Mean-field Boolean network model of a signal transduction network

In this paper we provide a mean-field Boolean network model for a signal transduction network of a generic fibroblast cell. The network consists of several main signaling pathways, including the receptor tyrosine kinase, the G-protein coupled receptor, and the Integrin signaling pathway. The network consists of 130 nodes, each representing a signaling molecule (mainly proteins). Nodes are governed by Boolean dynamics including canalizing functions as well as totalistic Boolean functions that depend only on the overall fraction of active nodes. We categorize the Boolean functions into several different classes. Using a mean-field approach we generate a mathematical formula for the probability of a node becoming active at any time step. The model is shown to be a good match for the actual network. This is done by iterating both the actual network and the model and comparing the results numerically. Using the Boolean model it is shown that the system is stable under a variety of parameter combinations. It is also shown that this model is suitable for assessing the dynamics of the network under protein mutations. Analytical results support the numerical observations that in the long-run at most half of the nodes of the network are active.     

神经前体细胞表达发育性下调蛋白4在消化系统肿瘤发生中的作用

神经前体细胞表达发育性下调蛋白4(neural precursor cell expressed,developmentally down-regulated protein 4,NEDD4-1,部分文章也称NEDD4)是近年来才备受关注的肿瘤相关基因,属于E3 HECT(homologous to E6 associated protein C terminus,E6蛋白c端同源基因)泛素连接酶NEDD4样家族成员。泛素连接酶,能够参与多种蛋白质的泛素化、溶酶体及蛋白酶体的降解、胞核-胞质转位等,间接影响不同恶性肿瘤的多种信号通路。随着大量NEDD4-1与肿瘤相关实验的不断深入,目前已发现其可通过调控细胞周期、癌细胞侵袭转移、拮抗耐药性等许多途径影响肿瘤的生物学行为。在消化系统肿瘤中,NEDD4-1主要通过PTEN/PI3K/AKT、TGF-β、Hippo、LDLRAD4等多条通路促进肝细胞癌的增殖、侵袭和迁移能力;在胰腺癌中发现,NEDD4-1在PI3K/AKT信号通路中发挥癌基因作用,但在与Myc-SIRT2所形成的信号环路中,却发挥抑癌基因的作用;在胃癌和结直肠癌中,NEDD4-1所参与的信号通路与其他消化系统肿瘤均不相同,NEDD4-1能独立于PTEN/PI3K/AKT通路而发挥促进胃癌恶化、转移(EGFR信号通路)和抑制结直肠癌肿瘤生长(WNT信号通路)的作用。NEDD4-1已经成为人们治愈肿瘤的热门研究方向。本文通过系统总结NEDD4-1在不同消化系统肿瘤中的功能、信号通路和潜在抑制剂等,进行探讨NEDD4-1与不同信号通路的关系,旨为临床在癌症治疗领域提供重要的参考数据。     

Overexpression of hypoxia-inducible factor and metabolic pathways: possible targets of cancer

Cancer, the main cause of human deaths in the modern world is a group of diseases. Anticancer drug discovery is a challenge for scientists because of involvement of multiple survival pathways of cancer cells. An extensive study on the regulation of each step of these pathways may help find a potential cancer target. Up-regulated HIF-1 expression and altered metabolic pathways are two classical characteristics of cancer. Oxygen-dependent (through pVHL, PHDs, calcium-mediated) and independent (through growth factor signaling pathway, mdm2 pathway, HSP90) regulation of HIF-1α leads to angiogenesis, metastasis, and cell survival. The two subunits of HIF-1 regulates in the same fashion through different mechanisms. HIF-1α translation upregulates via mammalian target of rapamycin and mitogen-activated protein kinase signaling pathways, whereas HIF-1β through calmodulin kinase. Further, the stabilized interactions of these two subunits are important for proper functioning. Also, metabolic pathways crucial for the formation of building blocks (pentose phosphate pathway) and energy generation (glycolysis, TCA cycle and catabolism of glutamine) are altered in cancer cells to protect them from oxidative stress and to meet the reduced oxygen and nutrient supply. Up-regulated anaerobic metabolism occurs through enhanced expression of hexokinase, phosphofructokinase, triosephosphate isomerase, glucose 6-phosphate dehydrogenase and down-regulation of aerobic metabolism via pyruvate dehydrogenase kinase and lactate dehydrogenase which compensate energy requirements along with high glucose intake. Controlled expression of these two pathways through their common intermediate may serve as potent cancer target in future.     

Molecular targets and signaling pathways regulated by interleukin (IL)-24 in mediating its antitumor activities

Cancer remains a major health issue in the world and the effectiveness of current therapies is limited resulting in disease recurrence and resistance to therapy. Therefore to overcome disease recurrence and have improved treatment efficacy there is a continued effort to develop and test new anticancer drugs that are natural or synthetic - (conventional chemotherapeutics, small molecule inhibitors) and biologic (antibody, tumor suppressor genes, oligonucleotide) product. In parallel, efforts for identifying molecular targets and signaling pathways to which cancer cells are “addicted” are underway. By inhibiting critical signaling pathways that is crucial for cancer cell survival, it is expected that the cancer cells will undergo a withdrawal symptom akin to “de-addiction” resulting in cell death. Thus, the key for having an improved and greater control on tumor growth and metastasis is to develop a therapeutic that is able to kill tumor cells efficiently by modulating critical signaling pathways on which cancer cells rely for their survival.Currently several small molecule inhibitors targeted towards unique molecular signaling pathways have been developed and tested in the clinic. Few of these inhibitors have shown efficacy while others have failed. Thus, targeting a single molecule or pathway may be insufficient to completely block cancer cell proliferation and survival. It is therefore important to identify and test an anticancer drug that can inhibit multiple signaling pathways in a cancer cell, control growth of both primary and metastatic tumors and is safe.One biologic agent that has the characteristics of serving as a potent anticancer drug is interleukin (IL)-24. IL-24 suppresses multiple signaling pathways in a broad-spectrum of human cancer cells leading to tumor cell death, inhibition of tumor angiogenesis and metastasis. Additionally, combining IL-24 with other therapies demonstrated additive to synergistic antitumor activity. Clinical testing of IL-24 as a gene-based therapeutic for the treatment of solid tumors demonstrated that IL-24 is efficacious and is safe. The unique features of IL-24 support its further development as an anticancer drug for cancer treatment.In this review we summarize the current understanding on the molecular targets and signaling pathways regulated by IL-24 in mediating its anticancer activity.     

Convergence of signaling pathways on the activation of ERK in B cells

The B cell receptor (BCR) initiates three major signaling pathways: the Ras pathway, which leads to extracellular signal-regulated kinase (ERK) activation; the phospholipase C-gamma pathway, which causes calcium mobilization; and the phosphoinositide 3-kinase (PI 3-kinase) pathway. These combine to induce different biological responses depending on the context of the BCR signal. Both the Ras and PI 3-kinase pathways are important for B cell development and activation. Several model systems show evidence of cross-regulation between these pathways. Here we demonstrate through the use of PI 3-kinase inhibitors and a dominant-negative PI 3-kinase construct that the BCR-induced phosphorylation and activation of ERK is dependent on PI 3-kinase. PI 3-kinase feeds into the Ras signaling cascade at multiple points, both upstream and downstream of Ras. We also show that ERK activation is dependent on phospholipase C-gamma, in keeping with its dependence on calcium mobilization. Last, the activation of PI 3-kinase itself is completely dependent on Ras. We conclude that the PI 3-kinase and Ras signaling cascades are intimately connected in B cells and that the activation of ERK is a signal integration point, since it requires simultaneous input from all three major signaling pathways.     

Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.     

Structural and Logical Analysis of a Comprehensive Hedgehog Signaling Pathway to Identify Alternative Drug Targets for Glioma,Colon and Pancreatic Cancer

Hedgehog is an evolutionarily conserved developmental pathway, widely implicated in controlling various cellular responses such as cellular proliferation and stem cell renewal in human and other organisms, through external stimuli. Aberrant activation of this pathway in human adult stem cell line may cause different types of cancers. Hence, targeting this pathway in cancer therapy has become indispensable, but the non availability of detailed molecular interactions, complex regulations by extra- and intra-cellular proteins and cross talks with other pathways pose a serious challenge to get a coherent understanding of this signaling pathway for making therapeutic strategy. This motivated us to perform a computational study of the pathway and to identify probable drug targets. In this work, from available databases and literature, we reconstructed a complete hedgehog pathway which reports the largest number of molecules and interactions to date. Using recently developed computational techniques, we further performed structural and logical analysis of this pathway. In structural analysis, the connectivity and centrality parameters were calculated to identify the important proteins from the network. To capture the regulations of the molecules, we developed a master Boolean model of all the interactions between the proteins and created different cancer scenarios, such as Glioma, Colon and Pancreatic. We performed perturbation analysis on these cancer conditions to identify the important and minimal combinations of proteins that can be used as drug targets. From our study we observed the under expressions of various oncoproteins in Hedgehog pathway while perturbing at a time the combinations of the proteins GLI1, GLI2 and SMO in Glioma; SMO, HFU, ULK3 and RAS in Colon cancer; SMO, HFU, ULK3, RAS and ERK12 in Pancreatic cancer. This reconstructed Hedgehog signaling pathway and the computational analysis for identifying new combinatory drug targets will be useful for future in-vitro and in-vivo analysis to control different cancers.     

Malfunctioning of adipocytes in obesity is linked to quantitative surfaceome changes

Increased triglyceride accumulation in adipocytes caused by a misbalance between energy intake and energy consumption, results in increased adipocyte size, excess adipose tissue, increased body weight and ultimately, obesity. It is well established that enlarged adipocytes exhibit malfunctions that contribute to whole body insulin resistance, a key factor for the development of type 2 diabetes. However, the underlying molecular cause for dysfunctional adipocyte behavior and signaling is poorly understood. Since the adipocyte cell surface proteome, or surfaceome, represents the cellular signaling gateway to the microenvironment, we studied the contribution of this subproteome to adipocyte malfunctions in obesity. By using the chemoproteomic Cell Surface Capture (CSC) technology, we established surfaceome maps of primary adipocytes derived from different mouse models for metabolic disorders. Relative quantitative comparison between these surfaceome maps revealed a set of cell surface glycoproteins with modulated location-specific abundance levels. RNAi mediated targeting of a subset of the detected obesity modulated cell surface glycoproteins in an in vitro model system provided functional evidence for their role in adiponectin secretion and the lipolytic activity of adipocytes. Thus, we conclude that the identified cell surface glycoproteins which exhibit obesity induced abundance changes and impact adipocyte function at the same time contribute to adipocyte malfunction in obesity. The regulation of their concerted activities could improve adipocyte function in obesity.     

Influence of kinetics of drug binding on EGFR signaling: a comparative study of three EGFR signaling pathway models

We used three models of the epidermal growth factor receptor (EGFR) signaling pathway mimicking three different cell lines to study the effects of kinetics of drug binding on influencing molecular signaling in the pathways. With no incubation of drugs before the external cue epidermal growth factor (EGF) was applied, we found that fast kinetics of binding to protein kinases was advantageous in suppressing the production of the Extracellular signal-regulated kinase (ERK) that triggers cell proliferation, with some exceptions. Incubation of a drug with a protein kinase target for an hour before a pathway was initiated with an external cue made kinetics less significant, so did high concentration of drugs. In addition, we found that applying a drug to a protein kinase mostly affected downstream signaling although upstream events were also affected in a few cases. In examining whether applying two drugs to two protein kinase targets in the pathways could produce synergistic effects, we found positive, negative, or no effects, depending on the protein kinases targeted and the pathway model considered.     

CDA: Combinatorial Drug Discovery Using Transcriptional Response Modules

Background

Anticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years.

Methodology

Here we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells.

Conclusions

CDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.     

Analysis of signaling pathways in 90 cancer cell lines by protein lysate array

Multiple signal transduction pathways play a crucial role in cancer development, progression, and response to different therapies. An important issue is whether common signal transduction pathways are ubiquitously altered in all cancer types and some unique pathways are involved in different cancer types. Another important issue is whether and how transduction signaling molecules are heterogeneously expressed and activated in different cancer cells within and between cancer cell types. METHODS: To gain insight into these issues, we assembled a protein lysate array with 90 different cell lines of 12 different cell types. Each sample is diluted 2-fold six times, and samples from the dilution series were printed three times on the array. We then measured the expression levels and phosphorylation status of 52 different signaling proteins with specific antibodies and carried out statistical hierarchical clustering analysis. RESULTS: The most significant finding based on the cluster analysis was that the cell lines did not group based on tumor types, suggesting that the signaling pathways studied were commonly activated in most of the tumor types cultured in vitro. As expected, related proteins associated with specific signaling pathways clustered together, and analysis of the 30 most differentially expressed proteins revealed the PI3-K signaling pathway was upregulated in several different tumor types and the VEGF-angiogenesis pathway was downregulated in hematopoetic cancers. Another important observation, with clinical implications was that EGFR was the most heterogeneous among all the cell lines. We also observed signaling pathways unique to specific types of cancers such as the inverse relationship between p16ink and Rb, and the EGFR mediated pathway activation characteristic of pancreatic cancers. CONCLUSIONS: Using reverse phase lysate array analysis in this study, we were able to determine potential relationships and signaling pathways, both common and unique, to different types of cancer using cell lines in vitro. This data could be utilized for mining information related to an individual cancer of interest and combined with morphological and genomic profiles would help in creating a combination of expression markers and/or functional signaling maps for specific cancer diagnosis and therapy.     

CAFET algorithm reveals Wnt/PCP signature in lung squamous cell carcinoma

We analyzed the gene expression patterns of 138 Non-Small Cell Lung Cancer (NSCLC) samples and developed a new algorithm called Coverage Analysis with Fisher's Exact Test (CAFET) to identify molecular pathways that are differentially activated in squamous cell carcinoma (SCC) and adenocarcinoma (AC) subtypes. Analysis of the lung cancer samples demonstrated hierarchical clustering according to the histological subtype and revealed a strong enrichment for the Wnt signaling pathway components in the cluster consisting predominantly of SCC samples. The specific gene expression pattern observed correlated with enhanced activation of the Wnt Planar Cell Polarity (PCP) pathway and inhibition of the canonical Wnt signaling branch. Further real time RT-PCR follow-up with additional primary tumor samples and lung cancer cell lines confirmed enrichment of Wnt/PCP pathway associated genes in the SCC subtype. Dysregulation of the canonical Wnt pathway, characterized by increased levels of β-catenin and epigenetic silencing of negative regulators, has been reported in adenocarcinoma of the lung. Our results suggest that SCC and AC utilize different branches of the Wnt pathway during oncogenesis.     

Hedgehog信号通路的作用机制及研究进展

HH(Hedgehog)信号通路参与多种生物学过程,包括细胞分化、细胞增殖、细胞衰老、肿瘤的发生、肿瘤恶性转化以及肿瘤耐药,HH信号通路相关基因的异常表达或突变会在生物发生发展的不同阶段引起各种疾病的发生.而HH信号通路通过复杂的机制调控诸多信号通路,进一步影响生物体的功能.所以深入了解HH信号通路在各种遗传疾病、肿瘤...     

Hedgehog Signaling: Progenitor Phenotype in Small-Cell Lung Cancer

Recently, we have shown that small cell lung cancer (SCLC) is dependent on activation of the Hedgehog signaling, an embryonic pathway implicated in development, morphogenesis and the regulation of stem cell fates. These findings form the framework for an emerging view of cancer as a process of aberrant organogenesis in which progenitor/ stem cells escape dependence on niche signaling through mutation in genes such as Ptch, or through persistent activation of progenitor cell pathways. Interestingly, the normally quiescent airway epithelial compartment uses the Hh pathway to repopulate itself when challenged by injury. How Hh signaling works to promote the malignant phenotype promises to be as important biologically as the promise of Hh pathway inhibitors are clinically.

Key words

Cancer, Hedgehog signaling, Morphogenesis, Stem cells     

Targeting secret handshakes of biological processes for novel drug development

In multicellular organisms, several biological processes control the rise and fall of life. Different cell types communicate and co-operate in response to different stimulus through cell to cell signaling and regulate biologic processes in the cell/organism. Signaling in multicellular organism has to be made very secretly so that only the target cell responds to the signal. Of all the biomolecules, nature chose mainly proteins for secret delivery of information both inside and outside the cell. During cell signaling, proteins physically interact and shake hands for transfer of secret information by a phenomenon called as protein–protein interactions (PPIs). In both, extra and intracellular signaling processes PPIs play a crucial role. PPIs involved in cellular signaling are the primary cause for cell proliferation, differentiation, movement, metabolism, death and various other biological processes not mentioned here. These secret handshakes are very specific for specific functions. Any alterations/malfunctions in particular PPIs results in diseased condition. An overview of signaling pathways and importance of PPIs in cellular function and possibilities of targeting PPIs for novel drug development are discussed in this review.     

Identification of novel metastasis suppressor signaling pathways for breast cancer

Cancer lethality is mainly caused by metastasis. Therefore, understanding the nature of the genes involved in this process has become a priority. Given the heterogeneity of mutations in cancer cells, considerable focus has been directed toward characterizing metastasis genes in the context of relevant signaling pathways rather than treating genes as independent and equal entities. One signaling cascade implicated in the regulation of cell growth, invasion and metastasis is the MAP kinase pathway. Raf kinase inhibitory protein (RKIP) functions as an inhibitor of the MAP kinase pathway and is a metastasis suppressor in different cancer models. By utilizing statistical analysis of clinical data integrated with experimental validation, we recently identified components of the RKIP signaling pathway relevant to breast cancer metastasis. Using the RKIP pathway as an example, we show how prior biological knowledge can be efficiently combined with genome-wide patient data to identify gene regulatory mechanisms that control metastasis.     

G-protein-coupled receptor prediction using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties

G-protein-coupled receptors (GPCRs) are the largest family of cell surface receptors that, via trimetric guanine nucleotide-binding proteins (G-proteins), initiate some signaling pathways in the eukaryotic cell. Many diseases involve malfunction of GPCRs making their role evident in drug discovery. Thus, the automatic prediction of GPCRs can be very helpful in the pharmaceutical industry. However, prediction of GPCRs, their families, and their subfamilies is a challenging task. In this article, GPCRs are classified into families, subfamilies, and sub-subfamilies using pseudo-amino-acid composition and multiscale energy representation of different physiochemical properties of amino acids. The aim of the current research is to assess different feature extraction strategies and to develop a hybrid feature extraction strategy that can exploit the discrimination capability in both the spatial and transform domains for GPCR classification. Support vector machine, nearest neighbor, and probabilistic neural network are used for classification purposes. The overall performance of each classifier is computed individually for each feature extraction strategy. It is observed that using the jackknife test the proposed GPCR–hybrid method provides the best results reported so far. The GPCR–hybrid web predictor to help researchers working on GPCRs in the field of biochemistry and bioinformatics is available at http://111.68.99.218/GPCR.     

Identification of novel metastasis suppressor signaling pathways for breast cancer

Cancer lethality is mainly caused by metastasis. Therefore, understanding the nature of the genes involved in this process has become a priority. Given the heterogeneity of mutations in cancer cells, considerable focus has been directed toward characterizing metastasis genes in the context of relevant signaling pathways rather than treating genes as independent and equal entities. One signaling cascade implicated in the regulation of cell growth, invasion and metastasis is the MAP kinase pathway. Raf kinase inhibitory protein (RKIP) functions as an inhibitor of the MAP kinase pathway and is a metastasis suppressor in different cancer models. By utilizing statistical analysis of clinical data integrated with experimental validation, we recently identified components of the RKIP signaling pathway relevant to breast cancer metastasis. Using the RKIP pathway as an example, we show how prior biological knowledge can be efficiently combined with genome-wide patient data to identify gene regulatory mechanisms that control metastasis.