target sequences such as miRNA, impose a significant barrier to subcellular visualization and present a major challenge to cell biologists. Here, we offer a generic and highly sensitive visualization approach (oligo fluorescent in situ hybridization, O-FISH) that can be used to detect such nucleic acids using a single-oligonucleotide probe of 19-26 nucleotides in length.

RESULTS

We used O-FISH to visualize miR146a in human and avian cells. Furthermore, we reveal the sensitivity of O-FISH detection by using a HIV-1 model system to show that as little as 1-2 copies of nucleic acids can be detected in a single cell. We were able to discern newly synthesized viral cDNA and, moreover, observed that certain HIV RNA sequences are only transiently available for O-FISH detection.

CONCLUSIONS

Taken together, these results suggest that the O-FISH method can potentially be used for in situ probing of, as few as, 1-2 copies of nucleic acid and, additionally, to visualize small RNA such as miRNA. We further propose that the O-FISH method could be extended to understand viral function by probing newly transcribed viral intermediates; and discern the localisation of nucleic acids of interest. Additionally, interrogating the conformation and structure of a particular nucleic acid in situ might also be possible, based on the accessibility of a target sequence.

Promoter sequence determines the relationship between expression level and noise

A single transcription factor can activate or repress expression by three different mechanisms: one that increases cell-to-cell variability in target gene expression (noise) and two that decrease noise.

The ability of cells to accurately control gene expression levels in response to extracellular cues is limited by the inherently stochastic nature of transcriptional regulation. A change in transcription factor (TF) activity results in changes in the expression of its targets, but the way in which cell-to-cell variability in expression (noise) changes as a function of TF activity, and whether targets of the same TF behave similarly, is not known. Here, we measure expression and noise as a function of TF activity for 16 native targets of the transcription factor Zap1 that are regulated by it through diverse mechanisms. For most activated and repressed Zap1 targets, noise decreases as expression increases. Kinetic modeling suggests that this is due to two distinct Zap1-mediated mechanisms that both change the frequency of transcriptional bursts. Notably, we found that another mechanism of repression by Zap1, which is encoded in the promoter DNA, likely decreases the size of transcriptional bursts, producing a unique transcriptional state characterized by low expression and low noise. In addition, we find that further reduction in noise is achieved when a single TF both activates and represses a single target gene. Our results suggest a global principle whereby at low TF concentrations, the dominant source of differences in expression between promoters stems from differences in burst frequency, whereas at high TF concentrations differences in burst size dominate. Taken together, we show that the precise amount by which noise changes with expression is specific to the regulatory mechanism of transcription and translation that acts at each gene.

In response to environmental changes, cells regulate the activity of transcription factors (TFs), which in turn change the expression of dozens of downstream target genes by binding to their promoters. The response of each target gene is determined by the interplay between TF concentration and the context in which TF binding sites occur in each target promoter. To examine the relationship between promoter sequence, mechanism of regulation, and response to TF activity, we measured expression of 16 target genes of a single TF in response to changes in TF concentration in single cells. We found that different native promoters that are all targets of the same TF exhibit diverse responses to changing TF levels in terms of both gene expression level and cell-to-cell variability (noise) in expression. Using computational modeling and mutations of specific promoter elements, we show that the molecular mechanisms of regulation can be inferred by measuring how noise changes with expression. These results show that a single TF can regulate transcription through multiple mechanisms, resulting in similar changes in mean expression but vastly different changes in cell-to-cell variability.

Single-molecule methods for studying gene regulation in vivo

The recent emergence of new experimental tools employing sensitive fluorescence detection in vivo has made it possible to visualize various aspects of gene regulation at the single-molecule level in the native, intracellular context. In this review, we will first describe general considerations for in vivo, single-molecule fluorescence detection of DNA, mRNA, and protein molecules involved in gene regulation. We will then give an overview of the rapidly evolving suite of molecular tools available for observing gene regulation in vivo and discuss new insights they have brought into gene regulation.

Transcription stochasticity of complex gene regulation models

Transcription is regulated by a multitude of factors that concertedly induce genes to switch between activity states. Eukaryotic transcription involves a multitude of complexes that sequentially assemble on chromatin under the influence of transcription factors and the dynamic state of chromatin. Prokaryotic transcription depends on transcription factors, sigma-factors, and, in some cases, on DNA looping. We present a stochastic model of transcription that considers these complex regulatory mechanisms. We coarse-grain the molecular details in such a way that the model can describe a broad class of gene-regulation mechanisms. We solve this model analytically for various measures of stochastic transcription and compare alternative gene-regulation designs. We find that genes with complex multiprotein regulation can have peaked burst-size distributions in contrast to the geometric distributions found for simple models of transcription regulation. Burst-size distributions are, in addition, shaped by mRNA degradation during transcription bursts. We derive the stochastic properties of genes in the limit of deterministic switch times. These genes typically have reduced transcription noise. Severe timescale separation between gene regulation and transcription initiation enhances noise and leads to bimodal mRNA copy number distributions. In general, complex mechanisms for gene regulation lead to nonexponential waiting-time distributions for gene switching and transcription initiation, which typically reduce noise in mRNA copy numbers and burst size. Finally, we discuss that qualitatively different gene regulation models can often fit the same experimental data on single-cell mRNA abundance even though they have qualitatively different burst-size statistics and regulatory parameters.

Chemical memory reactions induced bursting dynamics in gene expression

Memory is a ubiquitous phenomenon in biological systems in which the present system state is not entirely determined by the current conditions but also depends on the time evolutionary path of the system. Specifically, many memorial phenomena are characterized by chemical memory reactions that may fire under particular system conditions. These conditional chemical reactions contradict to the extant stochastic approaches for modeling chemical kinetics and have increasingly posed significant challenges to mathematical modeling and computer simulation. To tackle the challenge, I proposed a novel theory consisting of the memory chemical master equations and memory stochastic simulation algorithm. A stochastic model for single-gene expression was proposed to illustrate the key function of memory reactions in inducing bursting dynamics of gene expression that has been observed in experiments recently. The importance of memory reactions has been further validated by the stochastic model of the p53-MDM2 core module. Simulations showed that memory reactions is a major mechanism for realizing both sustained oscillations of p53 protein numbers in single cells and damped oscillations over a population of cells. These successful applications of the memory modeling framework suggested that this innovative theory is an effective and powerful tool to study memory process and conditional chemical reactions in a wide range of complex biological systems.

Logic of the inflammation-associated transcriptional response

Immune response to pathogens depends on coordinated regulation of numerous genes that contribute collectively to pathogen elimination and restoration of the integrity of the affected tissue. The pathogen-induced gene expression is governed largely by the signal-induced posttranslational histone modifications that facilitate assembly of the functionally distinct chromatin complexes. In this review, we describe the principles of chromatin-based gene regulation during innate immune responses. We discuss the ability of pathogens to hijack the host response by interfering with various arms of transcriptional machinery involved in the responses. In particular, we discuss the phenomenon of the histone mimicry where interaction between histones and transcriptional regulators is targeted by pathogens that carry the histone-like sequences (histone mimics). We show how the principle of isotone mimicry as an efficient way to control host gene expression has been sued for the development of novel anti-inflammatory pharmacological approaches.

Single-cell analysis of ribonucleotide reductase transcriptional and translational response to DNA damage

The ribonucleotide reductase (RNR) enzyme catalyzes an essential step in the production of deoxyribonucleotide triphosphates (dNTPs) in cells. Bulk biochemical measurements in synchronized Saccharomyces cerevisiae cells suggest that RNR mRNA production is maximal in late G(1) and S phases; however, damaged DNA induces RNR transcription throughout the cell cycle. But such en masse measurements reveal neither cell-to-cell heterogeneity in responses nor direct correlations between transcript and protein expression or localization in single cells which may be central to function. We overcame these limitations by simultaneous detection of single RNR transcripts and also Rnr proteins in the same individual asynchronous S. cerevisiae cells, with and without DNA damage by methyl methanesulfonate (MMS). Surprisingly, RNR subunit mRNA levels were comparably low in both damaged and undamaged G(1) cells and highly induced in damaged S/G(2) cells. Transcript numbers became correlated with both protein levels and localization only upon DNA damage in a cell cycle-dependent manner. Further, we showed that the differential RNR response to DNA damage correlated with variable Mec1 kinase activity in the cell cycle in single cells. The transcription of RNR genes was found to be noisy and non-Poissonian in nature. Our results provide vital insight into cell cycle-dependent RNR regulation under conditions of genotoxic stress.

Noise-mean relationship in mutated promoters

Gene expression depends on the frequency of transcription events (burst frequency) and on the number of mRNA molecules made per event (burst size). Both processes are encoded in promoter sequence, yet their dependence on mutations is poorly understood. Theory suggests that burst size and frequency can be distinguished by monitoring the stochastic variation (noise) in gene expression: Increasing burst size will increase mean expression without changing noise, while increasing burst frequency will increase mean expression and decrease noise. To reveal principles by which promoter sequence regulates burst size and frequency, we randomly mutated 22 yeast promoters chosen to span a range of expression and noise levels, generating libraries of hundreds of sequence variants. In each library, mean expression (m) and noise (coefficient of variation, η) varied together, defining a scaling curve: η(2) = b/m + η(ext)(2). This relation is expected if sequence mutations modulate burst frequency primarily. The estimated burst size (b) differed between promoters, being higher in promoter containing a TATA box and lacking a nucleosome-free region. The rare variants that significantly decreased b were explained by mutations in TATA, or by an insertion of an out-of-frame translation start site. The decrease in burst size due to mutations in TATA was promoter-dependent, but independent of other mutations. These TATA box mutations also modulated the responsiveness of gene expression to changing conditions. Our results suggest that burst size is a promoter-specific property that is relatively robust to sequence mutations but is strongly dependent on the interaction between the TATA box and promoter nucleosomes.

Cohesin proteins promote ribosomal RNA production and protein translation in yeast and human cells

Cohesin is a protein complex known for its essential role in chromosome segregation. However, cohesin and associated factors have additional functions in transcription, DNA damage repair, and chromosome condensation. The human cohesinopathy diseases are thought to stem not from defects in chromosome segregation but from gene expression. The role of cohesin in gene expression is not well understood. We used budding yeast strains bearing mutations analogous to the human cohesinopathy disease alleles under control of their native promoter to study gene expression. These mutations do not significantly affect chromosome segregation. Transcriptional profiling reveals that many targets of the transcriptional activator Gcn4 are induced in the eco1-W216G mutant background. The upregulation of Gcn4 was observed in many cohesin mutants, and this observation suggested protein translation was reduced. We demonstrate that the cohesinopathy mutations eco1-W216G and smc1-Q843Δ are associated with defects in ribosome biogenesis and a reduction in the actively translating fraction of ribosomes, eiF2α-phosphorylation, and (35)S-methionine incorporation, all of which indicate a deficit in protein translation. Metabolic labeling shows that the eco1-W216G and smc1-Q843Δ mutants produce less ribosomal RNA, which is expected to constrain ribosome biogenesis. Further analysis shows that the production of rRNA from an individual repeat is reduced while copy number remains unchanged. Similar defects in rRNA production and protein translation are observed in a human Roberts syndrome cell line. In addition, cohesion is defective specifically at the rDNA locus in the eco1-W216G mutant, as has been previously reported for Roberts syndrome. Collectively, our data suggest that cohesin proteins normally facilitate production of ribosomal RNA and protein translation, and this is one way they can influence gene expression. Reduced translational capacity could contribute to the human cohesinopathies.

Expression noise and acetylation profiles distinguish HDAC functions

Gene expression shows a significant variation (noise) between genetically identical cells. Noise depends on the gene expression process regulated by the chromatin environment. We screened for chromatin factors that modulate noise in S. cerevisiae and analyzed the results using a theoretical model that infers regulatory mechanisms from the noise versus mean relationship. Distinct activities of the Rpd3(L) and Set3 histone deacetylase complexes were predicted. Both HDACs repressed expression. Yet, Rpd3(L)C decreased the frequency of transcriptional bursts, while Set3C decreased the burst size, as did H2B monoubiquitination (ubH2B). We mapped the acetylation of H3 lysine 9 (H3K9ac) upon deletion of multiple subunits of Set3C and Rpd3(L)C and of ubH2B effectors. ubH2B and Set3C appear to function in the same pathway to reduce the probability that an elongating PolII produces a functional transcript (PolII processivity), while Rpd3(L)C likely represses gene expression at a step preceding elongation.

Using gene expression noise to understand gene regulation

Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression "noise," has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and messenger RNA levels, and they have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive "fingerprint" with which to explore fundamental questions of gene regulation. In this review, we highlight several studies that used gene expression variability to develop a quantitative understanding of the mechanisms and dynamics of gene regulation.

Transcription goes digital

Transcription is a complex process that integrates the state of the cell and its environment to generate adequate responses for cell fitness and survival. Recent microscopy experiments have been able to monitor transcription from single genes in individual cells. These observations have revealed two striking features: transcriptional activity can vary markedly from one cell to another, and is subject to large changes over time, sometimes within minutes. How the chromatin structure, transcription machinery assembly and signalling networks generate such patterns is still unclear. In this review, we present the techniques used to investigate transcription from single genes, introduce quantitative modelling tools, and discuss transcription mechanisms and their implications for gene expression regulation.

Evolution of the cancer genome

Human tumors result from an evolutionary process operating on somatic cells within tissues, whereby natural selection operates on the phenotypic variability generated by the accumulation of genetic, genomic and epigenetic alterations. This somatic evolution leads to adaptations such as increased proliferative, angiogenic, and invasive phenotypes. In this review we outline how cancer genomes are beginning to be investigated from an evolutionary perspective. We describe recent progress in the cataloging of somatic genetic and genomic alterations, and investigate the contributions of germline as well as epigenetic factors to cancer genome evolution. Finally, we outline the challenges facing researchers who investigate the processes driving the evolution of the cancer genome.

Dynamics of protein noise can distinguish between alternate sources of gene-expression variability

Within individual cells, two molecular processes have been implicated as sources of noise in gene expression: (i) Poisson fluctuations in mRNA abundance arising from random birth and death of individual mRNA transcripts or (ii) promoter fluctuations arising from stochastic promoter transitions between different transcriptional states. Steady-state measurements of variance in protein levels are insufficient to discriminate between these two mechanisms, and mRNA single-molecule fluorescence in situ hybridization (smFISH) is challenging when cellular mRNA concentrations are high. Here, we present a perturbation method that discriminates mRNA birth/death fluctuations from promoter fluctuations by measuring transient changes in protein variance and that can operate in the regime of high molecular numbers. Conceptually, the method exploits the fact that transcriptional blockage results in more rapid increases in protein variability when mRNA birth/death fluctuations dominate over promoter fluctuations. We experimentally demonstrate the utility of this perturbation approach in the HIV-1 model system. Our results support promoter fluctuations as the primary noise source in HIV-1 expression. This study illustrates a relatively simple method that complements mRNA smFISH hybridization and can be used with existing GFP-tagged libraries to include or exclude alternate sources of noise in gene expression.

Computation of steady-state probability distributions in stochastic models of cellular networks

Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.

General properties of transcriptional time series in Escherichia coli

Gene activity is described by the time-series of discrete, stochastic mRNA production events. This transcriptional time-series exhibits intermittent, bursty behavior. One consequence of this temporal intricacy is that gene expression can be tuned by varying different features of the time-series. What schemes for varying the transcriptional time-series are observed in the cell? Are the observed properties of these time-series optimized for cellular function? To address these questions, we characterize mRNA copy-number statistics at single-molecule resolution from multiple Escherichia coli promoters. We find that the degree of burstiness depends only on the gene expression level, while being independent of the details of gene regulation. The observed behavior is explained by the underlying variation in the duration of bursting events. Using information theory, we find that the properties of the transcriptional time series allow the cell to efficiently map the extracellular concentration of inducer molecules to intracellular levels of mRNA and proteins.

Validating transcripts with probes and imaging technology

High-throughput gene expression screens provide a quantitative picture of the average expression signature of biological samples. However, the analysis of spatial gene expression patterns with single-cell resolution requires quantitative in situ measurement techniques. Here we describe recent technological advances in RNA fluorescence in situ hybridization (FISH) techniques that facilitate detection of individual fluorescently labeled mRNA molecules of practically any endogenous gene. These methods, which are based on advances in probe design, imaging technology and image processing, enable the absolute measurement of transcript abundance in individual cells with single-molecule resolution.

Mammalian genes are transcribed with widely different bursting kinetics

In prokaryotes and eukaryotes, most genes appear to be transcribed during short periods called transcriptional bursts, interspersed by silent intervals. We describe how such bursts generate gene-specific temporal patterns of messenger RNA (mRNA) synthesis in mammalian cells. To monitor transcription at high temporal resolution, we established various gene trap cell lines and transgenic cell lines expressing a short-lived luciferase protein from an unstable mRNA, and recorded bioluminescence in real time in single cells. Mathematical modeling identified gene-specific on- and off-switching rates in transcriptional activity and mean numbers of mRNAs produced during the bursts. Transcriptional kinetics were markedly altered by cis-regulatory DNA elements. Our analysis demonstrated that bursting kinetics are highly gene-specific, reflecting refractory periods during which genes stay inactive for a certain time before switching on again.