Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing

Toll-like receptor 4 and MYD88-dependent signaling mechanisms of the innate immune system are essential for the response to lipopolysaccharide by epithelial and stromal cells of the bovine endometrium

Infection of the bovine endometrium with Gram-negative bacteria commonly causes uterine disease. Toll-like receptor 4 (TLR4) on cells of the immune system bind Gram-negative bacterial lipopolysaccharide (LPS), stimulating the secretion of the proinflammatory cytokines interleukin 1B (IL1B) and IL6, and the chemokine IL8. Because the endometrium is the first barrier to infection of the uterus, the signaling cascade triggered by LPS and the subsequent expression of inflammatory mediators were investigated in endometrial epithelial and stromal cells, and the key pathways identified using short interfering RNA (siRNA) and biochemical inhibitors. Treatment of endometrial cells with ultrapure LPS stimulated an inflammatory response characterized by increased IL1B, IL6, and IL8 mRNA expression, and IL6 protein accumulation in epithelial cells, and by increased IL1B and IL8 mRNA expression, and IL6 and IL8 protein accumulation in stromal cells. Treatment of endometrial cells with LPS also induced the degradation of IKB and the nuclear translocation of NFKB, as well as rapid phosphorylation of mitogen-activated protein kinase 3/1 (MAPK3/1) and MAPK14. Knockdown of TLR4 or its signaling adaptor molecule, myeloid differentiation factor 88 (MYD88), using siRNA reduced the inflammatory response to LPS in epithelial and stromal cells. Biochemical inhibition of MAPK3/1, but not JNK or MAPK14, reduced LPS-induced IL1B, IL6, and IL8 expression in endometrial cells. In conclusion, epithelial and stromal cells have an intrinsic role in innate immune surveillance in the endometrium, and in the case of LPS this recognition occurs via TLR4- and MYD88-dependent cell signaling pathways.

Regulation of nuclear PKA revealed by spatiotemporal manipulation of cyclic AMP

Understanding how specific cAMP signals are organized and relayed to their effectors in different compartments of the cell to achieve functional specificity requires molecular tools that allow precise manipulation of cAMP in these compartments. Here we characterize a new method using bicarbonate-activatable and genetically targetable soluble adenylyl cyclase (sAC) to control the location, kinetics and magnitude of the cAMP signal. Using this live-cell cAMP manipulation in conjunction with fluorescence imaging and mechanistic modeling, we uncover the activation of a resident pool of PKA holoenzyme in the nuclei of HEK-293 cells, modifying the existing dogma of cAMP-PKA signaling in the nucleus. Furthermore, we show that phosphodiesterases (PDE) and A-Kinase Anchoring Proteins (AKAP) are critical in shaping nuclear PKA responses. Collectively, our data suggests a new model where AKAP-localized PDEs tune an activation threshold for nuclear PKA holoenzyme, thereby converting spatially distinct second messenger signals to temporally controlled nuclear kinase activity.

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.

Higher-order chromatin regulation and differential gene expression in the human tumor necrosis factor/lymphotoxin locus in hepatocellular carcinoma cells

The three-dimensional context of endogenous chromosomal regions may contribute to the regulation of gene clusters by influencing interactions between transcriptional regulatory elements. In this study, we investigated the effects of tumor necrosis factor (TNF) signaling on spatiotemporal enhancer-promoter interactions in the human tumor necrosis factor (TNF)/lymphotoxin (LT) gene locus, mediated by CCCTC-binding factor (CTCF)-dependent chromatin insulators. The cytokine genes LTα, TNF, and LTβ are differentially regulated by NF-κB signaling in inflammatory and oncogenic responses. We identified at least four CTCF-enriched sites with enhancer-blocking activities and a TNF-responsive TE2 enhancer in the TNF/LT locus. One of the CTCF-enriched sites is located between the early-inducible LTα/TNF promoters and the late-inducible LTβ promoter. Depletion of CTCF reduced TNF expression and accelerated LTβ induction. After TNF stimulation, via intrachromosomal dynamics, these insulators mediated interactions between the enhancer and the LTα/TNF promoters, followed by interaction with the LTβ promoter. These results suggest that insulators mediate the spatiotemporal control of enhancer-promoter associations in the TNF/LT gene cluster.

A microchamber array for single cell isolation and analysis of intracellular biomolecules

We present a microfluidic device that enables the determination of intracellular biomolecules in multiple single cells. The cells are individually trapped and isolated in a microchamber array. Since the microchambers can be opened and closed reversibly, the cells can be exposed to different solutions sequentially, e.g. for incubation, washing steps, labelling and finally, for lysis. The tightly sealed microchambers enable the retention and analysis of cell lysate derived from single cells. The performance of the device is demonstrated by monitoring the levels of the cofactors NADPH and NADH both in healthy mammalian cells and in cells exposed to oxidative stress. The platform was also used to determine the toxic impact of the alkaloid camptothecin on the intracellular enzyme glucose-6-phosphate dehydrogenase levels. In general, the device is applicable for the analysis of cell auto-stimulation and the detection of intracellular metabolite concentration or expression levels of proteins.

Recent advances in single-cell studies of gene regulation

A mechanistic understanding of gene regulatory network dynamics requires quantitative single-cell data of multiple network components in response to well-defined perturbations. Recent advances in the development of fluorescent biomarkers for proteins, detection of RNA and interactions, microfluidic technology, and high-resolution imaging have set the stage for a host of new studies that elucidate the important roles of stochasticity and cell-cell variability in response to external perturbations. In this review, we briefly describe methods for high-resolution visualization and the control of gene expression, along with application of these novel methods to recent studies involving gene networks.

Differential gene expression regulated by oscillatory transcription factors

Cells respond to changes in the internal and external environment by a complex regulatory system whose end-point is the activation of transcription factors controlling the expression of a pool of ad-hoc genes. Recent experiments have shown that certain stimuli may trigger oscillations in the concentration of transcription factors such as NF-κB and p53 influencing the final outcome of the genetic response. In this study we investigate the role of oscillations in the case of three different well known gene regulatory mechanisms using mathematical models based on ordinary differential equations and numerical simulations. We considered the cases of direct regulation, two-step regulation and feed-forward loops, and characterized their response to oscillatory input signals both analytically and numerically. We show that in the case of indirect two-step regulation the expression of genes can be turned on or off in a frequency dependent manner, and that feed-forward loops are also able to selectively respond to the temporal profile of oscillating transcription factors.

Inferring Biological Mechanisms by Data-Based Mathematical Modelling: Compartment-Specific Gene Activation during Sporulation in Bacillus subtilis as a Test Case

Biological functionality arises from the complex interactions of simple components. Emerging behaviour is difficult to recognize with verbal models alone, and mathematical approaches are important. Even few interacting components can give rise to a wide range of different responses, that is, sustained, transient, oscillatory, switch-like responses, depending on the values of the model parameters. A quantitative comparison of model predictions and experiments is therefore important to distinguish between competing hypotheses and to judge whether a certain regulatory behaviour is at all possible and plausible given the observed type and strengths of interactions and the speed of reactions. Here I will review a detailed model for the transcription factor σ(F), a regulator of cell differentiation during sporulation in Bacillus subtilis. I will focus in particular on the type of conclusions that can be drawn from detailed, carefully validated models of biological signaling networks. For most systems, such detailed experimental information is currently not available, but accumulating biochemical data through technical advances are likely to enable the detailed modelling of an increasing number of pathways. A major challenge will be the linking of such detailed models and their integration into a multiscale framework to enable their analysis in a larger biological context.

Observing single cell NF-κB dynamics under stimulant concentration gradient

Study of cell signaling often requires examination of the cellular dynamics under variation in the stimulant concentration. Such variation has typically been conducted by dispensing cell populations in a number of chambers or wells containing discrete concentrations. Such practice adds to the complexity associated with experimental or device design and requires substantial labor for implementation. Furthermore, there is also potential risk of missing important results due to the often arbitrary selection of discrete concentration values for testing. In this Letter, we study NF-κB activation and translocation at the single cell level using a microfluidic device that generates continuously varying concentration gradient. We use only three device settings to cover stimulant (interleukin-1β) concentrations of 4 orders of magnitude (0.001-10 ng/mL). Such device allows us to study temporal dynamics of NF-κB in single cells under different stimulant concentrations by real-time imaging. Interestingly, our results reveal that, while the percent of cells with NF-κB translocation decreases with lower stimulant concentration in the range of 0.1-0.001 ng/mL, the response time of such translocation remains constant, reflected by the single cell data.

Microscale functional cytomics for studying hematologic cancers

An important problem in translational cancer research is our limited ability to functionally characterize behaviors of primary patient cancer cells and associated stromal cell types, and relate mechanistic understanding to therapy selection. Functional analyses of primary samples face at least 3 major challenges: limited availability of primary samples for testing, paucity of functional information extracted from samples, and lack of functional methods accessible to many researchers. We developed a microscale cell culture platform that overcomes these limitations, especially for hematologic cancers. A key feature of the platform is the ability to compartmentalize small populations of adherent and nonadherent cells in controlled microenvironments that can better reflect physiological conditions and enable cell-cell interaction studies. Custom image analysis was developed to measure cell viability and protein subcellular localizations in single cells to provide insights into heterogeneity of cellular responses. We validated our platform by assessing viability and nuclear translocations of NF-κB and STAT3 in multiple myeloma cells exposed to different conditions, including cocultured bone marrow stromal cells. We further assessed its utility by analyzing NF-κB activation in a primary chronic lymphocytic leukemia patient sample. Our platform can be applied to myriad biological questions, enabling high-content functional cytomics of primary hematologic malignancies.

Constant flow-driven microfluidic oscillator for different duty cycles

This paper presents microfluidic devices that autonomously convert two constant flow inputs into an alternating oscillatory flow output. We accomplish this hardware embedded self-control programming using normally closed membrane valves that have an inlet, an outlet, and a membrane-pressurization chamber connected to a third terminal. Adjustment of threshold opening pressures in these 3-terminal flow switching valves enabled adjustment of oscillation periods to between 57 and 360 s with duty cycles of 0.2-0.5. These values are in relatively good agreement with theoretical values, providing the way for rational design of an even wider range of different waveform oscillations. We also demonstrate the ability to use these oscillators to perform temporally patterned delivery of chemicals to living cells. The device only needs a syringe pump, thus removing the use of complex, expensive external actuators. These tunable waveform microfluidic oscillators are envisioned to facilitate cell-based studies that require temporal stimulation.

Communicating oscillatory networks: frequency domain analysis

Background

Constructing predictive dynamic models of interacting signalling networks remains one of the great challenges facing systems biology. While detailed dynamical data exists about individual pathways, the task of combining such data without further lengthy experimentation is highly nontrivial. The communicating links between pathways, implicitly assumed to be unimportant and thus excluded, are precisely what become important in the larger system and must be reinstated. To maintain the delicate phase relationships between signals, signalling networks demand accurate dynamical parameters, but parameters optimised in isolation and under varying conditions are unlikely to remain optimal when combined. The computational burden of estimating parameters increases exponentially with increasing system size, so it is crucial to find precise and efficient ways of measuring the behaviour of systems, in order to re-use existing work.

Results

Motivated by the above, we present a new frequency domain-based systematic analysis technique that attempts to address the challenge of network assembly by defining a rigorous means to quantify the behaviour of stochastic systems. As our focus we construct a novel coupled oscillatory model of p53, NF-kB and the mammalian cell cycle, based on recent experimentally verified mathematical models. Informed by online databases of protein networks and interactions, we distilled their key elements into simplified models containing the most significant parts. Having coupled these systems, we constructed stochastic models for use in our frequency domain analysis. We used our new technique to investigate the crosstalk between the components of our model and measure the efficacy of certain network-based heuristic measures.

Conclusions

We find that the interactions between the networks we study are highly complex and not intuitive: (i) points of maximum perturbation do not necessarily correspond to points of maximum proximity to influence; (ii) increased coupling strength does not necessarily increase perturbation; (iii) different perturbations do not necessarily sum and (iv) overall, susceptibility to perturbation is amplitude and frequency dependent and cannot easily be predicted by heuristic measures.

Our methodology is particularly relevant for oscillatory systems, though not limited to these, and is most revealing when applied to the results of stochastic simulation. The technique is able to characterise precisely the distance in behaviour between different models, different systems and different parts within the same system. It can also measure the difference between different simulation algorithms used on the same system and can be used to inform the choice of dynamic parameters. By measuring crosstalk between subsystems it can also indicate mechanisms by which such systems may be controlled in experiments and therapeutics. We have thus found our technique of frequency domain analysis to be a valuable benchmark systems-biological tool.

Single-cell protein analysis

Heterogeneity of cellular systems has been widely recognized but only recently have tools become available that allow probing of genes and proteins in single cells to understand it. While the advancement in single cell genomic analysis has been greatly aided by the power of amplification techniques (e.g. PCR), analysis of proteins in single cells has proven to be more challenging. However, recent advances in multi-parameter flow cytometry, microscopy, microfluidics and other techniques have made it possible to measure wide variety of proteins in single cells. In this review, we highlight key recent developments in analysis of proteins in a single cell (excluding imaging-based methods), and discuss their significance in biological research.

Signal-dependent dynamics of transcription factor translocation controls gene expression

Information about environmental stimuli is often transmitted using common signaling molecules, but the mechanisms that ensure signaling specificity are not entirely known. Here we show that the identities and intensities of different stresses are transmitted by modulation of the amplitude, duration or frequency of nuclear translocation of the Saccharomyces cerevisiae general stress response transcription factor Msn2. Through artificial control of the dynamics of Msn2 translocation, we reveal how distinct dynamical schemes differentially affect reporter gene expression. Using a simple model, we predict stress-induced reporter gene expression from single-cell translocation dynamics. We then demonstrate that the response of natural target genes to dynamical modulation of Msn2 translocation is influenced by differences in the kinetics of promoter transitions and transcription factor binding properties. Thus, multiple environmental signals can trigger qualitatively different dynamics of a single transcription factor and influence gene expression patterns.

Nanoparticle induced cell magneto-rotation: monitoring morphology, stress and drug sensitivity of a suspended single cancer cell

Single cell analysis has allowed critical discoveries in drug testing, immunobiology and stem cell research. In addition, a change from two to three dimensional growth conditions radically affects cell behavior. This already resulted in new observations on gene expression and communication networks and in better predictions of cell responses to their environment. However, it is still difficult to study the size and shape of single cells that are freely suspended, where morphological changes are highly significant. Described here is a new method for quantitative real time monitoring of cell size and morphology, on single live suspended cancer cells, unconfined in three dimensions. The precision is comparable to that of the best optical microscopes, but, in contrast, there is no need for confining the cell to the imaging plane. The here first introduced cell magnetorotation (CM) method is made possible by nanoparticle induced cell magnetization. By using a rotating magnetic field, the magnetically labeled cell is actively rotated, and the rotational period is measured in real-time. A change in morphology induces a change in the rotational period of the suspended cell (e.g. when the cell gets bigger it rotates slower). The ability to monitor, in real time, cell swelling or death, at the single cell level, is demonstrated. This method could thus be used for multiplexed real time single cell morphology analysis, with implications for drug testing, drug discovery, genomics and three-dimensional culturing.

Stochastic binary modeling of cells in continuous time as an alternative to biochemical reaction equations

We have developed a coarse-grained formulation for modeling the dynamic behavior of cells quantitatively, based on stochasticity and heterogeneity, rather than on biochemical reactions. We treat each reaction as a continuous-time stochastic process, while reducing each biochemical quantity to a binary value at the level of individual cells. The system can be analytically represented by a finite set of ordinary linear differential equations, which provides a continuous time course prediction of each molecular state. Here we introduce our formalism and demonstrate it with several examples.

From systems biology to systems biomedicine

Systems Biology is about combining theory, technology, and targeted experiments in a way that drives not only data accumulation but knowledge as well. The challenge in Systems Biomedicine is to furthermore translate mechanistic insights in biological systems to clinical application, with the central aim of improving patients' quality of life. The challenge is to find theoretically well-chosen models for the contextually correct and intelligible representation of multi-scale biological systems. In this review, we discuss the current state of Systems Biology, highlight the emergence of Systems Biomedicine, and highlight some of the topics and views that we think are important for the efficient application of Systems Theory in Biomedicine.

Enhancing apoptosis in TRAIL-resistant cancer cells using fundamental response rules

The tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induces apoptosis in malignant cells, while leaving other cells mostly unharmed. However, several carcinomas remain resistant to TRAIL. To investigate the resistance mechanisms in TRAIL-stimulated human fibrosarcoma (HT1080) cells, we developed a computational model to analyze the temporal activation profiles of cell survival (IκB, JNK, p38) and apoptotic (caspase-8 and -3) molecules in wildtype and several (FADD, RIP1, TRAF2 and caspase-8) knock-down conditions. Based on perturbation-response approach utilizing the law of information (signaling flux) conservation, we derived response rules for population-level average cell response. From this approach, i) a FADD-independent pathway to activate p38 and JNK, ii) a crosstalk between RIP1 and p38, and iii) a crosstalk between p62 and JNK are predicted. Notably, subsequent simulations suggest that targeting a novel molecule at p62/sequestosome-1 junction will optimize apoptosis through signaling flux redistribution. This study offers a valuable prospective to sensitive TRAIL-based therapy.

Analyzing inflammatory response as excitable media

The regulatory system of the transcription factor NF-κB plays a great role in many cell functions, including inflammatory response. Interestingly, the NF-κB system is known to up-regulate production of its own triggering signal-namely, inflammatory cytokines such as TNF, IL-1, and IL-6. In this paper we investigate a previously presented model of the NF-κB, which includes both spatial effects and the positive feedback from cytokines. The model exhibits the properties of an excitable medium and has the ability to propagate waves of high cytokine concentration. These waves represent an optimal way of sending an inflammatory signal through the tissue as they create a chemotactic signal able to recruit neutrophils to the site of infection. The simple model displays three qualitatively different states; low stimuli leads to no or very little response. Intermediate stimuli leads to reoccurring waves of high cytokine concentration. Finally, high stimuli leads to a sustained high cytokine concentration, a scenario which is toxic for the tissue cells and corresponds to chronic inflammation. Due to the few variables of the simple model, we are able to perform a phase-space analysis leading to a detailed understanding of the functional form of the model and its limitations. The spatial effects of the model contribute to the robustness of the cytokine wave formation and propagation.

Information transduction capacity of noisy biochemical signaling networks

Molecular noise restricts the ability of an individual cell to resolve input signals of different strengths and gather information about the external environment. Transmitting information through complex signaling networks with redundancies can overcome this limitation. We developed an integrative theoretical and experimental framework, based on the formalism of information theory, to quantitatively predict and measure the amount of information transduced by molecular and cellular networks. Analyzing tumor necrosis factor (TNF) signaling revealed that individual TNF signaling pathways transduce information sufficient for accurate binary decisions, and an upstream bottleneck limits the information gained via multiple integrated pathways. Negative feedback to this bottleneck could both alleviate and enhance its limiting effect, despite decreasing noise. Bottlenecks likewise constrain information attained by networks signaling through multiple genes or cells.

We are all individuals: causes and consequences of non-genetic heterogeneity in mammalian cells

The human body is formed by trillions of individual cells. These cells work together with remarkable precision, first forming an adult organism out of a single fertilized egg, and then keeping the organism alive and functional for decades. To achieve this precision, one would assume that each individual cell reacts in a reliable, reproducible way to a given input, faithfully executing the required task. However, a growing number of studies investigating cellular processes on the level of single cells revealed large heterogeneity even among genetically identical cells of the same cell type. Here we discuss the sources of heterogeneity in mammalian systems; how cells ensure reliable processing of information despite fluctuations in their molecular components; and what could be the benefit of cell-to-cell variability for mammalian cells.

Genomic analysis at the single-cell level

Studying complex biological systems such as a developing embryo, a tumor, or a microbial ecosystem often involves understanding the behavior and heterogeneity of the individual cells that constitute the system and their interactions. In this review, we discuss a variety of approaches to single-cell genomic analysis.

Mechanosignaling to the cell nucleus and gene regulation

Cells integrate physicochemical signals on the nanoscale from the local microenvironment, resulting in altered functional nuclear landscape and gene expression. These alterations regulate diverse biological processes including stem cell differentiation, establishing robust developmental genetic programs and cellular homeostatic control systems. The mechanisms by which these signals are integrated into the 3D spatiotemporal organization of the cell nucleus to elicit differential gene expression programs are poorly understood. In this review I analyze our current understanding of mechanosignal transduction mechanisms to the cell nucleus to induce differential gene regulation. A description of both physical and chemical coupling, resulting in a prestressed nuclear organization, is emphasized. I also highlight the importance of spatial dimension in chromosome assembly, as well as the temporal filtering and stochastic processes at gene promoters that may be important in understanding the biophysical design principles underlying mechanoregulation of gene transcription.

Stimulus-dependent dynamics of p53 in single cells

p53 exhibits a single non-excitable pulse in response to UV radiation, which contrasts with the excitable series of undamped pulses induced by DNA double-strand breaks. The authors find that these behaviors are conferred by specific regulatory connections in the p53 network.

Many biological networks respond to various inputs through a common signaling molecule that triggers distinct cellular outcomes. One potential mechanism for achieving specific input–output relationships is to trigger distinct dynamical patterns in response to different stimuli. Here we focused on the dynamics of p53, a tumor suppressor activated in response to cellular stress. We quantified the dynamics of p53 in individual cells in response to UV and observed a single pulse that increases in amplitude and duration in proportion to the UV dose. This graded response contrasts with the previously described series of fixed pulses in response to γ-radiation. We further found that while γ-triggered p53 pulses are excitable, the p53 response to UV is not excitable and depends on continuous signaling from the input-sensing kinases. Using mathematical modeling and experiments, we identified feedback loops that contribute to specific features of the stimulus-dependent dynamics of p53, including excitability and input-duration dependency. Our study shows that different stresses elicit different temporal profiles of p53, suggesting that modulation of p53 dynamics might be used to achieve specificity in this network.

Robustness portraits of diverse biological networks conserved despite order-of-magnitude parameter uncertainty

MOTIVATION

Biological networks are robust to a wide variety of internal and external perturbations, yet fragile or sensitive to a small minority of perturbations. Due to this rare sensitivity of networks to certain perturbations, it is unclear how precisely biochemical parameters must be experimentally measured in order to accurately predict network function.

RESULTS

Here, we examined a model of cardiac β-adrenergic signaling and found that its robustness portrait, a global measure of steady-state network function, was well conserved even when all parameters were rounded to their nearest 1-2 orders of magnitude. In contrast, β-adrenergic network kinetics were more sensitive to parameter precision. This analysis was then extended to 10 additional networks, including Escherichia coli chemotaxis, stem cell differentiation and cytokine signaling, of which nine exhibited conserved robustness portraits despite the order-of-magnitude approximation of their biochemical parameters. Thus, both fragile and robust aspects of diverse biological networks are largely shaped by network topology and can be predicted despite order-of-magnitude uncertainty in biochemical parameters. These findings suggest an iterative strategy where order-of-magnitude models are used to prioritize experiments toward the fragile network elements that require precise measurements, efficiently driving model revision.

CONTACT

jsaucerman@virginia.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sources of cell-to-cell variability in canonical nuclear factor-κB (NF-κB) signaling pathway inferred from single cell dynamic images

The canonical nuclear factor-κB (NF-κB) signaling pathway controls a gene network important in the cellular inflammatory response. Upon activation, NF-κB/RelA is released from cytoplasmic inhibitors, from where it translocates into the nucleus, subsequently activating negative feedback loops producing either monophasic or damped oscillatory nucleo-cytoplasmic dynamics. Although the population behavior of the NF-κB pathway has been extensively modeled, the sources of cell-to-cell variability are not well understood. We describe an integrated experimental-computational analysis of NF-κB/RelA translocation in a validated cell model exhibiting monophasic dynamics. Quantitative measures of cellular geometry and total cytoplasmic concentration and translocated RelA amounts were used as priors in Bayesian inference to estimate biophysically realistic parameter values based on dynamic live cell imaging studies of enhanced GFP-tagged RelA in stable transfectants. Bayesian inference was performed on multiple cells simultaneously, assuming identical reaction rate parameters, whereas cellular geometry and initial and total NF-κB concentration-related parameters were cell-specific. A subpopulation of cells exhibiting distinct kinetic profiles was identified that corresponded to differences in the IκBα translation rate. We conclude that cellular geometry, initial and total NF-κB concentration, IκBα translation, and IκBα degradation rates account for distinct cell-to-cell differences in canonical NF-κB translocation dynamics.

MicroRNA-155 promotes resolution of hypoxia-inducible factor 1alpha activity during prolonged hypoxia

The hypoxia-inducible factor (HIF) is a key regulator of the transcriptional response to hypoxia. While the mechanism underpinning HIF activation is well understood, little is known about its resolution. Both the protein and the mRNA levels of HIF-1α (but not HIF-2α) were decreased in intestinal epithelial cells exposed to prolonged hypoxia. Coincident with this, microRNA (miRNA) array analysis revealed multiple hypoxia-inducible miRNAs. Among these was miRNA-155 (miR-155), which is predicted to target HIF-1α mRNA. We confirmed the hypoxic upregulation of miR-155 in cultured cells and intestinal tissue from mice exposed to hypoxia. Furthermore, a role for HIF-1α in the induction of miR-155 in hypoxia was suggested by the identification of hypoxia response elements in the miR-155 promoter and confirmed experimentally. Application of miR-155 decreased the HIF-1α mRNA, protein, and transcriptional activity in hypoxia, and neutralization of endogenous miR-155 reversed the resolution of HIF-1α stabilization and activity. Based on these data and a mathematical model of HIF-1α suppression by miR-155, we propose that miR-155 induction contributes to an isoform-specific negative-feedback loop for the resolution of HIF-1α activity in cells exposed to prolonged hypoxia, leading to oscillatory behavior of HIF-1α-dependent transcription.

A precarious balance: pluripotency factors as lineage specifiers

Understanding the basis of the unrestricted multilineage differentiation potential of pluripotent cells will be of developmental and translational consequence. We propose that pluripotency transcription factors are lineage specifiers that direct commitment to specific fetal lineages. Individual factors bestow the ability to differentiate into particular cell types, and concomitant expression of multiple lineage specifiers within pluripotent cells enables differentiation into every fetal lineage. Moreover, we speculate that, rather than being an intrinsically stable "ground state," pluripotency is an inherently precarious condition in which rival lineage specifiers continually compete to specify differentiation along mutually exclusive lineages.

Microfluidic array cytometer based on refractive optical tweezers for parallel trapping, imaging and sorting of individual cells

Analysis of genetic and functional variability in populations of living cells requires experimental techniques capable of monitoring cellular processes such as cell signaling of many single cells in parallel while offering the possibility to sort interesting cell phenotypes for further investigations. Although flow cytometry is able to sequentially probe and sort thousands of cells per second, dynamic processes cannot be experimentally accessed on single cells due to the sub-second sampling time. Cellular dynamics can be measured by image cytometry of surface-immobilized cells, however, cell sorting is complicated under these conditions due to cell attachment. We here developed a cytometric tool based on refractive multiple optical tweezers combined with microfluidics and optical microscopy. We demonstrate contact-free immobilization of more than 200 yeast cells into a high-density array of optical traps in a microfluidic chip. The cell array could be moved to specific locations of the chip enabling us to expose in a controlled manner the cells to reagents and to analyze the responses of individual cells in a highly parallel format using fluorescence microscopy. We further established a method to sort single cells within the microfluidic device using an additional steerable optical trap. Ratiometric fluorescence imaging of intracellular pH of trapped yeast cells allowed us on the one hand to measure the effect of the trapping laser on the cells' viability and on the other hand to probe the dynamic response of the cells upon glucose sensing.

Hierarchies of NF-κB target-gene regulation

Members of the NF-κB family of transcription factors function as dominant regulators of inducible gene expression in almost all cell types in response to a broad range of stimuli, with particularly important roles in coordinating both innate and adaptive immunity. This review summarizes the present knowledge and recent progress toward elucidating the numerous regulatory layers that confer target-gene selectivity in response to an NF-κB-inducing stimulus.

Modeling the NF-κB mediated inflammatory response predicts cytokine waves in tissue

Background

Waves propagating in "excitable media" is a reliable way to transmit signals in space. A fascinating example where living cells comprise such a medium is Dictyostelium D. which propagates waves of chemoattractant to attract distant cells. While neutrophils chemotax in a similar fashion as Dictyostelium D., it is unclear if chemoattractant waves exist in mammalian tissues and what mechanisms could propagate them.

Results

We propose that chemoattractant cytokine waves may naturally develop as a result of NF-κB response. Using a heuristic mathematical model of NF-κB-like circuits coupled in space we show that the known characteristics of NF-κB response favor cytokine waves.

Conclusions

While the propagating wave of cytokines is generally beneficial for inflammation resolution, our model predicts that there exist special conditions that can cause chronic inflammation and re-occurrence of acute inflammatory response.

Simulating quantitative cellular responses using asynchronous threshold Boolean network ensembles

BACKGROUND

With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data.

RESULTS

We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature.

CONCLUSION

A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a) using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b) with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our results suggest that this approach is both quantitative, allowing statistical verification and calibration, and extensible, allowing modification and revision as guided by experimental evidence. The simulation methodology is part of the US EPA Virtual Liver, which is investigating the effects of everyday contaminants on living tissues. Future models will incorporate additional crosstalk surrounding proliferation as well as the putative effects of xenobiotics on these signaling cascades within hepatocytes.

Quantitative characterization and analysis of the dynamic NF-κB response in microglia

Background

Activation of the NF-κB transcription factor and its associated gene expression in microglia is a key component in the response to brain injury. Its activation is dynamic and is part of a network of biochemical species with multiple feedback regulatory mechanisms. Mathematical modeling, which has been instrumental for understanding the NF-κB response in other cell types, offers a valuable tool to investigate the regulation of NF-κB activation in microglia at a systems level.

Results

We quantify the dynamic response of NF-κB activation and activation of the upstream kinase IKK using ELISA measurements of a microglial cell line following treatment with the pro-inflammatory cytokine TNFα. A new mathematical model is developed based on these data sets using a modular procedure that exploits the feedback structure of the network. We show that the new model requires previously unmodeled dynamics involved in the stimulus-induced degradation of the inhibitor IκBα in order to properly describe microglial NF-κB activation in a statistically consistent manner. This suggests a more prominent role for the ubiquitin-proteasome system in regulating the activation of NF-κB to inflammatory stimuli. We also find that the introduction of nonlinearities in the kinetics of IKK activation and inactivation is essential for proper characterization of transient IKK activity and corresponds to known biological mechanisms. Numerical analyses of the model highlight key regulators of the microglial NF-κB response, as well as those governing IKK activation. Results illustrate the dynamic regulatory mechanisms and the robust yet fragile nature of the negative feedback regulated network.

Conclusions

We have developed a new mathematical model that incorporates previously unmodeled dynamics to characterize the dynamic response of the NF-κB signaling network in microglia. This model is the first of its kind for microglia and provides a tool for the quantitative, systems level study the dynamic cellular response to inflammatory stimuli.

Dynamic chromatin localization of Sirt6 shapes stress- and aging-related transcriptional networks

The sirtuin Sirt6 is a NAD-dependent histone deacetylase that is implicated in gene regulation and lifespan control. Sirt6 can interact with the stress-responsive transcription factor NF-κB and regulate some NF-κB target genes, but the full scope of Sirt6 target genes as well as dynamics of Sirt6 occupancy on chromatin are not known. Here we map Sirt6 occupancy on mouse promoters genome-wide and show that Sirt6 occupancy is highly dynamic in response to TNF-α. More than half of Sirt6 target genes are only revealed upon stress-signaling. The majority of genes bound by NF-κB subunit RelA recruit Sirt6, and dynamic Sirt6 relocalization is largely driven in a RelA-dependent manner. Integrative analysis with global gene expression patterns in wild-type, Sirt6−/−, and double Sirt6−/− RelA−/− cells reveals the epistatic relationships between Sirt6 and RelA in shaping diverse temporal patterns of gene expression. Genes under the direct joint control of Sirt6 and RelA include several with prominent roles in cell senescence and organismal aging. These data suggest dynamic chromatin relocalization of Sirt6 as a key output of NF-κB signaling in stress response and aging.

Sirtuins (Sirt) are a family of enzymes that modify chromatin and other proteins to affect gene activity. Loss of Sirt6 leads to a progeria-like phenotype in mice, but the target of SIRT6 action has been elusive. Here we show that Sirt6 binds to thousands of gene promoters in a stress-inducible fashion, guided by the stress-responsive transcription factor NF-κB. Both the departure and arrival of Sirt6 alter gene expression, shaping the temporal dynamics of NF-κB transcriptional response and directly controlling the expression of other key regulators of aging. These findings provide the first view of how an oscillatory transcription factor can drive a progression of chromatin changes over time.

Systems biology in immunology: a computational modeling perspective

Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and to conduct simulations of immune function. We provide descriptions of the key data-gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and the reasons why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease.

Follicular helper CD4 T cells (TFH)

T cell help to B cells is a fundamental aspect of adaptive immunity and the generation of immunological memory. Follicular helper CD4 T (T(FH)) cells are the specialized providers of B cell help. T(FH) cells depend on expression of the master regulator transcription factor Bcl6. Distinguishing features of T(FH) cells are the expression of CXCR5, PD-1, SAP (SH2D1A), IL-21, and ICOS, among other molecules, and the absence of Blimp-1 (prdm1). T(FH) cells are important for the formation of germinal centers. Once germinal centers are formed, T(FH) cells are needed to maintain them and to regulate germinal center B cell differentiation into plasma cells and memory B cells. This review covers T(FH) differentiation, T(FH) functions, and human T(FH) cells, discussing recent progress and areas of uncertainty or disagreement in the literature, and it debates the developmental relationship between T(FH) cells and other CD4 T cell subsets (Th1, Th2, Th17, iTreg).

Impulse control: temporal dynamics in gene transcription

Regulatory circuits controlling gene expression constantly rewire to adapt to environmental stimuli, differentiation cues, and disease. We review our current understanding of the temporal dynamics of gene expression in eukaryotes and prokaryotes and the molecular mechanisms that shape them. We delineate several prototypical temporal patterns, including "impulse" (or single-pulse) patterns in response to transient environmental stimuli, sustained (or state-transitioning) patterns in response to developmental cues, and oscillating patterns. We focus on impulse responses and their higher-order temporal organization in regulons and cascades and describe how core protein circuits and cis-regulatory sequences in promoters integrate with chromatin architecture to generate these responses.

Principles and strategies for developing network models in cancer

The flood of genome-wide data generated by high-throughput technologies currently provides biologists with an unprecedented opportunity: to manipulate, query, and reconstruct functional molecular networks of cells. Here, we outline three underlying principles and six strategies to infer network models from genomic data. Then, using cancer as an example, we describe experimental and computational approaches to infer "differential" networks that can identify genes and processes driving disease phenotypes. In conclusion, we discuss how a network-level understanding of cancer can be used to predict drug response and guide therapeutics.

Inflammatory gene regulatory networks in amnion cells following cytokine stimulation: translational systems approach to modeling human parturition

A majority of the studies examining the molecular regulation of human labor have been conducted using single gene approaches. While the technology to produce multi-dimensional datasets is readily available, the means for facile analysis of such data are limited. The objective of this study was to develop a systems approach to infer regulatory mechanisms governing global gene expression in cytokine-challenged cells in vitro, and to apply these methods to predict gene regulatory networks (GRNs) in intrauterine tissues during term parturition. To this end, microarray analysis was applied to human amnion mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially expressed transcripts were subjected to hierarchical clustering, temporal expression profiling, and motif enrichment analysis, from which a GRN was constructed. These methods were then applied to fetal membrane specimens collected in the absence or presence of spontaneous term labor. Analysis of cytokine-responsive genes in AMCs revealed a sterile immune response signature, with promoters enriched in response elements for several inflammation-associated transcription factors. In comparison to the fetal membrane dataset, there were 34 genes commonly upregulated, many of which were part of an acute inflammation gene expression signature. Binding motifs for nuclear factor-κB were prominent in the gene interaction and regulatory networks for both datasets; however, we found little evidence to support the utilization of pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens were also enriched for transcripts governed by hypoxia-inducible factor. The approach presented here provides an uncomplicated means to infer global relationships among gene clusters involved in cellular responses to labor-associated signals.

System Integration - A Major Step toward Lab on a Chip

Microfluidics holds great promise to revolutionize various areas of biological engineering, such as single cell analysis, environmental monitoring, regenerative medicine, and point-of-care diagnostics. Despite the fact that intensive efforts have been devoted into the field in the past decades, microfluidics has not yet been adopted widely. It is increasingly realized that an effective system integration strategy that is low cost and broadly applicable to various biological engineering situations is required to fully realize the potential of microfluidics. In this article, we review several promising system integration approaches for microfluidics and discuss their advantages, limitations, and applications. Future advancements of these microfluidic strategies will lead toward translational lab-on-a-chip systems for a wide spectrum of biological engineering applications.

Macroscopic law of conservation revealed in the population dynamics of Toll-like receptor signaling

Stimulating the receptors of a single cell generates stochastic intracellular signaling. The fluctuating response has been attributed to the low abundance of signaling molecules and the spatio-temporal effects of diffusion and crowding. At population level, however, cells are able to execute well-defined deterministic biological processes such as growth, division, differentiation and immune response. These data reflect biology as a system possessing microscopic and macroscopic dynamics. This commentary discusses the average population response of the Toll-like receptor (TLR) 3 and 4 signaling. Without requiring detailed experimental data, linear response equations together with the fundamental law of information conservation have been used to decipher novel network features such as unknown intermediates, processes and cross-talk mechanisms. For single cell response, however, such simplicity seems far from reality. Thus, as observed in any other complex systems, biology can be considered to possess order and disorder, inheriting a mixture of predictable population level and unpredictable single cell outcomes.