The ins and outs of signalling

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Toward a Logic of the Organism: A Process Philosophical Consideration

Mathematical models applied in contemporary theoretical and systems biology are based on some implicit ontological assumptions about the nature of organisms. This article aims to show that real organisms reveal a logic of internal causality transcending the tacit logic of biological modeling. Systems biology has focused on models consisting of static systems of differential equations operating with fixed control parameters that are measured or fitted to experimental data. However, the structure of real organisms is a highly dynamic process, the internal causality of which can only be captured by continuously changing systems of equations. In addition, in real physiological settings kinetic parameters can vary by orders of magnitude, i.e., organisms vary the value of internal quantities that in models are represented by fixed control parameters. Both the plasticity of organisms and the state dependence of kinetic parameters adds indeterminacy to the picture and asks for a new statistical perspective. This requirement could be met by the arising Biological Statistical Mechanics project, which promises to do more justice to the nature of real organisms than contemporary modeling. This article concludes that Biological Statistical Mechanics allows for a wider range of organismic ontologies than does the tacitly followed ontology of contemporary theoretical and systems biology, which are implicitly and explicitly based on systems theory.

Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy

Machine learning is one of the most potential ways to realize the function prediction of the incremental large-scale G-protein-coupled receptors (GPCR). Prior research reveals that the key to determining the overall classification accuracy of GPCR is extracting valuable features and filtering out redundancy. To achieve a more efficient classification model, we put the feature synonym problem into consideration and create a new method based on functional word clustering and integration. Through evaluating the evolution correlation between features using the transition scores in mature molecular substitution matrices, candidate features are clustered into synonym groups. Each group of the clustered features is then integrated and represented by a unique key functional word. These retained key functional words are used to form a feature knowledge base. The original GPCR sequences are then transferred into feature vectors based on a feature re-extraction strategy according to the features in the knowledge base before the training and testing stage. We create multiple machine learning models based on Naïve Bayesian (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The established model is applied to classify two public data sets containing 8354 and 12,731 GPCRs, respectively. These models achieve significant performance in almost all evaluation criteria in comparison with state-of-the art. This work demonstrated the potential of the novel feature extraction strategy and provided an effective theoretical design for the hierarchical classification of GPCRs.

Probing the effect of clustering on EphA2 receptor signaling efficiency by subcellular control of ligand-receptor mobility

Clustering of ligand:receptor complexes on the cell membrane is widely presumed to have functional consequences for subsequent signal transduction. However, it is experimentally challenging to selectively manipulate receptor clustering without altering other biochemical aspects of the cellular system. Here, we develop a microfabrication strategy to produce substrates displaying mobile and immobile ligands that are separated by roughly 1 µm, and thus experience an identical cytoplasmic signaling state, enabling precision comparison of downstream signaling reactions. Applying this approach to characterize the ephrinA1:EphA2 signaling system reveals that EphA2 clustering enhances both receptor phosphorylation and downstream signaling activity. Single-molecule imaging clearly resolves increased molecular binding dwell times at EphA2 clusters for both Grb2:SOS and NCK:N-WASP signaling modules. This type of intracellular comparison enables a substantially higher degree of quantitative analysis than is possible when comparisons must be made between different cells and essentially eliminates the effects of cellular response to ligand manipulation.

STIM1, STIM2, and PDI Participate in Cellular Fate Decisions in Low Energy Availability Induced by 3-NP in Male Rats

Impairment in the energetic function of mitochondria is seen in many neurologic disorders like neurodegeneration. It disrupts ATP production, gives rise to oxidative stress, and ultimately challenges the viability of neurons. In this situation, neural cells use complex crosstalk between various subcellular elements to make live-or-die decisions about their fate. This study aimed to describe a part of the molecular changes and the outcome of the cellular decision during an energy crisis in neural cells in a time-dependent manner in the striatum. Adult male rats were treated with single or multiple 3-nitropropionic acid (3-NP) doses, a mitochondrial toxin, for 1 to 5 days. We found that protein disulfide isomerase (PDI) activity was decreased on the third day and remained lower than the control group up to the fifth day. However, on the day 1 and day 2 of 3-NP treatment, the stromal interaction molecule (STIM) 1 and STIM2 significantly decreased. On the third day, STIM1 and STIM2 were increased and reached the level of controls and remained the same up to the fifth day. In this condition, cell death was significantly higher than the controls from the third day up to the fifth day. We also showed that even a single dose of 3-NP reduced the brain volume. These data suggest that the STIM1, STIM2, and PDI activity changes may be involved in the outcome of cellular fate decisions. It also suggests that cells may reduce STIM1 and STIM2 as a defense mechanism against low energy availability.

Bistability in cell signalling and its significance in identifying potential drug targets

MOTIVATION

Bistability is one of the salient dynamical features in various all-or-none kinds of decision-making processes. The presence of bistability in a cell signalling network plays a key role in input-output (I/O) relation. Our study is aiming to capture and emphasise the role of motif structure influencing the I/O relation between two nodes in the context of bistability. Here, a model-based analysis is made to investigate the critical conditions responsible for the emergence of different bistable protein-protein interaction (PPI) motifs and their possible applications to find the potential drug targets.

RESULTS

The global sensitivity analysis is used to identify sensitive parameters and their role in maintaining the bistability. Additionally, the bistable switching through hysteresis is explored to develop an understanding of the underlying mechanisms involved in the cell signalling processes, when significant motifs exhibiting bistability have emerged. Further, we elaborate the application of the results by the implication of the emerged PPI motifs to identify potential drug-targets in three cancer networks, which is validated with existing databases. The influence of stochastic perturbations that could hinder desired functionality of any signalling networks is also described here.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Characterization of an Aggregated Three-Dimensional Cell Culture Model by Multimodal Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is an established analytical tool capable of defining and understanding complex tissues by determining the spatial distribution of biological molecules. Three-dimensional (3D) cell culture models mimic the pathophysiological environment of in vivo tumors and are rapidly emerging as a valuable research tool. Here, multimodal MSI techniques were employed to characterize a novel aggregated 3D lung adenocarcinoma model, developed by the group to mimic the in vivo tissue. Regions of tumor heterogeneity and the hypoxic microenvironment were observed based on the spatial distribution of a variety of endogenous molecules. Desorption electrospray ionization (DESI)-MSI defined regions of a hypoxic core and a proliferative outer layer from metabolite distribution. Targeted metabolites (e.g., lactate, glutamine, and citrate) were mapped to pathways of glycolysis and the TCA cycle demonstrating tumor metabolic behavior. The first application of imaging mass cytometry (IMC) with 3D cell culture enabled single-cell phenotyping at 1 μm spatial resolution. Protein markers of proliferation (K_i-67) and hypoxia (glucose transporter 1) defined metabolic signaling in the aggregoid model, which complemented the metabolite data. Laser ablation inductively coupled plasma (LA-ICP)-MSI analysis localized endogenous elements including magnesium and copper, further differentiating the hypoxia gradient and validating the protein expression. Obtaining a large amount of molecular information on a complementary nature enabled an in-depth understanding of the biological processes within the novel tumor model. Combining powerful imaging techniques to characterize the aggregated 3D culture highlighted a future methodology with potential applications in cancer research and drug development.

Insights From the Ecology of Information to Cancer Control

Uniquely in nature, living systems must acquire, store, and act upon information. The survival and replicative fate of each normal cell in a multicellular organism is determined solely by information obtained from its surrounding tissue. In contrast, cancer cells as single-cell eukaryotes live in a disrupted, heterogeneous environment with opportunities and hazards. Thus, cancer cells, unlike normal somatic cells, must constantly obtain information from their environment to ensure survival and proliferation. In this study, we build upon a simple mathematical modeling framework developed to predict (1) how information promotes population persistence in a highly heterogeneous environment and (2) how disruption of information resulting from habitat fragmentation increases the probability of population extinction. Because (1) tumors grow in a highly heterogeneous microenvironment and (2) many cancer therapies fragment tumors into isolated, small cancer cell populations, we identify parallels between these 2 systems and develop ideas for cancer cure based on lessons gleaned from Anthropocene extinctions. In many Anthropocene extinctions, such as that of the North American heath hen (Tympanuchus cupido cupido), a large and widespread population was initially reduced and fragmented owing to overexploitation by humans (a "first strike"). After this, the small surviving populations are vulnerable to extinction from environmental or demographic stochastic disturbances (a "second strike"). Following this analogy, after a tumor is fragmented into small populations of isolated cancer cells by an initial therapy, additional treatment can be applied with the intent of extinction (cure). Disrupting a cancer cell's ability to acquire and use information in a heterogeneous environment may be an important tactic for causing extinction following an effective initial therapy. Thus, information, from the scale of cells within tumors to that of species within ecosystems, can be used to identify vulnerabilities to extinction and opportunities for novel treatment strategies.

The First 3D Model of the Full-Length KIT Cytoplasmic Domain Reveals a New Look for an Old Receptor

Receptor tyrosine kinases (RTKs) are key regulators of normal cellular processes and have a critical role in the development and progression of many diseases. RTK ligand-induced stimulation leads to activation of the cytoplasmic kinase domain that controls the intracellular signalling. Although the kinase domain of RTKs has been extensively studied using X-ray analysis, the kinase insert domain (KID) and the C-terminal are partially or fully missing in all reported structures. We communicate the first structural model of the full-length RTK KIT cytoplasmic domain, a crucial target for cancer therapy. This model was achieved by integration of ab initio KID and C-terminal probe models into an X-ray structure, and by their further exploration through molecular dynamics (MD) simulation. An extended (2-µs) MD simulation of the proper model provided insight into the structure and conformational dynamics of the full-length cytoplasmic domain of KIT, which can be exploited in the description of the KIT transduction processes.

A Live-Cell Screen for Altered Erk Dynamics Reveals Principles of Proliferative Control

Complex, time-varying responses have been observed widely in cell signaling, but how specific dynamics are generated or regulated is largely unknown. One major obstacle has been that high-throughput screens are typically incompatible with the live-cell assays used to monitor dynamics. Here, we address this challenge by screening a library of 429 kinase inhibitors and monitoring extracellular-regulated kinase (Erk) activity over 5 h in more than 80,000 single primary mouse keratinocytes. Our screen reveals both known and uncharacterized modulators of Erk dynamics, including inhibitors of non-epidermal growth factor receptor (EGFR) receptor tyrosine kinases (RTKs) that increase Erk pulse frequency and overall activity. Using drug treatment and direct optogenetic control, we demonstrate that drug-induced changes to Erk dynamics alter the conditions under which cells proliferate. Our work opens the door to high-throughput screens using live-cell biosensors and reveals that cell proliferation integrates information from Erk dynamics as well as additional permissive cues.

An ultrasensitive fiveplex activity assay for cellular kinases

Protein kinases are enzymes whose abundance, protein-protein interactions, and posttranslational modifications together determine net signaling activity in cells. Large-scale data on cellular kinase activity are limited, because existing assays are cumbersome, poorly sensitive, low throughput, and restricted to measuring one kinase at a time. Here, we surmount the conventional hurdles of activity measurement with a multiplexing approach that leverages the selectivity of individual kinase-substrate pairs. We demonstrate proof of concept by designing an assay that jointly measures activity of five pleiotropic signaling kinases: Akt, IκB kinase (IKK), c-jun N-terminal kinase (JNK), mitogen-activated protein kinase (MAPK)-extracellular regulated kinase kinase (MEK), and MAPK-activated protein kinase-2 (MK2). The assay operates in a 96-well format and specifically measures endogenous kinase activation with coefficients of variation less than 20%. Multiplex tracking of kinase-substrate pairs reduces input requirements by 25-fold, with ~75 µg of cellular extract sufficient for fiveplex activity profiling. We applied the assay to monitor kinase signaling during coxsackievirus B3 infection of two different host-cell types and identified multiple differences in pathway dynamics and coordination that warrant future study. Because the Akt–IKK–JNK–MEK–MK2 pathways regulate many important cellular functions, the fiveplex assay should find applications in inflammation, environmental-stress, and cancer research.

Protein Domain Level Cancer Drug Targets in the Network of MAPK Pathways

Proteins in the MAPK pathways considered as potential drug targets for cancer treatment. Pathways along with the cross-talks increase their scope to view them as a network of MAPK pathways. Side effect causing targeted domains act as a proxy for drug targets due to its structural similarity and frequent reuse of their variants. We proposed to identify non-repeatable protein domains as the drug targets to disrupt the signal transduction than targeting the whole protein. Network based approach is used to understand the contribution of 52 domains in non-hub, non-essential, and intra-pathway cancerous nodes and to identify potential drug target domains. 34 distinct domains in the cancerous proteins are playing vital roles in making cancer as a complex disease and pose challenges to identify potential drug targets. Distribution of domain families follows the power law in the network. Single promiscuous domains are contributing to the formation of hubs like Pkinease, Pkinease Tyr, and Ras. Hub nodes are positively correlated with the domain coverage and targeting them would disrupt functional properties of the proteins. EIF 4EBP, alpha Kinase, Sel1, ROKNT, and KH 1 are the domains identified as potential domain targets for the disruption of the signaling mechanism involved in cancer.

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites

As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein malonylation. In this study, we constructed a deep learning (DL) network classifier based on long short-term memory (LSTM) with word embedding (LSTM_WE) for the prediction of mammalian malonylation sites. LSTM_WE performs better than traditional classifiers developed with common pre-defined feature encodings or a DL classifier based on LSTM with a one-hot vector. The performance of LSTM_WE is sensitive to the size of the training set, but this limitation can be overcome by integration with a traditional machine learning (ML) classifier. Accordingly, an integrated approach called LEMP was developed, which includes LSTM_WE and the random forest classifier with a novel encoding of enhanced amino acid content. LEMP performs not only better than the individual classifiers but also superior to the currently-available malonylation predictors. Additionally, it demonstrates a promising performance with a low false positive rate, which is highly useful in the prediction application. Overall, LEMP is a useful tool for easily identifying malonylation sites with high confidence. LEMP is available at http://www.bioinfogo.org/lemp.

Topological alternate centrality measure capturing drug targets in the network of MAPK pathways

A new centrality of the nodes in the network is proposed called alternate centrality, which can isolate effective drug targets in the complex signalling network. Alternate centrality metric defined over the network substructure (four nodes - motifs). The nodes involving in alternative activation in the motifs gain in metric values. Targeting high alternative centrality nodes hypothesised to be destructive free to the network due to their alternative activation mechanism. Overlapping and crosstalk among the gene products in the conserved network of MAPK pathways selected for the study. In silico knock-out of high alternate centrality nodes causing rewiring in the network is investigated using MCF-7 breast cancer cell line-based data. Degree of top alternate centrality nodes lies between the degree of bridging and pagerank nodes. Node deletion of high alternate centrality on the centralities such as eccentricity, closeness, betweenness, stress, centroid and radiality causes low perturbation. The authors identified the following alternate centrality nodes ERK1, ERK2, MEKK2, MKK5, MKK4, MLK3, MLK2, MLK1, MEKK4, MEKK1, TAK1, P38alpha, ZAK, DLK, LZK, MLTKa/b and P38beta as efficient drug targets for breast cancer. Alternate centrality identifies effective drug targets and is free from intertwined biological processes and lethality.

KInhibition: A Kinase Inhibitor Selection Portal

Protein kinases constitute a large class of signaling molecules frequently targeted in research and clinical uses. However, kinase inhibitors are notoriously non-specific, making it difficult to select an appropriate inhibitor for a given kinase. Available data from large-scale kinase inhibitor screens are often difficult to query. Here, we present KInhibition (https://kinhibition.fredhutch.org), an online portal that allows users to search publicly available datasets to find selective inhibitors for a chosen kinase or group of kinases. Compounds are sorted by a KInhibition Selectivity Score, calculated based on compounds' activity against the selected kinase(s) versus activity against all other kinases for which that compound has been profiled. The current version allows users to query four datasets, with a framework that can easily accommodate additional datasets. KInhibition represents a powerful platform through which researchers from broad areas of biology, chemistry, and pharmacology can easily interrogate large datasets to help guide their selection of kinase inhibitors.

•

An easy-to-use tool that compiles datasets from kinase inhibitor screens

•

A KInhibition Selectivity Score quantifies each compound's selectivity

•

Users can select kinases, view compounds and selectivity, and download the results

•

KInhibition is broadly applicable across life science disciplines

Engineering Approaches to Study Cellular Decision Making

In their native environment, cells are immersed in a complex milieu of biochemical and biophysical cues. These cues may include growth factors, the extracellular matrix, cell-cell contacts, stiffness, and topography, and they are responsible for regulating cellular behaviors such as adhesion, proliferation, migration, apoptosis, and differentiation. The decision-making process used to convert these extracellular inputs into actions is highly complex and sensitive to changes both in the type of individual cue (e.g., growth factor dose/level, timing) and in how these individual cues are combined (e.g., homotypic/heterotypic combinations). In this review, we highlight recent advances in the development of engineering-based approaches to study the cellular decision-making process. Specifically, we discuss the use of biomaterial platforms that enable controlled and tailored delivery of individual and combined cues, as well as the application of computational modeling to analyses of the complex cellular decision-making networks.

Manatee invariants reveal functional pathways in signaling networks

Background

Signal transduction pathways are important cellular processes to maintain the cell’s integrity. Their imbalance can cause severe pathologies. As signal transduction pathways feature complex regulations, they form intertwined networks. Mathematical models aim to capture their regulatory logic and allow an unbiased analysis of robustness and vulnerability of the signaling network. Pathway detection is yet a challenge for the analysis of signaling networks in the field of systems biology. A rigorous mathematical formalism is lacking to identify all possible signal flows in a network model.

Results

In this paper, we introduce the concept of Manatee invariants for the analysis of signal transduction networks. We present an algorithm for the characterization of the combinatorial diversity of signal flows, e.g., from signal reception to cellular response. We demonstrate the concept for a small model of the TNFR1-mediated NF- κB signaling pathway. Manatee invariants reveal all possible signal flows in the network. Further, we show the application of Manatee invariants for in silico knockout experiments. Here, we illustrate the biological relevance of the concept.

Conclusions

The proposed mathematical framework reveals the entire variety of signal flows in models of signaling systems, including cyclic regulations. Thereby, Manatee invariants allow for the analysis of robustness and vulnerability of signaling networks. The application to further analyses such as for in silico knockout was shown. The new framework of Manatee invariants contributes to an advanced examination of signaling systems.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0448-7) contains supplementary material, which is available to authorized users.

Profiling Subcellular Protein Phosphatase Responses to Coxsackievirus B3 Infection of Cardiomyocytes

Cellular responses to stimuli involve dynamic and localized changes in protein kinases and phosphatases. Here, we report a generalized functional assay for high-throughput profiling of multiple protein phosphatases with subcellular resolution and apply it to analyze coxsackievirus B3 (CVB3) infection counteracted by interferon signaling. Using on-plate cell fractionation optimized for adherent cells, we isolate protein extracts containing active endogenous phosphatases from cell membranes, the cytoplasm, and the nucleus. The extracts contain all major classes of protein phosphatases and catalyze dephosphorylation of plate-bound phosphosubstrates in a microtiter format, with cellular activity quantified at the end point by phosphospecific ELISA. The platform is optimized for six phosphosubstrates (ERK2, JNK1, p38α, MK2, CREB, and STAT1) and measures specific activities from extracts of fewer than 50,000 cells. The assay was exploited to examine viral and antiviral signaling in AC16 cardiomyocytes, which we show can be engineered to serve as susceptible and permissive hosts for CVB3. Phosphatase responses were profiled in these cells by completing a full-factorial experiment for CVB3 infection and type I/II interferon signaling. Over 850 functional measurements revealed several independent, subcellular changes in specific phosphatase activities. During CVB3 infection, we found that type I interferon signaling increases subcellular JNK1 phosphatase activity, inhibiting nuclear JNK1 activity that otherwise promotes viral protein synthesis in the infected host cell. Our assay provides a high-throughput way to capture perturbations in important negative regulators of intracellular signal-transduction networks.

Quantification of growth factor signaling and pathway cross talk by live-cell imaging

Peptide growth factors stimulate cellular responses through activation of their transmembrane receptors. Multiple intracellular signaling cascades are engaged following growth factor-receptor binding, leading to short- and long-term biological effects. Each receptor-activated signaling pathway does not act in isolation but rather interacts at different levels with other pathways to shape signaling networks that are distinctive for each growth factor. To gain insights into the specifics of growth factor-regulated interactions among different signaling cascades, we developed a HeLa cell line stably expressing fluorescent live-cell imaging reporters that are readouts for two major growth factor-stimulated pathways, Ras-Raf-Mek-ERK and phosphatidylinositol (PI) 3-kinase-Akt. Incubation of cells with epidermal growth factor (EGF) resulted in rapid, robust, and sustained ERK signaling but shorter-term activation of Akt. In contrast, hepatocyte growth factor induced sustained Akt signaling but weak and short-lived ERK activity, and insulin-like growth factor-I stimulated strong long-term Akt responses but negligible ERK signaling. To address potential interactions between signaling pathways, we employed specific small-molecule inhibitors. In cells incubated with EGF or platelet-derived growth factor-AA, Raf activation and the subsequent stimulation of ERK reduced Akt signaling, whereas Mek inhibition, which blocked ERK activation, enhanced Akt and turned transient effects into sustained responses. Our results reveal that individual growth factors initiate signaling cascades that vary markedly in strength and duration and demonstrate in living cells the dramatic effects of cross talk from Raf and Mek to PI 3-kinase and Akt. Our data further indicate how specific growth factors can encode distinct cellular behaviors by promoting complex interactions among signaling pathways.

A Regulated Double-Negative Feedback Decodes the Temporal Gradient of Input Stimulation in a Cell Signaling Network

Revealing the hidden mechanism of how cells sense and react to environmental signals has been a central question in cell biology. We focused on the rate of increase of stimulation, or temporal gradient, known to cause different responses of cells. We have investigated all possible three-node enzymatic networks and identified a network motif that robustly generates a transient or sustained response by acute or gradual stimulation, respectively. We also found that a regulated double-negative feedback within the motif is essential for the temporal gradient-sensitive switching. Our analysis highlights the essential structure and mechanism enabling cells to properly respond to dynamic environmental changes.

Unraveling Growth Factor Signaling and Cell Cycle Progression in Individual Fibroblasts

Cultured cells require the actions of growth factors to enter the cell cycle, but how individual members of a population respond to the same stimulus remains unknown. Here we have employed continuous monitoring by live cell imaging in a dual-reporter cell model to investigate the regulation of short-term growth factor signaling (protein kinase B (PKB/Akt) activity) and longer-term progression through the cell cycle (cyclin-dependent kinase 2 activity). In the total population, insulin-like growth factor-I (IGF-I)-enhanced cell cycle entry by >5-fold compared with serum-free medium (from 13.5 to 78%), but at the single cell level we observed a broad distribution in the timing of G1 exit (4-24 h, mean ∼12 h) that did not vary with either the amount or duration of IGF-I treatment. Cells that failed to re-enter the cell cycle exhibited similar responses to IGF-I in terms of integrated Akt activity and migration distance compared with those that did. We made similar observations with EGF, PDGF-AA, and PDGF-BB. As potential thresholds of growth factor-mediated cell cycle progression appeared to be heterogeneous within the population, the longer-term proliferative outcomes of individual cells to growth factor stimulation could not be predicted based solely on acute Akt signaling responses, no matter how robust these might be. Thus, although we could define a relationship at the population level between growth factor-induced Akt signaling dynamics and cell cycle progression, we could not predict the fate of individual cells.

Mapping growth-factor-modulated Akt signaling dynamics

Growth factors alter cellular behavior through shared signaling cascades, raising the question of how specificity is achieved. Here, we have determined how growth factor actions are encoded into Akt signaling dynamics by real-time tracking of a fluorescent sensor. In individual cells, Akt activity was encoded in an analog pattern, with similar latencies (∼2 min) and half-maximal peak response times (range of 5-8 min). Yet, different growth factors promoted dose-dependent and heterogeneous changes in signaling dynamics. Insulin treatment caused sustained Akt activity, whereas EGF or PDGF-AA promoted transient signaling; PDGF-BB produced sustained responses at higher concentrations, but short-term effects at low doses, actions that were independent of the PDGF-α receptor. Transient responses to EGF were caused by negative feedback at the receptor level, as a second treatment yielded minimal responses, whereas parallel exposure to IGF-I caused full Akt activation. Small-molecule inhibitors reduced PDGF-BB signaling to transient responses, but only decreased the magnitude of IGF-I actions. Our observations reveal distinctions among growth factors that use shared components, and allow us to capture the consequences of receptor-specific regulatory mechanisms on Akt signaling.

Studying Cellular Signal Transduction with OMIC Technologies

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.

Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory

Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.

Food safety and nutritional quality for the prevention of non communicable diseases: the Nutrient, hazard Analysis and Critical Control Point process (NACCP)

Background

The important role of food and nutrition in public health is being increasingly recognized as crucial for its potential impact on health-related quality of life and the economy, both at the societal and individual levels. The prevalence of non-communicable diseases calls for a reformulation of our view of food. The Hazard Analysis and Critical Control Point (HACCP) system, first implemented in the EU with the Directive 43/93/CEE, later replaced by Regulation CE 178/2002 and Regulation CE 852/2004, is the internationally agreed approach for food safety control. Our aim is to develop a new procedure for the assessment of the Nutrient, hazard Analysis and Critical Control Point (NACCP) process, for total quality management (TMQ), and optimize nutritional levels.

Methods

NACCP was based on four general principles: i) guarantee of health maintenance; ii) evaluate and assure the nutritional quality of food and TMQ; iii) give correct information to the consumers; iv) ensure an ethical profit. There are three stages for the application of the NACCP process: 1) application of NACCP for quality principles; 2) application of NACCP for health principals; 3) implementation of the NACCP process. The actions are: 1) identification of nutritional markers, which must remain intact throughout the food supply chain; 2) identification of critical control points which must monitored in order to minimize the likelihood of a reduction in quality; 3) establishment of critical limits to maintain adequate levels of nutrient; 4) establishment, and implementation of effective monitoring procedures of critical control points; 5) establishment of corrective actions; 6) identification of metabolic biomarkers; 7) evaluation of the effects of food intake, through the application of specific clinical trials; 8) establishment of procedures for consumer information; 9) implementation of the Health claim Regulation EU 1924/2006; 10) starting a training program.

Results and discussion

We calculate the risk assessment as follows: Risk (R) = probability (P) × damage (D). The NACCP process considers the entire food supply chain “from farm to consumer”; in each point of the chain it is necessary implement a tight monitoring in order to guarantee optimal nutritional quality.

Microfluidic platforms for the investigation of intercellular signalling mechanisms

Intercellular signalling has been identified as a highly complex process, responsible for orchestrating many physiological functions. While conventional methods of investigation have been useful, their limitations are impeding further development. Microfluidics offers an opportunity to overcome some of these limitations. Most notably, microfluidic systems can emulate the in-vivo environments. Further, they enable exceptionally precise control of the microenvironment, allowing complex mechanisms to be selectively isolated and studied in detail. There has thus been a growing adoption of microfluidic platforms for investigation of cell signalling mechanisms. This review provides an overview of the different signalling mechanisms and discusses the methods used to study them, with a focus on the microfluidic devices developed for this purpose.

Resolving cancer-stroma interfacial signalling and interventions with micropatterned tumour-stromal assays

Tumor-stromal interactions are a determining factor in cancer progression. In vivo, the interaction interface is associated with spatially-resolved distributions of cancer and stromal phenotypes. Here, we establish a micropatterned tumor-stromal assay (μTSA) with laser capture microdissection to control the location of co-cultured cells and analyze bulk and interfacial tumor-stromal signaling in driving cancer progression. μTSA reveals a spatial distribution of phenotypes in concordance with human estrogen receptor-positive (ER+) breast cancer samples, and heterogeneous drug activity relative to the tumor-stroma interface. Specifically, an unknown mechanism of reversine is shown in targeting tumor-stromal interfacial interactions using ER+ MCF-7 breast cancer and bone marrow-derived stromal cells. Reversine suppresses MCF-7 tumor growth and bone metastasis in vivo by reducing tumor stromalization including collagen deposition and recruitment of activated stromal cells. This study advocates μTSA as a platform for studying tumor microenvironmental interactions and cancer field effects with applications in drug discovery and development.

Site-specific methylated reporter constructs for functional analysis of DNA methylation

Methods to experimentally alter and functionally evaluate cytosine methylation in a site-specific manner have proven elusive. We describe a site-specific DNA methylation method, using synthetically methylated primers and high fidelity PCR coupled with ligation of reporter constructs. We applied this method to introduce methylated cytosines into fragments of the respective DAPK and RASSF1A promoters that had been cloned into luciferase reporters. We found that methylation of 3-7 residue CpG clusters that were 5' adjacent to the transcription start site (TSS) of the DAPK gene produced up to a 54% decrease in promoter activity (p<0.01). Similarly, for RASSF1A promoter reporter constructs, the methylation of either of two clusters of four CpGs each, but not an intervening cluster, produced a 63% decrease in promoter activity (p<0.01), suggesting that precise mCpG position is crucial, and factors other than simple proximity to the TSS are at play. Chromatin immunoprecipitation analysis of these reporter constructs demonstrated that transcription factor Oct-1 and Sp1 preferentially bound the unmethylated vs. methylated DAPK or RASSF1A promoter reporter constructs at the functional CpG sites. Histone H1, hnRNP1, and MeCP2 showed preferential binding to methylated sequence at functional sites in these reporter constructs, as well as highly preferential (> 8-80-fold) binding to native methylated vs. unmethylated chromatin. These results suggest that: (1) site-specific, precision DNA methylation of a reporter construct can be used for functional analysis of commonly observed gene promoter methylation patterns; (2) the reporter system contains key elements of the endogenous chromatin machinery.

Simultaneous profiling of 194 distinct receptor transcripts in human cells

Many signal transduction cascades are initiated by transmembrane receptors with the presence or absence and abundance of receptors dictating cellular responsiveness. We provide a validated array of quantitative reverse transcription polymerase chain reaction (qRT-PCR) reagents for high-throughput profiling of the presence and relative abundance of transcripts for 194 transmembrane receptors in the human genome. We found that the qRT-PCR array had greater sensitivity and specificity for the detected receptor transcript profiles compared to conventional oligonucleotide microarrays or exon microarrays. The qRT-PCR array also distinguished functional receptor presence versus absence more accurately than deep sequencing of adenylated RNA species by RNA sequencing (RNA-seq). By applying qRT-PCR-based receptor transcript profiling to 40 human cell lines representing four main tissues (pancreas, skin, breast, and colon), we identified clusters of cell lines with enhanced signaling capabilities and revealed a role for receptor silencing in defining tissue lineage. Ectopic expression of the interleukin-10 (IL-10) receptor-encoding gene IL10RA in melanoma cells engaged an IL-10 autocrine loop not otherwise present in this cell type, which altered signaling, gene expression, and cellular responses to proinflammatory stimuli. Our array provides a rapid, inexpensive, and convenient means for assigning a receptor signature to any human cell or tissue type.

Branched motifs enable long-range interactions in signaling networks through retrograde propagation

Branched structures arise in the intra-cellular signaling network when a molecule is involved in multiple enzyme-substrate reaction cascades. Such branched motifs are involved in key biological processes, e.g., immune response activated by T-cell and B-cell receptors. In this paper, we demonstrate long-range communication through retrograde propagation between branches of signaling pathways whose molecules do not directly interact. Our numerical simulations and experiments on a system comprising branches with JNK and p38MAPK as terminal molecules respectively that share a common MAP3K enzyme MEKK3/4 show that perturbing an enzyme in one branch can result in a series of changes in the activity levels of molecules “upstream” to the enzyme that eventually reaches the branch-point and affects other branches. In the absence of any evidence for explicit feedback regulation between the functionally distinct JNK and p38MAPK pathways, the experimentally observed modulation of phosphorylation amplitudes in the two pathways when a terminal kinase is inhibited implies the existence of long-range coordination through retrograde information propagation previously demonstrated in single linear reaction pathways. An important aspect of retrograde propagation in branched pathways that is distinct from previous work on retroactivity focusing exclusively on single chains is that varying the type of perturbation, e.g., between pharmaceutical agent mediated inhibition of phosphorylation or suppression of protein expression, can result in opposing responses in the other branches. This can have potential significance in designing drugs targeting key molecules which regulate multiple pathways implicated in systems-level diseases such as cancer and diabetes.

Gene methylation in gastric cancer

Gastric cancer is one of the most common malignancies and remains the second leading cause of cancer-related death worldwide. Over 70% of new cases and deaths occur in developing countries. In the early years of the molecular biology revolution, cancer research mainly focuses on genetic alterations, including gastric cancer. Epigenetic mechanisms are essential for normal development and maintenance of tissue-specific gene expression patterns in mammals. Disruption of epigenetic processes can lead to altered gene function and malignant cellular transformation. Recent advancements in the rapidly evolving field of cancer epigenetics have shown extensive reprogramming of every component of the epigenetic machinery in cancer, including DNA methylation, histone modifications, nucleosome positioning, noncoding RNAs, and microRNAs. Aberrant DNA methylation in the promoter regions of gene, which leads to inactivation of tumor suppressor and other cancer-related genes in cancer cells, is the most well-defined epigenetic hallmark in gastric cancer. The advantages of gene methylation as a target for detection and diagnosis of cancer in biopsy specimens and non-invasive body fluids such as serum and gastric washes have led to many studies of application in gastric cancer. This review focuses on the most common and important phenomenon of epigenetics, DNA methylation, in gastric cancer and illustrates the impact epigenetics has had on this field.

CLE signaling systems during plant development and nematode infection

Plants contain numerous CLAVATA3 (CLV3)/EMBRYO SURROUNDING REGION (ESR) (CLE) genes encoding small secreted peptide hormones that function in a variety of developmental and physiological processes. The first known Arabidopsis CLE gene was originally discovered through the analysis of clv3 mutants, which exhibit fasciated stems and an increased number of floral organs. In total, 32 CLE genes have been identified in Arabidopsis. Amongst these are CLV3 and CLE40, which repress the expression of homeobox-containing genes WUSCHEL (WUS) and WUSCHEL-related homeobox 5 (WOX5) to control shoot apical meristem (SAM) and root columella initial cell activity, respectively. Interestingly, the CLE signaling pathway appears to be conserved amongst plants. In this review, we discuss the latest research uncovering the diverse functions and activities of CLE peptides in plants; especially during shoot, root and vascular development. In addition, we discuss the important role of CLE peptides during infection by phytoparasitic nematodes. Understanding the molecular properties of CLE peptides and their modes of action will provide further insight into plant cell-cell communication, which could also be applied to manipulate plant-nematode interactions.

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.

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.

Unraveling the design principle for motif organization in signaling networks

Cellular signaling networks display complex architecture. Defining the design principle of this architecture is crucial for our understanding of various biological processes. Using a mathematical model for three-node feed-forward loops, we identify that the organization of motifs in specific manner within the network serves as an important regulator of signal processing. Further, incorporating a systemic stochastic perturbation to the model we could propose a possible design principle, for higher-order organization of motifs into larger networks in order to achieve specific biological output. The design principle was then verified in a large, complex human cancer signaling network. Further analysis permitted us to classify signaling nodes of the network into robust and vulnerable nodes as a result of higher order motif organization. We show that distribution of these nodes within the network at strategic locations then provides for the range of features displayed by the signaling network.

The Function of the CLE Peptides in Plant Development and Plant-Microbe Interactions

The CLAVATA3 (CLV3)/ENDOSPERM SURROUNDING REGION (ESR) (CLE) peptides consist of 12 or 13 amino acids, including hydroxylated proline residues that may or may not contain sugar modifications, and function in a non-cell-autonomous fashion. The CLE gene was first reported in Zea mays (maize) as an endosperm-specific gene, ESR, in 1997 (Opsahl-Ferstad et al., 1997). CLE genes encode secreted peptides that function in the extracellular space as intercellular signaling molecules and bind to cellular surface receptor-like proteins to transmit a signal. CLE peptides regulate various physiological and developmental processes and its signaling pathway are conserved in diverse land plants. Recent CLE functional studies have pointed to their significance in regulating meristematic activity in plant meristems, through the CLE-receptor kinase-WOX signaling node. CLV3 and CLE40 are responsible for maintenance of shoot apical meristem (SAM) and root apical meristem (RAM) function, regulating homeodomain transcription factors, WUSCHEL (WUS) and WUSCHEL-related homeobox 5 (WOX5), respectively. CLE and WOX form an interconnected and self-correcting feedback loop to provide robustness to stem cell homeostasis. CLE peptides are required for certain plant-microbe interactions, such as those that occur during legume symbiosis and phytopathogenic nematode infection. Understanding the molecular properties of CLE peptides may provide insight into plant cell-cell communication, and therefore also into plant-microbe interactions.

Combined logical and data-driven models for linking signalling pathways to cellular response

Background

Signalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity.

Results

In this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible (i.e., construction of canonical pathways), whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines.

Conclusions

We developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion.

A molecular and co-evolutionary context for grazer induced toxin production in Alexandrium tamarense

Marine dinoflagellates of the genus Alexandrium are the proximal source of neurotoxins associated with Paralytic Shellfish Poisoning. The production of these toxins, the toxin biosynthesis and, thus, the cellular toxicity can be influenced by abiotic and biotic factors. There is, however, a lack of substantial evidence concerning the toxins' ecological function such as grazing defense. Waterborne cues from copepods have been previously found to induce a species-specific increase in toxin content in Alexandrium minutum. However, it remains speculative in which context these species-specific responses evolved and if it occurs in other Alexandrium species as well. In this study we exposed Alexandrium tamarense to three copepod species (Calanus helgolandicus, Acartia clausii, and Oithona similis) and their corresponding cues. We show that the species-specific response towards copepod-cues is not restricted to one Alexandrium species and that co-evolutionary processes might be involved in these responses, thus giving additional evidence for the defensive role of phycotoxins. Through a functional genomic approach we gained insights into the underlying molecular processes which could trigger the different outcomes of these species-specific responses and consequently lead to increased toxin content in Alexandrium tamarense. We propose that the regulation of serine/threonine kinase signaling pathways has a major influence in directing the external stimuli i.e. copepod-cues, into different intracellular cascades and networks in A. tamarense. Our results show that A. tamarense can sense potential predating copepods and respond to the received information by increasing its toxin production. Furthermore, we demonstrate how a functional genomic approach can be used to investigate species interactions within the plankton community.

Epigenetic inactivation of the NORE1 gene correlates with malignant progression of colorectal tumors

Background

NORE1 (RASSF5) is a newly described member of the RASSF family with Ras effector function. NORE1 expression is frequently inactivated by aberrant promoter hypermethylation in many human cancers, suggesting that NORE1 might be a putative tumor suppressor. However, expression and mutation status of NORE1 and its implication in colorectal tumorigenesis has not been evaluated.

Methods

Expression, mutation, and methylation status of NORE1A and NORE1B in 10 cancer cell lines and 80 primary tumors were characterized by quantitative PCR, SSCP, and bisulfite DNA sequencing analyses. Effect of NORE1A and NORE1B expression on tumor cell growth was evaluated using cell number counting, flow cytometry, and colony formation assays.

Results

Expression of NORE1A and NORE1B transcript was easily detectable in all normal colonic epithelial tissues, but substantially decreased in 7 (70%) and 4 (40%) of 10 cancer cell lines and 31 (38.8%) and 25 (31.3%) of 80 primary carcinoma tissues, respectively. Moreover, 46 (57.6%) and 38 (47.5%) of 80 matched tissue sets exhibited tumor-specific reduction of NORE1A and NORE1B, respectively. Abnormal reduction of NORE1 was more commonly observed in advanced stage and high grade tumors compared to early and low grade tumors. While somatic mutations of the gene were not identified, its expression was re-activated in all low expressor cells after treatment with the demethylating agent 5-aza-dC. Bisulfite DNA sequencing analysis of 31 CpG sites within the promoter region demonstrated that abnormal reduction of NORE1A is tightly associated with promoter CpG sites hypermethylation. Moreover, transient expression and siRNA-mediated knockdown assays revealed that both NORE1A and NORE1B decrease cellular growth and colony forming ability of tumor cells and enhance tumor cell response to apoptotic stress.

Conclusion

Our data indicate that epigenetic inactivation of NORE1 due to aberrant promoter hypermethylation is a frequent event in colorectal tumorigenesis and might be implicated in the malignant progression of colorectal tumors.

Effects of ionizing radiation on cell-to-cell communication

Cell-to-cell signaling has become a significant issue in radiation biology due to experimental evidence, accumulated primarily since the early 1990s, of radiation-induced bystander effects. Several candidate mediators involved in cell-to-cell communication have been investigated and proposed as being responsible for this phenomenon, but the current investigation techniques (both theoretical and experimental) of the mechanisms involved, due to the particular set-up of each experiment, result in experimental data that often are not directly comparable. In this study, a comprehensive approach was adopted to describe cell-to-cell communication (focusing on cytokine signaling) and its modulation by external agents such as ionizing radiation. The aim was also to provide integrated theoretical instruments and experimental data to help in understanding the peculiarities of in vitro experiments. Theoretical/modeling activities were integrated with experimental measurements by (1) redesigning a cybernetic model (proposed in its original form in the 1950s) to frame cell-to-cell communication processes, (2) implementing and developing a mathematical model, and (3) designing and carrying out experiments to quantify key parameters involved in intercellular signaling (focusing as a pilot study on the release and decay of IL-6 molecules and their modulation by radiation). This formalization provides an interpretative framework for understanding the intercellular signaling and in particular for focusing on the study of cell-to-cell communication in a "step-by-step" approach. Under this model, the complex phenomenon of signal transmission was reduced where possible into independent processes to investigate them separately, providing an evaluation of the role of cell communication to guarantee and maintain the robustness of the in vitro experimental systems against the effects of perturbations.

Proteomic analysis of colorectal cancer: prefractionation strategies using two-dimensional free-flow electrophoresis

This review deals with the application of a new prefractionation tool, free-flow electrophoresis (FFE), for proteomic analysis of colorectal cancer (CRC). CRC is a leading cause of cancer death in the Western world. Early detection is the single most important factor influencing outcome of CRC patients. If identified while the disease is still localized, CRC is treatable. To improve outcomes for CRC patients there is a pressing need to identify biomarkers for early detection (diagnostic markers), prognosis (prognostic indicators), tumour responses (predictive markers) and disease recurrence (monitoring markers). Despite recent advances in the use of genomic analysis for risk assessment, in the area of biomarker identification genomic methods alone have yet to produce reliable candidate markers for CRC. For this reason, attention is being directed towards proteomics as a complementary analytical tool for biomarker identification. Here we describe a proteomics separation tool, which uses a combination of continuous FFE, a liquid-based isoelectric focusing technique, in the first dimension, followed by rapid reversed-phase HPLC (1–6 min/analysis) in the second dimension. We have optimized imaging software to present the FFE/RP-HPLC data in a virtual 2D gel-like format. The advantage of this liquid based fractionation system over traditional gel-based fractionation systems is the ability to fractionate large quantity protein samples. Unlike 2D gels, the method is applicable to both high-M_r proteins and small peptides, which are difficult to separate, and in the case of peptides, are not retained in standard 2D gels.

Computational approaches for detecting protein complexes from protein interaction networks: a survey

Background

Most proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as yeast-two-hybrid) have enabled systematic screening of pairwise protein-protein interactions en masse. Thus computational approaches for detecting protein complexes from protein interaction data are useful complements to the limited experimental methods. They can be used together with the experimental methods for mapping the interactions of proteins to understand how different proteins are organized into higher-level substructures to perform various cellular functions.

Results

Given the abundance of pairwise protein interaction data from high-throughput genome-wide experimental screenings, a protein interaction network can be constructed from protein interaction data by considering individual proteins as the nodes, and the existence of a physical interaction between a pair of proteins as a link. This binary protein interaction graph can then be used for detecting protein complexes using graph clustering techniques. In this paper, we review and evaluate the state-of-the-art techniques for computational detection of protein complexes, and discuss some promising research directions in this field.

Conclusions

Experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes. In addition, the computational approaches have also improved in performance over the years. Further improvements could be achieved if the quality of the underlying protein interaction data can be considered adequately to minimize the undesirable effects from the irrelevant and noisy sources, and the various biological evidences can be better incorporated into the detection process to maximize the exploitation of the increasing wealth of biological knowledge available.

Cell-specific information processing in segregating populations of Eph receptor ephrin-expressing cells

Cells have self-organizing properties that control their behavior in complex tissues. Contact between cells expressing either B-type Eph receptors or their transmembrane ephrin ligands initiates bidirectional signals that regulate cell positioning. However, simultaneously investigating how information is processed in two interacting cell types remains a challenge. We implemented a proteomic strategy to systematically determine cell-specific signaling networks underlying EphB2- and ephrin-B1-controlled cell sorting. Quantitative mass spectrometric analysis of mixed populations of EphB2- and ephrin-B1-expressing cells that were labeled with different isotopes revealed cell-specific tyrosine phosphorylation events. Functional associations between these phosphotyrosine signaling networks and cell sorting were established with small interfering RNA screening. Data-driven network modeling revealed that signaling between mixed EphB2- and ephrin-B1-expressing cells is asymmetric and that the distinct cell types use different tyrosine kinases and targets to process signals induced by cell-cell contact. We provide systems- and cell-specific network models of contact-initiated signaling between two distinct cell types.

Matching biochemical reaction kinetics to the timescales of life: structural determinants that influence the autodephosphorylation rate of response regulator proteins

In two-component regulatory systems, covalent phosphorylation typically activates the response regulator signaling protein, and hydrolysis of the phosphoryl group reestablishes the inactive state. Despite highly conserved three-dimensional structures and active-site features, the rates of catalytic autodephosphorylation for different response regulators vary by a factor of almost 10(6). Previous studies identified two variable active-site residues, corresponding to Escherichia coli CheY residues 59 and 89, that modulate response regulator autodephosphorylation rates about 100-fold. Here, a set of five CheY mutants, which match other "model" response regulators (ArcA, CusR, DctD, FixJ, PhoB, or Spo0F) at variable active-site positions corresponding to CheY residues 14, 59, and 89, were characterized functionally and structurally in an attempt to identify mechanisms that modulate autodephosphorylation rate. As expected, the autodephosphorylation rates of the CheY mutants were reduced 6- to 40-fold relative to wild-type CheY, but all still autodephosphorylated 12- to 80-fold faster than their respective model response regulators. Comparison of X-ray crystal structures of the five CheY mutants (complexed with the phosphoryl group analogue BeF(3)(-)) to wild-type CheY or corresponding model response regulator structures gave strong evidence for steric obstruction of the phosphoryl group from the attacking water molecule as one mechanism to enhance phosphoryl group stability. Structural data also suggested that impeding the change of a response regulator from the active to the inactive conformation might retard the autodephosphorylation reaction if the two processes are coupled, and that the residue at position '58' may contribute to rate modulation. A given combination of amino acids at positions '14', '59', and '89' adopted similar conformations regardless of protein context (CheY or model response regulator), suggesting that knowledge of residue identity may be sufficient to predict autodephosphorylation rate, and hence the kinetics of the signaling response, in the response regulator family of proteins.

DNA-triggered innate immune responses are propagated by gap junction communication

Cells respond to infection by sensing pathogens and communicating danger signals to noninfected neighbors; however, little is known about this complex spatiotemporal process. Here we show that activation of the innate immune system by double-stranded DNA (dsDNA) triggers intercellular communication through a gap junction-dependent signaling pathway, recruiting colonies of cells to collectively secrete antiviral and inflammatory cytokines for the propagation of danger signals across the tissue at large. By using live-cell imaging of a stable IRF3-sensitive GFP reporter, we demonstrate that dsDNA sensing leads to multicellular colonies of IRF3-activated cells that express the majority of secreted cytokines, including IFNbeta and TNFalpha. Inhibiting gap junctions decreases dsDNA-induced IRF3 activation, cytokine production, and the resulting tissue-wide antiviral state, indicating that this immune response propagation pathway lies upstream of the paracrine action of secreted cytokines and may represent a host-derived mechanism for evading viral antiinterferon strategies.