Overview of the Alliance for Cellular Signaling

Interlinked switch circuits of biological intelligence

Eukaryotic cells learn and adapt via unknown network architectures. Recent work demonstrated a circuit of two GTPases used by cells to overcome growth factor scarcity, encouraging our view that artificial and biological intelligence share strikingly similar design principles and that cells function as deep reinforcement learning (RL) agents in uncertain environments.

Understanding molecular signaling cascades in neural disease using multi-resolution models

If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.

GPR174 signals via Gαs to control a CD86-containing gene expression program in B cells

GPR174 is abundantly expressed in B and T lymphocytes and has a role in restraining T cell responses, but the function of GPR174 in B cells is less clear. Here we report that upon in vitro culture B cells undergo a spontaneous GPR174-dependent activation process that is associated with marked changes in gene expression, including up-regulation of Cd86, Nr4a1, Ccr7, and phosphodiesterases. B cells lacking Gαs show a block in induction of the GPR174-dependent program. Spontaneous up-regulation of CD86 in cultured B cells is dependent on protein kinase A. Both GPR174- and Gαs-deficient B cells show enhanced survival in culture. In vivo, GPR174 contributes to NUR77 expression in follicular B cells and is needed for establishing a marginal zone compartment of normal size. Treatment of mice with lysophosphatidylserine (lysoPS), a GPR174 ligand, is sufficient to promote CD86 up-regulation by follicular B cells. These findings demonstrate that GPR174 can signal via Gαs to modulate B cell gene expression and show this can occur in vivo in response to lysoPS. Additionally, the findings illuminate a pathway that might be targeted to improve systems for the in vitro study of B cell responses.

Hypertrophy of human embryonic stem cell-derived cardiomyocytes supported by positive feedback between Ca2+ and diacylglycerol signals

Human embryonic stem cell-derived cardiomyocytes develop pronounced hypertrophy in response to angiotensin-2, endothelin-1, and a selected mix of three fatty acids. All three of these responses are accompanied by increases in both basal cytoplasmic Ca2+ and diacylglycerol, quantified with the Ca2+ sensor Fluo-4 and a FRET-based diacylglycerol sensor expressed in these cardiomyocytes. The heart glycoside, ouabain (30 nM), and a recently developed inhibitor of diacylglycerol lipases, DO34 (1 μM), cause similar hypertrophy responses, and both responses are accompanied by equivalent increases of basal Ca2+ and diacylglycerol. These results together suggest that basal Ca2+ and diacylglycerol form a positive feedback signaling loop that promotes execution of cardiac growth programs in these human myocytes. Given that basal Ca2+ in myocytes depends strongly on the Na+ gradient, we also tested whether nanomolar ouabain concentrations might stimulate Na+/K+ pumps, as described by others, and thereby prevent hypertrophy. However, stimulatory effects of nanomolar ouabain (1.5 nM) were not verified on Na+/K+ pump currents in stem cell-derived myocytes, nor did nanomolar ouabain block hypertrophy induced by endothelin-1. Thus, low-dose ouabain is not a "protective" intervention under the conditions of these experiments in this human myocyte model. To summarize, the major aim of this study has been to characterize the progression of hypertrophy in human embryonic stem cell-derived cardiac myocytes in dependence on diacylglycerol and Na+ gradient changes, developing a case that positive feedback coupling between these mechanisms plays an important role in the initiation of hypertrophy programs.

FV-429 Induced Apoptosis Through ROS-Mediated ERK2 Nuclear Translocation and p53 Activation in Gastric Cancer Cells

Following our previous finding which revealed that FV-429 induces apoptosis in human hepatocellular carcinoma HepG2 cells, in this study, we found that FV-429 could also induce apoptosis in human gastric cancer cells. Firstly, FV-429 inhibited the viability of BGC-823 and MGC-803 cells with IC50 values in the range of 38.10 ± 6.28 and 31.53 ± 6.84 µM for 24 h treatment by MTT-assay. Secondly, FV-429 induced apoptosis in BGC-823 and MGC-803 cells through the mitochondrial-mediated pathway, showing an increase in Bax/Bcl-2 ratios, and caspase-9 activation, without change in caspase-8. Further research revealed that the mitogen-activated protein kinases, including c-Jun N-terminal kinase, extracellular regulated kinase, and p38 mitogen-activated protein kinase, could be activated by FV-429-induced high level ROS. Moreover, FV-429 also promoted the ERK2 nuclear translocation, resulting in the co-translocation of p53 to the nucleus and increased transcription of p53-regulated proapoptotic genes. FV-429 significantly inhibited the nude mice xenograft tumors growth of BGC-823 or MGC-803 cells in vivo.

Nuclear Receptor Signaling Atlas: Opening Access to the Biology of Nuclear Receptor Signaling Pathways

Signaling pathways involving nuclear receptors (NRs), their ligands and coregulators, regulate tissue-specific transcriptomes in diverse processes, including development, metabolism, reproduction, the immune response and neuronal function, as well as in their associated pathologies. The Nuclear Receptor Signaling Atlas (NURSA) is a Consortium focused around a Hub website (www.nursa.org) that annotates and integrates diverse ‘omics datasets originating from the published literature and NURSA-funded Data Source Projects (NDSPs). These datasets are then exposed to the scientific community on an Open Access basis through user-friendly data browsing and search interfaces. Here, we describe the redesign of the Hub, version 3.0, to deploy “Web 2.0” technologies and add richer, more diverse content. The Molecule Pages, which aggregate information relevant to NR signaling pathways from myriad external databases, have been enhanced to include resources for basic scientists, such as post-translational modification sites and targeting miRNAs, and for clinicians, such as clinical trials. A portal to NURSA’s Open Access, PubMed-indexed journal Nuclear Receptor Signaling has been added to facilitate manuscript submissions. Datasets and information on reagents generated by NDSPs are available, as is information concerning periodic new NDSP funding solicitations. Finally, the new website integrates the Transcriptomine analysis tool, which allows for mining of millions of richly annotated public transcriptomic data points in the field, providing an environment for dataset re-use and citation, bench data validation and hypothesis generation. We anticipate that this new release of the NURSA database will have tangible, long term benefits for both basic and clinical research in this field.

Structural Insights into GIRK Channel Function

G protein-gated inwardly rectifying potassium (GIRK; Kir3) channels, which are members of the large family of inwardly rectifying potassium channels (Kir1-Kir7), regulate excitability in the heart and brain. GIRK channels are activated following stimulation of G protein-coupled receptors that couple to the G(i/o) (pertussis toxin-sensitive) G proteins. GIRK channels, like all other Kir channels, possess an extrinsic mechanism of inward rectification involving intracellular Mg(2+) and polyamines that occlude the conduction pathway at membrane potentials positive to E(K). In the past 17 years, more than 20 high-resolution atomic structures containing GIRK channel cytoplasmic domains and transmembrane domains have been solved. These structures have provided valuable insights into the structural determinants of many of the properties common to all inward rectifiers, such as permeation and rectification, as well as revealing the structural bases for GIRK channel gating. In this chapter, we describe advances in our understanding of GIRK channel function based on recent high-resolution atomic structures of inwardly rectifying K(+) channels discussed in the context of classical structure-function experiments.

Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages

BACKGROUND

High-throughput methods for biological measurements generate vast amounts of quantitative data, which necessitate the development of advanced approaches to data analysis to help understand the underlying mechanisms and networks. Reconstruction of biological networks from measured data of different components is a significant challenge in systems biology.

RESULTS

We use an information theoretic approach to reconstruct phosphoprotein-cytokine networks in RAW 264.7 macrophage cells. Cytokines are secreted upon activation of a wide range of regulatory signals transduced by the phosphoprotein network. Identifying these components can help identify regulatory modules responsible for the inflammatory phenotype. The information theoretic approach is based on estimation of mutual information of interactions by using kernel density estimators. Mutual information provides a measure of statistical dependencies between interacting components. Using the topology of the network derived, we develop a data-driven parsimonious input-output model of the phosphoprotein-cytokine network.

CONCLUSIONS

We demonstrate the applicability of our information theoretic approach to reconstruction of biological networks. For the phosphoprotein-cytokine network, this approach not only captures most of the known signaling components involved in cytokine release but also predicts new signaling components involved in the release of cytokines. The results of this study are important for gaining a clear understanding of macrophage activation during the inflammation process.

Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data

The inference of cellular architectures from detailed time-series measurements of intracellular variables is an active area of research. High throughput measurements of responses to cellular perturbations are usually analyzed using a variety of machine learning methods that typically only work within one type of measurement. Here, summaries of some recent research attempts are presented–these studies have expanded the scope of the problem by systematically integrating measurements across multiple layers of regulation including second messengers, protein phosphorylation markers, transcript levels, and functional phenotypes into signaling vectors or signatures of signal transduction. Data analyses through simple unsupervised methods provide rich insight into the biology of the underlying network, and in some cases reconstruction of key architectures of the underlying network from perturbation data. The methodological advantages provided by these efforts are examined using data from a publicly available database of responses to systematic perturbations of cellular signaling networks generated by the Alliance for Cellular Signaling (AfCS).

Complexity in cancer biology: is systems biology the answer?

Complex phenotypes emerge from the interactions of thousands of macromolecules that are organized in multimolecular complexes and interacting functional modules. In turn, modules form functional networks in health and disease. Omics approaches collect data on changes for all genes and proteins and statistical analysis attempts to uncover the functional modules that perform the functions that characterize higher levels of biological organization. Systems biology attempts to transcend the study of individual genes/proteins and to integrate them into higher order information. Cancer cells exhibit defective genetic and epigenetic networks formed by altered complexes and network modules arising in different parts of tumor tissues that sustain autonomous cell behavior which ultimately lead tumor growth. We suggest that an understanding of tumor behavior must address not only molecular but also, and more importantly, tumor cell heterogeneity, by considering cancer tissue genetic and epigenetic networks, by characterizing changes in the types, composition, and interactions of complexes and networks in the different parts of tumor tissues, and by identifying critical hubs that connect them in time and space.

Lgr5-expressing cells are sufficient and necessary for postnatal mammary gland organogenesis

Mammary epithelial stem cells are vital to tissue expansion and remodeling during various phases of postnatal mammary development. Basal mammary epithelial cells are enriched in Wnt-responsive cells and can reconstitute cleared mammary fat pads upon transplantation into mice. Lgr5 is a Wnt-regulated target gene and was identified as a major stem cell marker in the small intestine, colon, stomach, and hair follicle, as well as in kidney nephrons. Here, we demonstrate the outstanding regenerative potential of a rare population of Lgr5-expressing (Lgr5(+)) mammary epithelial cells (MECs). We found that Lgr5(+) cells reside within the basal population, are superior to other basal cells in regenerating functional mammary glands (MGs), are exceptionally efficient in reconstituting MGs from single cells, and exhibit regenerative capacity in serial transplantations. Loss-of-function and depletion experiments of Lgr5(+) cells from transplanted MECs or from pubertal MGs revealed that these cells are not only sufficient but also necessary for postnatal mammary organogenesis.

In silico models for B-cell epitope recognition and signaling

Tremendous technological advances in peptide synthesis and modification in recent years have resolved the major limitations of peptide-based vaccines. B-cell epitopes are major components of these vaccines (besides having other biological applications). Researchers have been developing in silico or computational models for the prediction of both linear and conformational B-cell epitopes, enabling immunologists and clinicians to identify the most promising epitopes for characterization in the laboratory. Attempts are also ongoing in systems biology to delineate the signaling networks in immune cells. Here we present all possible in silico models developed thus far in these areas.

Multiscale models of cell signaling

Computational models of signal transduction face challenges of scale below the resolution of a single cell. Here, we organize these challenges around three key interfaces for multiscale models of cell signaling: molecules to pathways, pathways to networks, and networks to outcomes. Each interface requires its own set of computational approaches and systems-level data, and no single approach or dataset can effectively bridge all three interfaces. This suggests that realistic "whole-cell" models of signaling will need to agglomerate different model types that span critical intracellular scales. Future multiscale models will be valuable for understanding the impact of signaling mutations or population variants that lead to cellular diseases such as cancer.

Lipidomics: when apocrypha becomes canonical

Lipidomics is a branch of the field of metabolomics. Although only about a decade since its inception, lipidomics has already had a major influence on the way in which questions about lipid metabolism and signaling are posed. The field is intertwined in the culture and rich history of mass spectrometry. Early work emphasized analytical issues such as limits of detection and numbers of molecular species quantitated in single injections. Increased sophistication in applications of lipidomic analysis and emerging technologies, such as imaging mass spectrometry, are facilitating the study of lipid metabolism and signaling species in cellular functions and human diseases. In the coming years we anticipate a richer understanding of how specific lipid species mediate complex biological processes and interconnections between cellular pathways that were thought to be disparate.

Ten years of pathway analysis: current approaches and outstanding challenges

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.

Silver spoons and other personal reflections

The intent is to tell a story-hopefully one that is at various times serious, light-hearted, or provocative-that describes my life in biomedical science, especially focusing on the 50 years from 1961 (as a college senior) to the present.

A mouse macrophage lipidome

We report the lipidomic response of the murine macrophage RAW cell line to Kdo(2)-lipid A, the active component of an inflammatory lipopolysaccharide functioning as a selective TLR4 agonist and compactin, a statin inhibitor of cholesterol biosynthesis. Analyses of lipid molecular species by dynamic quantitative mass spectrometry and concomitant transcriptomic measurements define the lipidome and demonstrate immediate responses in fatty acid metabolism represented by increases in eicosanoid synthesis and delayed responses characterized by sphingolipid and sterol biosynthesis. Lipid remodeling of glycerolipids, glycerophospholipids, and prenols also take place, indicating that activation of the innate immune system by inflammatory mediators leads to alterations in a majority of mammalian lipid categories, including unanticipated effects of a statin drug. Our studies provide a systems-level view of lipid metabolism and reveal significant connections between lipid and cell signaling and biochemical pathways that contribute to innate immune responses and to pharmacological perturbations.

Cytopede: a three-dimensional tool for modeling cell motility on a flat surface

When cultured on flat surfaces, fibroblasts and many other cells spread to form thin lamellar sheets. Motion then occurs by extension of the sheet at the leading edge and retraction at the trailing edge. Comprehensive quantitative models of these phenomena have so far been lacking and to address this need, we have designed a three-dimensional code called Cytopede specialized for the simulation of the mechanical and signaling behavior of plated cells. Under assumptions by which the cytosol and the cytoskeleton are treated from a continuum mechanical perspective, Cytopede uses the finite element method to solve mass and momentum equations for each phase, and thus determine the time evolution of cellular models. We present the physical concepts that underlie Cytopede together with the algorithms used for their implementation. We then validate the approach by a computation of the spread of a viscous sessile droplet. Finally, to exemplify how Cytopede enables the testing of ideas about cell mechanics, we simulate a simple fibroblast model. We show how Cytopede allows computation, not only of basic characteristics of shape and velocity, but also of maps of cell thickness, cytoskeletal density, cytoskeletal flow, and substratum tractions that are readily compared with experimental data.

G-protein coupled receptor signaling architecture of mammalian immune cells

A series of recent studies on large-scale networks of signaling and metabolic systems revealed that a certain network structure often called “bow-tie network” are observed. In signaling systems, bow-tie network takes a form with diverse and redundant inputs and outputs connected via a small numbers of core molecules. While arguments have been made that such network architecture enhances robustness and evolvability of biological systems, its functional role at a cellular level remains obscure. A hypothesis was proposed that such a network function as a stimuli-reaction classifier where dynamics of core molecules dictate downstream transcriptional activities, hence physiological responses against stimuli. In this study, we examined whether such hypothesis can be verified using experimental data from Alliance for Cellular Signaling (AfCS) that comprehensively measured GPCR related ligands response for B-cell and macrophage. In a GPCR signaling system, cAMP and Ca²⁺ act as core molecules. Stimuli-response for 32 ligands to B-Cells and 23 ligands to macrophages has been measured. We found that ligands with correlated changes of cAMP and Ca²⁺ tend to cluster closely together within the hyperspaces of both cell types and they induced genes involved in the same cellular processes. It was found that ligands inducing cAMP synthesis activate genes involved in cell growth and proliferation; cAMP and Ca²⁺ molecules that increased together form a feedback loop and induce immune cells to migrate and adhere together. In contrast, ligands without a core molecules response are scattered throughout the hyperspace and do not share clusters. G-protein coupling receptors together with immune response specific receptors were found in cAMP and Ca²⁺ activated clusters. Analyses have been done on the original software applicable for discovering ‘bow-tie’ network architectures within the complex network of intracellular signaling where ab initio clustering has been implemented as well. Groups of potential transcription factors for each specific group of genes were found to be partly conserved across B-Cell and macrophage. A series of findings support the hypothesis.

Robust spatial sensing of mating pheromone gradients by yeast cells

Projecting or moving up a chemical gradient is a universal behavior of living organisms. We tested the ability of S. cerevisiaea-cells to sense and respond to spatial gradients of the mating pheromone α-factor produced in a microfluidics chamber; the focus was on bar1Δ strains, which do not degrade the pheromone input. The yeast cells exhibited good accuracy with the mating projection typically pointing in the correct direction up the gradient (∼80% under certain conditions), excellent sensitivity to shallow gradients, and broad dynamic range so that gradient-sensing was relatively robust over a 1000-fold range of average α-factor concentrations. Optimal directional sensing occurred at lower concentrations (5 nM) close to the K_d of the receptor and with steeper gradient slopes. Pheromone supersensitive mutations (sst2Δ and ste2^300Δ) that disrupt the down-regulation of heterotrimeric G-protein signaling caused defects in both sensing and response. Interestingly, yeast cells employed adaptive mechanisms to increase the robustness of the process including filamentous growth (i.e. directional distal budding) up the gradient at low pheromone concentrations, bending of the projection to be more aligned with the gradient, and forming a more accurate second projection when the first projection was in the wrong direction. Finally, the cells were able to amplify a shallow external gradient signal of α-factor to produce a dramatic polarization of signaling proteins at the front of the cell. Mathematical modeling revealed insights into the mechanism of this amplification and how the supersensitive mutants can disrupt accurate polarization. Together, these data help to specify and elucidate the abilities of yeast cells to sense and respond to spatial gradients of pheromone.

Bioinformatics analyses for signal transduction networks

Research in signaling networks contributes to a deeper understanding of organism living activities. With the development of experimental methods in the signal transduction field, more and more mechanisms of signaling pathways have been discovered. This paper introduces such popular bioinformatics analysis methods for signaling networks as the common mechanism of signaling pathways and database resource on the Internet, summerizes the methods of analyzing the structural properties of networks, including structural Motif finding and automated pathways generation, and discusses the modeling and simulation of signaling networks in detail, as well as the research situation and tendency in this area. Now the investigation of signal transduction is developing from small-scale experiments to large-scale network analysis, and dynamic simulation of networks is closer to the real system. With the investigation going deeper than ever, the bioinformatics analysis of signal transduction would have immense space for development and application.

Introducing spatial information into predictive NF-kappaB modelling--an agent-based approach

Nature is governed by local interactions among lower-level sub-units, whether at the cell, organ, organism, or colony level. Adaptive system behaviour emerges via these interactions, which integrate the activity of the sub-units. To understand the system level it is necessary to understand the underlying local interactions. Successful models of local interactions at different levels of biological organisation, including epithelial tissue and ant colonies, have demonstrated the benefits of such 'agent-based' modelling. Here we present an agent-based approach to modelling a crucial biological system--the intracellular NF-kappaB signalling pathway. The pathway is vital to immune response regulation, and is fundamental to basic survival in a range of species. Alterations in pathway regulation underlie a variety of diseases, including atherosclerosis and arthritis. Our modelling of individual molecules, receptors and genes provides a more comprehensive outline of regulatory network mechanisms than previously possible with equation-based approaches. The method also permits consideration of structural parameters in pathway regulation; here we predict that inhibition of NF-kappaB is directly affected by actin filaments of the cytoskeleton sequestering excess inhibitors, therefore regulating steady-state and feedback behaviour.

Operating regimes of signaling cycles: statics, dynamics, and noise filtering

A ubiquitous building block of signaling pathways is a cycle of covalent modification (e.g., phosphorylation and dephosphorylation in MAPK cascades). Our paper explores the kind of information processing and filtering that can be accomplished by this simple biochemical circuit. Signaling cycles are particularly known for exhibiting a highly sigmoidal (ultrasensitive) input–output characteristic in a certain steady-state regime. Here, we systematically study the cycle's steady-state behavior and its response to time-varying stimuli. We demonstrate that the cycle can actually operate in four different regimes, each with its specific input–output characteristics. These results are obtained using the total quasi–steady-state approximation, which is more generally valid than the typically used Michaelis-Menten approximation for enzymatic reactions. We invoke experimental data that suggest the possibility of signaling cycles operating in one of the new regimes. We then consider the cycle's dynamic behavior, which has so far been relatively neglected. We demonstrate that the intrinsic architecture of the cycles makes them act—in all four regimes—as tunable low-pass filters, filtering out high-frequency fluctuations or noise in signals and environmental cues. Moreover, the cutoff frequency can be adjusted by the cell. Numerical simulations show that our analytical results hold well even for noise of large amplitude. We suggest that noise filtering and tunability make signaling cycles versatile components of more elaborate cell-signaling pathways.

A cell is subjected to constantly changing environments and time-varying stimuli. Signals sensed at the cell surface are transmitted inside the cell by signaling pathways. Such pathways can transform signals in diverse ways and perform some preliminary information processing. A ubiquitous building block of signaling pathways is a simple biochemical cycle involving covalent modification of an enzyme–substrate pair. Our paper is devoted to fully characterizing the static and dynamic behavior of this simple cycle, an essential first step in understanding the behavior of interconnections of such cycles. It is known that a signaling cycle can function as a static switch, with the steady-state output being an “ultrasensitive” function of the input, i.e., changing from a low to high value for only a small change in the input. We show that there are in fact precisely four major regimes of static and dynamic operation (with ultrasensitive being one of the static regimes). Each regime has its own input–output characteristics. Despite the distinctive features of these four regimes, they all respond to time-varying stimuli by filtering out high-frequency fluctuations or noise in their inputs, while passing through the lower-frequency information-bearing variations. A cell can select the regime and tune the noise-filtering characteristics of the individual cycles in a specific signaling pathway. This tunability makes signaling cycles versatile components of elaborate cell-signaling pathways.

Empirical multiscale networks of cellular regulation

Grouping genes by similarity of expression across multiple cellular conditions enables the identification of cellular modules. The known functions of genes enable the characterization of the aggregate biological functions of these modules. In this paper, we use a high-throughput approach to identify the effective mutual regulatory interactions between modules composed of mouse genes from the Alliance for Cell Signaling (AfCS) murine B-lymphocyte database which tracks the response of ∼15,000 genes following chemokine perturbation. This analysis reveals principles of cellular organization that we discuss along four conceptual axes. (1) Regulatory implications: the derived collection of influences between any two modules quantifies intuitive as well as unexpected regulatory interactions. (2) Behavior across scales: trends across global networks of varying resolution (composed of various numbers of modules) reveal principles of assembly of high-level behaviors from smaller components. (3) Temporal behavior: tracking the mutual module influences over different time intervals provides features of regulation dynamics such as duration, persistence, and periodicity. (4) Gene Ontology correspondence: the association of modules to known biological roles of individual genes describes the organization of functions within coexpressed modules of various sizes. We present key specific results in each of these four areas, as well as derive general principles of cellular organization. At the coarsest scale, the entire transcriptional network contains five divisions: two divisions devoted to ATP production/biosynthesis and DNA replication that activate all other divisions, an “extracellular interaction” division that represses all other divisions, and two divisions (proliferation/differentiation and membrane infrastructure) that activate and repress other divisions in specific ways consistent with cell cycle control.

In a eukaryotic organism such as the mouse, the complete transcriptional network contains ∼15,000 genes and up to 225 million regulatory relationships between pairs of genes. Determining all of these relationships is currently intractable using traditional experimental techniques, and, thus, a comprehensive description of the entire mouse transcriptional network is elusive. Alternatively, one can apply the limited amount of experimental data to determine the entire transcriptional network at a less detailed, higher level. This is analogous to considering a map of the world resolved to the kilometer rather than to the millimeter. Here, we derive from mouse microarray data several high-scale transcriptional networks by determining the mutual effective regulatory influences of large modules of genes. In particular, global transcriptional networks containing 12 to 72 modules are derived, and analysis of these multiscale networks reveals properties of the transcriptional network that are universal at all scales (e.g., maintenance of homeostasis) and properties that vary as a function of scale (e.g., the fractions of module pairs that exert mutual regulation). In addition, we describe how cellular functions associated with large modules (those containing many genes) are composed of more specific functions associated with smaller modules.

A versatile approach to multiple gene RNA interference using microRNA-based short hairpin RNAs

Background

Effective and stable knockdown of multiple gene targets by RNA interference is often necessary to overcome isoform redundancy, but it remains a technical challenge when working with intractable cell systems.

Results

We have developed a flexible platform using RNA polymerase II promoter-driven expression of microRNA-like short hairpin RNAs which permits robust depletion of multiple target genes from a single transcript. Recombination-based subcloning permits expression of multi-shRNA transcripts from a comprehensive range of plasmid or viral vectors. Retroviral delivery of transcripts targeting isoforms of cAMP-dependent protein kinase in the RAW264.7 murine macrophage cell line emphasizes the utility of this approach and provides insight to cAMP-dependent transcription.

Conclusion

We demonstrate functional consequences of depleting multiple endogenous target genes using miR-shRNAs, and highlight the versatility of the described vector platform for multiple target gene knockdown in mammalian cells.

The Molecule Pages database

The UCSD-Nature Signaling Gateway Molecule Pages (http://www.signaling-gateway.org/molecule) provides essential information on more than 3800 mammalian proteins involved in cellular signaling. The Molecule Pages contain expert-authored and peer-reviewed information based on the published literature, complemented by regularly updated information derived from public data source references and sequence analysis. The expert-authored data includes both a full-text review about the molecule, with citations, and highly structured data for bioinformatics interrogation, including information on protein interactions and states, transitions between states and protein function. The expert-authored pages are anonymously peer reviewed by the Nature Publishing Group. The Molecule Pages data is present in an object-relational database format and is freely accessible to the authors, the reviewers and the public from a web browser that serves as a presentation layer. The Molecule Pages are supported by several applications that along with the database and the interfaces form a multi-tier architecture. The Molecule Pages and the Signaling Gateway are routinely accessed by a very large research community.

Both ligand- and cell-specific parameters control ligand agonism in a kinetic model of g protein-coupled receptor signaling

G protein–coupled receptors (GPCRs) exist in multiple dynamic states (e.g., ligand-bound, inactive, G protein–coupled) that influence G protein activation and ultimately response generation. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult to intuit. Here we identify the ligand- and cell-specific parameters that are important determinants of cell-response behavior in a dynamic model of GPCR signaling using parameter variation and sensitivity analysis. The character of response (i.e., positive/neutral/inverse agonism) is, not surprisingly, significantly influenced by a ligand's ability to bias the receptor into an active conformation. We also find that several cell-specific parameters, including the ratio of active to inactive receptor species, the rate constant for G protein activation, and expression levels of receptors and G proteins also dramatically influence agonism. Expressing either receptor or G protein in numbers several fold above or below endogenous levels may result in system behavior inconsistent with that measured in endogenous systems. Finally, small variations in cell-specific parameters identified by sensitivity analysis as significant determinants of response behavior are found to change ligand-induced responses from positive to negative, a phenomenon termed protean agonism. Our findings offer an explanation for protean agonism reported in β₂--adrenergic and α_2A-adrenergic receptor systems.

G protein–coupled receptors (GPCRs) are transmembrane proteins involved in physiological functions ranging from vasodilation and immune response to memory. The binding of both endogenous ligands (e.g., hormones, neurotransmitters) and exogenous ligands (e.g., pharmaceuticals) to these receptors initiates intracellular events that ultimately lead to cell responses. We describe a dynamic model for G protein activation, an immediate outcome of GPCR signaling, and use it together with efficient parameter variation and sensitivity analysis techniques to identify the key cell- and ligand-specific parameters that influence G protein activation. Our results show that although ligand-specific parameters do strongly influence cell response (either causing increases or decreases in G protein activation), cellular parameters may also dictate the magnitude and direction of G protein activation. We apply our findings to describe how protean agonism, a phenomenon in which the same ligand may induce both positive and negative responses, may result from changes in cell-specific parameters. These findings may be used to understand the molecular basis of different responses of cell types and tissues to pharmacological treatment. In addition, these methods may be applied generally to models of cellular signaling and will help guide experimental resources toward further characterization of the key parameters in these networks.

Cardiac beta-adrenergic signaling: from subcellular microdomains to heart failure

beta-adrenergic signaling plays a central role in the neurohumoral regulation of the heart and the progression of heart failure. Initially thought to be a simple linear cascade, this complex network is now recognized to utilize cross-talk with numerous other pathways, spatial compartmentation, and feedback control to coordinate cardiac electrophysiology, contractility, and adaptive remodeling. Here, we review recent basic insights and novel quantitative approaches that are leading to a more comprehensive understanding of beta-adrenergic signaling and thus motivate new therapeutic strategies for cardiac disease.

The alliance for cellular signaling plasmid collection: a flexible resource for protein localization studies and signaling pathway analysis

Cellular responses to inputs that vary both temporally and spatially are determined by complex relationships between the components of cell signaling networks. Analysis of these relationships requires access to a wide range of experimental reagents and techniques, including the ability to express the protein components of the model cells in a variety of contexts. As part of the Alliance for Cellular Signaling, we developed a robust method for cloning large numbers of signaling ORFs into Gateway entry vectors, and we created a wide range of compatible expression platforms for proteomics applications. To date, we have generated over 3000 plasmids that are available to the scientific community via the American Type Culture Collection. We have established a website at www.signaling-gateway.org/data/plasmid/ that allows users to browse, search, and blast Alliance for Cellular Signaling plasmids. The collection primarily contains murine signaling ORFs with an emphasis on kinases and G protein signaling genes. Here we describe the cloning, databasing, and application of this proteomics resource for large scale subcellular localization screens in mammalian cell lines.

An evaluation of human protein-protein interaction data in the public domain

Background

Protein-protein interaction (PPI) databases have become a major resource for investigating biological networks and pathways in cells. A number of publicly available repositories for human PPIs are currently available. Each of these databases has their own unique features with a large variation in the type and depth of their annotations.

Results

We analyzed the major publicly available primary databases that contain literature curated PPI information for human proteins. This included BIND, DIP, HPRD, IntAct, MINT, MIPS, PDZBase and Reactome databases. The number of binary non-redundant human PPIs ranged from 101 in PDZBase and 346 in MIPS to 11,367 in MINT and 36,617 in HPRD. The number of genes annotated with at least one interactor was 9,427 in HPRD, 4,975 in MINT, 4,614 in IntAct, 3,887 in BIND and <1,000 in the remaining databases. The number of literature citations for the PPIs included in the databases was 43,634 in HPRD, 11,480 in MINT, 10,331 in IntAct, 8,020 in BIND and <2,100 in the remaining databases.

Conclusion

Given the importance of PPIs, we suggest that submission of PPIs to repositories be made mandatory by scientific journals at the time of manuscript submission as this will minimize annotation errors, promote standardization and help keep the information up to date. We hope that our analysis will help guide biomedical scientists in selecting the most appropriate database for their needs especially in light of the dramatic differences in their content.

SigFlux: a novel network feature to evaluate the importance of proteins in signal transduction networks

Background

Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks.

Results

We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network.

Conclusion

SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project

Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

Stimulation of mast cells via FcvarepsilonR1 and TLR2: the type of ligand determines the outcome

Little is known about the interplay between pathophysiological processes of allergy and infection, particularly with respect to mast cell (MC)-mediated responses. The presence and recognition of pathogen-associated molecular patterns (PAMPs) might have broad impact on the development and severity of diseases. In this study, we assessed the influence of toll-like receptor 2 (TLR 2)-dependent synthetic analogs of bacterial lipopeptides (LPs), Pam(3)CSK(4) and MALP-2, on Ag (DNP-HSA)-triggered responses in bone marrow-derived MCs (BMMCs). Both LPs strongly synergized with sub-optimal amounts of Ag in the stimulation of cytokine release. Intriguingly, Pam(3)CSK(4), but not MALP-2 suppressed Ag-induced degranulation of BMMCs (together with early tyrosine phosphorylation and calcium mobilization) in a TLR2-independent manner. Further analysis revealed that Pam(3)CSK(4), most probably by electrostatic forces, reduced the level of active DNP-HSA and that this, in turn, was responsible for the suppression of Ag-induced degranulation. Thus, our work demonstrates that LPs can synergize with IgE+Ag in stimulating the production of IL-6 by BMMCs. As well, our findings with Pam(3)CSK(4) indicate that one must be cautious when interpretating results obtained with "model" substances and the combination of ligands must be carefully chosen when functional interactions between the high-affinity receptor for IgE (FcepsilonR1) and TLR2 are examined.

Data-driven modelling of signal-transduction networks

New technologies are permitting large-scale quantitative studies of signal-transduction networks. Such data are hard to understand completely by inspection and intuition. 'Data-driven models' help users to analyse large data sets by simplifying the measurements themselves. Data-driven modelling approaches such as clustering, principal components analysis and partial least squares can derive biological insights from large-scale experiments. These models are emerging as standard tools for systems-level research in signalling networks.

Parameter estimate of signal transduction pathways

BACKGROUND

The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway.

RESULTS

Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions.

CONCLUSION

Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data.

Imaging of cell migration

Cell migration is an essential process during many phases of development and adult life. Cells can either migrate as individuals or move in the context of tissues. Movement is controlled by internal and external signals, which activate complex signal transduction cascades resulting in highly dynamic and localised remodelling of the cytoskeleton, cell-cell and cell-substrate interactions. To understand these processes, it will be necessary to identify the critical structural cytoskeletal components, their spatio-temporal dynamics as well as those of the signalling pathways that control them. Imaging plays an increasingly important and powerful role in the analysis of these spatio-temporal dynamics. We will highlight a variety of imaging techniques and their use in the investigation of various aspects of cell motility, and illustrate their role in the characterisation of chemotaxis in Dictyostelium and cell movement during gastrulation in chick embryos in more detail.

Lipidomics: An analysis of cellular lipids by ESI-MS

Recognition of the importance of lipid signaling in cellular function has led to rapid progress in the technology of lipid analysis. Measurements of lipid species changes are central to defining the networks of cell signaling (e.g., receptor activation by hormones or drugs) and lipids are involved in many biochemical and pathological processes. During the last several years our laboratory has focused on developing efficient methods for extraction of glycerophospholipids from biological systems and their detection and identification by mass spectrometry. We analyze phospholipid changes in mammalian cells as a result of a defined ligand stimulation strategy that supports the research questions of the consortium. The improvement of mass spectrometry techniques for phospholipid analysis combined with sophisticated computational methods developed in our group has facilitated simultaneous analysis of hundreds of phospholipid species in mammalian cells. This information is presented as Lipid Arrays (or more precisely as virtual arrays) and allows identification of temporal changes in membrane phospholipid species between two contrasting biological conditions (e.g., unstimulated basal vs. stimulated or as a contrast between normal and disease stages). Using the lipidomics approach, we are able to identify approximately 450 phospholipid species from total membrane extracts and qualitatively measure pattern response changes initiated by cell surface receptors. As such, this approach facilitates the elucidation of the metabolic changes induced by a perturbation in the cell and recognition of patterns of signaling.

A global analysis of cross-talk in a mammalian cellular signalling network

Cellular information processing requires the coordinated activity of a large network of intracellular signalling pathways. Cross-talk between pathways provides for complex non-linear responses to combinations of stimuli, but little is known about the density of these interactions in any specific cell. Here, we have analysed a large-scale survey of pathway interactions carried out by the Alliance for Cellular Signalling (AfCS) in RAW 264.7 macrophages. Twenty-two receptor-specific ligands were studied, both alone and in all pairwise combinations, for Ca2+ mobilization, cAMP synthesis, phosphorylation of many signalling proteins and for cytokine production. A large number of non-additive interactions are evident that are consistent with known mechanisms of cross-talk between pathways, but many novel interactions are also revealed. A global analysis of cross-talk suggests that many external stimuli converge on a relatively small number of interaction mechanisms to provide for context-dependent signalling.

A computational framework for the topological analysis and targeted disruption of signal transduction networks

In this article, optimization-based frameworks are introduced for elucidating the input-output structure of signaling networks and for pinpointing targeted disruptions leading to the silencing of undesirable outputs in therapeutic interventions. The frameworks are demonstrated on a large-scale reconstruction of a signaling network composed of nine signaling pathways implicated in prostate cancer. The Min-Input framework is used to exhaustively identify all input-output connections implied by the signaling network structure. Results reveal that there exist two distinct types of outputs in the signaling network that either can be elicited by many different input combinations or are highly specific requiring dedicated inputs. The Min-Interference framework is next used to precisely pinpoint key disruptions that negate undesirable outputs while leaving unaffected necessary ones. In addition to identifying disruptions of terminal steps, we also identify complex disruption combinations in upstream pathways that indirectly negate the targeted output by propagating their action through the signaling cascades. By comparing the obtained disruption targets with lists of drug molecules we find that many of these targets can be acted upon by existing drug compounds, whereas the remaining ones point at so-far unexplored targets. Overall the proposed computational frameworks can help elucidate input/output relationships of signaling networks and help to guide the systematic design of interference strategies.

Identification of signaling components required for the prediction of cytokine release in RAW 264.7 macrophages

BACKGROUND

Release of immuno-regulatory cytokines and chemokines during inflammatory response is mediated by a complex signaling network. Multiple stimuli produce different signals that generate different cytokine responses. Current knowledge does not provide a complete picture of these signaling pathways. However, using specific markers of signaling pathways, such as signaling proteins, it is possible to develop a 'coarse-grained network' map that can help understand common regulatory modules for various cytokine responses and help differentiate between the causes of their release.

RESULTS

Using a systematic profiling of signaling responses and cytokine release in RAW 264.7 macrophages made available by the Alliance for Cellular Signaling, an analysis strategy is presented that integrates principal component regression and exhaustive search-based model reduction to identify required signaling factors necessary and sufficient to predict the release of seven cytokines (G-CSF, IL-1alpha, IL-6, IL-10, MIP-1alpha, RANTES, and TNFalpha) in response to selected ligands. This study provides a model-based quantitative estimate of cytokine release and identifies ten signaling components involved in cytokine production. The models identified capture many of the known signaling pathways involved in cytokine release and predict potentially important novel signaling components, like p38 MAPK for G-CSF release, IFNgamma- and IL-4-specific pathways for IL-1a release, and an M-CSF-specific pathway for TNFalpha release.

CONCLUSION

Using an integrative approach, we have identified the pathways responsible for the differential regulation of cytokine release in RAW 264.7 macrophages. Our results demonstrate the power of using heterogeneous cellular data to qualitatively and quantitatively map intermediate cellular phenotypes.

Microfluidics-based systems biology

Systems biology seeks to develop a complete understanding of cellular mechanisms by studying the functions of intra- and inter-cellular molecular interactions that trigger and coordinate cellular events. However, the complexity of biological systems causes accurate and precise systems biology experimentation to be a difficult task. Most biological experimentation focuses on highly detailed investigation of a single signaling mechanism, which lacks the throughput necessary to reconstruct the entirety of the biological system, while high-throughput testing often lacks the fidelity and detail necessary to fully comprehend the mechanisms of signal propagation. Systems biology experimentation, however, can benefit greatly from the progress in the development of microfluidic devices. Microfluidics provides the opportunity to study cells effectively on both a single- and multi-cellular level with high-resolution and localized application of experimental conditions with biomimetic physiological conditions. Additionally, the ability to massively array devices on a chip opens the door for high-throughput, high fidelity experimentation to aid in accurate and precise unraveling of the intertwined signaling systems that compose the inner workings of the cell.

A novel strategy to identify the regulatory DNA-organized cooperations among transcription factors

To identify the functional contributions of cooperations among transcription factors on regulatory DNA is critical for understanding transcription activation. But so far there is a great lack of effective identifying methods. Here we describe a novel strategy, based on comprehensively perturbed experiments and a computational model, to identify the cooperations among NF-kappaB (p65), CREB, and AP-1 in transcription activation of human cytomegalovirus major IE1 promoter/enhancer (MIEP). In this strategy, functional profiles of protein-MIEP association and RNA synthesis are achieved through comprehensively perturbing the association of p65, CREB or AP-1 with MIEP and then subjected to the computational model. Consequently, the 'real' cooperations contributing to MIEP activation are found to comprise five but not seven types of potential cooperations. Thus, our research provides a facile systematic approach to identifying the DNA-organized cooperations among transcription factors and understanding transcription activation.

A Compendium of Signals and Responses Triggered by Prodeath and Prosurvival Cytokines

Cell-signaling networks consist of proteins with a variety of functions (receptors, adaptor proteins, GTPases, kinases, proteases, and transcription factors) working together to control cell fate. Although much is known about the identities and biochemical activities of these signaling proteins, the ways in which they are combined into networks to process and transduce signals are poorly understood. Network-level understanding of signaling requires data on a wide variety of biochemical processes such as posttranslational modification, assembly of macromolecular complexes, enzymatic activity, and localization. No single method can gather such heterogeneous data in high throughput, and most studies of signal transduction therefore rely on series of small, discrete experiments. Inspired by the power of systematic datasets in genomics, we set out to build a systematic signaling dataset that would enable the construction of predictive models of cell-signaling networks. Here we describe the compilation and fusion of approximately 10,000 signal and response measurements acquired from HT-29 cells treated with tumor necrosis factor-alpha, a proapoptotic cytokine, in combination with epidermal growth factor or insulin, two prosurvival growth factors. Nineteen protein signals were measured over a 24-h period using kinase activity assays, quantitative immunoblotting, and antibody microarrays. Four different measurements of apoptotic response were also collected by flow cytometry for each time course. Partial least squares regression models that relate signaling data to apoptotic response data reveal which aspects of compendium construction and analysis were important for the reproducibility, internal consistency, and accuracy of the fused set of signaling measurements. We conclude that it is possible to build self-consistent compendia of cell-signaling data that can be mined computationally to yield important insights into the control of mammalian cell responses.

Electronic data sources for kinetic models of cell signaling

Functional understanding of signaling pathways requires detailed information about the constituent molecules and their interactions. Simulations of signaling pathways therefore build upon a great deal of data from various sources. We first survey electronic data resources for cell signaling modeling and then based on the type of data representation the data sources are broadly classified into five groups. None of the data sources surveyed provide all required data in a ready-to-be-modeled fashion. We then put forward a "wish list" for the desired attributes for an ideal modeling centric database. Finally, we close with perspectives on how electronic data sources for cell signaling modeling have developed. We suggest that future directions in such data sources are largely model-driven and are hinged on interoperability of data sources.

Toward predictive models of mammalian cells

Progress in experimental and theoretical biology is likely to provide us with the opportunity to assemble detailed predictive models of mammalian cells. Using a functional format to describe the organization of mammalian cells, we describe current approaches for developing qualitative and quantitative models using data from a variety of experimental sources. Recent developments and applications of graph theory to biological networks are reviewed. The use of these qualitative models to identify the topology of regulatory motifs and functional modules is discussed. Cellular homeostasis and plasticity are interpreted within the framework of balance between regulatory motifs and interactions between modules. From this analysis we identify the need for detailed quantitative models on the basis of the representation of the chemistry underlying the cellular process. The use of deterministic, stochastic, and hybrid models to represent cellular processes is reviewed, and an initial integrated approach for the development of large-scale predictive models of a mammalian cell is presented.