A comprehensive pathway map of epidermal growth factor receptor signaling

Troubleshooting and deconvoluting label-free cell phenotypic assays in drug discovery

INTRODUCTION

Central to drug discovery and development is to comprehend the target(s), potency, efficacy and safety of drug molecules using pharmacological assays. Owing to their ability to provide a holistic view of drug actions in native cells, label-free biosensor-enabled cell phenotypic assays have been emerging as new generation phenotypic assays for drug discovery. Despite the benefits associated with wide pathway coverage, high sensitivity, high information content, non-invasiveness and real-time kinetics, label-free cell phenotypic assays are often viewed to be a blackbox in the era of target-centric drug discovery.

METHODS

This article first reviews the biochemical and biological complexity of drug-target interactions, and then discusses the key characteristics of label-free cell phenotypic assays and presents a five-step strategy to troubleshooting and deconvoluting the label-free cell phenotypic profiles of drugs.

RESULTS

Drug-target interactions are intrinsically complicated. Label-free cell phenotypic signatures of drugs mirror the innate complexity of drug-target interactions, and can be effectively deconvoluted using the five-step strategy.

DISCUSSION

The past decades have witnessed dramatic expansion of pharmacological assays ranging from molecular to phenotypic assays, which is coincident with the realization of the innate complexity of drug-target interactions. The clinical features of a drug are defined by how it operates at the system level and by its distinct polypharmacology, ontarget, phenotypic and network pharmacology. Approaches to examine the biochemical, cellular and molecular mechanisms of action of drugs are essential to increase the efficiency of drug discovery and development. Label-free cell phenotypic assays and the troubleshooting and deconvoluting approach presented here may hold great promise in drug discovery and development.

Why nature really chose phosphate

Phosphoryl transfer plays key roles in signaling, energy transduction, protein synthesis, and maintaining the integrity of the genetic material. On the surface, it would appear to be a simple nucleophile displacement reaction. However, this simplicity is deceptive, as, even in aqueous solution, the low-lying d-orbitals on the phosphorus atom allow for eight distinct mechanistic possibilities, before even introducing the complexities of the enzyme catalyzed reactions. To further complicate matters, while powerful, traditional experimental techniques such as the use of linear free-energy relationships (LFER) or measuring isotope effects cannot make unique distinctions between different potential mechanisms. A quarter of a century has passed since Westheimer wrote his seminal review, 'Why Nature Chose Phosphate' (Science 235 (1987), 1173), and a lot has changed in the field since then. The present review revisits this biologically crucial issue, exploring both relevant enzymatic systems as well as the corresponding chemistry in aqueous solution, and demonstrating that the only way key questions in this field are likely to be resolved is through careful theoretical studies (which of course should be able to reproduce all relevant experimental data). Finally, we demonstrate that the reason that nature really chose phosphate is due to interplay between two counteracting effects: on the one hand, phosphates are negatively charged and the resulting charge-charge repulsion with the attacking nucleophile contributes to the very high barrier for hydrolysis, making phosphate esters among the most inert compounds known. However, biology is not only about reducing the barrier to unfavorable chemical reactions. That is, the same charge-charge repulsion that makes phosphate ester hydrolysis so unfavorable also makes it possible to regulate, by exploiting the electrostatics. This means that phosphate ester hydrolysis can not only be turned on, but also be turned off, by fine tuning the electrostatic environment and the present review demonstrates numerous examples where this is the case. Without this capacity for regulation, it would be impossible to have for instance a signaling or metabolic cascade, where the action of each participant is determined by the fine-tuned activity of the previous piece in the production line. This makes phosphate esters the ideal compounds to facilitate life as we know it.

Functional expression of the voltage-gated sodium channel, Nav1.7, underlies epidermal growth factor-mediated invasion in human [R1.S1] non-small cell lung cancer cells

Various ion channels are expressed in human cancers where they are intimately involved in proliferation, angiogenesis, invasion and metastasis. Expression of functional voltage-gated sodium channels (Nav) is implicated in the metastatic potential of breast, prostate, lung and colon cancer cells. However, the cellular mechanisms that regulate Nav expression in cancer remain largely unknown. Growth factors are attractive candidates; they not only play crucial roles in cancer progression but are also key regulators of ion channel expression and activity in non-cancerous cells. Here, we examine the role of epidermal growth factor receptor (EGFR) signalling and Nav in non-small cell lung carcinoma (NSCLC) cell lines. We show unequivocally, that functional expression of Nav1.7 promotes invasion in H460 NSCLC cells. Inhibition of Nav1.7 activity (tetrodotoxin), or, expression (small interfering RNA), reduces H460 cell invasion by up to 50%. Crucially, non-invasive wild type A549 cells lack functional Nav whereas exogenous over-expression of Nav1.7 is sufficient to promote TTX-sensitive invasion of these cells. EGF/EGFR signalling enhances proliferation, migration and invasion of H460 cells but we find that EGFR-mediated up-regulation of Nav1.7 specifically, is necessary for invasive behaviour in these cells. Examination of Nav1.7 expression at the mRNA, protein and functional levels further reveals that EGF/EGFR signalling via the ERK1/2 pathway controls transcriptional regulation of channel expression to promote cellular invasion. Immunohistochemistry of patient biopsies confirms the clinical relevance of Nav1.7 expression in NSCLC. Thus, Nav1.7 has significant potential as a novel target for therapeutic intervention and/or as a diagnostic/prognostic marker in NSCLC.

How scaffolds shape MAPK signaling: what we know and opportunities for systems approaches

Scaffolding proteins add a new layer of complexity to the dynamics of cell signaling. Above their basic function to bring several components of a signaling pathway together, recent experimental research has found that scaffolds influence signaling in a much more complex way: scaffolds can exert some catalytic function, influence signaling by allosteric mechanisms, are feedback-regulated, localize signaling activity to distinct regions of the cell or increase pathway fidelity. Here we review experimental and theoretical approaches that address the function of two MAPK scaffolds, Ste5, a scaffold of the yeast mating pathway and KSR1/2, a scaffold of the classical mammalian MAPK signaling pathway. For the yeast scaffold Ste5, detailed mechanistic models have been valuable for the understanding of its function. For scaffolds in mammalian signaling, however, models have been rather generic and sketchy. For example, these models predicted narrow optimal scaffold concentrations, but when revisiting these models by assuming typical concentrations, rather a range of scaffold levels optimally supports signaling. Thus, more realistic models are needed to understand the role of scaffolds in mammalian signal transduction, which opens a big opportunity for systems biology.

Evaluating Inhibition of the Epidermal Growth Factor (EGF)-Induced Response of Mutant MCF10A Cells with an Acoustic Sensor

Many cancer treatments rely on inhibition of epidermal growth factor (EGF)-induced cellular responses. Evaluating drug effects on such responses becomes critical to the development of new cancer therapeutics. In this report, we have employed a label-free acoustic sensor, the quartz crystal microbalance with dissipation monitoring (QCM-D), to track the EGF-induced response of mutant MCF10A cells under various inhibitory conditions. We have identified a complex cell de-adhesion process, which can be distinctly altered by inhibitors of signaling pathways and cytoskeleton formation in a dose-dependent manner. The dose dependencies of the inhibitors provide IC₅₀ values which are in strong agreement with the values reported in the literature, demonstrating the sensitivity and reliability of the QCM-D as a screening tool. Using immunofluorescence imaging, we have also verified the quantitative relationship between the ΔD-response (change in energy dissipation factor) and the level of focal adhesions quantified with the areal density of immunostained vinculin under those inhibitory conditions. Such a correlation suggests that the dynamic restructuring of focal adhesions can be assessed based on the time-dependent change in ΔD-response. Overall, this report has shown that the QCM-D has the potential to become an effective sensing platform for screening therapeutic agents that target signaling and cytoskeletal proteins.

Modeling of tumor progression in NSCLC and intrinsic resistance to TKI in loss of PTEN expression

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

Molecular-targeted therapies in head and neck cancer

The increasing understanding of tumor biology has opened the door to a new class of biological agents directed at specific molecular targets in the treatment of squamous cell carcinomas of the head and neck. These targeted agents present the opportunity to more effectively attack the crucial cellular pathways contributing to tumor growth and survival, while minimizing toxicity. Cetuximab, which targets epidermal growth factor (EGF) receptor signaling, was the first such biological agent to be proven effective in head and neck squamous cell cancers. Currently, there are dozens of targeted agents at various stages of testing for use in the treatment of head and neck cancers. In this article, we review strategies aimed at key pathways, including EGF receptor signaling, the Vascular Endothelial Growth Factor (VEGF) pathway, and PI3K/AKT/mammalian target of rapamycin activation.

Personalized medicine and treatment approaches in non-small-cell lung carcinoma

Chemotherapy has been the traditional backbone for the management of metastatic lung cancer. Multiple trials have shown the benefits of treatment with platinum doublets in lung cancer. This "one treatment fits all" approach was further refined by the introduction of targeted agents and discovery of subpopulations of patients who benefited from treatment with these agents. It has also become evident that certain histologic subtypes of non-small-cell lung cancer respond better to one cytotoxic chemotherapy versus others. This has led to the concept of using histology to guide therapy. With the introduction of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and the discovery of activating mutations in the EGFR gene, further personalization of treatment for subgroups of patients has become a reality. More recently, the presence of a fusion gene, echinoderm microtubule-associated protein-like 4 - anaplastic lymphoma kinase (EML4-ALK), was identified as the driver mutation in yet another subgroup of patients, and subsequent studies have led to approval of crizotinib in this group of patients. In this article, efforts in personalizing delivery of care based on the histological subtypes of lung cancer and the role of K-RAS and EGFR mutations, EML4/ALK translocation, and ERCC1 (excision repair cross-complementing 1) and EGFR expression in choosing appropriate treatments for patients with advanced lung cancer are discussed. This article also reviews the problem of resistance to EGFR tyrosine kinase inhibitors and the ongoing trials that target novel pathways and mechanisms that are implicated in resistance.

Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

BACKGROUND

Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions.

RESULTS

Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map.

CONCLUSIONS

With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.

Dysfunctions associated with methylation, microRNA expression and gene expression in lung cancer

Integrating high-throughput data obtained from different molecular levels is essential for understanding the mechanisms of complex diseases such as cancer. In this study, we integrated the methylation, microRNA and mRNA data from lung cancer tissues and normal lung tissues using functional gene sets. For each Gene Ontology (GO) term, three sets were defined: the methylation set, the microRNA set and the mRNA set. The discriminating ability of each gene set was represented by the Matthews correlation coefficient (MCC), as evaluated by leave-one-out cross-validation (LOOCV). Next, the MCCs in the methylation sets, the microRNA sets and the mRNA sets were ranked. By comparing the MCC ranks of methylation, microRNA and mRNA for each GO term, we classified the GO sets into six groups and identified the dysfunctional methylation, microRNA and mRNA gene sets in lung cancer. Our results provide a systematic view of the functional alterations during tumorigenesis that may help to elucidate the mechanisms of lung cancer and lead to improved treatments for patients.

Cetuximab and biomarkers in non-small-cell lung carcinoma

Cancer progression is a highly complex process that is driven by a constellation of deregulated signaling pathways and key molecular events. In non-small-cell lung cancer (NSCLC), as in several other cancer types, the epidermal growth factor receptor (EGFR) and its downstream signaling components represent a key axis that has been found not only to trigger cancer progression but also to support advanced disease leading to metastasis. Two major therapeutic approaches comprising monoclonal antibodies and small molecule tyrosine kinase inhibitors have so far been used to target this pathway, with a combination of positive, negative, and inconsequential results, as judged by patient survival indices. Since these drugs are expensive and not all patients derive benefits from taking them, it has become both pertinent and paramount to identify biomarkers that can predict not only beneficial response but also resistance. This review focuses on the chimeric monoclonal antibody, cetuximab, its application in the treatment of NSCLC, and the biomarkers that may guide its use in the clinical setting. A special emphasis is placed on the EGFR, including its structural and mechanistic attributes.

The ERBB network: at last, cancer therapy meets systems biology

Although it is broadly agreed that the improved treatment of patients with cancer will depend on a deeper molecular understanding of the underlying pathogenesis, only a few examples are already available. This Timeline article focuses on the ERBB (also known as HER) network of receptor tyrosine kinases (RTKs), which exemplifies how a constant dialogue between basic research and medical oncology can translate into both a sustained pipeline of novel drugs and ways to overcome acquired treatment resistance in patients. We track the key early discoveries that linked this RTK family to oncogenesis, the course of pioneering clinical research and their merger into a systems-biology framework that is likely to inspire further generations of effective therapeutic strategies.

Lipid rafts and Alzheimer's disease: protein-lipid interactions and perturbation of signaling

Lipid rafts are membrane domains, more ordered than the bulk membrane and enriched in cholesterol and sphingolipids. They represent a platform for protein-lipid and protein–protein interactions and for cellular signaling events. In addition to their normal functions, including membrane trafficking, ligand binding (including viruses), axonal development and maintenance of synaptic integrity, rafts have also been implicated in the pathogenesis of several neurodegenerative diseases including Alzheimer’s disease (AD). Lipid rafts promote interaction of the amyloid precursor protein (APP) with the secretase (BACE-1) responsible for generation of the amyloid β peptide, Aβ. Rafts also regulate cholinergic signaling as well as acetylcholinesterase and Aβ interaction. In addition, such major lipid raft components as cholesterol and GM1 ganglioside have been directly implicated in pathogenesis of the disease. Perturbation of lipid raft integrity can also affect various signaling pathways leading to cellular death and AD. In this review, we discuss modulation of APP cleavage by lipid rafts and their components, while also looking at more recent findings on the role of lipid rafts in signaling events.

Antiepidermal growth factor receptor therapy in squamous cell carcinoma of the head and neck

Squamous cell carcinoma of head and neck (SCCHN) is the most common neoplasm of the upper aerodigestive tract. In this paper, we attempt to summarize the role and applications of the epidermal growth factor receptor (EGFR) inhibitors monoclonal antibodies (moAbs) and tyrosine kinase inhibitors (TKIs) locally advanced as well as metastatic SCCHN. Targeted therapy in SCCHN is now incorporated in the first-line regimes for advanced disease. Novel targeted agents, including the EGFR antibody, cetuximab, have been approved for use as single agents or in combination with radiation therapy or chemotherapy in treatment of recurrent metastatic or locally advanced SCCHN. Refractory mechanisms that bypass the pathway of EGFR inhibitors activity are identified explaining resistance to targeted therapy. Strategies of cotargeting EGFR and other pathways are under investigation. Examples of targeted therapy being used include mammalian target of rapamycin (mtor) inhibitors, antivascular endothelial growth factor (VEGF) moAb, and other inhibitors. We will be focusing our paper on the preclinical and clinical aspects of EGFR inhibition in SCCHN and touch upon other targeted therapies in application.

Quantification of degeneracy in biological systems for characterization of functional interactions between modules

There is an evolutionary advantage in having multiple components with overlapping functionality (i.e degeneracy) in organisms. While theoretical considerations of degeneracy have been well established in neural networks using information theory, the same concepts have not been developed for differential systems, which form the basis of many biochemical reaction network descriptions in systems biology. Here we establish mathematical definitions of degeneracy, complexity and robustness that allow for the quantification of these properties in a system. By exciting a dynamical system with noise, the mutual information associated with a selected observable output and the interacting subspaces of input components can be used to define both complexity and degeneracy. The calculation of degeneracy in a biological network is a useful metric for evaluating features such as the sensitivity of a biological network to environmental evolutionary pressure. Using a two-receptor signal transduction network, we find that redundant components will not yield high degeneracy whereas compensatory mechanisms established by pathway crosstalk will. This form of analysis permits interrogation of large-scale differential systems for non-identical, functionally equivalent features that have evolved to maintain homeostasis during disruption of individual components.

AlzPathway: a comprehensive map of signaling pathways of Alzheimer's disease

Background

Alzheimer’s disease (AD) is the most common cause of dementia among the elderly. To clarify pathogenesis of AD, thousands of reports have been accumulating. However, knowledge of signaling pathways in the field of AD has not been compiled as a database before.

Description

Here, we have constructed a publicly available pathway map called “AlzPathway” that comprehensively catalogs signaling pathways in the field of AD. We have collected and manually curated over 100 review articles related to AD, and have built an AD pathway map using CellDesigner. AlzPathway is currently composed of 1347 molecules and 1070 reactions in neuron, brain blood barrier, presynaptic, postsynaptic, astrocyte, and microglial cells and their cellular localizations. AlzPathway is available as both the SBML (Systems Biology Markup Language) map for CellDesigner and the high resolution image map. AlzPathway is also available as a web service (online map) based on Payao system, a community-based, collaborative web service platform for pathway model curation, enabling continuous updates by AD researchers.

Conclusions

AlzPathway is the first comprehensive map of intra, inter and extra cellular AD signaling pathways which can enable mechanistic deciphering of AD pathogenesis. The AlzPathway map is accessible at http://alzpathway.org/.

A linearized constraint-based approach for modeling signaling networks

With the unparalleled increase in the availability of biological data over the last couple of decades, accurate and computable models are becoming increasingly important for unraveling complex biological phenomena. Past efforts to model signaling networks have utilized various computational methods, including Boolean and constraint-based modeling (CBM) approaches. These approaches are based on solving mixed integer linear programs; hence, they may not scale up for the analysis of large networks and are not amenable for applications based on sampling the full spectrum of the solution space. Here we propose a new CBM approach that is fully linear and does not involve integer variables, thereby overcoming the aforementioned limitations. We describe a novel optimization procedure for model construction and demonstrate the utility of our approach on a reconstructed model of the human epidermal growth factor receptor (EGFR) pathway, spanning 322 species and 211 connections. We compare our model's predictions to experimental phosphorylation data and to the predictions inferred via an additional Boolean-based EGFR signaling model. Our results show high prediction accuracy (75%) and high similarity to the Boolean model. Considering the marked computational advantages in terms of scalability and sampling utilization obtained by having a linear mode, these results demonstrate the potential promise of this framework for the study of cellular signaling.

Mitochondrial DNA mutations in respiratory complex-I in never-smoker lung cancer patients contribute to lung cancer progression and associated with EGFR gene mutation

Mitochondrial DNA (mtDNA) mutations were reported in different cancers. However, the nature and role of mtDNA mutation in never-smoker lung cancer patients including patients with epidermal growth factor receptor (EGFR) and KRAS gene mutation are unknown. In the present study, we sequenced entire mitochondrial genome (16.5 kb) in matched normal and tumors obtained from 30 never-smoker and 30 current-smoker lung cancer patients, and determined the mtDNA content. All the patients' samples were sequenced for KRAS (exon 2) and EGFR (exon 19 and 21) gene mutation. The impact of forced overexpression of a respiratory complex-I gene mutation was evaluated in a lung cancer cell line. We observed significantly higher (P = 0.006) mtDNA mutation in the never-smokers compared to the current-smoker lung cancer patients. MtDNA mutation was significantly higher (P = 0.026) in the never-smoker Asian compared to the current-smoker Caucasian patients' population. MtDNA mutation was significantly (P = 0.007) associated with EGFR gene mutation in the never-smoker patients. We also observed a significant increase (P = 0.037) in mtDNA content among the never-smoker lung cancer patients. The majority of the coding mtDNA mutations targeted respiratory complex-I and forced overexpression of one of these mutations resulted in increased in vitro proliferation, invasion, and superoxide production in lung cancer cells. We observed a higher prevalence and new relationship between mtDNA alterations among never-smoker lung cancer patients and EGFR gene mutation. Moreover, a representative mutation produced strong growth effects after forced overexpression in lung cancer cells. Signature mtDNA mutations provide a basis to develop novel biomarkers and therapeutic strategies for never-smoker lung cancer patients.

An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes

During liver regeneration, quiescent hepatocytes re-enter the cell cycle to proliferate and compensate for lost tissue. Multiple signals including hepatocyte growth factor, epidermal growth factor, tumor necrosis factor α, interleukin-6, insulin and transforming growth factor β orchestrate these responses and are integrated during the G₁ phase of the cell cycle. To investigate how these inputs influence DNA synthesis as a measure for proliferation, we established a large-scale integrated logical model connecting multiple signaling pathways and the cell cycle. We constructed our model based upon established literature knowledge, and successively improved and validated its structure using hepatocyte-specific literature as well as experimental DNA synthesis data. Model analyses showed that activation of the mitogen-activated protein kinase and phosphatidylinositol 3-kinase pathways was sufficient and necessary for triggering DNA synthesis. In addition, we identified key species in these pathways that mediate DNA replication. Our model predicted oncogenic mutations that were compared with the COSMIC database, and proposed intervention targets to block hepatocyte growth factor-induced DNA synthesis, which we validated experimentally. Our integrative approach demonstrates that, despite the complexity and size of the underlying interlaced network, logical modeling enables an integrative understanding of signaling-controlled proliferation at the cellular level, and thus can provide intervention strategies for distinct perturbation scenarios at various regulatory levels.

Multiscale models of cell signaling

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

Combinatorial complexity and compositional drift in protein interaction networks

The assembly of molecular machines and transient signaling complexes does not typically occur under circumstances in which the appropriate proteins are isolated from all others present in the cell. Rather, assembly must proceed in the context of large-scale protein-protein interaction (PPI) networks that are characterized both by conflict and combinatorial complexity. Conflict refers to the fact that protein interfaces can often bind many different partners in a mutually exclusive way, while combinatorial complexity refers to the explosion in the number of distinct complexes that can be formed by a network of binding possibilities. Using computational models, we explore the consequences of these characteristics for the global dynamics of a PPI network based on highly curated yeast two-hybrid data. The limited molecular context represented in this data-type translates formally into an assumption of independent binding sites for each protein. The challenge of avoiding the explicit enumeration of the astronomically many possibilities for complex formation is met by a rule-based approach to kinetic modeling. Despite imposing global biophysical constraints, we find that initially identical simulations rapidly diverge in the space of molecular possibilities, eventually sampling disjoint sets of large complexes. We refer to this phenomenon as "compositional drift". Since interaction data in PPI networks lack detailed information about geometric and biological constraints, our study does not represent a quantitative description of cellular dynamics. Rather, our work brings to light a fundamental problem (the control of compositional drift) that must be solved by mechanisms of assembly in the context of large networks. In cases where drift is not (or cannot be) completely controlled by the cell, this phenomenon could constitute a novel source of phenotypic heterogeneity in cell populations.

Insulin signaling - mathematical modeling comes of age

Signaling pathways that only a few years ago appeared simple and understandable, albeit far from complete, have evolved into very complex multi-layered networks of cellular control mechanisms, which in turn are integrated in a similarly complex whole-body level of control mechanisms. This complexity sets limits for classical biochemical reasoning, such that a correct and complete analysis of experimental data while taking the full complexity into account is not possible. In this Opinion we propose that mathematical modeling can be used as a tool in insulin signaling research, and we demonstrate how recent developments in modeling - and the integration of modeling in the experimental process - provide new possibilities to approach and decipher complex biological systems more efficiently.

Effects of diabetes and/or hypercholesterolemia on skin development of rat fetuses

OBJECTIVE

To investigate the effects of diabetes and/or hypercholesterolemia on skin development during in utero life at 15, 17 & 19 days old.

METHODS

Sixty pregnant female albino Wistar rats were arranged into three groups: control, diabetic (single i.p. 60 mg streptozotocin/kg B.wt) and hypercholesterolemic (diet supplement 3% cholesterol 6 week prior to conception and throughout gestation). Pregnant rats were sacrificed at 15, 17 & 19 days prenatal). Vibrissae skin biopsies were removed and allowed for scanning (SEM), light, and transmission electron microscopic (TEM) investigation. Also, DNA fragmentation and sodium dodecyl polyacrylamides gel electrophoresis (SDS-PAGE) were carried out.

RESULTS

Scanning electron microscopic observations revealed retarded hair follicle growth and deformations of their pattern structure. At light microscopic level, skin exhibited decreased epidermal cornification, as well as degeneration of hair follicles in fetuses of both diabetic and hypercholesterolemic groups. Transmission electron microscopy revealed abundant vacuolar spaces in the epidermis. Degenerative phases become more abundant in keratinocytes as well as in stratum germinativum cells. Fetal skin possessed altered protein expression and missing bands as well as separation of genomic DNA to several degraded bands in skin of 15-, 17-, and 19-day-old, maternally diabetic and/or hypercholesterolemic fetuses.

CONCLUSION

These findings showed that maternal diabetes and/or hypercholesterolemia increased average deformation of hair follicles, vacuolation, and degeneration of epidermal cell layers. The observed findings resulted from altered protein expression and increased DNA fragmentation, which, in turn, disrupt epidermal cell differentiation.

[HPV-associated head and neck cancer. The basics of molecular and translational research]

Translational research refers to the interfaces between preclinical research and targeted short- and medium-term developments through to clinical standards. There are two distinct groups of oropharyngeal malignancies: those caused by tobacco and alcohol abuse and those caused by HPV infection. Although the prognosis of patients in the latter group is significantly better, this is not taken into consideration in the choice of treatment. However, less intensive use of radiotherapy, chemotherapy, or surgery, as well as targeted multimodal therapeutic approaches, is under research. This article summarizes the main events in the HPV life cycle, with emphasis on carcinogenic mechanisms and potential new molecular targets. Identifying distinct tumor entities of the oropharynx enables the design and development of new preventive and therapeutic strategies to reduce the incidence and mortality of HPV-associated oropharyngeal cancers in the near future.

The cell surface glycoprotein CUB domain-containing protein 1 (CDCP1) contributes to epidermal growth factor receptor-mediated cell migration

Epidermal growth factor (EGF) activation of the EGF receptor (EGFR) is an important mediator of cell migration, and aberrant signaling via this system promotes a number of malignancies including ovarian cancer. We have identified the cell surface glycoprotein CDCP1 as a key regulator of EGF/EGFR-induced cell migration. We show that signaling via EGF/EGFR induces migration of ovarian cancer Caov3 and OVCA420 cells with concomitant up-regulation of CDCP1 mRNA and protein. Consistent with a role in cell migration CDCP1 relocates from cell-cell junctions to punctate structures on filopodia after activation of EGFR. Significantly, disruption of CDCP1 either by silencing or the use of a function blocking antibody efficiently reduces EGF/EGFR-induced cell migration of Caov3 and OVCA420 cells. We also show that up-regulation of CDCP1 is inhibited by pharmacological agents blocking ERK but not Src signaling, indicating that the RAS/RAF/MEK/ERK pathway is required downstream of EGF/EGFR to induce increased expression of CDCP1. Our immunohistochemical analysis of benign, primary, and metastatic serous epithelial ovarian tumors demonstrates that CDCP1 is expressed during progression of this cancer. These data highlight a novel role for CDCP1 in EGF/EGFR-induced cell migration and indicate that targeting of CDCP1 may be a rational approach to inhibit progression of cancers driven by EGFR signaling including those resistant to anti-EGFR drugs because of activating mutations in the RAS/RAF/MEK/ERK pathway.

EGFR, CD10 and proliferation marker Ki67 expression in ameloblastoma: possible role in local recurrence

Background

Ameloblastoma is an odontogenic neoplasm characterized by local invasiveness and tendency towards recurrence.

Aims

Studying the role played by EGFR, CD10 and Ki67 in the recurrence of ameloblastoma.

Methods

This study was carried out on 22 retrospective cases of mandibular ameloblastoma from the period from Jan 2002 to Jan 2008 with follow up period until Jan 2011 (3 to 8 years follow up peroid). Archival materials were obtained from pathology department, Mansoura university. Paraffin sections of tumor tissue from all cases were submitted for routine H&E stains and immunohistochemistry using EGFR, CD10 and Ki67 monoclonal antibodies. Statistical analysis using of clinical data for all patients, tumor type, EGFR, CD10 and Ki67 expression in relation to recurrence were evaluated.

Results

Among the 22 cases, 10 cases were males and 12 were females with sex ratio 1:1.2. Age ranged from 34 to 59 years old with a mean age 44.18 year. Five cases showed local recurrence within studied period and proved by biopsy. No statistically significant relation was found between local recurrence and patient age, tumor size, tumor type, EGFR expression. There was a significant relation between CD10 expression as well as Ki67 labelling index and recurrence (P value = 0.003, 0.000 respectively).

Conclusion

Evaluation of CD10 and Ki67 status together with conventional histological evaluation can help in providing more information about the biologic behavior of the tumor, while EGFR could be a target of an expanding class of anticancer therapies.

Since ameloblastomas are EGFR-positive tumors, anti-EGFR agents could be considered to reduce the size of large tumors and to treat unresectable tumors that are in close proximity to vital structures.

Virtual Slides

The virtual slide(s) for this article can be found here:

http://www.diagnosticpathology.diagnomx.eu/vs/1902106905645651

Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.

Phosphoproteomics-based systems analysis of signal transduction networks

Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer.

Cytokines, chemokines and growth factors in wound healing

In wound healing, a variety of mediators have been identified throughout the years. The mediators discussed here comprise growth factors, cytokines and chemokines. These mediators act via multiple (specific) receptors to facilitate wound closure. As research in the last years has led to many new findings, there is a need to give an overview on what is known, and on what might possibly play a role as a molecular target for future wound therapy. This review aims to keep the reader up to date with selected important and novel findings regarding growth factors, cytokines and chemokines in wound healing.

EGR1 and the ERK-ERF axis drive mammary cell migration in response to EGF

The signaling pathways that commit cells to migration are incompletely understood. We employed human mammary cells and two stimuli: epidermal growth factor (EGF), which induced cellular migration, and serum factors, which stimulated cell growth. In addition to strong activation of ERK by EGF, and AKT by serum, early transcription remarkably differed: while EGF induced early growth response-1 (EGR1), and this was required for migration, serum induced c-Fos and FosB to enhance proliferation. We demonstrate that induction of EGR1 involves ERK-mediated down-regulation of microRNA-191 and phosphorylation of the ETS2 repressor factor (ERF) repressor, which subsequently leaves the nucleus. Unexpectedly, knockdown of ERF inhibited migration, which implies migratory roles for exported ERF molecules. On the other hand, chromatin immunoprecipitation identified a subset of direct EGR1 targets, including EGR1 autostimulation and SERPINB2, whose transcription is essential for EGF-induced cell migration. In summary, EGR1 and the EGF-ERK-ERF axis emerge from our study as major drivers of growth factor-induced mammary cell migration.

The black box illuminated: signals and signaling

Unraveling the signaling pathways that transmit information from the cell surface to the nucleus has been a major accomplishment of modern cell and molecular biology. The benefit to humans is seen in the multitude of new therapeutics based on the illumination of these pathways. Although considerable insight has been gained in understanding homeostatic and pathological signaling in the epidermis and other skin compartments, the translation into therapy has been lacking. This review will outline advances made in understanding fundamental signaling in several of the most prominent pathways that control cutaneous development, cell-fate decisions, and keratinocyte growth and differentiation with the anticipation that this insight will contribute to new treatments for troubling skin diseases.

Synthetic biology: modulating the MAP kinase module

In a recent study, the MAP kinase module involved in many human cancers has been reconstructed in yeast, in order to tinker with its behavior.

Systems biology modeling reveals a possible mechanism of the tumor cell death upon oncogene inactivation in EGFR addicted cancers

Despite many evidences supporting the concept of “oncogene addiction” and many hypotheses rationalizing it, there is still a lack of detailed understanding to the precise molecular mechanism underlying oncogene addiction. In this account, we developed a mathematic model of epidermal growth factor receptor (EGFR) associated signaling network, which involves EGFR-driving proliferation/pro-survival signaling pathways Ras/extracellular-signal-regulated kinase (ERK) and phosphoinositol-3 kinase (PI3K)/AKT, and pro-apoptotic signaling pathway apoptosis signal-regulating kinase 1 (ASK1)/p38. In the setting of sustained EGFR activation, the simulation results show a persistent high level of proliferation/pro-survival effectors phospho-ERK and phospho-AKT, and a basal level of pro-apoptotic effector phospho-p38. The potential of p38 activation (apoptotic potential) due to the elevated level of reactive oxygen species (ROS) is largely suppressed by the negative crosstalk between PI3K/AKT and ASK1/p38 pathways. Upon acute EGFR inactivation, the survival signals decay rapidly, followed by a fast increase of the apoptotic signal due to the release of apoptotic potential. Overall, our systems biology modeling together with experimental validations reveals that inhibition of survival signals and concomitant release of apoptotic potential jointly contribute to the tumor cell death following the inhibition of addicted oncogene in EGFR addicted cancers.

Information routing driven by background chatter in a signaling network

Living systems are capable of processing multiple sources of information simultaneously. This is true even at the cellular level, where not only coexisting signals stimulate the cell, but also the presence of fluctuating conditions is significant. When information is received by a cell signaling network via one specific input, the existence of other stimuli can provide a background activity –or chatter– that may affect signal transmission through the network and, therefore, the response of the cell. Here we study the modulation of information processing by chatter in the signaling network of a human cell, specifically, in a Boolean model of the signal transduction network of a fibroblast. We observe that the level of external chatter shapes the response of the system to information carrying signals in a nontrivial manner, modulates the activity levels of the network outputs, and effectively determines the paths of information flow. Our results show that the interactions and node dynamics, far from being random, confer versatility to the signaling network and allow transitions between different information-processing scenarios.

Far from being silent and static, the habitat of a cell is usually composed by multiple and simultaneous signals. We can consider nutrients, hormones, temperature, light, and other stimuli as elements building a default environment in which cells grow, divide and die. This environment, which has an intrinsically fluctuating nature, is the setting in which cells process all incoming stimuli. Here we examine the role that this background activity –or signaling chatter– plays in the transmission of information in a typical human cell. We address this question using a cellular model of signal transduction that we simulate using both random and periodic stimuli. We find that the level of background chatter determines the response of the whole signaling network to external stimuli. Different areas of the network are activated by specific levels of background activity, routing the information through chatter-dependent paths. In this way, different levels of chatter allow the network to select between different responses, given the same stimulus. These features depend on the architecture and functional connectivity of a truly biological network, since we find that randomized versions of the model are incapable of showing this behavior.

Relevant Networks involving the p53 Signalling Pathway in Renal Cell Carcinoma

INTRODUCTION

Renal cell carcinoma is the most common type of kidney cancer. A better understanding of the critical pathways and interactions associated with alterations in renal function and renal tumour properties is required. Our final goal is to combine the knowledge provided by a regulatory network with experimental observations provided by the dataset.

METHODS

In this study, a systems biology approach was used, integrating immunohistochemistry protein expression profiles and protein interaction information with the STRING and MeV bioinformatics tools. A group consisting of 80 patients with renal cell carcinoma was studied. The expression of selected markers was assessed using tissue microarray technology on immunohistochemically stained slides. The immunohistochemical data of the molecular factors studied were analysed using a parametric statistical test, Pearson's correlation coefficient test.

RESULTS

Bioinformatics analysis of tumour samples resulted in 2 protein networks. The first network consists of proteins involved in the angiogenesis pathway and the apoptosis suppressor, BCL2, and includes both positive and negative correlations. The second network shows a negative interaction between the p53 tumour suppressor protein and the glucose transporter type 4.

CONCLUSION

The comprehensive pathway network will help us to realise the cooperative behaviours among pathways. Regulation of metabolic pathways is an important role of p53. The pathway involving the tumour suppressor gene p53 could regulate tumour angiogenesis. Further investigation of the proteins that interact with this pathway in this type of tumour may provide new strategies for cancer therapies to specifically inhibit the molecules that play crucial roles in tumour progression.

Chronic activation of wild-type epidermal growth factor receptor and loss of Cdkn2a cause mouse glioblastoma formation

Glioblastoma multiforme (GBM) is characterized by overexpression of epidermal growth factor receptor (EGFR) and loss of the tumor suppressors Ink4a/Arf. Efforts at modeling GBM using wild-type EGFR in mice have proven unsuccessful. Here, we present a unique mouse model of wild-type EGFR-driven gliomagenesis. We used a combination of somatic conditional overexpression and ligand-mediated chronic activation of EGFR in cooperation with Ink4a/Arf loss in the central nervous system of adult mice to generate tumors with the histopathologic and molecular characteristics of human GBMs. Sustained, ligand-mediated activation of EGFR was necessary for gliomagenesis, functionally substantiating the clinical observation that EGFR-positive GBMs from patients express EGFR ligands. To gain a better understanding of the clinically disappointing EGFR-targeted therapies for GBM, we investigated the molecular responses to EGFR tyrosine kinase inhibitor (TKI) treatment in this model. Gefitinib treatment of primary GBM cells resulted in a robust apoptotic response, partially conveyed by mitogen-activated protein kinase (MAPK) signaling attenuation and accompanied by BIM(EL) expression. In human GBMs, loss-of-function mutations in the tumor suppressor PTEN are a common occurrence. Elimination of PTEN expression in GBM cells posttumor formation did not confer resistance to TKI treatment, showing that PTEN status in our model is not predictive. Together, these findings offer important mechanistic insights into the genetic determinants of EGFR gliomagenesis and sensitivity to TKIs and provide a robust discovery platform to better understand the molecular events that are associated with predictive markers of TKI therapy.

Molecular targeted agents for gastric and gastroesophageal junction cancer

Despite recent improvements in surgical techniques and chemotherapy, advanced cancers of the stomach and gastroesophageal junction (GEJ) continue to have poor clinical outcomes. However, molecules intimately related to cancer cell proliferation, invasion, and metastasis have been studied as candidates for molecular targeted agents. Target molecules, such as the epidermal growth factor receptor, vascular endothelial growth factor receptor, and P13k/Akt/mTor pathway, as well as the insulin-like growth factor receptor, c-Met pathways, fibroblast growth factor receptor, and other pathways are considered to be promising candidates for molecular targeted therapy for gastric and GEJ cancer. In this review we focus on the recent developments in targeting relevant pathways in these types of cancer.

Identification of biochemical network modules based on shortest retroactive distances

Modularity analysis offers a route to better understand the organization of cellular biochemical networks as well as to derive practically useful, simplified models of these complex systems. While there is general agreement regarding the qualitative properties of a biochemical module, there is no clear consensus on the quantitative criteria that may be used to systematically derive these modules. In this work, we investigate cyclical interactions as the defining characteristic of a biochemical module. We utilize a round trip distance metric, termed Shortest Retroactive Distance (ShReD), to characterize the retroactive connectivity between any two reactions in a biochemical network and to group together network components that mutually influence each other. We evaluate the metric on two types of networks that feature feedback interactions: (i) epidermal growth factor receptor (EGFR) signaling and (ii) liver metabolism supporting drug transformation. For both networks, the ShReD partitions found hierarchically arranged modules that confirm biological intuition. In addition, the partitions also revealed modules that are less intuitive. In particular, ShReD-based partition of the metabolic network identified a ‘redox’ module that couples reactions of glucose, pyruvate, lipid and drug metabolism through shared production and consumption of NADPH. Our results suggest that retroactive interactions arising from feedback loops and metabolic cycles significantly contribute to the modularity of biochemical networks. For metabolic networks, cofactors play an important role as allosteric effectors that mediate the retroactive interactions.

Mathematical models are powerful tools to understand and predict the behavior of complex systems. However, the complexity presents many challenges in developing such models. In the case of a biological cell, a fully detailed and comprehensive model of a major function such as signaling and metabolism remains out of reach, due to the very large number of interdependent biochemical reactions that are required to carry out the function. In this regard, one practical approach is to develop simplified models that nevertheless preserve the essential features of the cell as a complex system by better understanding the chemical organization of the cell, or the layout of the biochemical network. In this work, we describe a computational method to systematically identify closely interacting groups of biochemical reactions by recognizing the modular hierarchy inherent in biochemical networks. We focus on cyclical interactions based on the rationale that reactions that mutually influence each other belong in the same group. We demonstrate our method on a signaling and metabolic network and show that the results confirm biological intuition as well as provide new insights into the coordination of biochemical pathways. Prospectively, our modularization method could be used to systematically derive simplified and practically useful models of complex biological networks.

Modeling the transcriptional consequences of epidermal growth factor receptor ablation in Ras-initiated squamous cancer

PURPOSE

Epidermal growth factor receptor (EGFR)-targeted therapy is in clinical use to treat squamous cell carcinoma of the head and neck and other cancers of lining epithelium. RAS mutations in these tumors are a negative prognostic factor for response, and skin inflammation is an adverse reaction to therapy. We investigated transcriptional and biochemical changes that could account for the confounding effects of RAS activation and inflammation in a squamous tissue.

EXPERIMENTAL DESIGN

We carried out gene expression profiling on oncogenic Ras-transformed and wild-type mouse and human keratinocytes with EGFR ablated chronically by genetic deletion or acutely by drug treatment and followed leads provided by pathway analysis with biochemical studies.

RESULTS

We identified a 25-gene signature specific to the Ras-EGFR ablation interaction and a distinct 19-gene EGFR ablation signature on normal keratinocytes. EGFR ablation in the context of wild-type Ras reduces ontologies favoring cell-cycle control and transcription, whereas oncogenic Ras enriches ontologies for ion channels and membrane transporters, particularly focused on calcium homeostasis. Ontologies between chronic EGFR ablation and acute pharmacologic ablation were unique, both with and without Ras activation. p38α is activated in response to abrogation of EGFR signaling under conditions of Ras activation in both mouse and human keratinocytes and in RAS-transformed tumor orthografts of EGFR-ablated mouse keratinocytes. EGFR ablation in the absence of oncogenic Ras revealed Erk and interleukin-1β-related pathways.

CONCLUSION

These findings reveal unrecognized interactions between Ras and EGFR signaling in squamous tumor cells that could influence the therapeutic response to EGFR ablation therapy.

Software for systems biology: from tools to integrated platforms

Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.

Nitric oxide and protein phosphatase 2A provide novel therapeutic opportunities in ER-negative breast cancer

Basal-like breast cancer is an aggressive disease with limited therapeutic options because these tumors frequently express the 'triple-negative' phenotype. We have recently reported that inducible nitric oxide synthase (NOS2) is a strong predictor of survival in patients with estrogen receptor negative [ER(-)] breast cancer, and that NOS2 expression is correlated with a basal-like phenotype. Recent reports also describe the pro-tumor effects of NO in breast and many other types of cancer. NO promotes cancer progression by activating several oncogenic signaling pathways such as extracellular signal-regulated kinases (ERK)-1/2, phosphoinositide 3-kinases (PI3K)/Akt, and c-Myc. Protein phosphatase 2A (PP2A) is a tumor suppressor that negatively regulates the same cancer-related signaling pathways that are activated by NO. PP2A activity is suppressed in tumor cells, but potential pharmacological agents have recently been described to increase PP2A activity in ER(-) breast cancer cells. We examine here the various functions of NO and PP2A in breast cancer and propose a novel mechanism by which activation of PP2A antagonizes NO signaling that promotes ER(-) breast cancer.

The secret life of kinases: functions beyond catalysis

Protein phosphorylation participates in the regulation of all fundamental biological processes, and protein kinases have been intensively studied. However, while the focus was on catalytic activities, accumulating evidence suggests that non-catalytic properties of protein kinases are essential, and in some cases even sufficient for their functions. These non-catalytic functions include the scaffolding of protein complexes, the competition for protein interactions, allosteric effects on other enzymes, subcellular targeting, and DNA binding. This rich repertoire often is used to coordinate phosphorylation events and enhance the specificity of substrate phosphorylation, but also can adopt functions that do not rely on kinase activity. Here, we discuss such kinase independent functions of protein and lipid kinases focussing on kinases that play a role in the regulation of cell proliferation, differentiation, apoptosis, and motility.

Epidermal regeneration by ent-16α, 17-dihydroxy-kauran-19-oic acid isolated from Siegesbeckia pubescens

OBJECTIVES

  Keratinocyte stem/progenitor cells (KSCs) are known to regenerate epidermal tissue which they perform through to their great regenerative capacity.

MATERIALS AND METHODS

  Because stimulation of resident KSCs may regenerate epidermal tissue, we devised a strategy to find an appropriate KSC activator from natural products and to develop it as a skin-rejuvenating agent.

RESULTS

  Ent-16α, 17-dihydroxy-kauran-19-oic acid (DHK) isolated from Siegesbeckia pubescens exhibited a KSC-stimulating effect during screening of natural products. DHK increased proliferation and migration of KSCs using the Akt/ERK pathway. We further examined the mechanism of KSC stimulation and found that phosphorylation of Y1068 epithelial growth factor receptor (EGFR) was significantly increased. Functional inhibition of EGFR using neutralizing antibody and a chemical inhibitor, AG1478, attenuated DHK-induced KSC stimulation. In a 3D culture model of KSCs, DHK treatment significantly induced establishment of fully stratified epidermis and increased numbers of p63-positive cells. Likewise, DHK treatment significantly accelerated healing of epidermal wounds created by laser and dermatome, and increased p63-positive cells, in animal models.

CONCLUSION

  Collectively, these results indicate that DHK regenerates epidermal tissue mainly through EGFR phosphorylation. As DHK has diverse advantages over recombinant growth factors for commercialization (that is long-term stability and skin permeability), DHK might be applied to wound-healing agents and to a basic materials used in cosmetics.