Varying virulence: epigenetic control of expression noise and disease processes

Exact burst-size distributions for gene-expression models with complex promoter structure

In prokaryotic and eukaryotic cells, most genes are transcribed in a bursty fashion on one hand and complex gene regulations may lead to complex promoter structure on the other hand. This raises an unsolved issue: how does promoter structure shape transcriptional bursting kinetics characterized by burst size and frequency? Here we analyze stochastic models of gene transcription, which consider complex regulatory mechanisms. Notably, we develop an efficient method to derive exact burst-size distributions. The analytical results show that if the promoter of a gene contains only one active state, the burst size indeed follows a geometric distribution, in agreement with the previous result derived under certain limiting conditions. However, if it contains a multitude of active states, the burst size in general obeys a non-geometric distribution, which is a linearly weighted sum of geometric distributions. This superposition principle reveals the essential feature of bursting kinetics in complex cases of transcriptional regulation although it seems that there has been no direct experimental confirmation. The derived burst-size distributions not only highlight the importance of promoter structure in regulating bursting kinetics, but can be also used in the exact inference of this kinetics based on experimental data.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

Memory of stochastic single-cell apoptotic signaling promotes chemoresistance in neuroblastoma

Gene expression noise is known to promote stochastic drug resistance through the elevated expression of individual genes in rare cancer cells. However, we now demonstrate that chemoresistant neuroblastoma cells emerge at a much higher frequency when the influence of noise is integrated across multiple components of an apoptotic signaling network. Using a JNK activity biosensor with longitudinal high-content and in vivo intravital imaging, we identify a population of stochastic, JNK-impaired, chemoresistant cells that exist because of noise within this signaling network. Furthermore, we reveal that the memory of this initially random state is retained following chemotherapy treatment across a series of in vitro, in vivo, and patient models. Using matched PDX models established at diagnosis and relapse from individual patients, we show that HDAC inhibitor priming cannot erase the memory of this resistant state within relapsed neuroblastomas but improves response in the first-line setting by restoring drug-induced JNK activity within the chemoresistant population of treatment-naïve tumors.

UL135 and UL136 Epistasis Controls Reactivation of Human Cytomegalovirus

Human cytomegalovirus (HCMV) is beta herpesvirus that persists indefinitely in the human host through a protracted, latent infection. The polycistronic UL133-UL138 gene locus of HCMV encodes genes regulating latency and reactivation. While UL138 is pro-latency, restricting virus replication in CD34+ hematopoietic progenitor cells (HPCs), UL135 overcomes this restriction for reactivation. By contrast, UL136 is expressed with later kinetics and encodes multiple protein isoforms with differential roles in latency and reactivation. Like UL135, the largest UL136 isoform, UL136p33, is required for reactivation from latency in hematopoietic cells. Furthermore, UL136p33 is unstable, and its instability is important for the establishment of latency and sufficient accumulation of UL136p33 is a checkpoint for reactivation. We hypothesized that stabilizing UL136p33 might overcome the requirement of UL135 for reactivation. To test this, we generated recombinant viruses lacking UL135 that expressed a stabilized variant of UL136p33. Stabilizing UL136p33 did not impact replication of the UL135-mutant virus in fibroblasts. However, in the context of infection in hematopoietic cells, stabilization of UL136p33 strikingly compensated for the loss of UL135, resulting in increased replication in CD34+ HPCs and in humanized NOD- scid IL2Rγ _c ^null (NSG) mice. This finding suggests that while UL135 is essential for reactivation, it functions at steps preceding the accumulation of UL136p33 and that stabilized expression of UL136p33 largely overcomes the requirement for UL135 in reactivation. Taken together, our genetic evidence indicates an epistatic relationship between UL136p33 and UL135 whereby UL135 may initiate events early in reactivation that will result in the accumulation of UL136p33 to a threshold required for productive reactivation.

SIGNIFICANCE

Human cytomegalovirus (HCMV) is one of nine human herpesviruses and a significant human pathogen. While HCMV establishes a life-long latent infection that is typically asymptomatic in healthy individuals, its reactivation from latency can have devastating consequences in the immune compromised. Defining virus-host and virus-virus interactions important for HCMV latency, reactivation and replication is critical to defining the molecular basis of latent and replicative states and in controlling infection and CMV disease. Here we define a genetic relationship between two viral genes in controlling virus reactivation from latency using primary human hematopoietic progenitor cell and humanized mouse models.

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways.

Recent progress in marine noise pollution: A thorough review

The increase in urbanization and the progressive development of marine industries have led to the appearance of a new kind of pollution called "noise pollution". This pollution exerts an increasing pressure on marine mammals, fish species, and invertebrates, which constitutes a new debate that must be controlled in a sustainable way by environmental and noise approaches with the objective of preserving marine and human life. Despite, noise pollution can travel long distances underwater, cover large areas, and have secondary effects on marine animals; by masking their ability to hear their prey or predators, finding their way, or connecting group members. During the COVID-19 pandemic, except for the transportation of essential goods and emergency services, all the public transport services were suspended including aircraft and ships. This lockdown has impacted positively on the marine environment through reduction of the noise sources. In this article, we are interested in noise pollution in general, its sources, impacts, and the management and future actions to follow. And since this pollution is not studied in Morocco, we focused on the different sources that can generate it on the Moroccan coasts. This is the first review article, which focuses on the impact of the COVID 19 pandemic on this type of pollution in the marine environment; which we aim to identify the impact of this pandemic on underwater noise and marine species. Finally, and given the increase in noise levels, preventive management, both at the national and international level, is required before irreversible damage is caused to biodiversity and the marine ecosystem.

Volumetric compression develops noise-driven single-cell heterogeneity

Recent studies have revealed that extensive heterogeneity of biological systems arises through various routes ranging from intracellular chromosome segregation to spatiotemporally varying biochemical stimulations. However, the contribution of physical microenvironments to single-cell heterogeneity remains largely unexplored. Here, we show that a homogeneous population of non-small-cell lung carcinoma develops into heterogeneous subpopulations upon application of a homogeneous physical compression, as shown by single-cell transcriptome profiling. The generated subpopulations stochastically gain the signature genes associated with epithelial-mesenchymal transition (EMT; VIM, CDH1, EPCAM, ZEB1, and ZEB2) and cancer stem cells (MKI67, BIRC5, and KLF4), respectively. Trajectory analysis revealed two bifurcated paths as cells evolving upon the physical compression, along each path the corresponding signature genes (epithelial or mesenchymal) gradually increase. Furthermore, we show that compression increases gene expression noise, which interplays with regulatory network architecture and thus generates differential cell-fate outcomes. The experimental observations of both single-cell sequencing and single-molecule fluorescent in situ hybridization agrees well with our computational modeling of regulatory network in the EMT process. These results demonstrate a paradigm of how mechanical stimulations impact cell-fate determination by altering transcription dynamics; moreover, we show a distinct path that the ecology and evolution of cancer interplay with their physical microenvironments from the view of mechanobiology and systems biology, with insight into the origin of single-cell heterogeneity.

Quantifying the Stability of Coupled Genetic and Epigenetic Switches With Variational Methods

The Waddington landscape provides an intuitive metaphor to view development as a ball rolling down the hill, with distinct phenotypes as basins and differentiation pathways as valleys. Since, at a molecular level, cell differentiation arises from interactions among the genes, a mathematical definition for the Waddington landscape can, in principle, be obtained by studying the gene regulatory networks. For eukaryotes, gene regulation is inextricably and intimately linked to histone modifications. However, the impact of such modifications on both landscape topography and stability of attractor states is not fully understood. In this work, we introduced a minimal kinetic model for gene regulation that combines the impact of both histone modifications and transcription factors. We further developed an approximation scheme based on variational principles to solve the corresponding master equation in a second quantized framework. By analyzing the steady-state solutions at various parameter regimes, we found that histone modification kinetics can significantly alter the behavior of a genetic network, resulting in qualitative changes in gene expression profiles. The emerging epigenetic landscape captures the delicate interplay between transcription factors and histone modifications in driving cell-fate decisions.

Identifying chromatin features that regulate gene expression distribution

Gene expression variability, differences in the number of mRNA per cell across a population of cells, is ubiquitous across diverse organisms with broad impacts on cellular phenotypes. The role of chromatin in regulating average gene expression has been extensively studied. However, what aspects of the chromatin contribute to gene expression variability is still underexplored. Here we addressed this problem by leveraging chromatin diversity and using a systematic investigation of randomly integrated expression reporters to identify what aspects of chromatin microenvironment contribute to gene expression variability. Using DNA barcoding and split-pool decoding, we created a large library of isogenic reporter clones and identified reporter integration sites in a massive and parallel manner. By mapping our measurements of reporter expression at different genomic loci with multiple epigenetic profiles including the enrichment of transcription factors and the distance to different chromatin states, we identified new factors that impact the regulation of gene expression distributions.

Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion

BACKGROUND

RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification.

RESULTS

EMBR-seq results in 90% of the sequenced RNA molecules from an E. coli culture deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq uses a single or a few oligonucleotides per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits for bacterial rRNA depletion, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA.

CONCLUSIONS

EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.

Differences in Transcriptional Dynamics Between T-cells and Macrophages as Determined by a Three-State Mathematical Model

HIV-1 viral transcription persists in patients despite antiretroviral treatment, potentially due to intermittent HIV-1 LTR activation. While several mathematical models have been explored in the context of LTR-protein interactions, in this work for the first time HIV-1 LTR model featuring repressed, intermediate, and activated LTR states is integrated with generation of long (env) and short (TAR) RNAs and proteins (Tat, Pr55, and p24) in T-cells and macrophages using both cell lines and infected primary cells. This type of extended modeling framework allows us to compare and contrast behavior of these two cell types. We demonstrate that they exhibit unique LTR dynamics, which ultimately results in differences in the magnitude of viral products generated. One of the distinctive features of this work is that it relies on experimental data in reaction rate computations. Two RNA transcription rates from the activated promoter states are fit by comparison of experimental data to model predictions. Fitting to the data also provides estimates for the degradation/exit rates for long and short viral RNA. Our experimentally generated data is in reasonable agreement for the T-cell as well macrophage population and gives strong evidence in support of using the proposed integrated modeling paradigm. Sensitivity analysis performed using Latin hypercube sampling method confirms robustness of the model with respect to small parameter perturbations. Finally, incorporation of a transcription inhibitor (F07#13) into the governing equations demonstrates how the model can be used to assess drug efficacy. Collectively, our model indicates transcriptional differences between latently HIV-1 infected T-cells and macrophages and provides a novel platform to study various transcriptional dynamics leading to latency or activation in numerous cell types and physiological conditions.

Transcriptional processes: Models and inference

Many biochemical events involve multistep reactions. One of the most important biological processes that involve multistep reaction is the transcriptional process. Models for multistep reaction necessarily need multiple states and it is a challenge to compute model parameters that best agree with experimental data. Therefore, the aim of this work is to design a multistep promoter model which accurately characterizes transcriptional bursting and is consistent with observed data. To address this issue, we develop a model for promoters with several OFF states and a single ON state using Erlang distribution. To explore the combined effects of model and data, we combine Monte Carlo extension of Expectation Maximization (MCEM) and delay Stochastic Simulation Algorithm (DSSA) and call the resultant algorithm as delay Bursty MCEM. We apply this algorithm to time-series data of endogenous mouse glutaminase promoter to validate the model assumptions and infer the kinetic parameters. Our results show that with multiple OFF states, we are able to infer and produce a model which is more consistent with experimental data. Our results also show that delay Bursty MCEM inference is more efficient.

Defining Division of Labor in Microbial Communities

In order to survive and reproduce, organisms must perform a multitude of tasks. However, trade-offs limit their ability to allocate energy and resources to all of these different processes. One strategy to solve this problem is to specialize in some traits and team up with other organisms that can help by providing additional, complementary functions. By reciprocally exchanging metabolites and/or services in this way, both parties benefit from the interaction. This phenomenon, which has been termed functional specialization or division of labor, is very common in nature and exists on all levels of biological organization. Also, microorganisms have evolved different types of synergistic interactions. However, very often, it remains unclear whether or not a given example represents a true case of division of labor. Here we aim at filling this gap by providing a list of criteria that clearly define division of labor in microbial communities. Furthermore, we propose a set of diagnostic experiments to verify whether a given interaction fulfills these conditions. In contrast to the common use of the term, our analysis reveals that both intraspecific and interspecific interactions meet the criteria defining division of labor. Moreover, our analysis identified non-cooperators of intraspecific public goods interactions as growth specialists that divide labor with conspecific producers, rather than being social parasites. By providing a conceptual toolkit, our work will help to unambiguously identify cases of division of labor and stimulate more detailed investigations of this important and widespread type of inter-microbial interaction.

Intratumoral Heterogeneity: More Than Just Mutations

Most human tumors are composed of genetically and phenotypically heterogeneous cancer cell populations, which poses a major challenge for the clinical management of cancer patients. Advances of single-cell technologies have allowed the profiling of tumors at unprecedented depth, which, in combination with newly developed computational tools, enable the dissection of tumor evolution with increasing precision. However, our understanding of mechanisms that regulate intratumoral heterogeneity and our ability to modulate it has been lagging behind. Recent data demonstrate that epigenetic regulators, including histone demethylases, may control the cell-to-cell variability of transcriptomes and chromatin profiles and they may modulate therapeutic responses via this function. Thus, the therapeutic targeting of epigenetic enzymes may be used to decrease intratumoral cellular heterogeneity and treatment resistance, when used in combination with other types of agents.

Multi-modality in gene regulatory networks with slow promoter kinetics

Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks (GRNs). In this view, different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions. We provide theoretical and practical characterizations of these landscapes, specifically focusing on stochastic Slow Promoter Kinetics (SPK), a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling. In this case, largely unexplored except for numerical simulations, adiabatic approximations of promoter kinetics are not appropriate. In contrast to the existing literature, we provide rigorous analytic characterizations of multiple modes. A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring, for example through mutations or artificial interventions, impact the possible number, location, and likelihood of alternative states. We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system. As illustrations, the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models, and applications are given to various model systems, such as toggle switches in isolation or in communicating populations, a synthetic oscillator, and a trans-differentiation network.

Stochastic multistationarity in a model of the hematopoietic stem cell differentiation network

A central issue in the analysis of multi-stable systems is that of controlling the relative size of the basins of attraction of alternative states through suitable choices of system parameters. We are interested here mainly in the stochastic version of this problem, that of shaping the stationary probability distribution of a Markov chain so that various alternative modes become more likely than others. Although many of our results are more general, we were motivated by an important biological question, that of cell differentiation. In the mathematical modeling of cell differentiation, it is common to think of internal states of cells (quanfitied by activation levels of certain genes) as determining the different cell types. Specifically, we study here the "PU.1/GATA-1 circuit" which is involved in the control of the development of mature blood cells from hematopoietic stem cells (HSCs). All mature, specialized blood cells have been shown to be derived from multipotent HSCs. Our first contribution is to introduce a rigorous chemical reaction network model of the PU.1/GATA-1 circuit, which incorporates current biological knowledge. We then find that the resulting ODE model of these biomolecular reactions is incapable of exhibiting multistability, contradicting the fact that differentiation networks have, by definition, alternative stable steady states. When considering instead the stochastic version of this chemical network, we analytically construct the stationary distribution, and are able to show that this distribution is indeed capable of admitting a multiplicity of modes. Finally, we study how a judicious choice of system parameters serves to bias the probabilities towards different stationary states. We remark that certain changes in system parameters can be physically implemented by a biological feedback mechanism; tuning this feedback gives extra degrees of freedom that allow one to assign higher likelihood to some cell types over others.

Mechanisms of Plastic Rescue in Novel Environments

Adaptive phenotypic plasticity provides a mechanism of developmental rescue in novel and rapidly changing environments. Understanding the underlying mechanism of plasticity is important for predicting both the likelihood that a developmental response is adaptive and associated life-history trade-offs that could influence patterns of subsequent evolutionary rescue. Although evolved developmental switches may move organisms toward a new adaptive peak in a novel environment, such mechanisms often result in maladaptive responses. The induction of generalized physiological mechanisms in new environments is relatively more likely to result in adaptive responses to factors such as novel toxins, heat stress, or pathogens. Developmental selection forms of plasticity, which rely on within-individual selective processes, such as shaping of tissue architecture, trial-and-error learning, or acquired immunity, are particularly likely to result in adaptive plasticity in a novel environment. However, both the induction of plastic responses and the ability to be plastic through developmental selection come with significant costs, resulting in delays in reproduction, increased individual investment, and reduced fecundity. Thus, we might expect complex interactions between plastic responses that allow survival in novel environments and subsequent evolutionary responses at the population level.

Modulation of first-passage time for bursty gene expression via random signals

The stochastic nature of cell-specific signal molecules (such as transcription factor, ribosome, etc.) and the intrinsic stochastic nature of gene expression process result in cell-to-cell variations at protein levels. Increasing experimental evidences suggest that cell phenotypic variations often depend on the accumulation of some special proteins. Hence, a natural and fundamental question is: How does input signal affect the timing of protein count up to a given threshold? To this end, we study effects of input signal on the first-passage time (FPT), the time at which the number of proteins crosses a given threshold. Input signal is distinguished into two types: constant input signal and random input signal, regulating only burst frequency (or burst size) of gene expression. Firstly, we derive analytical formulae for FPT moments in each case of constant signal regulation and random signal regulation. Then, we find that random input signal tends to increases the mean and noise of FPT compared with constant input signal. Finally, we observe that different regulation ways of random signal have different effects on FPT, that is, burst size modulation tends to decrease the mean of FPT and increase the noise of FPT compared with burst frequency modulation. Our findings imply a fundamental mechanism that random fluctuating environment may prolong FPT. This can provide theoretical guidance for studies of some cellular key events such as latency of HIV and lysis time of bacteriophage λ. In conclusion, our results reveal impacts of external signal on FPT and aid understanding the regulation mechanism of gene expression.

Links between evolutionary processes and phenotypic robustness in microbes

The costs and benefits of random phenotypic heterogeneity in microbes have been vigorously debated and experimental tested for decades; yet, this conversation is largely independent from discussion of phenotypic robustness in other disciplines. In this review I connect microbial examples of stochasticity with studies on the ecological and population-genetic consequences of phenotypic variability. These topics illustrate the complexity of selection pressures on phenotypic robustness and provide inspiration that this complexity can be parsed with theoretical advances and the experimental power of microbial systems.

The evolutionary advantage of heritable phenotypic heterogeneity

Phenotypic plasticity is an evolutionary driving force in diverse biological processes, including the adaptive immune system, the development of neoplasms, and the persistence of pathogens despite drug pressure. It is essential, therefore, to understand the evolutionary advantage of an allele that confers on cells the ability to express a range of phenotypes. Here, we study the fate of a new mutation that allows the expression of multiple phenotypic states, introduced into a finite population of individuals that can express only a single phenotype. We show that the advantage of such a mutation depends on the degree of phenotypic heritability between generations, called phenotypic memory. We analyze the fixation probability of the phenotypically plastic allele as a function of phenotypic memory, the variance of expressible phenotypes, the rate of environmental changes, and the population size. We find that the fate of a phenotypically plastic allele depends fundamentally on the environmental regime. In constant environments, plastic alleles are advantageous and their fixation probability increases with the degree of phenotypic memory. In periodically fluctuating environments, by contrast, there is an optimum phenotypic memory that maximizes the probability of the plastic allele's fixation. This same optimum memory also maximizes geometric mean fitness, in steady state. We interpret these results in the context of previous studies in an infinite-population framework. We also discuss the implications of our results for the design of therapies that can overcome persistence and, indirectly, drug resistance.

Heterogeneous regulation of bacterial natural product biosynthesis via a novel transcription factor

Biological diversity arises among genetically equal subpopulations in the same environment, a phenomenon called phenotypic heterogeneity. The life cycle of the enteric bacterium Photorhabdus luminescens involves a symbiotic interaction with nematodes as well as a pathogenic association with insect larvae. P. luminescens exists in two distinct phenotypic forms designated as primary (1°) and secondary (2°). In contrast to 1° cells, 2° cells are non-pigmented due to the absence of natural compounds, especially anthraquinones (AQs). We identified a novel type of transcriptional regulator, AntJ, which activates expression of the antA-I operon responsible for AQ production. AntJ heterogeneously activates the AQ production in single P. luminescens 1° cells, and blocks AQ production in 2° cells. AntJ contains a proposed ligand-binding WYL-domain, which is widespread among bacteria. AntJ is one of the rare examples of regulators that mediate heterogeneous gene expression by altering activity rather than copy number in single cells.

Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers

BACKGROUND

The dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries.

METHODS

In the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules.

RESULTS

We show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers.

Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences

This article reviews the production of different phenotypes from the same genotype in the same environment by stochastic cellular events, nonlinear mechanisms during patterning and morphogenesis, and probabilistic self-reinforcing circuitries in the adult life. These aspects of phenotypic variation are summarized under the term 'stochastic developmental variation' (SDV) in the following. In the past, SDV has been viewed primarily as a nuisance, impairing laboratory experiments, pharmaceutical testing, and true-to-type breeding. This article also emphasizes the positive biological effects of SDV and discusses implications for genotype-to-phenotype mapping, biological individuation, ecology, evolution, and applied biology. There is strong evidence from experiments with genetically identical organisms performed in narrowly standardized laboratory set-ups that SDV is a source of phenotypic variation in its own right aside from genetic variation and environmental variation. It is obviously mediated by molecular and higher-order epigenetic mechanisms. Comparison of SDV in animals, plants, fungi, protists, bacteria, archaeans, and viruses suggests that it is a ubiquitous and phylogenetically old phenomenon. In animals, it is usually smallest for morphometric traits and highest for life history traits and behaviour. SDV is thought to contribute to phenotypic diversity in all populations but is particularly relevant for asexually reproducing and genetically impoverished populations, where it generates individuality despite genetic uniformity. In each generation, SDV produces a range of phenotypes around a well-adapted target phenotype, which is interpreted as a bet-hedging strategy to cope with the unpredictability of dynamic environments. At least some manifestations of SDV are heritable, adaptable, selectable, and evolvable, and therefore, SDV may be seen as a hitherto overlooked evolution factor. SDV is also relevant for husbandry, agriculture, and medicine because most pathogens are asexuals that exploit this third source of phenotypic variation to modify infectivity and resistance to antibiotics. Since SDV affects all types of organisms and almost all aspects of life, it urgently requires more intense research and a better integration into biological thinking.

Distinct promoter activation mechanisms modulate noise-driven HIV gene expression

Latent human immunodeficiency virus (HIV) infections occur when the virus occupies a transcriptionally silent but reversible state, presenting a major obstacle to cure. There is experimental evidence that random fluctuations in gene expression, when coupled to the strong positive feedback encoded by the HIV genetic circuit, act as a ‘molecular switch’ controlling cell fate, i.e., viral replication versus latency. Here, we implemented a stochastic computational modeling approach to explore how different promoter activation mechanisms in the presence of positive feedback would affect noise-driven activation from latency. We modeled the HIV promoter as existing in one, two, or three states that are representative of increasingly complex mechanisms of promoter repression underlying latency. We demonstrate that two-state and three-state models are associated with greater variability in noisy activation behaviors, and we find that Fano factor (defined as variance over mean) proves to be a useful noise metric to compare variability across model structures and parameter values. Finally, we show how three-state promoter models can be used to qualitatively describe complex reactivation phenotypes in response to therapeutic perturbations that we observe experimentally. Ultimately, our analysis suggests that multi-state models more accurately reflect observed heterogeneous reactivation and may be better suited to evaluate how noise affects viral clearance.

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks.

Variability of Gene Expression Identifies Transcriptional Regulators of Early Human Embryonic Development

An analysis of gene expression variability can provide an insightful window into how regulatory control is distributed across the transcriptome. In a single cell analysis, the inter-cellular variability of gene expression measures the consistency of transcript copy numbers observed between cells in the same population. Application of these ideas to the study of early human embryonic development may reveal important insights into the transcriptional programs controlling this process, based on which components are most tightly regulated. Using a published single cell RNA-seq data set of human embryos collected at four-cell, eight-cell, morula and blastocyst stages, we identified genes with the most stable, invariant expression across all four developmental stages. Stably-expressed genes were found to be enriched for those sharing indispensable features, including essentiality, haploinsufficiency, and ubiquitous expression. The stable genes were less likely to be associated with loss-of-function variant genes or human recessive disease genes affected by a DNA copy number variant deletion, suggesting that stable genes have a functional impact on the regulation of some of the basic cellular processes. Genes with low expression variability at early stages of development are involved in regulation of DNA methylation, responses to hypoxia and telomerase activity, whereas by the blastocyst stage, low-variability genes are enriched for metabolic processes as well as telomerase signaling. Based on changes in expression variability, we identified a putative set of gene expression markers of morulae and blastocyst stages. Experimental validation of a blastocyst-expressed variability marker demonstrated that HDDC2 plays a role in the maintenance of pluripotency in human ES and iPS cells. Collectively our analyses identified new regulators involved in human embryonic development that would have otherwise been missed using methods that focus on assessment of the average expression levels; in doing so, we highlight the value of studying expression variability for single cell RNA-seq data.

Orthogonal control of expression mean and variance by epigenetic features at different genomic loci

While gene expression noise has been shown to drive dramatic phenotypic variations, the molecular basis for this variability in mammalian systems is not well understood. Gene expression has been shown to be regulated by promoter architecture and the associated chromatin environment. However, the exact contribution of these two factors in regulating expression noise has not been explored. Using a dual-reporter lentiviral model system, we deconvolved the influence of the promoter sequence to systematically study the contribution of the chromatin environment at different genomic locations in regulating expression noise. By integrating a large-scale analysis to quantify mRNA levels by smFISH and protein levels by flow cytometry in single cells, we found that mean expression and noise are uncorrelated across genomic locations. Furthermore, we showed that this independence could be explained by the orthogonal control of mean expression by the transcript burst size and noise by the burst frequency. Finally, we showed that genomic locations displaying higher expression noise are associated with more repressed chromatin, thereby indicating the contribution of the chromatin environment in regulating expression noise.

Dynamical analysis of mCAT2 gene models with CTN-RNA nuclear retention

As an experimentally well-studied nuclear-retained RNA, CTN-RNA plays a significant role in many aspects of mouse cationic amino acid transporter 2 (mCAT2) gene expression, but relevant dynamical mechanisms have not been completely clarified. Here we first show that CTN-RNA nuclear retention can not only reduce pre-mCAT2 RNA noise but also mediate its coding partner noise. Then, by collecting experimental observations, we conjecture a heterodimer formed by two proteins, p54(nrb) and PSP1, named p54(nrb)-PSP1, by which CTN-RNA can positively regulate the expression of nuclear mCAT2 RNA. Therefore, we construct a sequestration model at the molecular level. By analyzing the dynamics of this model system, we demonstrate why most nuclear-retained CTN-RNAs stabilize at the periphery of paraspeckles, how CTN-RNA regulates its protein-coding partner, and how the mCAT2 gene can maintain a stable expression. In particular, we obtain results that can easily explain the experimental phenomena observed in two cases, namely, when cells are stressed and unstressed. Our entire analysis not only reveals that CTN-RNA nuclear retention may play an essential role in indirectly preventing diseases but also lays the foundation for further study of other members of the nuclear-regulatory RNA family with more complicated molecular mechanisms.

Essential gene disruptions reveal complex relationships between phenotypic robustness, pleiotropy, and fitness

The concept of robustness in biology has gained much attention recently, but a mechanistic understanding of how genetic networks regulate phenotypic variation has remained elusive. One approach to understand the genetic architecture of variability has been to analyze dispensable gene deletions in model organisms; however, the most important genes cannot be deleted. Here, we have utilized two systems in yeast whereby essential genes have been altered to reduce expression. Using high-throughput microscopy and image analysis, we have characterized a large number of morphological phenotypes, and their associated variation, for the majority of essential genes in yeast. Our results indicate that phenotypic robustness is more highly dependent upon the expression of essential genes than on the presence of dispensable genes. Morphological robustness appears to be a general property of a genotype that is closely related to pleiotropy. While the fitness profile across a range of expression levels is idiosyncratic to each gene, the global pattern indicates that there is a window in which phenotypic variation can be released before fitness effects are observable.

Individual T helper cells have a quantitative cytokine memory

The probabilistic expression of cytokine genes in differentiated T helper (Th) cell populations remains ill defined. By single-cell analyses and mathematical modeling, we show that one stimulation featured stable cytokine nonproducers as well as stable producers with wide cell-to-cell variability in the magnitude of expression. Focusing on interferon-γ (IFN-γ) expression by Th1 cells, mathematical modeling predicted that this behavior reflected different cell-intrinsic capacities and not mere gene-expression noise. In vivo, Th1 cells sort purified by secreted IFN-γ amounts preserved a quantitative memory for both probability and magnitude of IFN-γ re-expression for at least 1 month. Mechanistically, this memory resulted from quantitatively distinct transcription of individual alleles and was controlled by stable expression differences of the Th1 cell lineage-specifying transcription factor T-bet. Functionally, Th1 cells with graded IFN-γ production competence differentially activated Salmonella-infected macrophages for bacterial killing. Thus, individual Th cells commit to produce distinct amounts of a given cytokine, thereby generating functional intrapopulation heterogeneity.

Promoter-mediated transcriptional dynamics

Genes in eukaryotic cells are typically regulated by complex promoters containing multiple binding sites for a variety of transcription factors, but how promoter dynamics affect transcriptional dynamics has remained poorly understood. In this study, we analyze gene models at the transcriptional regulation level, which incorporate the complexity of promoter structure (PS) defined as transcriptional exits (i.e., ON states of the promoter) and the transition pattern (described by a matrix consisting of transition rates among promoter activity states). We show that multiple exits of transcription are the essential origin of generating multimodal distributions of mRNA, but promoters with the same transition pattern can lead to multimodality of different modes, depending on the regulation of transcriptional factors. In turn, for similar mRNA distributions in the models, the mean ON or OFF time distributions may exhibit different characteristics, thus providing the supplemental information on PS. In addition, we demonstrate that the transcriptional noise can be characterized by a nonlinear function of mean ON and OFF times. These results not only reveal essential characteristics of promoter-mediated transcriptional dynamics but also provide signatures useful for inferring PS based on characteristics of transcriptional outputs.

Epigenetic diversity in hematopoietic neoplasms

Tumor cell populations display a remarkable extent of variability in non-genetic characteristics such as DNA methylation, histone modification patterns, and differentiation levels of individual cells. It remains to be elucidated whether non-genetic heterogeneity is simply a byproduct of tumor evolution or instead a manifestation of a higher-order tissue organization that is maintained within the neoplasm to establish a differentiation hierarchy, a favorable microenvironment, or a buffer against changing selection pressures during tumorigenesis. Here, we review recent findings on epigenetic diversity, particularly heterogeneity in DNA methylation patterns in hematologic malignancies. We also address the implications of epigenetic heterogeneity for the clonal evolution of tumors and discuss its effects on gene expression and other genome functions in cancer.

Stem cell differentiation as a many-body problem

Stem cell differentiation has been viewed as coming from transitions between attractors on an epigenetic landscape that governs the dynamics of a regulatory network involving many genes. Rigorous definition of such a landscape is made possible by the realization that gene regulation is stochastic, owing to the small copy number of the transcription factors that regulate gene expression and because of the single-molecule nature of the gene itself. We develop an approximation that allows the quantitative construction of the epigenetic landscape for large realistic model networks. Applying this approach to the network for embryonic stem cell development explains many experimental observations, including the heterogeneous distribution of the transcription factor Nanog and its role in safeguarding the stem cell pluripotency, which can be understood by finding stable steady-state attractors and the most probable transition paths between those attractors. We also demonstrate that the switching rate between attractors can be significantly influenced by the gene expression noise arising from the fluctuations of DNA occupancy when binding to a specific DNA site is slow.

An effective method for computing the noise in biochemical networks

We present a simple yet effective method, which is based on power series expansion, for computing exact binomial moments that can be in turn used to compute steady-state probability distributions as well as the noise in linear or nonlinear biochemical reaction networks. When the method is applied to representative reaction networks such as the ON-OFF models of gene expression, gene models of promoter progression, gene auto-regulatory models, and common signaling motifs, the exact formulae for computing the intensities of noise in the species of interest or steady-state distributions are analytically given. Interestingly, we find that positive (negative) feedback does not enlarge (reduce) noise as claimed in previous works but has a counter-intuitive effect and that the multi-OFF (or ON) mechanism always attenuates the noise in contrast to the common ON-OFF mechanism and can modulate the noise to the lowest level independently of the mRNA mean. Except for its power in deriving analytical expressions for distributions and noise, our method is programmable and has apparent advantages in reducing computational cost.

Genetic selection for context-dependent stochastic phenotypes: Sp1 and TATA mutations increase phenotypic noise in HIV-1 gene expression

The sequence of a promoter within a genome does not uniquely determine gene expression levels and their variability; rather, promoter sequence can additionally interact with its location in the genome, or genomic context, to shape eukaryotic gene expression. Retroviruses, such as human immunodeficiency virus-1 (HIV), integrate their genomes into those of their host and thereby provide a biomedically-relevant model system to quantitatively explore the relationship between promoter sequence, genomic context, and noise-driven variability on viral gene expression. Using an in vitro model of the HIV Tat-mediated positive-feedback loop, we previously demonstrated that fluctuations in viral Tat-transactivating protein levels generate integration-site-dependent, stochastically-driven phenotypes, in which infected cells randomly ‘switch’ between high and low expressing states in a manner that may be related to viral latency. Here we extended this model and designed a forward genetic screen to systematically identify genetic elements in the HIV LTR promoter that modulate the fraction of genomic integrations that specify ‘Switching’ phenotypes. Our screen identified mutations in core promoter regions, including Sp1 and TATA transcription factor binding sites, which increased the Switching fraction several fold. By integrating single-cell experiments with computational modeling, we further investigated the mechanism of Switching-fraction enhancement for a selected Sp1 mutation. Our experimental observations demonstrated that the Sp1 mutation both impaired Tat-transactivated expression and also altered basal expression in the absence of Tat. Computational analysis demonstrated that the observed change in basal expression could contribute significantly to the observed increase in viral integrations that specify a Switching phenotype, provided that the selected mutation affected Tat-mediated noise amplification differentially across genomic contexts. Our study thus demonstrates a methodology to identify and characterize promoter elements that affect the distribution of stochastic phenotypes over genomic contexts, and advances our understanding of how promoter mutations may control the frequency of latent HIV infection.

The sequence of a gene within a cellular genome does not uniquely determine its expression level, even for a single type of cell under fixed conditions. Numerous other factors, including gene location on the chromosome and random gene-expression “noise,” can alter expression patterns and cause differences between otherwise identical cells. This poses new challenges for characterizing the genotype–phenotype relationship. Infection by the human immunodeficiency virus-1 (HIV-1) provides a biomedically important example in which transcriptional noise and viral genomic location impact the decision between viral replication and latency, a quiescent but reversible state that cannot be eliminated by anti-viral therapies. Here, we designed a forward genetic screen to systematically identify mutations in the HIV promoter that alter the fraction of genomic integrations that specify noisy/reactivating expression phenotypes. The mechanisms by which the selected mutations specify the observed phenotypic enrichments are investigated through a combination of single-cell experiments and computational modeling. Our study provides a framework for identifying genetic sequences that alter the distribution of stochastic expression phenotypes over genomic locations and for characterizing their mechanisms of regulation. Our results also may yield further insights into the mechanisms by which HIV sequence evolution can alter the propensity for latent infections.

Asymmetric flies: the control of developmental noise in Drosophila

What are the sources of phenotypic variation and which factors shape this variation are fundamental questions of developmental and evolutionary biology. Despite this simple formulation and intense research, controversy remains. Three points are particularly discussed: (1) whether adaptive developmental mechanisms buffering variation exist at all; (2) if yes, do they involve specific genes and processes, i.e., different from those involved in the development of the traits that are buffered?; and (3) whether different mechanisms specifically buffer the various sources of variation, i.e., genetic, environmental and stochastic, or whether a generalist process buffers them all at once. We advocate that experimental work integrating different levels of analysis will improve our understanding of the origin of phenotypic variation and thus help answering these contentious questions. In this paper, we first survey the current views on these issues, highlighting potential sources of controversy. We then focus on the stochastic part of phenotypic variation, as measured by fluctuating asymmetry, and on current knowledge about the genetic basis of developmental stability. We report our recent discovery that an individual gene, Cyclin G, plays a central role-adaptive or not-in developmental stability in Drosophila. ( 1) We discuss the implications of this discovery on the regulation of organ size and shape, and finally point out open questions.

Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts

BACKGROUND

A number of studies have established that stochasticity in gene expression may play an important role in many biological phenomena. This therefore calls for further investigations to identify the molecular mechanisms at stake, in order to understand and manipulate cell-to-cell variability. In this work, we explored the role played by chromatin dynamics in the regulation of stochastic gene expression in higher eukaryotic cells.

RESULTS

For this purpose, we generated isogenic chicken-cell populations expressing a fluorescent reporter integrated in one copy per clone. Although the clones differed only in the genetic locus at which the reporter was inserted, they showed markedly different fluorescence distributions, revealing different levels of stochastic gene expression. Use of chromatin-modifying agents showed that direct manipulation of chromatin dynamics had a marked effect on the extent of stochastic gene expression. To better understand the molecular mechanism involved in these phenomena, we fitted these data to a two-state model describing the opening/closing process of the chromatin. We found that the differences between clones seemed to be due mainly to the duration of the closed state, and that the agents we used mainly seem to act on the opening probability.

CONCLUSIONS

In this study, we report biological experiments combined with computational modeling, highlighting the importance of chromatin dynamics in stochastic gene expression. This work sheds a new light on the mechanisms of gene expression in higher eukaryotic cells, and argues in favor of relatively slow dynamics with long (hours to days) periods of quiet state.

Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data

Background

Genetically identical populations of cells grown in the same environmental condition show substantial variability in gene expression profiles. Although single-cell RNA-seq provides an opportunity to explore this phenomenon, statistical methods need to be developed to interpret the variability of gene expression counts.

Results

We develop a statistical framework for studying the kinetics of stochastic gene expression from single-cell RNA-seq data. By applying our model to a single-cell RNA-seq dataset generated by profiling mouse embryonic stem cells, we find that the inferred kinetic parameters are consistent with RNA polymerase II binding and chromatin modifications. Our results suggest that histone modifications affect transcriptional bursting by modulating both burst size and frequency. Furthermore, we show that our model can be used to identify genes with slow promoter kinetics, which are important for probabilistic differentiation of embryonic stem cells.

Conclusions

We conclude that the proposed statistical model provides a flexible and efficient way to investigate the kinetics of transcription.

Live imaging of nascent RNA dynamics reveals distinct types of transcriptional pulse regulation

Transcription of genes can be discontinuous, occurring in pulses or bursts. It is not clear how properties of transcriptional pulses vary between different genes. We compared the pulsing of five housekeeping and five developmentally induced genes by direct imaging of single gene transcriptional events in individual living Dictyostelium cells. Each gene displayed its own transcriptional signature, differing in probability of firing and pulse duration, frequency, and intensity. In contrast to the prevailing view from both prokaryotes and eukaryotes that transcription displays binary behavior, strongly expressed housekeeping genes altered the magnitude of their transcriptional pulses during development. These nonbinary "tunable" responses may be better suited than stochastic switch behavior for housekeeping functions. Analysis of RNA synthesis kinetics using fluorescence recovery after photobleaching implied modulation of housekeeping-gene pulse strength occurs at the level of transcription initiation rather than elongation. In addition, disparities between single cell and population measures of transcript production suggested differences in RNA stability between gene classes. Analysis of stability using RNAseq revealed no major global differences in stability between developmental and housekeeping transcripts, although strongly induced RNAs showed unusually rapid decay, indicating tight regulation of expression.

Duel of the fates: the role of transcriptional circuits and noise in CD4+ cells

CD4+ T cells play key roles in orchestrating adaptive immune responses, and are a popular model for mammalian cell differentiation. While immune regulation would seem to require exactly adjusted mRNA and protein expression levels of key factors, there is little evidence that this is strictly the case. Stochastic gene expression and plasticity of cell types contrast the apparent need for precision. Recent work has provided insight into the magnitude of molecular noise, as well as the relationship between noise, transcriptional circuits and epigenetic modifications in a variety of cell types. These processes and their interplay will also govern gene expression patterns in the different CD4+ cell types, and the determination of their cellular fates.

Analytical distribution and tunability of noise in a model of promoter progress

Chromatin template (CT), which accumulates over time until the promoter becomes active, determines upstream dynamics of transcription, but how upstream sequential steps impact downstream dynamics qualitatively and quantitatively is unclear. Here, we analyze a stochastic gene model with a simple yet typical CT that contains one active state and several inactive states of the promoter. We derive the analytical expressions for the noise in mRNA probability distributions governed by master equations. The derived results extend previous work by including the effects of promoter progress on variability and bimodality. Specifically, given a CT for transcription, we analytically demonstrate that inactive phases of the promoter can modulate the noise intensity to the minimum independently of the mean expression of mRNA. If one new inactive state is added to the CT, then the resulting noise will be reduced, implying that the multi-off mechanism plays a role of attenuating the noise. In contrast to the simple on-off mechanism, the multi-off mechanism can also narrow bimodal regions in a certain parameter plane and obscure two peaks, explaining why bimodal distributions are rarely observed in experiments. Our results provide insight into the role of promoter progress in determining the level of cell-to-cell variability in gene expression.

Regulated noise in the epigenetic landscape of development and disease

In this Perspective, we synthesize past and present observations in the field of epigenetics to propose a model in which the epigenome can modulate cellular plasticity in development and disease by regulating the effects of noise. In this model, the epigenome facilitates phase transitions in development and reprogramming and mediates canalization, or the ability to produce a consistent phenotypic outcome despite being challenged by variable conditions, during cell fate commitment. After grounding our argument in a discussion of stochastic noise and nongenetic heterogeneity, we explore the hypothesis that distinct chromatin domains, which are known to be dysregulated in disease and remodeled during development, might underlie cellular plasticity more generally. We then present a modern portrayal of Waddington's epigenetic landscape through a mathematical formalism. We speculate that this new framework might impact how we approach disease mechanisms. In particular, it may help to explain the observation that the variability of DNA methylation and gene expression are increased in cancer, thus contributing to tumor cell heterogeneity.

Interplay between gene expression noise and regulatory network architecture

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.