Probability in transcriptional regulation and its implications for leukocyte differentiation and inducible gene expression

Economy of Effort or Sophisticated Programming? The Prevalence of Bidirectional Promoter Complexes in the Human Genome

An abundance of antisense promoters in the vicinity of the transcriptional start site of coding genes suggests that they play an important role in gene regulation. The divergent transcription of housekeeping genes by a common central promoter region allows for coordinated regulation of genes in related pathways and is also linked to higher promoter activity. However, closely positioned transcription start sites can also result in competition between overlapping promoter elements and generate a binary switch element. Furthermore, the direct competition resulting from the presence of an antisense promoter immediately downstream of the transcription start site of the gene produces an element that can exist in only one of two stable transcriptional states: sense or antisense. In this review, we summarize analyses of the prevalence of antisense transcription in higher eukaryotes and viruses, with a focus on the antisense promoters competing with the promoters of coding genes. The structures of bidirectional promoters driving the simultaneous expression of housekeeping genes are compared with examples of human bidirectional elements that have been shown to act as switches. Since many bidirectional elements contain a noncoding RNA as the divergent transcript, we describe examples of functional noncoding antisense transcripts that affect the epigenetic landscape and alter the expression of their host gene. Finally, we discuss opportunities for additional research on competing sense/antisense promoters, uncovering their potential role in programming cell differentiation.

Understanding Cell Differentiation Through Single-Cell Approaches: Conceptual Challenges of the Systemic Approach

The cells of a multicellular organism are derived from a single zygote and genetically almost identical. Yet, they are phenotypically very different. This difference is the result of a process commonly called cell differentiation. How the phenotypic diversity emerges during ontogenesis or regeneration is a central and intensely studied but still unresolved issue in biology. Cell biology is facing conceptual challenges that are frequently confused with methodological difficulties. How to define a cell type? What stability or change means in the context of cell differentiation and how to deal with the ubiquitous molecular variations seen in the living cells? What are the driving forces of the change? We propose to reframe the problem of cell differentiation in a systemic way by incorporating different theoretical approaches. The new conceptual framework is able to capture the insights made at different levels of cellular organization and considered previously as contradictory. It also provides a formal strategy for further experimental studies.

Evolution of hierarchy and irreversibility in theoretical cell differentiation model

The process of cell differentiation in multicellular organisms is characterized by hierarchy and irreversibility in many cases. However, the conditions and selection pressures that give rise to these characteristics remain poorly understood. By using a mathematical model, here we show that the network of differentiation potency (differentiation diagram) becomes necessarily hierarchical and irreversible by increasing the number of terminally differentiated states under certain conditions. The mechanisms generating these characteristics are clarified using geometry in the cell state space. The results demonstrate that the hierarchical organization and irreversibility can manifest independently of direct selection pressures associated with these characteristics, instead they appear to evolve as byproducts of selective forces favoring a diversity of differentiated cell types. The study also provides a new perspective on the structure of gene regulatory networks that produce hierarchical and irreversible differentiation diagrams. These results indicate some constraints on cell differentiation, which are expected to provide a starting point for theoretical discussion of the implicit limits and directions of evolution in multicellular organisms.

Macrophage heterogeneity in the single-cell era: facts and artifacts

In this spotlight, we review technical issues that compromise single-cell analysis of tissue macrophages, including limited and unrepresentative yields, fragmentation and generation of remnants, and activation during tissue disaggregation. These issues may lead to a misleading definition of subpopulations of macrophages and the expression of macrophage-specific transcripts by unrelated cells. Recognition of the technical limitations of single-cell approaches is required in order to map the full spectrum of tissue-resident macrophage heterogeneity and assess its biological significance.

Random Penetrance of Mutations Among Individuals: A New Type of Genetic Drift in Molecular Evolution

The determinative view of mutation penetrance is a fundamental assumption for the building of molecular evolutionary theory: individuals in the population with the same genotype have the same fitness effect. Since this view has been constantly challenged by experimental evidence, it is desirable to examine to what extent violation of this view could affect our understanding of molecular evolution. To this end, the author formulated a new theory of molecular evolution under a random model of penetrance: for any individual with the same mutational genotype, the coefficient of selection is a random variable. It follows that, in addition to the conventional N _e-genetic drift (N _e is the effective population size), the variance of penetrance among individuals (ε ²) represents a new type of genetic drift, coined by the ε ²-genetic drift. It has been demonstrated that these two genetic drifts together provided new insights on the nearly neutral evolution: the evolutionary rate is inversely related to the log-of-N _e when the ε ²-genetic drift is nontrivial. This log-of-N _e feature of ε ²-genetic drift did explain well why the d _N /d _S ratio (the nonsynonymous rate to the synonymous rate) in humans is only as twofold as that in mice, while the effective population size (N _e) of mice is about two-magnitude larger than that of humans. It was estimated that, for the first time, the variance of random penetrance in mammalian genes was approximately ε ²≈ 5.89 × 10^-3.

Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlights the power of large data sets to identify the diversity of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages, functions, or subpopulations.

Pinpointing Cell Identity in Time and Space

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

Analysis on gene modular network reveals morphogen-directed development robustness in Drosophila

Genetic robustness is an important characteristic to tolerate genetic or nongenetic perturbations and ensure phenotypic stability. Morphogens, a type of evolutionarily conserved diffusible molecules, govern tissue patterns in a direction-dependent or concentration-dependent manner by differentially regulating downstream gene expression. However, whether the morphogen-directed gene regulatory network possesses genetic robustness remains elusive. In the present study, we collected 4217 morphogen-responsive genes along A-P axis of Drosophila wing discs from the RNA-seq data, and clustered them into 12 modules. By applying mathematical model to the measured data, we constructed a gene modular network (GMN) to decipher the module regulatory interactions and robustness in morphogen-directed development. The computational analyses on asymptotical dynamics of this GMN demonstrated that this morphogen-directed GMN is robust to tolerate a majority of genetic perturbations, which has been further validated by biological experiments. Furthermore, besides the genetic alterations, we further demonstrated that this morphogen-directed GMN can well tolerate nongenetic perturbations (Hh production changes) via computational analyses and experimental validation. Therefore, these findings clearly indicate that the morphogen-directed GMN is robust in response to perturbations and is important for Drosophila to ensure the proper tissue patterning in wing disc.

Order by chance: origins and benefits of stochasticity in immune cell fate control

To protect against diverse challenges, the immune system must continuously generate an arsenal of specialized cell types, each of which can mount a myriad of effector responses upon detection of potential threats. To do so, it must generate multiple differentiated cell populations with defined sizes and proportions, often from rare starting precursor cells. Here, we discuss the emerging view that inherently probabilistic mechanisms, involving rare, rate-limiting regulatory events in single cells, control fate decisions and population sizes and fractions during immune development and function. We first review growing evidence that key fate control points are gated by stochastic signaling and gene regulatory events that occur infrequently over decision-making timescales, such that initially homogeneous cells can adopt variable outcomes in response to uniform signals. We next discuss how such stochastic control can provide functional capabilities that are harder to achieve with deterministic control strategies, and may be central to robust immune system function.

Paf1c defects challenge the robustness of flower meristem termination in Arabidopsis thaliana

Although accumulating evidence suggests that gene regulation is highly stochastic, genetic screens have successfully uncovered master developmental regulators, questioning the relationship between transcriptional noise and intrinsic robustness of development. To identify developmental modules that are more or less resilient to large-scale genetic perturbations, we used the Arabidopsis polymerase II-associated factor 1 complex (Paf1c) mutant vip3, which is impaired in several RNA polymerase II-dependent transcriptional processes. We found that the control of flower termination was not as robust as classically pictured. In angiosperms, the floral female organs, called carpels, display determinate growth: their development requires the arrest of stem cell maintenance. In vip3 mutant flowers, carpels displayed a highly variable morphology, with different degrees of indeterminacy defects up to wild-type size inflorescence emerging from carpels. This phenotype was associated with variable expression of two key regulators of flower termination and stem cell maintenance in flowers, WUSCHEL and AGAMOUS. The phenotype was also dependent on growth conditions. Together, these results highlight the surprisingly plastic nature of stem cell maintenance in plants and its dependence on Paf1c.

Summary: Using a mutant with increased transcriptional noise, we reveal that stem cell maintenance is not as robust as anticipated in plants, even leading to major defects in essential developmental processes such as flower indeterminacy.

Is the cell really a machine?

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes.

Conceptual Challenges of the Systemic Approach in Understanding Cell Differentiation

The cells of a multicellular organism are derived from a single zygote and genetically identical. Yet, they are phenotypically very different. This difference is the result of a process commonly called cell differentiation. How the phenotypic diversity emerges during ontogenesis or regeneration is a central and intensely studied but still unresolved issue in biology. Cell biology is facing conceptual challenges that are frequently confused with methodological difficulties. How to define a cell type? What stability or change means in the context of cell differentiation and how to deal with the ubiquitous molecular variations seen in the living cells? What are the driving forces of the change? We propose to reframe the problem of cell differentiation in a systemic way by incorporating different theoretical approaches. The new conceptual framework is able to capture the insights made at different levels of cellular organization and considered previously as contradictory. It also provides a formal strategy for further experimental studies.

Reprogramming, oscillations and transdifferentiation in epigenetic landscapes

Waddington’s epigenetic landscape provides a phenomenological understanding of the cell differentiation pathways from the pluripotent to mature lineage-committed cell lines. In light of recent successes in the reverse programming process there has been significant interest in quantifying the underlying landscape picture through the mathematics of gene regulatory networks. We investigate the role of time delays arising from multi-step chemical reactions and epigenetic rearrangement on the cell differentiation landscape for a realistic two-gene regulatory network, consisting of self-promoting and mutually inhibiting genes. Our work provides the first theoretical basis of the transdifferentiation process in the presence of delays, where one differentiated cell type can transition to another directly without passing through the undifferentiated state. Additionally, the interplay of time-delayed feedback and a time dependent chemical drive leads to long-lived oscillatory states in appropriate parameter regimes. This work emphasizes the important role played by time-delayed feedback loops in gene regulatory circuits and provides a framework for the characterization of epigenetic landscapes.

The evolution of the macrophage-specific enhancer (Fms intronic regulatory element) within the CSF1R locus of vertebrates

The Csf1r locus encodes the receptor for macrophage colony-stimulating factor, which controls the proliferation, differentiation and survival of macrophages. The 300 bp Fms intronic regulatory element (FIRE), within the second intron of Csf1r, is necessary and sufficient to direct macrophage-specific transcription. We have analysed the conservation and divergence of the FIRE DNA sequence in vertebrates. FIRE is present in the same location in the Csf1r locus in reptile, avian and mammalian genomes. Nearest neighbor analysis based upon this element alone largely recapitulates phylogenies inferred from much larger genomic sequence datasets. One core element, containing binding sites for AP1 family and the macrophage-specific transcription factor, PU.1, is conserved from lizards to humans. Around this element, the FIRE sequence is conserved within clades with the most conserved elements containing motifs for known myeloid-expressed transcription factors. Conversely, there is little alignment between clades outside the AP1/PU.1 element. The analysis favours a hybrid between “enhanceosome” and “smorgasbord” models of enhancer function, in which elements cooperate to bind components of the available transcription factor milieu.

Piecewise-Deterministic Markov Processes as Limits of Markov Jump Processes

A classical result about Markov jump processes states that a certain class of dynamical systems given by ordinary differential equations are obtained as the limit of a sequence of scaled Markov jump processes. This approach fails if the scaling cannot be carried out equally across all entities. In the present paper we present a convergence theorem for such an unequal scaling. In contrast to an equal scaling the limit process is not purely deterministic but still possesses randomness. We show that these processes constitute a rich subclass of piecewise-deterministic processes. Such processes apply in molecular biology where entities often occur in different scales of numbers.

Piecewise-Deterministic Markov Processes as Limits of Markov Jump Processes

A classical result about Markov jump processes states that a certain class of dynamical systems given by ordinary differential equations are obtained as the limit of a sequence of scaled Markov jump processes. This approach fails if the scaling cannot be carried out equally across all entities. In the present paper we present a convergence theorem for such an unequal scaling. In contrast to an equal scaling the limit process is not purely deterministic but still possesses randomness. We show that these processes constitute a rich subclass of piecewise-deterministic processes. Such processes apply in molecular biology where entities often occur in different scales of numbers.

A monoallelic-to-biallelic T-cell transcriptional switch regulates GATA3 abundance

Ku et al. show that loss of one Gata3 allele leads to diminished expansion of immature T cells as well as aberrant induction of myeloid transcription factor PU.1. Gata3 is monoallelically expressed in hematopoietic stem cells and early T-cell progenitors. Half of the developing cells switch to biallelic Gata3 transcription abruptly at midthymopoiesis.

Protein abundance must be precisely regulated throughout life, and nowhere is the stringency of this requirement more evident than during T-cell development: A twofold increase in the abundance of transcription factor GATA3 results in thymic lymphoma, while reduced GATA3 leads to diminished T-cell production. GATA3 haploinsufficiency also causes human HDR (hypoparathyroidism, deafness, and renal dysplasia) syndrome, often accompanied by immunodeficiency. Here we show that loss of one Gata3 allele leads to diminished expansion (and compromised development) of immature T cells as well as aberrant induction of myeloid transcription factor PU.1. This effect is at least in part mediated transcriptionally: We discovered that Gata3 is monoallelically expressed in a parent of origin-independent manner in hematopoietic stem cells and early T-cell progenitors. Curiously, half of the developing cells switch to biallelic Gata3 transcription abruptly at midthymopoiesis. We show that the monoallelic-to-biallelic transcriptional switch is stably maintained and therefore is not a stochastic phenomenon. This unique mechanism, if adopted by other regulatory genes, may provide new biological insights into the rather prevalent phenomenon of monoallelic expression of autosomal genes as well as into the variably penetrant pathophysiological spectrum of phenotypes observed in many human syndromes that are due to haploinsufficiency of the affected gene.

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.

A straightforward method to compute average stochastic oscillations from data samples

BACKGROUND

Many biological systems exhibit sustained stochastic oscillations in their steady state. Assessing these oscillations is usually a challenging task due to the potential variability of the amplitude and frequency of the oscillations over time. As a result of this variability, when several stochastic replications are averaged, the oscillations are flattened and can be overlooked. This can easily lead to the erroneous conclusion that the system reaches a constant steady state.

RESULTS

This paper proposes a straightforward method to detect and asses stochastic oscillations. The basis of the method is in the use of polar coordinates for systems with two species, and cylindrical coordinates for systems with more than two species. By slightly modifying these coordinate systems, it is possible to compute the total angular distance run by the system and the average Euclidean distance to a reference point. This allows us to compute confidence intervals, both for the average angular speed and for the distance to a reference point, from a set of replications.

CONCLUSIONS

The use of polar (or cylindrical) coordinates provides a new perspective of the system dynamics. The mean trajectory that can be obtained by averaging the usual cartesian coordinates of the samples informs about the trajectory of the center of mass of the replications. In contrast to such a mean cartesian trajectory, the mean polar trajectory can be used to compute the average circular motion of those replications, and therefore, can yield evidence about sustained steady state oscillations. Both, the coordinate transformation and the computation of confidence intervals, can be carried out efficiently. This results in an efficient method to evaluate stochastic oscillations.

The Many Alternative Faces of Macrophage Activation

Monocytes and macrophages provide the first line of defense against pathogens. They also initiate acquired immunity by processing and presenting antigens and provide the downstream effector functions. Analysis of large gene expression datasets from multiple cells and tissues reveals sets of genes that are co-regulated with the transcription factors that regulate them. In macrophages, the gene clusters include lineage-specific genes, interferon-responsive genes, early inflammatory genes, and genes required for endocytosis and lysosome function. Macrophages enter tissues and alter their function to deal with a wide range of challenges related to development and organogenesis, tissue injury, malignancy, sterile, or pathogenic inflammatory stimuli. These stimuli alter the gene expression to produce "activated macrophages" that are better equipped to eliminate the cause of their influx and to restore homeostasis. Activation or polarization states of macrophages have been classified as "classical" and "alternative" or M1 and M2. These proposed states of cells are not supported by large-scale transcriptomic data, including macrophage-associated signatures from large cancer tissue datasets, where the supposed markers do not correlate with other. Individual macrophage cells differ markedly from each other, and change their functions in response to doses and combinations of agonists and time. The most studied macrophage activation response is the transcriptional cascade initiated by the TLR4 agonist lipopolysaccharide. This response is reviewed herein. The network topology is conserved across species, but genes within the transcriptional network evolve rapidly and differ between mouse and human. There is also considerable divergence in the sets of target genes between mouse strains, between individuals, and in other species such as pigs. The deluge of complex information related to macrophage activation can be accessed with new analytical tools and new databases that provide access for the non-expert.

Transcriptomic analysis of mononuclear phagocyte differentiation and activation

Monocytes and macrophages differentiate from progenitor cells under the influence of colony-stimulating factors. Genome-scale data have enabled the identification of the sets of genes that are associated with specific functions and the mechanisms by which thousands of genes are regulated in response to pathogen challenge. In large datasets, it is possible to identify large sets of genes that are coregulated with the transcription factors that regulate them. They include macrophage-specific genes, interferon-responsive genes, early inflammatory genes, and those associated with endocytosis. Such analyses can also extract macrophage-associated signatures from large cancer tissue datasets. However, cluster analysis provides no support for a signature that distinguishes macrophages from antigen-presenting dendritic cells, nor the classification of macrophage activation states as classical versus alternative, or M1 versus M2. Although there has been a focus on a small subset of lineage-enriched transcription factors, such as PU.1, more than half of the transcription factors in the genome can be expressed in macrophage lineage cells under some state of activation, and they interact in a complex network. The network architecture is conserved across species, but many of the target genes evolve rapidly and differ between mouse and human. The data and publication deluge related to macrophage biology require the development of new analytical tools and ways of presenting information in an accessible form.

On the impact of discreteness and abstractions on modelling noise in gene regulatory networks

In this paper, we explore the impact of different forms of model abstraction and the role of discreteness on the dynamical behaviour of a simple model of gene regulation where a transcriptional repressor negatively regulates its own expression. We first investigate the relation between a minimal set of parameters and the system dynamics in a purely discrete stochastic framework, with the twofold purpose of providing an intuitive explanation of the different behavioural patterns exhibited and of identifying the main sources of noise. Then, we explore the effect of combining hybrid approaches and quasi-steady state approximations on model behaviour (and simulation time), to understand to what extent dynamics and quantitative features such as noise intensity can be preserved.

Integrating mitosis, toxicity, and transgene expression in a telecommunications packet-switched network model of lipoplex-mediated gene delivery

Gene delivery systems transport exogenous genetic information to cells or biological systems with the potential to directly alter endogenous gene expression and behavior with applications in functional genomics, tissue engineering, medical devices, and gene therapy. Nonviral systems offer advantages over viral systems because of their low immunogenicity, inexpensive synthesis, and easy modification but suffer from lower transfection levels. The representation of gene transfer using models offers perspective and interpretation of complex cellular mechanisms,including nonviral gene delivery where exact mechanisms are unknown. Here, we introduce a novel telecommunications model of the nonviral gene delivery process in which the delivery of the gene to a cell is synonymous with delivery of a packet of information to a destination computer within a packet-switched computer network. Such a model uses nodes and layers to simplify the complexity of modeling the transfection process and to overcome several challenges of existing models. These challenges include a limited scope and limited time frame, which often does not incorporate biological effects known to affect transfection. The telecommunication model was constructed in MATLAB to model lipoplex delivery of the gene encoding the green fluorescent protein to HeLa cells. Mitosis and toxicity events were included in the model resulting in simulation outputs of nuclear internalization and transfection efficiency that correlated with experimental data. A priori predictions based on model sensitivity analysis suggest that increasing endosomal escape and decreasing lysosomal degradation, protein degradation, and GFP-induced toxicity can improve transfection efficiency by three-fold. Application of the telecommunications model to nonviral gene delivery offers insight into the development of new gene delivery systems with therapeutically relevant transfection levels.

Identifying Intracellular pDNA Losses From a Model of Nonviral Gene Delivery

Nonviral gene delivery systems are a type of nanocommunication system that transmit plasmid packets (i.e., pDNA packets) that are programmed at the nanoscale to biological systems at the microscopic cellular level. This engineered nanocommunication system suffers large pDNA losses during transmission of the genetically encoded information, preventing its use in biotechnological and medical applications. The pDNA losses largely remain uncharacterized, and the ramifications of reducing pDNA loss from newly designed gene delivery systems remain difficult to predict. Here, the pDNA losses during primary and secondary transmission chains were identified utilizing a MATLAB model employing queuing theory simulating delivery of pEGFPLuc transgene to HeLa cells carried by Lipofectamine 2000 nonviral DNA carrier. Minimizing pDNA loss during endosomal escape of the primary transmission process results in increased number of pDNA in the nucleus with increased transfection, but with increased probability of cell death. The number of pDNA copies in the nucleus and the amount of time the pDNAs are in the nucleus directly correlates to improved transfection efficiency. During secondary transmission, pDNAs are degraded during distribution to daughter cells. Reducing pDNA losses improves transfection, but a balance in quantity of nuclear pDNA, mitosis, and toxicity must be considered in order to achieve therapeutically relevant transfection levels.

An animated landscape representation of CD4+ T-cell differentiation, variability, and plasticity: insights into the behavior of populations versus cells

Recent advances in understanding CD4(+) T-cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well-characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program "LAVA" visualizes T-cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semistable differentiation states. The model illustrates several principles, including: (i) cell populations may behave more predictably than individual cells; (ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well-defined pathway; (iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; (iv) intrapopulation variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; (v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T-cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states.

Investigating the relation between stochastic differentiation, homeostasis and clonal expansion in intestinal crypts via multiscale modeling

Colorectal tumors originate and develop within intestinal crypts. Even though some of the essential phenomena that characterize crypt structure and dynamics have been effectively described in the past, the relation between the differentiation process and the overall crypt homeostasis is still only partially understood. We here investigate this relation and other important biological phenomena by introducing a novel multiscale model that combines a morphological description of the crypt with a gene regulation model: the emergent dynamical behavior of the underlying gene regulatory network drives cell growth and differentiation processes, linking the two distinct spatio-temporal levels. The model relies on a few a priori assumptions, yet accounting for several key processes related to crypt functioning, such as: dynamic gene activation patterns, stochastic differentiation, signaling pathways ruling cell adhesion properties, cell displacement, cell growth, mitosis, apoptosis and the presence of biological noise. We show that this modeling approach captures the major dynamical phenomena that characterize the regular physiology of crypts, such as cell sorting, coordinate migration, dynamic turnover, stem cell niche correct positioning and clonal expansion. All in all, the model suggests that the process of stochastic differentiation might be sufficient to drive the crypt to homeostasis, under certain crypt configurations. Besides, our approach allows to make precise quantitative inferences that, when possible, were matched to the current biological knowledge and it permits to investigate the role of gene-level perturbations, with reference to cancer development. We also remark the theoretical framework is general and may be applied to different tissues, organs or organisms.

Quantal and graded stimulation of B lymphocytes as alternative strategies for regulating adaptive immune responses

Lymphocytes undergo a typical response pattern following stimulation in vivo: they proliferate, differentiate to effector cells, cease dividing and predominantly die, leaving a small proportion of long-lived memory and effector cells. This pattern results from cell-intrinsic processes following activation and the influence of external regulation. Here we apply quantitative methods to study B-cell responses in vitro. Our results reveal that B cells stimulated through two Toll-like receptors (TLRs) require minimal external direction to undergo the basic pattern typical of immunity. Altering the stimulus strength regulates the outcome in a quantal manner by varying the number of cells that participate in the response. In contrast, the T-cell-dependent CD40 activation signal induces a response where division times and differentiation rates vary in relation to stimulus strength. These studies offer insight into how the adaptive antibody response may have evolved from simple autonomous response patterns to the highly regulable state that is now observed in mammals.

Microglial diversity by responses and responders

Microglia are the principal resident innate immune cells of the CNS. Their contributions to the normal development of the CNS, the maintenance and plasticity of neuronal networks and the safeguarding of proper functionality are becoming more and more evident. Microglia also survey the tissue homeostasis to respond rapidly to exogenous and endogenous threats, primarily with a protective outcome. However, excessive acute activation, chronic activity or an improper adaptation of their functional performance can foster neuropathologies. A key to the versatile response behavior of these cells is their ability to commit to reactive phenotypes, which reveal enormous complexity. Yet the respective profiles of induced genes and installed functions may build up on heterogeneous contributions of cellular subsets. Here, we discuss findings and concepts that consider the variety of microglial activities and response options as being based-at least in part-on a diversity of the engaged cells. Whether it is the production of proinflammatory cytokines, clearance of tissue debris, antigen presentation or the ability to sense neurotransmitters, microglial cells present with an unanticipated heterogeneity of their constitutive and inducible features. While the organizational principles of this heterogeneity are still largely unknown, functional implications are already perceptible.

Shaping the murine macrophage phenotype: IL-4 and cyclic AMP synergistically activate the arginase I promoter

Arginase I is a marker of murine M2 macrophages and is highly expressed in many inflammatory diseases. The basis for high arginase I expression in macrophages in vivo is incompletely understood but likely reflects integrated responses to combinations of stimuli. Our objective was to elucidate mechanisms involved in modulating arginase I induction by IL-4, the prototypical activator of M2 macrophages. IL-4 and 8-bromo-cAMP individually induce arginase I, but together they rapidly and synergistically induce arginase I mRNA, protein, and promoter activity in murine macrophage cells. Arginase I induction by IL-4 requires binding of the transcription factors STAT6 and C/EBPβ to the IL-4 response element of the arginase I gene. Chromatin immunoprecipitation showed that the synergistic response involves binding of both transcription factors to the IL-4 response element at levels significantly greater than in response to IL-4 alone. The results suggest that C/EBPβ is a limiting factor for the level of STAT6 bound to the IL-4 response element. The enhanced binding in the synergistic response was not due to increased expression of either STAT6 or C/EBPβ but was correlated primarily with increased nuclear abundance of C/EBPβ. Our findings also suggest that induction of arginase I expression is stochastic; that is, differences in induction reflect differences in probability of transcriptional activation and not simply differences in rate of transcription. Results of the present study also may be useful for understanding mechanisms underlying regulated expression of other genes in macrophages and other myeloid-derived cells in health and disease.

Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates

An experimental and theoretical study of T cell differentiation in response to mixed-input conditions reveals that cells can tune between Th1 and Th2 states through a continuum of mixed phenotypes.

Cell differentiation is typically directed by external signals that drive opposing regulatory pathways. Studying differentiation under polarizing conditions, with only one input signal provided, is limited in its ability to resolve the logic of interactions between opposing pathways. Dissection of this logic can be facilitated by mapping the system's response to mixtures of input signals, which are expected to occur in vivo, where cells are simultaneously exposed to various signals with potentially opposing effects. Here, we systematically map the response of naïve T cells to mixtures of signals driving differentiation into the Th1 and Th2 lineages. We characterize cell state at the single cell level by measuring levels of the two lineage-specific transcription factors (T-bet and GATA3) and two lineage characteristic cytokines (IFN-γ and IL-4) that are driven by these transcription regulators. We find a continuum of mixed phenotypes in which individual cells co-express the two lineage-specific master regulators at levels that gradually depend on levels of the two input signals. Using mathematical modeling we show that such tunable mixed phenotype arises if autoregulatory positive feedback loops in the gene network regulating this process are gradual and dominant over cross-pathway inhibition. We also find that expression of the lineage-specific cytokines follows two independent stochastic processes that are biased by expression levels of the master regulators. Thus, cytokine expression is highly heterogeneous under mixed conditions, with subpopulations of cells expressing only IFN-γ, only IL-4, both cytokines, or neither. The fraction of cells in each of these subpopulations changes gradually with input conditions, reproducing the continuous internal state at the cell population level. These results suggest a differentiation scheme in which cells reflect uncertainty through a continuously tuneable mixed phenotype combined with a biased stochastic decision rather than a binary phenotype with a deterministic decision.

During cell differentiation, progenitor cells respond to external signals that drive the expression of genes that are characteristic of the differentiated cell states. This process is controlled by gene regulatory networks that typically involve positive autoregulation and cross-inhibition between master regulators of the two differentiated states. Mapping the system's response to mixtures of external signals can help us to understand the operational logic of these binary cell fate decisions. Here, we study differentiation of CD4⁺ T cells into Th1 and Th2 lineages under mixed-input conditions, at the single cell level. We reveal that cell state is not restricted to a small number of well-defined phenotypes, but rather tunes through a continuum of mixed-phenotype states in which levels of lineage-specifying transcription factors gradually change with the levels of the two inputs. Using mathematical modeling we establish the conditions under which the system has one stable steady state that continuously tunes in response to changes in levels of the inputs. Results of this model qualitatively explain our experimental observations. We further characterize expression patterns of downstream lineage-specific genes—cytokines that are driven by the two master regulators upon cell re-stimulation. We find a highly heterogeneous population with cells expressing either one of the cytokines, both cytokines, or neither. Of note, the fraction of cells in these subpopulations continuously tunes with input levels, thus reproducing a tunable state at the cell population level. Our results can be explained by a two-stage scheme in which the gene regulatory network is responsible for a continuously tunable cell state, which is translated into a heterogeneous cytokine expression pattern through uncorrelated and biased stochastic processes.

Theoretical aspects of Systems Biology

The natural world consists of hierarchical levels of complexity that range from subatomic particles and molecules to ecosystems and beyond. This implies that, in order to explain the features and behavior of a whole system, a theory might be required that would operate at the corresponding hierarchical level, i.e. where self-organization processes take place. In the past, biological research has focused on questions that could be answered by a reductionist program of genetics. The organism (and its development) was considered an epiphenomenona of its genes. However, a profound rethinking of the biological paradigm is now underway and it is likely that such a process will lead to a conceptual revolution emerging from the ashes of reductionism. This revolution implies the search for general principles on which a cogent theory of biology might rely. Because much of the logic of living systems is located at higher levels, it is imperative to focus on them. Indeed, both evolution and physiology work on these levels. Thus, by no means Systems Biology could be considered a 'simple' 'gradual' extension of Molecular Biology.

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.

Transition probability in cell proliferation, stochasticity in cell differentiation, and the restriction point of the cell cycle in one package

Clonal cells are known to display stochastically varying interdivision times (IMT) and stochastic choices of cell fates. These features are suggested in the present paper to stem from discrete transitions of genes between different modes of their engagement in transcription. These transitions are explained by stochastic events of assembly/disassembly of huge ensembles of transcription factors needed to built-up gene-specific transcription preinitiation complexes (PIC). The time required to assemble a PIC at a gene promoter by random collisions of numerous proteins may be long enough to be comparable with the cell cycle. Independently published findings are reviewed to show that active genes may display discontinuous patterns of transcriptional output consistent with stochastically varying periods of PIC presence or absence at their promoters, and that these periods may reach several hours. This timescale matches the time needed for synchronised clonal cells to pass the restriction point (RP) of the cell cycle. RP is suggested to correspond to cell state where cell fate is determined by competing discrete transcriptional events. Cell fate choice depends on the event that, by chance, has outpaced other events able to commit the cell to alternative fates. Simple modelling based on these premises is consistent with general features of cell kinetics, including RP passage dependance on mitogenic stimulation, IMT distributions conformance to exponentially modified Gaussian, the limited proliferative potential of untransformed cells, relationships between changes in cell proliferation and differentiation, and bimodal distributions of cells over expression levels of genes involved in stem cell differentiation.

Evolving sensitivity balances Boolean Networks

We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman’s Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. Insilico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks.

Genes at work in random bouts: stochastically discontinuous gene activity makes cell cycle duration and cell fate decisions variable, thus providing for stem cells plasticity

Cell interdivision periods (IDP) in homogenous cell populations vary stochastically. Another aspect of probabilistic cell behavior is randomness in cell differentiation. These features are suggested to result from competing stochastic events of assembly/disassembly of the transcription pre-initiation complex (PIC) at gene promoters. The time needed to assemble a proper PIC from different proteins, which must be numerous enough to make their combination gene specific, may be comparable to the IDP. Nascent mRNA visualization at defined genes and inferences from protein level fluctuations in single cells suggest that some genes do operate in this way. The onset of mRNA production by such genes may miss the time windows provided by the cell cycle, resulting in cells differentiating into those in which the respective mRNAs are either present or absent. This creates a way to generate cell phenotype diversity in multicellular organisms.

Multiscale stochastic modelling of gene expression

Stochastic phenomena in gene regulatory networks can be modelled by the chemical master equation for gene products such as mRNA and proteins. If some of these elements are present in significantly higher amounts than the rest, or if some of the reactions between these elements are substantially faster than others, it is often possible to reduce the master equation to a simpler problem using asymptotic methods. We present examples of such a procedure and analyse the relationship between the reduced models and the original.

Non-genetic heterogeneity and cell fate choice in Dictyostelium discoideum

From microbes to metazoans, it is now clear that fluctuations in the abundance of mRNA transcripts and protein molecules enable genetically identical cells to oscillate between several distinct states (Kaern et al. 2005). Since this cell-cell variability does not derive from physical differences in the genetic code it is termed non-genetic heterogeneity. Non-genetic heterogeneity endows cell populations with useful capabilities they could never achieve if each cell were the same as its neighbors (Raj & van Oudenaarden 2008; Eldar & Elowitz 2010). One such example is seen during multicellular development and "salt and pepper" cell type differentiation. In this review, we will first examine the importance of non-genetic heterogeneity in initiating "salt and pepper" pattern formation during Dictyostelium discoideum development. Second, we will discuss the various ways in which non-genetic heterogeneity might be generated, as well as recent advances in understanding the molecular basis of heterogeneity in this system.

A dynamical model of genetic networks for cell differentiation

A mathematical model is proposed which is able to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions which drive the phenomenon. On the contrary, cell differentiation is described here as an emergent property of a generic model of the underlying gene regulatory network, and it can therefore be applied to a variety of different organisms. The model points to a peculiar role of cellular noise in differentiation and leads to non trivial predictions which could be subject to experimental testing. Moreover, a single model proves able to describe several different phenomena observed in various differentiation processes.

Quantification of miRNA abundance in single cells using locked nucleic acid-FISH and enzyme-labeled fluorescence

The ability to quantify miRNA abundance at the single-cell level and image its spatial distribution could lead to unique insight into the biological roles of miRNAs and miRNA-associated gene regulatory networks. This protocol describes a method for quantitatively imaging miRNAs in single cells using fluorescence in situ hybridization (FISH). The method combines the unique miRNA recognition properties of locked nucleic acid (LNA) with the signal amplification technology known as enzyme-labeled fluorescence (ELF). Although both approaches have previously been shown to increase detection specificity and/or sensitivity in FISH, combining these techniques into one protocol allows for single molecule detection. Specifically, individual miRNAs are identified as bright, photostable fluorescent spots. The dynamic range was found to span over three orders of magnitude and the average miRNA copy number per cell was within 17.5% of measurements acquired by quantitative RT-PCR.

Bimodal gene expression in noncooperative regulatory systems

Bimodality of gene expression, as a mechanism contributing to phenotypic diversity, enhances the survival of cells in a fluctuating environment. To date, the bimodal response of a gene regulatory system has been attributed to the cooperativity of transcription factor binding or to feedback loops. It has remained unclear whether noncooperative binding of transcription factors can give rise to bimodality in an open-loop system. We study a theoretical model of gene expression in a two-step cascade (a deterministically monostable system) in which the regulatory gene produces transcription factors that have a nonlinear effect on the activity of the target gene. We show that a unimodal distribution of transcription factors over the cell population can generate a bimodal steady-state output without cooperative transcription factor binding. We introduce a simple method of geometric construction that allows one to predict the onset of bimodality. The construction only involves the parameters of bursting of the regulatory gene and the dose-response curve of the target gene. Using this method, we show that the gene expression may switch between unimodal and bimodal as the concentration of inducers or corepressors is varied. These findings may explain the experimentally observed bimodal response of cascades consisting of a fluorescent protein reporter controlled by the tetracycline repressor. The geometric construction provides a useful tool for designing experiments and for interpretation of their results. Our findings may have important implications for understanding the strategies adopted by cell populations to survive in changing environments.

Microglia: key innate immune cells of the brain

Microglia are the histiocytes of the central nervous system. These long-lived cells undergo very little turnover in normal physiological states; however, in pathological conditions, increased egress from the bone marrow and chemoattractive signals in the brain can substantially modulate the indigenous population. Although they were initially conceived of as "resting" cells, recent data suggest that they would be more aptly described as "surveillance" cells. Microglia are specifically adapted to sense various types of danger and differentially react with a classical or alternative reparative response. Our understanding of macrophage function has shifted away from focusing on cell lineage to a more systems-based biology of gene networks accomplishing the detoxification and immune functions. With our greater appreciation of microglial involvement in the innate immune response, we have entered a new era in which the modulation of microglia can be proposed as a means of modulating neurological disease.

The potential landscape of genetic circuits imposes the arrow of time in stem cell differentiation

Differentiation from a multipotent stem or progenitor state to a mature cell is an essentially irreversible process. The associated changes in gene expression patterns exhibit time-directionality. This "arrow of time" in the collective change of gene expression across multiple stable gene expression patterns (attractors) is not explained by the regulated activation, the suppression of individual genes which are bidirectional molecular processes, or by the standard dynamical models of the underlying gene circuit which only account for local stability of attractors. To capture the global dynamics of this nonequilibrium system and gain insight in the time-asymmetry of state transitions, we computed the quasipotential landscape of the stochastic dynamics of a canonical gene circuit that governs branching cell fate commitment. The potential landscape reveals the global dynamics and permits the calculation of potential barriers between cell phenotypes imposed by the circuit architecture. The generic asymmetry of barrier heights indicates that the transition from the uncommitted multipotent state to differentiated states is inherently unidirectional. The model agrees with observations and predicts the extreme conditions for reprogramming cells back to the undifferentiated state.

Random discrete competing events vs. dynamic bistable switches in cell proliferation in differentiation

Several recent experiments related to fundamental aspects of cell behaviour, such as passing of the restriction point of cell cycle, which are generally interpreted in accordance with the dynamic paradigm implying the use of differential equations operating with the concentrations of cellular components and rate constants of their interactions, are shown in the present paper to be consistent with a simple model based on discrete competing stochastic events interpreted as assembly of alternative complexes of transcription factors at gene promoters. The model conforms to the transition probability model of cell cycle and to the stochastic approaches to cell differentiation and integrates them with the restriction point concept.

Short-term memory in gene induction reveals the regulatory principle behind stochastic IL-4 expression

Combining experiments on primary T cells and mathematical modeling, we characterized the stochastic expression of the interleukin-4 cytokine gene in its physiologic context, showing that a two-step model of transcriptional regulation acting on chromatin rearrangement and RNA polymerase recruitment accounts for the level, kinetics, and population variability of expression.

A rate-limiting step upstream of transcription initiation, but occurring at the level of an individual allele, controls whether the interleukin-4 gene is expressed during antigenic stimulation, suggesting that the observed stochasticity of expression is linked to the dynamics of chromatin rearrangement.

The computational analysis predicts that the probability to re-express an interleukin-4 gene that has been expressed once is transiently increased. In support, we experimentally demonstrate a short-term memory for interleukin-4 expression at the predicted time scale of several days.

The model provides a unifying framework that accounts for both graded and binary modes of gene regulation. Graded changes in expression level can be achieved by controlling transcription initiation, whereas binary regulation acts at the level of chromatin rearrangement and is targeted during the differentiation of T cells that specialize in interleukin-4 production.

Cell populations are typically heterogeneous with respect to protein expression even when clonally derived from a single progenitor. In bacteria and yeast, such heterogeneity has been shown to be due to intrinsically stochastic dynamics of gene expression (Raj and van Oudenaarden, 2008). Thus, cross-population heterogeneity may be an unavoidable by-product of random fluctuations in molecular interactions (Raser and O'Shea, 2004; Pedraza and van Oudenaarden, 2005). The phenotypic variability deriving from it may also be beneficial for cell function, differentiation, or adaptation to changing environments (Chang et al, 2008; Feinerman et al, 2008; Losick and Desplan, 2008). However, little is known about how gene-expression variability is caused in mammalian cells.

Two principal modes of gene regulation have been identified: graded and binary. In the graded mode, transcriptional regulators can tune the level of a gene product in a continuous manner (Hazzalin and Mahadevan, 2002). In the binary mode, the gene is expressed at an invariant level, whereas its probability of being expressed in a given cell is regulated, so that the gene has discrete ‘on' and ‘off' states (Walters et al, 1995; Hume, 2000; Biggar and Crabtree, 2001). In humans and mice, cytokine genes are expressed in a binary manner (Bix and Locksley, 1998; Riviere et al, 1998; Hu-Li et al, 2001; Apostolou and Thanos, 2008). A particularly well-studied case is the interleukin-4 (il4) gene that is critical for antibody-based immune responses. This gene is expressed by antigen-stimulated T cells initially with low probability, so that in most IL-4-positive cells only one allele is active (Bix and Locksley, 1998; Riviere et al, 1998). The expressed allele is not imprinted but chosen stochastically during each cell stimulation (Hu-Li et al, 2001).

Here, we have studied the dynamics of IL-4 expression quantitatively. Primary murine CD4⁺ T cells have been differentiated uniformly into type-2 T-helper (Th2) cells that express the lineage-specifying transcription factor (TF) Gata-3 and are competent to activate the il4 gene upon challenge with antigen. Using T cells heterozygous for an il4 wild-type allele and an il4 allele with GFP knock-in after the promoter, the alleles are found to be expressed stochastically and in an uncorrelated manner (Figure 2A; Hu-Li et al, 2001). To account for the observed stochastic dynamics of IL-4 expression, we considered a basic model of gene transcription, mRNA translation, turnover, and protein secretion (Figure 2B). However, our experimental estimates of the intracellular life times of IL-4 mRNA and protein (∼1 h) and their absolute numbers (mRNA∼10³, protein∼10⁵) rule out random fluctuations in transcription, translation as well as mRNA and protein turnover as an explanation for the observed stochastic properties of IL-4 expression (Thattai and van Oudenaarden, 2001; Paulsson, 2004).

As il4 is known to be strongly regulated at the chromatin level (Ansel et al, 2006), we included in the model a reversible step of chromatin opening that is permissive for transcription (Figure 2C and D). Both chromatin opening and transcription initiation are driven by TFs that are transiently activated during the antigen stimulus, with NFAT1 playing a prominent role (Agarwal et al, 2000; Avni et al, 2002; Guo et al, 2004). The model accounts for the kinetics of NFAT1 TF activity (Figure 2E) (Loh et al, 1996). Using a best-fit procedure for estimating the kinetics of the chromatin transition and TF activity from experimental data, we found that the model accurately reproduces the distribution of IL-4 expression within the cell population over the entire time course of a stimulation (Figure 3A). At the same time, it accounts for the measured kinetics of IL-4 mRNA, intracellular and secreted protein (Figure 3B). Additional data show that the model can also explain IL-4 expression at different stages of Th2 differentiation and upon pharmacological inhibition of NFAT1 activity. In each case, the model predicts a slow and stochastic chromatin opening (Step 1 in Figure 2C) that is the limiting step for the activation of the gene.

The slowness of chromatin opening inferred by the model implies an extended lifetime of the open chromatin state (several days), which lasts longer than TF activity during antigenic stimulation (several hours). This indicates that acute IL-4 expression is terminated by the cessation of TF activity (Step 2 in Figure 2C), rather than by the closing of the chromatin (Step 1). In support of this prediction, we observed an elevated fraction of IL-4-producing cells after secondary stimulations administered within a few days of the primary stimulus. Consistent with the model, this elevation disappeared with a half-life of ∼3 days (Figure 4B). To test whether this ‘short-term memory' for activation of the il4 gene is indeed due to the IL-4 producers in the primary stimulation, we sorted stimulated Th2 cells into viable IL-4-producing and non-producing fractions using the cytokine secretion assay (Ouyang et al, 2000) and cultured them separately for different resting periods. The probability of IL-4 re-expression in the positive-sorted cells was consistently larger than in negative-sorted cells and decreased progressively over several days (Figure 4C). By contrast, the sorted IL-4 negative cells exhibited a constant induction probability indistinguishable from the unsorted population. This behavior was not due to differential cell proliferation in the sorted populations or different success of Th2 differentiation. Moreover, using heterozygous il4-wild-type/il4-gfp cells, and sorting for expression of the wild-type allele, we observed that expression of the il4-gfp allele was similar in IL-4-positive and negative sorted fractions. Taken together, these findings imply that stochastic, slow chromatin changes at individual il4 genes govern the binary expression pattern of this cytokine.

In conclusion, we propose an experimentally based model of inducible gene expression where strong stochasticity arises from slow (hours to days) chromatin opening and closing transitions, rather than being due to small numbers of mRNA or protein molecules or transcriptional bursting (Raj et al, 2006). This rate-limiting step upstream of transcription initiation (which may entail several interacting epigenetic processes) naturally gives rise to a binary expression pattern of the gene. By contrast, regulation at the level of transcription initiation can have a graded effect on the expression level. We provide evidence that both binary and graded regulation can occur for the il4 gene. Physiological regulation of il4 seems to be mainly binary, thus enabling a dose–response within a population while producing an unequivocal all-or-none signal at the single-cell level.

Although cell-to-cell variability has been recognized as an unavoidable consequence of stochasticity in gene expression, it may also serve a functional role for tuning physiological responses within a cell population. In the immune system, remarkably large variability in the expression of cytokine genes has been observed in homogeneous populations of lymphocytes, but the underlying molecular mechanisms are incompletely understood. Here, we study the interleukin-4 gene (il4) in T-helper lymphocytes, combining mathematical modeling with the experimental quantification of expression variability and critical parameters. We show that a stochastic rate-limiting step upstream of transcription initiation, but acting at the level of an individual allele, controls il4 expression. Only a fraction of cells reaches an active, transcription-competent state in the transient time window determined by antigen stimulation. We support this finding by experimental evidence of a previously unknown short-term memory that was predicted by the model to arise from the long lifetime of the active state. Our analysis shows how a stochastic mechanism acting at the chromatin level can be integrated with transcriptional regulation to quantitatively control cell-to-cell variability.

Unravelling mononuclear phagocyte heterogeneity

When Ralph Steinman and Zanvil Cohn first described dendritic cells (DCs) in 1973 it took many years to convince the immunology community that these cells were truly distinct from macrophages. Almost four decades later, the DC is regarded as the key initiator of adaptive immune responses; however, distinguishing DCs from macrophages still leads to confusion and debate in the field. Here, Nature Reviews Immunology asks five experts to discuss the issue of heterogeneity in the mononuclear phagocyte system and to give their opinion on the importance of defining these cells for future research.

Meta-analysis of lineage-specific gene expression signatures in mouse leukocyte populations

In order to address fundamental questions associated with the relationships between mononuclear phagocytes and other myeloid and lymphoid cell populations, we have taken advantage of the growing body of expression data available in the public domain. We collated a large number of published expression studies on mouse haemopoietic cell lineages comprising 304 cell samples from 29 independent experiments performed on a single microarray platform (Affymetrix MOE430-2). The data were subjected to network-based cluster analysis using Biolayout Express(3D). Genes with related function clustered together in distinct regions of the graph reaffirming many known associations between gene expression and role in specific pathways and defining most major cell types of the immune system. Promoters of genes within individual clusters were distinguished by over-representation of regulatory motifs recognised by specific transcription factors. However, these data indicate that commonly used myeloid subpopulation markers, such as CD11c (Itgax), do not correlate with expression of other genes, and further bring into question their use in defining myeloid cell lineage, activation (M1 vs. M2) and antigen-presenting cell function. In particular, there were few mRNA markers that clearly distinguished classical dendritic cells (DC) from macrophages, other than low expression of genes required for phagocytic activity. Bone marrow-derived DC, grown in GM-CSF, were clearly identified as phagocytes and distinguished from isolated lymphoid tissue DC. Thus, through pooling datasets from public data and examining the gene expression clusters within, we can learn a great deal about the transcriptional networks that underpin the differences in functional activities between cell populations of the immune system.