A comprehensive pathway map of epidermal growth factor receptor signaling

Advanced glycation end-products activate extracellular signal-regulated kinase via the oxidative stress-EGF receptor pathway in renal fibroblasts

Advanced glycation end-products (AGEs), epidermal growth factor receptor (EGFR), reactive oxygen species (ROS), and extracellular signal-regulated kinases (ERK) are implicated in diabetic nephropathy (DN). Therefore, we asked if AGEs-induced ERK protein phosphorylation and mitogenesis are dependent on the receptor for AGEs (RAGE)-ROS-EGFR pathway in normal rat kidney interstitial fibroblast (NRK-49F) cells. We found that AGEs (100 microg/ml) activated EGFR and ERK1/2, which was attenuated by RAGE short-hairpin RNA (shRNA). AGEs also increased RAGE protein and intracellular ROS levels while RAGE shRNA and N-acetylcysteine (NAC) attenuated AGEs-induced intracellular ROS. Hydrogen peroxide (5-25 microM) increased RAGE protein level while activating both EGFR and ERK1/2. Low-dose hydrogen peroxide (5 microM) increased whereas high-dose hydrogen peroxide (100 microM) decreased mitogenesis at 3 days. AGEs-activated EGFR and ERK1/2 were attenuated by an anti-oxidant (NAC) and an EGFR inhibitor (Iressa). Moreover, AGEs-induced mitogenesis was attenuated by RAGE shRNA, NAC, Iressa, and an ERK1/2 inhibitor (PD98059). In conclusion, it was found that AGEs-induced mitogenesis is dependent on the RAGE-ROS-EGFR-ERK1/2 pathway whereas AGEs-activated ERK1/2 is dependent on the RAGE-ROS-EGFR pathway in NRK-49F cells.

Systematic prediction of human membrane receptor interactions

Membrane receptor-activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high-throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system-wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.

Bridging the Divide between Neuroprosthetic Design, Tissue Engineering and Neurobiology

Neuroprosthetic devices have made a major impact in the treatment of a variety of disorders such as paralysis and stroke. However, a major impediment in the advancement of this technology is the challenge of maintaining device performance during chronic implantation (months to years) due to complex intrinsic host responses such as gliosis or glial scarring. The objective of this review is to bring together research communities in neurobiology, tissue engineering, and neuroprosthetics to address the major obstacles encountered in the translation of neuroprosthetics technology into long-term clinical use. This article draws connections between specific challenges faced by current neuroprosthetics technology and recent advances in the areas of nerve tissue engineering and neurobiology. Within the context of the device-nervous system interface and central nervous system implants, areas of synergistic opportunity are discussed, including platforms to present cells with multiple cues, controlled delivery of bioactive factors, three-dimensional constructs and in vitro models of gliosis and brain injury, nerve regeneration strategies, and neural stem/progenitor cell biology. Finally, recent insights gained from the fields of developmental neurobiology and cancer biology are discussed as examples of exciting new biological knowledge that may provide fresh inspiration toward novel technologies to address the complexities associated with long-term neuroprosthetic device performance.

Biocharts: a visual formalism for complex biological systems

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis.

The EGFR network in bone biology and pathology

The resorption, formation and maintenance of bone are coordinated by the action of several hormones, growth factors and transcription factors. Recent experiments based on genetically modified mouse models, gene microarrays and pharmacological intervention indicate that the epidermal growth factor receptor (EGFR) system plays important roles in skeletal biology and pathology. This network, including a family of seven growth factors - the EGFR ligands - and the related tyrosine kinase receptors EGFR (ERBB1), ERBB2, ERBB3 and ERBB4, regulates aspects such as proliferation and differentiation of osteoblasts, chondrocytes and osteoclasts, parathyroid hormone-mediated bone formation and cancer metastases in bone. Here, we summarize and discuss the role of the EGFR and its ligands in skeletal biology and pathology.

Nutritional systems biology modeling: from molecular mechanisms to physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.

Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering

Background

Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments.

Results

We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification.

Conclusion

We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich-data poor' paradox in Systems Biology.

Towards a systems biology approach to mammalian cell cycle: modeling the entrance into S phase of quiescent fibroblasts after serum stimulation

Background

The cell cycle is a complex process that allows eukaryotic cells to replicate chromosomal DNA and partition it into two daughter cells. A relevant regulatory step is in the G₀/G₁ phase, a point called the restriction (R) point where intracellular and extracellular signals are monitored and integrated.

Subcellular localization of cell cycle proteins is increasingly recognized as a major factor that regulates cell cycle transitions. Nevertheless, current mathematical models of the G₁/S networks of mammalian cells do not consider this aspect. Hence, there is a need for a computational model that incorporates this regulatory aspect that has a relevant role in cancer, since altered localization of key cell cycle players, notably of inhibitors of cyclin-dependent kinases, has been reported to occur in neoplastic cells and to be linked to cancer aggressiveness.

Results

The network of the model components involved in the G₁ to S transition process was identified through a literature and web-based data mining and the corresponding wiring diagram of the G₁ to S transition drawn with Cell Designer notation. The model has been implemented in Mathematica using Ordinary Differential Equations. Time-courses of level and of sub-cellular localization of key cell cycle players in mouse fibroblasts re-entering the cell cycle after serum starvation/re-feeding have been used to constrain network design and parameter determination. The model allows to recapitulate events from growth factor stimulation to the onset of S phase. The R point estimated by simulation is consistent with the R point experimentally determined.

Conclusion

The major element of novelty of our model of the G₁ to S transition is the explicit modeling of cytoplasmic/nuclear shuttling of cyclins, cyclin-dependent kinases, their inhibitor and complexes. Sensitivity analysis of the network performance newly reveals that the biological effect brought about by Cki overexpression is strictly dependent on whether the Cki is promoting nuclear translocation of cyclin/Cdk containing complexes.

Data recovery and integration from public databases uncovers transformation-specific transcriptional downregulation of cAMP-PKA pathway-encoding genes

BACKGROUND

The integration of data from multiple genome-wide assays is essential for understanding dynamic spatio-temporal interactions within cells. Such integration, which leads to a more complete view of cellular processes, offers the opportunity to rationalize better the high amount of "omics" data freely available in several public databases.In particular, integration of microarray-derived transcriptome data with other high-throughput analyses (genomic and mutational analysis, promoter analysis) may allow us to unravel transcriptional regulatory networks under a variety of physio-pathological situations, such as the alteration in the cross-talk between signal transduction pathways in transformed cells.

RESULTS

Here we sequentially apply web-based and statistical tools to a case study: the role of oncogenic activation of different signal transduction pathways in the transcriptional regulation of genes encoding proteins involved in the cAMP-PKA pathway. To this end, we first re-analyzed available genome-wide expression data for genes encoding proteins of the downstream branch of the PKA pathway in normal tissues and human tumor cell lines. Then, in order to identify mutation-dependent transcriptional signatures, we classified cancer cells as a function of their mutational state. The results of such procedure were used as a starting point to analyze the structure of PKA pathway-encoding genes promoters, leading to identification of specific combinations of transcription factor binding sites, which are neatly consistent with available experimental data and help to clarify the relation between gene expression, transcriptional factors and oncogenes in our case study.

CONCLUSIONS

Genome-wide, large-scale "omics" experimental technologies give different, complementary perspectives on the structure and regulatory properties of complex systems. Even the relatively simple, integrated workflow presented here offers opportunities not only for filtering data noise intrinsic in high throughput data, but also to progressively extract novel information that would have remained hidden otherwise. In fact we have been able to detect a strong transcriptional repression of genes encoding proteins of cAMP/PKA pathway in cancer cells of different genetic origins. The basic workflow presented herein may be easily extended by incorporating other tools and can be applied even by researchers with poor bioinformatics skills.

An extensive survey of tyrosine phosphorylation revealing new sites in human mammary epithelial cells

Protein tyrosine phosphorylation represents a central regulatory mechanism in cell signaling. Here, we present an extensive survey of tyrosine phosphorylation sites in a normal-derived human mammary epithelial cell (HMEC) line by applying antiphosphotyrosine peptide immunoaffinity purification coupled with high sensitivity capillary liquid chromatography tandem mass spectrometry. A total of 481 tyrosine phosphorylation sites (covered by 716 unique peptides) from 285 proteins were confidently identified in HMEC following the analysis of both the basal condition and acute stimulation with epidermal growth factor (EGF). The estimated false discovery rate was 1.0% as determined by searching against a scrambled database. Comparison of these data with existing literature showed significant agreement for previously reported sites. However, we observed 281 sites that were not previously reported for HMEC cultures and 29 of which have not been reported for any human cell or tissue system. The analysis showed that a majority of highly phosphorylated proteins were relatively low-abundance. Large differences in phosphorylation stoichiometry for sites within the same protein were also observed, raising the possibility of more important functional roles for such highly phosphorylated pTyr sites. By mapping to major signaling networks, such as the EGF receptor and insulin growth factor-1 receptor signaling pathways, many known proteins involved in these pathways were revealed to be tyrosine phosphorylated, which provides interesting targets for future hypothesis-driven and targeted quantitative studies involving tyrosine phosphorylation in HMEC or other human systems.

An analysis of trends and growth factor receptor expression of GI carcinoid tumors

INTRODUCTION

The purpose of our study was twofold: (1) to determine the incidence, patient and tumor characteristics, and outcome of patients with gastrointestinal carcinoid tumors using the Surveillance, Epidemiology and End Results (SEER) database, and (2) to delineate the expression pattern of growth factor receptors (GFRs) in carcinoid tumors.

MATERIALS AND METHODS

The SEER database search provided information on patients diagnosed with carcinoid tumors from 1990 to 2002. Carcinoid tumor sections (n = 46) were stained for the GFRs: epidermal growth factor receptor, insulin-like growth factor receptor (IGFR), vascular endothelial growth factor receptor (VEGFR), and HER-2/neu.

RESULTS

Over the 12-year analysis period, 18,180 patients were identified with carcinoid tumors of the foregut, midgut, and hindgut; the incidence of carcinoid tumors increased approximately 2-fold during this time period. Of the patients with carcinoid tumors, there was a trend of increased expression of VEGFR and IGFR, particularly in the foregut and midgut carcinoids. Analysis of the SEER database confirms that the incidence of carcinoid tumors is increasing with an approximate doubling in the number of carcinoid cases from 1990 to 2002. Furthermore, an increase in VEGFR and IGFR expression suggests that GFR inhibitors may be effective adjuvant therapy for carcinoid cancer.

An analysis of trends and growth factor receptor expression of GI carcinoid tumors

INTRODUCTION

The purpose of our study was twofold: (1) to determine the incidence, patient and tumor characteristics, and outcome of patients with gastrointestinal carcinoid tumors using the Surveillance, Epidemiology and End Results (SEER) database, and (2) to delineate the expression pattern of growth factor receptors (GFRs) in carcinoid tumors.

MATERIALS AND METHODS

The SEER database search provided information on patients diagnosed with carcinoid tumors from 1990 to 2002. Carcinoid tumor sections (n = 46) were stained for the GFRs: epidermal growth factor receptor, insulin-like growth factor receptor (IGFR), vascular endothelial growth factor receptor (VEGFR), and HER-2/neu.

RESULTS

Over the 12-year analysis period, 18,180 patients were identified with carcinoid tumors of the foregut, midgut, and hindgut; the incidence of carcinoid tumors increased approximately 2-fold during this time period. Of the patients with carcinoid tumors, there was a trend of increased expression of VEGFR and IGFR, particularly in the foregut and midgut carcinoids. Analysis of the SEER database confirms that the incidence of carcinoid tumors is increasing with an approximate doubling in the number of carcinoid cases from 1990 to 2002. Furthermore, an increase in VEGFR and IGFR expression suggests that GFR inhibitors may be effective adjuvant therapy for carcinoid cancer.

Refinement of the population pharmacokinetic model for the monoclonal antibody matuzumab: external model evaluation and simulations

OBJECTIVES

A developed population pharmacokinetic model of the humanized monoclonal antibody (mAb) matuzumab was evaluated by external evaluation. Based on the estimates of the final model, simulations of different dosing regimens and the covariate effect were performed.

METHODS

The development dataset included 90 patients, and the evaluation dataset included 81 patients; the two sets of patients were from three different studies. In all studies, the patients had different types of advanced carcinoma - mainly colon, rectal and pancreatic cancer. They received matuzumab as multiple 1-hour intravenous infusions in a wide range of dosing regimens (development dataset: from 400 mg every 3 weeks to 2000 mg in the first week followed by 1600 mg weekly; evaluation dataset: from 100 mg weekly to 800 mg weekly). In addition to 1256 serum mAb concentrations for model development, there were 1124 concentrations available for model evaluation. Serum concentration-time data were simultaneously fitted using NONMEM software. The developed two-compartment model - with the parameters central volume of distribution (V(1)) and peripheral volume of distribution (V(2)), intercompartmental clearance and linear clearance (CLL), an additional nonlinear elimination pathway (Michaelis-Menten constant: the concentration with the half-maximal elimination rate and V(max): the maximum elimination rate) and covariate relations - was evaluated by an external dataset. Different simulation scenarios were performed to demonstrate the impact of the incorporated covariate effect and the influence of different dosing regimens and dosing strategies on the concentration-time profiles.

RESULTS

The developed model included the covariate fat-free mass (FFM) on V(1) and on CLL. The evaluation did not support the covariate FFM on V(1) and, after deletion of this covariate, the model parameters of the refined model were estimated. The model showed good precision for all parameters: the relative standard errors (RSEs) were <42% for the development dataset and < or = 51% for the evaluation dataset (excluding the higher RSEs for the correlation between V(2) and V(max) and the interindividual variability on V(2) for the evaluation dataset). The model showed good robustness for the ability to estimate highly precise parameters for the combined dataset of 171 patients (RSE <29%). Simulations revealed that variability in concentration-time profiles for minimum and maximum steady-state concentrations was reduced to a marginal extent by a proposed dose adaptation.

CONCLUSION

The population pharmacokinetic model for matuzumab was improved by evaluation with an external dataset. The new model obtained precise parameter estimates and demonstrated robustness. After correlation with efficacy data simulation results in particular could serve as a tool to guide dose selection for this 'targeted' cancer therapy.

Stochastic simulation of signal transduction: impact of the cellular architecture on diffusion

The transduction of signals depends on the translocation of signaling molecules to specific targets. Undirected diffusion processes play a key role in the bridging of spaces between different cellular compartments. The diffusion of the molecules is, in turn, governed by the intracellular architecture. Molecular crowding and the cytoskeleton decrease macroscopic diffusion. This article shows the use of a stochastic simulation method to study the effects of the cytoskeleton structure on the mobility of macromolecules. Brownian dynamics and single particle tracking were used to simulate the diffusion process of individual molecules through a model cytoskeleton. The resulting average effective diffusion is in line with data obtained in the in vitro and in vivo experiments. It shows that the cytoskeleton structure strongly influences the diffusion of macromolecules. The simulation method used also allows the inclusion of reactions in order to model complete signaling pathways in their spatio-temporal dynamics, taking into account the effects of the cellular architecture.

A comprehensive catalogue of functional genetic variations in the EGFR pathway: protein-protein interaction analysis reveals novel genes and polymorphisms important for cancer research

The EGFR pathway is a critical signaling pathway deregulated in many solid tumors. In addition to the initiation and progression of cancer, the EGFR pathway is also implicated in variable treatment responses and prognoses. Genetic variation in the form of Single Nucleotide Polymorphisms (SNPs) can affect the function/expression of the EGFR pathway genes. Here, we applied a systematic and comprehensive approach utilizing diverse public databases and in silico analysis tools to select putative functional genetic variations from 244 genes involved in the EGFR pathway. Our data comprises 649 SNPs. Three hundred sixty SNPs are predicted to have biological consequences (functional SNPs). These SNPs can be directly used in further studies to test their association with risk, treatment response and prognosis in cancer. To systematically cover the EGFR pathway, we also performed a network-based analysis to further select putative functional SNPs from the genes whose protein products physically interact with the EGFR pathway proteins. We utilized protein-protein interaction information and focused on 14 proteins that have a high degree of connectivity (interacting with > or = 10 proteins) with the EGFR pathway genes identified to have functional SNPs (f-EGFR genes). Two of these proteins (FYN and LCK) had interactions with 17 of the f-EGFR genes, yet both lacked any putative functional SNP. However, our analysis indicated the presence of potentially functional SNPs in 9 other highly interactive proteins. The genes and their SNPs identified in the network-based analysis represent potential candidates for gene-gene and SNP-SNP interaction studies in cancer research.

The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data

The epidermal growth factor receptor (EGFR) signaling pathway is probably the best-studied receptor system in mammalian cells, and it also has become a popular example for employing mathematical modeling to cellular signaling networks. Dynamic models have the highest explanatory and predictive potential; however, the lack of kinetic information restricts current models of EGFR signaling to smaller sub-networks. This work aims to provide a large-scale qualitative model that comprises the main and also the side routes of EGFR/ErbB signaling and that still enables one to derive important functional properties and predictions. Using a recently introduced logical modeling framework, we first examined general topological properties and the qualitative stimulus-response behavior of the network. With species equivalence classes, we introduce a new technique for logical networks that reveals sets of nodes strongly coupled in their behavior. We also analyzed a model variant which explicitly accounts for uncertainties regarding the logical combination of signals in the model. The predictive power of this model is still high, indicating highly redundant sub-structures in the network. Finally, one key advance of this work is the introduction of new techniques for assessing high-throughput data with logical models (and their underlying interaction graph). By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions. Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2. Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway.

Knowledge based identification of essential signaling from genome-scale siRNA experiments

Background

A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.

Results

We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK cytoskeletal remodeling pathway. Different branches of the FAK pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.

Conclusion

PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets.

Pathway databases and tools for their exploitation: benefits, current limitations and challenges

In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue.

Network architecture of signaling from uncoupled helicase-polymerase to cell cycle checkpoints and trans-lesion DNA synthesis

When replication is blocked by a template lesion or polymerase inhibitor while helicase continues unwinding the DNA, single stranded DNA (ssDNA) accumulates and becomes coated with RPA, which then initiates signals via PCNA mono-ubiquitination to activate trans-lesion polymerases and via ATR and Chk1 to inhibit Cdk2-dependent cell cycle progression. The signals are conveyed by way of a complex network of molecular interactions. To clarify those complexities, we have constructed a molecular interaction map (MIM) using a novel hierarchical assembly procedure. Molecules were arranged on the map in hierarchical levels according to interaction step distance from the DNA region of stalled replication. The hierarchical MIM allows us to disentangle the network's interlocking pathways and loops and to suggest functionally significant features of network architecture. The MIM shows how parallel pathways and multiple feedback loops can provide failsafe and robust switch-like responses to replication stress. Within the central level of hierarchy ATR and Claspin together appear to function as a nexus that conveys signals from many sources to many destinations. We noted a division of labor between those two molecules, separating enzymatic and structural roles. In addition, the network architecture disclosed by the hierarchical map, suggested a speculative model for how molecular crowding and the granular localization of network components in the cell nucleus can facilitate function.

Clique-based data mining for related genes in a biomedical database

BACKGROUND

Progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. There is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. Here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph.

RESULTS

We constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the Online Mendelian Inheritance in Man (OMIM) database. We obtained over 20,000 sets of related genes (called 'gene modules') by enumerating cliques computationally. The modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. The results of experiments using 'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes.

CONCLUSION

We presented a data mining approach extracting related genes by enumerating cliques. The extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.

Integration of signals from the B-cell antigen receptor and the IL-4 receptor leads to a cooperative shift in the cellular response axis

Although intracellular signaling events activated through individual cell surface receptors have been characterized in detail, cells are often exposed to multiple stimuli simultaneously in physiological situations. The response elicited then is defined through the cooperative interactions between signals activated by these multiple stimuli. Examples of such instances include cooperativity between individual isoforms of G-protein-coupled receptors, between different growth factor receptors, or between growth factor and integrin receptors. Mechanisms by which the integration of signals emanating from independent receptors influences cellular responses, however, are unknown. In this report, we studied interactions between the antigen and the IL-4 receptors in immature B cells. While stimulation through the B-cell antigen receptor alone causes cell cycle arrest and subsequent apoptosis, the inclusion of IL-4 during stimulation provides a protective effect. We therefore sought to obtain a systems view on how crosstalk between the two respective cell surface receptors modulates the cellular response. We found that, in comparison to the effects of B-cell receptor activation alone, combined stimulation through both receptors enforced a marked reorientation in the 'survival vs. apoptosis' axis of the signaling machinery. The consequent modulation of transcription factor activities yielded an integrated network, spanning the signaling and the transcriptional regulatory components, that was now biased towards the recruitment of molecules with a pro-survival function. This alteration in network properties influenced early-induced gene expression, in a manner that could rationalize the antagonistic effect of the IL-4 receptor on B-cell receptor signaling. Importantly, this antagonism was achieved through an expansion in the repertoire of the genes expressed, wherein the newly generated products counteracted the effects of the B-cell receptor-specific subset. Thus the plasticity of the regulatory networks is also experienced at the level of gene expression, and is the resultant pattern obtained that then modulates cell-fate decisions.

Genistein synergizes with RNA interference inhibiting survivin for inducing DU-145 of prostate cancer cells to apoptosis

To further investigate the effect of a combination of genistein with survivin of RNA interference on the proliferation and apoptosis of DU-145 cells, the effect of genistein on the proliferation of DU-145 cells was detected by the MTT method and cytometry, and the apoptosis of cells was observed with fluorescence microscopy. In order to test combined genistein with transfection of small interfering RNA (siRNA) against survivin, a survivin siRNA plasmid was constructed and transfected into DU-145 cells. Genistein inhibited proliferation and induced apoptosis of cancerous DU-145 and Hela cells, whereas genistein had minimal effects for normal L-O2 cells. The stable transfected cell lines of DU-145, knockdown survivin by siRNA, displayed stronger apoptotic than untransfected DU-145, the transfected cell of DU-145 treated with genistein demonstrated the inhibition of proliferation and induction of apoptosis significantly; it showed genistein synergistic effect with RNAi in survivin for inhibition of prostate cancer cells.

A general map of iron metabolism and tissue-specific subnetworks

Iron is required for survival of mammalian cells. Recently, understanding of iron metabolism and trafficking has increased dramatically, revealing a complex, interacting network largely unknown just a few years ago. This provides an excellent model for systems biology development and analysis. The first step in such an analysis is the construction of a structural network of iron metabolism, which we present here. This network was created using CellDesigner version 3.5.2 and includes reactions occurring in mammalian cells of numerous tissue types. The iron metabolic network contains 151 chemical species and 107 reactions and transport steps. Starting from this general model, we construct iron networks for specific tissues and cells that are fundamental to maintaining body iron homeostasis. We include subnetworks for cells of the intestine and liver, tissues important in iron uptake and storage, respectively, as well as the reticulocyte and macrophage, key cells in iron utilization and recycling. The addition of kinetic information to our structural network will permit the simulation of iron metabolism in different tissues as well as in health and disease.

Current concepts in research related to oncogenes implicated in salivary gland tumourigenesis: a review of the literature

BACKGROUND

Salivary gland tumours are relatively uncommon and there exists considerable difficulty in decisions regarding prognosis and management, as well as diagnostic uncertainty that has implications for treatment.

METHOD

Literature pertaining to individual oncogenes has been reviewed and commented upon, specifically looking at the role of these as diagnostic and prognostic markers and as potential targets for treatments.

RESULTS

kit, PLAG1, Mect1-Maml2, HMGIC, HER2/neu, ras, c-fos and Sox-4 all have seminal small-scale studies in the literature with potential for further research and eventual clinical applications.

CONCLUSION

A wide variety of oncogenes are implicated in salivary gland tumourigenesis, with evidence being confined to small murine or in vitro studies more often than not. There are possible roles for different oncogenes in therapeutics, prognosis and management of specific salivary gland tumours.

Osteoblast-induced EGFR/ERBB2 signaling in androgen-sensitive prostate carcinoma cells characterized by multiplex kinase activity profiling

Bone metastases in prostate cancer are predominantly osteoblastic. To study regulatory mechanisms underlying the establishment of prostate cancer within an osteoblastic microenvironment, human androgen-sensitive prostate carcinoma cells (LNCaP) were treated with culture medium conditioned by human osteoblast-derived sarcoma cells (OHS), and activated signalling pathways in the carcinoma cells were analyzed using microarrays with tyrosine kinase substrates. Network interaction analysis of substrates with significantly increased phosphorylation levels revealed that signalling pathways mediated by EGFR and ERBB2 were activated in LNCaP cells under OHS influence but also by androgen treatment. Activation of EGFR/ERBB2 signalling was also found in LNCaP cells in cocultures with OHS cells or osteoblastic cells that had been differentiated from human mesenchymal stem cells. Our experimental data suggests osteoblast-directed induction of signalling activity via EGFR and ERBB2 in prostate carcinoma cells and may provide a rationale for the use of EGFR or ERBB2 inhibition in systemic prevention or treatment of metastatic prostate cancer in the androgen-sensitive stage of the disease.

EGF receptor signaling blocks aryl hydrocarbon receptor-mediated transcription and cell differentiation in human epidermal keratinocytes

Dioxin is an extremely potent carcinogen. In highly exposed people, the most commonly observed toxicity is chloracne, a pathological response of the skin. Most of the effects of dioxin are attributed to its activation of the aryl hydrocarbon receptor (AHR), a transcription factor that binds to the Ah receptor nuclear translocator (ARNT) to regulate the transcription of numerous genes, including CYP1A1 and CYP1B1. In cultures of normal human epidermal keratinocytes dioxin accelerates cell differentiation, as measured by the formation of cornified envelopes. We show that this acceleration is mediated by the AHR; also, that dioxin increases the expression of several genes known to be regulated by ARNT, which have critical roles in the cornification and epidermal barrier function of the skin. Importantly, we demonstrate that all of these responses are opposed by ligand-activation of the EGF receptor (R), an important regulator of keratinocyte cell fate. In the CYP1A1 enhancer, EGFR activation prevents recruitment of the p300 coactivator, although not affecting the binding of the AHR or ARNT. The total cellular level of p300 protein does not decrease, and overexpression of p300 relieves EGFR-mediated repression of transcription, indicating that p300 is a critical target for the repression of the AHR complex by EGFR signaling. These results provide a mechanism by which 2,3,7,8-tetrachlorodibenzo-p-dioxin is able to disrupt epidermal homeostasis and identify EGFR signaling as a regulator of the AHR. This signaling may modulate the incidence and severity of chloracne and be of therapeutic relevance to human poisonings by dioxin.

Ligand-induced ErbB receptor dimerization

Structural studies have provided important new insights into how ligand binding promotes homodimerization and activation of the EGF receptor and the other members of the ErbB family of receptor tyrosine kinases. These structures have also suggested possible explanations for the unique properties of ErbB2, which has no known ligand and can cause cell transformation (and tumorigenesis) by simple overexpression. In parallel with these advances, studies of the EGF receptor at the cell surface increasingly argue that the structural studies are missing key mechanistic components. This is particularly evident in the structural prediction that EGF binding linked to receptor dimerization should be positively cooperative, whereas cell-surface EGF-binding studies suggest negative cooperativity. In this review, I summarize studies of ErbB receptor extracellular regions in solution and of intact receptors at the cell surface, and attempt to reconcile the differences suggested by the two approaches. By combining results obtained with receptor 'parts', it is qualitatively possible to explain some models for the properties of the whole receptor. These considerations underline the need to consider the intact ErbB receptors as intact allosterically regulated enzymes, and to combine cellular and structural studies into a complete picture.

Estrogen receptor-alpha mediates the epidermal growth factor-stimulated prolactin expression and release in lactotrophs

Epidermal growth factor (EGF) is a potent regulator of cell function in many cell types. EGF-receptor (EGFR/ErbB1)-activated Erk1/2 has been reported to activate estrogen receptor (ER) in an estrogen (E2)-independent manner. In the pituitary lactotrophs, both EGF and E2 stimulate prolactin (PRL) release, but the nature of interactions between ErbB and ERalpha signaling is unknown. Our objectives were to 1) characterize EGF-induced PRL release, 2) determine whether this effect requires ERalpha, and 3) determine the molecular basis for cross talk between ErbB and ERalpha signaling pathways. Using GH3 cells, a rat lactotroph cell line, we report that EGF stimulates PRL gene expression and release in a dose- and time-dependent manner. EGF caused a rapid and robust activation of Erk1/2 via ErbB1 and induced phosphorylation of S118 on ERalpha in an Erk1/2-dependent manner. The global antiestrogen ICI 182780 and the ERalpha-specific antagonist 1,3-bis(4-hydroxyphenyl)-4-methyl-5-[4-(2-piperidinylet hoxy)phenol]-1H-pyrazole dihydrochloride (MPP), but not the ERbeta-specific antagonist 4-[2-Phenyl-5,7-bis(trifluoromethyl) pyrazolo[1,5-a]pyrimidin-3-yl]phenol (PHTPP), blocked the EGF-induced PRL release, indicating an ERalpha requirement. This was further supported by using ERalpha knockdown by small interfering RNA. Because the antiestrogens did not block EGF-induced Mek-1 or Erk1/2 phosphorylation, ERalpha is placed downstream from the ErbB1-activated Erk1/2. These results provide the first evidence that ErbB1-induced PRL release is ERalpha dependent.

Systems biology of vascular endothelial growth factors

Several cytokine families have roles in the development, maintenance, and remodeling of the microcirculation. Of these, the vascular endothelial growth factor (VEGF) family is one of the best studied and one of the most complex. Five VEGF ligand genes and five cell-surface receptor genes are known in the human, and each of these may be transcribed as multiple splice isoforms to generate an extensive family of proteins, many of which are subject to further proteolytic processing. Using the VEGF family as an example, we describe the current knowledge of growth-factor expression, processing, and transport in vivo. Experimental studies and computational simulations are being used to measure and predict the activity of these molecules, and we describe avenues of research that seek to fill the remaining gaps in our understanding of VEGF family behavior.

Linear combinations of docking affinities explain quantitative differences in RTK signaling

Receptor tyrosine kinases (RTKs) process extracellular cues by activating a broad array of signaling proteins. Paradoxically, they often use the same proteins to elicit diverse and even opposing phenotypic responses. Binary, ‘on–off' wiring diagrams are therefore inadequate to explain their differences. Here, we show that when six diverse RTKs are placed in the same cellular background, they activate many of the same proteins, but to different quantitative degrees. Additionally, we find that the relative phosphorylation levels of upstream signaling proteins can be accurately predicted using linear models that rely on combinations of receptor-docking affinities and that the docking sites for phosphoinositide 3-kinase (PI3K) and Shc1 provide much of the predictive information. In contrast, we find that the phosphorylation levels of downstream proteins cannot be predicted using linear models. Taken together, these results show that information processing by RTKs can be segmented into discrete upstream and downstream steps, suggesting that the challenging task of constructing mathematical models of RTK signaling can be parsed into separate and more manageable layers.

Reconstruction and logical modeling of glucose repression signaling pathways in Saccharomyces cerevisiae

Background

In the yeast Saccharomyces cerevisiae, the presence of high levels of glucose leads to an array of down-regulatory effects known as glucose repression. This process is complex due to the presence of feedback loops and crosstalk between different pathways, complicating the use of intuitive approaches to analyze the system.

Results

We established a logical model of yeast glucose repression, formalized as a hypergraph. The model was constructed based on verified regulatory interactions and it includes 50 gene transcripts, 22 proteins, 5 metabolites and 118 hyperedges. We computed the logical steady states of all nodes in the network in order to simulate wildtype and deletion mutant responses to different sugar availabilities. Evaluation of the model predictive power was achieved by comparing changes in the logical state of gene nodes with transcriptome data. Overall, we observed 71% true predictions, and analyzed sources of errors and discrepancies for the remaining.

Conclusion

Though the binary nature of logical (Boolean) models entails inherent limitations, our model constitutes a primary tool for storing regulatory knowledge, searching for incoherencies in hypotheses and evaluating the effect of deleting regulatory elements involved in glucose repression.

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.

Tyrosine kinase activity of epidermal growth factor receptor is regulated by GM3 binding through carbohydrate to carbohydrate interactions

Epidermal growth factor receptor (EGFR), an N-glycosylated transmembrane protein with an intracellular kinase domain, undergoes dimerization by ligand binding resulting in activation of the kinase domain and phosphorylation. Ganglioside GM3 containing sialyllactose inhibits the tyrosine kinase activity of EGFR through carbohydrate to carbohydrate interactions (CCI) between N-glycans with GlcNAc termini on EGFR and oligosaccharides on GM3. In this study, we provide further evidence for CCI between EGFR and GM3. (i) In vitro and in situ, the inhibitory effect of GM3 on EGFR tyrosine kinase was much higher in A431 cells upon exposure of the GlcNAc termini of the N-glycans to glycosidase treatment (neuraminidase and beta-galactosidase) than in untreated A431 cells. Furthermore, the GM3-mediated inhibition was abrogated by co-incubation with N-glycan containing terminal GlcNAc. (ii) In situ, inhibition of EGFR phosphorylation by GM3 was not observed in alpha-mannosidase IB (ManIB)-knocked down A431 cells that accumulate high mannose-type N-glycans. (iii) EGFR binding to GM3 was enhanced in glycosidase-treated cells that accumulated GlcNAc termini, whereas GM3 did not bind to EGFR from ManIB-knocked down cells that accumulated high mannose-type N-glycans. These results indicate that GM3-mediated inhibition of EGFR phosphorylation is caused by interaction of GM3 with GlcNAc-terminated N-glycan on EGFR.

Molecular pathogenesis of progression and recurrence in breast phyllodes tumors

Breast phyllodes tumors are rare fibroepithelial neoplasms that need to be distinguished from the common morphologically similar fibroadenomas, because phyllodes tumors can recur and progress to malignancy. Their potentially recurring and metastasizing behavior is attributed to their stromal characteristics, for which categorization between benign, borderline and malignant tumors have not been universally established. Previous clonality studies revealing monoclonal stromal cells versus a polyclonal epithelial component theorized that phyllodes tumors are mainly stromal neoplasms, possibly arising from fibroadenomas. More recent chromosomal imbalances in both epithelium and stroma have challenged this theory to favor neoplasia of both epithelium and stroma, with initial interdependence between the two components. Inverse correlations between epithelial and stromal overexpression for various biological markers like estrogen receptor, p53, c-kit, Ki-67, endothelin-1, epidermal growth factor receptor, heparan sulfate, in addition to findings of epithelial Wnt signalling with stromal insulin growth factors and beta-catenin expression, suggest an initial epithelial-stromal interdependence at the benign phase. Upon progression to malignancy, the stroma is hypothesized to assume an autonomous growth overriding any epithelial influence. Frequent genetic alterations are chromosomal gains of 1q and losses at chromosome 13. Acquisition of new genetic imbalances within the tumor consistent with intratumoral heterogeneity, and subclones within histologically benign phyllodes tumors that recur or metastasize are the current theories explaining these tumors' unpredictable clinical behavior.

Systemic analysis of TGFbeta proteomics revealed involvement of Plag1/CNK1/RASSF1A/Src network in TGFbeta1-dependent activation of Erk1/2 and cell proliferation

Transforming growth factor-beta (TGFbeta) is a key regulator of cell proliferation, death, migration, and differentiation. To explore mechanisms of TGFbeta action, we performed systemic analysis of functional dependencies between 153 proteins which changed their expression and synthesis upon treatment of human breast epithelial cells with TGFbeta1. We found that TGFbeta1 initiated signaling via a scale-free network of proteins which affect primary cellular metabolism, stress response, signal transduction, transport, transcription, cytoskeleton, and cell death. Multiple inputs into each functional domain were observed, emphasizing robustness of TGFbeta1 signaling. Network analysis demonstrated importance of a Plag1/CNK1/RASSF1A/Src-dependent prozone effect, as a systemic feature which is crucial for TGFbeta1-dependent activation of Erk1/2 and regulation of cell proliferation. We showed that the balance between Plag1, CNK1, RASSF1A and Src defined whether TGFbeta1 will stimulate, inhibit or will have no effect on a long-term activation of Erk1/2 and subsequent TGFbeta1 inhibitory or stimulatory effect on cell proliferation. This is the first demonstration of importance of systemic features for incorporation of Erk1/2 activation into TGFbeta1 signaling.

Phosphoproteomics, oncogenic signaling and cancer research

The past 5 years have seen an explosion of phosphoproteomics methods development. In this review, using epidermal growth-factor signaling as a model, we will discuss how phosphoproteomics, along with bioinformatics and computational modeling, have impacted key aspects of oncogenic signaling such as in the temporal fine mapping of phosphorylation events, and the identification of novel tyrosine kinase substrates and phosphorylation sites. We submit that the next decade will see considerable exploitation of phosphoproteomics in cancer research. Such a phenomenon is already happening as exemplified by its use in promoting the understanding of the molecular etiology of cancer and target-directed therapeutics.

In silico and in vitro modeling of hepatocyte drug transport processes: importance of ABCC2 expression levels in the disposition of carboxydichlorofluroscein

The impact of transport proteins in the disposition of chemicals is becoming increasingly evident. Alteration in disposition can cause altered pharmacokinetic and pharmacodynamic parameters, potentially leading to reduced efficacy or overt toxicity. We have developed a quantitative in silico model, based upon literature and experimentally derived data, to model the disposition of carboxydichlorofluroscein (CDF), a substrate for the SLCO1A/B and ABCC subfamilies of transporters. Kinetic parameters generated by the in silico model closely match both literature and experimentally derived kinetic values, allowing this model to be used for the examination of transporter action in primary rat hepatocytes. In particular, we show that the in silico model is suited to the rapid, accurate determination of K(i) values, using 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethylsulfanyl)methylsulfanyl] propionic acid (MK571) as a prototypical pan-ABCC inhibitor. In vitro-derived data are often used to predict in vivo response, and we have examined how differences in protein expression levels between these systems may affect chemical disposition. We show that ABCC2 and ABCC3 are overexpressed in sandwich culture hepatocytes by 3.5- and 2.3-fold, respectively, at the protein level. Correction for this in markedly different disposition of CDF, with the area under the concentration versus time curve and C(max) of intracellular CDF increasing by 365 and 160%, respectively. Finally, using kinetic simulations we show that ABCC2 represents a fragile node within this pathway, with alterations in ABCC2 having the most prominent effects on both the K(m) and V(max) through the pathway. This is the first demonstration of the utility of modeling approaches to estimate the impact of drug transport processes on chemical disposition.

Receptor overexpression or inhibition alters cell surface dynamics of EGF-EGFR interaction: new insights from real-time single molecule analysis

Binding of Epidermal growth factor (EGF) to epidermal growth factor receptor (EGFR) in two types of cancer cells (HeLa; 5 x 10(4) EGFR/cell) and MDA-MB-468; 2 x 10(6) EGFR/cell) was studied using Total Internal Reflectance Fluorescence (TIRF) microscopy at single molecule precision. Mathematical modeling of the binding kinetics revealed that cells respond differently to the same concentration of EGF depending on the expression level of EGFR. Compared to Hela, MDA-MB-468 cells show; (a) higher number of pre-formed dimers, (b) improved EGF-EGFR interaction at lower ligand concentrations, and (c) shorter time-lapse between first and second EGF binding to the dimer. Treatment with a pharmacological inhibitor of EGFR, AG1478, produced strikingly different binding kinetics where the extent of pre-formed EGFR dimers increased substantially. Thus, single molecule approaches produce novel, quantitative information on signaling mechanisms of significant biological importance. Surface kinetics could also serve as surrogate markers to predict biological outcome of signaling pathways.

Myricetin directly targets JAK1 to inhibit cell transformation

JAK1/STAT3 pathway has been suggested to play a role in cell transformation and carcinogenesis. In the present study, we found that myricetin (3, 3', 4', 5, 5', 7-hexahydroxyflavone), a typical flavonol existing in many fruits and vegetables, could directly bind to JAK1/STAT3 molecules to inhibit cell transformation in epidermal growth factor (EGF)-activated mouse JB6 P(+) cells. Colony assay revealed that myricetin had the strongest inhibitory effect on cell transformation among three flavonols including myricetin, quercetin and kaempferol. Molecular data revealed that myricetin inhibited DNA- binding and transcriptional activity of STAT3. Furthermore, myricetin inhibited the phosphorylation of STAT3 at Tyr705 and Ser727. Cellular signaling analyses revealed that EGF could induce the phosphorylation of Janus Kinase (JAK) 1, but not JAK2. Myricetin inhibited the phosphorylation of JAK1 and increased the autophosphorylation of EGF receptor (EGFR). Moreover, ex vivo and in vitro pull-down assay revealed that myricetin bound to JAK1 and STAT3, but not EGFR. Affinity data further demonstrated that myricetin had a higher affinity for JAK1 than STAT3. Thus, our data indicate that myricetin might directly target JAK1 to block cell transformation in mouse JB6 cells.

Quantitative modeling perspectives on the ErbB system of cell regulatory processes

The complexities of the processes involved in ErbB-mediated regulation of cellular phenotype are broadly appreciated, so much so that it might be reasonably argued that this highly studied system provided significant impetus for the systems perspective on cell signaling processes in general. Recent years have seen major advances in the level of characterization of the ErbB system as well as our ability to make measurements of the system. This new data provides significant new insight, while at the same time creating new challenges for making quantitative statements and predictions with certainty. Here, we discuss recent advances in each of these directions and the interplay between them, with a particular focus on quantitative modeling approaches to interpret data and provide predictive power. Our discussion follows the sequential order of ErbB pathway activation, beginning with considerations of receptor/ligand interactions and dynamics, proceeding to the generation of intracellular signals, and ending with determination of cellular phenotype. As discussed herein, these processes become increasingly difficult to describe or interpret in terms of traditional models, and we review emerging methodologies to address this complexity.

Characterization of human skin-derived mesenchymal stem cell proliferation rate in different growth conditions

This study investigated conditions for optimal in vitro propagation of human skin-derived mesenchymal stem cells (S-MSC). Forty primary skin-derived precursor cell (SKP) cultures were established from both male and female donors (age 29-65 years) and eight of them were randomly selected for in-depth characterization. Effects of basic fibroblast growth factor (FGF-2), epidermal growth factor (EGF), leukemia inhibiting factor (LIF) and dibutyryl-cyclic adenosine monophosphate (db-cAMP) on S-MSC proliferation were investigated. Primary SKP cultures were >95% homogenous for CD90, CD73, and CD105 marker expression enabling to classify these cells as S-MSC. FGF-2 dose-dependent stimulation was observed in low serum medium only, whereas EGF neither stimulated S-MSC proliferation nor potentates the effect of FGF-2. Pronounced donor to donor differences among S-MSC cultures were observed in 3-day proliferation assay. This study demonstrates that homogenous S-MSC populations can be reproducibly isolated from individual donors of different age. Optimal cell culture conditions for in vitro propagation of S-MSC are B27 supplemented or low serum media with FGF-2 (4 ng/ml). EGF and LIF as well as db-cAMP are dispensable for S-MSC proliferation.

Automatic decomposition of kinetic models of signaling networks minimizing the retroactivity among modules

MOTIVATION

The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks de.ned as chemical systems. Such a decomposition would be very useful as most quantitative models are de.ned using the latter, more detailed formalism.

RESULTS

Here, we introduce a novel method to decompose biochemical networks into modules so that the bidirectional (retroactive) couplings among the modules are minimized. Our approach adapts a method to detect community structures, and applies it to the so-called retroactivity matrix that characterizes the couplings of the network. Only the structure of the network, e.g. in SBML format, is required. Furthermore, the modularized models can be loaded into ProMoT, a modeling tool which supports modular modeling. This allows visualization of the models, exploiting their modularity and easy generation of models of one or several modules for further analysis. The method is applied to several relevant cases, including an entangled model of the EGF-induced MAPK cascade and a comprehensive model of EGF signaling, demonstrating its ability to uncover meaningful modules. Our approach can thus help to analyze large networks, especially when little a priori knowledge on the structure of the network is available.

AVAILABILITY

The decomposition algorithms implemented in MATLAB (Mathworks, Inc.) are freely available upon request. ProMoT is freely available at http://www.mpi-magdeburg.mpg.de/projects/promot.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.