Protein Signaling Networks Research Articles

Abstract 3465: Impact of intratumoral protein signaling network activation heterogeneity: Implication for precision medicine

Abstract Background: The evaluation of intratumor heterogeneity has become a major topic in the oncological field and it has major implications in the delivery of individualized therapy. While a number of genomic studies have reported significant intratumoral heterogeneity the analysis of the activation of protein kinase-driven signaling networks, which constitute the targets of most of the targeted compounds, has been sparse, or did not focus specifically on the tumor cells. This study evaluated the activation/phosphorylation of the protein signaling networks and a number of major precision medicine-oriented drug target to assess whether the intratumoral heterogeneity could impact patient stratification for targeted treatment in the context of metastatic disease. Methods: 3 independent study sets were used for this analysis: 1) metastatic colorectal cancer (mCRC) liver biopsies were collected from 6 patients; for each biopsy, 2 different regions were microdissected, 2) 18 needle biopsies were collected from 2 different regions of the same lesion of 9 mCRC liver metastases, 3) 7 serial liver biopsies were collected from 3 mCRC before and after treatment. Specimens were frozen upon collection, stored at -80°C and subjected to laser capture microdissection to isolate pure tumor epithelium. Protein pathway activation mapping was performed using reverse phase protein microarray. To assess changes in the activation level of the members of the MAPK and the AKT-mTOR pathway, unsupervised hierarchical clustering analysis and fold change between the different areas of the tumors were evaluated. Results: Hierarchical clustering analysis of the activation of 47 key signaling protein drug targets revealed that the signaling activation of any given protein or pathway was largely the same within a patient's tumor, no matter where the biopsy was obtained, but large interpatient variation was observed. When we evaluated activation of the MAPK and AKT-mTOR pathways, we found that the relative levels of pathway activation were found to be similar in different regions of the tumor, while each patient looked distinct. Conclusions: This first of its kind protein drug target activation analysis of microdissected tumor cells from human metastatic tumors revealed that the activated signaling architecture is largely maintained within the same tumor while each patient's tumor has a distinct patient-specific signaling portrait. The data suggest that interpatient protein drug target activation heterogeneity is much larger than intratumoral heterogeneity and point to the need to determine precision medicine based profiling on a patient-by-patient basis. Citation Format: Erika M. Parasido, Alessandra Silvestri, Claudio Belluco, Vincenzo Canzonieri, Maria Grazia Diodoro, Massimo Milione, Lance A. Liotta, Emanuel F. Petricoin, Mariaelena Pierobon. Impact of intratumoral protein signaling network activation heterogeneity: Implication for precision medicine. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3465. doi:10.1158/1538-7445.AM2014-3465

Circulating biomarker panels for targeted therapy in brain tumors.

An important goal of oncology is the development of cancer risk-identifier biomarkers that aid early detection and target therapy. High-throughput profiling represents a major concern for cancer research, including brain tumors. A promising approach for efficacious monitoring of disease progression and therapy could be circulating biomarker panels using molecular proteomic patterns. Tailoring treatment by targeting specific protein-protein interactions and signaling networks, microRNA and cancer stem cell signaling in accordance with tumor phenotype or patient clustering based on biomarker panels represents the future of personalized medicine for brain tumors. Gathering current data regarding biomarker candidates, we address the major challenges surrounding the biomarker field of this devastating tumor type, exploring potential perspectives for the development of more effective predictive biomarker panels.

New gene evolution in the bonus-TIF1-γ/TRIM33 family impacted the architecture of the vertebrate dorsal-ventral patterning network.

Uncovering how a new gene acquires its function and understanding how the function of a new gene influences existing genetic networks are important topics in evolutionary biology. Here, we demonstrate nonconservation for the embryonic functions of Drosophila Bonus and its newest vertebrate relative TIF1-γ/TRIM33. We showed previously that TIF1-γ/TRIM33 functions as an ubiquitin ligase for the Smad4 signal transducer and antagonizes the Bone Morphogenetic Protein (BMP) signaling network underlying vertebrate dorsal-ventral axis formation. Here, we show that Bonus functions as an agonist of the Decapentaplegic (Dpp) signaling network underlying dorsal-ventral axis formation in flies. The absence of conservation for the roles of Bonus and TIF1-γ/TRIM33 reveals a shift in the dorsal-ventral patterning networks of flies and mice, systems that were previously considered wholly conserved. The shift occurred when the new gene TIF1-γ/TRIM33 replaced the function of the ubiquitin ligase Nedd4L in the lineage leading to vertebrates. Evidence of this replacement is our demonstration that Nedd4 performs the function of TIF1-γ/TRIM33 in flies during dorsal-ventral axis formation. The replacement allowed vertebrate Nedd4L to acquire novel functions as a ubiquitin ligase of vertebrate-specific Smad proteins. Overall our data reveal that the architecture of the Dpp/BMP dorsal-ventral patterning network continued to evolve in the vertebrate lineage, after separation from flies, via the incorporation of new genes.

Multiplexing oscillatory biochemical signals

In recent years it has been increasingly recognized that biochemical signals are not necessarily constant in time and that the temporal dynamics of a signal can be the information carrier. Moreover, it is now well established that the protein signaling network of living cells has a bow-tie structure and that components are often shared between different signaling pathways. Here we show by mathematical modeling that living cells can multiplex a constant and an oscillatory signal: they can transmit these two signals simultaneously through a common signaling pathway, and yet respond to them specifically and reliably. We find that information transmission is reduced not only by noise arising from the intrinsic stochasticity of biochemical reactions, but also by crosstalk between the different channels. Yet, under biologically relevant conditions more than 2 bits of information can be transmitted per channel, even when the two signals are transmitted simultaneously. These observations suggest that oscillatory signals are ideal for multiplexing signals.

Interplay between phosphoinositide lipids and calcium signals at the leading edge of chemotaxing ameboid cells

The chemotactic migration of eukaryotic ameboid cells up concentration gradients is among the most advanced forms of cellular behavior. Chemotaxis is controlled by a complex network of signaling proteins bound to specific lipids on the cytoplasmic surface of the plasma membrane at the front of the cell, or the leading edge. The central lipid players in this leading edge signaling pathway include the phosphoinositides PI(4,5)P2 (PIP2) and PI(3,4,5)P3 (PIP3), both of which play multiple roles. The products of PI(4,5)P2 hydrolysis, diacylglycerol (DAG) and Ins(1,4,5)P3 (IP3), are also implicated as important players. Together, these leading edge phosphoinositides and their degradation products, in concert with a local Ca2+ signal, control the recruitment and activities of many peripheral membrane proteins that are crucial to the leading edge signaling network. The present critical review summarizes the current molecular understanding of chemotactic signaling at the leading edge, including newly discovered roles of phosphoinositide lipids and Ca2+, while highlighting key questions for future research.

FANCA Controls Mitotic Phosphosignaling Networks To Ensure Genome Stability During Cell Division

Fanconi anemia (FA) is a heterogenous genome instability syndrome with a high risk of cancer. The FA proteins are essential for interphase DNA damage repair. However, it is incompletely understood why FA-deficient cells also develop gross aneuploidy and multinucleation, which are symptoms of error-prone chromosome segregation. Emerging evidence indicates that the FA signaling network functions as a guardian of the genome throughout the cell cycle, including chromosome segregation during mitosis. However, the mechanistic aspects of the critical role of the FA signaling in mitosis remain poorly understood.We have recently shown that the FA signaling network localizes to the mitotic apparatus to control the spindle assembly checkpoint and centrosome maintenance (J Clin Invest 2013, in press). The spindle assembly checkpoint (SAC) is a complex tumor suppressor signaling network that prevents premature separation of sister chromatids by delaying the metaphase-to-anaphase transition until all the kinetochores are properly attached to the mitotic spindle. Since weakened SAC promotes stochastic chromosome segregation, mutagenesis and cancer, these findings shed new light on the role of FA signaling in maintenance of genomic stability. We found the subcellular localization of FA proteins to the mitotic apparatus is spatiotemporally regulated as cells divide.Our new data revealed the pathways connecting the FANCA protein with canonical mitotic phosphosignaling networks. We have employed unbiased kinome-wide phospho-mass spectrometry to compare the landscape of abnormalities of mitotic signaling pathways in primary FANCA-/- patient cells and gene-corrected isogenic cells. These experiments led us to identify and quantify a wide range of phosphorylation abnormalities of multiple FANCA-dependent centrosome-, kinetochore- and chromosome-associated regulators of mitosis. Our data illuminated the role for FA signaling in three critical stages of cell division: (1) the spindle assembly checkpoint, (2) anaphase and (3) cytokinesis. Thus, we employed live phase-contrast imaging of primary FANCA-/- patient cells in comparison to gene-corrected cells to separately quantify aberrations in (1) chromosome congression and metaphase-anaphase transition (SAC malfunction), (2) execution of anaphase and (3) completion of cytokinesis.Our findings further our understanding of human cell cycle control and provide new insights into the origins of genomic instability in Fanconi anemia by establishing mechanistic connection between the FANCA protein and key mitotic signaling networks. The identification of cell division pathways regulated by FANCA has implications for future targeted drug development in Fanconi anemia and FA-deficient malignancies in the general population. Disclosures:No relevant conflicts of interest to declare.

All Roads Lead to Rome: Phenotypic Channeling to Uniform Differentiation Structures from Diverse Leukemia Genotypes by Mass Cytometry

Emerging single-cell technologies have been pivotal in uncovering an extensive degree of heterogeneity between and within tissues (1). Analysis of single-cell data has shed light on many different cellular processes (2-7) and recent technological advances have enabled the study of a large number of parameters in single cells at unparalleled resolution. One such technology, mass cytometry (8), can measure up to 45 parameters simultaneously in tens of thousands of individual cells. Using mass cytometry and genomic sequencing of conventionally sorted subpopulations show that acute myelogenous leukemia (AML) in a given patient can simultaneously occupy multiple stages of differentiation. Occupation of these stages was correlated with the presence, or absence, of unique exonic mutation fingerprints. In another cancer, B-cell acute lymphoblastic leukemia (ALL), outgrowth of tumor at pro and pre-B cell stages was nearly always uniquely at a single stage - contrary to the results in AML. This suggests that evolutionary “niche” searching is not only for physical space in cancers, but also involves utilization of differentiation machinery as an additional elaboration mechanism. Each differentiation stage in both AML and B-cell ALL was characterized by utilization of cognate signaling networks which showed differential susceptibility to drug action. Using such deep profiling and signaling delineation approaches at the single-cell level will allow for fine structured indexing of patient disease and further tailoring of disease management. In addition, it will allow “heterogeneous” tumors to be organized by a maturation index associated with a granular catalog of mutations that drive cells to occupy these pseudo-differentiation niches.1. Bendall, S.C., et al., A deep profiler's guide to cytometry.Trends Immunol, 2012. 33(7): p. 323-32.2. Petilla Interneuron Nomenclature Group, et al., Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.Nat Rev Neurosci, 2008. 9(7): p. 557-68.3. Irish, J.M., et al., Single cell profiling of potentiated phospho-protein networks in cancer cells.Cell, 2004. 118(2): p. 217-28.4. Sachs, K., et al., Causal protein-signaling networks derived from multiparameter single-cell data.Science, 2005. 308(5721): p. 523-9.5. Majeti, R., C.Y. Park, and I.L. Weissman, Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell, 2007. 1(6): p. 635-45.6. Tarnok, A., H. Ulrich, and J. Bocsi, Phenotypes of stem cells from diverse origin.Cytometry A, 2010. 77(1): p. 6-10.7. O'Brien, C.A., A. Kreso, and J.E. Dick, Cancer stem cells in solid tumors: an overview.Semin Radiat Oncol, 2009. 19(2): p. 71-7.8. Bandura, D.R., et al., Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem, 2009. 81(16): p. 6813-22. Disclosures:No relevant conflicts of interest to declare.

Proteomic Profiling Identifies Distinct Protein Patterns in Acute Myelogenous Leukemia CD34+CD38- Stem-Like Cells

Acute myeloid leukemia (AML) is believed to arise from leukemic stem-like cells (LSC) making understanding the biological differences between LSC and normal stem cells (HSC) or common myeloid progenitors (CMP) crucial to understanding AML biology. To determine if protein expression patterns were different in LSC compared to other AML and CD34+ populations, we measured the expression of 121 proteins by Reverse Phase Protein Arrays (RPPA) in 5 purified fractions from AML marrow and blood samples: Bulk (CD3/CD19 depleted), CD34-, CD34+(CMP), CD34+CD38+ and CD34+CD38-(LSC). LSC protein expression differed markedly from Bulk (n=31 cases, 93/121 proteins) and CD34+ cells (n= 30 cases, 88/121 proteins) with 54 proteins being significantly different (31 higher, 23 lower) in LSC than in either Bulk or CD34+ cells. Sixty-seven proteins differed significantly between CD34+ and Bulk blasts (n=69 cases). Protein expression patterns in LSC and CD34+ differed markedly from normal CD34+ cells. LSC were distinct from CD34+ and Bulk cells by principal component and by protein signaling network analysis which confirmed individual protein analysis. Potential targetable submodules in LSC included the proteins PU.1(SP1), P27, Mcl1, HIF1α, cMET, P53, Yap, and phospho-Stats 1, 5 and 6. Protein expression and activation in LSC differs markedly from other blast populations suggesting that studies of AML biology should be performed in LSC.

7th International Symposium on Enabling Technologies for Life Sciences (ETP)

7^th International Symposium on Enabling Technologies for Life Sciences (ETP)

CFTR: a hub for kinases and crosstalk of cAMP and Ca2+

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator (CFTR). The resulting disease is pleiotropic consistent with the idea that CFTR acts as a node within a network of signalling proteins. CFTR is not only a regulator of multiple transport proteins and controlled by numerous kinases but also participates in many signalling pathways that are disrupted after expression of its commonest mutant (F508del-CFTR). It operates in membrane compartments creating a scaffold for cytoskeletal elements, surface receptors, kinases and phosphodiesterases. CFTR is exposed to membrane-local second messengers such that a CFTR-interacting, low cellular energy sensor kinase (AMP- and ADP-activated kinase, AMPK) signals through a high energy phosphohistidine protein kinase (nucleoside diphosphate kinase, NDPK). CFTR also translocates a Ca(2+)-dependent adenylate cyclase to its proximity so that a rigid separation between cAMP-dependent and Ca(2+)-dependent regulation of Cl(-) transport becomes obsolete. In the presence of wild-type CFTR, parallel activation of CFTR and outwardly rectifying anoctamin 6 Cl(-) channels is observed, while the Ca(2+)-activated anoctamin 1 Cl(-) channel is inhibited. In contrast, in CF cells, CFTR is missing/mislocalized and the outwardly rectifying chloride channel is attenuated while Ca(2+)-dependent Cl(-) secretion (anoctamin 1) appears upregulated. Additionally, we consider the idea that F508del-CFTR when trapped in the endoplasmic reticulum augments IP3-mediated Ca(2+) release by providing a shunt pathway for Cl(-). CFTR and the IP3 receptor share the characteristic that they both assemble their partner proteins to increase the plasticity of their hub responses. In CF, the CFTR hub fails to form at the plasma membrane, with widespread detrimental consequences for cell signalling.

The preventive role of breadfruit against inflammation‐associated epithelial carcinogenesis in mice

Artocarpus communis has been identified as a rich source of flavonoids and has been gaining attention for its potential chemopreventive abilities. In this study, methanol extracts from the fruit of A. communis (MEFA) and leaf of A. communis (MELA) were prepared, and their effects on inflammation-associated skin tumorigenesis were assessed using mouse models, including 12-O-tetradecanoylphorbol-13-acetate (TPA) induced cutaneous inflammation as well as 7,12-dimethylbenz[α]anthracene (DMBA) initiated and TPA-promoted skin tumorigenesis. According to the results, both MEFA and MELA decreased the intensity of leukocyte infiltration in mouse dorsal skin and cutaneous edema induced by TPA, which appeared to be mediated by inhibition of proinflammatory genes (inducible nitric oxide synthase, cyclooxygenase-2 (COX-2), tumor necrosis factor-α (TNF-α), IL-1β, and IL-6) and proinflammatory mediators (TNF-α, IL-1β, and Prostaglandin E2 ). In addition, topical application with MEFA or MELA effectively attenuated tumor incidence, multiplicity, volume, malignancy as well as angiogenesis of TPA-stimulated skin tumor promotion in DMBA-initiated mice. Notably, immunohistochemical stain showed that MEFA and MELA attenuated COX-2 expression of both skin and tumor tissues in different animal tests, which may be closely related to the suppression of nuclear factor kappa B/activator protein signaling networks. These findings first demonstrate that flavonoid-rich A. communis may exert potent anti-inflammatory activity through modulation of COX-2 in TPA-activated skin and tumor tissues.

PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data

Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed. We developed PhosphoChain to predict kinases, phosphatases and chains of phosphorylation events in signaling networks by combining mRNA expression levels of regulators and targets with a motif detection algorithm and optional prior information. PhosphoChain correctly reconstructed ∼78% of the yeast mitogen-activated protein kinase pathway from publicly available data. When tested on yeast phosphoproteomic data from large-scale mass spectrometry experiments, PhosphoChain correctly identified ∼27% more phosphorylation sites than existing motif detection tools (NetPhosYeast and GPS2.0), and predictions of kinase-phosphatase interactions overlapped with ∼59% of known interactions present in yeast databases. PhosphoChain provides a valuable framework for predicting condition-specific phosphorylation events from high-throughput data. PhosphoChain is implemented in Java and available at http://virgo.csie.ncku.edu.tw/PhosphoChain/ or http://aitchisonlab.com/PhosphoChain

Signaling networks in Leishmania macrophages deciphered through integrated systems biology: a mathematical modeling approach

Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines.

Systems Analysis of the NCI-60 Cancer Cell Lines by Alignment of Protein Pathway Activation Modules with “-OMIC” Data Fields and Therapeutic Response Signatures

The NCI-60 cell line set is likely the most molecularly profiled set of human tumor cell lines in the world. However, a critical missing component of previous analyses has been the inability to place the massive amounts of "-omic" data in the context of functional protein signaling networks, which often contain many of the drug targets for new targeted therapeutics. We used reverse-phase protein array (RPPA) analysis to measure the activation/phosphorylation state of 135 proteins, with a total analysis of nearly 200 key protein isoforms involved in cell proliferation, survival, migration, adhesion, etc., in all 60 cell lines. We aggregated the signaling data into biochemical modules of interconnected kinase substrates for 6 key cancer signaling pathways: AKT, mTOR, EGF receptor (EGFR), insulin-like growth factor-1 receptor (IGF-1R), integrin, and apoptosis signaling. The net activation state of these protein network modules was correlated to available individual protein, phosphoprotein, mutational, metabolomic, miRNA, transcriptional, and drug sensitivity data. Pathway activation mapping identified reproducible and distinct signaling cohorts that transcended organ-type distinctions. Direct correlations with the protein network modules involved largely protein phosphorylation data but we also identified direct correlations of signaling networks with metabolites, miRNA, and DNA data. The integration of protein activation measurements into biochemically interconnected modules provided a novel means to align the functional protein architecture with multiple "-omic" data sets and therapeutic response correlations. This approach may provide a deeper understanding of how cellular biochemistry defines therapeutic response. Such "-omic" portraits could inform rational anticancer agent screenings and drive personalized therapeutic approaches.

OCSANA: optimal combinations of interventions from network analysis

Targeted therapies interfering with specifically one protein activity are promising strategies in the treatment of diseases like cancer. However, accumulated empirical experience has shown that targeting multiple proteins in signaling networks involved in the disease is often necessary. Thus, one important problem in biomedical research is the design and prioritization of optimal combinations of interventions to repress a pathological behavior, while minimizing side-effects. OCSANA (optimal combinations of interventions from network analysis) is a new software designed to identify and prioritize optimal and minimal combinations of interventions to disrupt the paths between source nodes and target nodes. When specified by the user, OCSANA seeks to additionally minimize the side effects that a combination of interventions can cause on specified off-target nodes. With the crucial ability to cope with very large networks, OCSANA includes an exact solution and a novel selective enumeration approach for the combinatorial interventions' problem. The latest version of OCSANA, implemented as a plugin for Cytoscape and distributed under LGPL license, is available together with source code at http://bioinfo.curie.fr/projects/ocsana.

Functional Interactions of Intrinsically Disordered Proteins in Signaling Networks

Intrinsically disordered proteins (IDPs) mediate critical regulatory functions in the cell, including regulation of transcription, translation, the cell cycle, and numerous signal transduction events. The lack of stable globular structure can confer numerous functional advantages, including, paradoxically, both binding promiscuity and high specificity in target interactions. IDPs frequently function in dynamic regulatory networks, where their propensity for posttranslational modifications, their rapid binding and dissociation kinetics, and their ability to interact with multiple target proteins makes them well adapted for precise transduction of cellular signals. The role of IDPs in dynamic cellular signaling will be illustrated by reference to pathways regulated by the general transcriptional coactivators CBP and p300, the tumor suppressor p53, and the adenovirus E1A oncoprotein. CBP and p300 are central nodes in eukaryotic transcriptional regulatory networks and transcription factors must compete for binding to the limiting concentrations of CBP/p300 present in the cell. Many intrinsically disordered proteins contain multipartite interaction motifs that perform an essential function in the integration of complex signaling networks. The role of multipartite binding motifs and post translational modifications in regulation of p53‐mediated signaling pathways will be discussed.

Network inference using steady-state data and Goldbeter–Koshland kinetics

Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference.Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.mukherjeelab.nki.nl/CODE/GK_Kinetics.zipc.j.oates@warwick.ac.uk; s.mukherjee@nki.nlSupplementary data are available at Bioinformatics online.

Allo-Network Drugs: Extension of the Allosteric Drug Concept to Protein- Protein Interaction and Signaling Networks

Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design.

A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis

BackgroundThe yeast Schizosaccharomyces pombe is frequently used as a model for studying the cell cycle. The cells are rod-shaped and divide by medial fission. The process of cell division, or cytokinesis, is controlled by a network of signaling proteins called the Septation Initiation Network (SIN); SIN proteins associate with the SPBs during nuclear division (mitosis). Some SIN proteins associate with both SPBs early in mitosis, and then display strongly asymmetric signal intensity at the SPBs in late mitosis, just before cytokinesis. This asymmetry is thought to be important for correct regulation of SIN signaling, and coordination of cytokinesis and mitosis. In order to study the dynamics of organelles or large protein complexes such as the spindle pole body (SPB), which have been labeled with a fluorescent protein tag in living cells, a number of the image analysis problems must be solved; the cell outline must be detected automatically, and the position and signal intensity associated with the structures of interest within the cell must be determined.ResultsWe present a new 2D and 3D image analysis system that permits versatile and robust analysis of motile, fluorescently labeled structures in rod-shaped cells. We have designed an image analysis system that we have implemented as a user-friendly software package allowing the fast and robust image-analysis of large numbers of rod-shaped cells. We have developed new robust algorithms, which we combined with existing methodologies to facilitate fast and accurate analysis. Our software permits the detection and segmentation of rod-shaped cells in either static or dynamic (i.e. time lapse) multi-channel images. It enables tracking of two structures (for example SPBs) in two different image channels. For 2D or 3D static images, the locations of the structures are identified, and then intensity values are extracted together with several quantitative parameters, such as length, width, cell orientation, background fluorescence and the distance between the structures of interest. Furthermore, two kinds of kymographs of the tracked structures can be established, one representing the migration with respect to their relative position, the other representing their individual trajectories inside the cell. This software package, called “RodCellJ”, allowed us to analyze a large number of S. pombe cells to understand the rules that govern SIN protein asymmetry. (Continued on next page)(Continued from previous page)Conclusions“RodCellJ” is freely available to the community as a package of several ImageJ plugins to simultaneously analyze the behavior of a large number of rod-shaped cells in an extensive manner. The integration of different image-processing techniques in a single package, as well as the development of novel algorithms does not only allow to speed up the analysis with respect to the usage of existing tools, but also accounts for higher accuracy. Its utility was demonstrated on both 2D and 3D static and dynamic images to study the septation initiation network of the yeast Schizosaccharomyces pombe. More generally, it can be used in any kind of biological context where fluorescent-protein labeled structures need to be analyzed in rod-shaped cells.AvailabilityRodCellJ is freely available under http://bigwww.epfl.ch/algorithms.html.

A Pilot Characterization of Human Lung NSCLC by Protein Pathway Activation Mapping

An understanding of the activated protein signaling architecture in non-small-cell lung cancer (NSCLC) is of critical importance to the development of new therapeutic approaches and identification of predictive and prognostic biomarkers for patient stratification. We used reverse-phase protein microarrays to map the activated protein signaling networks of 47 NSCLC tumors, 28 of which were node negative, which were subjected to tumor cellular enrichment using laser capture microdissection. The phosphorylation/cleavage levels of 111 key signaling proteins and total levels of 17 proteins were measured for broadscale signaling analysis. Pathway activation mapping of NSCLC revealed distinct subgroups composed of epidermal growth factor receptor (ERBB1), v-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (ERBB2), v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (ERBB3), v-erb-a erythroblastic leukemia viral oncogene homolog 4 (ERBB4), v-akt murine thymoma viral oncogene homolog 1- mammalian target of rapamycin (AKT-mTOR), protein kinase, AMP-activated, alpha 2 catalytic subunit (AMPK), and autophagy-related signaling, along with transforming growth factor-beta-signaling protein 1 (SMAD), insulin-line growth factor receptor (IGFR), rearranged during transfection proto-oncogene (RET), and activated CDC42-associated kinase (ACK) activation. Investigation of epidermal growth factor receptor (EGFR)-driven signaling identified a unique cohort of tumors with low EGFR protein expression yet high relative levels of phosphorylated EGFR and high EGFR total protein with low relative levels of phosphorylation. Last, mapping analysis of patients with NSCLC with N0 disease revealed a pilot pathway activation signature composed of linked epidermal growth factor receptor family (HER)-AMPK-AKT-mTOR signaling network along with focal adhesion kinase- LIM domain kinase-1 (FAK-LIMK) and janus kinase (JAK)-signal transducers and activators of transcription (STAT) pathways that correlated with short-term survival and aggressive disease. Functional protein pathway activation mapping of NSCLC reveals distinct activation subgroups that are underpinned by important therapeutic targets and that patients with early-stage node negative disease and poor prognosis may be identified by activation of defined, biochemically linked protein signaling events. Such findings, if confirmed in larger study sets, could help select and stratify patients for personalized targeted therapies.